● Open Interpreter
Get early access to the desktop app | Documentation
**Open Interpreter** lets LLMs run code (Python, Javascript, Shell, and more) locally. You can chat with Open Interpreter through a ChatGPT-like interface in your terminal by running `$ interpreter` after installing. This provides a natural-language interface to your computer's general-purpose capabilities: - Create and edit photos, videos, PDFs, etc. - Control a Chrome browser to perform research - Plot, clean, and analyze large datasets - ...etc. **⚠️ Note: You'll be asked to approve code before it's run.**
## Demo https://github.com/OpenInterpreter/open-interpreter/assets/63927363/37152071-680d-4423-9af3-64836a6f7b60 #### An interactive demo is also available on Google Colab: [](https://colab.research.google.com/drive/1WKmRXZgsErej2xUriKzxrEAXdxMSgWbb?usp=sharing) #### Along with an example voice interface, inspired by _Her_: [](https://colab.research.google.com/drive/1NojYGHDgxH6Y1G1oxThEBBb2AtyODBIK) ## Quick Start ### Install ```shell pip install git+https://github.com/OpenInterpreter/open-interpreter.git ``` > Not working? Read our [setup guide](https://docs.openinterpreter.com/getting-started/setup). ### Terminal After installation, simply run `interpreter`: ```shell interpreter ``` ### Python ```python from interpreter import interpreter interpreter.chat("Plot AAPL and META's normalized stock prices") # Executes a single command interpreter.chat() # Starts an interactive chat ``` ### GitHub Codespaces Press the `,` key on this repository's GitHub page to create a codespace. After a moment, you'll receive a cloud virtual machine environment pre-installed with open-interpreter. You can then start interacting with it directly and freely confirm its execution of system commands without worrying about damaging the system. ## Comparison to ChatGPT's Code Interpreter OpenAI's release of [Code Interpreter](https://openai.com/blog/chatgpt-plugins#code-interpreter) with GPT-4 presents a fantastic opportunity to accomplish real-world tasks with ChatGPT. However, OpenAI's service is hosted, closed-source, and heavily restricted: - No internet access. - [Limited set of pre-installed packages](https://wfhbrian.com/mastering-chatgpts-code-interpreter-list-of-python-packages/). - 100 MB maximum upload, 120.0 second runtime limit. - State is cleared (along with any generated files or links) when the environment dies. --- Open Interpreter overcomes these limitations by running in your local environment. It has full access to the internet, isn't restricted by time or file size, and can utilize any package or library. This combines the power of GPT-4's Code Interpreter with the flexibility of your local development environment. ## Commands **Update:** The Generator Update (0.1.5) introduced streaming: ```python message = "What operating system are we on?" for chunk in interpreter.chat(message, display=False, stream=True): print(chunk) ``` ### Interactive Chat To start an interactive chat in your terminal, either run `interpreter` from the command line: ```shell interpreter ``` Or `interpreter.chat()` from a .py file: ```python interpreter.chat() ``` **You can also stream each chunk:** ```python message = "What operating system are we on?" for chunk in interpreter.chat(message, display=False, stream=True): print(chunk) ``` ### Programmatic Chat For more precise control, you can pass messages directly to `.chat(message)`: ```python interpreter.chat("Add subtitles to all videos in /videos.") # ... Streams output to your terminal, completes task ... interpreter.chat("These look great but can you make the subtitles bigger?") # ... ``` ### Start a New Chat In Python, Open Interpreter remembers conversation history. If you want to start fresh, you can reset it: ```python interpreter.messages = [] ``` ### Save and Restore Chats `interpreter.chat()` returns a List of messages, which can be used to resume a conversation with `interpreter.messages = messages`: ```python messages = interpreter.chat("My name is Killian.") # Save messages to 'messages' interpreter.messages = [] # Reset interpreter ("Killian" will be forgotten) interpreter.messages = messages # Resume chat from 'messages' ("Killian" will be remembered) ``` ### Customize System Message You can inspect and configure Open Interpreter's system message to extend its functionality, modify permissions, or give it more context. ```python interpreter.system_message += """ Run shell commands with -y so the user doesn't have to confirm them. """ print(interpreter.system_message) ``` ### Change your Language Model Open Interpreter uses [LiteLLM](https://docs.litellm.ai/docs/providers/) to connect to hosted language models. You can change the model by setting the model parameter: ```shell interpreter --model gpt-3.5-turbo interpreter --model claude-2 interpreter --model command-nightly ``` In Python, set the model on the object: ```python interpreter.llm.model = "gpt-3.5-turbo" ``` [Find the appropriate "model" string for your language model here.](https://docs.litellm.ai/docs/providers/) ### Running Open Interpreter locally #### Terminal Open Interpreter can use OpenAI-compatible server to run models locally. (LM Studio, jan.ai, ollama etc) Simply run `interpreter` with the api_base URL of your inference server (for LM studio it is `http://localhost:1234/v1` by default): ```shell interpreter --api_base "http://localhost:1234/v1" --api_key "fake_key" ``` Alternatively you can use Llamafile without installing any third party software just by running ```shell interpreter --local ``` for a more detailed guide check out [this video by Mike Bird](https://www.youtube.com/watch?v=CEs51hGWuGU?si=cN7f6QhfT4edfG5H) **How to run LM Studio in the background.** 1. Download [https://lmstudio.ai/](https://lmstudio.ai/) then start it. 2. Select a model then click **↓ Download**. 3. Click the **↔️** button on the left (below 💬). 4. Select your model at the top, then click **Start Server**. Once the server is running, you can begin your conversation with Open Interpreter. > **Note:** Local mode sets your `context_window` to 3000, and your `max_tokens` to 1000. If your model has different requirements, set these parameters manually (see below). #### Python Our Python package gives you more control over each setting. To replicate and connect to LM Studio, use these settings: ```python from interpreter import interpreter interpreter.offline = True # Disables online features like Open Procedures interpreter.llm.model = "openai/x" # Tells OI to send messages in OpenAI's format interpreter.llm.api_key = "fake_key" # LiteLLM, which we use to talk to LM Studio, requires this interpreter.llm.api_base = "http://localhost:1234/v1" # Point this at any OpenAI compatible server interpreter.chat() ``` #### Context Window, Max Tokens You can modify the `max_tokens` and `context_window` (in tokens) of locally running models. For local mode, smaller context windows will use less RAM, so we recommend trying a much shorter window (~1000) if it's failing / if it's slow. Make sure `max_tokens` is less than `context_window`. ```shell interpreter --local --max_tokens 1000 --context_window 3000 ``` ### Verbose mode To help you inspect Open Interpreter we have a `--verbose` mode for debugging. You can activate verbose mode by using its flag (`interpreter --verbose`), or mid-chat: ```shell $ interpreter ... > %verbose true <- Turns on verbose mode > %verbose false <- Turns off verbose mode ``` ### Interactive Mode Commands In the interactive mode, you can use the below commands to enhance your experience. Here's a list of available commands: **Available Commands:** - `%verbose [true/false]`: Toggle verbose mode. Without arguments or with `true` it enters verbose mode. With `false` it exits verbose mode. - `%reset`: Resets the current session's conversation. - `%undo`: Removes the previous user message and the AI's response from the message history. - `%tokens [prompt]`: (_Experimental_) Calculate the tokens that will be sent with the next prompt as context and estimate their cost. Optionally calculate the tokens and estimated cost of a `prompt` if one is provided. Relies on [LiteLLM's `cost_per_token()` method](https://docs.litellm.ai/docs/completion/token_usage#2-cost_per_token) for estimated costs. - `%help`: Show the help message. ### Configuration / Profiles Open Interpreter allows you to set default behaviors using `yaml` files. This provides a flexible way to configure the interpreter without changing command-line arguments every time. Run the following command to open the profiles directory: ``` interpreter --profiles ``` You can add `yaml` files there. The default profile is named `default.yaml`. #### Multiple Profiles Open Interpreter supports multiple `yaml` files, allowing you to easily switch between configurations: ``` interpreter --profile my_profile.yaml ``` ## Sample FastAPI Server The generator update enables Open Interpreter to be controlled via HTTP REST endpoints: ```python # server.py from fastapi import FastAPI from fastapi.responses import StreamingResponse from interpreter import interpreter app = FastAPI() @app.get("/chat") def chat_endpoint(message: str): def event_stream(): for result in interpreter.chat(message, stream=True): yield f"data: {result}\n\n" return StreamingResponse(event_stream(), media_type="text/event-stream") @app.get("/history") def history_endpoint(): return interpreter.messages ``` ```shell pip install fastapi uvicorn uvicorn server:app --reload ``` You can also start a server identical to the one above by simply running `interpreter.server()`. ## Android The step-by-step guide for installing Open Interpreter on your Android device can be found in the [open-interpreter-termux repo](https://github.com/MikeBirdTech/open-interpreter-termux). ## Safety Notice Since generated code is executed in your local environment, it can interact with your files and system settings, potentially leading to unexpected outcomes like data loss or security risks. **⚠️ Open Interpreter will ask for user confirmation before executing code.** You can run `interpreter -y` or set `interpreter.auto_run = True` to bypass this confirmation, in which case: - Be cautious when requesting commands that modify files or system settings. - Watch Open Interpreter like a self-driving car, and be prepared to end the process by closing your terminal. - Consider running Open Interpreter in a restricted environment like Google Colab or Replit. These environments are more isolated, reducing the risks of executing arbitrary code. There is **experimental** support for a [safe mode](https://github.com/OpenInterpreter/open-interpreter/blob/main/docs/SAFE_MODE.md) to help mitigate some risks. ## How Does it Work? Open Interpreter equips a [function-calling language model](https://platform.openai.com/docs/guides/gpt/function-calling) with an `exec()` function, which accepts a `language` (like "Python" or "JavaScript") and `code` to run. We then stream the model's messages, code, and your system's outputs to the terminal as Markdown. # Access Documentation Offline The full [documentation](https://docs.openinterpreter.com/) is accessible on-the-go without the need for an internet connection. [Node](https://nodejs.org/en) is a pre-requisite: - Version 18.17.0 or any later 18.x.x version. - Version 20.3.0 or any later 20.x.x version. - Any version starting from 21.0.0 onwards, with no upper limit specified. Install [Mintlify](https://mintlify.com/): ```bash npm i -g mintlify@latest ``` Change into the docs directory and run the appropriate command: ```bash # Assuming you're at the project's root directory cd ./docs # Run the documentation server mintlify dev ``` A new browser window should open. The documentation will be available at [http://localhost:3000](http://localhost:3000) as long as the documentation server is running. # Contributing Thank you for your interest in contributing! We welcome involvement from the community. Please see our [contributing guidelines](https://github.com/OpenInterpreter/open-interpreter/blob/main/docs/CONTRIBUTING.md) for more details on how to get involved. # Roadmap Visit [our roadmap](https://github.com/OpenInterpreter/open-interpreter/blob/main/docs/ROADMAP.md) to preview the future of Open Interpreter. **Note**: This software is not affiliated with OpenAI.  > Having access to a junior programmer working at the speed of your fingertips ... can make new workflows effortless and efficient, as well as open the benefits of programming to new audiences. > > — _OpenAI's Code Interpreter Release_
================================================ FILE: docs/CONTRIBUTING.md ================================================ # ● **Open Interpreter is large, open-source initiative to build a standard interface between language models and computers.** There are many ways to contribute, from helping others on [Github](https://github.com/OpenInterpreter/open-interpreter/issues) or [Discord](https://discord.gg/6p3fD6rBVm), writing documentation, or improving code. We depend on contributors like you. Let's build this. ## What should I work on? First, please familiarize yourself with our [project scope](https://github.com/OpenInterpreter/open-interpreter/blob/main/docs/ROADMAP.md#whats-in-our-scope). Then, pick up a task from our [roadmap](https://github.com/OpenInterpreter/open-interpreter/blob/main/docs/ROADMAP.md) or work on solving an [issue](https://github.com/OpenInterpreter/open-interpreter/issues). If you encounter a bug or have a feature in mind, don't hesitate to [open a new issue](https://github.com/OpenInterpreter/open-interpreter/issues/new/choose). ## Philosophy This is a minimalist, **tightly scoped** project that places a premium on simplicity. We're skeptical of new extensions, integrations, and extra features. We would rather not extend the system if it adds nonessential complexity. # Contribution Guidelines 1. Before taking on significant code changes, please discuss your ideas on [Discord](https://discord.gg/6p3fD6rBVm) to ensure they align with our vision. We want to keep the codebase simple and unintimidating for new users. 2. Fork the repository and create a new branch for your work. 3. Follow the [Running Your Local Fork](https://github.com/OpenInterpreter/open-interpreter/blob/main/docs/CONTRIBUTING.md#running-your-local-fork) guide below. 4. Make changes with clear code comments explaining your approach. Try to follow existing conventions in the code. 5. Follow the [Code Formatting and Linting](https://github.com/OpenInterpreter/open-interpreter/blob/main/docs/CONTRIBUTING.md#code-formatting-and-linting) guide below. 6. Open a PR into `main` linking any related issues. Provide detailed context on your changes. We will review PRs when possible and work with you to integrate your contribution. Please be patient as reviews take time. Once approved, your code will be merged. ## Running Your Local Fork **Note: for anyone testing the new `--local`, `--os`, and `--local --os` modes: When you run `poetry install` you aren't installing the optional dependencies and it'll throw errors. To test `--local` mode, run `poetry install -E local`. To test `--os` mode, run `poetry install -E os`. To test `--local --os` mode, run `poetry install -E local -E os`. You can edit the system messages for these modes in `interpreter/terminal_interface/profiles/defaults`.** Once you've forked the code and created a new branch for your work, you can run the fork in CLI mode by following these steps: 1. CD into the project folder by running `cd open-interpreter`. 2. Install `poetry` [according to their documentation](https://python-poetry.org/docs/#installing-with-pipx), which will create a virtual environment for development + handle dependencies. 3. Install dependencies by running `poetry install`. 4. Run the program with `poetry run interpreter`. Run tests with `poetry run pytest -s -x`. **Note**: This project uses [`black`](https://black.readthedocs.io/en/stable/index.html) and [`isort`](https://pypi.org/project/isort/) via a [`pre-commit`](https://pre-commit.com/) hook to ensure consistent code style. If you need to bypass it for some reason, you can `git commit` with the `--no-verify` flag. ### Installing New Dependencies If you wish to install new dependencies into the project, please use `poetry add package-name`. ### Installing Developer Dependencies If you need to install dependencies specific to development, like testing tools, formatting tools, etc. please use `poetry add package-name --group dev`. ### Known Issues For some, `poetry install` might hang on some dependencies. As a first step, try to run the following command in your terminal: `export PYTHON_KEYRING_BACKEND=keyring.backends.fail.Keyring` Then run `poetry install` again. If this doesn't work, please join our [Discord community](https://discord.gg/6p3fD6rBVm) for help. ## Code Formatting and Linting Our project uses `black` for code formatting and `isort` for import sorting. To ensure consistency across contributions, please adhere to the following guidelines: 1. **Install Pre-commit Hooks**: If you want to automatically format your code every time you make a commit, install the pre-commit hooks. ```bash pip install pre-commit pre-commit install ``` After installing, the hooks will automatically check and format your code every time you commit. 2. **Manual Formatting**: If you choose not to use the pre-commit hooks, you can manually format your code using: ```bash black . isort . ``` # Licensing Contributions to Open Interpreter would be under the MIT license before version 0.2.0, or under AGPL for subsequent contributions. # Questions? Join our [Discord community](https://discord.gg/6p3fD6rBVm) and post in the #General channel to connect with contributors. We're happy to guide you through your first open source contribution to this project! **Thank you for your dedication and understanding as we continue refining our processes. As we explore this extraordinary new technology, we sincerely appreciate your involvement.** ================================================ FILE: docs/NCU_MIGRATION_GUIDE.md ================================================ # `0.2.0` Migration Guide Open Interpreter is [changing](https://changes.openinterpreter.com/log/the-new-computer-update). This guide will help you migrate your application to `0.2.0`, also called the _New Computer Update_ (NCU), the latest major version of Open Interpreter. ## A New Start To start using Open Interpreter in Python, we now use a standard **class instantiation** format: ```python # From the module `interpreter`, import the class `OpenInterpreter` from interpreter import OpenInterpreter # Create an instance of `OpenInterpreter` to use it agent = OpenInterpreter() agent.chat() ``` For convenience, we also provide an instance of `interpreter`, which you can import from the module (also called `interpreter`): ```python # From the module `interpreter`, import the included instance of `OpenInterpreter` from interpreter import interpreter interpreter.chat() ``` ## New Parameters All stateless LLM attributes have been moved to `interpreter.llm`: - `interpreter.model` → `interpreter.llm.model` - `interpreter.api_key` → `interpreter.llm.api_key` - `interpreter.llm_supports_vision` → `interpreter.llm.supports_vision` - `interpreter.supports_function_calling` → `interpreter.llm.supports_functions` - `interpreter.max_tokens` → `interpreter.llm.max_tokens` - `interpreter.context_window` → `interpreter.llm.context_window` - `interpreter.temperature` → `interpreter.llm.temperature` - `interpreter.api_version` → `interpreter.llm.api_version` - `interpreter.api_base` → `interpreter.llm.api_base` This is reflected **1)** in Python applications using Open Interpreter and **2)** in your profile for OI's terminal interface, which can be edited via `interpreter --profiles`. ## New Static Messages Structure - The array of messages is now flat, making the architecture more modular, and easier to adapt to new kinds of media in the future. - Each message holds only one kind of data. This yields more messages, but prevents large nested messages that can be difficult to parse. - This allows you to pass the full `messages` list into Open Interpreter as `interpreter.messages = message_list`. - Every message has a "role", which can be "assistant", "computer", or "user". - Every message has a "type", specifying the type of data it contains. - Every message has "content", which contains the data for the message. - Some messages have a "format" key, to specify the format of the content, like "path" or "base64.png". - The recipient of the message is specified by the "recipient" key, which can be "user" or "assistant". This is used to inform the LLM of who the message is intended for. ```python [ {"role": "user", "type": "message", "content": "Please create a plot from this data and display it as an image and then as HTML."}, # implied format: text (only one format for type message) {"role": "user", "type": "image", "format": "path", "content": "path/to/image.png"} {"role": "user", "type": "file", "content": "/path/to/file.pdf"} # implied format: path (only one format for type file) {"role": "assistant", "type": "message", "content": "Processing your request to generate a plot."} # implied format: text {"role": "assistant", "type": "code", "format": "python", "content": "plot = create_plot_from_data('data')\ndisplay_as_image(plot)\ndisplay_as_html(plot)"} {"role": "computer", "type": "image", "format": "base64.png", "content": "base64"} {"role": "computer", "type": "code", "format": "html", "content": "Plot in HTML format"} {"role": "computer", "type": "console", "format": "output", "content": "{HTML errors}"} {"role": "assistant", "type": "message", "content": "Plot generated successfully."} # implied format: text ] ``` ## New Streaming Structure - The streaming data structure closely matches the static messages structure, with only a few differences. - Every streaming chunk has a "start" and "end" key, which are booleans that specify whether the chunk is the first or last chunk in the stream. This is what you should use to build messages from the streaming chunks. - There is a "confirmation" chunk type, which is used to confirm with the user that the code should be run. The "content" key of this chunk is a dictionary with a `code` and a `language` key. - Introducing more information per chunk is helpful in processing the streaming responses. Please take a look below for example code for processing streaming responses, in JavaScript. ```python {"role": "assistant", "type": "message", "start": True} {"role": "assistant", "type": "message", "content": "Pro"} {"role": "assistant", "type": "message", "content": "cessing"} {"role": "assistant", "type": "message", "content": "your request"} {"role": "assistant", "type": "message", "content": "to generate a plot."} {"role": "assistant", "type": "message", "end": True} {"role": "assistant", "type": "code", "format": "python", "start": True} {"role": "assistant", "type": "code", "format": "python", "content": "plot = create_plot_from_data"} {"role": "assistant", "type": "code", "format": "python", "content": "('data')\ndisplay_as_image(plot)"} {"role": "assistant", "type": "code", "format": "python", "content": "\ndisplay_as_html(plot)"} {"role": "assistant", "type": "code", "format": "python", "end": True} # The computer will emit a confirmation chunk *before* running the code. You can break here to cancel the execution. {"role": "computer", "type": "confirmation", "format": "execution", "content": { "type": "code", "format": "python", "content": "plot = create_plot_from_data('data')\ndisplay_as_image(plot)\ndisplay_as_html(plot)", }} {"role": "computer", "type": "console", "start": True} {"role": "computer", "type": "console", "format": "output", "content": "a printed statement"} {"role": "computer", "type": "console", "format": "active_line", "content": "1"} {"role": "computer", "type": "console", "format": "active_line", "content": "2"} {"role": "computer", "type": "console", "format": "active_line", "content": "3"} {"role": "computer", "type": "console", "format": "output", "content": "another printed statement"} {"role": "computer", "type": "console", "end": True} ``` ## Tips and Best Practices - Adding an `id` and a `created_at` field to messages can be helpful to manipulate the messages later on. - If you want your application to run the code instead of OI, then your app will act as the `computer`. This means breaking from the stream once OI emits a confirmation chunk (`{'role': 'computer', 'type': 'confirmation' ...}`) to prevent OI from running the code. When you run code, grab the message history via `messages = interpreter.messages`, then simply mimic the `computer` format above by appending new `{'role': 'computer' ...}` messages, then run `interpreter.chat(messages)`. - Open Interpreter is designed to stop code execution when the stream is disconnected. Use this to your advantage to add a "Stop" button to the UI. - Setting up your Python server to send errors and exceptions to the client can be helpful for debugging and generating error messages. ## Example Code ### Types Python: ```python class Message: role: Union["user", "assistant", "computer"] type: Union["message", "code", "image", "console", "file", "confirmation"] format: Union["output", "path", "base64.png", "base64.jpeg", "python", "javascript", "shell", "html", "active_line", "execution"] recipient: Union["user", "assistant"] content: Union[str, dict] # dict should have 'code' and 'language' keys, this is only for confirmation messages class StreamingChunk(Message): start: bool end: bool ``` TypeScript: ```typescript interface Message { role: "user" | "assistant" | "computer"; type: "message" | "code" | "image" | "console" | "file", | "confirmation"; format: "output" | "path" | "base64.png" | "base64.jpeg" | "python" | "javascript" | "shell" | "html" | "active_line", | "execution"; recipient: "user" | "assistant"; content: string | { code: string; language: string }; } ``` ```typescript interface StreamingChunk extends Message { start: boolean; end: boolean; } ``` ### Handling streaming chunks Here is a minimal example of how to handle streaming chunks in JavaScript. This example assumes that you are using a Python server to handle the streaming requests, and that you are using a JavaScript client to send the requests and handle the responses. See the main repository README for an example FastAPI server. ```javascript //Javascript let messages = []; //variable to hold all messages let currentMessageIndex = 0; //variable to keep track of the current message index let isGenerating = false; //variable to stop the stream // Function to send a POST request to the OI async function sendRequest() { // Temporary message to hold the message that is being processed try { // Define parameters for the POST request, add at least the full messages array, but you may also consider adding any other OI parameters here, like auto_run, local, etc. const params = { messages, }; //Define a controller to allow for aborting the request const controller = new AbortController(); const { signal } = controller; // Send the POST request to your Python server endpoint const interpreterCall = await fetch("https://YOUR_ENDPOINT/", { method: "POST", headers: { "Content-Type": "application/json", }, body: JSON.stringify(params), signal, }); // Throw an error if the request was not successful if (!interpreterCall.ok) { console.error("Interpreter didn't respond with 200 OK"); return; } // Initialize a reader for the response body const reader = interpreterCall.body.getReader(); isGenerating = true; while (true) { const { value, done } = await reader.read(); // Break the loop if the stream is done if (done) { break; } // If isGenerating is set to false, cancel the reader and break the loop. This will halt the execution of the code run by OI as well if (!isGenerating) { await reader.cancel(); controller.abort(); break; } // Decode the stream and split it into lines const text = new TextDecoder().decode(value); const lines = text.split("\n"); lines.pop(); // Remove last empty line // Process each line of the response for (const line of lines) { const chunk = JSON.parse(line); await processChunk(chunk); } } //Stream has completed here, so run any code that needs to be run after the stream has finished if (isGenerating) isGenerating = false; } catch (error) { console.error("An error occurred:", error); } } //Function to process each chunk of the stream, and create messages function processChunk(chunk) { if (chunk.start) { const tempMessage = {}; //add the new message's data to the tempMessage tempMessage.role = chunk.role; tempMessage.type = chunk.type; tempMessage.content = ""; if (chunk.format) tempMessage.format = chunk.format; if (chunk.recipient) tempMessage.recipient = chunk.recipient; //add the new message to the messages array, and set the currentMessageIndex to the index of the new message messages.push(tempMessage); currentMessageIndex = messages.length - 1; } //Handle active lines for code blocks if (chunk.format === "active_line") { messages[currentMessageIndex].activeLine = chunk.content; } else if (chunk.end && chunk.type === "console") { messages[currentMessageIndex].activeLine = null; } //Add the content of the chunk to current message, avoiding adding the content of the active line if (chunk.content && chunk.format !== "active_line") { messages[currentMessageIndex].content += chunk.content; } } ``` ================================================ FILE: docs/README_DE.md ================================================
● Open Interpreter
Lassen Sie Sprachmodelle Code auf Ihrem Computer ausführen.
Eine Open-Source, lokal laufende Implementierung von OpenAIs Code-Interpreter.
Erhalten Sie frühen Zugang zur Desktop-Anwendung.
```shell pip install open-interpreter ``` ```shell interpreter ```
**Open Interpreter** ermöglicht es LLMs (Language Models), Code (Python, Javascript, Shell und mehr) lokal auszuführen. Sie können mit Open Interpreter über eine ChatGPT-ähnliche Schnittstelle in Ihrem Terminal chatten, indem Sie $ interpreter nach der Installation ausführen. Dies bietet eine natürliche Sprachschnittstelle zu den allgemeinen Fähigkeiten Ihres Computers: - Erstellen und bearbeiten Sie Fotos, Videos, PDFs usw. - Steuern Sie einen Chrome-Browser, um Forschungen durchzuführen - Darstellen, bereinigen und analysieren Sie große Datensätze - ...usw. **⚠️ Hinweis: Sie werden aufgefordert, Code zu genehmigen, bevor er ausgeführt wird.**
## Demo https://github.com/OpenInterpreter/open-interpreter/assets/63927363/37152071-680d-4423-9af3-64836a6f7b60 #### Eine interaktive Demo ist auch auf Google Colab verfügbar: [](https://colab.research.google.com/drive/1WKmRXZgsErej2xUriKzxrEAXdxMSgWbb?usp=sharing) ## Schnellstart ```shell pip install open-interpreter ``` ### Terminal Nach der Installation führen Sie einfach `interpreter` aus: ```shell interpreter ``` ### Python ```python from interpreter import interpreter interpreter.chat("Stellen Sie AAPL und METAs normalisierte Aktienpreise dar") # Führt einen einzelnen Befehl aus interpreter.chat() # Startet einen interaktiven Chat ``` ## Vergleich zu ChatGPTs Code Interpreter OpenAIs Veröffentlichung des [Code Interpreters](https://openai.com/blog/chatgpt-plugins#code-interpreter) mit GPT-4 bietet eine fantastische Möglichkeit, reale Aufgaben mit ChatGPT zu erledigen. Allerdings ist OpenAIs Dienst gehostet, Closed-Source und stark eingeschränkt: - Kein Internetzugang. - [Begrenzte Anzahl vorinstallierter Pakete](https://wfhbrian.com/mastering-chatgpts-code-interpreter-list-of-python-packages/). - 100 MB maximale Uploadgröße, 120.0 Sekunden Laufzeitlimit. - Der Zustand wird gelöscht (zusammen mit allen generierten Dateien oder Links), wenn die Umgebung abstirbt. --- Open Interpreter überwindet diese Einschränkungen, indem es in Ihrer lokalen Umgebung läuft. Es hat vollen Zugang zum Internet, ist nicht durch Zeit oder Dateigröße eingeschränkt und kann jedes Paket oder jede Bibliothek nutzen. Dies kombiniert die Kraft von GPT-4s Code Interpreter mit der Flexibilität Ihrer lokalen Maschine. ## Sicherheitshinweis Da generierter Code in deiner lokalen Umgebung ausgeführt wird, kann er mit deinen Dateien und Systemeinstellungen interagieren, was potenziell zu unerwarteten Ergebnissen wie Datenverlust oder Sicherheitsrisiken führen kann. **⚠️ Open Interpreter wird um Nutzerbestätigung bitten, bevor Code ausgeführt wird.** Du kannst `interpreter -y` ausführen oder `interpreter.auto_run = True` setzen, um diese Bestätigung zu umgehen, in diesem Fall: - Sei vorsichtig bei Befehlsanfragen, die Dateien oder Systemeinstellungen ändern. - Beobachte Open Interpreter wie ein selbstfahrendes Auto und sei bereit, den Prozess zu beenden, indem du dein Terminal schließt. - Betrachte die Ausführung von Open Interpreter in einer eingeschränkten Umgebung wie Google Colab oder Replit. Diese Umgebungen sind isolierter und reduzieren das Risiko der Ausführung willkürlichen Codes. Es gibt **experimentelle** Unterstützung für einen [Sicherheitsmodus](docs/SAFE_MODE.md), um einige Risiken zu mindern. ## Wie funktioniert es? Open Interpreter rüstet ein [funktionsaufrufendes Sprachmodell](https://platform.openai.com/docs/guides/gpt/function-calling) mit einer `exec()`-Funktion aus, die eine `language` (wie "Python" oder "JavaScript") und auszuführenden `code` akzeptiert. Wir streamen dann die Nachrichten des Modells, Code und die Ausgaben deines Systems zum Terminal als Markdown. # Mitwirken Danke für dein Interesse an der Mitarbeit! Wir begrüßen die Beteiligung der Gemeinschaft. Bitte sieh dir unsere [Richtlinien für Mitwirkende](docs/CONTRIBUTING.md) für weitere Details an, wie du dich einbringen kannst. ## Lizenz Open Interpreter ist unter der MIT-Lizenz lizenziert. Du darfst die Software verwenden, kopieren, modifizieren, verteilen, unterlizenzieren und Kopien der Software verkaufen. **Hinweis**: Diese Software ist nicht mit OpenAI affiliiert. > Zugriff auf einen Junior-Programmierer zu haben, der mit der Geschwindigkeit deiner Fingerspitzen arbeitet ... kann neue Arbeitsabläufe mühelos und effizient machen sowie das Programmieren einem neuen Publikum öffnen. > > — _OpenAIs Code Interpreter Release_
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● Intérprete Abierto
Obtenga acceso temprano a la aplicación de escritorio | Documentación
La Nueva Actualización del Computador presenta --os y una nueva API de Computadora. Lea más →
```shell pip install open-interpreter ``` > ¿No funciona? Lea nuestra [guía de configuración](https://docs.openinterpreter.com/getting-started/setup). ```shell interpreter ```
**Intérprete Abierto** permite a los LLMs ejecutar código (Python, JavaScript, Shell, etc.) localmente. Puede chatear con Intérprete Abierto a través de una interfaz de chat como ChatGPT en su terminal después de instalar. Esto proporciona una interfaz de lenguaje natural para las capacidades generales de su computadora: - Crear y editar fotos, videos, PDF, etc. - Controlar un navegador de Chrome para realizar investigaciones - Graficar, limpiar y analizar conjuntos de datos grandes - ... etc. **⚠️ Nota: Se le pedirá que apruebe el código antes de ejecutarlo.**
## Demo https://github.com/OpenInterpreter/open-interpreter/assets/63927363/37152071-680d-4423-9af3-64836a6f7b60 #### También hay disponible una demo interactiva en Google Colab: [](https://colab.research.google.com/drive/1WKmRXZgsErej2xUriKzxrEAXdxMSgWbb?usp=sharing) #### Además, hay un ejemplo de interfaz de voz inspirada en _Her_: [](https://colab.research.google.com/drive/1NojYGHDgxH6Y1G1oxThEBBb2AtyODBIK) ## Inicio Rápido ```shell pip install open-interpreter ``` ### Terminal Después de la instalación, simplemente ejecute `interpreter`: ```shell interpreter ``` ### Python ```python from interpreter import interpreter interpreter.chat("Plot AAPL and META's normalized stock prices") # Ejecuta un comando sencillo interpreter.chat() # Inicia una sesión de chat interactiva ``` ### GitHub Codespaces Presione la tecla `,` en la página de GitHub de este repositorio para crear un espacio de códigos. Después de un momento, recibirá un entorno de máquina virtual en la nube con Interprete Abierto pre-instalado. Puede entonces empezar a interactuar con él directamente y confirmar su ejecución de comandos del sistema sin preocuparse por dañar el sistema. ## Comparación con el Intérprete de Código de ChatGPT El lanzamiento de [Intérprete de Código](https://openai.com/blog/chatgpt-plugins#code-interpreter) de OpenAI con GPT-4 presenta una oportunidad fantástica para realizar tareas del mundo real con ChatGPT. Sin embargo, el servicio de OpenAI está alojado, su codigo es cerrado y está fuertemente restringido: - No hay acceso a Internet. - [Conjunto limitado de paquetes preinstalados](https://wfhbrian.com/mastering-chatgpts-code-interpreter-list-of-python-packages/). - Límite de 100 MB de carga, límite de tiempo de 120.0 segundos. - El estado se elimina (junto con cualquier archivo generado o enlace) cuando el entorno se cierra. --- Intérprete Abierto supera estas limitaciones al ejecutarse en su entorno local. Tiene acceso completo a Internet, no está restringido por tiempo o tamaño de archivo y puede utilizar cualquier paquete o libreria. Esto combina el poder del Intérprete de Código de GPT-4 con la flexibilidad de su entorno de desarrollo local. ## Comandos **Actualización:** La Actualización del Generador (0.1.5) introdujo streaming: ```python message = "¿Qué sistema operativo estamos utilizando?" for chunk in interpreter.chat(message, display=False, stream=True): print(chunk) ``` ### Chat Interactivo Para iniciar una sesión de chat interactiva en su terminal, puede ejecutar `interpreter` desde la línea de comandos: ```shell interpreter ``` O `interpreter.chat()` desde un archivo `.py`: ```python interpreter.chat() ``` **Puede también transmitir cada trozo:** ```python message = "¿Qué sistema operativo estamos utilizando?" for chunk in interpreter.chat(message, display=False, stream=True): print(chunk) ``` ### Chat Programático Para un control más preciso, puede pasar mensajes directamente a `.chat(message)`: ```python interpreter.chat("Añade subtítulos a todos los videos en /videos.") # ... Transmite salida a su terminal, completa tarea ... interpreter.chat("Estos se ven bien, pero ¿pueden hacer los subtítulos más grandes?") # ... ``` ### Iniciar un nuevo chat En Python, Intérprete Abierto recuerda el historial de conversación. Si desea empezar de nuevo, puede resetearlo: ```python interpreter.messages = [] ``` ### Guardar y Restaurar Chats `interpreter.chat()` devuelve una lista de mensajes, que puede utilizar para reanudar una conversación con `interpreter.messages = messages`: ```python messages = interpreter.chat("Mi nombre es Killian.") # Guarda mensajes en 'messages' interpreter.messages = [] # Resetear Intérprete ("Killian" será olvidado) interpreter.messages = messages # Reanuda chat desde 'messages' ("Killian" será recordado) ``` ### Personalizar el Mensaje del Sistema Puede inspeccionar y configurar el mensaje del sistema de Intérprete Abierto para extender su funcionalidad, modificar permisos o darle más contexto. ```python interpreter.system_message += """ Ejecute comandos de shell con -y para que el usuario no tenga que confirmarlos. """ print(interpreter.system_message) ``` ### Cambiar el Modelo de Lenguaje Intérprete Abierto utiliza [LiteLLM](https://docs.litellm.ai/docs/providers/) para conectarse a modelos de lenguaje hospedados. Puede cambiar el modelo estableciendo el parámetro de modelo: ```shell interpreter --model gpt-3.5-turbo interpreter --model claude-2 interpreter --model command-nightly ``` En Python, establezca el modelo en el objeto: ```python interpreter.llm.model = "gpt-3.5-turbo" ``` [Encuentre la cadena adecuada para su modelo de lenguaje aquí.](https://docs.litellm.ai/docs/providers/) ### Ejecutar Intérprete Abierto localmente #### Terminal Intérprete Abierto puede utilizar un servidor de OpenAI compatible para ejecutar modelos localmente. (LM Studio, jan.ai, ollama, etc.) Simplemente ejecute `interpreter` con la URL de base de API de su servidor de inferencia (por defecto, `http://localhost:1234/v1` para LM Studio): ```shell interpreter --api_base "http://localhost:1234/v1" --api_key "fake_key" ``` O puede utilizar Llamafile sin instalar software adicional simplemente ejecutando: ```shell interpreter --local ``` Para una guía mas detallada, consulte [este video de Mike Bird](https://www.youtube.com/watch?v=CEs51hGWuGU?si=cN7f6QhfT4edfG5H) **Cómo ejecutar LM Studio en segundo plano.** 1. Descargue [https://lmstudio.ai/](https://lmstudio.ai/) luego ejecutelo. 2. Seleccione un modelo, luego haga clic **↓ Descargar**. 3. Haga clic en el botón **↔️** en la izquierda (debajo de 💬). 4. Seleccione su modelo en la parte superior, luego haga clic **Iniciar Servidor**. Una vez que el servidor esté funcionando, puede empezar su conversación con Intérprete Abierto. > **Nota:** El modo local establece su `context_window` en 3000 y su `max_tokens` en 1000. Si su modelo tiene requisitos diferentes, ajuste estos parámetros manualmente (ver a continuación). #### Python Nuestro paquete de Python le da más control sobre cada ajuste. Para replicar y conectarse a LM Studio, utilice estos ajustes: ```python from interpreter import interpreter interpreter.offline = True # Desactiva las características en línea como Procedimientos Abiertos interpreter.llm.model = "openai/x" # Indica a OI que envíe mensajes en el formato de OpenAI interpreter.llm.api_key = "fake_key" # LiteLLM, que utilizamos para hablar con LM Studio, requiere esto interpreter.llm.api_base = "http://localhost:1234/v1" # Apunta esto a cualquier servidor compatible con OpenAI interpreter.chat() ``` #### Ventana de Contexto, Tokens Máximos Puede modificar los `max_tokens` y `context_window` (en tokens) de los modelos locales. Para el modo local, ventanas de contexto más cortas utilizarán menos RAM, así que recomendamos intentar una ventana mucho más corta (~1000) si falla o si es lenta. Asegúrese de que `max_tokens` sea menor que `context_window`. ```shell interpreter --local --max_tokens 1000 --context_window 3000 ``` ### Modo Detallado Para ayudarle a inspeccionar Intérprete Abierto, tenemos un modo `--verbose` para depuración. Puede activar el modo detallado utilizando el parámetro (`interpreter --verbose`), o en plena sesión: ```shell $ interpreter ... > %verbose true <- Activa el modo detallado > %verbose false <- Desactiva el modo verbose ``` ### Comandos de Modo Interactivo En el modo interactivo, puede utilizar los siguientes comandos para mejorar su experiencia. Aquí hay una lista de comandos disponibles: **Comandos Disponibles:** - `%verbose [true/false]`: Activa o desactiva el modo detallado. Sin parámetros o con `true` entra en modo detallado. Con `false` sale del modo verbose. - `%reset`: Reinicia la sesión actual de conversación. - `%undo`: Elimina el mensaje de usuario previo y la respuesta del AI del historial de mensajes. - `%tokens [prompt]`: (_Experimental_) Calcula los tokens que se enviarán con el próximo prompt como contexto y estima su costo. Opcionalmente, calcule los tokens y el costo estimado de un `prompt` si se proporciona. Depende de [LiteLLM's `cost_per_token()` method](https://docs.litellm.ai/docs/completion/token_usage#2-cost_per_token) para costos estimados. - `%help`: Muestra el mensaje de ayuda. ### Configuración / Perfiles Intérprete Abierto permite establecer comportamientos predeterminados utilizando archivos `yaml`. Esto proporciona una forma flexible de configurar el intérprete sin cambiar los argumentos de línea de comandos cada vez. Ejecutar el siguiente comando para abrir el directorio de perfiles: ``` interpreter --profiles ``` Puede agregar archivos `yaml` allí. El perfil predeterminado se llama `default.yaml`. #### Perfiles Múltiples Intérprete Abierto admite múltiples archivos `yaml`, lo que permite cambiar fácilmente entre configuraciones: ``` interpreter --profile my_profile.yaml ``` ## Servidor de FastAPI de ejemplo El generador actualiza permite controlar Intérprete Abierto a través de puntos de conexión HTTP REST: ```python # server.py from fastapi import FastAPI from fastapi.responses import StreamingResponse from interpreter import interpreter app = FastAPI() @app.get("/chat") def chat_endpoint(message: str): def event_stream(): for result in interpreter.chat(message, stream=True): yield f"data: {result}\n\n" return StreamingResponse(event_stream(), media_type="text/event-stream") @app.get("/history") def history_endpoint(): return interpreter.messages ``` ```shell pip install fastapi uvicorn uvicorn server:app --reload ``` Puede iniciar un servidor idéntico al anterior simplemente ejecutando `interpreter.server()`. ## Android La guía paso a paso para instalar Intérprete Abierto en su dispositivo Android se encuentra en el [repo de open-interpreter-termux](https://github.com/MikeBirdTech/open-interpreter-termux). ## Aviso de Seguridad Ya que el código generado se ejecuta en su entorno local, puede interactuar con sus archivos y configuraciones del sistema, lo que puede llevar a resultados inesperados como pérdida de datos o riesgos de seguridad. **⚠️ Intérprete Abierto le pedirá que apruebe el código antes de ejecutarlo.** Puede ejecutar `interpreter -y` o establecer `interpreter.auto_run = True` para evitar esta confirmación, en cuyo caso: - Sea cuidadoso al solicitar comandos que modifican archivos o configuraciones del sistema. - Vigile Intérprete Abierto como si fuera un coche autónomo y esté preparado para terminar el proceso cerrando su terminal. - Considere ejecutar Intérprete Abierto en un entorno restringido como Google Colab o Replit. Estos entornos son más aislados, reduciendo los riesgos de ejecutar código arbitrario. Hay soporte **experimental** para un [modo seguro](docs/SAFE_MODE.md) para ayudar a mitigar algunos riesgos. ## ¿Cómo Funciona? Intérprete Abierto equipa un [modelo de lenguaje de llamada a funciones](https://platform.openai.com/docs/guides/gpt/function-calling) con una función `exec()`, que acepta un `lenguaje` (como "Python" o "JavaScript") y `código` para ejecutar. Luego, transmite los mensajes del modelo, el código y las salidas del sistema a la terminal como Markdown. # Acceso a la Documentación Offline La documentación completa está disponible en línea sin necesidad de conexión a Internet. [Node](https://nodejs.org/en) es un requisito previo: - Versión 18.17.0 o cualquier versión posterior 18.x.x. - Versión 20.3.0 o cualquier versión posterior 20.x.x. - Cualquier versión a partir de 21.0.0 sin límite superior especificado. Instale [Mintlify](https://mintlify.com/): ```bash npm i -g mintlify@latest ``` Cambia a la carpeta de documentos y ejecuta el comando apropiado: ```bash # Suponiendo que estás en la carpeta raíz del proyecto cd ./docs # Ejecute el servidor de documentación mintlify dev ``` Una nueva ventana del navegador debería abrirse. La documentación estará disponible en [http://localhost:3000](http://localhost:3000) mientras el servidor de documentación esté funcionando. # Contribuyendo ¡Gracias por su interés en contribuir! Damos la bienvenida a la implicación de la comunidad. Por favor, consulte nuestras [directrices de contribución](docs/CONTRIBUTING.md) para obtener más detalles sobre cómo involucrarse. # Roadmap Visite [nuestro roadmap](https://github.com/OpenInterpreter/open-interpreter/blob/main/docs/ROADMAP.md) para ver el futuro de Intérprete Abierto. **Nota:** Este software no está afiliado con OpenAI.  > Tener acceso a un programador junior trabajando a la velocidad de su dedos... puede hacer que los nuevos flujos de trabajo sean sencillos y eficientes, además de abrir los beneficios de la programación a nuevas audiencias. > > — _Lanzamiento del intérprete de código de OpenAI_
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● Open Interpreter
अपने कंप्यूटर पर कोड चलाने के लिए भाषा मॉडल को चलाएं।
ओपनएआई कोड इंटरप्रेटर का एक ओपन-सोर्स, स्थानीय चलने वाला अमल।
डेस्कटॉप एप्लिकेशन को पहले से ही उपयोग करने के लिए एरली एक्सेस प्राप्त करें।
```shell pip install open-interpreter ``` ```shell interpreter ```
**ओपन इंटरप्रेटर** एलएलएम कोड (पायथन, जावास्क्रिप्ट, शेल, और अधिक) को स्थानीय रूप से चलाने की अनुमति देता है। आप इंस्टॉल करने के बाद अपने टर्मिनल में `$ interpreter` चलाकर ओपन इंटरप्रेटर के साथ एक चैटजीपीटी-जैसे इंटरफ़ेस के माध्यम से चैट कर सकते हैं। यह आपके कंप्यूटर की सामान्य-उद्देश्य क्षमताओं के लिए एक प्राकृतिक भाषा इंटरफ़ेस प्रदान करता है: - फ़ोटो, वीडियो, पीडीएफ़ आदि बनाएँ और संपादित करें। - अनुसंधान करने के लिए एक क्रोम ब्राउज़र को नियंत्रित करें। - बड़े डेटासेट को प्लॉट करें, साफ करें और विश्लेषण करें। - ...आदि। **⚠️ ध्यान दें: कोड को चलाने से पहले आपसे मंज़ूरी मांगी जाएगी।**
## डेमो [](https://colab.research.google.com/drive/1WKmRXZgsErej2xUriKzxrEAXdxMSgWbb?usp=sharing) ## त्वरित प्रारंभ ```shell pip install open-interpreter ``` ### टर्मिनल इंस्टॉलेशन के बाद, सीधे `interpreter` चलाएं: ```shell interpreter ``` ### पायथन ```python from interpreter import interpreter interpreter.chat("AAPL और META के मानकीकृत स्टॉक मूल्यों का चित्रित करें") # एकल कमांड को निष्पादित करता है interpreter.chat() # एक इंटरैक्टिव चैट शुरू करता है ``` ## ChatGPT के कोड इंटरप्रेटर के साथ तुलना ओपनएआई द्वारा [कोड इंटरप्रेटर](https://openai.com/blog/chatgpt-plugins#code-interpreter) का विमोचन। GPT-4 के साथ यह एक शानदार अवसर प्रस्तुत करता है जिससे ChatGPT के साथ वास्तविक दुनिया के कार्यों को पूरा करने का संभावना होती है। हालांकि, ओपनएआई की सेवा होस्ट की जाती है, क्लोज़-स्रोत है और गहरी प्रतिबंधित है। यहां दिए गए नियमों के अनुसार, चैटजीपीटी कोड इंटरप्रेटर के लिए निर्धारित नियमों को हिंदी में अनुवाद किया जा सकता है: - कोई इंटरनेट पहुंच नहीं होती। - [प्रतिष्ठित सेट की सीमित संख्या के पहले स्थापित पैकेज](https://wfhbrian.com/mastering-chatgpts-code-interpreter-list-of-python-packages/) होते हैं। - 100 एमबी तक की अधिकतम अपलोड सीमा होती है। - 120.0 सेकंड की रनटाइम सीमा होती है। - जब एनवायरनमेंट समाप्त होता है, तो स्थिति साफ हो जाती है (साथ ही उत्पन्न किए गए फ़ाइल या लिंक भी)। --- ओपन इंटरप्रेटर इन सीमाओं को पार करता है जो आपके स्थानीय वातावरण पर चलता है। इसके पास इंटरनेट का पूरा उपयोग होता है, समय या फ़ाइल का आकार पर प्रतिबंध नहीं होता है, और किसी भी पैकेज या लाइब्रेरी का उपयोग कर सकता है। यह GPT-4 के कोड इंटरप्रेटर की शक्ति को आपके स्थानीय विकास वातावरण की लचीलापन के साथ मिलाता है। ## Commands ### Interactive Chat To start an interactive chat in your terminal, either run `interpreter` from the command line: ```shell interpreter ``` Or `interpreter.chat()` from a .py file: ```python interpreter.chat() ``` ## कमांड ### इंटरैक्टिव चैट अपने टर्मिनल में इंटरैक्टिव चैट शुरू करने के लिए, या तो कमांड लाइन से `interpreter` चलाएँ: ```shell interpreter ``` या एक .py फ़ाइल से `interpreter.chat()` चलाएँ: ````python interpreter.chat() ### प्रोग्रामेटिक चैट और सटीक नियंत्रण के लिए, आप सीधे `.chat(message)` को संदेश पास कर सकते हैं: ```python interpreter.chat("सभी वीडियो में उपशीर्षक जोड़ें /videos में।") # ... आपके टर्मिनल में आउटपुट स्ट्रीम करता है, कार्य पूरा करता है ... interpreter.chat("ये बड़े दिख रहे हैं लेकिन क्या आप उपशीर्षक को और बड़ा कर सकते हैं?") # ... ```` ### नया चैट शुरू करें Python में, ओपन इंटरप्रेटर संवाद इतिहास को याद रखता है। यदि आप एक नया आरंभ करना चाहते हैं, तो आप इसे रीसेट कर सकते हैं: ```python interpreter.messages = [] ``` ### चैट सहेजें और पुनर्स्थापित करें ```python messages = interpreter.chat("मेरा नाम किलियन है।") # संदेशों को 'messages' में सहेजें interpreter.messages = messages # 'messages' से चैट को फिर से शुरू करें ("किलियन" याद रखा जाएगा) ``` ### सिस्टम संदेश कस्टमाइज़ करें आप ओपन इंटरप्रेटर के सिस्टम संदेश की जांच और कॉन्फ़िगर कर सकते हैं ताकि इसकी क्षमता को विस्तारित किया जा सके, अनुमतियों को संशोधित किया जा सके, या इसे अधिक संदर्भ दिया जा सके। ```python interpreter.system_message += """ यूज़र को पुष्टि करने की आवश्यकता न हो, -y के साथ शेल कमांड चलाएँ। """ print(interpreter.system_message) ``` ### मॉडल बदलें `gpt-3.5-turbo` के लिए तेज़ मोड का उपयोग करें: ```shell interpreter --fast ``` Python में, आपको मॉडल को मैन्युअली सेट करने की आवश्यकता होगी: ```python interpreter.llm.model = "gpt-3.5-turbo" ``` ### ओपन इंटरप्रेटर को स्थानीय रूप से चलाना ```shell interpreter --local ``` #### स्थानीय मॉडल पैरामीटर आप स्थानीय रूप से चल रहे मॉडल की `max_tokens` और `context_window` (टोकन में) आसानी से संशोधित कर सकते हैं। छोटे संदर्भ विंडो का उपयोग करने से कम RAM का उपयोग होगा, इसलिए यदि GPU असफल हो रहा है तो हम एक छोटी विंडो की कोशिश करने की सलाह देते हैं। ```shell interpreter --max_tokens 2000 --context_window 16000 ``` ### डीबग मोड सहयोगियों को ओपन इंटरप्रेटर की जांच करने में मदद करने के लिए, `--verbose` मोड अत्यधिक वर्बोस होता है। आप डीबग मोड को उसके फ़्लैग (`interpreter --verbose`) का उपयोग करके या चैट के बीच में सक्षम कर सकते हैं: ```shell $ interpreter ... > %verbose true <- डीबग मोड चालू करता है > %verbose false <- डीबग मोड बंद करता है ``` ### इंटरैक्टिव मोड कमांड्स इंटरैक्टिव मोड में, आप निम्नलिखित कमांडों का उपयोग करके अपने अनुभव को बेहतर बना सकते हैं। यहां उपलब्ध कमांडों की सूची है: **उपलब्ध कमांड:** • `%verbose [true/false]`: डीबग मोड को टॉगल करें। कोई तर्क नहीं या 'true' के साथ, यह डीबग मोड में प्रवेश करता है। 'false' के साथ, यह डीबग मोड से बाहर निकलता है। • `%reset`: वर्तमान सत्र को रीसेट करता है। • `%undo`: पिछले संदेश और उसके जवाब को संदेश इतिहास से हटा देता है। • `%save_message [पथ]`: संदेशों को एक निर्दिष्ट JSON पथ पर सहेजता है। यदि कोई पथ निर्दिष्ट नहीं किया गया है, तो यह डिफ़ॉल्ट रूप से 'messages.json' पर जाता है। • `%load_message [पथ]`: एक निर्दिष्ट JSON पथ से संदेश लोड करता है। यदि कोई पथ निर्दिष्ट नहीं किया गया है, तो यह डिफ़ॉल्ट रूप से 'messages.json' पर जाता है। • `%help`: मदद संदेश दिखाएं। इन कमांडों का प्रयोग करके अपनी प्रतिक्रिया दें और हमें अपनी प्रतिक्रिया दें! ## सुरक्षा सूचना क्योंकि उत्पन्न कोड आपके स्थानीय वातावरण में निष्पादित किया जाता है, इसलिए यह आपके फ़ाइलों और सिस्टम सेटिंग्स के साथ संवाद कर सकता है, जिससे अप्रत्याशित परिणाम जैसे डेटा हानि या सुरक्षा जोखिम हो सकता है। **⚠️ Open Interpreter कोड को निष्पादित करने से पहले उपयोगकर्ता की पुष्टि के लिए पूछेगा।** आप `interpreter -y` चला सकते हैं या ... ... `interpreter.auto_run = True` सेट कर सकते हैं ताकि इस पुष्टि को छोड़ दें, जिसके बाद: - फ़ाइलों या सिस्टम सेटिंग्स को संशोधित करने वाले कमांडों के लिए सतर्क रहें। - ओपन इंटरप्रेटर को एक स्व-चालित कार की तरह देखें और अपने टर्मिनल को बंद करके प्रक्रिया को समाप्त करने के लिए तत्पर रहें। - Google Colab या Replit जैसे प्रतिबंधित वातावरण में ओपन इंटरप्रेटर को चलाने का विचार करें। ये वातावरण अधिक संगठित होते हैं और अनियंत्रित कोड के साथ जुड़े जोखिमों को कम करते हैं। ## यह कार्य कैसे करता है? Open Interpreter एक [फ़ंक्शन-कॉलिंग भाषा मॉडल](https://platform.openai.com/docs/guides/gpt/function-calling) को एक `exec()` फ़ंक्शन के साथ लैस करता है, जो एक `language` (जैसे "Python" या "JavaScript") और `code` को चलाने के लिए स्वीकार करता है। फिर हम मॉडल के संदेश, कोड और आपके सिस्टम के आउटपुट को टर्मिनल में मार्कडाउन के रूप में स्ट्रीम करते हैं। # योगदान योगदान करने के लिए आपकी रुचि के लिए धन्यवाद! हम समुदाय से सहभागिता का स्वागत करते हैं। अधिक जानकारी के लिए कृपया हमारे [योगदान दिशानिर्देश](CONTRIBUTING.md) देखें। ## लाइसेंस Open Interpreter MIT लाइसेंस के तहत लाइसेंस है। आपको सॉफ़्टवेयर की प्रतिलिपि का उपयोग, प्रतिलिपि, संशोधन, वितरण, सबलाइसेंस और बेचने की अनुमति है। **ध्यान दें**: यह सॉफ़्टवेयर OpenAI से संबद्ध नहीं है। > अपनी उंगलियों की गति से काम करने वाले एक जूनियर प्रोग्रामर तक पहुंच ... नए वर्कफ़्लो को सरल और कुशल बना सकता है, साथ ही ... प्रोग्रामिंग के लाभों को नए दरबारों तक पहुंचा सकता है। > > — _OpenAI's Code Interpreter Release_
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● Open Interpreter
自然言語で指示するだけでコードを書いて実行までしてくれる。
ローカルに実装したOpenAI Code Interpreterのオープンソース版。
デスクトップアプリへの早期アクセス | ドキュメント
**Update:** ● 0.1.12 アップデートで `interpreter --vision` 機能が導入されました。([ドキュメント](https://docs.openinterpreter.com/usage/terminal/vision))
```shell pip install open-interpreter ``` ```shell interpreter ```
**Open Interpreter**は、言語モデルに指示し、コード（Python、Javascript、Shell など）をローカル環境で実行できるようにします。インストール後、`$ interpreter` を実行するとターミナル経由で ChatGPT のようなインターフェースを介し、Open Interpreter とチャットができます。 これにより、自然言語のインターフェースを通して、パソコンの一般的な機能が操作できます。 - 写真、動画、PDF などの作成や編集 - Chrome ブラウザの制御とリサーチ作業 - 大規模なデータセットのプロット、クリーニング、分析 - 等々 **⚠️ 注意: 実行する前にコードを承認するよう求められます。**
## デモ https://github.com/OpenInterpreter/open-interpreter/assets/63927363/37152071-680d-4423-9af3-64836a6f7b60 #### Google Colab でも対話形式のデモを利用できます: [](https://colab.research.google.com/drive/1WKmRXZgsErej2xUriKzxrEAXdxMSgWbb?usp=sharing) #### 音声インターフェースの実装例 (_Her_ からインスピレーションを得たもの): [](https://colab.research.google.com/drive/1NojYGHDgxH6Y1G1oxThEBBb2AtyODBIK) ## クイックスタート ```shell pip install open-interpreter ``` ### ターミナル インストール後、`interpreter` を実行するだけです: ```shell interpreter ``` ### Python ```python from interpreter import interpreter interpreter.chat("AAPLとMETAの株価グラフを描いてください") # コマンドを実行 interpreter.chat() # 対話形式のチャットを開始 ``` ## ChatGPT の Code Interpreter との違い GPT-4 で実装された OpenAI の [Code Interpreter](https://openai.com/blog/chatgpt-plugins#code-interpreter) は、実世界のタスクを ChatGPT で操作できる素晴らしい機会を提供しています。 しかし、OpenAI のサービスはホスティングされていてるクローズドな環境で、かなり制限がされています: - インターネットに接続できない。 - [プリインストールされているパッケージが限られている](https://wfhbrian.com/mastering-chatgpts-code-interpreter-list-of-python-packages/)。 - 最大アップロードは 100MB で、120 秒という実行時間の制限も。 - 生成されたファイルやリンクとともに状態がリセットされる。 --- Open Interpreter は、ローカル環境で操作することで、これらの制限を克服しています。インターネットにフルアクセスでき、時間やファイルサイズの制限を受けず、どんなパッケージやライブラリも利用できます。 Open Interpter は、GPT-4 Code Interpreter のパワーとローカル開発環境の柔軟性を組み合わせたものです。 ## コマンド **更新:** アップデート(0.1.5)でストリーミング機能が導入されました: ```python message = "どのオペレーティングシステムを使用していますか？" for chunk in interpreter.chat(message, display=False, stream=True): print(chunk) ``` ### 対話型チャット ターミナルで対話形式のチャットを開始するには、コマンドラインから `interpreter` を実行します。 ```shell interpreter ``` または、.py ファイルから `interpreter.chat()` も利用できます。 ```python interpreter.chat() ``` **ストリーミングすることで chunk 毎に処理することも可能です:** ```python message = "What operating system are we on?" for chunk in interpreter.chat(message, display=False, stream=True): print(chunk) ``` ### プログラム的なチャット より精確な制御のために、メッセージを直接`.chat(message)`に渡すことができます。 ```python interpreter.chat("/videos フォルダにあるすべての動画に字幕を追加する。") # ... ターミナルに出力をストリームし、タスクを完了 ... interpreter.chat("ついでに、字幕を大きくできますか？") # ... ``` ### 新しいチャットを開始 プログラム的チャットで Open Interpreter は、会話の履歴を記憶しています。新しくやり直したい場合は、リセットすることができます: ```python interpreter.messages = [] ``` ### チャットの保存と復元 `interpreter.chat()` はメッセージのリストを返し, `interpreter.messages = messages` のように使用することで会話を再開することが可能です: ```python messages = interpreter.chat("私の名前は田中です。") # 'messages'にメッセージを保存 interpreter.messages = [] # インタープリタをリセット（"田中"は忘れられる） interpreter.messages = messages # 'messages'からチャットを再開（"田中"は記憶される） ``` ### システムメッセージのカスタマイズ Open Interpreter のシステムメッセージを確認し、設定することで、機能を拡張したり、権限を変更したり、またはより多くのコンテキストを与えたりすることができます。 ```python interpreter.system_message += """ シェルコマンドを '-y' フラグ付きで実行し、ユーザーが確認する必要がないようにする。 """ print(interpreter.system_message) ``` ### モデルの変更 Open Interpreter は、ホストされた言語モデルへの接続に [LiteLLM](https://docs.litellm.ai/docs/providers/) を使用しています。 model パラメータを設定することで、モデルを変更することが可能です: ```shell interpreter --model gpt-3.5-turbo interpreter --model claude-2 interpreter --model command-nightly ``` Python では、オブジェクト上でモデルを設定します: ```python interpreter.llm.model = "gpt-3.5-turbo" ``` [適切な "model" の値はこちらから検索してください。](https://docs.litellm.ai/docs/providers/) ### ローカルのモデルを実行する Open Interpreter は、OpenAI 互換サーバーを使用してモデルをローカルで実行できます。 (LM Studio、jan.ai、ollam など) 推論サーバーの api_base URL を指定して「interpreter」を実行するだけです (LM Studio の場合、デフォルトでは「http://localhost:1234/v1」です)。 ```shell interpreter --api_base "http://localhost:1234/v1" --api_key "fake_key" ``` あるいは、サードパーティのソフトウェアをインストールせずに、単に実行するだけで Llamafile を使用することもできます。 ```shell interpreter --local ``` より詳細なガイドについては、[Mike Bird によるこのビデオ](https://www.youtube.com/watch?v=CEs51hGWuGU?si=cN7f6QhfT4edfG5H) をご覧ください。 **LM Studio をバックグラウンドで使用する方法** 1. [https://lmstudio.ai/](https://lmstudio.ai/)からダウンロードして起動します。 2. モデルを選択し、**↓ ダウンロード** をクリックします。 3. 左側の **↔️** ボタン（💬 の下）をクリックします。 4. 上部でモデルを選択し、**サーバーを起動** をクリックします。 サーバーが稼働を開始したら、Open Interpreter との会話を開始できます。 > **注意:** ローカルモードでは、`context_window` を 3000 に、`max_tokens` を 1000 に設定します。モデルによって異なる要件がある場合、これらのパラメータを手動で設定してください（下記参照）。 #### コンテキストウィンドウ、最大トークン数 ローカルで実行しているモデルの `max_tokens` と `context_window`（トークン単位）を変更することができます。 ローカルモードでは、小さいコンテキストウィンドウは RAM を少なく使用するので、失敗する場合や遅い場合は、より短いウィンドウ（〜1000）を試すことをお勧めします。`max_tokens` が `context_window` より小さいことを確認してください。 ```shell interpreter --local --max_tokens 1000 --context_window 3000 ``` ### デバッグモード コントリビューターが Open Interpreter を調査するのを助けるために、`--verbose` モードは非常に便利です。 デバッグモードは、フラグ（`interpreter --verbose`）を使用するか、またはチャットの中から有効にできます: ```shell $ interpreter ... > %verbose true # <- デバッグモードを有効にする > %verbose false # <- デバッグモードを無効にする ``` ### 対話モードのコマンド 対話モードでは、以下のコマンドを使用して操作を便利にすることができます。利用可能なコマンドのリストは以下の通りです: **利用可能なコマンド:** - `%verbose [true/false]`: デバッグモードを切り替えます。引数なしまたは `true` でデバッグモードに入ります。`false` でデバッグモードを終了します。 - `%reset`: 現在のセッションの会話をリセットします。 - `%undo`: メッセージ履歴から前のユーザーメッセージと AI の応答を削除します。 - `%save_message [path]`: メッセージを指定した JSON パスに保存します。パスが指定されていない場合、デフォルトは `messages.json` になります。 - `%load_message [path]`: 指定した JSON パスからメッセージを読み込みます。パスが指定されていない場合、デフォルトは `messages.json` になります。 - `%tokens [prompt]`: (_実験的_) 次のプロンプトのコンテキストとして送信されるトークンを計算し、そのコストを見積もります。オプションで、`prompt` が提供された場合のトークンと見積もりコストを計算します。見積もりコストは [LiteLLM の `cost_per_token()` メソッド](https://docs.litellm.ai/docs/completion/token_usage#2-cost_per_token)に依存します。 - `%help`: ヘルプメッセージを表示します。 ### 設定 Open Interpreter では、`config.yaml` ファイルを使用してデフォルトの動作を設定することができます。 これにより、毎回コマンドライン引数を変更することなく柔軟に設定することができます。 以下のコマンドを実行して設定ファイルを開きます: ``` interpreter --config ``` #### 設定ファイルの複数利用 Open Interpreter は複数の `config.yaml` ファイルをサポートしており、`--config_file` 引数を通じて簡単に設定を切り替えることができます。 **注意**: `--config_file` はファイル名またはファイルパスを受け入れます。ファイル名はデフォルトの設定ディレクトリを使用し、ファイルパスは指定されたパスを使用します。 新しい設定を作成または編集するには、次のコマンドを実行します: ``` interpreter --config --config_file $config_path ``` 特定の設定ファイルをロードして Open Interpreter を実行するには、次のコマンドを実行します: ``` interpreter --config_file $config_path ``` **注意**: `$config_path` をあなたの設定ファイルの名前またはパスに置き換えてください。 ##### 対話モードでの使用例 1. 新しい `config.turbo.yaml` ファイルを作成します ``` interpreter --config --config_file config.turbo.yaml ``` 2. `config.turbo.yaml` ファイルを編集して、`model` を `gpt-3.5-turbo` に設定します 3. `config.turbo.yaml` 設定で、Open Interpreter を実行します ``` interpreter --config_file config.turbo.yaml ``` ##### Python での使用例 Python のスクリプトから Open Interpreter を呼び出すときにも設定ファイルをロードできます: ```python import os from interpreter import interpreter currentPath = os.path.dirname(os.path.abspath(__file__)) config_path=os.path.join(currentPath, './config.test.yaml') interpreter.extend_config(config_path=config_path) message = "What operating system are we on?" for chunk in interpreter.chat(message, display=False, stream=True): print(chunk) ``` ## FastAPI サーバーのサンプル アップデートにより Open Interpreter は、HTTP REST エンドポイントを介して制御できるようになりました: ```python # server.py from fastapi import FastAPI from fastapi.responses import StreamingResponse from interpreter import interpreter app = FastAPI() @app.get("/chat") def chat_endpoint(message: str): def event_stream(): for result in interpreter.chat(message, stream=True): yield f"data: {result}\n\n" return StreamingResponse(event_stream(), media_type="text/event-stream") @app.get("/history") def history_endpoint(): return interpreter.messages ``` ```shell pip install fastapi uvicorn uvicorn server:app --reload ``` ## 安全に関する注意 生成されたコードはローカル環境で実行されるため、ファイルやシステム設定と相互作用する可能性があり、データ損失やセキュリティリスクなど予期せぬ結果につながる可能性があります。 **⚠️ Open Interpreter はコードを実行する前にユーザーの確認を求めます。** この確認を回避するには、`interpreter -y` を実行するか、`interpreter.auto_run = True` を設定します。その場合: - ファイルやシステム設定を変更するコマンドを要求するときは注意してください。 - Open Interpreter を自動運転車のように監視し、ターミナルを閉じてプロセスを終了できるように準備しておいてください。 - Google Colab や Replit のような制限された環境で Open Interpreter を実行することを検討してください。これらの環境はより隔離されており、任意のコードの実行に関連するリスクを軽減します。 一部のリスクを軽減するための[セーフモード](docs/SAFE_MODE.md)と呼ばれる **実験的な** サポートがあります。 ## Open Interpreter はどのように機能するのか？ Open Interpreter は、[関数が呼び出せる言語モデル](https://platform.openai.com/docs/guides/gpt/function-calling)に `exec()` 関数を装備し、実行する言語（"python"や"javascript"など）とコードが渡せるようになっています。 そして、モデルからのメッセージ、コード、システムの出力を Markdown としてターミナルにストリーミングします。 # 貢献 貢献に興味を持っていただき、ありがとうございます！コミュニティからの参加を歓迎しています。 詳しくは、[貢献ガイドライン](CONTRIBUTING.md)を参照してください。 # ロードマップ Open Interpreter の未来を一足先に見るために、[私たちのロードマップ](https://github.com/OpenInterpreter/open-interpreter/blob/main/docs/ROADMAP.md)をご覧ください。 **注意**: このソフトウェアは OpenAI とは関連していません。 > あなたの指先のスピードで作業するジュニアプログラマーにアクセスすることで、… 新しいワークフローを楽で効率的なものにし、プログラミングの利点を新しいオーディエンスに開放することができます。 > > — _OpenAI Code Interpreter リリース_
================================================ FILE: docs/README_UK.md ================================================
● Open Interpreter
Отримайте ранній доступ до десктопної програми | Документація
Нове комп'ютерне оновлення представило --os та новий Computer API. Читати далі →
```shell pip install open-interpreter ``` > Не працює? Прочитайте наш [посібник з налаштування](https://docs.openinterpreter.com/getting-started/setup). ```shell interpreter ```
**Open Interpreter** дозволяє LLM локально запускати код (Python, Javascript, Shell тощо). Ви можете спілкуватися з Open Interpreter через інтерфейс, схожий на ChatGPT, у вашому терміналі, запустивши `$ interpreter` після встановлення. Це забезпечує інтерфейс природною мовою для загального використання можливостей вашого комп’ютера: - Створювати та редагувати фотографії, відео, PDF-файли тощо. - Керувати браузером Chrome для проведення досліджень - Створювати, очищати та аналізувати великі набори даних - ...і т.д. **⚠️ Увага: Вам буде запропоновано підтвердити код перед його запуском.**
## Demo https://github.com/OpenInterpreter/open-interpreter/assets/63927363/37152071-680d-4423-9af3-64836a6f7b60 #### Інтерактивна демонстрація також доступна на Google Colab: [](https://colab.research.google.com/drive/1WKmRXZgsErej2xUriKzxrEAXdxMSgWbb?usp=sharing) #### Разом із прикладом голосового інтерфейсу, натхненного _Her_: [](https://colab.research.google.com/drive/1NojYGHDgxH6Y1G1oxThEBBb2AtyODBIK) ## Швидкий старт ```shell pip install open-interpreter ``` ### Термінал Після встановлення просто запустіть `interpreter`: ```shell interpreter ``` ### Python ```python from interpreter import interpreter interpreter.chat("Plot AAPL and META's normalized stock prices") # Виконує одну команду interpreter.chat() # Починає інтерактивний чат ``` ### GitHub Codespaces Натисніть клавішу `,` на сторінці GitHub цього репозиторію, щоб створити Codespace. Через деякий час ви отримаєте хмарне середовище віртуальної машини, попередньо встановлене з відкритим інтерпретатором. Потім ви можете почати взаємодіяти з ним безпосередньо та вільно підтверджувати виконання ним системних команд, не турбуючись про пошкодження системи. ## Порівняння з інтерпретатором коду ChatGPT Випуск OpenAI [Code Interpreter](https://openai.com/blog/chatgpt-plugins#code-interpreter) з GPT-4 надає фантастичну можливість виконувати реальні завдання за допомогою ChatGPT. Однак служба OpenAI є хмарною, з закритим вихідним кодом і суворо обмежена: - Немає доступу до Інтернету. - [Обмежений набір попередньо встановлених пакетів](https://wfhbrian.com/mastering-chatgpts-code-interpreter-list-of-python-packages/). - Максимальний розмір завантаження - 100 МБ, обмеження часу виконання - 120,0 секунд. - Стан очищається (разом із будь-якими згенерованими файлами чи посиланнями), коли середовище зупиняється. --- Open Interpreter долає ці обмеження, запускаючись у вашому локальному середовищі. Він має повний доступ до Інтернету, не обмежений часом або розміром файлу, і може використовувати будь-який пакет або бібліотеку. Це поєднує потужність інтерпретатора коду GPT-4 із гнучкістю вашого локального середовища розробки. ## Команди **Оновлення:** Оновлення Generator (0.1.5) представило потокове передавання: ```python message = "What operating system are we on?" for chunk in interpreter.chat(message, display=False, stream=True): print(chunk) ``` ### Інтерактивний чат Щоб почати інтерактивний чат у вашому терміналі, запустіть `interpreter` з командного рядка: ```shell interpreter ``` Або `interpreter.chat()` з файлу .py: ```python interpreter.chat() ``` **Ви також можете транслювати кожен фрагмент:** ```python message = "На якій операційній системі ми працюємо?" for chunk in interpreter.chat(message, display=False, stream=True): print(chunk) ``` ### Програмований чат Для більш точного керування ви можете передавати повідомлення безпосередньо до `.chat(message)`: ```python interpreter.chat("Додайте субтитри до всіх відео в /videos.") # ... Потік виведення на ваш термінал, виконання завдання ... interpreter.chat("Виглядає чудово, але чи можеш ти збільшити субтитри?") # ... ``` ### Почати новий чат В Python, Open Interpreter запам’ятовує історію розмов. Якщо ви хочете почати заново, ви можете скинути її: ```python interpreter.messages = [] ``` ### Зберегти та відновити чати `interpreter.chat()` повертає список повідомлень, який можна використовувати для відновлення розмови за допомогою `interpreter.messages = messages`: ```python messages = interpreter.chat("Мене звати Степан.") # Зберегти повідомлення в "messages" interpreter.messages = [] # Скинути інтерпретатор ("Степан" буде забутий) interpreter.messages = messages # Відновити чат із "messages" ("Степан" запам’ятається) ``` ### Кастомізувати системне повідомлення Ви можете перевірити та налаштувати системне повідомлення Open Interpreter, щоб розширити його функціональність, змінити дозволи або надати йому більше контексту. ```python interpreter.system_message += """ Виконуй команди оболонки з -y, щоб користувачеві не потрібно було їх підтверджувати. """ print(interpreter.system_message) ``` ### Змініть свою мовну модель Open Interpreter використовує [LiteLLM](https://docs.litellm.ai/docs/providers/) для підключення до розміщених мовних моделей. Ви можете змінити модель, встановивши параметр моделі: ```shell interpreter --model gpt-3.5-turbo interpreter --model claude-2 interpreter --model command-nightly ``` В Pythonб встановити модель на об’єкт: ```python interpreter.llm.model = "gpt-3.5-turbo" ``` [Знайдіть відповідний рядок «model» для вашої мовної моделі тут.](https://docs.litellm.ai/docs/providers/) ### Запуск Open Interpreter локально #### Термінал Open Interpreter може використовувати OpenAI-сумісний сервер для запуску моделей локально. (LM Studio, jan.ai, ollama тощо) Просто запустіть `interpreter` з URL-адресою api_base вашого сервера interference (для LM Studio це `http://localhost:1234/v1` за замовчуванням): ```shell interpreter --api_base "http://localhost:1234/v1" --api_key "fake_key" ``` Крім того, ви можете використовувати Llamafile без встановлення стороннього програмного забезпечення, просто запустивши його ```shell interpreter --local ``` for a more detailed guide check out [this video by Mike Bird](https://www.youtube.com/watch?v=CEs51hGWuGU?si=cN7f6QhfT4edfG5H) **Як запустити LM Studio у фоновому режимі.** 1. Завантажте [https://lmstudio.ai/](https://lmstudio.ai/), після чого запустіть його. 2. Виберіть модель і натисніть **↓ Завантажити**. 3. Натисніть кнопку **↔️** ліворуч (нижче 💬). 4. Виберіть свою модель угорі, а потім натисніть **Запустити сервер**. Коли сервер запущено, ви можете почати розмову за допомогою Open Interpreter. > **Примітка.** Локальний режим встановлює ваше `context_window` на 3000, а `max_tokens` на 1000. Якщо ваша модель має інші вимоги, установіть ці параметри вручну (див. нижче). #### Python Наш пакет Python дає вам більше контролю над кожним параметром. Для реплікації та підключення до LM Studio використовуйте ці налаштування: ```python from interpreter import interpreter interpreter.offline = True # Вимикає такі онлайн-функції, як Open Procedures interpreter.llm.model = "openai/x" # Каже AI надсилати повідомлення у форматі OpenAI interpreter.llm.api_key = "fake_key" # LiteLLM, який ми використовуємо для спілкування з LM Studio, вимагає api-ключ interpreter.llm.api_base = "http://localhost:1234/v1" # Познчате це на будь-якому сервері, сумісному з OpenAI interpreter.chat() ``` #### Контекстне вікно, максимальна кількість токенів Ви можете змінити `max_tokens` і `context_window` (у токенах) локально запущених моделей. У локальному режимі менші контекстні вікна використовуватимуть менше оперативної пам’яті, тому ми рекомендуємо спробувати набагато коротше вікно (~1000), якщо воно не вдається або працює повільно. Переконайтеся, що `max_tokens` менший за `context_window`. ```shell interpreter --local --max_tokens 1000 --context_window 3000 ``` ### Режим "verbose" Щоб допомогти вам перевірити Open Interpreter, у нас є режим `--verbose` для налагодження. Ви можете активувати режим "verbose", використовуючи його прапорець (`interpreter --verbose`) або в середині чату: ```shell $ interpreter ... > %verbose true <- Вмикає режим verbose > %verbose false <- Вимикає режим verbose ``` ### Команди інтерактивного режиму В інтерактивному режимі ви можете використовувати наведені нижче команди, щоб покращити свій досвід. Ось список доступних команд: **Доступні команди:** - `%verbose [true/false]`: увімкнути режим verbose. Без аргументів або з `true`. переходить у багатослівний режим. З `false` він виходить із багатослівного режиму. - `%reset`: скидає розмову поточного сеансу. - `% undo`: видаляє попереднє повідомлення користувача та відповідь ШІ з історії повідомлень. - `%tokens [prompt]`: (_Експериментально_) Розрахувати токени, які будуть надіслані з наступним запитом як контекст, і оцінити їх вартість. Додатково обчисліть токени та приблизну вартість «підказки», якщо вона надається. Покладається на [метод `cost_per_token()` LiteLLM](https://docs.litellm.ai/docs/completion/token_usage#2-cost_per_token) для оцінки витрат. - `%help`: Показати повідомлення довідки. ### Конфігурація / Профілі Open Interpreter дозволяє встановлювати поведінку за замовчуванням за допомогою файлів `yaml`. Це забезпечує гнучкий спосіб налаштування інтерпретатора, не змінюючи щоразу аргументи командного рядка. Виконайте цю команду, щоб відкрити каталог профілів: ``` interpreter --profiles ``` Ви можете додати файли `yaml`. Профіль за замовчуванням має назву `default.yaml`. #### Кілька профілів Open Interpreter підтримує декілька файлів `yaml`, що дозволяє легко перемикатися між конфігураціями: ``` interpreter --profile my_profile.yaml ``` ## Зразок сервера FastAPI Оновлення генератора дозволяє керувати Open Interpreter через кінцеві точки HTTP REST: ```python # server.py from fastapi import FastAPI from fastapi.responses import StreamingResponse from interpreter import interpreter app = FastAPI() @app.get("/chat") def chat_endpoint(message: str): def event_stream(): for result in interpreter.chat(message, stream=True): yield f"data: {result}\n\n" return StreamingResponse(event_stream(), media_type="text/event-stream") @app.get("/history") def history_endpoint(): return interpreter.messages ``` ```shell pip install fastapi uvicorn uvicorn server:app --reload ``` Ви також можете запустити сервер, ідентичний наведеному вище, просто запустивши `interpreter.server()`. ## Android Покроковий посібник із встановлення Open Interpreter на вашому пристрої Android можна знайти в [репозиторії open-interpreter-termux](https://github.com/MikeBirdTech/open-interpreter-termux). ## Повідомлення про безпеку Оскільки згенерований код виконується у вашому локальному середовищі, він може взаємодіяти з вашими файлами та налаштуваннями системи, потенційно призводячи до неочікуваних результатів, як-от втрати даних або ризиків для безпеки. **⚠️ Open Interpreter попросить підтвердження користувача перед виконанням коду.** Ви можете запустити `interpreter -y` або встановити `interpreter.auto_run = True`, щоб обійти це підтвердження, у такому випадку: - Будьте обережні, запитуючи команди, які змінюють файли або налаштування системи. - Дивіться на Open Interpreter як на самокерований автомобіль і будьте готові завершити процес, закривши термінал. - Спробуйте запустити Open Interpreter у обмеженому середовищі, наприклад Google Colab або Replit. Ці середовища більш ізольовані, що зменшує ризики виконання довільного коду. Існує **експериментальна** підтримка [безпечного режиму](https://github.com/OpenInterpreter/open-interpreter/blob/main/docs/SAFE_MODE.md), щоб зменшити деякі ризики. ## Як це працює? Open Interpreter оснащує [модель мови виклику функцій](https://platform.openai.com/docs/guides/gpt/function-calling) функцією `exec()`, яка приймає `мову` (як "Python" або "JavaScript") і `code` для запуску. Потім ми передаємо повідомлення моделі, код і результати вашої системи на термінал як Markdown. # Доступ до документації в автономному режимі Повна [документація](https://docs.openinterpreter.com/) доступна в дорозі без підключення до Інтернету. [Node](https://nodejs.org/en) є необхідною умовою: - Версія 18.17.0 або будь-яка пізніша версія 18.x.x. - Версія 20.3.0 або будь-яка пізніша версія 20.x.x. - Будь-яка версія, починаючи з 21.0.0 і далі, без вказівки верхньої межі. Встановіть [Mintlify](https://mintlify.com/): ```bash npm i -g mintlify@latest ``` Перейдіть у каталог документів і виконайте відповідну команду: ```bash # Якщо ви перебуваєте в кореневому каталозі проекту cd ./docs # Запустіть сервер документації mintlify dev ``` Має відкритися нове вікно браузера. Документація буде доступна за адресою [http://localhost:3000](http://localhost:3000), поки працює сервер документації. # Вклади Дякуємо за ваш інтерес до участі! Ми вітаємо участь спільноти. Щоб дізнатися більше про те, як взяти участь, ознайомтеся з нашими [інструкціями щодо створення внеску](https://github.com/OpenInterpreter/open-interpreter/blob/main/docs/CONTRIBUTING.md). # Дорожня карта Відвідайте [нашу дорожню карту](https://github.com/OpenInterpreter/open-interpreter/blob/main/docs/ROADMAP.md), щоб переглянути майбутнє Open Interpreter. **Примітка**: це програмне забезпечення не пов’язане з OpenAI.  > Маючи доступ до джуніора, який працює зі швидкістю ваших пальців ... ви можете зробити нові робочі процеси легкими та ефективними, а також відкрити переваги програмування новій аудиторії. > > — _OpenAI's Code Interpreter Release_
================================================ FILE: docs/README_VN.md ================================================
● Open Interpreter
chạy mô hình ngôn ngữ trí tuệ nhân tạo trên máy tính của bạn.
Mã nguồn mở và ứng dụng phát triển dựa trên code của OpenAI.
Quyền truy cập sớm dành cho máy tính cá nhân | Tài liệu đọc tham khảo
```shell pip install open-interpreter ``` ```shell interpreter ```
**Open Interpreter** Chạy LLMs trên máy tính cục bộ (Có thể sử dụng ngôn ngữ Python, Javascript, Shell, và nhiều hơn thế). Bạn có thể nói chuyện với Open Interpreter thông qua giao diện giống với ChatGPT ngay trên terminal của bạn bằng cách chạy lệnh `$ interpreter` sau khi tải thành công. Các tính năng chung giao diện ngôn ngữ mang llại - Tạo và chỉnh sửa ảnh, videos, PDF, vân vân... - Điều khiển trình duyệt Chrome để tiến hành nghiên cứu - Vẽ, làm sạch và phân tích các tập dữ liệu lớn (large datasets) - ...vân vân. **⚠️ Lưu ý: Bạn sẽ được yêu cầu phê duyệt mã trước khi chạy.**
## Thử nghiệm https://github.com/OpenInterpreter/open-interpreter/assets/63927363/37152071-680d-4423-9af3-64836a6f7b60 #### Bản thử nghiệm có sẵn trên Google Colab: [](https://colab.research.google.com/drive/1WKmRXZgsErej2xUriKzxrEAXdxMSgWbb?usp=sharing) #### Đi kèm với ứng dụng mẫu qua tương tác giọng nói (Lấy cảm hứng từ _Cô ấy_ (Giọng nữ)): [](https://colab.research.google.com/drive/1NojYGHDgxH6Y1G1oxThEBBb2AtyODBIK) ## Hướng dẫn khởi dộng ngắn ```shell pip install open-interpreter ``` ### Terminal Sau khi cài đặt, chạy dòng lệnh `interpreter`: ```shell interpreter ``` ### Python ```python from interpreter import interpreter interpreter.chat("Vẽ giá cổ phiếu đã bình hoá của AAPL và META ") # Chạy trên 1 dòng lệnh interpreter.chat() # Khởi động chat có khả năng tương tác ``` ## So sánh Code Interpreter của ChatGPT Bản phát hành của OpenAI [Code Interpreter](https://openai.com/blog/chatgpt-plugins#code-interpreter) sử dụng GPT-4 tăng khả năng hoàn thiện vấn đề thực tiễn với ChatGPT. Tuy nhiên, dịch vụ của OpenAI được lưu trữ, mã nguồn đóng, và rất hạn chế: - Không có truy cập Internet. - [Số lượng gói cài đặt hỗ trỡ có sẵn giới hạn](https://wfhbrian.com/mastering-chatgpts-code-interpreter-list-of-python-packages/). - tốc độ tải tối đa 100 MB , thời gian chạy giới hạn 120.0 giây . - Trạng thái tin nhắn bị xoá kèm với các tệp và liên kết được tạo trước đó khi đóng môi trường lại. --- Open Interpreter khắc phục những hạn chế này bằng cách chạy cục bộ trobộ môi trường máy tính của bạn. Nó có toàn quyền truy cập vào Internet, không bị hạn chế về thời gian hoặc kích thước tệp và có thể sử dụng bất kỳ gói hoặc thư viện nào. Đây là sự kết hợp sức mạnh của mã nguồn của GPT-4 với tính linh hoạt của môi trường phát triển cục bộ của bạn. ## Dòng lệnh **Update:** Cập nhật trình tạo lệnh (0.1.5) giới thiệu tính năng trực tuyến: ```python message = "Chúng ta đang ở trên hệ điều hành nào?" for chunk in interpreter.chat(message, display=False, stream=True): print(chunk) ``` ### Trò chuyện tương tác Để tạo một cuộc trò chuyện tương tác từ terminal của bạn, chạy `interpreter` bằng dòng lệnh: ```shell interpreter ``` hoặc `interpreter.chat()` từ file có đuôi .py : ```python interpreter.chat() ``` **Bạn cũng có thể phát trực tuyến từng đoạn:** ```python message = "Chúng ta đang chạy trên hệ điều hành nào?" for chunk in interpreter.chat(message, display=False, stream=True): print(chunk) ``` ### Trò chuyện lập trình được Để kiểm soát tốt hơn, bạn chuyển tin nhắn qua `.chat(message)`: ```python interpreter.chat("Truyền phụ đề tới tất cả videos vào /videos.") # ... Truyền đầu ra đến thiết bị đầu cuối của bạn (terminal) hoàn thành tác vụ ... interpreter.chat("Nhìn đẹp đấy nhưng bạn có thể làm cho phụ đề lớn hơn được không?") # ... ``` ### Tạo một cuộc trò chuyện mới: Trong Python, Open Interpreter ghi nhớ lịch sử hội thoại, nếu muốn bắt đầu lại từ đầu, bạn có thể cài thứ: ```python interpreter.messages = [] ``` ### Lưu và khôi phục cuộc trò chuyện `interpreter.chat()` trả về danh sách tin nhắn, có thể được sử dụng để tiếp tục cuộc trò chuyện với `interpreter.messages = messages`: ```python messages = interpreter.chat("Tên của tôi là Killian.") # Lưu tin nhắn tới 'messages' interpreter.messages = [] # Khởi động lại trình phiên dịch ("Killian" sẽ bị lãng quên) interpreter.messages = messages # Tiếp tục cuộc trò chuyện từ 'messages' ("Killian" sẽ được ghi nhớ) ``` ### Cá nhân hoá tin nhắn từ hệ thống Bạn có thể kiếm tra và điều chỉnh tin nhắn hệ thống từ Optừ Interpreter để mở rộng chức năng của nó, thay đổi quyền, hoặc đưa cho nó nhiều ngữ cảnh hơn. ```python interpreter.system_message += """ Chạy shell commands với -y để người dùng không phải xác nhận chúng. """ print(interpreter.system_message) ``` ### Thay đổi mô hình ngôn ngữ Open Interpreter sử dụng mô hình [LiteLLM](https://docs.litellm.ai/docs/providers/) để kết nối tới các mô hình ngôn ngữ được lưu trữ trước đó. Bạn có thể thay đổi mô hình ngôn ngữ bằng cách thay đổi tham số mô hình: ```shell interpreter --model gpt-3.5-turbo interpreter --model claude-2 interpreter --model command-nightly ``` Ở trong Python, đổi model bằng cách thay đổi đối tượng: ```python interpreter.llm.model = "gpt-3.5-turbo" ``` [Tìm tên chuỗi "mô hình" phù hợp cho mô hình ngôn ngữ của bạn ở đây.](https://docs.litellm.ai/docs/providers/) ### Chạy Open Interpreter trên máy cục bộ Open Interpreter có thể sử dụng máy chủ tương thích với OpenAI để chạy các mô hình cục bộ. (LM Studio, jan.ai, ollama, v.v.) Chỉ cần chạy `interpreter` với URL api_base của máy chủ suy luận của bạn (đối với LM studio, nó là `http://localhost:1234/v1` theo mặc định): ```vỏ trình thông dịch --api_base "http://localhost:1234/v1" --api_key "fake_key" ``` Ngoài ra, bạn có thể sử dụng Llamafile mà không cần cài đặt bất kỳ phần mềm bên thứ ba nào chỉ bằng cách chạy ```vỏ thông dịch viên --local ``` để biết hướng dẫn chi tiết hơn, hãy xem [video này của Mike Bird](https://www.youtube.com/watch?v=CEs51hGWuGU?si=cN7f6QhfT4edfG5H) **Để chạy LM Studio ở chế độ nền.** 1. Tải [https://lmstudio.ai/](https://lmstudio.ai/) và khởi động. 2. Chọn một mô hình rồi nhấn **↓ Download**. 3. Nhấn vào nút **↔️** ở bên trái (dưới 💬). 4. Chọn mô hình của bạn ở phía trên, rồi nhấn chạy **Start Server**. Một khi server chạy, bạn có thể bắt đầu trò chuyện với Open Interpreter. > **Lưu ý:** Chế độ cục bộ chỉnh `context_window` của bạn tới 3000, và `max_tokens` của bạn tới 600. Nếu mô hình của bạn có các yêu cầu khác, thì hãy chỉnh các tham số thủ công (xem bên dưới). #### Cửa sổ ngữ cảnh (Context Window), (Max Tokens) Bạn có thể thay đổi `max_tokens` và `context_window` (ở trong các) of locally running models. Ở chế độ cục bộ, các cửa sổ ngữ cảnh sẽ tiêu ít RAM hơn, vậy nên chúng tôi khuyến khích dùng cửa sổ nhỏ hơn (~1000) nếu như nó chạy không ổn định / hoặc nếu nó chậm. Đảm bảo rằng `max_tokens` ít hơn `context_window`. ```shell interpreter --local --max_tokens 1000 --context_window 3000 ``` ### Chế độ sửa lỗi Để giúp đóng góp kiểm tra Open Interpreter, thì chế độ `--verbose` hơi dài dòng. Bạn có thể khởi động chế độ sửa lỗi bằng cách sử dụng cờ (`interpreter --verbose`), hoặc mid-chat: ```shell $ interpreter ... > %verbose true <- Khởi động chế độ gỡ lỗi > %verbose false <- Tắt chế độ gỡ lỗi ``` ### Lệnh chế độ tương tác Trong chế độ tương tác, bạn có thể sử dụng những dòng lệnh sau để cải thiện trải nghiệm của mình. Đây là danh sách các dòng lệnh có sẵn: **Các lệnh có sẵn:** - `%verbose [true/false]`: Bật chế độ gỡ lỗi. Có hay không có `true` đều khởi động chế độ gỡ lỗi. Với `false` thì nó tắt chế độ gỡ lỗi. - `%reset`: Khởi động lại toàn bộ phiên trò chuyện hiện tại. - `%undo`: Xóa tin nhắn của người dùng trước đó và phản hồi của AI khỏi lịch sử tin nhắn. - `%save_message [path]`: Lưu tin nhắn vào một đường dẫn JSON được xác định từ trước. Nếu không có đường dẫn nào được cung cấp, nó sẽ mặc định là `messages.json`. - `%load_message [path]`: Tải tin nhắn từ một đường dẫn JSON được chỉ định. Nếu không có đường dẫn nào được cung cấp, nó sẽ mặc định là `messages.json`. - `%tokens [prompt]`: (_Experimental_) Tính toán các token sẽ được gửi cùng với lời nhắc tiếp theo dưới dạng ngữ cảnh và hao tổn. Tùy chọn tính toán token và hao tổn ước tính của một `prompt` nếu được cung cấp. Dựa vào [hàm `cost_per_token()` của mô hình LiteLLM](https://docs.litellm.ai/docs/completion/token_usage#2-cost_per_token) để tính toán hao tổn. - `%help`: Hiện lên trợ giúp cho cuộc trò chuyện. ### Cấu hình cài Open Interpreter cho phép bạn thiết lập các tác vụ mặc định bằng cách sử dụng file `config.yaml`. Điều này cung cấp một cách linh hoạt để định cấu hình trình thông dịch mà không cần thay đổi đối số dòng lệnh mỗi lần Chạy lệnh sau để mở tệp cấu hình: ``` interpreter --config ``` #### Cấu hình cho nhiều tệp Open Interpreter hỗ trợ nhiều file `config.yaml`, cho phép bạn dễ dàng chuyển đổi giữa các cấu hình thông qua lệnh `--config_file`. **Chú ý**: `--config_file` chấp nhận tên tệp hoặc đường dẫn tệp. Tên tệp sẽ sử dụng thư mục cấu hình mặc định, trong khi đường dẫn tệp sẽ sử dụng đường dẫn đã chỉ định. Để tạo hoặc chỉnh sửa cấu hình mới, hãy chạy: ``` interpreter --config --config_file $config_path ``` Để yêu cầu Open Interpreter chạy một tệp cấu hình cụ thể, hãy chạy: ``` interpreter --config_file $config_path ``` **Chú ý**: Thay đổi `$config_path` với tên hoặc đường dẫn đến tệp cấu hình của bạn. ##### Ví dụ CLI 1. Tạo mới một file `config.turbo.yaml` ``` interpreter --config --config_file config.turbo.yaml ``` 2. Chạy file `config.turbo.yaml`để đặt lại `model` thành `gpt-3.5-turbo` 3. Chạy Open Interpreter với cấu hình `config.turbo.yaml ``` interpreter --config_file config.turbo.yaml ``` ##### Ví dụ Python Bạn cũng có thể tải các tệp cấu hình khi gọi Open Interpreter từ tập lệnh Python: ```python import os from interpreter import interpreter currentPath = os.path.dirname(os.path.abspath(__file__)) config_path=os.path.join(currentPath, './config.test.yaml') interpreter.extend_config(config_path=config_path) message = "What operating system are we on?" for chunk in interpreter.chat(message, display=False, stream=True): print(chunk) ``` ## Máy chủ FastAPI mẫu Bản cập nhật trình tạo cho phép điều khiển Trình thông dịch mở thông qua các điểm cuối HTTP REST: ```python # server.py from fastapi import FastAPI from fastapi.responses import StreamingResponse from interpreter import interpreter app = FastAPI() @app.get("/chat") def chat_endpoint(message: str): def event_stream(): for result in interpreter.chat(message, stream=True): yield f"data: {result}\n\n" return StreamingResponse(event_stream(), media_type="text/event-stream") @app.get("/history") def history_endpoint(): return interpreter.messages ``` ```shell pip install fastapi uvicorn uvicorn server:app --reload ``` ## Hướng dẫn an toàn Vì mã được tạo được thực thi trong môi trường cục bộ của bạn nên nó có thể tương tác với các tệp và cài đặt hệ thống của bạn, có khả năng dẫn đến các kết quả không mong muốn như mất dữ liệu hoặc rủi ro bảo mật. **⚠️ Open Interpreter sẽ yêu cầu xác nhận của người dùng trước khi chạy code.** Bạn có thể chạy `interpreter -y` hoặc đặt `interpreter.auto_run = True` để bỏ qua xác nhận này, trong trường hợp đó: - Hãy thận trọng khi yêu cầu các lệnh sửa đổi tệp hoặc cài đặt hệ thống. - Theo dõi Open Interpreter giống như một chiếc ô tô tự lái và sẵn sàng kết thúc quá trình bằng cách đóng terminal của bạn. - Cân nhắc việc chạy Open Interpreter trong môi trường bị hạn chế như Google Colab hoặc Replit. Những môi trường này biệt lập hơn, giảm thiểu rủi ro khi chạy code tùy ý. Đây là hỗ trợ **thử nghiệm** cho [chế độ an toàn](docs/SAFE_MODE.md) giúp giảm thiểu rủi ro. ## Nó hoạt động thế nào? Open Interpreter trang bị [mô hình ngôn ngữ gọi hàm](https://platform.openai.com/docs/guides/gpt/function-calling) với một hàm `exec()`, chấp nhận một `language` (như "Python" hoặc "JavaScript") và `code` để chạy. Sau đó, chúng tôi truyền trực tuyến thông báo, mã của mô hình và kết quả đầu ra của hệ thống của bạn đến terminal dưới dạng Markdown. # Đóng góp Cảm ơn bạn đã quan tâm đóng góp! Chúng tôi hoan nghênh sự tham gia của cộng đồng. Vui lòng xem [Hướng dẫn đóng góp](CONTRIBUTING.md) để biết thêm chi tiết cách tham gia. ## Giấy phép Open Interpreter được cấp phép theo Giấy phép MIT. Bạn được phép sử dụng, sao chép, sửa đổi, phân phối, cấp phép lại và bán các bản sao của phần mềm. **Lưu ý**: Phần mềm này không liên kết với OpenAI. > Có quyền truy cập vào một lập trình viên cấp dưới làm việc nhanh chóng trong tầm tay bạn ... có thể khiến quy trình làm việc mới trở nên dễ dàng và hiệu quả, cũng như mở ra những lợi ích của việc lập trình cho người mới. > > — _Phát hành trình thông dịch mã của OpenAI_
================================================ FILE: docs/README_ZH.md ================================================
● Open Interpreter（开放解释器）
让语言模型在您的计算机上运行代码。
在本地实现的开源OpenAI的代码解释器。
登记以提前获取Open Interpreter（开放解释器）桌面应用程序 | 阅读新文档
```shell pip install open-interpreter ``` ```shell interpreter ```
**Open Interpreter（开放解释器）** 可以让大语言模型（LLMs）在本地运行代码（比如 Python、JavaScript、Shell 等）。安装后，在终端上运行 `$ interpreter` 即可通过类似 ChatGPT 的界面与 Open Interpreter 聊天。 本软件为计算机的通用功能提供了一个自然语言界面，比如： - 创建和编辑照片、视频、PDF 等 - 控制 Chrome 浏览器进行搜索 - 绘制、清理和分析大型数据集 - ... **⚠️ 注意：在代码运行前都会要求您批准执行代码。**
## 演示 https://github.com/OpenInterpreter/open-interpreter/assets/63927363/37152071-680d-4423-9af3-64836a6f7b60 #### Google Colab 上也提供了交互式演示： [](https://colab.research.google.com/drive/1WKmRXZgsErej2xUriKzxrEAXdxMSgWbb?usp=sharing) ## 快速开始 ```shell pip install open-interpreter ``` ### 终端 安装后，运行 `interpreter`： ```shell interpreter ``` ### Python ```python from interpreter import interpreter interpreter.chat("Plot AAPL and META's normalized stock prices") # 执行单一命令 interpreter.chat() # 开始交互式聊天 ``` ## 与 ChatGPT 的代码解释器比较 OpenAI 发布的 [Code Interpreter](https://openai.com/blog/chatgpt-plugins#code-interpreter) 和 GPT-4 提供了一个与 ChatGPT 完成实际任务的绝佳机会。 但是，OpenAI 的服务是托管的，闭源的，并且受到严格限制： - 无法访问互联网。 - [预装软件包数量有限](https://wfhbrian.com/mastering-chatgpts-code-interpreter-list-of-python-packages/)。 - 允许的最大上传为 100 MB，且最大运行时间限制为 120.0 秒 - 当运行环境中途结束时，之前的状态会被清除（包括任何生成的文件或链接）。 --- Open Interpreter（开放解释器）通过在本地环境中运行克服了这些限制。它可以完全访问互联网，不受运行时间或是文件大小的限制，也可以使用任何软件包或库。 它将 GPT-4 代码解释器的强大功能与本地开发环境的灵活性相结合。 ## 命令 ### 交互式聊天 要在终端中开始交互式聊天，从命令行运行 `interpreter`： ```shell interpreter ``` 或者从.py 文件中运行 `interpreter.chat()`： ```python interpreter.chat() ``` ### 程序化聊天 为了更精确的控制，您可以通过 `.chat(message)` 直接传递消息 ： ```python interpreter.chat("Add subtitles to all videos in /videos.") # ... Streams output to your terminal, completes task ... interpreter.chat("These look great but can you make the subtitles bigger?") # ... ``` ### 开始新的聊天 在 Python 中，Open Interpreter 会记录历史对话。如果你想从头开始，可以进行重置： ```python interpreter.messages = [] ``` ### 保存和恢复聊天 ```python messages = interpreter.chat("My name is Killian.") # 保存消息到 'messages' interpreter.messages = [] # 重置解释器 ("Killian" 将被遗忘) interpreter.messages = messages # 从 'messages' 恢复聊天 ("Killian" 将被记住) ``` ### 自定义系统消息 你可以检查和配置 Open Interpreter 的系统信息，以扩展其功能、修改权限或赋予其更多上下文。 ```python interpreter.system_message += """ 使用 -y 运行 shell 命令，这样用户就不必确认它们。 """ print(interpreter.system_message) ``` ### 更改模型 Open Interpreter 使用[LiteLLM](https://docs.litellm.ai/docs/providers/)连接到语言模型。 您可以通过设置模型参数来更改模型： ```shell interpreter --model gpt-3.5-turbo interpreter --model claude-2 interpreter --model command-nightly ``` 在 Python 环境下，您需要手动设置模型： ```python interpreter.llm.model = "gpt-3.5-turbo" ``` ### 在本地运行 Open Interpreter（开放解释器） ```shell interpreter --local ``` ### 调试模式 为了帮助贡献者检查和调试 Open Interpreter，`--verbose` 模式提供了详细的日志。 您可以使用 `interpreter --verbose` 来激活调试模式，或者直接在终端输入： ```shell $ interpreter ... > %verbose true <- 开启调试模式 > %verbose false <- 关闭调试模式 ``` ## 安全提示 由于生成的代码是在本地环境中运行的，因此会与文件和系统设置发生交互，从而可能导致本地数据丢失或安全风险等意想不到的结果。 **⚠️ 所以在执行任何代码之前，Open Interpreter 都会询问用户是否运行。** 您可以运行 `interpreter -y` 或设置 `interpreter.auto_run = True` 来绕过此确认，此时： - 在运行请求修改本地文件或系统设置的命令时要谨慎。 - 请像驾驶自动驾驶汽车一直握着方向盘一样留意 Open Interpreter，并随时做好通过关闭终端来结束进程的准备。 - 考虑在 Google Colab 或 Replit 等受限环境中运行 Open Interpreter 的主要原因是这些环境更加独立，从而降低执行任意代码导致出现问题的风险。 ## 它是如何工作的？ Open Interpreter 为[函数调用语言模型](https://platform.openai.com/docs/guides/gpt/function-calling)配备了 `exec()` 函数，该函数接受 `编程语言`（如 "Python "或 "JavaScript"）和要运行的 `代码`。 然后，它会将模型的信息、代码和系统的输出以 Markdown 的形式流式传输到终端。 # 作出贡献 感谢您对本项目参与的贡献！我们欢迎所有人贡献到本项目里面。 请参阅我们的 [贡献准则](CONTRIBUTING.md)，了解如何参与贡献的更多详情。 ## 规划图 若要预览 Open Interpreter 的未来，请查看[我们的路线图](https://github.com/OpenInterpreter/open-interpreter/blob/main/docs/ROADMAP.md) 。 **请注意**：此软件与 OpenAI 无关。  ================================================ FILE: docs/ROADMAP.md ================================================ # Roadmap ## Documentation - [ ] Work with Mintlify to translate docs. How does Mintlify let us translate our documentation automatically? I know there's a way. - [ ] Better comments throughout the package (they're like docs for contributors) - [ ] Show how to replace interpreter.llm so you can use a custom llm ## New features - [ ] Figure out how to get OI to answer to user input requests like python's `input()`. Do we somehow detect a delay in the output..? Is there some universal flag that TUIs emit when they expect user input? Should we do this semantically with embeddings, then ask OI to review it and respond..? - [ ] Placeholder text that gives a compelling example OI request. Probably use `textual` - [ ] Everything else `textual` offers, like could we make it easier to select text? Copy paste in and out? Code editing interface? - [x] Let people turn off the active line highlighting - [ ] Add a --plain flag which doesn't use rich, just prints stuff in plain text - [ ] Use iPython stuff to track the active line, instead of inserting print statements, which makes debugging weird (From ChatGPT: For deeper insights into what's happening behind the scenes, including which line of code is being executed, you can increase the logging level of the IPython kernel. You can configure the kernel's logger to a more verbose setting, which logs each execution request. However, this requires modifying the kernel's startup settings, which might involve changing logging configurations in the IPython kernel source or when launching the kernel.) - [ ] Let people edit the code OI writes. Could just open it in the user's preferred editor. Simple. [Full description of how to implement this here.](https://github.com/OpenInterpreter/open-interpreter/pull/830#issuecomment-1854989795) - [ ] Display images in the terminal interface - [ ] There should be a function that just renders messages to the terminal, so we can revive conversation navigator, and let people look at their conversations - [ ] ^ This function should also render the last like 5 messages once input() is about to be run, so we don't get those weird stuttering `rich` artifacts - [ ] Let OI use OI, add `interpreter.chat(async=True)` bool. OI can use this to open OI on a new thread - [ ] Also add `interpreter.await()` which waits for `interpreter.running` (?) to = False, and `interpreter.result()` which returns the last assistant messages content. - [ ] Allow for limited functions (`interpreter.functions`) using regex - [ ] If `interpreter.functions != []`: - [ ] set `interpreter.computer.languages` to only use Python - [ ] Use regex to ensure the output of code blocks conforms to just using those functions + other python basics - [ ] (Maybe) Allow for a custom embedding function (`interpreter.embed` or `computer.ai.embed`) which will let us do semantic search - [ ] (Maybe) if a git is detected, switch to a mode that's good for developers, like showing nested file structure in dynamic system message, searching for relevant functions (use computer.files.search) - [x] Allow for integrations somehow (you can replace interpreter.llm.completions with a wrapped completions endpoint for any kind of logging. need to document this tho) - [ ] Document this^ - [ ] Expand "safe mode" to have proper, simple Docker support, or maybe Cosmopolitan LibC - [ ] Make it so core can be run elsewhere from terminal package — perhaps split over HTTP (this would make docker easier too) - [ ] For OS mode, experiment with screenshot just returning active window, experiment with it just showing the changes, or showing changes in addition to the whole thing, etc. GAIA should be your guide ## Future-proofing - [ ] Really good tests / optimization framework, to be run less frequently than Github actions tests - [x] Figure out how to run us on [GAIA](https://huggingface.co/gaia-benchmark) - [x] How do we just get the questions out of this thing? - [x] How do we assess whether or not OI has solved the task? - [ ] Loop over GAIA, use a different language model every time (use Replicate, then ask LiteLLM how they made their "mega key" to many different LLM providers) - [ ] Loop over that ↑ using a different prompt each time. Which prompt is best across all LLMs? - [ ] (For the NCU) might be good to use a Google VM with a display - [ ] (Future future) Use GPT-4 to assess each result, explaining each failure. Summarize. Send it all to GPT-4 + our prompt. Let it redesign the prompt, given the failures, rinse and repeat - [ ] Stateless (as in, doesn't use the application directory) core python package. All `appdir` or `platformdirs` stuff should be only for the TUI - [ ] `interpreter.__dict__` = a dict derived from config is how the python package should be set, and this should be from the TUI. `interpreter` should not know about the config - [ ] Move conversation storage out of the core and into the TUI. When we exit or error, save messages same as core currently does - [ ] Further split TUI from core (some utils still reach across) - [ ] Better storage of different model keys in TUI / config file. All keys, to multiple providers, should be stored in there. Easy switching - [ ] Automatically migrate users from old config to new config, display a message of this - [ ] On update, check for new system message and ask user to overwrite theirs, or only let users pass in "custom instructions" which adds to our system message - [ ] I think we could have a config that's like... system_message_version. If system_message_version is below the current version, ask the user if we can overwrite it with the default config system message of that version. (This somewhat exists now but needs to be robust) # What's in our scope? Open Interpreter contains two projects which support each other, whose scopes are as follows: 1. `core`, which is dedicated to figuring out how to get LLMs to safely control a computer. Right now, this means creating a real-time code execution environment that language models can operate. 2. `terminal_interface`, a text-only way for users to direct the code-running LLM running inside `core`. This includes functions for connecting the `core` to various local and hosted LLMs (which the `core` itself should not know about). # What's not in our scope? Our guiding philosophy is minimalism, so we have also decided to explicitly consider the following as **out of scope**: 1. Additional functions in `core` beyond running code. 2. More complex interactions with the LLM in `terminal_interface` beyond text (but file paths to more complex inputs, like images or video, can be included in that text). --- This roadmap gets pretty rough from here. More like working notes. # Working Notes ## \* Roughly, how to build `computer.browser`: First I think we should have a part, like `computer.browser.ask(query)` which just hits up [perplexity](https://www.perplexity.ai/) for fast answers to questions. Then we want these sorts of things: - `browser.open(url)` - `browser.screenshot()` - `browser.click()` It should actually be based closely on Selenium. Copy their API so the LLM knows it. Other than that, basically should be = to the computer module itself, at least the IO / keyboard and mouse parts. However, for non vision models, `browser.screenshot()` can return the accessibility tree, not an image. And for `browser.click(some text)` we can use the HTML to find that text. **Here's how GPT suggests we implement the first steps of this:** Creating a Python script that automates the opening of Chrome with the necessary flags and then interacts with it to navigate to a URL and retrieve the accessibility tree involves a few steps. Here's a comprehensive approach: 1. **Script to Launch Chrome with Remote Debugging**: - This script will start Chrome with the `--remote-debugging-port=9222` flag. - It will handle different platforms (Windows, macOS, Linux). 2. **Python Script for Automation**: - This script uses `pychrome` to connect to the Chrome instance, navigate to a URL, and retrieve the accessibility tree. ### Step 1: Launching Chrome with Remote Debugging You'll need a script to launch Chrome. This script varies based on the operating system. Below is an example for Windows. You can adapt it for macOS or Linux by changing the path and command to start Chrome. ```python import subprocess import sys import os def launch_chrome(): chrome_path = "C:\\Program Files (x86)\\Google\\Chrome\\Application\\chrome.exe" # Update this path for your system url = "http://localhost:9222/json/version" subprocess.Popen([chrome_path, '--remote-debugging-port=9222'], shell=True) print("Chrome launched with remote debugging on port 9222.") if __name__ == "__main__": launch_chrome() ``` ### Step 2: Python Script to Navigate and Retrieve Accessibility Tree Next, you'll use `pychrome` to connect to this Chrome instance. Ensure you've installed `pychrome`: ```bash pip install pychrome ``` Here's the Python script: ```python import pychrome import time def get_accessibility_tree(tab): # Enable the Accessibility domain tab.call_method("Accessibility.enable") # Get the accessibility tree tree = tab.call_method("Accessibility.getFullAXTree") return tree def main(): # Create a browser instance browser = pychrome.Browser(url="http://127.0.0.1:9222") # Create a new tab tab = browser.new_tab() # Start the tab tab.start() # Navigate to a URL tab.set_url("https://www.example.com") time.sleep(3) # Wait for page to load # Retrieve the accessibility tree accessibility_tree = get_accessibility_tree(tab) print(accessibility_tree) # Stop the tab (closes it) tab.stop() # Close the browser browser.close() if __name__ == "__main__": main() ``` This script will launch Chrome, connect to it, navigate to "https://www.example.com", and then print the accessibility tree to the console. **Note**: The script to launch Chrome assumes a typical installation path on Windows. You will need to modify this path according to your Chrome installation location and operating system. Additionally, handling different operating systems requires conditional checks and respective commands for each OS. ================================================ FILE: docs/SAFE_MODE.md ================================================ # Safe Mode **⚠️ Safe mode is experimental and does not provide any guarantees of safety or security.** Open Interpreter is working on providing an experimental safety toolkit to help you feel more confident running the code generated by Open Interpreter. Install Open Interpreter with the safety toolkit dependencies as part of the bundle: ```shell pip install open-interpreter[safe] ``` Alternatively, you can install the safety toolkit dependencies separately in your virtual environment: ```shell pip install semgrep ``` ## Features - **No Auto Run**: Safe mode disables the ability to automatically execute code - **Code Scanning**: Scan generated code for vulnerabilities with [`semgrep`](https://semgrep.dev/) ## Enabling Safe Mode You can enable safe mode by passing the `--safe` flag when invoking `interpreter` or by configuring `safe_mode` in your [config file](https://github.com/OpenInterpreter/open-interpreter#configuration). The safe mode setting has three options: - `off`: disables the safety toolkit (_default_) - `ask`: prompts you to confirm that you want to scan code - `auto`: automatically scans code ### Example Config: ```yaml model: gpt-4 temperature: 0 verbose: false safe_mode: ask ``` ## Roadmap Some upcoming features that enable even more safety: - [Execute code in containers](https://github.com/OpenInterpreter/open-interpreter/pull/459) ## Tips & Tricks You can adjust the `system_message` in your [config file](https://github.com/OpenInterpreter/open-interpreter#configuration) to include instructions for the model to scan packages with [`guarddog`]() before installing them. ```yaml model: gpt-4 verbose: false safe_mode: ask system_message: | # normal system message here BEFORE INSTALLING ANY PACKAGES WITH pip OR npm YOU MUST SCAN THEM WITH `guarddog` FIRST. Run `guarddog pypi scan $package` for pip packages and `guarddog npm scan $package` for npm packages. `guarddog` only accepts one package name at a time. ``` ================================================ FILE: docs/SECURITY.md ================================================ # Open Interpreter Security Policy We take security seriously. Responsible reporting and disclosure of security vulnerabilities is important for the protection and privacy of our users. If you discover any security vulnerabilities, please follow these guidelines. Published security advisories are available on our [GitHub Security Advisories] page. To report a vulnerability, please draft a [new security advisory on GitHub]. Any fields that you are unsure of or don't understand can be left at their default values. The important part is that the vulnerability is reported. Once the security advisory draft has been created, we will validate the vulnerability and coordinate with you to fix it, release a patch, and responsibly disclose the vulnerability to the public. Read GitHub's documentation on [privately reporting a security vulnerability] for details. Please do not report undisclosed vulnerabilities on public sites or forums, including GitHub issues and pull requests. Reporting vulnerabilities to the public could allow attackers to exploit vulnerable applications before we have been able to release a patch and before applications have had time to install the patch. Once we have released a patch and sufficient time has passed for applications to install the patch, we will disclose the vulnerability to the public, at which time you will be free to publish details of the vulnerability on public sites and forums. If you have a fix for a security vulnerability, please do not submit a GitHub pull request. Instead, report the vulnerability as described in this policy. Once we have verified the vulnerability, we can create a [temporary private fork] to collaborate on a patch. We appreciate your cooperation in helping keep our users safe by following this policy. [github security advisories]: https://github.com/OpenInterpreter/open-interpreter/security/advisories [new security advisory on github]: https://github.com/OpenInterpreter/open-interpreter/security/advisories/new [privately reporting a security vulnerability]: https://docs.github.com/en/code-security/security-advisories/guidance-on-reporting-and-writing/privately-reporting-a-security-vulnerability [temporary private fork]: https://docs.github.com/en/code-security/security-advisories/repository-security-advisories/collaborating-in-a-temporary-private-fork-to-resolve-a-repository-security-vulnerability ================================================ FILE: docs/code-execution/computer-api.mdx ================================================ --- title: Computer API --- The following functions are designed for language models to use in Open Interpreter, currently only supported in [OS Mode](/guides/os-mode/). ### Display - View Takes a screenshot of the primary display. ```python interpreter.computer.display.view() ``` ### Display - Center Gets the x, y value of the center of the screen. ```python x, y = interpreter.computer.display.center() ``` ### Keyboard - Hotkey Performs a hotkey on the computer ```python interpreter.computer.keyboard.hotkey(" ", "command") ``` ### Keyboard - Write Writes the text into the currently focused window. ```python interpreter.computer.keyboard.write("hello") ``` ### Mouse - Click Clicks on the specified coordinates, or an icon, or text. If text is specified, OCR will be run on the screenshot to find the text coordinates and click on it. ```python # Click on coordinates interpreter.computer.mouse.click(x=100, y=100) # Click on text on the screen interpreter.computer.mouse.click("Onscreen Text") # Click on a gear icon interpreter.computer.mouse.click(icon="gear icon") ``` ### Mouse - Move Moves to the specified coordinates, or an icon, or text. If text is specified, OCR will be run on the screenshot to find the text coordinates and move to it. ```python # Click on coordinates interpreter.computer.mouse.move(x=100, y=100) # Click on text on the screen interpreter.computer.mouse.move("Onscreen Text") # Click on a gear icon interpreter.computer.mouse.move(icon="gear icon") ``` ### Mouse - Scroll Scrolls the mouse a specified number of pixels. ```python # Scroll Down interpreter.computer.mouse.scroll(-10) # Scroll Up interpreter.computer.mouse.scroll(10) ``` ### Clipboard - View Returns the contents of the clipboard. ```python interpreter.computer.clipboard.view() ``` ### OS - Get Selected Text Get the selected text on the screen. ```python interpreter.computer.os.get_selected_text() ``` ### Mail - Get Retrieves the last `number` emails from the inbox, optionally filtering for only unread emails. (Mac only) ```python interpreter.computer.mail.get(number=10, unread=True) ``` ### Mail - Send Sends an email with the given parameters using the default mail app. (Mac only) ```python interpreter.computer.mail.send("john@email.com", "Subject", "Body", ["path/to/attachment.pdf", "path/to/attachment2.pdf"]) ``` ### Mail - Unread Count Retrieves the count of unread emails in the inbox. (Mac only) ```python interpreter.computer.mail.unread_count() ``` ### SMS - Send Send a text message using the default SMS app. (Mac only) ```python interpreter.computer.sms.send("2068675309", "Hello from Open Interpreter!") ``` ### Contacts - Get Phone Number Returns the phone number of a contact name. (Mac only) ```python interpreter.computer.contacts.get_phone_number("John Doe") ``` ### Contacts - Get Email Address Returns the email of a contact name. (Mac only) ```python interpreter.computer.contacts.get_phone_number("John Doe") ``` ### Calendar - Get Events Fetches calendar events for the given date or date range from all calendars. (Mac only) ```python interpreter.computer.calendar.get_events(start_date=datetime, end_date=datetime) ``` ### Calendar - Create Event Creates a new calendar event. Uses first calendar if none is specified (Mac only) ```python interpreter.computer.calendar.create_event(title="Title", start_date=datetime, end_date=datetime, location="Location", notes="Notes", calendar="Work") ``` ### Calendar - Delete Event Delete a specific calendar event. (Mac only) ```python interpreter.computer.calendar.delete_event(event_title="Title", start_date=datetime, calendar="Work") ``` ================================================ FILE: docs/code-execution/custom-languages.mdx ================================================ --- title: Custom Languages --- You can add or edit the programming languages that Open Interpreter's computer runs. In this example, we'll swap out the `python` language for a version of `python` that runs in the cloud. We'll use `E2B` to do this. ([`E2B`](https://e2b.dev/) is a secure, sandboxed environment where you can run arbitrary code.) First, [get an API key here](https://e2b.dev/), and set it: ```python import os os.environ["E2B_API_KEY"] = "
Introduction
**Open Interpreter** lets language models run code.
You can chat with Open Interpreter through a ChatGPT-like interface in your terminal by running `interpreter` after installing.
This provides a natural-language interface to your computer's general-purpose capabilities:
- Create and edit photos, videos, PDFs, etc.
- Control a Chrome browser to perform research
- Plot, clean, and analyze large datasets
- ...etc.
Quick start
If you already use Python, you can install Open Interpreter via `pip`:\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": { "id": "N72lFowc2GZv" }, "source": [ "# Setup" ] }, { "cell_type": "markdown", "metadata": { "id": "rW6EmHRli1fN" }, "source": [ "First, let's install `open-interpreter`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4pDQ5xE-lokf" }, "outputs": [], "source": [ "!pip install open-interpreter\n", "# Google Colab users: restart your runtime here." ] }, { "cell_type": "markdown", "metadata": { "id": "iYHe0KLri7lU" }, "source": [ "Open Interpreter is best used with GPT-4. You can [grab an API key for it here](https://platform.openai.com/account/api-keys).\n", "\n", "❗ **Remember to restart your runtime** (`Runtime` > `Restart`) before running the next cell, or you will get an error." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "SCEh9l8LZqcL" }, "outputs": [], "source": [ "from interpreter import interpreter\n", "\n", "# Paste your OpenAI API key below.\n", "interpreter.llm.api_key = \"your_openai_api_key\"" ] }, { "cell_type": "markdown", "source": [ "**By default, Open Interpreter will ask for confirmation before executing any code.**\n", "\n", "Since Google Colab is a safe, isolated environment, we recommend enabling `auto_run`. This mimics the behavior of OpenAI's code interpreter." ], "metadata": { "id": "6hQubS1mFe8r" } }, { "cell_type": "code", "source": [ "interpreter.auto_run = True" ], "metadata": { "id": "CrI5b2IAFeDk" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "A1wGFVu9MNpM" }, "source": [ "# Basic Examples" ] }, { "cell_type": "markdown", "metadata": { "id": "loqh8HJ-oQAG" }, "source": [ "## Hello, World!" ] }, { "cell_type": "markdown", "metadata": { "id": "KQGYgFehnumE" }, "source": [ "Let's start by asking the interpreter to print hello world." ] }, { "cell_type": "code", "source": [ "interpreter.chat(\"Please print hello world.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 182, "referenced_widgets": [ "8e4d22ad1dcc44ec882bb9c6663de431", "915e05fbbcd44fb1b0402d5a51b9dcf6", "ed7c54937ec94948915fbe589e04d312", "ef25bc033b0b419bb7b33e05aed61b43", "1672ae981ae041ae8b26126e30f29882", "84e676b87e204fa9b36f1ec2ccefc396" ] }, "id": "sasDzlwm0JAZ", "outputId": "b47ab4ba-6134-42b4-e288-5fd55a1dad89" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "\n", "\n" ], "text/html": [ "
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"cell_type": "markdown",
"metadata": {
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"source": [
"Great! The model decided to run a **code block** then tell us its output."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2VwbdspIofFC"
},
"source": [
"## Math"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sf-CMTm2oizg"
},
"source": [
"For this example, we're going to open an interactive chat in our terminal with `interpreter.chat()`.\n",
"\n",
"💬 **The interactive chat behaves exactly like ChatGPT.** 💬"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uytMTyy6qFMd"
},
"source": [
"Try this:\n",
"\n",
"1. Ask Open Interpreter to solve an equation like `10x + 14 = 12`\n",
"2. Watch it use a Python library called `sympy` to solve it.\n",
"3. Stop the cell to leave the interactive chat."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 336,
"referenced_widgets": [
"1326436b350b45ed82d6a1be8d9402ce",
"ef334d3eb6a043608312c67aa3c7e3b8",
"75a72b485eb0422a820209bef270a311",
"065b7774a5a34beebb77f7e6a1501617",
"50e4adc0bc80423e9549ba0ca3c1125d",
"9ad527758ac44f7ba5cf5501f0a2ccc1"
]
},
"id": "ZpwadxmIoiIR",
"outputId": "bea0ba64-593d-41dc-c805-bb3f047413ce"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Type 'exit' to leave the chat.\n",
"\n",
"> Can you solve this equation? 10x + 14 = 21 / 3\n"
]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"> exit\n"
]
}
],
"source": [
"interpreter.chat()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Y-TzGmZ2rVPd"
},
"source": [
"## Web Browsing"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2QWXsSwarjav"
},
"source": [
"Let's ask Open Interpreter to browse the web.\n",
"\n",
"1. Start by opening an interactive chat again with `interpreter.chat()`.\n",
"2. Type a query like \"what are the last 10 BBC news headlines?\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
"referenced_widgets": [
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},
"id": "2WlB4ArZlbOE",
"outputId": "e549febb-35f1-48e2-90ef-320beeec313a"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Type 'exit' to leave the chat.\n",
"\n",
"> What are the last 10 BBC news headlines?\n"
]
},
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"\n"
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},
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"text": [
"> exit\n"
]
}
],
"source": [
"interpreter.chat()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ace0nkv8s66H"
},
"source": [
"Here it likely ran two code blocks:\n",
"\n",
"1. The first code block installed `feedparser` from pip.\n",
"1. The second used `feedparser` to read the BBC's RSS feed.\n",
"\n",
"Notice that the first code block **was a shell command.** Open Interpreter uses Jupyter internally, so it can run shell commands *and* Python."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bLWX5tB0rzyW"
},
"source": [
"## Resetting the Chat"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bZmlAKzjtWJZ"
},
"source": [
"In Python, the Open Interpreter instance remembers your conversation history.\n",
"\n",
"If you want it to forget previous messages, you can reset it:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DQzDB8qetlYH"
},
"outputs": [],
"source": [
"interpreter.messages = []"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6clx6Pyhtmzj"
},
"source": [
"Now it won't remember the previous examples.\n",
"\n",
"\n", "\n", "`Why might I want to do this?`\n", "\n", "
\n", "\n", "To reduce the number of tokens that get sent to OpenAI.\n", "\n", "We need to send OpenAI your entire conversation history (automatically limited to the maximum amount GPT-4 can handle) everytime 1) we send it a message or 2) a code block produces an output.\n", "\n", "
\n", "\n", "**Note:** The command-line version of Open Interpreter resets itself automatically." ] }, { "cell_type": "markdown", "metadata": { "id": "eC27gX51wCm-" }, "source": [ "# Advanced Examples" ] }, { "cell_type": "markdown", "metadata": { "id": "c73LbswP4YQF" }, "source": [ "## YouTube Link -> Animation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 341 }, "id": "WBAR6xgMSUWX", "outputId": "7623079d-e3a2-44b6-e84f-1e571c70506f" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "
\n", "\n", "`Doesn't this require logging into \"replicate\"?`\n", "\n", "
\n", "\n", "Yes, and this is a great reason to use Open Interpreter! We can simply log in to `replicate`, then tell the interpreter how to use it ([I just pasted in replicate's quick start](https://replicate.com/jagilley/controlnet-pose/api)).\n", "Because code is run locally, the interpreter is \"logged in\" too." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-oLsybTEwelU" }, "outputs": [], "source": [ "# First, let's install Replicate and log in.\n", "\n", "!pip install replicate\n", "import os\n", "os.environ[\"REPLICATE_API_TOKEN\"] = \"api_token\" # Get yours: https://replicate.com/account" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "referenced_widgets": [ "0fc2ff891df145cd802b1b0f1c369adb", "a6ddbeb9b5ef4ea58858eabac8fde3c6", "daba6cb342374830be9830b57d96a599", "41707e7b17314b17a249388903e0ce71", "a21bd572aad1457890fd77e77ae60cde", "800d0a52c55346119c920db787b7f229", "c39c77408c5e4ab99f127a1306f27554", "ece854c5547443d78f1e8c0d8960ad47", 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"source": [
"# Now we'll ask interpreter to redraw the video using Replicate.\n",
"\n",
"message = \"\"\"\n",
"\n",
"I want to edit a video so it's in 12 fps and each frame is redrawn with ControlNet\n",
"to give it a stop motion / hand-drawn animation feeling.\n",
"\n",
"Here's how you use ControlNet on a single frame, from Replicate's quick start:\n",
"\n",
"import replicate\n",
"output = replicate.run(\n",
" \"jagilley/controlnet-canny:aff48af9c68d162388d230a2ab003f68d2638d88307bdaf1c2f1ac95079c9613\",\n",
" input={\"image\": open(\"path/to/file\", \"rb\"), \"prompt\": \"\n", "\n", "Just for fun, imagine a function that takes a `youtube_url`, passes it into `interpreter.chat(message)` with the instructions above, then returns the processed filename:\n", "\n", "```python\n", "def animate_video(youtube_url):\n", " interpreter.messages = []\n", " interpreter.message(f'Animate {youtube_url} with the following steps then save to final.mp4. ...')\n", " return 'final.mp4'\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "I1WSa-Jw0KM0" }, "source": [ "## Create documents" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "0f4453a4442c4304b22d673c9b1a72ab", "4ed501ea235a4e77a2d7f4e9ef49eb23", "85a2c311f8cd4fd484dd5a95079d9dcb", "bd5ea7bd835a483095abe490c447dd32", "affa3daf1b964d0995f83ab890e1e214", "45d35e811af74d4b9798eb2efa6fbe19", "42895ff661844f4b8e03699a3ac93267", "fc51add87d6744bc8fb46911b671eac5", "d045b96ac66249c6af3b39562216c469", "e32b943cf46c49b49a53275b33b06072", "00ba9165fc8c406e9ac0fffa01cac05d", "4a16bcbbc01e40dfb0b835c4af36cc54", "faa25b85f22545f09f98b5d43cbd21b1", "74a068f07b49454bba51058b0c1772ae" ] }, "id": "mvkatiSHQr8Z", "outputId": "e80ed572-d01f-4a09-9a0d-108634b97775" }, "outputs": [ { "data": { "text/html": [ "
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"interpreter.messages = [] # Reset the chat\n",
"interpreter.chat(\"\"\"Can you make a folder called documents and put five .docx files in it\n",
"and write a sentence about machine learning in each of them?\"\"\") # Pass a message directly into chat"
]
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{
"cell_type": "markdown",
"metadata": {
"id": "cM7Yf5pHgeDW"
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"source": [
"## Edit all documents in a folder"
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"message = \"Can you slow + reverb this song? https://www.youtube.com/watch?v=8GW6sLrK40k\"\n",
"\n",
"interpreter.messages = []\n",
"interpreter.chat(message)"
]
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"metadata": {
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"base_uri": "https://localhost:8080/",
"height": 75
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"id": "3_uywJFkISTi",
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"message = \"\"\"\n",
"\n",
"Hello! I'd like your help making some artwork for the Open Interpreter project.\n",
"\n",
"It's going to be pixel art, ~160 wide and 14 pixels tall. Black background.\n",
"\n",
"I'd like to see rectangles on every other row. These should be anywhere from\n",
"~6 to 36 pixels in width. They should be placed randomly around the image, never touching eachother\n",
"(the space between them should be ~16-64 pixels). They can go off screen / butt up against the edges.\n",
"\n",
"Half of these rectangles should be white, half should be a powerful purple color: R138 G43 B226\n",
"\n",
"Once you've created it, please scale it up with nearest-neighbor 10x.\n",
"\n",
"Please make ~10 options I can review, like banner_1.png, banner_2.png, etc.\n",
"\n",
"\"\"\"\n",
"\n",
"interpreter.messages = []\n",
"interpreter.chat(message)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OTTkCcIshgmB"
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"source": [
"## Add subtitles to videos"
]
},
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"cell_type": "markdown",
"source": [
"`replicate` also has a speech-to-text service that generates subtitle files (.srt).\n",
"\n",
"Let's ask Open Interpreter to use some code [copied from replicate's quickstart](https://replicate.com/m1guelpf/whisper-subtitles/api) to add hardcoded subtitles to a video:"
],
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"message = \"\"\"\n",
"Hello! I just logged into Replicate on this machine. You have my permission to run any code.\n",
"\n",
"Could you use their speech-to-text service to hardcode subtitles to the bottom of billyking.mp4 and make billy_subbed.mp4?\n",
"\"\"\"\n",
"\n",
"# Again, let's give Open Interpreter an example of how to use the service.\n",
"message += \"\"\"\n",
"Here's some code that Replicate provides for how to use their service:\n",
"\n",
"import replicate\n",
"output = replicate.run(\n",
" \"m1guelpf/whisper-subtitles:7f686e243a96c7f6f0f481bcef24d688a1369ed3983cea348d1f43b879615766\",\n",
" input={\"audio_path\": open(\"path/to/file\", \"rb\")} # Can also be a video path\n",
")\n",
"print(output)\n",
"\n",
"\"\"\"\n",
"\n",
"# Now let's reset and run Open Interpreter.\n",
"interpreter.messages = []\n",
"interpreter.chat(message)"
]
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]
},
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]
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"message = \"\"\"\n",
"Hi, I want to make a video that's comprised of 12 frames. The 12 frames should be of the image\n",
"victoria.png run through controlnet with different materials as prompts.\n",
"\n",
"I'm logged into replicate on this machine. Here's how to use replicate's controlnet:\n",
"\n",
"output = replicate.run(\n",
" \"jagilley/controlnet-canny:aff48af9c68d162388d230a2ab003f68d2638d88307bdaf1c2f1ac95079c9613\",\n",
" input={\"image\": open(\"path/to/file\", \"rb\"), \"prompt\": \"metal\"}\n",
")\n",
"print(output)\n",
"\n",
"Can you run victoria.png through this 12 times with different materials each time like \"metal\", \"embroidery\", and \"crayon\"?\n",
"Then stitch together the 12 pictures into a 1 second video clip. Thank you!\n",
"\"\"\"\n",
"\n",
"interpreter.messages = []\n",
"interpreter.chat(message)"
]
},
{
"cell_type": "code",
"source": [
"# Watch the final output:"
],
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"\n from docx import Document \n \n # Creating 5 documents with sentences about Machine Learning. \n doc_texts = [ \n 'Machine learning is a method of data analysis that automates analytical model building.', \n 'It is a branch of artificial intelligence based on the idea that systems can learn from data.', \n 'Machine learning is an application of artificial intelligence (AI) that provides systems the ability to au \n 'Machine learning focuses on the development of computer programs that can access data and use it learn for \n 'The process of learning begins with observations or data, such as examples, direct experience, or instruct \n ] \n \n def create_doc(path, text): \n doc = Document() \n doc.add_paragraph(text) \n doc.save(path) \n \n # Creating and saving the documents. \n for i in range(5): \n create_doc(f'documents/doc{i+1}.docx', doc_texts[i]) \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mfrom\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdocx\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mDocument\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Creating 5 documents with sentences about Machine Learning.\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdoc_texts\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mMachine learning is a method of data analysis that automates analytical model building.\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mIt is a branch of artificial intelligence based on the idea that systems can learn from data.\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mMachine learning is an application of artificial intelligence (AI) that provides systems the ability to au\u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mMachine learning focuses on the development of computer programs that can access data and use it learn for\u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mThe process of learning begins with observations or data, such as examples, direct experience, or instruct\u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m 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\n import feedparser \n \n feed = feedparser.parse('http://feeds.bbci.co.uk/news/rss.xml') \n [entry.title for entry in feed.entries[:10]] \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfeedparser\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfeed\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfeedparser\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mparse\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mhttp://feeds.bbci.co.uk/news/rss.xml\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mentry\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtitle\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mfor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mentry\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34min\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfeed\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mentries\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m10\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "0cbcc3ce78ac4df28d0326e591a2418e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", 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null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "0d24fe34dd454245ab16fd84734a063d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "0e33aaf3c5dd44dda84ebc379dd78417": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "0ef2a15fe9594494b7957e9dab6166b0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, 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\n import os \n os.mkdir('documents') \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mos\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mos\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmkdir\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mdocuments\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "0fc2ff891df145cd802b1b0f1c369adb": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_a6ddbeb9b5ef4ea58858eabac8fde3c6", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n This is a multi-step task. Here's the plan: \n \n 1 Install necessary packages: yt-dlp for downloading the YouTube video, moviepy for video editing, and \n replicate for using ControlNet. \n 2 Download the video from YouTube. \n 3 Extract the audio from the video to add it back later. \n 4 Cut the video to the specified time frame (0:10 to 0:17). \n 5 Reduce the video to 12 frames per second. \n 6 For each frame in the video, use ControlNet to redraw it with a random prompt that evokes a stop motion / \n hand-drawn animation. \n 7 Combine the redrawn frames into a new video. \n 8 Add the original audio back to the new video. \n \n Let's start with the first step, which is installing the necessary packages. \n \n\n", "text/plain": " \n This is a multi-step task. Here's the plan: \n \n \u001b[1;33m 1 \u001b[0mInstall necessary packages: \u001b[1;36;40myt-dlp\u001b[0m for downloading the YouTube video, \u001b[1;36;40mmoviepy\u001b[0m for video editing, and \n \u001b[1;33m \u001b[0m\u001b[1;36;40mreplicate\u001b[0m for using ControlNet. \n \u001b[1;33m 2 \u001b[0mDownload the video from YouTube. \n \u001b[1;33m 3 \u001b[0mExtract the audio from the video to add it back later. \n \u001b[1;33m 4 \u001b[0mCut the video to the specified time frame (0:10 to 0:17). \n \u001b[1;33m 5 \u001b[0mReduce the video to 12 frames per second. \n \u001b[1;33m 6 \u001b[0mFor each frame in the video, use ControlNet to redraw it with a random prompt that evokes a stop motion / \n \u001b[1;33m \u001b[0mhand-drawn animation. \n \u001b[1;33m 7 \u001b[0mCombine the redrawn frames into a new video. \n \u001b[1;33m 8 \u001b[0mAdd the original audio back to the new video. \n \n Let's start with the first step, which is installing the necessary packages. \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "1038b781d07d4e8e8df69bff5b2df9ad": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "1276e768ba0143ac98e1a1369dedd755": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_0cd08b7a5f5a4d8c9cd61e5df2a43dc3", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n import replicate \n \n material_prompts = ['metal', 'embroidery', 'crayon'] \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mreplicate\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmaterial_prompts\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mmetal\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34membroidery\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mcrayon\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "1326436b350b45ed82d6a1be8d9402ce": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_ef334d3eb6a043608312c67aa3c7e3b8", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n import sympy as sp \n \n x = sp.symbols('x') \n equation = 10*x + 14 - 21/3 \n solution = sp.solve(equation, x) \n solution \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msympy\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mas\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msp\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mx\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m 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\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msp\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msolve\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mequation\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mx\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msolution\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "1356d617a0c74b9fb9dc4f94d78f62aa": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", 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"overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "155ee92669cc4e1180a1568bfffb8720": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_7da8ca4865a647d6ac46a64af17fd02d", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n {'language': 'english', 'subtitles': \"WEBVTT\\n00:00.000 --> 00:02.000\\n Well I'm a stoner man\\n00:04.000 --> \n 00:07.000\\n That's right I took in man\\n00:08.560 --> 00:10.560\\n Put a joint in my hand\\n00:12.240 --> \n 00:14.240\\n And lose control\\n00:16.840 --> 00:18.840\\n Well, I'm a lunatic man\\n00:21.440 --> 00:23.440\\n I \n got right crazy man\\n00:25.560 --> 00:28.720\\n Then take you long if you're out\\n00:28.720 --> 00:30.720\\n I'm \n out of game trouble\\n00:35.720 --> 00:37.720\\n I could\\n00:37.720 --> 00:39.720\\n No\\n00:39.720 --> 00:41.720\\n \n Oh\\n00:41.720 --> 00:43.720\\n No\\n\", 'text': \" Well I'm a stoner man That's right I took in man Put a joint in \n my hand And lose control Well, I'm a lunatic man I got right crazy man Then take you long if you're out I'm out \n of game trouble I could No Oh No\"} \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m{'language': 'english', 'subtitles': \"WEBVTT\\n00:00.000 --> 00:02.000\\n Well I'm a stoner man\\n00:04.000 --> \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m00:07.000\\n That's right I took in man\\n00:08.560 --> 00:10.560\\n Put a joint in my hand\\n00:12.240 --> \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m00:14.240\\n And lose control\\n00:16.840 --> 00:18.840\\n Well, I'm a lunatic man\\n00:21.440 --> 00:23.440\\n I \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mgot right crazy man\\n00:25.560 --> 00:28.720\\n Then take you long if you're out\\n00:28.720 --> 00:30.720\\n I'm \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mout of game trouble\\n00:35.720 --> 00:37.720\\n I could\\n00:37.720 --> 00:39.720\\n No\\n00:39.720 --> 00:41.720\\n\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mOh\\n00:41.720 --> 00:43.720\\n No\\n\", 'text': \" Well I'm a stoner man That's right I took in man Put a joint in \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mmy hand And lose control Well, I'm a lunatic man I got right crazy man Then take you long if you're out I'm out\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mof game trouble I could No Oh No\"}\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "163a7b62b46f463b991bba3140df45f4": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_2ced3555ea7a4e358c5ee24ad61affb5", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n !pip install pydub \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;150;0;80;48;2;30;0;16m!\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34minstall\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpydub\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "18a380ea6a594f64b8381b1cd1bb2594": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "1ab1dc792ecf498fb44978beb9fad234": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_6bd4412598a24f4b82c9fd8918a3e0ae", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Traceback (most recent call last): \n Cell In[45], line 3 \n print(clip.write_videofile('video.mp4', codec='mpeg4')) \n File <decorator-gen-73>:2 in write_videofile \n File /usr/local/lib/python3.10/dist-packages/moviepy/decorators.py:54 in requires_duration \n return f(clip, *a, **k) \n File <decorator-gen-72>:2 in write_videofile \n File /usr/local/lib/python3.10/dist-packages/moviepy/decorators.py:130 in use_clip_fps_by_default \n new_a = [fun(arg) if (name=='fps') else arg \n File /usr/local/lib/python3.10/dist-packages/moviepy/decorators.py:130 in <listcomp> \n new_a = [fun(arg) if (name=='fps') else arg \n File /usr/local/lib/python3.10/dist-packages/moviepy/decorators.py:117 in fun \n raise AttributeError(\"No 'fps' (frames per second) attribute specified\" \n AttributeError: No 'fps' (frames per second) attribute specified for function write_videofile and the clip has \n no 'fps' attribute. Either provide e.g. fps=24 in the arguments of the function, or define the clip's fps with \n `clip.fps=24` \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mTraceback (most recent call last):\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m Cell In[45], line 3\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m print(clip.write_videofile('video.mp4', codec='mpeg4'))\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m File
\n import pedalboard \n print(dir(pedalboard)) \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpedalboard\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprint\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdir\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpedalboard\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "1c0545391e074baf8a5fe4f7b892fdd1": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_57f2e054ead24f71a958cae8de41c2b7", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n from docx import Document \n \n # Open and read each document, replacing 'Machine Learning' with 'AI' \n for filename in files: \n print(f'Processing {filename}') \n \n # Open the document \n doc = Document(os.path.join('documents', filename)) \n \n # Loop through each paragraph in the document \n for paragraph in doc.paragraphs: \n # Replace all occurrences of 'Machine Learning' (any case) with 'AI' \n paragraph.text = paragraph.text.replace('Machine Learning', 'AI').replace('machine learning', 'AI').rep \n \n # Save the modified document to a new file \n doc.save(os.path.join('documents', f'new_{filename}')) \n print(f'Finished processing {filename}') \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mfrom\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdocx\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mDocument\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Open and read each document, replacing 'Machine Learning' with 'AI'\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mfor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfilename\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34min\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfiles\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprint\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mf\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mProcessing \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m{\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfilename\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m}\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Open the document\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdoc\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m 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\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Loop through each paragraph in the document\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mfor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mparagraph\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34min\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdoc\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mparagraphs\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Replace all occurrences of 'Machine Learning' (any case) with 'AI'\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mparagraph\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtext\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mparagraph\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtext\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mreplace\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mMachine Learning\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mAI\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mreplace\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mmachine learning\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mAI\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrep\u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Save the modified document to a new file\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdoc\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msave\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mos\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpath\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mjoin\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mdocuments\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mf\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mnew_\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m{\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfilename\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m}\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprint\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mf\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mFinished processing \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m{\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfilename\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m}\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "1c0e5743735d4490a8f6a010c77aeb40": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, 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\n clip.fps = 12 \n clip.write_videofile('video.mp4', codec='mpeg4') \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mclip\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfps\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m12\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mclip\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwrite_videofile\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mvideo.mp4\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcodec\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mmpeg4\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "2044f647ec5b4bb6a3a363f40bfc45b1": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "267f3fe5e392407d88b0be2f178ba149": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_6f7b2b5688184ea5aa32b7eba22546e6", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n print(Pedalboard.__doc__) \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprint\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mPedalboard\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m__doc__\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "26d0efdbe3624615a7bf632af1e8c60b": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_cc23e29c55ac41fa9bfbb35230fb67e7", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n !pip install feedparser \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;150;0;80;48;2;30;0;16m!\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34minstall\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfeedparser\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "2882213b1cce4520b8e0ee44be5f1924": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_1038b781d07d4e8e8df69bff5b2df9ad", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n from moviepy.editor import VideoFileClip, TextClip, CompositeVideoClip \n \n # Load the video \n video = VideoFileClip(\"billyking.mp4\") \n \n subtitles_dict = { \n '00:00.000 --> 00:02.000': \"Well I'm a stoner man\", \n '00:04.000 --> 00:07.000': \"That's right I took in man\", \n '00:08.560 --> 00:10.560': 'Put a joint in my hand', \n '00:12.240 --> 00:14.240': 'And lose control', \n '00:16.840 --> 00:18.840': \"Well, I'm a lunatic man\", \n '00:21.440 --> 00:23.440': \"I got right crazy man\", \n '00:25.560 --> 00:28.720': \"Then take you long if you're out\", \n '00:28.720 --> 00:30.720': \"I'm out of game trouble\", \n '00:35.720 --> 00:37.720': 'I could', \n '00:37.720 --> 00:39.720': 'No', \n '00:39.720 --> 00:41.720': 'Oh', \n '00:41.720 --> 00:43.720': 'No', \n } \n \n # Create the text clips for each subtitle and add them to the video \n video_clips = [video] \n for timestamp, subtitle in subtitles_dict.items(): \n start, end = timestamp.split(' --> ') \n start_time = sum(float(x) * 60 ** i for i,x in enumerate(reversed(start.split(':')))) \n end_time = sum(float(x) * 60 ** i for i,x in enumerate(reversed(end.split(':')))) \n txt_clip = TextClip(txt=subtitle, fontsize=24, color='white') \n txt_clip = txt_clip.set_start(start_time).set_duration(end_time - start_time).set_position(('bottom')) \n video_clips.append(txt_clip) \n \n final = CompositeVideoClip(video_clips) \n final.write_videofile('billy_subbed.mp4') \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mfrom\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmoviepy\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34meditor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mVideoFileClip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mTextClip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mCompositeVideoClip\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Load the video\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mvideo\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mVideoFileClip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mbillyking.mp4\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msubtitles_dict\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m{\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m00:00.000 --> 00:02.000\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mWell I\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mm a stoner man\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m00:04.000 --> 00:07.000\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mThat\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34ms right I took in man\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m00:08.560 --> 00:10.560\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mPut a joint in my hand\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m00:12.240 --> 00:14.240\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mAnd lose control\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m00:16.840 --> 00:18.840\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mWell, I\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mm a lunatic man\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m00:21.440 --> 00:23.440\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mI got right crazy man\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m 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00:30.720\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mI\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mm out of game trouble\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m00:35.720 --> 00:37.720\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m 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\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msum\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfloat\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mx\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m*\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m60\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m*\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m*\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mfor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mx\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34min\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m 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\n I have completed the task. The final video has been saved as final_video.mp4. It has been edited to have a \n frame rate of 12 fps, and each frame has been redrawn with ControlNet using random prompts that evoke a stop \n motion / hand-drawn animation feeling. The original sound has been added back to the video. \n \n\n",
"text/plain": " \n I have completed the task. The final video has been saved as \u001b[1;36;40mfinal_video.mp4\u001b[0m. It has been edited to have a \n frame rate of 12 fps, and each frame has been redrawn with ControlNet using random prompts that evoke a stop \n motion / hand-drawn animation feeling. The original sound has been added back to the video. \n \n"
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"text/html": " \n It seems like the error occurred because the frames per second (fps) information was missing while writing the \n video file. I am going to run the code again but this time with fps=12 added. This means there will be 12 \n frames image shown in one second which is what we want. Let's try that. \n \n\n",
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"text/html": "\n It seems like there was an error because the Stretch class could not be imported from the pedalboard module. \n This could be because the Stretch class does not exist in the pedalboard module. \n \n Let's verify the available classes and functions in the pedalboard module. \n \n\n", "text/plain": " \n It seems like there was an error because the \u001b[1;36;40mStretch\u001b[0m class could not be imported from the \u001b[1;36;40mpedalboard\u001b[0m module. \n This could be because the \u001b[1;36;40mStretch\u001b[0m class does not exist in the \u001b[1;36;40mpedalboard\u001b[0m module. \n \n Let's verify the available classes and functions in the \u001b[1;36;40mpedalboard\u001b[0m module. \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "2e56b90a86e94cb5b51cf56b86837410": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": 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\n I have finished processing all the .docx files in your 'documents' folder. Here's what I did: \n \n 1 I went through each document in the 'documents' folder. \n 2 Opened each document and replaced all occurrences of 'Machine Learning' (ain any form of capitalization) \n with 'AI'. \n 3 Saved the modified documents with a 'new_' prefix in their original location. \n \n You should see the updated files in your 'documents' folder named as 'new_doc1.docx', 'new_doc2.docx', etc. \n Feel free to check them and let me know if everything looks good! \n \n\n", "text/plain": " \n I have finished processing all the .docx files in your 'documents' folder. Here's what I did: \n \n \u001b[1;33m 1 \u001b[0mI went through each document in the 'documents' folder. \n \u001b[1;33m 2 \u001b[0mOpened each document and replaced all occurrences of 'Machine Learning' (ain any form of capitalization) \n \u001b[1;33m \u001b[0mwith 'AI'. \n \u001b[1;33m 3 \u001b[0mSaved the modified documents with a 'new_' prefix in their original location. \n \n You should see the updated files in your 'documents' folder named as 'new_doc1.docx', 'new_doc2.docx', etc. \n Feel free to check them and let me know if everything looks good! \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "38bd644aee574f47b65793c655bcc9c9": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_5e77c72502414e0ba7c79c1cc8324b95", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n from moviepy.editor import ImageSequenceClip \n \n # Create a video using the generated frames \n clip = ImageSequenceClip(images, durations=[1/12]*len(images)) \n clip.write_videofile('video.mp4', codec='mpeg4') \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mfrom\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmoviepy\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34meditor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mImageSequenceClip\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Create a video using the generated frames\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mclip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mImageSequenceClip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mimages\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m 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\n !yt-dlp -f 'bestaudio' -o 'original_song.%(ext)s' 'https://www.youtube.com/watch?v=8GW6sLrK40k' \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;150;0;80;48;2;30;0;16m!\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34myt\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdlp\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mf\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mbestaudio\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mo\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34moriginal_song.\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m%(ext)s\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mhttps://www.youtube.com/watch?v=8GW6sLrK40k\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "42895ff661844f4b8e03699a3ac93267": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_fc51add87d6744bc8fb46911b671eac5", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n !pip install python-docx \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;150;0;80;48;2;30;0;16m!\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34minstall\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpython\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdocx\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "43008ef3259f4bc0aa7662907abe1c03": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_dfae26728db147cb98311156e758816b", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n import yt_dlp \n \n url = 'https://www.youtube.com/watch?v=VF-FGf_ZZiI' \n \n # Download the video \n yt_dlp.YoutubeDL({'outtmpl': 'original_video.mp4'}).download([url]) \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34myt_dlp\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34murl\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mhttps://www.youtube.com/watch?v=VF-FGf_ZZiI\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Download the video\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34myt_dlp\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mYoutubeDL\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m{\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mouttmpl\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34moriginal_video.mp4\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m}\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdownload\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34murl\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "44646c403c814456a6a4665c3236a79d": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_5bb4cd46aed946578d102122a0944a71", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n !pip install replicate moviepy pysrt \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;150;0;80;48;2;30;0;16m!\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34minstall\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mreplicate\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmoviepy\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpysrt\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "456f954392e84f46b7f68b50101bd62e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "45d35e811af74d4b9798eb2efa6fbe19": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "45e937a0dcaf405b8865cceb68551be4": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_6731e56abeba4af69d33942800665902", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Extracting URL: https://www.youtube.com/watch?v=8GW6sLrK40k \n 8GW6sLrK40k: Downloading webpage \n 8GW6sLrK40k: Downloading ios player API JSON \n 8GW6sLrK40k: Downloading android player API JSON \n 8GW6sLrK40k: Downloading m3u8 information \n 8GW6sLrK40k: Downloading 1 format(s): 251 \n Destination: original_song.webm \n 0.0% of 3.49MiB at 426.25KiB/s ETA 00:08 0.1% of 3.49MiB at 882.45KiB/s ETA 00:04 0.2% of \n 3.49MiB at 1.69MiB/s ETA 00:02 0.4% of 3.49MiB at 3.13MiB/s ETA 00:01 0.9% of 3.49MiB at \n 2.66MiB/s ETA 00:01 1.8% of 3.49MiB at 3.07MiB/s ETA 00:01 3.6% of 3.49MiB at 4.12MiB/s ETA \n 00:00 7.1% of 3.49MiB at 5.58MiB/s ETA 00:00 14.3% of 3.49MiB at 9.78MiB/s ETA 00:00 28.6% of \n 3.49MiB at 15.31MiB/s ETA 00:00 57.2% of 3.49MiB at 24.85MiB/s ETA 00:00 100.0% of 3.49MiB at \n 35.39MiB/s ETA 00:00 100% of 3.49MiB in 00:00:00 at 22.41MiB/s \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m Extracting URL: https://www.youtube.com/watch?v=8GW6sLrK40k\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 8GW6sLrK40k: Downloading webpage\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 8GW6sLrK40k: Downloading ios player API JSON\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 8GW6sLrK40k: Downloading android player API JSON\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 8GW6sLrK40k: Downloading m3u8 information\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 8GW6sLrK40k: Downloading 1 format(s): 251\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m Destination: original_song.webm\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 0.0% of 3.49MiB at 426.25KiB/s ETA 00:08\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 0.1% of 3.49MiB at 882.45KiB/s ETA 00:04\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 0.2% of \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m3.49MiB at 1.69MiB/s ETA 00:02\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 0.4% of 3.49MiB at 3.13MiB/s ETA 00:01\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 0.9% of 3.49MiB at \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m2.66MiB/s ETA 00:01\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 1.8% of 3.49MiB at 3.07MiB/s ETA 00:01\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 3.6% of 3.49MiB at 4.12MiB/s ETA \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m00:00\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 7.1% of 3.49MiB at 5.58MiB/s ETA 00:00\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 14.3% of 3.49MiB at 9.78MiB/s ETA 00:00\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 28.6% of \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m3.49MiB at 15.31MiB/s ETA 00:00\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 57.2% of 3.49MiB at 24.85MiB/s ETA 00:00\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 100.0% of 3.49MiB at \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m35.39MiB/s ETA 00:00\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 100% of 3.49MiB in 00:00:00 at 22.41MiB/s\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "46c63dde8afb4b87b56913bc55b59ade": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "48b7cb834ffe4597b7589228a8b7d11d": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_46c63dde8afb4b87b56913bc55b59ade", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n I have successfully created a 12-frame video, 'video.mp4', using different material prompts for the image \n 'victoria.png'. Each frame was generated using the controlnet model via replicate and the material prompts \n 'metal', 'embroidery', and 'crayon'. The frames were stitched together into a 1-second video clip. Please check \n the on your computer for the generated video file 'video.mp4'. \n \n\n", "text/plain": " \n I have successfully created a 12-frame video, 'video.mp4', using different material prompts for the image \n 'victoria.png'. Each frame was generated using the controlnet model via replicate and the material prompts \n 'metal', 'embroidery', and 'crayon'. The frames were stitched together into a 1-second video clip. Please check \n the on your computer for the generated video file 'video.mp4'. \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "49254282d5644edeb2f217b59b9bf28b": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_b3e874cde5924727b3c2cdb93de1e68a", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n import os \n \n # List all files \n files = os.listdir('documents') \n \n # Filter out only .docx files \n files = [file for file in files if file.endswith('.docx')] \n \n files \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mos\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# List all files\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfiles\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mos\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlistdir\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mdocuments\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Filter out only .docx files\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfiles\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfile\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mfor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfile\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34min\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfiles\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mif\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfile\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mendswith\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m.docx\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfiles\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } 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\n Traceback (most recent call last): \n Cell In[9], line 2 \n board = Pedalboard([PitchShift(semitones=-5), Reverb(room_size=0.5, \n TypeError: Pedalboard.__init__() got an unexpected keyword argument 'sample_rate' \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mTraceback (most recent call last):\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m Cell In[9], line 2\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m board = Pedalboard([PitchShift(semitones=-5), Reverb(room_size=0.5,\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mTypeError: Pedalboard.__init__() got an unexpected keyword argument 'sample_rate'\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "4f2343ab60004171a8768a8339e82c0e": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_a46c38d5d2864821ba81ad63259670db", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n output_urls = [] \n for prompt in material_prompts: \n for _ in range(4): \n output = replicate.run( \n 'jagilley/controlnet-canny:aff48af9c68d162388d230a2ab003f68d2638d88307bdaf1c2f1ac95079c9613', \n input={'image': open('victoria.png', 'rb'), 'prompt': prompt} \n ) \n output_urls.append(output[1]) \n output_urls \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34moutput_urls\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mfor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m 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\u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mjagilley/controlnet-canny:aff48af9c68d162388d230a2ab003f68d2638d88307bdaf1c2f1ac95079c9613\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34minput\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m{\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mimage\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mopen\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mvictoria.png\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mrb\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mprompt\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m 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\n ['https://replicate.delivery/pbxt/zrqhegeMmzmWu06Tq2Rq4Df0Mh0eBMVwfPT7OjzJB1NIkSCKC/output_1.png', \n 'https://replicate.delivery/pbxt/pZfDRfLvrhge6oZRxzHihoPcm0VEg8DprAzlYctVdgkMpkgiA/output_1.png', \n 'https://replicate.delivery/pbxt/cSM7xxMjC67INlxGYf655U3lYpgzxbxWoh2GgZ3J5INVKJoIA/output_1.png', \n 'https://replicate.delivery/pbxt/f2PilmS7o0SyA6CpYIJ8SHES9lQjstnggWaGcvvcXUyXKJoIA/output_1.png', \n 'https://replicate.delivery/pbxt/3LqcWxCYn6rfFyH9LqjDMJV9RCc8Cnt13eCGHnVYuRM4USQRA/output_1.png', \n 'https://replicate.delivery/pbxt/3ZtaSRvsA6p6INT1JEqvQwPPdipZOoZzgwpDcKLIf1keUSQRA/output_1.png', \n 'https://replicate.delivery/pbxt/i9eesm9VJvstfpT8USw3oSiWprHbWJf6UUg0f7URK4kIoSCKC/output_1.png', \n 'https://replicate.delivery/pbxt/TXTCbLIEdWKzFF1rv7Nk7db0bZYSZjJdmfPBl129eO9GVSQRA/output_1.png', \n 'https://replicate.delivery/pbxt/6mTEtWiXPhpDFN5902KGv9woakleMcxvVPfAKAujtYSKVSQRA/output_1.png', \n 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\n The solution to the equation 10x + 14 = 21 / 3 is x = -0.7. \n \n\n", "text/plain": " \n The solution to the equation \u001b[1;36;40m10x + 14 = 21 / 3\u001b[0m is \u001b[1;36;40mx = -0.7\u001b[0m. \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "523049f89f904844ab674b9410ee61c4": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_73078cc9fc5741fcb130caa028914bf9", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n To get latest news headlines from BBC, we can use web scraping libraries in python like BeautifulSoup. However, \n the usage of such tools may violate terms of service of the website. Alternatively, we can use the BBC RSS feed \n to get the latest news. \n \n Let's try this using the feedparser library in Python. I will install this library if isn't already installed. \n After this, I'll get the last 10 BBC news headlines using their RSS feed. \n \n\n",
"text/plain": " \n To get latest news headlines from BBC, we can use web scraping libraries in python like BeautifulSoup. However, \n the usage of such tools may violate terms of service of the website. Alternatively, we can use the BBC RSS feed \n to get the latest news. \n \n Let's try this using the \u001b[1;36;40mfeedparser\u001b[0m library in Python. I will install this library if isn't already installed. \n After this, I'll get the last 10 BBC news headlines using their RSS feed. \n \n"
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"text/html": "\n It seems like the video was downloaded as a .webm file instead of a .mp4 file. I'll adjust the code to use the \n correct filename. \n \n\n", "text/plain": " \n It seems like the video was downloaded as a \u001b[1;36;40m.webm\u001b[0m file instead of a \u001b[1;36;40m.mp4\u001b[0m file. I'll adjust the code to use the \n correct filename. \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "540e7806e7c145d3a26fceb5c0ef8aa1": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "554e5912797c4fa88f6e557ca6367711": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_723d3e051e9649a392ae0a54e4399a73", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n https://replicate.delivery/pbxt/IavszBht716pMhxufdF8Ch7cv0Pun1eTQarLmQVxkbAPUSQRA/output_1.png \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mhttps://replicate.delivery/pbxt/IavszBht716pMhxufdF8Ch7cv0Pun1eTQarLmQVxkbAPUSQRA/output_1.png\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "5635e4c8e7734b3b95693f789c9239ba": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_f944a4b42ef14ae889efef3f5369e173", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n\n", "text/plain": "\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "56f46ba6d7254804843b932c97007f85": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_2b36d066449447c1817a4a5f945aada3", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Great, let's start by using the provided Replicate code to convert the audio of billyking.mp4 to text. Then, \n I'll use moviepy to add these as subtitles to your video. I'll need the replicate, moviepy, and possibly the \n pysrt Python packages to do this. \n \n I will first install the needed packages. After that, we will proceed to running the Replicate code and \n processing the output. \n \n\n", "text/plain": " \n Great, let's start by using the provided Replicate code to convert the audio of \u001b[1;36;40mbillyking.mp4\u001b[0m to text. Then, \n I'll use moviepy to add these as subtitles to your video. I'll need the \u001b[1;36;40mreplicate\u001b[0m, \u001b[1;36;40mmoviepy\u001b[0m, and possibly the \n \u001b[1;36;40mpysrt\u001b[0m Python packages to do this. \n \n I will first install the needed packages. After that, we will proceed to running the Replicate code and \n processing the output. \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "575438c672314d12b722af00426a8971": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_2e56b90a86e94cb5b51cf56b86837410", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n All files have been updated. \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mAll files have been updated.\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "57f2e054ead24f71a958cae8de41c2b7": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "58ce723038e04fd2810f4985e864544a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "5a9695b9f4f8410b8c3cc92819ad081c": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_cc1a30ae29f5499d8156470700faae6c", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n The last 10 news headlines from BBC are: \n \n 1 'Disposable vapes: Councils call for total ban by 2024' \n 2 'SAG strike: Actors join writers on Hollywood picket lines' \n 3 'Bournemouth beach deaths: No police action to be taken' \n 4 'Summer holidays: Will there be more travel chaos this year?' \n 5 'Just Stop Oil protesters interrupt the Proms' \n 6 'Stare at smokers to stop them, Hong Kong health chief urges public' \n 7 'Europe heatwave: More record temperatures expected' \n 8 \"Extra energy bill scheme was 'staggering failure', says MP\" \n 9 \"Shekhar Kapur: Hollywood's diversity push is guilt driven\" \n 10 'NI officers told not to wear police uniforms at Pride' \n \n\n", "text/plain": " \n The last 10 news headlines from BBC are: \n \n \u001b[1;33m 1 \u001b[0m'Disposable vapes: Councils call for total ban by 2024' \n \u001b[1;33m 2 \u001b[0m'SAG strike: Actors join writers on Hollywood picket lines' \n \u001b[1;33m 3 \u001b[0m'Bournemouth beach deaths: No police action to be taken' \n \u001b[1;33m 4 \u001b[0m'Summer holidays: Will there be more travel chaos this year?' \n \u001b[1;33m 5 \u001b[0m'Just Stop Oil protesters interrupt the Proms' \n \u001b[1;33m 6 \u001b[0m'Stare at smokers to stop them, Hong Kong health chief urges public' \n \u001b[1;33m 7 \u001b[0m'Europe heatwave: More record temperatures expected' \n \u001b[1;33m 8 \u001b[0m\"Extra energy bill scheme was 'staggering failure', says MP\" \n \u001b[1;33m 9 \u001b[0m\"Shekhar Kapur: Hollywood's diversity push is guilt driven\" \n \u001b[1;33m 10 \u001b[0m'NI officers told not to wear police uniforms at Pride' \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "5a9e09aff4cd4665912a50bc8ad5fe93": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_dde784ca98904ba49ba23dda2100076a", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n The first image transformation with the 'metal' prompt was successful. The output image URL is \n 'https://replicate.delivery/pbxt/IavszBht716pMhxufdF8Ch7cv0Pun1eTQarLmQVxkbAPUSQRA/output_1.png'. \n \n Next, I'll use the material prompts 'embroidery' and 'crayon' to transform the image. Each of these \n transformations will then be performed four times to attain 12 frames. Let's start by transforming the image \n with each of the prompts. \n \n\n", "text/plain": " \n The first image transformation with the 'metal' prompt was successful. The output image URL is \n 'https://replicate.delivery/pbxt/IavszBht716pMhxufdF8Ch7cv0Pun1eTQarLmQVxkbAPUSQRA/output_1.png'. \n \n Next, I'll use the material prompts 'embroidery' and 'crayon' to transform the image. Each of these \n transformations will then be performed four times to attain 12 frames. Let's start by transforming the image \n with each of the prompts. \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "5b414a1b78f44530a2e4d127641b88d7": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_1c0e5743735d4490a8f6a010c77aeb40", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Sure, I can do that. We will use the yt-dlp library to download the audio from the YouTube video, and then the \n pedalboard library to apply the slow and reverb effects. Let's start by downloading the audio. \n \n\n", "text/plain": " \n Sure, I can do that. We will use the \u001b[1;36;40myt-dlp\u001b[0m library to download the audio from the YouTube video, and then the \n \u001b[1;36;40mpedalboard\u001b[0m library to apply the slow and reverb effects. Let's start by downloading the audio. \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "5bb4cd46aed946578d102122a0944a71": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "5e5a5fd06a6d46e3b85e9587d50adfdb": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "5e77c72502414e0ba7c79c1cc8324b95": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "6031e59c120a48b98b4597dcb10a2d9a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "61c835b06ac043c0a06cecf241d7fa48": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "626af5cf397440b2baec64ed98ea5c3f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, 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"overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "673dce80b4b54a7384a460b0075759fb": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "6aae7d9824224170be7f43c8c423c488": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_1c79566727f944fcbe35f3d793f110e3", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n import replicate \n \n output = replicate.run( \n \"m1guelpf/whisper-subtitles:7f686e243a96c7f6f0f481bcef24d688a1369ed3983cea348d1f43b879615766\", \n input={\"audio_path\": open(\"billyking.mp4\", \"rb\")} \n ) \n output \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mreplicate\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34moutput\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mreplicate\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrun\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mm1guelpf/whisper-subtitles:7f686e243a96c7f6f0f481bcef24d688a1369ed3983cea348d1f43b879615766\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34minput\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m{\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34maudio_path\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mopen\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mbillyking.mp4\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mrb\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m}\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34moutput\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "6bd4412598a24f4b82c9fd8918a3e0ae": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "6c4817e94e354e6998e9665f6a509d38": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_78f2b7f6451d4315964aea44ac02db78", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Requirement already satisfied: opencv-python-headless in /usr/local/lib/python3.10/dist-packages (4.8.0.74) \n Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (2.27.1) \n Requirement already satisfied: numpy>=1.21.2 in /usr/local/lib/python3.10/dist-packages (from \n opencv-python-headless) (1.22.4) \n Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests) \n (1.26.16) \n Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests) \n (2023.5.7) \n Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from \n requests) (2.0.12) \n Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests) (3.4) \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 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"display_data" } ] } }, "6d14d7db445b4f829275aea79bd0dbd1": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "6f7b2b5688184ea5aa32b7eba22546e6": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "723d3e051e9649a392ae0a54e4399a73": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, 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\n The .docx files in your 'documents' folder are: \n \n • doc1.docx \n • doc2.docx \n • doc3.docx \n • doc4.docx \n • doc5.docx \n \n Now, I'm going to install a Python package called python-docx that allows us to operate on Microsoft Word \n (.docx) files. After the installation, I will read each .docx file and perform the replacement operation. Let's \n proceed with the installation! \n \n\n", "text/plain": " \n The .docx files in your 'documents' folder are: \n \n \u001b[1;33m • \u001b[0mdoc1.docx \n \u001b[1;33m • \u001b[0mdoc2.docx \n \u001b[1;33m • \u001b[0mdoc3.docx \n \u001b[1;33m • \u001b[0mdoc4.docx \n \u001b[1;33m • \u001b[0mdoc5.docx \n \n Now, I'm going to install a Python package called \u001b[1;36;40mpython-docx\u001b[0m that allows us to operate on Microsoft Word \n (.docx) files. After the installation, I will read each .docx file and perform the replacement operation. Let's \n proceed with the installation! \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "73078cc9fc5741fcb130caa028914bf9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "7388e925607147d0903d6b59642d21c1": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_7978261c27704717bf0f5df5231c3dee", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Traceback (most recent call last): \n Cell In[3], line 2 \n video = VideoFileClip('original_video.mp4') \n File /usr/local/lib/python3.10/dist-packages/moviepy/video/io/VideoFileClip.py:88 in __init__ \n self.reader = FFMPEG_VideoReader(filename, pix_fmt=pix_fmt, \n File /usr/local/lib/python3.10/dist-packages/moviepy/video/io/ffmpeg_reader.py:35 in __init__ \n infos = ffmpeg_parse_infos(filename, print_infos, check_duration, \n File /usr/local/lib/python3.10/dist-packages/moviepy/video/io/ffmpeg_reader.py:270 in ffmpeg_parse_infos \n raise IOError((\"MoviePy error: the file %s could not be found!\\n\" \n OSError: MoviePy error: the file original_video.mp4 could not be found! \n Please check that you entered the correct path. \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mTraceback (most recent call last):\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m Cell In[3], line 2\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m video = VideoFileClip('original_video.mp4')\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m File /usr/local/lib/python3.10/dist-packages/moviepy/video/io/VideoFileClip.py:88 in __init__\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m self.reader = FFMPEG_VideoReader(filename, pix_fmt=pix_fmt,\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m File /usr/local/lib/python3.10/dist-packages/moviepy/video/io/ffmpeg_reader.py:35 in __init__\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m infos = ffmpeg_parse_infos(filename, print_infos, check_duration,\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m File /usr/local/lib/python3.10/dist-packages/moviepy/video/io/ffmpeg_reader.py:270 in ffmpeg_parse_infos\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m raise IOError((\"MoviePy error: the file %s could not be found!\\n\"\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mOSError: MoviePy error: the file original_video.mp4 could not be found!\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mPlease check that you entered the correct path.\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, 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\n Certainly, we can do this task in a few steps: \n \n 1 Run the controlnet model on victoria.png with each material prompt. \n 2 Download the resulting images. \n 3 Stitch together these images into a video. \n \n Before we start, there are some things we need to clarify: \n \n • We need to know the full list of material prompts. You've mentioned \"metal\", \"embroidery\", and \"crayon\". We \n need 9 more. \n • The frame rate of the video. With 12 images in 1 second, it's assumed that we will have a frame rate of 12 \n frames per second (fps). \n \n Let's go ahead with 3 materials you provided for now and we will add more later. We'll set the frame rate to 12 \n fps. \n \n To proceed, I will first need to: \n \n 1 Import the package replicate which should be installed on your system. \n 2 Create a list of material prompts. \n 3 Run the controlnet model with each material prompt and store the URLs of the output images. \n \n Let's start with importing replicate and preparing the material prompts. \n \n (Note that you won't see the code execution output in this platform due to our backend design. However, you'll \n still get any output relevant to our task.) \n \n\n", "text/plain": " \n Certainly, we can do this task in a few steps: \n \n \u001b[1;33m 1 \u001b[0mRun the \u001b[1;36;40mcontrolnet\u001b[0m model on \u001b[1;36;40mvictoria.png\u001b[0m with each material prompt. \n \u001b[1;33m 2 \u001b[0mDownload the resulting images. \n \u001b[1;33m 3 \u001b[0mStitch together these images into a video. \n \n Before we start, there are some things we need to clarify: \n \n \u001b[1;33m • \u001b[0mWe need to know the full list of material prompts. You've mentioned \"metal\", \"embroidery\", and \"crayon\". We \n \u001b[1;33m \u001b[0mneed 9 more. \n \u001b[1;33m • \u001b[0mThe frame rate of the video. With 12 images in 1 second, it's assumed that we will have a frame rate of 12 \n \u001b[1;33m \u001b[0mframes per second (fps). \n \n Let's go ahead with 3 materials you provided for now and we will add more later. We'll set the frame rate to 12 \n fps. \n \n To proceed, I will first need to: \n \n \u001b[1;33m 1 \u001b[0mImport the package \u001b[1;36;40mreplicate\u001b[0m which should be installed on your system. \n \u001b[1;33m 2 \u001b[0mCreate a list of material prompts. \n \u001b[1;33m 3 \u001b[0mRun the \u001b[1;36;40mcontrolnet\u001b[0m model with each material prompt and store the URLs of the output images. \n \n Let's start with importing \u001b[1;36;40mreplicate\u001b[0m and preparing the material prompts. \n \n (Note that you won't see the code execution output in this platform due to our backend design. However, you'll \n still get any output relevant to our task.) \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "74a068f07b49454bba51058b0c1772ae": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "75a72b485eb0422a820209bef270a311": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_065b7774a5a34beebb77f7e6a1501617", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n [-0.700000000000000] \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m[-0.700000000000000]\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "76227155ec874b2bbdd633477275162d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "7735b6aa07bb48e198f2e79bb8483006": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "77d06811ac04459ba507adec1b053905": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_f25805a2402f4a7ba505fb75554bdbed", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Requirement already satisfied: pydub in /usr/local/lib/python3.10/dist-packages (0.25.1) \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mRequirement already satisfied: pydub in /usr/local/lib/python3.10/dist-packages (0.25.1)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "77e0222fd2c54dbca7932d57e517601a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "78f2b7f6451d4315964aea44ac02db78": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "7924e96ff4484d9888b85bf7f7630cdc": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_ee9b39134cf84f089d4868aae1851900", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n I've set up the three material prompts that you mentioned - 'metal', 'embroidery', and 'crayon'. \n \n To ensure the video clip contains 12 equally-spaced frames from these three materials, we'll run the \n replication four times for each of the material prompts. \n \n Let's create these images first by running victoria.png through replicate's controlnet with different material \n prompts. I'll store these images locally so we can later combine them into a video. \n \n For this task, I need to write a loop that will iterate over each material, generate the image using \n replicate's controlnet and save the generated image locally. Let's do it step by step. First, let's try it with \n one material to ensure that the image generation process is working correctly. Here we go... \n \n\n", "text/plain": " \n I've set up the three material prompts that you mentioned - 'metal', 'embroidery', and 'crayon'. \n \n To ensure the video clip contains 12 equally-spaced frames from these three materials, we'll run the \n replication four times for each of the material prompts. \n \n Let's create these images first by running victoria.png through replicate's controlnet with different material \n prompts. I'll store these images locally so we can later combine them into a video. \n \n For this task, I need to write a loop that will iterate over each material, generate the image using \n replicate's controlnet and save the generated image locally. Let's do it step by step. First, let's try it with \n one material to ensure that the image generation process is working correctly. Here we go... \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "7978261c27704717bf0f5df5231c3dee": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "7bc0945fc38c4f7c958bdafed2823093": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_dcfeecca01c345f58eebb9b6209cb5ad", "msg_id": "", "outputs": [ { "data": { "text/html": "
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\n import os \n import numpy as np \n from PIL import Image \n \n # Create a directory to store the frames \n os.makedirs('frames', exist_ok=True) \n \n # Extract frames from the video \n for i, frame in enumerate(video.iter_frames()): \n # Convert the frame to an image \n image = Image.fromarray(np.uint8(frame)) \n \n # Save the image \n image.save(f'frames/frame_{i}.png') \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mos\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnumpy\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m 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\u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Create a directory to store the frames\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mos\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmakedirs\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mframes\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mexist_ok\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mTrue\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Extract frames from the video\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mfor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mframe\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34min\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34menumerate\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mvideo\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34miter_frames\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Convert the frame to an image\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mimage\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mImage\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfromarray\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnp\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34muint8\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mframe\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Save the image\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mimage\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msave\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mf\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mframes/frame_\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m{\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m}\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m.png\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "800d0a52c55346119c920db787b7f229": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { 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\n ['AudioProcessorParameter', 'Bitcrush', 'Chain', 'Chorus', 'Clipping', 'Compressor', 'Convolution', 'Delay', \n 'Distortion', 'ExternalPlugin', 'GSMFullRateCompressor', 'Gain', 'HighShelfFilter', 'HighpassFilter', \n 'IIRFilter', 'Invert', 'LadderFilter', 'Limiter', 'LowShelfFilter', 'LowpassFilter', 'MP3Compressor', 'Mix', \n 'NoiseGate', 'PeakFilter', 'Pedalboard', 'Phaser', 'PitchShift', 'Plugin', 'PluginContainer', 'Resample', \n 'Reverb', 'VST3Plugin', '_AVAILABLE_PLUGIN_CLASSES', '__builtins__', '__cached__', '__doc__', '__file__', \n '__loader__', '__name__', '__package__', '__path__', '__spec__', '__version__', '_klass', '_pedalboard', 'io', \n 'load_plugin', 'numpy', 'process', 'utils', 'version'] \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m['AudioProcessorParameter', 'Bitcrush', 'Chain', 'Chorus', 'Clipping', 'Compressor', 'Convolution', 'Delay', \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m'Distortion', 'ExternalPlugin', 'GSMFullRateCompressor', 'Gain', 'HighShelfFilter', 'HighpassFilter', \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m'IIRFilter', 'Invert', 'LadderFilter', 'Limiter', 'LowShelfFilter', 'LowpassFilter', 'MP3Compressor', 'Mix', \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m'NoiseGate', 'PeakFilter', 'Pedalboard', 'Phaser', 'PitchShift', 'Plugin', 'PluginContainer', 'Resample', \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m'Reverb', 'VST3Plugin', '_AVAILABLE_PLUGIN_CLASSES', '__builtins__', '__cached__', '__doc__', '__file__', \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m'__loader__', '__name__', '__package__', '__path__', '__spec__', '__version__', '_klass', '_pedalboard', 'io', \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m'load_plugin', 'numpy', 'process', 'utils', 'version']\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "83897e5d417c4801b9033373ab047563": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_a2766ca73ab9400d87bbaa96008ebb3b", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n from moviepy.video.io.VideoFileClip import VideoFileClip \n \n # Load the video \n video = VideoFileClip('original_video.mp4') \n \n # Extract the segment from 0:10 to 0:17 \n video = video.subclip(10, 17) \n \n # Reduce the frame rate to 12 fps \n video = video.set_duration(7).set_fps(12) \n \n # Save the modified video \n video.write_videofile('modified_video.mp4', codec='libx264') \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mfrom\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmoviepy\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mvideo\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mVideoFileClip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mVideoFileClip\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Load the video\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mvideo\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mVideoFileClip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34moriginal_video.mp4\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Extract the segment from 0:10 to 0:17\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mvideo\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mvideo\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msubclip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m10\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m17\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Reduce the frame rate to 12 fps\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mvideo\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mvideo\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mset_duration\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m7\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mset_fps\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m12\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Save the modified video\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mvideo\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwrite_videofile\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mmodified_video.mp4\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcodec\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mlibx264\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "8497cd04499f48eb9a305e6c3db8b38d": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_e64b5e68e0e84553a9e303ba55d389d0", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Traceback (most recent call last): \n Cell In[9], line 3 \n audio, sample_rate = sf.read('original_song.webm') \n File /usr/local/lib/python3.10/dist-packages/soundfile.py:285 in read \n with SoundFile(file, 'r', samplerate, channels, \n File /usr/local/lib/python3.10/dist-packages/soundfile.py:658 in __init__ \n self._file = self._open(file, mode_int, closefd) \n File /usr/local/lib/python3.10/dist-packages/soundfile.py:1216 in _open \n raise LibsndfileError(err, prefix=\"Error opening {0!r}: \".format(self.name)) \n LibsndfileError: Error opening 'original_song.webm': Format not recognised. \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mTraceback (most recent call last):\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m Cell In[9], line 3\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m audio, sample_rate = sf.read('original_song.webm')\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m File /usr/local/lib/python3.10/dist-packages/soundfile.py:285 in read\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m with SoundFile(file, 'r', samplerate, channels,\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m File /usr/local/lib/python3.10/dist-packages/soundfile.py:658 in __init__\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m self._file = self._open(file, mode_int, closefd)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m File /usr/local/lib/python3.10/dist-packages/soundfile.py:1216 in _open\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m raise LibsndfileError(err, prefix=\"Error opening {0!r}: \".format(self.name))\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mLibsndfileError: Error opening 'original_song.webm': Format not recognised.\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "85a2c311f8cd4fd484dd5a95079d9dcb": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_bd5ea7bd835a483095abe490c447dd32", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "87f4047550f14103ac55a7ee2736f3ef": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_e6feb1cf757b44bba8fbdecd9b185178", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n The video has been successfully downloaded and saved as \"original_video.mp4\". \n \n Next, I'll extract the segment from 0:10 to 0:17 and reduce its frame rate to 12 fps. \n \n Let's proceed with this plan: \n \n 1 Load the video using MoviePy. \n 2 Extract the segment from 0:10 to 0:17. \n 3 Reduce the frame rate to 12 fps. \n 4 Save the modified video. \n \n\n", "text/plain": " \n The video has been successfully downloaded and saved as \"original_video.mp4\". \n \n Next, I'll extract the segment from 0:10 to 0:17 and reduce its frame rate to 12 fps. \n \n Let's proceed with this plan: \n \n \u001b[1;33m 1 \u001b[0mLoad the video using MoviePy. \n \u001b[1;33m 2 \u001b[0mExtract the segment from 0:10 to 0:17. \n \u001b[1;33m 3 \u001b[0mReduce the frame rate to 12 fps. \n \u001b[1;33m 4 \u001b[0mSave the modified video. \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "886daf8914584873b21fb95604cfa84f": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_456f954392e84f46b7f68b50101bd62e", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Requirement already satisfied: moviepy in /usr/local/lib/python3.10/dist-packages (1.0.3) \n Requirement already satisfied: decorator<5.0,>=4.0.2 in /usr/local/lib/python3.10/dist-packages (from moviepy) \n (4.4.2) \n Requirement already satisfied: tqdm<5.0,>=4.11.2 in /usr/local/lib/python3.10/dist-packages (from moviepy) \n (4.65.0) \n Requirement already satisfied: requests<3.0,>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from moviepy) \n (2.27.1) \n Requirement already satisfied: proglog<=1.0.0 in /usr/local/lib/python3.10/dist-packages (from moviepy) \n (0.1.10) \n Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from moviepy) (1.22.4) \n Requirement already satisfied: imageio<3.0,>=2.5 in /usr/local/lib/python3.10/dist-packages (from moviepy) \n (2.25.1) \n Requirement already satisfied: imageio-ffmpeg>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from moviepy) \n (0.4.8) \n Requirement already satisfied: pillow>=8.3.2 in /usr/local/lib/python3.10/dist-packages (from \n imageio<3.0,>=2.5->moviepy) (8.4.0) \n Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from \n requests<3.0,>=2.8.1->moviepy) (1.26.16) \n Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from \n requests<3.0,>=2.8.1->moviepy) (2023.5.7) \n Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from \n requests<3.0,>=2.8.1->moviepy) (2.0.12) \n Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from \n requests<3.0,>=2.8.1->moviepy) (3.4) \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mRequirement already satisfied: moviepy in /usr/local/lib/python3.10/dist-packages 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\n According to the output received from Replicate's speech-to-text service, the subtitles generated are in the \n WEBVTT format. Note that the file format consists of timestamped text data representing spoken dialogue. Looks \n like the presented text is: \n \n \n WEBVTT \n 00:00.000 --> 00:02.000 \n Well I'm a stoner man \n 00:04.000 --> 00:07.000 \n That's right I took in man \n 00:08.560 --> 00:10.560 \n Put a joint in my hand \n 00:12.240 --> 00:14.240 \n And lose control \n 00:16.840 --> 00:18.840 \n Well, I'm a lunatic man \n 00:21.440 --> 00:23.440 \n I got right crazy man \n 00:25.560 --> 00:28.720 \n Then take you long if you're out \n 00:28.720 --> 00:30.720 \n I'm out of game trouble \n 00:35.720 --> 00:37.720 \n I could \n 00:37.720 --> 00:39.720 \n No \n 00:39.720 --> 00:41.720 \n Oh \n 00:41.720 --> 00:43.720 \n No \n \n \n Though we have the subtitles now, we need to impose these onto the video using MoviePy and save it as \n \"billy_subbed.mp4\". Let's proceed with that. If the final video doesn't look right, it might be due to the \n inaccuracies in the timestamps or in the transcription. Let's give it a try, though. \n \n\n", "text/plain": " \n According to the output received from Replicate's speech-to-text service, the subtitles generated are in the \n WEBVTT format. Note that the file format consists of timestamped text data representing spoken dialogue. Looks \n like the presented text is: \n \n \u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mWEBVTT\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m00:00.000 --> 00:02.000\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m Well I'm a stoner man\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m00:04.000 --> 00:07.000\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m That's right I took in man\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m00:08.560 --> 00:10.560\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m Put a joint in my hand\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m00:12.240 --> 00:14.240\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m And lose control\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m00:16.840 --> 00:18.840\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m Well, I'm a lunatic man\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m00:21.440 --> 00:23.440\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m I got right crazy man\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m00:25.560 --> 00:28.720\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m Then take you long if you're out\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m00:28.720 --> 00:30.720\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m I'm out of game trouble\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m00:35.720 --> 00:37.720\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m I could\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m00:37.720 --> 00:39.720\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m No\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m00:39.720 --> 00:41.720\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m Oh\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m00:41.720 --> 00:43.720\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m No\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \n \u001b[48;2;39;40;34m \u001b[0m \n \n Though we have the subtitles now, we need to impose these onto the video using MoviePy and save it as \n \"billy_subbed.mp4\". Let's proceed with that. If the final video doesn't look right, it might be due to the \n inaccuracies in the timestamps or in the transcription. Let's give it a try, though. \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "8b67d19d967b4ec8a22e595c3aea1f79": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_4ecb19ab194440e7962991fe4c9332a7", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n ['Disposable vapes: Councils call for total ban by 2024', 'SAG strike: Actors join writers on Hollywood picket \n lines', 'Bournemouth beach deaths: No police action to be taken', 'Summer holidays: Will there be more travel \n chaos this year?', 'Just Stop Oil protesters interrupt the Proms', 'Stare at smokers to stop them, Hong Kong \n health chief urges public', 'Europe heatwave: More record temperatures expected', \"Extra energy bill scheme was \n 'staggering failure', says MP\", \"Shekhar Kapur: Hollywood's diversity push is guilt driven\", 'NI officers told \n not to wear police uniforms at Pride'] \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m['Disposable vapes: Councils call for total ban by 2024', 'SAG strike: Actors join writers on Hollywood picket \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mlines', 'Bournemouth beach deaths: No police action to be taken', 'Summer holidays: Will there be more travel \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mchaos this year?', 'Just Stop Oil protesters interrupt the Proms', 'Stare at smokers to stop them, Hong Kong \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mhealth chief urges public', 'Europe heatwave: More record temperatures expected', \"Extra energy bill scheme was\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m'staggering failure', says MP\", \"Shekhar Kapur: Hollywood's diversity push is guilt driven\", 'NI officers told \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mnot to wear police uniforms at Pride']\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } 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\n from moviepy.editor import ImageSequenceClip \n \n # Create a list of the redrawn frames \n frames = [f'redrawn_frames/frame_{i}.png' for i in range(len(os.listdir('redrawn_frames')))] \n \n # Create a clip from the redrawn frames \n clip = ImageSequenceClip(frames, fps=12) \n \n # Add the original audio to the clip \n clip = clip.set_audio(video.audio) \n \n # Save the final video \n clip.write_videofile('final_video.mp4', codec='libx264') \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mfrom\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmoviepy\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34meditor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mImageSequenceClip\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Create a list of the redrawn frames\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mframes\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mf\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mredrawn_frames/frame_\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m{\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m}\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m.png\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mfor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34min\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrange\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlen\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mos\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlistdir\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mredrawn_frames\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Create a clip from the redrawn frames\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mclip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mImageSequenceClip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mframes\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfps\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m12\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Add the original audio to the clip\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mclip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mclip\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mset_audio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mvideo\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34maudio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Save the final video\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mclip\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwrite_videofile\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mfinal_video.mp4\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcodec\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mlibx264\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "9201ae6c615b46ec8c97b198f56c38ed": { 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\n I have successfully created 12 images using ControlNet with different material prompts, we now have their URLs. \n The next step is to download these images and then merge them into a single video. \n \n In order to accomplish this, we will first need to install the opencv-python library for manipulating our \n images and creating our video, as well as the requests library for downloading our images from the internet. \n Let's do that. \n \n\n", "text/plain": " \n I have successfully created 12 images using ControlNet with different material prompts, we now have their URLs. \n The next step is to download these images and then merge them into a single video. \n \n In order to accomplish this, we will first need to install the \u001b[1;36;40mopencv-python\u001b[0m library for manipulating our \n images and creating our video, as well as the \u001b[1;36;40mrequests\u001b[0m library for downloading our images from the internet. \n Let's do that. \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "a21bd572aad1457890fd77e77ae60cde": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_800d0a52c55346119c920db787b7f229", "msg_id": "", "outputs": [ { "data": { "text/html": "
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\n from pedalboard import Pedalboard, Reverb, PitchShift \n \n # Create a Pedalboard instance \n board = Pedalboard([ \n PitchShift(semitones=-5), # Lower the pitch \n Reverb(room_size=0.5, dry_level=0.5, wet_level=0.5) # Add reverb \n ]) \n \n # Process the audio \n processed_audio = board.process(audio, sample_rate=sample_rate) \n \n # Save the processed audio to a new file \n sf.write('processed_song.wav', processed_audio, sample_rate) \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mfrom\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpedalboard\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mPedalboard\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mReverb\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mPitchShift\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Create a Pedalboard instance\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mboard\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mPedalboard\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mPitchShift\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msemitones\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m-\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m5\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Lower the pitch\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mReverb\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mroom_size\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0.5\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdry_level\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0.5\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwet_level\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0.5\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Add reverb\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Process the audio\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprocessed_audio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mboard\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprocess\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34maudio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msample_rate\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msample_rate\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Save the processed audio to a new file\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msf\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwrite\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mprocessed_song.wav\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprocessed_audio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msample_rate\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "a6ddbeb9b5ef4ea58858eabac8fde3c6": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "a7c6c1912ff3442e878fe2f19a7bab01": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_2044f647ec5b4bb6a3a363f40bfc45b1", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Output truncated. Showing the last 2000 characters. 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Showing the last 2000 characters. Adjust via `interpreter.max_output_chars`.\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m | 29/84 [00:01<00:02, 22.13it/s, now=None]t: 38%|###8 | 32/84 [00:01<00:02, 23.60it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m42%|####1 | 35/84 [00:01<00:02, 23.60it/s, now=None]t: 45%|####5 | 38/84 [00:01<00:02, 22.13it/s, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mnow=None]t: 49%|####8 | 41/84 [00:01<00:02, 21.33it/s, now=None]t: 52%|#####2 | 44/84 [00:01<00:01, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m20.25it/s, now=None]t: 56%|#####5 | 47/84 [00:02<00:02, 16.81it/s, now=None]t: 58%|#####8 | 49/84 \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m[00:02<00:02, 15.82it/s, now=None]t: 61%|###### | 51/84 [00:02<00:02, 14.81it/s, now=None]t: 63%|######3 \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m| 53/84 [00:02<00:02, 13.42it/s, now=None]t: 65%|######5 | 55/84 [00:02<00:02, 13.78it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m68%|######7 | 57/84 [00:03<00:01, 13.98it/s, now=None]t: 70%|####### | 59/84 [00:03<00:01, 14.07it/s, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 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\u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "a8733b66c6594a9b82309adeda08104f": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_d6a7ae990c474869934f5862a89dd274", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n output = replicate.run( \n 'jagilley/controlnet-canny:aff48af9c68d162388d230a2ab003f68d2638d88307bdaf1c2f1ac95079c9613', \n input={'image': open('victoria.png', 'rb'), 'prompt': 'metal'} \n ) \n output_second = output[1] \n output_second \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34moutput\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mreplicate\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrun\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mjagilley/controlnet-canny:aff48af9c68d162388d230a2ab003f68d2638d88307bdaf1c2f1ac95079c9613\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34minput\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m{\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mimage\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mopen\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mvictoria.png\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mrb\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mprompt\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mmetal\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m}\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34moutput_second\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34moutput\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m1\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34moutput_second\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "aa6c553f705d413fb4908e0d97aa017d": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_8df28d357a604e64b6d7353891fbdaa3", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n It appears that the Stretch class is not available in the pedalboard module. However, there is a PitchShift \n class which might be used to achieve a similar effect. Let's try using that instead. \n \n\n", "text/plain": " \n It appears that the \u001b[1;36;40mStretch\u001b[0m class is not available in the \u001b[1;36;40mpedalboard\u001b[0m module. However, there is a \u001b[1;36;40mPitchShift\u001b[0m \n class which might be used to achieve a similar effect. Let's try using that instead. \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "aae61f21148843148e082f1349b130ae": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "ad1cdb2076be4851b5d3a9af551e2e1e": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_0cbcc3ce78ac4df28d0326e591a2418e", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Moviepy - Building video video.mp4. \n Moviepy - Writing video video.mp4 \n \n \n t: 0%| | 0/12 [00:00<?, ?it/s, now=None] \n Moviepy - Done ! \n Moviepy - video ready video.mp4 \n None \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mMoviepy - Building video video.mp4.\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mMoviepy - Writing video video.mp4\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mt: 0%| | 0/12 [00:00 \n It appears that the folder named 'documents' was created successfully. Now, let's create the document files \n with sentences about machine learning. For this, I'm going to use the python-docx package. I'll install it now. \n Let's do this step-by-step for clarity. \n \n First, I'll install the package. \n \n\n", "text/plain": " \n It appears that the folder named 'documents' was created successfully. Now, let's create the document files \n with sentences about machine learning. For this, I'm going to use the python-docx package. I'll install it now. \n Let's do this step-by-step for clarity. \n \n First, I'll install the package. \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "b1ed6a8960964d01937082e84b55cf3a": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_2d23c9f050f745cdaadd6806811fe0ff", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n from PIL import Image, ImageDraw \n import random \n \n # Define the image dimensions and colors \n width, height = 160, 14 \n black = (0, 0, 0) \n white = (255, 255, 255) \n purple = (138, 43, 226) \n \n # Function to create a single banner \n def create_banner(filename): \n # Create a new image with a black background \n img = Image.new('RGB', (width, height), black) \n draw = ImageDraw.Draw(img) \n \n # Loop over every other row \n for y in range(0, height, 2): \n x = 0 \n # Randomly decide whether to start with a rectangle or a space \n start_with_rectangle = random.choice([True, False]) \n while x < width: \n if start_with_rectangle: \n # Draw a rectangle \n rectangle_width = random.randint(6, 36) \n color = random.choice([white, purple]) \n draw.rectangle([(x, y), (x + rectangle_width, y)], fill=color) \n x += rectangle_width \n else: \n # Leave a space \n space_width = random.randint(16, 64) \n x += space_width \n # Flip the flag for the next iteration \n start_with_rectangle = not start_with_rectangle \n \n # Scale up the image \n img = img.resize((width * 10, height * 10), Image.NEAREST) \n \n # Save the image \n img.save(filename) \n \n # Create 10 banners \n for i in range(1, 11): \n create_banner(f'banner_{i}.png') \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mfrom\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mPIL\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mImage\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mImageDraw\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrandom\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Define the image dimensions and colors\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwidth\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mheight\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m160\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m14\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mblack\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwhite\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m255\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m255\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m255\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpurple\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m138\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m43\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m226\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Function to create a single banner\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mdef\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;166;226;46;48;2;39;40;34mcreate_banner\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfilename\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Create a new image with a black background\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mimg\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mImage\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnew\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mRGB\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwidth\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mheight\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mblack\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdraw\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mImageDraw\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mDraw\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mimg\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Loop over every other row\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mfor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34my\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34min\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrange\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mheight\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m2\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mx\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Randomly decide whether to start with a rectangle or a space\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mstart_with_rectangle\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrandom\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mchoice\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mTrue\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mFalse\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mwhile\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mx\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m<\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwidth\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mif\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mstart_with_rectangle\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Draw a rectangle\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrectangle_width\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrandom\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrandint\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m6\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m36\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcolor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrandom\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mchoice\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwhite\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpurple\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdraw\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrectangle\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mx\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34my\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mx\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m+\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrectangle_width\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34my\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfill\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcolor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mx\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m+\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrectangle_width\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34melse\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Leave a space\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mspace_width\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrandom\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrandint\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m16\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m64\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mx\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m+\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mspace_width\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Flip the flag for the next iteration\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mstart_with_rectangle\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mnot\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mstart_with_rectangle\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Scale up the image\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mimg\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mimg\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mresize\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwidth\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m*\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m10\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mheight\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m*\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m10\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mImage\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mNEAREST\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Save the image\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mimg\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msave\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfilename\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Create 10 banners\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mfor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34min\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrange\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m1\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m11\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m 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\n Requirement already satisfied: pedalboard in /usr/local/lib/python3.10/dist-packages (0.7.5) \n Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pedalboard) (1.22.4) \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mRequirement already satisfied: pedalboard in /usr/local/lib/python3.10/dist-packages (0.7.5)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pedalboard) (1.22.4)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "b5508ace4d4844b68a2fc0f23e320db9": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_2acee6da53c748b194bd4445bdba02a4", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n I have deleted the old files and renamed the new ones with the same names as the original files. You should now \n see only the updated files in your 'documents' folder. All references to 'Machine Learning' have been replaced \n with 'AI' in these files. Feel free to check them and let me know if you have any other requests! \n \n\n", "text/plain": " \n I have deleted the old files and renamed the new ones with the same names as the original files. You should now \n see only the updated files in your 'documents' folder. All references to 'Machine Learning' have been replaced \n with 'AI' in these files. Feel free to check them and let me know if you have any other requests! \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "b821ba1c3698465092f5d390725fd535": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_93d564a6d8c842f29e3fcb5e8b8d2a18", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n ['.config', 'original_video.mp4.webm', 'sample_data'] \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m['.config', 'original_video.mp4.webm', 'sample_data']\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "b94baeecf44949ee9e76b2c080f0b3db": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_3164bb1a7475448e9f5bcaaadd74c63d", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n !pip install yt-dlp \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;150;0;80;48;2;30;0;16m!\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34minstall\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34myt\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdlp\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "bbf6af49c88c4c5d909b61ad8b67b5fa": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "bc0abb5adb504f1395bbff2e8a5404ef": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_18a380ea6a594f64b8381b1cd1bb2594", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Processing doc2.docx \n Finished processing doc2.docx \n Processing doc4.docx \n Finished processing doc4.docx \n Processing doc5.docx \n Finished processing doc5.docx \n Processing doc1.docx \n Finished processing doc1.docx \n Processing doc3.docx \n Finished processing doc3.docx \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mProcessing doc2.docx\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mFinished processing doc2.docx\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mProcessing doc4.docx\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mFinished processing doc4.docx\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mProcessing doc5.docx\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mFinished processing doc5.docx\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mProcessing doc1.docx\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mFinished processing doc1.docx\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mProcessing doc3.docx\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mFinished processing doc3.docx\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "bd5ea7bd835a483095abe490c447dd32": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "bfffa91fc96f4f5b9ca25b29025938c6": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_d007873a1ec540b9b73cba17251362b0", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n from pydub import AudioSegment \n \n # Convert webm file to wav \n song = AudioSegment.from_file('original_song.webm') \n song.export('original_song.wav', format='wav') \n \n # Load the audio file \n audio, sample_rate = sf.read('original_song.wav') \n \n # Create a Pedalboard instance \n board = Pedalboard([ \n PitchShift(semitones=-5), # Lower the pitch \n Reverb(room_size=0.5, dry_level=0.5, wet_level=0.5) # Add reverb \n ]) \n \n # Process the audio \n processed_audio = board.process(audio, sample_rate=sample_rate) \n \n # Save the processed audio to a new file \n sf.write('processed_song.wav', processed_audio, sample_rate) \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mfrom\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpydub\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mAudioSegment\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Convert webm file to wav\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msong\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mAudioSegment\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfrom_file\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34moriginal_song.webm\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msong\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mexport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34moriginal_song.wav\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mformat\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mwav\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Load the audio file\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34maudio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msample_rate\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msf\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mread\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34moriginal_song.wav\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Create a Pedalboard instance\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mboard\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mPedalboard\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mPitchShift\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msemitones\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m-\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m5\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Lower the pitch\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mReverb\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mroom_size\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0.5\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdry_level\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0.5\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwet_level\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0.5\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Add reverb\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Process the audio\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprocessed_audio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mboard\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprocess\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34maudio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msample_rate\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msample_rate\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Save the processed audio to a new file\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msf\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwrite\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mprocessed_song.wav\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprocessed_audio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msample_rate\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "c0a1d45a04b7406683954288d08915b1": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "c18416a315554529bbf7f4f4543ecaa3": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "c22ab4bfa43a460088c58b6679d60360": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_2e9f43e734694e76829a9ed2a61439be", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n The video has been successfully downloaded and modified to have a frame rate of 12 fps for the segment from \n 0:10 to 0:17. The next step is to redraw each frame using ControlNet with random prompts that evoke a stop \n motion / hand-drawn animation feeling. \n \n Let's start by extracting all frames from the modified video. Then we will process each frame with ControlNet. \n \n\n", "text/plain": " \n The video has been successfully downloaded and modified to have a frame rate of 12 fps for the segment from \n 0:10 to 0:17. The next step is to redraw each frame using ControlNet with random prompts that evoke a stop \n motion / hand-drawn animation feeling. \n \n Let's start by extracting all frames from the modified video. Then we will process each frame with ControlNet. \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "c31a702d90604293b9494824157c5493": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_77e0222fd2c54dbca7932d57e517601a", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Traceback (most recent call last): \n Cell In[9], line 2 \n from pedalboard import Pedalboard, Reverb, Stretch \n ImportError: cannot import name 'Stretch' from 'pedalboard' \n (/usr/local/lib/python3.10/dist-packages/pedalboard/__init__.py) \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mTraceback (most recent call last):\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m Cell In[9], line 2\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m from pedalboard import Pedalboard, Reverb, Stretch\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mImportError: cannot import name 'Stretch' from 'pedalboard' \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m(/usr/local/lib/python3.10/dist-packages/pedalboard/__init__.py)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "c39c77408c5e4ab99f127a1306f27554": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_ece854c5547443d78f1e8c0d8960ad47", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Great, the necessary packages have been installed. Now, let's proceed with the following steps: \n \n 1 Download the video from the provided YouTube link. \n 2 Extract the audio from the video for later use. \n 3 Cut the video to the desired time range (0:10 to 0:17). \n 4 Reduce the frame rate of the video to 12 fps. \n 5 Save each frame of the video as an image. \n 6 Use ControlNet to redraw each image with a random prompt. \n 7 Combine the redrawn images into a new video. \n 8 Add the original audio back to the new video. \n \n Let's start with step 1: Downloading the video. \n \n\n", "text/plain": " \n Great, the necessary packages have been installed. Now, let's proceed with the following steps: \n \n \u001b[1;33m 1 \u001b[0mDownload the video from the provided YouTube link. \n \u001b[1;33m 2 \u001b[0mExtract the audio from the video for later use. \n \u001b[1;33m 3 \u001b[0mCut the video to the desired time range (0:10 to 0:17). \n \u001b[1;33m 4 \u001b[0mReduce the frame rate of the video to 12 fps. \n \u001b[1;33m 5 \u001b[0mSave each frame of the video as an image. \n \u001b[1;33m 6 \u001b[0mUse ControlNet to redraw each image with a random prompt. \n \u001b[1;33m 7 \u001b[0mCombine the redrawn images into a new video. \n \u001b[1;33m 8 \u001b[0mAdd the original audio back to the new video. \n \n Let's start with step 1: Downloading the video. \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "c72c1bac807b40d297fdba74958df4de": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_fe21edae84cc4778a3ddc2fcaedfbc3d", "msg_id": "", "outputs": [ { "data": { "text/html": "
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\n I have successfully hardcoded the subtitles into the video and created a new file named billy_subbed.mp4. Now, \n you should be able to play the video with the subtitles displayed at the bottom. Let me know if there is \n anything else I can help you with. \n \n\n",
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"text/html": "\n Traceback (most recent call last): \n Cell In[9], line 4 \n processed_audio = board.process(audio, sample_rate=sample_rate) \n NameError: name 'audio' is not defined \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mTraceback (most recent call last):\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m Cell In[9], line 4\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m processed_audio = board.process(audio, sample_rate=sample_rate)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mNameError: name 'audio' is not defined\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "cc1a30ae29f5499d8156470700faae6c": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "cc23e29c55ac41fa9bfbb35230fb67e7": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "cf53e6745eb14d44a6ce84d8f1efb3e9": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_9d35e8077b634453a9da6c9bdf55c104", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n import soundfile as sf \n from pedalboard import Pedalboard, Reverb, Stretch \n \n # Load the audio file \n audio, sample_rate = sf.read('original_song.webm') \n \n # Create a Pedalboard instance \n board = Pedalboard([ \n Stretch(factor=0.8), # Slow down by 20% \n Reverb(room_size=0.5, dry_level=0.5, wet_level=0.5) # Add reverb \n ], sample_rate=sample_rate) \n \n # Process the audio \n processed_audio = board(audio) \n \n # Save the processed audio to a new file \n sf.write('processed_song.wav', processed_audio, sample_rate) \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msoundfile\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mas\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msf\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mfrom\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpedalboard\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mPedalboard\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mReverb\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mStretch\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Load the audio file\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34maudio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msample_rate\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msf\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mread\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34moriginal_song.webm\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Create a Pedalboard instance\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mboard\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mPedalboard\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mStretch\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfactor\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0.8\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Slow down by 20%\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mReverb\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mroom_size\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0.5\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdry_level\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0.5\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwet_level\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0.5\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Add reverb\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msample_rate\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msample_rate\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Process the audio\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprocessed_audio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mboard\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34maudio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Save the processed audio to a new file\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msf\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwrite\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mprocessed_song.wav\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprocessed_audio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msample_rate\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "d007873a1ec540b9b73cba17251362b0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, 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\n Collecting python-docx \n Downloading python-docx-0.8.11.tar.gz (5.6 MB) \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/5.6 MB ? eta -:--:-- \n ━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.8/5.6 MB 50.5 MB/s eta 0:00:01 \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━ 4.2/5.6 MB 69.5 MB/s eta 0:00:01 \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 5.6/5.6 MB 67.5 MB/s eta 0:00:01 \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5.6/5.6 MB 46.8 MB/s eta 0:00:00 \n Preparing metadata (setup.py) ... done \n Requirement already satisfied: lxml>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from python-docx) \n (4.9.3) \n Building wheels for collected packages: python-docx \n Building wheel for python-docx (setup.py) ... done \n Created wheel for python-docx: filename=python_docx-0.8.11-py3-none-any.whl size=184491 \n sha256=6df55c1b8dfc526237f9d47c2bee330c560a1087b2525c78bb00a4a383104b5d \n Stored in directory: /root/.cache/pip/wheels/80/27/06/837436d4c3bd989b957a91679966f207bfd71d358d63a8194d \n Successfully built python-docx \n Installing collected packages: python-docx \n Successfully installed python-docx-0.8.11 \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mCollecting python-docx\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m Downloading python-docx-0.8.11.tar.gz (5.6 MB)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 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\u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mInstalling collected packages: python-docx\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mSuccessfully installed python-docx-0.8.11\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "d0aafd6be71f4f548e2ef6df1fb0dde1": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", 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\n !pip install pedalboard \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;150;0;80;48;2;30;0;16m!\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34minstall\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpedalboard\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "d6a7ae990c474869934f5862a89dd274": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "d70ab8f629f446c19c41c3293b905d83": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "d77d9d8269874d54b6762f634e916206": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_9d1e55aa8c544a3aa780fa2230b82c21", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Output truncated. 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\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m90%|######### | 1319/1463 [02:42<00:15, 9.50it/s, now=None]t: 90%|######### | 1321/1463 [02:43<00:15, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m9.40it/s, now=None]t: 90%|######### | 1322/1463 [02:43<00:14, 9.42it/s, now=None]t: 90%|######### | \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m1324/1463 [02:43<00:15, 9.11it/s, now=None]t: 91%|######### | 1325/1463 [02:43<00:14, 9.25it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m91%|######### | 1327/1463 [02:43<00:12, 11.31it/s, now=None]t: 91%|######### | 1329/1463 [02:43<00:14, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m9.57it/s, now=None]t: 91%|######### | 1331/1463 [02:44<00:15, 8.62it/s, now=None]t: 91%|#########1| \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m1333/1463 [02:44<00:12, 10.34it/s, now=None]t: 91%|#########1| 1335/1463 [02:44<00:13, 9.21it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m91%|#########1| 1337/1463 [02:44<00:12, 10.30it/s, now=None]t: 92%|#########1| 1339/1463 [02:44<00:13, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m9.06it/s, now=None]t: 92%|#########1| 1341/1463 [02:45<00:12, 10.11it/s, now=None]t: 92%|#########1| \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m1343/1463 [02:45<00:13, 9.08it/s, now=None]t: 92%|#########1| 1345/1463 [02:45<00:12, 9.61it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m92%|#########2| 1347/1463 [02:45<00:10, 11.20it/s, now=None]t: 92%|#########2| 1349/1463 [02:45<00:11, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m9.58it/s, now=None]t: 92%|#########2| 1351/1463 [02:46<00:11, 9.57it/s, now=None]t: 92%|#########2| \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m1353/1463 [02:46<00:10, 10.70it/s, now=None]t: 93%|#########2| 1355/1463 [02:46<00:10, 10.17it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m93%|#########2| 1357/1463 [02:46<00:10, 10.28it/s, now=None]t: 93%|#########2| 1359/1463 [02:46<00:09, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m10.94it/s, now=None]t: 93%|#########3| 1361/1463 [02:47<00:12, 8.09it/s, now=None]t: 93%|#########3| \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m1363/1463 [02:47<00:11, 8.84it/s, now=None]t: 93%|#########3| 1365/1463 [02:47<00:12, 7.58it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m93%|#########3| 1367/1463 [02:48<00:14, 6.47it/s, now=None]t: 94%|#########3| 1369/1463 [02:48<00:12, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m7.48it/s, now=None]t: 94%|#########3| 1370/1463 [02:48<00:12, 7.29it/s, now=None]t: 94%|#########3| \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m1371/1463 [02:48<00:15, 5.85it/s, now=None]t: 94%|#########3| 1372/1463 [02:48<00:14, 6.33it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m94%|#########3| 1373/1463 [02:49<00:14, 6.11it/s, now=None]t: 94%|#########3| 1374/1463 [02:49<00:14, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m6.17it/s, now=None]t: 94%|#########3| 1375/1463 [02:49<00:15, 5.59it/s, now=None]t: 94%|#########4| \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m1376/1463 [02:49<00:13, 6.28it/s, now=None]t: 94%|#########4| 1377/1463 [02:49<00:12, 6.97it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m94%|#########4| 1378/1463 [02:49<00:14, 6.04it/s, now=None]t: 94%|#########4| 1379/1463 [02:50<00:16, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m5.17it/s, now=None]t: 94%|#########4| 1380/1463 [02:50<00:15, 5.41it/s, now=None]t: 94%|#########4| \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m1382/1463 [02:50<00:11, 7.30it/s, now=None]t: 95%|#########4| 1383/1463 [02:50<00:13, 5.78it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m95%|#########4| 1384/1463 [02:50<00:13, 5.78it/s, now=None]t: 95%|#########4| 1386/1463 [02:51<00:12, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m6.03it/s, now=None]t: 95%|#########4| 1387/1463 [02:51<00:11, 6.52it/s, now=None]t: 95%|#########4| \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m1388/1463 [02:51<00:10, 7.11it/s, now=None]t: 95%|#########4| 1389/1463 [02:51<00:10, 7.06it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m95%|#########5| 1390/1463 [02:51<00:13, 5.49it/s, now=None]t: 95%|#########5| 1391/1463 [02:52<00:12, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m5.90it/s, now=None]t: 95%|#########5| 1393/1463 [02:52<00:08, 8.30it/s, now=None]t: 95%|#########5| \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 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5.35it/s, now=None]t: 96%|#########5| \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m1401/1463 [02:53<00:10, 5.92it/s, now=None]t: 96%|#########5| 1403/1463 [02:54<00:10, 5.85it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m96%|#########6| 1405/1463 [02:54<00:07, 7.74it/s, now=None]t: 96%|#########6| 1407/1463 [02:54<00:09, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m6.09it/s, now=None]t: 96%|#########6| 1409/1463 [02:54<00:07, 6.86it/s, now=None]t: 96%|#########6| \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m1410/1463 [02:55<00:09, 5.53it/s, now=None]t: 97%|#########6| 1412/1463 [02:55<00:07, 6.96it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m97%|#########6| 1413/1463 [02:55<00:07, 6.90it/s, now=None]t: 97%|#########6| 1414/1463 [02:55<00:09, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m5.34it/s, now=None]t: 97%|#########6| 1416/1463 [02:56<00:08, 5.72it/s, now=None]t: 97%|#########6| \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m1417/1463 [02:56<00:07, 6.29it/s, now=None]t: 97%|#########6| 1419/1463 [02:56<00:05, 7.89it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m97%|#########7| 1420/1463 [02:56<00:07, 6.11it/s, now=None]t: 97%|#########7| 1422/1463 [02:57<00:06, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m6.33it/s, now=None]t: 97%|#########7| 1423/1463 [02:57<00:05, 6.70it/s, now=None]t: 97%|#########7| \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m1424/1463 [02:57<00:05, 7.00it/s, now=None]t: 97%|#########7| 1425/1463 [02:57<00:05, 7.39it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 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[02:58<00:04, 7.63it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m98%|#########7| 1433/1463 [02:58<00:03, 7.50it/s, now=None]t: 98%|#########8| 1434/1463 [02:58<00:04, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m6.56it/s, now=None]t: 98%|#########8| 1436/1463 [02:58<00:03, 7.82it/s, now=None]t: 98%|#########8| \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m1438/1463 [02:59<00:02, 9.68it/s, now=None]t: 98%|#########8| 1440/1463 [02:59<00:02, 8.59it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m99%|#########8| 1442/1463 [02:59<00:02, 10.36it/s, now=None]t: 99%|#########8| 1444/1463 [02:59<00:02, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m9.01it/s, now=None]t: 99%|#########8| 1446/1463 [02:59<00:01, 9.91it/s, now=None]t: 99%|#########8| \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 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now=None]t: 100%|#########9| 1456/1463 [03:00<00:00, 10.56it/s, now=None]t: 100%|#########9| \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m1458/1463 [03:00<00:00, 11.16it/s, now=None]t: 100%|#########9| 1460/1463 [03:01<00:00, 10.43it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m100%|#########9| 1462/1463 [03:01<00:00, 11.59it/s, now=None] \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mMoviepy - Done !\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mMoviepy - video ready billy_subbed.mp4\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mNone\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "d7e431b748474de0a69eee41e44d6eb7": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_e343d276aac5477bb4f9917eb5d8d8db", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n import os \n \n # List all files in the current directory \n os.listdir() \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mos\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# List all files in the current directory\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mos\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlistdir\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "d849c6a21c8b41a8bbb0abc8a95997c5": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "d911c9e7aedd42519ee06ef45a0224d2": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_e660cf408ab34428a4f579f4b3770f5e", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n video = VideoFileClip('original_video.mp4.webm') \n \n # Extract the segment from 0:10 to 0:17 \n video = video.subclip(10, 17) \n \n # Reduce the frame rate to 12 fps \n video = video.set_duration(7).set_fps(12) \n \n # Save the modified video \n video.write_videofile('modified_video.mp4', codec='libx264') \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mvideo\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mVideoFileClip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34moriginal_video.mp4.webm\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Extract the segment from 0:10 to 0:17\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mvideo\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m 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\u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Save the modified video\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mvideo\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwrite_videofile\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mmodified_video.mp4\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcodec\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mlibx264\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "daba6cb342374830be9830b57d96a599": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_41707e7b17314b17a249388903e0ce71", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n !pip install yt-dlp moviepy replicate \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;150;0;80;48;2;30;0;16m!\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34minstall\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34myt\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdlp\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmoviepy\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mreplicate\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "dbb96798015249abbad237f9086ac8ff": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_0b001f96904d4c91bf26ad72420e9cca", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n import replicate \n import random \n \n # Define the prompts \n prompts = ['embroidery', 'pencil art', 'claymation', 'yarn on a table'] \n \n # Create a directory to store the redrawn frames \n os.makedirs('redrawn_frames', exist_ok=True) \n \n # Redraw each frame with ControlNet \n for i in range(len(os.listdir('frames'))): \n # Open the frame \n with open(f'frames/frame_{i}.png', 'rb') as f: \n # Run ControlNet on the frame \n output = replicate.run( \n 'jagilley/controlnet-canny:aff48af9c68d162388d230a2ab003f68d2638d88307bdaf1c2f1ac95079c9613', \n input={'image': f, 'prompt': random.choice(prompts)} \n ) \n \n # Download the redrawn frame \n os.system(f'wget {output[1]} -O redrawn_frames/frame_{i}.png') \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mreplicate\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrandom\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Define the prompts\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprompts\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34membroidery\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mpencil art\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mclaymation\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34myarn on a table\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Create a directory to store the redrawn frames\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mos\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmakedirs\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mredrawn_frames\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mexist_ok\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mTrue\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Redraw each frame with ControlNet\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mfor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34min\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrange\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlen\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mos\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlistdir\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mframes\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Open the frame\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mwith\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mopen\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mf\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mframes/frame_\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m{\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m}\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m.png\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mrb\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mas\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mf\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Run ControlNet on the frame\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34moutput\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mreplicate\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrun\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mjagilley/controlnet-canny:aff48af9c68d162388d230a2ab003f68d2638d88307bdaf1c2f1ac95079c9613\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34minput\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m{\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mimage\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mf\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mprompt\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrandom\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mchoice\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprompts\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m}\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Download the redrawn frame\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mos\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msystem\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mf\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mwget 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\n It seems like the video file original_video.mp4 could not be found. This could be because the video was \n downloaded with a different filename. Let's check the current directory to see if the video file is present and \n what its exact filename is. \n \n\n",
"text/plain": " \n It seems like the video file \u001b[1;36;40moriginal_video.mp4\u001b[0m could not be found. This could be because the video was \n downloaded with a different filename. Let's check the current directory to see if the video file is present and \n what its exact filename is. \n \n"
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"text/html": "\n from pedalboard import Pedalboard, Reverb, PitchShift \n \n # Create a Pedalboard instance \n board = Pedalboard([ \n PitchShift(semitones=-5), # Lower the pitch \n Reverb(room_size=0.5, dry_level=0.5, wet_level=0.5) # Add reverb \n ], sample_rate=sample_rate) \n \n # Process the audio \n processed_audio = board(audio) \n \n # Save the processed audio to a new file \n sf.write('processed_song.wav', processed_audio, sample_rate) \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mfrom\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpedalboard\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mPedalboard\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mReverb\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mPitchShift\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Create a Pedalboard instance\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mboard\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mPedalboard\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mPitchShift\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msemitones\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m-\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m5\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Lower the pitch\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mReverb\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mroom_size\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0.5\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdry_level\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0.5\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwet_level\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0.5\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Add reverb\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msample_rate\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msample_rate\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Process the audio\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprocessed_audio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mboard\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34maudio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Save the processed audio to a new file\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msf\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwrite\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mprocessed_song.wav\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprocessed_audio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msample_rate\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "e280512ec6404a2e9849072c1ac4a74a": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_626af5cf397440b2baec64ed98ea5c3f", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Output truncated. Showing the last 2000 characters. Adjust via `interpreter.max_output_chars`. \n \n 2.75MiB/s ETA 00:00 (frag 42/43) 101.3% of ~ 44.85MiB at 2.99MiB/s ETA 00:00 (frag 42/43) 100.0% of ~ \n 45.44MiB at 2.75MiB/s ETA 00:00 (frag 43/43) 102.3% of ~ 44.41MiB at 30.40KiB/s ETA -1:59:26 (frag 43/43) \n 102.3% of ~ 44.41MiB at 59.29KiB/s ETA -1:59:43 (frag 43/43) 102.3% of ~ 44.41MiB at 105.76KiB/s ETA \n -1:59:50 (frag 43/43) 102.4% of ~ 44.41MiB at 170.58KiB/s ETA -1:59:54 (frag 43/43) 102.4% of ~ 44.41MiB at \n 297.14KiB/s ETA -1:59:57 (frag 43/43) 102.5% of ~ 44.41MiB at 521.75KiB/s ETA -1:59:58 (frag 43/43) 102.6% of \n ~ 44.41MiB at 912.53KiB/s ETA -1:59:59 (frag 43/43) 102.9% of ~ 44.41MiB at 1.60MiB/s ETA 00:00 (frag \n 43/43) 103.3% of ~ 44.41MiB at 2.49MiB/s ETA 00:00 (frag 43/43) 102.3% of ~ 44.82MiB at 2.28MiB/s \n ETA 00:00 (frag 44/43) 100% of 45.87MiB in 00:00:09 at 4.83MiB/s \n Destination: original_video.mp4.f251.webm \n 0.0% of 3.80MiB at 72.52KiB/s ETA 00:53 0.1% of 3.80MiB at 109.40KiB/s ETA 00:35 0.2% of \n 3.80MiB at 169.45KiB/s ETA 00:22 0.4% of 3.80MiB at 272.40KiB/s ETA 00:14 0.8% of 3.80MiB at \n 450.28KiB/s ETA 00:08 1.6% of 3.80MiB at 750.69KiB/s ETA 00:05 3.3% of 3.80MiB at 1.22MiB/s ETA \n 00:03 6.6% of 3.80MiB at 2.08MiB/s ETA 00:01 13.1% of 3.80MiB at 3.59MiB/s ETA 00:00 26.3% of \n 3.80MiB at 6.15MiB/s ETA 00:00 52.7% of 3.80MiB at 10.69MiB/s ETA 00:00 100.0% of 3.80MiB at \n 16.74MiB/s ETA 00:00 100% of 3.80MiB in 00:00:00 at 14.53MiB/s \n [Merger] Merging formats into \"original_video.mp4.webm\" \n Deleting original file original_video.mp4.f251.webm (pass -k to keep) \n Deleting original file original_video.mp4.f616.mp4 (pass -k to keep) \n 0 \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mOutput truncated. Showing the last 2000 characters. Adjust via `interpreter.max_output_chars`.\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 2.75MiB/s ETA 00:00 (frag 42/43)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 101.3% of ~ 44.85MiB at 2.99MiB/s ETA 00:00 (frag 42/43)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 100.0% of ~ \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m45.44MiB at 2.75MiB/s ETA 00:00 (frag 43/43)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 102.3% of ~ 44.41MiB at 30.40KiB/s ETA -1:59:26 (frag 43/43)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m102.3% of ~ 44.41MiB at 59.29KiB/s ETA -1:59:43 (frag 43/43)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 102.3% of ~ 44.41MiB at 105.76KiB/s ETA \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m-1:59:50 (frag 43/43)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 102.4% of ~ 44.41MiB at 170.58KiB/s ETA -1:59:54 (frag 43/43)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 102.4% of ~ 44.41MiB at \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m297.14KiB/s ETA -1:59:57 (frag 43/43)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 102.5% of ~ 44.41MiB at 521.75KiB/s ETA -1:59:58 (frag 43/43)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 102.6% of\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m~ 44.41MiB at 912.53KiB/s ETA -1:59:59 (frag 43/43)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 102.9% of ~ 44.41MiB at 1.60MiB/s ETA 00:00 (frag \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m43/43) \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 103.3% of ~ 44.41MiB at 2.49MiB/s ETA 00:00 (frag 43/43)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 102.3% of ~ 44.82MiB at 2.28MiB/s \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mETA 00:00 (frag 44/43)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 100% of 45.87MiB in 00:00:09 at 4.83MiB/s \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m Destination: original_video.mp4.f251.webm\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 0.0% of 3.80MiB at 72.52KiB/s ETA 00:53\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 0.1% of 3.80MiB at 109.40KiB/s ETA 00:35\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 0.2% of \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m3.80MiB at 169.45KiB/s ETA 00:22\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 0.4% of 3.80MiB at 272.40KiB/s ETA 00:14\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 0.8% of 3.80MiB at \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m450.28KiB/s ETA 00:08\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 1.6% of 3.80MiB at 750.69KiB/s ETA 00:05\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 3.3% of 3.80MiB at 1.22MiB/s ETA \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m00:03\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 6.6% of 3.80MiB at 2.08MiB/s ETA 00:01\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 13.1% of 3.80MiB at 3.59MiB/s ETA 00:00\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 26.3% of \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m3.80MiB at 6.15MiB/s ETA 00:00\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 52.7% of 3.80MiB at 10.69MiB/s ETA 00:00\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 100.0% of 3.80MiB at \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m16.74MiB/s ETA 00:00\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 100% of 3.80MiB in 00:00:00 at 14.53MiB/s \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m[Merger] Merging formats into \"original_video.mp4.webm\"\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mDeleting original file original_video.mp4.f251.webm (pass -k to keep)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 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\n Sure, to achieve this, we will need the following steps: \n \n 1 List all the .docx files in the 'documents' folder. \n 2 Open each .docx file and search for the term 'Machine Learning' (regardless of the case). \n 3 Replace 'Machine Learning' with 'AI'. \n 4 Save the changes in each respective file. \n \n We would need to use the os and python-docx modules for this task. However, you might need to install the \n python-docx module, should it not be already installed. Let's proceed with the operation. \n \n First, I will write a small piece of code to list all .docx files in the directory. Let's make sure we have the \n right files. \n \n\n", "text/plain": " \n Sure, to achieve this, we will need the following steps: \n \n \u001b[1;33m 1 \u001b[0mList all the .docx files in the 'documents' folder. \n \u001b[1;33m 2 \u001b[0mOpen each .docx file and search for the term 'Machine Learning' (regardless of the case). \n \u001b[1;33m 3 \u001b[0mReplace 'Machine Learning' with 'AI'. \n \u001b[1;33m 4 \u001b[0mSave the changes in each respective file. \n \n We would need to use the \u001b[1;36;40mos\u001b[0m and \u001b[1;36;40mpython-docx\u001b[0m modules for this task. However, you might need to install the \n \u001b[1;36;40mpython-docx\u001b[0m module, should it not be already installed. Let's proceed with the operation. \n \n First, I will write a small piece of code to list all .docx files in the directory. Let's make sure we have the \n right files. \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "e632cf8148b5443eb888ad154820dac2": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_942032ef5a7b46a0866adf65a07ca9b6", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n !pip install opencv-python-headless requests \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;150;0;80;48;2;30;0;16m!\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34minstall\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mopencv\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpython\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mheadless\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrequests\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "e64b5e68e0e84553a9e303ba55d389d0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "e660cf408ab34428a4f579f4b3770f5e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "e6e713a9e71d495baa5a435abcb27ee1": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_d849c6a21c8b41a8bbb0abc8a95997c5", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n !pip install python-docx \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;150;0;80;48;2;30;0;16m!\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34minstall\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpython\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdocx\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "e6feb1cf757b44bba8fbdecd9b185178": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "e738acc7f303409f9af430957bb73425": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_7735b6aa07bb48e198f2e79bb8483006", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Output truncated. Showing the last 2000 characters. 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\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m6.50it/s, now=None] Moviepy - Done !\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mMoviepy - video ready modified_video.mp4\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mNone\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "e96fdcfcde144e78b7977830628e77f9": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_afddf4997e1a4d009f278b34486a71aa", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n I've created 10 pixel art banners as per your specifications. They are saved as 'banner_1.png', 'banner_2.png', \n and so on up to 'banner_10.png'. Please review them and let me know if you need any changes. \n \n\n", "text/plain": " \n I've created 10 pixel art banners as per your specifications. They are saved as 'banner_1.png', 'banner_2.png', \n and so on up to 'banner_10.png'. Please review them and let me know if you need any changes. \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "eb83234ed4724410aec6bde226f104a1": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_b37d96a06ec148fab77a1a5121bfc74b", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n !pip install moviepy \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;150;0;80;48;2;30;0;16m!\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34minstall\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmoviepy\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "ece854c5547443d78f1e8c0d8960ad47": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "ed07889e87744f3aa90e1a211b5b81ff": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_673dce80b4b54a7384a460b0075759fb", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n ['doc2.docx', 'doc4.docx', 'doc5.docx', 'doc1.docx', 'doc3.docx'] \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m['doc2.docx', 'doc4.docx', 'doc5.docx', 'doc1.docx', 'doc3.docx']\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "ed507d688c4747158749a94ce44b26b9": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_8e94b43d08f848159b7c621693e26878", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n # Delete the old files and rename the new ones \n for filename in files: \n new_filename = os.path.join('documents', f'new_{filename}') \n old_filename = os.path.join('documents', filename) \n \n # Remove the old file \n os.remove(old_filename) \n \n # Rename the new file to the original filename \n os.rename(new_filename, old_filename) \n \n print('All files have been updated.') \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Delete the old files and rename the new ones\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mfor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfilename\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m 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\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfilename\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Remove the old file\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m 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updated.\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "ee9b39134cf84f089d4868aae1851900": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, 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"_view_name": "OutputView", "layout": "IPY_MODEL_d70ab8f629f446c19c41c3293b905d83", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n import cv2 \n import requests \n import numpy as np \n \n # Here we are downloading all the frames generated by the model and \n # saving it as an image locally \n images = [] \n for url in output_urls: \n response = requests.get(url) \n array = np.frombuffer(response.content, dtype=np.uint8) \n image = cv2.imdecode(array, flags=cv2.IMREAD_COLOR) \n images.append(image) \n \n # Verify if we have the correct number of frames \n len(images) \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcv2\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrequests\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnumpy\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mas\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnp\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Here we are downloading all the frames generated by the model and\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# saving it as an image locally\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mimages\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mfor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m 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\n A container for a series of :class:`Plugin` objects, to use for processing audio, like a \n `guitar pedalboard <https://en.wikipedia.org/wiki/Guitar_pedalboard>`_. \n \n :class:`Pedalboard` objects act like regular Python ``List`` objects, \n but come with an additional :py:meth:`process` method (also aliased to :py:meth:`__call__`), \n allowing audio to be passed through the entire :class:`Pedalboard` object for processing:: \n \n my_pedalboard = Pedalboard() \n my_pedalboard.append(Reverb()) \n output_audio = my_pedalboard(input_audio) \n \n .. warning:: \n :class:`Pedalboard` objects may only contain effects plugins (i.e.: those for which \n :attr:`is_effect` is ``True``), and cannot contain instrument plugins (i.e.: those \n for which :attr:`is_instrument` is ``True``). \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mA container for a series of :class:`Plugin` objects, to use for processing audio, like a\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m `guitar pedalboard
\n Collecting feedparser \n Downloading feedparser-6.0.10-py3-none-any.whl (81 kB) \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/81.1 kB ? eta -:--:-- \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 81.1/81.1 kB 4.3 MB/s eta 0:00:00 \n Collecting sgmllib3k (from feedparser) \n Downloading sgmllib3k-1.0.0.tar.gz (5.8 kB) \n Preparing metadata (setup.py) ... done \n Building wheels for collected packages: sgmllib3k \n Building wheel for sgmllib3k (setup.py) ... done \n Created wheel for sgmllib3k: filename=sgmllib3k-1.0.0-py3-none-any.whl size=6046 \n sha256=651ecac15cda67cfda9e6d19a71d7b727ffed85714612ec195412ea27ea7b66c \n Stored in directory: /root/.cache/pip/wheels/f0/69/93/a47e9d621be168e9e33c7ce60524393c0b92ae83cf6c6e89c5 \n Successfully built sgmllib3k \n Installing collected packages: sgmllib3k, feedparser \n Successfully installed feedparser-6.0.10 sgmllib3k-1.0.0 \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mCollecting feedparser\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m Downloading feedparser-6.0.10-py3-none-any.whl (81 kB)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/81.1 kB ? eta -:--:-- \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 81.1/81.1 kB 4.3 MB/s eta 0:00:00\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mCollecting sgmllib3k (from feedparser)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m Downloading sgmllib3k-1.0.0.tar.gz (5.8 kB)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m Preparing metadata (setup.py) ... done\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mBuilding wheels for collected packages: sgmllib3k\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m Building wheel for sgmllib3k (setup.py) ... done\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m Created wheel for sgmllib3k: filename=sgmllib3k-1.0.0-py3-none-any.whl size=6046 \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55msha256=651ecac15cda67cfda9e6d19a71d7b727ffed85714612ec195412ea27ea7b66c\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m Stored in directory: /root/.cache/pip/wheels/f0/69/93/a47e9d621be168e9e33c7ce60524393c0b92ae83cf6c6e89c5\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mSuccessfully built sgmllib3k\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mInstalling collected packages: sgmllib3k, feedparser\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mSuccessfully installed feedparser-6.0.10 sgmllib3k-1.0.0\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "f491c55d72df4b589454a8861197376b": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_3bba172d1af2465181679c8b336704a7", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n import soundfile as sf \n from pedalboard import Pedalboard, Reverb, PitchShift \n \n # Load the audio file \n audio, sample_rate = sf.read('original_song.webm') \n \n # Create a Pedalboard instance \n board = Pedalboard([ \n PitchShift(semitones=-5), # Lower the pitch \n Reverb(room_size=0.5, dry_level=0.5, wet_level=0.5) # Add reverb \n ]) \n \n # Process the audio \n processed_audio = board.process(audio, sample_rate=sample_rate) \n \n # Save the processed audio to a new file \n sf.write('processed_song.wav', processed_audio, sample_rate) \n \n\n", "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msoundfile\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mas\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m 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\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msf\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mread\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34moriginal_song.webm\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Create a Pedalboard instance\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mboard\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mPedalboard\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mPitchShift\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msemitones\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m-\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m5\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Lower the pitch\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mReverb\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mroom_size\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0.5\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdry_level\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0.5\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwet_level\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0.5\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Add reverb\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Process the audio\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprocessed_audio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mboard\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprocess\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34maudio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msample_rate\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msample_rate\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Save the processed audio to a new file\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msf\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwrite\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mprocessed_song.wav\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprocessed_audio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msample_rate\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "f86e25f5432b4589b9045ba52ebed7ae": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_99b83169a2c5408dbfa7b835db2d7171", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Requirement already satisfied: yt-dlp in /usr/local/lib/python3.10/dist-packages (2023.7.6) \n Requirement already satisfied: mutagen in /usr/local/lib/python3.10/dist-packages (from yt-dlp) (1.46.0) \n Requirement already satisfied: pycryptodomex in /usr/local/lib/python3.10/dist-packages (from yt-dlp) (3.18.0) \n Requirement already satisfied: websockets in /usr/local/lib/python3.10/dist-packages (from yt-dlp) (11.0.3) \n Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from yt-dlp) (2023.5.7) \n Requirement already satisfied: brotli in /usr/local/lib/python3.10/dist-packages (from yt-dlp) (1.0.9) \n \n\n", "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mRequirement already satisfied: yt-dlp in /usr/local/lib/python3.10/dist-packages (2023.7.6)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mRequirement already satisfied: mutagen in /usr/local/lib/python3.10/dist-packages (from yt-dlp) (1.46.0)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mRequirement already satisfied: pycryptodomex in /usr/local/lib/python3.10/dist-packages (from yt-dlp) (3.18.0)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mRequirement already satisfied: websockets in /usr/local/lib/python3.10/dist-packages (from yt-dlp) (11.0.3)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mRequirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from yt-dlp) (2023.5.7)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mRequirement already satisfied: brotli in /usr/local/lib/python3.10/dist-packages (from yt-dlp) (1.0.9)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "f944a4b42ef14ae889efef3f5369e173": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "faa25b85f22545f09f98b5d43cbd21b1": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_74a068f07b49454bba51058b0c1772ae", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n I have created a folder named 'documents' and added 5 docx files in it, each containing a unique sentence about \n Machine Learning. The files are named as 'doc1.docx', 'doc2.docx', 'doc3.docx', 'doc4.docx', and 'doc5.docx'. \n \n\n", "text/plain": " \n I have created a folder named 'documents' and added 5 docx files in it, each containing a unique sentence about \n Machine Learning. The files are named as 'doc1.docx', 'doc2.docx', 'doc3.docx', 'doc4.docx', and 'doc5.docx'. \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "fb34c40c88f7423a891c85730730d011": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "fc51add87d6744bc8fb46911b671eac5": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "fe21edae84cc4778a3ddc2fcaedfbc3d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "feb8283edfbb49b989a8d29c98701bf0": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_0e33aaf3c5dd44dda84ebc379dd78417", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n I have successfully applied the slow and reverb effects to the song. The processed song has been saved as \n 'processed_song.wav'. You can listen to it to hear the effects. \n \n\n", "text/plain": " \n I have successfully applied the slow and reverb effects to the song. The processed song has been saved as \n 'processed_song.wav'. You can listen to it to hear the effects. \n \n" }, "metadata": {}, "output_type": "display_data" } ] } }, "ff2383ba5a3943cf8ab4f0cedcb40c3f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "8e4d22ad1dcc44ec882bb9c6663de431": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_915e05fbbcd44fb1b0402d5a51b9dcf6", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprint\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mHello, world!\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n", "text/html": "
\n print('Hello, world!') \n \n\n" }, "metadata": {} } ] } }, "915e05fbbcd44fb1b0402d5a51b9dcf6": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "ed7c54937ec94948915fbe589e04d312": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_ef25bc033b0b419bb7b33e05aed61b43", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mHello, world!\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n", "text/html": "
\n Hello, world! \n \n\n" }, "metadata": {} } ] } }, "ef25bc033b0b419bb7b33e05aed61b43": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "1672ae981ae041ae8b26126e30f29882": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_84e676b87e204fa9b36f1ec2ccefc396", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": " \n The code was executed successfully and it printed \"Hello, world!\". \n \n", "text/html": "
\n The code was executed successfully and it printed \"Hello, world!\". \n \n\n" }, "metadata": {} } ] } }, "84e676b87e204fa9b36f1ec2ccefc396": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "dfd7d092f15c4b33b4d9b4c299a56577": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_46f16ac02db04d13bdf9461b2e268ad3", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": " \n Alright, here's the plan: \n \n \u001b[1;33m 1 \u001b[0mDownload the YouTube video using \u001b[1;36;40myt-dlp\u001b[0m. \n \u001b[1;33m 2 \u001b[0mExtract the audio from the video using \u001b[1;36;40mmoviepy\u001b[0m. \n \u001b[1;33m 3 \u001b[0mCut the video to the specified time range (0:15 to 0:38) using \u001b[1;36;40mmoviepy\u001b[0m. \n \u001b[1;33m 4 \u001b[0mUse a face detection library like \u001b[1;36;40mdlib\u001b[0m or \u001b[1;36;40mopencv\u001b[0m to detect the face in each frame. \n \u001b[1;33m 5 \u001b[0mCrop each frame to a 9:16 aspect ratio around the detected face, making sure to smoothly average out the \n \u001b[1;33m \u001b[0mmotion between confident face detections. \n \u001b[1;33m 6 \u001b[0mCombine the cropped frames back into a video using \u001b[1;36;40mmoviepy\u001b[0m. \n \u001b[1;33m 7 \u001b[0mAdd the audio back into the video using \u001b[1;36;40mmoviepy\u001b[0m. \n \u001b[1;33m 8 \u001b[0mSave the final video. \n \n Let's start by installing the necessary packages. We'll need \u001b[1;36;40myt-dlp\u001b[0m for downloading the video, \u001b[1;36;40mmoviepy\u001b[0m for \n video editing, and \u001b[1;36;40mdlib\u001b[0m for face detection. We'll also need \u001b[1;36;40mnumpy\u001b[0m for numerical operations and \u001b[1;36;40mopencv-python\u001b[0m \n for image processing. \n \n", "text/html": "
\n Alright, here's the plan: \n \n 1 Download the YouTube video using yt-dlp. \n 2 Extract the audio from the video using moviepy. \n 3 Cut the video to the specified time range (0:15 to 0:38) using moviepy. \n 4 Use a face detection library like dlib or opencv to detect the face in each frame. \n 5 Crop each frame to a 9:16 aspect ratio around the detected face, making sure to smoothly average out the \n motion between confident face detections. \n 6 Combine the cropped frames back into a video using moviepy. \n 7 Add the audio back into the video using moviepy. \n 8 Save the final video. \n \n Let's start by installing the necessary packages. We'll need yt-dlp for downloading the video, moviepy for \n video editing, and dlib for face detection. We'll also need numpy for numerical operations and opencv-python \n for image processing. \n \n\n" }, "metadata": {} } ] } }, "46f16ac02db04d13bdf9461b2e268ad3": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "894485c6aa7a4560b1625b0667c4b0ad": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_c2678de51ec648b78d4965a3b898ffef", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;150;0;80;48;2;30;0;16m!\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34minstall\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34myt\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdlp\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmoviepy\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdlib\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnumpy\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mopencv\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpython\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n", "text/html": "
\n !pip install yt-dlp moviepy dlib numpy opencv-python \n \n\n" }, "metadata": {} } ] } }, "c2678de51ec648b78d4965a3b898ffef": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "1ba7ab9f477040ccb236598ef1085084": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_58c456a67739434b87b368aefc561eff", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mOutput truncated. Showing the last 2000 characters. Adjust via `interpreter.max_output_chars`.\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55msatisfied: certifi in /usr/local/lib/python3.10/dist-packages (from yt-dlp) (2023.5.7)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mCollecting brotli (from 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\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mSuccessfully installed brotli-1.0.9 mutagen-1.46.0 pycryptodomex-3.18.0 websockets-11.0.3 yt-dlp-2023.7.6\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n", "text/html": "
\n Output truncated. Showing the last 2000 characters. Adjust via `interpreter.max_output_chars`. \n \n satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from yt-dlp) (2023.5.7) \n Collecting brotli (from yt-dlp) \n Downloading \n Brotli-1.0.9-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl \n (2.7 MB) \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/2.7 MB ? eta -:--:-- \n ━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━ 1.6/2.7 MB 48.1 MB/s eta 0:00:01 \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 2.7/2.7 MB 49.9 MB/s eta 0:00:01 \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.7/2.7 MB 37.8 MB/s eta 0:00:00 \n Requirement already satisfied: decorator<5.0,>=4.0.2 in /usr/local/lib/python3.10/dist-packages (from moviepy) \n (4.4.2) \n Requirement already satisfied: tqdm<5.0,>=4.11.2 in /usr/local/lib/python3.10/dist-packages (from moviepy) \n (4.65.0) \n Requirement already satisfied: requests<3.0,>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from moviepy) \n (2.27.1) \n Requirement already satisfied: proglog<=1.0.0 in /usr/local/lib/python3.10/dist-packages (from moviepy) \n (0.1.10) \n Requirement already satisfied: imageio<3.0,>=2.5 in /usr/local/lib/python3.10/dist-packages (from moviepy) \n (2.25.1) \n Requirement already satisfied: imageio-ffmpeg>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from moviepy) \n (0.4.8) \n Requirement already satisfied: pillow>=8.3.2 in /usr/local/lib/python3.10/dist-packages (from \n imageio<3.0,>=2.5->moviepy) (8.4.0) \n Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from \n requests<3.0,>=2.8.1->moviepy) (1.26.16) \n Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from \n requests<3.0,>=2.8.1->moviepy) (2.0.12) \n Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from \n requests<3.0,>=2.8.1->moviepy) (3.4) \n Installing collected packages: brotli, websockets, pycryptodomex, mutagen, yt-dlp \n Successfully installed brotli-1.0.9 mutagen-1.46.0 pycryptodomex-3.18.0 websockets-11.0.3 yt-dlp-2023.7.6 \n \n\n" }, "metadata": {} } ] } }, "58c456a67739434b87b368aefc561eff": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "ba8772dae7fd44e4937b74a810235984": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_4ea12542e3134a719e8ca95bf443d710", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": " \n The necessary packages have been installed. The next steps are as follows: \n \n \u001b[1;33m 1 \u001b[0mDownload the YouTube video using \u001b[1;36;40myt-dlp\u001b[0m. \n \u001b[1;33m 2 \u001b[0mExtract the required segment from the video using \u001b[1;36;40mmoviepy\u001b[0m. \n \u001b[1;33m 3 \u001b[0mDetect and track the face in the video frames using \u001b[1;36;40mdlib\u001b[0m and \u001b[1;36;40mopencv\u001b[0m. \n \u001b[1;33m 4 \u001b[0mCrop the video around the face and resize it to a 9:16 aspect ratio. \n \u001b[1;33m 5 \u001b[0mSave the final video. \n \n Let's start by downloading the YouTube video. \n \n", "text/html": "
\n The necessary packages have been installed. The next steps are as follows: \n \n 1 Download the YouTube video using yt-dlp. \n 2 Extract the required segment from the video using moviepy. \n 3 Detect and track the face in the video frames using dlib and opencv. \n 4 Crop the video around the face and resize it to a 9:16 aspect ratio. \n 5 Save the final video. \n \n Let's start by downloading the YouTube video. \n \n\n" }, "metadata": {} } ] } }, "4ea12542e3134a719e8ca95bf443d710": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "7e39eac50d9b4e6e83818eccc7003bab": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": 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\n import yt_dlp \n \n url = 'https://www.youtube.com/watch?v=KgHkAwaW_lk' \n \n ydl_opts = { \n 'format': 'bestvideo+bestaudio', \n 'outtmpl': 'original_video.%(ext)s' \n } \n \n with yt_dlp.YoutubeDL(ydl_opts) as ydl: \n ydl.download([url]) \n \n\n" }, "metadata": {} } ] } }, "c1d7c09472ef40fa94bc348a0ce07711": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "70e01c3e03814cf2891e77d0f27f5e0a": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_5401202e0cbc4b7d93601ffceee34823", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mOutput truncated. 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Adjust via `interpreter.max_output_chars`.\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mad] 95.7% of ~ 8.25MiB at 144.14KiB/s ETA 00:02 (frag 18/20)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 96.5% of ~ 8.25MiB at 275.72KiB/s ETA \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m00:01 (frag 18/20)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 98.0% of ~ 8.25MiB at 527.81KiB/s ETA 00:00 (frag 18/20)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 100.2% of ~ 8.25MiB at \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m868.98KiB/s ETA 00:00 (frag 18/20)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 95.0% of ~ 8.70MiB at 867.54KiB/s ETA 00:00 (frag 19/20)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 100.0% of ~ \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m8.27MiB at 1.66KiB/s ETA 00:00 (frag 19/20)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 100.0% of ~ 8.27MiB at 4.98KiB/s ETA 00:00 (frag 19/20)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m100.1% of ~ 8.27MiB at 11.60KiB/s ETA 00:00 (frag 19/20)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 100.2% of ~ 8.27MiB at 24.81KiB/s ETA 00:00 \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m(frag 19/20)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 100.4% of ~ 8.27MiB at 51.20KiB/s ETA 00:00 (frag 19/20)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 100.7% of ~ 8.27MiB at 100.31KiB/s\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mETA 00:00 (frag 19/20)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 101.5% of ~ 8.27MiB at 192.41KiB/s ETA 00:00 (frag 19/20)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 102.6% of ~ 8.27MiB at \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m329.49KiB/s ETA 00:00 (frag 19/20)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 100.0% of ~ 8.48MiB at 329.08KiB/s ETA 00:00 (frag 20/20)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 100% of \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m8.48MiB in 00:00:10 at 860.64KiB/s \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m Destination: original_video.f140.m4a\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 0.1% of 1.50MiB at 607.17KiB/s ETA 00:02\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 0.2% of 1.50MiB at 1.30MiB/s ETA 00:01\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 0.5% of \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m1.50MiB at 2.48MiB/s ETA 00:00\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 1.0% of 1.50MiB at 4.58MiB/s ETA 00:00\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 2.0% of 1.50MiB at \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m1.05MiB/s ETA 00:01\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 4.1% of 1.50MiB at 1.24MiB/s ETA 00:01\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 8.3% of 1.50MiB at 1.59MiB/s ETA \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m00:00\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 16.6% of 1.50MiB at 2.41MiB/s ETA 00:00\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 33.3% of 1.50MiB at 3.87MiB/s ETA 00:00\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 66.6% of \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m1.50MiB at 6.42MiB/s ETA 00:00\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 100.0% of 1.50MiB at 8.82MiB/s ETA 00:00\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 100% of 1.50MiB in 00:00:00\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mat 4.24MiB/s \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m[Merger] Merging formats into \"original_video.mkv\"\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mDeleting original file original_video.f247.webm (pass -k to keep)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mDeleting original file original_video.f140.m4a (pass -k to keep)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n", "text/html": "
\n Output truncated. Showing the last 2000 characters. Adjust via `interpreter.max_output_chars`. \n \n ad] 95.7% of ~ 8.25MiB at 144.14KiB/s ETA 00:02 (frag 18/20) 96.5% of ~ 8.25MiB at 275.72KiB/s ETA \n 00:01 (frag 18/20) 98.0% of ~ 8.25MiB at 527.81KiB/s ETA 00:00 (frag 18/20) 100.2% of ~ 8.25MiB at \n 868.98KiB/s ETA 00:00 (frag 18/20) 95.0% of ~ 8.70MiB at 867.54KiB/s ETA 00:00 (frag 19/20) 100.0% of ~ \n 8.27MiB at 1.66KiB/s ETA 00:00 (frag 19/20) 100.0% of ~ 8.27MiB at 4.98KiB/s ETA 00:00 (frag 19/20) \n 100.1% of ~ 8.27MiB at 11.60KiB/s ETA 00:00 (frag 19/20) 100.2% of ~ 8.27MiB at 24.81KiB/s ETA 00:00 \n (frag 19/20) 100.4% of ~ 8.27MiB at 51.20KiB/s ETA 00:00 (frag 19/20) 100.7% of ~ 8.27MiB at 100.31KiB/s \n ETA 00:00 (frag 19/20) 101.5% of ~ 8.27MiB at 192.41KiB/s ETA 00:00 (frag 19/20) 102.6% of ~ 8.27MiB at \n 329.49KiB/s ETA 00:00 (frag 19/20) 100.0% of ~ 8.48MiB at 329.08KiB/s ETA 00:00 (frag 20/20) 100% of \n 8.48MiB in 00:00:10 at 860.64KiB/s \n Destination: original_video.f140.m4a \n 0.1% of 1.50MiB at 607.17KiB/s ETA 00:02 0.2% of 1.50MiB at 1.30MiB/s ETA 00:01 0.5% of \n 1.50MiB at 2.48MiB/s ETA 00:00 1.0% of 1.50MiB at 4.58MiB/s ETA 00:00 2.0% of 1.50MiB at \n 1.05MiB/s ETA 00:01 4.1% of 1.50MiB at 1.24MiB/s ETA 00:01 8.3% of 1.50MiB at 1.59MiB/s ETA \n 00:00 16.6% of 1.50MiB at 2.41MiB/s ETA 00:00 33.3% of 1.50MiB at 3.87MiB/s ETA 00:00 66.6% of \n 1.50MiB at 6.42MiB/s ETA 00:00 100.0% of 1.50MiB at 8.82MiB/s ETA 00:00 100% of 1.50MiB in 00:00:00 \n at 4.24MiB/s \n [Merger] Merging formats into \"original_video.mkv\" \n Deleting original file original_video.f247.webm (pass -k to keep) \n Deleting original file original_video.f140.m4a (pass -k to keep) \n \n\n" }, "metadata": {} } ] } }, "5401202e0cbc4b7d93601ffceee34823": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "acfb473b29e44673a1fff74002d1a9fb": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_c04640e4839d455b9faf31eb0f6cc24a", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": " \n The video has been successfully downloaded and saved as \"original_video.mkv\". \n \n Next, I will extract the required clip from the video (from 0:15 to 0:38). I will use the MoviePy library to \n accomplish this. \n \n After that, I will use the dlib and OpenCV libraries to detect and track the face in the video. I will crop the \n video around the face, maintaining the aspect ratio as 9:16. I will also ensure that the motion is smoothly \n averaged out between confident face detections. \n \n Finally, I will add the audio back into the cropped video. \n \n Let's start by extracting the required clip from the video. \n \n", "text/html": "
\n The video has been successfully downloaded and saved as \"original_video.mkv\". \n \n Next, I will extract the required clip from the video (from 0:15 to 0:38). I will use the MoviePy library to \n accomplish this. \n \n After that, I will use the dlib and OpenCV libraries to detect and track the face in the video. I will crop the \n video around the face, maintaining the aspect ratio as 9:16. I will also ensure that the motion is smoothly \n averaged out between confident face detections. \n \n Finally, I will add the audio back into the cropped video. \n \n Let's start by extracting the required clip from the video. \n \n\n" }, "metadata": {} } ] } }, "c04640e4839d455b9faf31eb0f6cc24a": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "e34e60e6f4a748fb856638e13dcea9ee": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_ea5366ee0804463b8ef07de383494750", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mfrom\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmoviepy\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mvideo\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mffmpeg_tools\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mffmpeg_extract_subclip\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mstart_time\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m15\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mend_time\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m38\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Extract the subclip\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mffmpeg_extract_subclip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34moriginal_video.mkv\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mstart_time\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mend_time\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtargetname\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mclip.mkv\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n", "text/html": "
\n from moviepy.video.io.ffmpeg_tools import ffmpeg_extract_subclip \n \n start_time = 15 \n end_time = 38 \n \n # Extract the subclip \n ffmpeg_extract_subclip('original_video.mkv', start_time, end_time, targetname='clip.mkv') \n \n\n" }, "metadata": {} } ] } }, "ea5366ee0804463b8ef07de383494750": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "e6b9ad18bbf94231be36619c23829365": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_1379bc8127c34125a9edcaa6ff80cb29", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mMoviepy - Running:\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m>>> \"+ \" \".join(cmd)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mMoviepy - Command successful\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mNone\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n", "text/html": "
\n Moviepy - Running: \n >>> \"+ \" \".join(cmd) \n Moviepy - Command successful \n None \n \n\n" }, "metadata": {} } ] } }, "1379bc8127c34125a9edcaa6ff80cb29": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "633eb737a8a540618283944da6587395": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_0422283e06104822a0b17e36537887ba", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": " \n The video has been successfully downloaded and the desired clip has been extracted. The next step is to detect \n and track the face in the video. We will use dlib's HOG-based model for this. \n \n After detecting the face in each frame, we will calculate the center of the face. We will then use these \n centers to crop the video around the face. We will also apply a smoothing function to these centers to ensure \n the motion is smooth. \n \n Let's start by loading the video and initializing the face detector. \n \n", "text/html": "
\n The video has been successfully downloaded and the desired clip has been extracted. The next step is to detect \n and track the face in the video. We will use dlib's HOG-based model for this. \n \n After detecting the face in each frame, we will calculate the center of the face. We will then use these \n centers to crop the video around the face. We will also apply a smoothing function to these centers to ensure \n the motion is smooth. \n \n Let's start by loading the video and initializing the face detector. \n \n\n" }, "metadata": {} } ] } }, "0422283e06104822a0b17e36537887ba": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "9c4a0269b2f545fd91690a0e05916486": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_54ba24de07fd495da0709275580522c4", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcv2\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdlib\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnumpy\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mas\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnp\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mfrom\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmoviepy\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34meditor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mVideoFileClip\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Load the video\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mclip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mVideoFileClip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mclip.mkv\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Initialize the face detector\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdetector\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdlib\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mget_frontal_face_detector\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n", "text/html": "
\n import cv2 \n import dlib \n import numpy as np \n from moviepy.editor import VideoFileClip \n \n # Load the video \n clip = VideoFileClip('clip.mkv') \n \n # Initialize the face detector \n detector = dlib.get_frontal_face_detector() \n \n\n" }, "metadata": {} } ] } }, "54ba24de07fd495da0709275580522c4": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "5c29036bf1394564ac06e60a277c2f4b": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_13288eb48c78450bb9c29905f1cfe5cc", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\n", "text/html": "
\n\n" }, "metadata": {} } ] } }, "13288eb48c78450bb9c29905f1cfe5cc": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "0a2bad1e724c412d9400b8ec3757d5fe": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_71be2ada61ca4eeca6c9c3169bf5a981", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": " \n Now that we have loaded the video and initialized the face detector, the next step is to process each frame of \n the video. For each frame, we will detect the face, calculate its center, and keep track of these centers. We \n will then calculate the average center of the face in the last few frames to smooth out the motion. After that, \n we will crop the frame around this average center to create a portrait video. \n \n Let's start by processing the frames and detecting the face in each frame. \n \n", "text/html": "
\n Now that we have loaded the video and initialized the face detector, the next step is to process each frame of \n the video. For each frame, we will detect the face, calculate its center, and keep track of these centers. We \n will then calculate the average center of the face in the last few frames to smooth out the motion. After that, \n we will crop the frame around this average center to create a portrait video. \n \n Let's start by processing the frames and detecting the face in each frame. \n \n\n" }, "metadata": {} } ] } }, "71be2ada61ca4eeca6c9c3169bf5a981": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "05159ee8a8ce4d1f842636561b767c1e": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_d41a011efd5148b998b27e7ecb43d6d6", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mface_centers\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Process each frame\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mfor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mframe\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34min\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34menumerate\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mclip\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34miter_frames\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Convert the frame to grayscale\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mgray\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcv2\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcvtColor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mframe\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcv2\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mCOLOR_RGB2GRAY\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Detect faces in the frame\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfaces\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdetector\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mgray\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# If a face is detected\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mif\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlen\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfaces\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m>\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Calculate the center of the face\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mx\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfaces\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mleft\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m+\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfaces\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwidth\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m/\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m/\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m2\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34my\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfaces\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtop\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m+\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfaces\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mheight\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m/\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m/\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m2\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mface_centers\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mappend\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mx\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34my\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Print progress\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mif\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m%\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m100\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m==\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprint\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mf\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mProcessed \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m{\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m}\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m/\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m{\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlen\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mclip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m}\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m frames\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n", "text/html": "
\n face_centers = [] \n \n # Process each frame \n for i, frame in enumerate(clip.iter_frames()): \n # Convert the frame to grayscale \n gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) \n \n # Detect faces in the frame \n faces = detector(gray) \n \n # If a face is detected \n if len(faces) > 0: \n # Calculate the center of the face \n x = faces[0].left() + faces[0].width() // 2 \n y = faces[0].top() + faces[0].height() // 2 \n face_centers.append((x, y)) \n \n # Print progress \n if i % 100 == 0: \n print(f'Processed {i}/{len(clip)} frames') \n \n\n" }, "metadata": {} } ] } }, "d41a011efd5148b998b27e7ecb43d6d6": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "f89766aa83954606822ac810550089a4": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_c092298defd44aea990d9b4e7368fb39", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mTraceback (most recent call last):\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m Cell In[3], line 10\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m print(f'Processed {i}/{len(clip)} frames')\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mTypeError: object of type 'VideoFileClip' has no len()\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n", "text/html": "
\n Traceback (most recent call last): \n Cell In[3], line 10 \n print(f'Processed {i}/{len(clip)} frames') \n TypeError: object of type 'VideoFileClip' has no len() \n \n\n" }, "metadata": {} } ] } }, "c092298defd44aea990d9b4e7368fb39": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "f9f0fbe9b4e8458daff8f0b208afe06d": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_4a0fea39019242139a82c33d7513ccb7", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": " \n It seems like I made a mistake by trying to get the length of the video clip using \u001b[1;36;40mlen(clip)\u001b[0m. The \u001b[1;36;40mVideoFileClip\u001b[0m \n object doesn't support the \u001b[1;36;40mlen()\u001b[0m function. Instead, I should use \u001b[1;36;40mclip.duration\u001b[0m to get the duration of the clip \n in seconds and multiply it by the frame rate to get the total number of frames. Let's correct this and try \n again. \n \n", "text/html": "
\n It seems like I made a mistake by trying to get the length of the video clip using len(clip). The VideoFileClip \n object doesn't support the len() function. Instead, I should use clip.duration to get the duration of the clip \n in seconds and multiply it by the frame rate to get the total number of frames. Let's correct this and try \n again. \n \n\n" }, "metadata": {} } ] } }, "4a0fea39019242139a82c33d7513ccb7": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "e94d50d8f12d4e97a9e4a147e1717a44": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_903ff249dcad4e2bbb19f036ee19d2cb", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mface_centers\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Get the total number of frames\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtotal_frames\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mint\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mclip\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mduration\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m*\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mclip\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfps\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Process each frame\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mfor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mframe\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34min\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34menumerate\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mclip\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34miter_frames\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Convert the frame to grayscale\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mgray\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcv2\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcvtColor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mframe\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcv2\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mCOLOR_RGB2GRAY\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Detect faces in the frame\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfaces\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdetector\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mgray\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# If a face is detected\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mif\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlen\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfaces\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m>\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Calculate the center of the face\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mx\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfaces\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mleft\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m+\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfaces\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwidth\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m 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\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m{\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m}\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m/\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m{\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtotal_frames\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m}\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m frames\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n", "text/html": "
\n face_centers = [] \n \n # Get the total number of frames \n total_frames = int(clip.duration * clip.fps) \n \n # Process each frame \n for i, frame in enumerate(clip.iter_frames()): \n # Convert the frame to grayscale \n gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) \n \n # Detect faces in the frame \n faces = detector(gray) \n \n # If a face is detected \n if len(faces) > 0: \n # Calculate the center of the face \n x = faces[0].left() + faces[0].width() // 2 \n y = faces[0].top() + faces[0].height() // 2 \n face_centers.append((x, y)) \n \n # Print progress \n if i % 100 == 0: \n print(f'Processed {i}/{total_frames} frames') \n \n\n" }, "metadata": {} } ] } }, "903ff249dcad4e2bbb19f036ee19d2cb": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", 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\n Processed 0/819 frames \n Processed 100/819 frames \n Processed 200/819 frames \n Processed 300/819 frames \n Processed 400/819 frames \n Processed 500/819 frames \n Processed 600/819 frames \n Processed 700/819 frames \n Processed 800/819 frames \n \n\n" }, "metadata": {} } ] } }, "2484cf82892e4459972b079943d9fe4d": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "7f88ac26e8cb4dce8d372d38501bdec2": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_646e8aa419f3420cbc453d285634a114", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": " \n The face detection process has been completed for all frames in the video. The next step is to calculate the \n average position of the face in each frame to create a smooth motion. After that, we will crop the video around \n the face and maintain the original height of the video. Let's proceed with these steps. \n \n", "text/html": "
\n The face detection process has been completed for all frames in the video. The next step is to calculate the \n average position of the face in each frame to create a smooth motion. After that, we will crop the video around \n the face and maintain the original height of the video. Let's proceed with these steps. \n \n\n" }, "metadata": {} } ] } }, "646e8aa419f3420cbc453d285634a114": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "2d1cd08ae5be4df8bef9f9ebc9347349": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_a954618bf6d344c285f74578e496dc0c", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Calculate the moving average of the face centers\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m 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\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnp\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mconvolve\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mface_centers\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnp\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mones\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwindow_size\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mvalid\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m/\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwindow_size\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Add the first and last few values to the start and end of the list\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mface_centers_avg\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnp\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mconcatenate\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mface_centers\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwindow_size\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m-\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m1\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mface_centers_avg\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mface_centers\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwindow_size\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m+\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m1\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n", "text/html": "
\n # Calculate the moving average of the face centers \n window_size = 10 \n face_centers_avg = np.convolve(face_centers, np.ones(window_size), 'valid') / window_size \n \n # Add the first and last few values to the start and end of the list \n face_centers_avg = np.concatenate((face_centers[:window_size-1], face_centers_avg, face_centers[-window_size+1: \n \n\n" }, "metadata": {} } ] } }, "a954618bf6d344c285f74578e496dc0c": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "a5da8f42711545d09911118485e8e83e": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_303c046d8d1e44c8b20e21678d26ee71", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mTraceback (most recent call last):\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m Cell In[3], line 2\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m face_centers_avg = np.convolve(face_centers, np.ones(window_size), 'valid'\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m File <__array_function__ internals>:180 in convolve\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m File /usr/local/lib/python3.10/dist-packages/numpy/core/numeric.py:844 in convolve\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m return multiarray.correlate(a, v[::-1], mode)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mValueError: object too deep for desired array\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n", "text/html": "
\n Traceback (most recent call last): \n Cell In[3], line 2 \n face_centers_avg = np.convolve(face_centers, np.ones(window_size), 'valid' \n File <__array_function__ internals>:180 in convolve \n File /usr/local/lib/python3.10/dist-packages/numpy/core/numeric.py:844 in convolve \n return multiarray.correlate(a, v[::-1], mode) \n ValueError: object too deep for desired array \n \n\n" }, "metadata": {} } ] } }, "303c046d8d1e44c8b20e21678d26ee71": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "71346df88e4c4123b952c6606eb2acf0": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_cf0ee63e4bab46acacb822a12586db47", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": " \n It seems like the \u001b[1;36;40mnp.convolve\u001b[0m function is not able to handle the list of tuples \u001b[1;36;40mface_centers\u001b[0m. This is because \n \u001b[1;36;40mnp.convolve\u001b[0m is designed to work with 1D arrays, and \u001b[1;36;40mface_centers\u001b[0m is a list of 2D points (tuples). \n \n To solve this, I will separate the x and y coordinates into two separate lists and calculate the moving \n averages for each list separately. Let's try this. \n \n", "text/html": "
\n It seems like the np.convolve function is not able to handle the list of tuples face_centers. This is because \n np.convolve is designed to work with 1D arrays, and face_centers is a list of 2D points (tuples). \n \n To solve this, I will separate the x and y coordinates into two separate lists and calculate the moving \n averages for each list separately. Let's try this. \n \n\n" }, "metadata": {} } ] } }, "cf0ee63e4bab46acacb822a12586db47": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, 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\u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mface_centers_x\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mface_centers_y\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mzip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m*\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mface_centers\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Calculate the moving average of the x and y coordinates separately\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mface_centers_avg_x\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnp\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mconvolve\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mface_centers_x\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m 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\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmoviepy\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mvideo\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfx\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcrop\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcrop\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Get the dimensions of the video\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwidth\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mheight\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mclip\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msize\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Calculate the dimensions of the cropped video\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnew_width\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mint\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mheight\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m*\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m9\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m/\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m16\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnew_height\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mheight\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Initialize a list to store the cropped frames\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcropped_frames\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Process each frame\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mfor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mframe\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcenter\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34min\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34menumerate\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mzip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mclip\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34miter_frames\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mface_centers_avg\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Calculate the left and right boundaries of the crop\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mleft\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmax\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmin\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwidth\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnew_width\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcenter\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnew_width\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m/\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m/\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m2\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mright\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mleft\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m+\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnew_width\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Crop the frame\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcropped_frame\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcrop\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mframe\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mx1\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mleft\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34my1\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mx2\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mright\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34my2\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnew_height\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcropped_frames\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mappend\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcropped_frame\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Print progress\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mif\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m%\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m100\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m==\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprint\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mf\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mCropped \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m{\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m}\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m/\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m{\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtotal_frames\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m}\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m frames\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n", "text/html": "
\n from moviepy.video.fx.crop import crop \n \n # Get the dimensions of the video \n width, height = clip.size \n \n # Calculate the dimensions of the cropped video \n new_width = int(height * 9 / 16) \n new_height = height \n \n # Initialize a list to store the cropped frames \n cropped_frames = [] \n \n # Process each frame \n for i, (frame, center) in enumerate(zip(clip.iter_frames(), face_centers_avg)): \n # Calculate the left and right boundaries of the crop \n left = max(0, min(width - new_width, center[0] - new_width // 2)) \n right = left + new_width \n \n # Crop the frame \n cropped_frame = crop(frame, x1=left, y1=0, x2=right, y2=new_height) \n cropped_frames.append(cropped_frame) \n \n # Print progress \n if i % 100 == 0: \n print(f'Cropped {i}/{total_frames} frames') \n \n\n" }, "metadata": {} } ] } }, "a96323047b04490aa28cb68056a3242d": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "ca48e8fcc96b4e0d985c7a3096aa04bb": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_8775c6e74caa4e2e85935960e6d9bf1a", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mTraceback (most recent call last):\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m Cell In[3], line 9\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m cropped_frame = crop(frame, x1=left, y1=0, x2=right, y2=new_height)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m File /usr/local/lib/python3.10/dist-packages/moviepy/video/fx/crop.py:53 in crop\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m return clip.fl_image(lambda pic: pic\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m, apply_to=[\"mask\"])\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mAttributeError: 'numpy.ndarray' object has no attribute 'fl_image'\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n", "text/html": "
\n Traceback (most recent call last): \n Cell In[3], line 9 \n cropped_frame = crop(frame, x1=left, y1=0, x2=right, y2=new_height) \n File /usr/local/lib/python3.10/dist-packages/moviepy/video/fx/crop.py:53 in crop \n return clip.fl_image(lambda pic: pic, apply_to=[\"mask\"]) \n AttributeError: 'numpy.ndarray' object has no attribute 'fl_image' \n \n\n" }, "metadata": {} } ] } }, "8775c6e74caa4e2e85935960e6d9bf1a": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "8b359f3ad34840ebbf9e40348d7fcc44": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_1d5dd9f4d7cd41ab98e463f7fe8dc5d3", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": " \n It seems like the \u001b[1;36;40mcrop\u001b[0m function from \u001b[1;36;40mmoviepy\u001b[0m is intended to be used on a \u001b[1;36;40mVideoClip\u001b[0m object, not a numpy array. \n However, we are trying to apply it to individual frames, which are numpy arrays. \n \n To fix this, we can use OpenCV's array slicing capabilities to crop the frames. Let's try that. \n \n", "text/html": "
\n It seems like the crop function from moviepy is intended to be used on a VideoClip object, not a numpy array. \n However, we are trying to apply it to individual frames, which are numpy arrays. \n \n To fix this, we can use OpenCV's array slicing capabilities to crop the frames. Let's try that. \n \n\n" }, "metadata": {} } ] } }, "1d5dd9f4d7cd41ab98e463f7fe8dc5d3": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "e8c4baed02d34a21a3e7de3a69b3a158": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_9ec4005929104fd9aa041b7460b5298f", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcropped_frames\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Process each frame\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mfor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mframe\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcenter\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34min\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34menumerate\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mzip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mclip\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34miter_frames\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mface_centers_avg\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Calculate the left and right boundaries of the crop\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mleft\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmax\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmin\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwidth\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnew_width\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcenter\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnew_width\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m/\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m/\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m2\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mright\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mleft\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m+\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnew_width\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Crop the frame\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcropped_frame\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mframe\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mleft\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mright\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcropped_frames\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mappend\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcropped_frame\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Print progress\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mif\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m%\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m100\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m==\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprint\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mf\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mCropped \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m{\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m}\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m/\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m{\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtotal_frames\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m}\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m frames\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n", "text/html": "
\n cropped_frames = [] \n \n # Process each frame \n for i, (frame, center) in enumerate(zip(clip.iter_frames(), face_centers_avg)): \n # Calculate the left and right boundaries of the crop \n left = max(0, min(width - new_width, center[0] - new_width // 2)) \n right = left + new_width \n \n # Crop the frame \n cropped_frame = frame[:, left:right] \n cropped_frames.append(cropped_frame) \n \n # Print progress \n if i % 100 == 0: \n print(f'Cropped {i}/{total_frames} frames') \n \n\n" }, "metadata": {} } ] } }, "9ec4005929104fd9aa041b7460b5298f": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "f0115b2d6740493484333cccfcbcf41c": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_a60d203e2b3b466da2f420c1396b90b2", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mTraceback (most recent call last):\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m Cell In[3], line 5\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m cropped_frame = frame[:, left:right]\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mTypeError: slice indices must be integers or None or have an __index__ method\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n", "text/html": "
\n Traceback (most recent call last): \n Cell In[3], line 5 \n cropped_frame = frame[:, left:right] \n TypeError: slice indices must be integers or None or have an __index__ method \n \n\n" }, "metadata": {} } ] } }, "a60d203e2b3b466da2f420c1396b90b2": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "26fea9a4ea31438c8bbe14445910c8e5": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_6887887f49b14c249cb892a9c936070b", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcropped_frames\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Process each frame\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mfor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mframe\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcenter\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34min\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34menumerate\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mzip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mclip\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34miter_frames\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mface_centers_avg\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Calculate the left and right boundaries of the crop\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mleft\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmax\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmin\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwidth\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnew_width\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mint\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcenter\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnew_width\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m/\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m/\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m2\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mright\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mleft\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m+\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnew_width\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Crop the frame\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcropped_frame\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mframe\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mleft\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mright\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcropped_frames\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mappend\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcropped_frame\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Print progress\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mif\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m%\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m100\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m==\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprint\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mf\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mCropped \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m{\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m}\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m/\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m{\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtotal_frames\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m}\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m frames\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n", "text/html": "
\n cropped_frames = [] \n \n # Process each frame \n for i, (frame, center) in enumerate(zip(clip.iter_frames(), face_centers_avg)): \n # Calculate the left and right boundaries of the crop \n left = max(0, min(width - new_width, int(center[0] - new_width // 2))) \n right = left + new_width \n \n # Crop the frame \n cropped_frame = frame[:, left:right] \n cropped_frames.append(cropped_frame) \n \n # Print progress \n if i % 100 == 0: \n print(f'Cropped {i}/{total_frames} frames') \n \n\n" }, "metadata": {} } ] } }, "6887887f49b14c249cb892a9c936070b": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, 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frames\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mCropped 600/819 frames\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mCropped 700/819 frames\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mCropped 800/819 frames\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n", "text/html": "
\n Cropped 0/819 frames \n Cropped 100/819 frames \n Cropped 200/819 frames \n Cropped 300/819 frames \n Cropped 400/819 frames \n Cropped 500/819 frames \n Cropped 600/819 frames \n Cropped 700/819 frames \n Cropped 800/819 frames \n \n\n" }, "metadata": {} } ] } }, "a68ae6eec13d47d182388c12e8874457": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": 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\u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mfrom\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmoviepy\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mvideo\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mVideoClip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mImageSequenceClip\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Create a new video clip from the cropped frames\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnew_clip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mImageSequenceClip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcropped_frames\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfps\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mclip\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfps\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Write the new video clip to a file\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnew_clip\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwrite_videofile\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mcropped_clip.mkv\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcodec\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mlibx264\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n", "text/html": "
\n from moviepy.video.VideoClip import ImageSequenceClip \n \n # Create a new video clip from the cropped frames \n new_clip = ImageSequenceClip(cropped_frames, fps=clip.fps) \n \n # Write the new video clip to a file \n new_clip.write_videofile('cropped_clip.mkv', codec='libx264') \n \n\n" }, "metadata": {} } ] } }, "edf044df54ef451781627f91bac2a9c3": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "e468488a3ec14569adfd1bcdaefd9a35": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_cf2aaac773e442b0bc8fbbc152dd0b23", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mTraceback (most recent call last):\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m Cell In[3], line 1\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m from moviepy.video.VideoClip import ImageSequenceClip\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mImportError: cannot import name 'ImageSequenceClip' from 'moviepy.video.VideoClip' \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m(/usr/local/lib/python3.10/dist-packages/moviepy/video/VideoClip.py)\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n", "text/html": "
\n Traceback (most recent call last): \n Cell In[3], line 1 \n from moviepy.video.VideoClip import ImageSequenceClip \n ImportError: cannot import name 'ImageSequenceClip' from 'moviepy.video.VideoClip' \n (/usr/local/lib/python3.10/dist-packages/moviepy/video/VideoClip.py) \n \n\n" }, "metadata": {} } ] } }, "cf2aaac773e442b0bc8fbbc152dd0b23": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "b4827858c2fe4cc28f9fc38fef5040c3": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_86328979d39d4e689f787d27e0989adc", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mfrom\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmoviepy\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mvideo\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mImageSequenceClip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mImageSequenceClip\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Create a new video clip from the cropped frames\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnew_clip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mImageSequenceClip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcropped_frames\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m 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\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnew_clip\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwrite_videofile\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mcropped_clip.mkv\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcodec\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mlibx264\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n", "text/html": "
\n from moviepy.video.io.ImageSequenceClip import ImageSequenceClip \n \n # Create a new video clip from the cropped frames \n new_clip = ImageSequenceClip(cropped_frames, fps=clip.fps) \n \n # Write the new video clip to a file \n new_clip.write_videofile('cropped_clip.mkv', codec='libx264') \n \n\n" }, "metadata": {} } ] } }, "86328979d39d4e689f787d27e0989adc": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "b6a181f821044dcc875efcbc2fed3747": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_c9e6274288f640af92d3224b0393390a", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mOutput truncated. 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Adjust via `interpreter.max_output_chars`.\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m#2 | 597/820 [00:07<00:03, 69.95it/s, now=None]t: 74%|#######3 | 605/820 [00:07<00:03, 64.47it/s, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mnow=None]t: 75%|#######4 | 612/820 [00:07<00:03, 62.44it/s, now=None]t: 75%|#######5 | 619/820 \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m[00:07<00:03, 64.15it/s, now=None]t: 76%|#######6 | 626/820 [00:08<00:03, 58.65it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m77%|#######7 | 633/820 [00:08<00:03, 59.87it/s, now=None]t: 78%|#######8 | 640/820 [00:08<00:03, 58.11it/s, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mnow=None]t: 79%|#######8 | 647/820 [00:08<00:02, 58.78it/s, now=None]t: 80%|#######9 | 655/820 \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m[00:08<00:02, 60.11it/s, now=None]t: 81%|######## | 663/820 [00:08<00:02, 59.37it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m82%|########1 | 671/820 [00:08<00:02, 63.42it/s, now=None]t: 83%|########2 | 678/820 [00:08<00:02, 61.95it/s, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mnow=None]t: 84%|########3 | 685/820 [00:08<00:02, 62.49it/s, now=None]t: 84%|########4 | 692/820 \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m[00:09<00:02, 62.57it/s, now=None]t: 85%|########5 | 699/820 [00:09<00:01, 63.71it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m86%|########6 | 706/820 [00:09<00:01, 64.65it/s, now=None]t: 87%|########7 | 714/820 [00:09<00:01, 65.82it/s, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mnow=None]t: 88%|########7 | 721/820 [00:09<00:01, 65.85it/s, now=None]t: 89%|########8 | 729/820 \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m[00:09<00:01, 64.37it/s, now=None]t: 90%|########9 | 736/820 [00:09<00:01, 65.42it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m91%|######### | 745/820 [00:09<00:01, 70.02it/s, now=None]t: 92%|#########1| 753/820 [00:09<00:00, 69.63it/s, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mnow=None]t: 93%|#########2| 761/820 [00:10<00:00, 70.53it/s, now=None]t: 94%|#########3| 769/820 \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m[00:10<00:00, 72.95it/s, now=None]t: 95%|#########4| 777/820 [00:10<00:00, 72.42it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m96%|#########5| 785/820 [00:10<00:00, 72.45it/s, now=None]t: 97%|#########6| 793/820 [00:10<00:00, 71.54it/s, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mnow=None]t: 98%|#########7| 801/820 [00:10<00:00, 70.71it/s, now=None]t: 99%|#########8| 809/820 \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m[00:10<00:00, 72.52it/s, now=None]t: 100%|#########9| 818/820 [00:10<00:00, 75.42it/s, now=None] \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mMoviepy - Done !\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mMoviepy - video ready cropped_clip.mkv\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mNone\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n", "text/html": "
\n Output truncated. Showing the last 2000 characters. Adjust via `interpreter.max_output_chars`. \n \n #2 | 597/820 [00:07<00:03, 69.95it/s, now=None]t: 74%|#######3 | 605/820 [00:07<00:03, 64.47it/s, \n now=None]t: 75%|#######4 | 612/820 [00:07<00:03, 62.44it/s, now=None]t: 75%|#######5 | 619/820 \n [00:07<00:03, 64.15it/s, now=None]t: 76%|#######6 | 626/820 [00:08<00:03, 58.65it/s, now=None]t: \n 77%|#######7 | 633/820 [00:08<00:03, 59.87it/s, now=None]t: 78%|#######8 | 640/820 [00:08<00:03, 58.11it/s, \n now=None]t: 79%|#######8 | 647/820 [00:08<00:02, 58.78it/s, now=None]t: 80%|#######9 | 655/820 \n [00:08<00:02, 60.11it/s, now=None]t: 81%|######## | 663/820 [00:08<00:02, 59.37it/s, now=None]t: \n 82%|########1 | 671/820 [00:08<00:02, 63.42it/s, now=None]t: 83%|########2 | 678/820 [00:08<00:02, 61.95it/s, \n now=None]t: 84%|########3 | 685/820 [00:08<00:02, 62.49it/s, now=None]t: 84%|########4 | 692/820 \n [00:09<00:02, 62.57it/s, now=None]t: 85%|########5 | 699/820 [00:09<00:01, 63.71it/s, now=None]t: \n 86%|########6 | 706/820 [00:09<00:01, 64.65it/s, now=None]t: 87%|########7 | 714/820 [00:09<00:01, 65.82it/s, \n now=None]t: 88%|########7 | 721/820 [00:09<00:01, 65.85it/s, now=None]t: 89%|########8 | 729/820 \n [00:09<00:01, 64.37it/s, now=None]t: 90%|########9 | 736/820 [00:09<00:01, 65.42it/s, now=None]t: \n 91%|######### | 745/820 [00:09<00:01, 70.02it/s, now=None]t: 92%|#########1| 753/820 [00:09<00:00, 69.63it/s, \n now=None]t: 93%|#########2| 761/820 [00:10<00:00, 70.53it/s, now=None]t: 94%|#########3| 769/820 \n [00:10<00:00, 72.95it/s, now=None]t: 95%|#########4| 777/820 [00:10<00:00, 72.42it/s, now=None]t: \n 96%|#########5| 785/820 [00:10<00:00, 72.45it/s, now=None]t: 97%|#########6| 793/820 [00:10<00:00, 71.54it/s, \n now=None]t: 98%|#########7| 801/820 [00:10<00:00, 70.71it/s, now=None]t: 99%|#########8| 809/820 \n [00:10<00:00, 72.52it/s, now=None]t: 100%|#########9| 818/820 [00:10<00:00, 75.42it/s, now=None] \n Moviepy - Done ! \n Moviepy - video ready cropped_clip.mkv \n None \n \n\n" }, "metadata": {} } ] } }, "c9e6274288f640af92d3224b0393390a": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "2cf83094efd74450a44684621cec09ba": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_9c56ce6494ae4d13a9c652e93d08d34f", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": " \n The video has been successfully cropped and saved as 'cropped_clip.mkv'. The next step is to extract the audio \n from the original clip and add it to the cropped video. Let's do that. \n \n", "text/html": "
\n The video has been successfully cropped and saved as 'cropped_clip.mkv'. The next step is to extract the audio \n from the original clip and add it to the cropped video. Let's do that. \n \n\n" }, "metadata": {} } ] } }, "9c56ce6494ae4d13a9c652e93d08d34f": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, 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\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmoviepy\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34meditor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mAudioFileClip\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Extract the audio from the original clip\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34maudio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mAudioFileClip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34moriginal_video.mkv\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msubclip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mstart_time\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mend_time\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m 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\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnew_clip\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mset_audio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34maudio\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;117;113;94;48;2;39;40;34m# Write the new video clip with audio to a file\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcropped_clip_with_audio\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwrite_videofile\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mcropped_clip_with_audio.mkv\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcodec\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mlibx264\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n", "text/html": "
\n from moviepy.editor import AudioFileClip \n \n # Extract the audio from the original clip \n audio = AudioFileClip('original_video.mkv').subclip(start_time, end_time) \n \n # Set the audio of the new clip \n cropped_clip_with_audio = new_clip.set_audio(audio) \n \n # Write the new video clip with audio to a file \n cropped_clip_with_audio.write_videofile('cropped_clip_with_audio.mkv', codec='libx264') \n \n\n" }, "metadata": {} } ] } }, "62b5f3bf807f4e17a0fee6e73b19b50f": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "4d37ead524de46239d479f3d5660af3f": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_fd62af45f4c74a9a93a93ed62415f62f", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mOutput truncated. 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\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mnow=None]t: 100%|##########| 820/820 [00:11<00:00, 74.23it/s, now=None] \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mMoviepy - Done !\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mMoviepy - video ready cropped_clip_with_audio.mkv\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mNone\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n", "text/html": "
\n Output truncated. Showing the last 2000 characters. Adjust via `interpreter.max_output_chars`. \n \n 20 [00:08<00:03, 63.24it/s, now=None]t: 74%|#######4 | 607/820 [00:08<00:03, 67.05it/s, now=None]t: \n 75%|#######4 | 614/820 [00:08<00:03, 61.46it/s, now=None]t: 76%|#######5 | 621/820 [00:08<00:03, 60.96it/s, \n now=None]t: 77%|#######6 | 628/820 [00:08<00:03, 58.23it/s, now=None]t: 77%|#######7 | 634/820 \n [00:08<00:03, 56.61it/s, now=None]t: 78%|#######8 | 640/820 [00:09<00:03, 54.12it/s, now=None]t: \n 79%|#######8 | 646/820 [00:09<00:03, 55.23it/s, now=None]t: 80%|#######9 | 654/820 [00:09<00:02, 61.50it/s, \n now=None]t: 81%|######## | 661/820 [00:09<00:02, 60.69it/s, now=None]t: 81%|########1 | 668/820 \n [00:09<00:02, 60.97it/s, now=None]t: 82%|########2 | 675/820 [00:09<00:02, 60.73it/s, now=None]t: \n 83%|########3 | 684/820 [00:09<00:02, 62.14it/s, now=None]t: 84%|########4 | 692/820 [00:09<00:02, 62.95it/s, \n now=None]t: 85%|########5 | 699/820 [00:10<00:01, 62.96it/s, now=None]t: 86%|########6 | 706/820 \n [00:10<00:01, 63.58it/s, now=None]t: 87%|########7 | 714/820 [00:10<00:01, 67.93it/s, now=None]t: \n 88%|########7 | 721/820 [00:10<00:01, 68.32it/s, now=None]t: 89%|########8 | 728/820 [00:10<00:01, 66.30it/s, \n now=None]t: 90%|########9 | 737/820 [00:10<00:01, 70.97it/s, now=None]t: 91%|######### | 745/820 \n [00:10<00:01, 66.52it/s, now=None]t: 92%|#########1| 753/820 [00:10<00:00, 69.11it/s, now=None]t: \n 93%|#########2| 762/820 [00:10<00:00, 72.09it/s, now=None]t: 94%|#########3| 770/820 [00:11<00:00, 73.50it/s, \n now=None]t: 95%|#########4| 778/820 [00:11<00:00, 75.04it/s, now=None]t: 96%|#########5| 787/820 \n [00:11<00:00, 77.95it/s, now=None]t: 97%|#########6| 795/820 [00:11<00:00, 77.10it/s, now=None]t: \n 98%|#########8| 804/820 [00:11<00:00, 77.64it/s, now=None]t: 99%|#########9| 812/820 [00:11<00:00, 75.04it/s, \n now=None]t: 100%|##########| 820/820 [00:11<00:00, 74.23it/s, now=None] \n Moviepy - Done ! \n Moviepy - video ready cropped_clip_with_audio.mkv \n None \n \n\n" }, "metadata": {} } ] } }, "fd62af45f4c74a9a93a93ed62415f62f": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "5ca82a2033fb4b0c93ba168d64f6ef9e": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_6c4cadeb9ad74ad5984cb26042f1b36a", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": " \n I have successfully created the TikTok clip. The video has been cropped to a 9:16 aspect ratio around the face \n in the frame, with the motion between confident face detections smoothly averaged out. The audio from the \n original video has been added back in. The final video file is named 'cropped_clip_with_audio.mkv'. \n \n", "text/html": "
\n I have successfully created the TikTok clip. The video has been cropped to a 9:16 aspect ratio around the face \n in the frame, with the motion between confident face detections smoothly averaged out. The audio from the \n original video has been added back in. The final video file is named 'cropped_clip_with_audio.mkv'. \n \n\n" }, "metadata": {} } ] } }, "6c4cadeb9ad74ad5984cb26042f1b36a": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "647b95ec7a5b40979432289c033ccd79": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_85e5e774473d4d629a91899373d2602b", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcropped_clip_with_audio\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwrite_videofile\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mcropped_clip_with_audio.mp4\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcodec\u001b[0m\u001b[38;2;249;38;114;48;2;39;40;34m=\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mlibx264\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\u001b[48;2;39;39;34m \u001b[0m\n\u001b[48;2;39;39;34m \u001b[0m\n", "text/html": "
\n cropped_clip_with_audio.write_videofile('cropped_clip_with_audio.mp4', codec='libx264') \n \n\n" }, "metadata": {} } ] } }, "85e5e774473d4d629a91899373d2602b": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "82db7edca0184c31a689eb8ad4785611": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_5988deb63066470bb3225344289929e9", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": "\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mOutput truncated. 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\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m76%|#######5 | 623/820 [00:07<00:02, 74.47it/s, now=None]t: 77%|#######7 | 632/820 [00:07<00:02, 76.20it/s, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mnow=None]t: 78%|#######8 | 640/820 [00:08<00:02, 75.49it/s, now=None]t: 79%|#######9 | 648/820 \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m[00:08<00:02, 76.29it/s, now=None]t: 80%|######## | 656/820 [00:08<00:02, 74.20it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m 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\u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m[00:09<00:01, 82.65it/s, now=None]t: 90%|######### | 741/820 [00:09<00:01, 71.51it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m92%|#########1| 751/820 [00:09<00:00, 77.55it/s, now=None]t: 93%|#########2| 760/820 [00:09<00:00, 78.10it/s, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mnow=None]t: 94%|#########3| 768/820 [00:09<00:00, 73.98it/s, now=None]t: 95%|#########4| 777/820 \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m[00:09<00:00, 76.13it/s, now=None]t: 96%|#########5| 786/820 [00:09<00:00, 78.54it/s, now=None]t: \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m97%|#########6| 794/820 [00:10<00:00, 74.12it/s, now=None]t: 98%|#########7| 802/820 [00:10<00:00, 74.97it/s, \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mnow=None]t: 99%|#########9| 812/820 [00:10<00:00, 79.79it/s, now=None] \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mMoviepy - Done !\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mMoviepy - video ready cropped_clip_with_audio.mp4\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55mNone\u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n\u001b[38;2;255;255;255;48;2;59;59;55m \u001b[0m\n", "text/html": "
\n Output truncated. Showing the last 2000 characters. Adjust via `interpreter.max_output_chars`. \n \n 20 [00:06<00:03, 73.39it/s, now=None]t: 69%|######9 | 569/820 [00:07<00:03, 78.56it/s, now=None]t: \n 70%|####### | 578/820 [00:07<00:03, 76.98it/s, now=None]t: 72%|#######1 | 588/820 [00:07<00:02, 81.45it/s, \n now=None]t: 73%|#######2 | 597/820 [00:07<00:02, 82.24it/s, now=None]t: 74%|#######3 | 606/820 \n [00:07<00:02, 75.18it/s, now=None]t: 75%|#######5 | 615/820 [00:07<00:02, 76.71it/s, now=None]t: \n 76%|#######5 | 623/820 [00:07<00:02, 74.47it/s, now=None]t: 77%|#######7 | 632/820 [00:07<00:02, 76.20it/s, \n now=None]t: 78%|#######8 | 640/820 [00:08<00:02, 75.49it/s, now=None]t: 79%|#######9 | 648/820 \n [00:08<00:02, 76.29it/s, now=None]t: 80%|######## | 656/820 [00:08<00:02, 74.20it/s, now=None]t: \n 81%|######## | 664/820 [00:08<00:02, 75.04it/s, now=None]t: 82%|########1 | 672/820 [00:08<00:02, 72.15it/s, \n now=None]t: 83%|########2 | 680/820 [00:08<00:01, 74.17it/s, now=None]t: 84%|########4 | 689/820 \n [00:08<00:01, 75.94it/s, now=None]t: 85%|########5 | 697/820 [00:08<00:01, 75.41it/s, now=None]t: \n 86%|########5 | 705/820 [00:08<00:01, 75.99it/s, now=None]t: 87%|########7 | 714/820 [00:08<00:01, 78.50it/s, \n now=None]t: 88%|########8 | 722/820 [00:09<00:01, 77.38it/s, now=None]t: 89%|########9 | 732/820 \n [00:09<00:01, 82.65it/s, now=None]t: 90%|######### | 741/820 [00:09<00:01, 71.51it/s, now=None]t: \n 92%|#########1| 751/820 [00:09<00:00, 77.55it/s, now=None]t: 93%|#########2| 760/820 [00:09<00:00, 78.10it/s, \n now=None]t: 94%|#########3| 768/820 [00:09<00:00, 73.98it/s, now=None]t: 95%|#########4| 777/820 \n [00:09<00:00, 76.13it/s, now=None]t: 96%|#########5| 786/820 [00:09<00:00, 78.54it/s, now=None]t: \n 97%|#########6| 794/820 [00:10<00:00, 74.12it/s, now=None]t: 98%|#########7| 802/820 [00:10<00:00, 74.97it/s, \n now=None]t: 99%|#########9| 812/820 [00:10<00:00, 79.79it/s, now=None] \n Moviepy - Done ! \n Moviepy - video ready cropped_clip_with_audio.mp4 \n None \n \n\n" }, "metadata": {} } ] } }, "5988deb63066470bb3225344289929e9": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "4bb6ea2b3ce6407cb1b85c32ad828415": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_09287fc3783049898cdb624d0cc4bf81", "msg_id": "", "outputs": [ { "output_type": "display_data", "data": { "text/plain": " \n I have successfully converted the video to an mp4 format. The final video file is named \n 'cropped_clip_with_audio.mp4'. \n \n", "text/html": "
\n I have successfully converted the video to an mp4 format. The final video file is named \n 'cropped_clip_with_audio.mp4'. \n \n\n" }, "metadata": {} } ] } }, "09287fc3783049898cdb624d0cc4bf81": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } } } } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: examples/README.md ================================================ # Open Interpreter Examples This directory contains various examples demonstrating how to use Open Interpreter in different scenarios and configurations. Each example is designed to provide a practical guide to integrating and leveraging Open Interpreter's capabilities in your projects. ## Colab Notebooks [Google Colab](https://colab.google/) provides a sandboxed development environment for you to run code in. Here are some Jupyter Notebooks on Colab that you can try: ### Local 3 [](https://colab.research.google.com/drive/1jWKKwVCQneCTB5VNQNWO0Wxqg1vG_E1T#scrollTo=13ISLtY9_v7g) ### Interactive Demo [](https://colab.research.google.com/drive/1WKmRXZgsErej2xUriKzxrEAXdxMSgWbb?usp=sharing) ### Voice Interface [](https://colab.research.google.com/drive/1NojYGHDgxH6Y1G1oxThEBBb2AtyODBIK) ================================================ FILE: examples/custom_tool.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Add a Custom Tool to your Instance\n", "\n", "You can create custom tools for your instance of Open Interpreter. This is extremely helpful for adding new functionality in a reliable way.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, create a profile and configure your instance:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Configure Open Interpreter\n", "from interpreter import interpreter\n", "\n", "interpreter.llm.model = \"claude-3-5-sonnet-20240620\"\n", "interpreter.computer.import_computer_api = True\n", "interpreter.llm.supports_functions = True\n", "interpreter.llm.supports_vision = True\n", "interpreter.llm.context_window = 100000\n", "interpreter.llm.max_tokens = 4096" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then you will define your custom tool by writing valid Python code within a comment. This example is for searching the AWS documentation using Perplexity:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "custom_tool = \"\"\"\n", "\n", "import os\n", "import requests\n", "\n", "def search_aws_docs(query):\n", "\n", " url = \"https://api.perplexity.ai/chat/completions\"\n", "\n", " payload = {\n", " \"model\": \"llama-3.1-sonar-small-128k-online\",\n", " \"messages\": [\n", " {\n", " \"role\": \"system\",\n", " \"content\": \"Be precise and concise.\"\n", " },\n", " {\n", " \"role\": \"user\",\n", " \"content\": query\n", " }\n", " ],\n", " \"temperature\": 0.2,\n", " \"top_p\": 0.9,\n", " \"return_citations\": True,\n", " \"search_domain_filter\": [\"docs.aws.amazon.com\"],\n", " \"return_images\": False,\n", " \"return_related_questions\": False,\n", " #\"search_recency_filter\": \"month\",\n", " \"top_k\": 0,\n", " \"stream\": False,\n", " \"presence_penalty\": 0,\n", " \"frequency_penalty\": 1\n", " }\n", " headers = {\n", " \"Authorization\": f\"Bearer {os.environ.get('PPLX_API_KEY')}\",\n", " \"Content-Type\": \"application/json\"\n", " }\n", "\n", " response = requests.request(\"POST\", url, json=payload, headers=headers)\n", "\n", " print(response.text)\n", "\n", " return response.text\n", "\n", "\"\"\"\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, you add the tool to the OI instance's computer:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "interpreter.computer.run(\"python\", custom_tool)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Note: You can define and set multiple tools in a single instance." ] } ], "metadata": { "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: examples/interactive_quickstart.py ================================================ # This is all you need to get started from interpreter import interpreter interpreter.chat() ================================================ FILE: examples/jan_computer_control.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Jan Computer Control\n", "\n", "We love Jan as an A.I. inference server. It also has a chat interface to chat with LLMs. But did you know that you can use this same chat interface as a computer control interface? Read on!\n", "\n", "[View on YouTube](https://www.youtube.com/watch?v=1l3B0AzbbjQ)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Install and set up Jan\n", "\n", "https://jan.ai/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Install Open Interpreter\n", "\n", "https://docs.openinterpreter.com/getting-started/introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the Open Interpreter OpenAI-compatible server.\n", "\n", "`interpreter --server`\n", "\n", "Add flags to set the `--model`, `--context_window`, or any other [setting](https://docs.openinterpreter.com/settings/all-settings) you want" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Edit Jan's OpenAI settings to point to the local server.\n", "\n", "Settings => OpenAI => Chat Competion endpoint `http://127.0.0.1:8000/openai/chat/completions`.\n", "\n", "Jan has a requirement to set a dummy OpenAI API key." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Go to Jan's chat window to start a new thread.\n", "\n", "Set `Model` to an OpenAI model. \n", "\n", "Start controlling your computer!" ] } ], "metadata": { "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: examples/local3.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook replicates running Open Interpreter locally and uses Llama3 via llamafile" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "## Download LLama3\n", "\n", "# Download the Meta-Llama-3-8B-Instruct.llamafile\n", "!curl -L -o Meta-Llama-3-8B-Instruct.Q5_K_M.llamafile 'https://huggingface.co/Mozilla/Meta-Llama-3-8B-Instruct-llamafile/resolve/main/Meta-Llama-3-8B-Instruct.Q5_K_M.llamafile?download=true'\n", "\n", "# Make the downloaded file executable\n", "!chmod +x Meta-Llama-3-8B-Instruct.Q5_K_M.llamafile" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "## Install OI\n", "\n", "!pip install open-interpreter --quiet\n", "!pip install opencv-python --quiet" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "## Configure OI\n", "\n", "import cv2\n", "import subprocess\n", "from interpreter import interpreter\n", "\n", "interpreter.offline = True\n", "\n", "interpreter.llm.model = \"openai/local\" # Tells OI to use an OpenAI-compatible server\n", "interpreter.llm.api_key = \"dummy_key\"\n", "interpreter.llm.api_base = \"http://localhost:8081/v1\"\n", "interpreter.llm.context_window = 7000\n", "interpreter.llm.max_tokens = 1000\n", "interpreter.llm.supports_functions = False" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "## Start server, run OI\n", "\n", "import subprocess\n", "import threading\n", "import os\n", "\n", "def read_output(process):\n", " while True:\n", " output = process.stdout.readline()\n", " if output == b'' and process.poll() is not None:\n", " break\n", " if output:\n", " print(output.decode().strip())\n", "\n", "# Check if the file exists and has execute permissions\n", "file_path = os.path.abspath('Meta-Llama-3-8B-Instruct.Q5_K_M.llamafile')\n", "\n", "# Why are the arguments not being used??\n", "command = [file_path, \"--nobrowser\", \"-ngl\", \"9999\"]\n", "print(command)\n", "\n", "# Setting up the Popen call with stderr redirected to stdout\n", "process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True)\n", "\n", "# Thread to handle the output asynchronously\n", "thread = threading.Thread(target=read_output, args=(process,))\n", "thread.start()\n", "\n", "# Here you can do other tasks concurrently\n", "# For example:\n", "interpreter.chat()\n", "\n", "# Wait for the thread to finish if the process completes\n", "thread.join()\n", "\n", "# Ensure the process has completed\n", "process.wait()" ] } ], "metadata": { "kernelspec": { "display_name": "oi", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: examples/local_server.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Build a local Open Interpreter server for a custom front end" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from flask import Flask, request, jsonify\n", "from interpreter import interpreter\n", "import json" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "app = Flask(__name__)\n", "\n", "# Configure Open Interpreter\n", "\n", "## Local Model\n", "# interpreter.offline = True\n", "# interpreter.llm.model = \"ollama/llama3.1\"\n", "# interpreter.llm.api_base = \"http://localhost:11434\"\n", "# interpreter.llm.context_window = 4000\n", "# interpreter.llm.max_tokens = 3000\n", "# interpreter.auto_run = True\n", "# interpreter.verbose = True\n", "\n", "## Hosted Model\n", "interpreter.llm.model = \"gpt-4o\"\n", "interpreter.llm.context_window = 10000\n", "interpreter.llm.max_tokens = 4096\n", "interpreter.auto_run = True\n", "\n", "# Create an endpoint\n", "@app.route('/chat', methods=['POST'])\n", "def chat():\n", " # Expected payload: {\"prompt\": \"User's message or question\"}\n", " data = request.json\n", " prompt = data.get('prompt')\n", " \n", " if not prompt:\n", " return jsonify({\"error\": \"No prompt provided\"}), 400\n", "\n", " full_response = \"\"\n", " try:\n", " for chunk in interpreter.chat(prompt, stream=True, display=False):\n", " if isinstance(chunk, dict):\n", " if chunk.get(\"type\") == \"message\":\n", " full_response += chunk.get(\"content\", \"\")\n", " elif isinstance(chunk, str):\n", " # Attempt to parse the string as JSON\n", " try:\n", " json_chunk = json.loads(chunk)\n", " full_response += json_chunk.get(\"response\", \"\")\n", " except json.JSONDecodeError:\n", " # If it's not valid JSON, just add the string\n", " full_response += chunk\n", " except Exception as e:\n", " return jsonify({\"error\": str(e)}), 500\n", "\n", " return jsonify({\"response\": full_response.strip()})\n", "\n", "if __name__ == '__main__':\n", " app.run(host='0.0.0.0', port=5001)\n", "\n", "print(\"Open Interpreter server is running on http://0.0.0.0:5001\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Make a request to the server" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "curl -X POST http://localhost:5001/chat \\\n", " -H \"Content-Type: application/json\" \\\n", " -d '{\"prompt\": \"Hello, how are you?\"}'" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: examples/organize_photos.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Organize your photos with Open Interpreter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can use Open Interpreter with a vision model to organize photos based on their contents. This is limited by the ability of the LLM as well as the organization of the directories storing photos. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Note: It is always recommended to back up photos and files on a regular basis. Most models are intelligent enough to know the difference between `move` and `delete` but on rare occasions, files can be deleted during some operations. It is important to test on duplicated photos and to keep an eye on code written by an LLM." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a profile. This example uses GPT-4o but you can use any vision model." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", "This is an Open Interpreter profile to organize a directory of photos. \n", "\"\"\"\n", "\n", "from interpreter import interpreter\n", "\n", "\n", "# LLM settings\n", "interpreter.llm.model = \"gpt-4o\"\n", "#interpreter.llm.model = \"ollama/codestral\"\n", "interpreter.llm.supports_vision = True\n", "interpreter.llm.execution_instructions = False\n", "interpreter.llm.max_tokens = 1000\n", "interpreter.llm.context_window = 7000\n", "interpreter.llm.load() # Loads Ollama models\n", "\n", "# Computer settings\n", "interpreter.computer.import_computer_api = True\n", "\n", "# Misc settings\n", "interpreter.auto_run = False\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following custom instruction is intended for a directory containing one sub-directory of unorganized photos and multiple empty sub-directories with names for the intended organization. Please update the custom instructions to match your use-case. This will take some trial and error, depending on the model used." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Custom Instructions\n", "interpreter.custom_instructions=f\"\"\"\n", " Recap the plan before answering the user's query!\n", " Your job is to organize photos. You love organizing photos.\n", " You will be given a parent directory with sub-directories. \n", " One sub-directory will be of unorganized photos.\n", " The other sub-directories will be categories that you move the photos in to.\n", " Remember the sub-directories's names because they will be the categories for organizing.\n", " It is extremely important because these are the only options for where you move the photos.\n", " Loop through every photo in the unorganized photos directory. \n", " Skip over non-photo files by checking for common photo extensions (.jpg, .jpeg, .png, etc).\n", " In this loop you will determine the description of each image one at a time. \n", " Use `computer.vision.query()` to get a description of the image.\n", " `computer.vision.query()` takes a `path=` argument to know which photo to describe. \n", " Print out the description so you can get the full context.\n", " Determine which sub-directory the photo should go in to.\n", " Every photo needs to go into one of the sub-directories.\n", " Make sure you actually move the photo. \n", " Your task is done when every photo in the unorganized photos directory has been moved to another directory. \n", " **Confirm that the unorganized photos directory has no more photos**.\n", " \"\"\"\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Save the profile with a descriptive name. Then run interpreter with:\n", "\n", "`interpreter --profile