[ { "path": ".gitignore", "content": "# Editors\n.idea/\n.vscode/\n\n# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# macOS dir files\n.DS_Store\n\n# Distribution / packaging\n.Python\nenv/\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\n*.egg-info/\n.installed.cfg\n*.egg\n\n# Checkpoints\ncheckpoints\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n.hypothesis/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# SageMath parsed files\n*.sage.py\n\n# dotenv\n.env\n\n# virtualenv\n.venv\nvenv/\nENV/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n\n# Generated files\n/fairseq/temporal_convolution_tbc\n/fairseq/modules/*_layer/*_forward.cu\n/fairseq/modules/*_layer/*_backward.cu\n/fairseq/version.py\n\n# data\ndata-bin/\n\n# reranking\n/examples/reranking/rerank_data\n\n# Cython-generated C++ source files\n/fairseq/data/data_utils_fast.cpp\n/fairseq/data/token_block_utils_fast.cpp\n\n# VSCODE\n.vscode/ftp-sync.json\n.vscode/settings.json\n\n# Experimental Folder\nexperimental/*\n\n# Weights and Biases logs\nwandb/\n\n# Hydra artifacts\nnohup.out\nmultirun\noutputs\n\n\n# symlinks\nseamless_communication\n# ignore src/seamless_communication \n!*/seamless_communication\nm4t_scripts\n/ggml/test_data/\n" }, { "path": ".gitmodules", "content": "[submodule \"ggml/tracy\"]\n\tpath = ggml/tracy\n\turl = git@github.com:wolfpld/tracy.git\n" }, { "path": ".pre-commit-config.yaml", "content": "repos:\n - repo: https://github.com/pre-commit/pre-commit-hooks\n rev: v4.1.0\n hooks:\n - id: trailing-whitespace\n - id: check-ast\n - id: check-merge-conflict\n - id: check-added-large-files\n args: [\"--maxkb=2000\"]\n - id: end-of-file-fixer\n\n - repo: https://github.com/psf/black\n rev: 22.3.0\n hooks:\n - id: black\n" }, { "path": "ACCEPTABLE_USE_POLICY", "content": "Seamless Acceptable Use Policy\n\nMeta is committed to promoting safe and fair use of its tools and features, including Seamless. If you access or use Seamless, Seamless Materials or the Seamless Demo, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [ai.meta.com/seamless/use-policy].\n\nProhibited Uses\n\nWe want everyone to use Seamless safely and responsibly. You agree you will not use, or allow others to use, Seamless to:\n\n1. Violate the law or others’ rights, including to:\n a. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\n i. Violence or terrorism\n ii. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\n iii. Human trafficking, exploitation, and sexual violence\n iv. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials\n v. Sexual solicitation\n vi. Any other criminal activity\n b. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n c. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\n d. Collect, process, disclose, generate, or infer health, demographic, biometric, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\n e. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using Seamless\n f. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system\n2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Seamless related to the following:\n a. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\n b. Guns and illegal weapons (including weapon development)\n c. Illegal drugs and regulated/controlled substances\n d. Operation of critical infrastructure, transportation technologies, or heavy machinery\n e. Self-harm or harm to others, including suicide, cutting, and eating disorders\n f. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive or mislead others, including use of Seamless related to the following:\n a. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n b. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n c. Generating, promoting, or further distributing spam\n d. Impersonating another individual by depiction of their voice or likeness without consent, authorization, or legal right, including non-consensual sexual imagery\n e. Representing that the use of Seamless or outputs are human-generated\n f. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement\n4. Fail to appropriately disclose to end users any known dangers of your AI system\n5. Engage in automated government decision-making in high risk contexts, including, for example, law enforcement, criminal justice, immigration, or asylum, without a qualified person reviewing the outputs\n6. Engage in any decision-making related to health, financial, safety, or legal matters\n7. Create, develop, access, or disseminate adult content, including in relation to:\n a. Erotic, sexual, or romantic chats\n b. Sexual solicitation\n c. Pornography\n d. Content that describes or promotes sexual or adult services\n\n\nPlease report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means:\n- Reporting issues with the model: github.com/facebookresearch/seamless_communication\n- Reporting bugs and security concerns: facebook.com/whitehat/info\n- Reporting violations of the Acceptable Use Policy or unlicensed uses of Seamless: SeamlessUseReport@meta.com\n" }, { "path": "CODE_OF_CONDUCT.md", "content": "# Code of Conduct\n\n## Our Pledge\n\nIn the interest of fostering an open and welcoming environment, we as\ncontributors and maintainers pledge to make participation in our project and\nour community a harassment-free experience for everyone, regardless of age, body\nsize, disability, ethnicity, sex characteristics, gender identity and expression,\nlevel of experience, education, socio-economic status, nationality, personal\nappearance, race, religion, or sexual identity and orientation.\n\n## Our Standards\n\nExamples of behavior that contributes to creating a positive environment\ninclude:\n\n* Using welcoming and inclusive language\n* Being respectful of differing viewpoints and experiences\n* Gracefully accepting constructive criticism\n* Focusing on what is best for the community\n* Showing empathy towards other community members\n\nExamples of unacceptable behavior by participants include:\n\n* The use of sexualized language or imagery and unwelcome sexual attention or\nadvances\n* Trolling, insulting/derogatory comments, and personal or political attacks\n* Public or private harassment\n* Publishing others' private information, such as a physical or electronic\naddress, without explicit permission\n* Other conduct which could reasonably be considered inappropriate in a\nprofessional setting\n\n## Our Responsibilities\n\nProject maintainers are responsible for clarifying the standards of acceptable\nbehavior and are expected to take appropriate and fair corrective action in\nresponse to any instances of unacceptable behavior.\n\nProject maintainers have the right and responsibility to remove, edit, or\nreject comments, commits, code, wiki edits, issues, and other contributions\nthat are not aligned to this Code of Conduct, or to ban temporarily or\npermanently any contributor for other behaviors that they deem inappropriate,\nthreatening, offensive, or harmful.\n\n## Scope\n\nThis Code of Conduct applies within all project spaces, and it also applies when\nan individual is representing the project or its community in public spaces.\nExamples of representing a project or community include using an official\nproject e-mail address, posting via an official social media account, or acting\nas an appointed representative at an online or offline event. Representation of\na project may be further defined and clarified by project maintainers.\n\nThis Code of Conduct also applies outside the project spaces when there is a\nreasonable belief that an individual's behavior may have a negative impact on\nthe project or its community.\n\n## Enforcement\n\nInstances of abusive, harassing, or otherwise unacceptable behavior may be\nreported by contacting the project team at . All\ncomplaints will be reviewed and investigated and will result in a response that\nis deemed necessary and appropriate to the circumstances. The project team is\nobligated to maintain confidentiality with regard to the reporter of an incident.\nFurther details of specific enforcement policies may be posted separately.\n\nProject maintainers who do not follow or enforce the Code of Conduct in good\nfaith may face temporary or permanent repercussions as determined by other\nmembers of the project's leadership.\n\n## Attribution\n\nThis Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,\navailable at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html\n\n[homepage]: https://www.contributor-covenant.org\n\nFor answers to common questions about this code of conduct, see\nhttps://www.contributor-covenant.org/faq\n" }, { "path": "CONTRIBUTING.md", "content": "# Contributing to `seamless_communication`\nWe want to make contributing to this project as easy and transparent as\npossible.\n\n## Our Development Process\n\n`seamless_communication` is built for Meta AI Seamless Communication team public release.\nWe engage in multiple projects internally and will update this repository with our progress upon reaching specific milestones.\n\n## Pull Requests\nWe actively welcome your pull requests.\n\n1. Fork the repo and create your branch from `main`.\n2. If you've added code that should be tested, add tests.\n3. If you've changed APIs, update the documentation.\n4. Ensure the test suite passes.\n5. Make sure your code lints.\n6. If you haven't already, complete the Contributor License Agreement (\"CLA\").\n\n## Contributor License Agreement (\"CLA\")\nIn order to accept your pull request, we need you to submit a CLA. You only need\nto do this once to work on any of Meta's open source projects.\n\nComplete your CLA here: \n\n## Issues\nWe use GitHub issues to track public bugs. Please ensure your description is\nclear and has sufficient instructions to be able to reproduce the issue.\n\nMeta has a [bounty program](https://www.facebook.com/whitehat/) for the safe\ndisclosure of security bugs. In those cases, please go through the process\noutlined on that page and do not file a public issue.\n\n\n## License\nBy contributing to `seamless_communication`, you agree that your contributions will be licensed\nunder the MIT_LICENSE file in the root directory of this source tree.\n" }, { "path": "LICENSE", "content": "\nAttribution-NonCommercial 4.0 International\n\n=======================================================================\n\nCreative Commons Corporation (\"Creative Commons\") is not a law firm and\ndoes not provide legal services or legal advice. Distribution of\nCreative Commons public licenses does not create a lawyer-client or\nother relationship. Creative Commons makes its licenses and related\ninformation available on an \"as-is\" basis. Creative Commons gives no\nwarranties regarding its licenses, any material licensed under their\nterms and conditions, or any related information. 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For\nthe avoidance of doubt, this paragraph does not form part of the\npublic licenses.\n\nCreative Commons may be contacted at creativecommons.org.\n" }, { "path": "MIT_LICENSE", "content": "MIT License\n\nCopyright (c) Meta Platforms, Inc. and affiliates.\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "README.md", "content": "![](23-11_SEAMLESS_BlogHero_11.17.jpg)\n# Seamless Intro\n\nSeamless is a family of AI models that enable more natural and authentic communication across languages. SeamlessM4T is a massive multilingual multimodal machine translation model supporting around 100 languages. SeamlessM4T serves as foundation for SeamlessExpressive, a model that preserves elements of prosody and voice style across languages and SeamlessStreaming, a model supporting simultaneous translation and streaming ASR for around 100 languages. SeamlessExpressive and SeamlessStreaming are combined into Seamless, a unified model featuring multilinguality, real-time and expressive translations.\n\n## Links\n\n### Demos\n\n| | SeamlessM4T v2 | SeamlessExpressive | SeamlessStreaming |\n| ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------- |\n| Demo | [SeamlessM4T v2 Demo](https://seamless.metademolab.com/m4t?utm_source=github&utm_medium=web&utm_campaign=seamless&utm_content=readme) | [SeamlessExpressive Demo](https://seamless.metademolab.com/expressive?utm_source=github&utm_medium=web&utm_campaign=seamless&utm_content=readme) | |\n| HuggingFace Space Demo | [🤗 SeamlessM4T v2 Space](https://huggingface.co/spaces/facebook/seamless-m4t-v2-large) | [🤗 SeamlessExpressive Space](https://huggingface.co/spaces/facebook/seamless-expressive) | [🤗 SeamlessStreaming Space](https://huggingface.co/spaces/facebook/seamless-streaming) |\n\n### Papers\n[Seamless](https://ai.facebook.com/research/publications/seamless-multilingual-expressive-and-streaming-speech-translation/)\n\n[EMMA](https://ai.meta.com/research/publications/efficient-monotonic-multihead-attention/)\n\n[SONAR](https://ai.meta.com/research/publications/sonar-expressive-zero-shot-expressive-speech-to-speech-translation/)\n\n### Blog\n[AI at Meta Blog](https://ai.meta.com/research/seamless-communication/)\n\n## Tutorial\nAn exhaustive [tutorial](Seamless_Tutorial.ipynb) given at the NeurIPS 2023 - Seamless EXPO, which is a one-stop shop to learn how to use the entire suite of Seamless models. Please feel free to play with the notebook.\n\n## SeamlessM4T\nSeamlessM4T is our foundational all-in-one **M**assively **M**ultilingual and **M**ultimodal **M**achine **T**ranslation model delivering high-quality translation for speech and text in nearly 100 languages.\n\nSeamlessM4T models support the tasks of:\n- Speech-to-speech translation (S2ST)\n- Speech-to-text translation (S2TT)\n- Text-to-speech translation (T2ST)\n- Text-to-text translation (T2TT)\n- Automatic speech recognition (ASR)\n\n:star2: We are releasing SeamlessM4T v2, an updated version with our novel *UnitY2* architecture. This new model improves over SeamlessM4T v1 in quality as well as inference latency in speech generation tasks.\n\nTo learn more about the collection of SeamlessM4T models, the approach used in each, their language coverage and their performance, visit the [SeamlessM4T README](docs/m4t/README.md) or [🤗 Model Card](https://huggingface.co/facebook/seamless-m4t-v2-large).\n\n> [!NOTE]\n> Seamless M4T is also available in the 🤗 Transformers library. Visit [this section](docs/m4t/README.md#transformers-usage) for more details.\n\n## SeamlessExpressive\n\nSeamlessExpressive is a speech-to-speech translation model that captures certain underexplored aspects of prosody such as speech rate and pauses, while preserving the style of one's voice and high content translation quality.\n\nTo learn more about SeamlessExpressive models, visit the [SeamlessExpressive README](docs/expressive/README.md) or [🤗 Model Card](https://huggingface.co/facebook/seamless-expressive)\n\n\n## SeamlessStreaming\n\nSeamlessStreaming is a streaming translation model. The model supports speech as input modality and speech/text as output modalities.\n\nThe SeamlessStreaming model supports the following tasks:\n- Speech-to-speech translation (S2ST)\n- Speech-to-text translation (S2TT)\n- Automatic speech recognition (ASR)\n\nTo learn more about SeamlessStreaming models, visit the [SeamlessStreaming README](docs/streaming/README.md) or [🤗 Model Card](https://huggingface.co/facebook/seamless-streaming)\n\n## Seamless\n\nThe Seamless model is the unified model for expressive streaming speech-to-speech translations.\n\n## What's new\n- [12/18/2023] We are open-sourcing our Conformer-based [W2v-BERT 2.0 speech encoder](#w2v-bert-20-speech-encoder) as described in Section 3.2.1 of the [paper](https://arxiv.org/pdf/2312.05187.pdf), which is at the core of our Seamless models.\n- [12/14/2023] We are releasing the Seamless [tutorial](#tutorial) given at NeurIPS 2023.\n\n# Quick Start\n## Installation\n> [!NOTE]\n> One of the prerequisites is [fairseq2](https://github.com/facebookresearch/fairseq2) which has pre-built packages available only\n> for Linux x86-64 and Apple-silicon Mac computers. In addition it has a dependency on [libsndfile](https://github.com/libsndfile/libsndfile) which\n> might not be installed on your machine. If you experience any installation issues, please refer to its\n> [README](https://github.com/facebookresearch/fairseq2) for further instructions.\n\n```\npip install .\n```\n\n> [!NOTE]\n> Transcribing inference audio for computing metric uses [Whisper](https://github.com/openai/whisper#setup), which is automatically installed. Whisper in turn requires the command-line tool [`ffmpeg`](https://ffmpeg.org/) to be installed on your system, which is available from most package managers.\n\n\n## Running inference\n\n### SeamlessM4T Inference\nHere’s an example of using the CLI from the root directory to run inference.\n\nS2ST task:\n```bash\nm4t_predict --task s2st --tgt_lang --output_path \n```\nT2TT task:\n```bash\nm4t_predict --task t2tt --tgt_lang --src_lang \n```\nPlease refer to the [inference README](src/seamless_communication/cli/m4t/predict) for detailed instruction on how to run inference and the list of supported languages on the source, target sides for speech, text modalities.\n\nFor running S2TT/ASR natively (without Python) using GGML, please refer to [the unity.cpp section](#unitycpp).\n\n### SeamlessExpressive Inference\n> [!NOTE]\n> Please check the [section](#seamlessexpressive-models) on how to download the model.\n\nHere’s an example of using the CLI from the root directory to run inference.\n\n```bash\nexpressivity_predict --tgt_lang --model_name seamless_expressivity --vocoder_name vocoder_pretssel --output_path \n```\n\n### SeamlessStreaming and Seamless Inference\n\n[Streaming Evaluation README](src/seamless_communication/cli/streaming) has detailed instructions for running evaluations for the SeamlessStreaming and Seamless models. The CLI has an `--no-scoring` option that can be used to skip the scoring part and just run inference.\n\nPlease check the inference [README](src/seamless_communication/inference) for more details.\n\n## Running SeamlessStreaming Demo\nYou can duplicate the [SeamlessStreaming HF space](https://huggingface.co/spaces/facebook/seamless-streaming?duplicate=true) to run the streaming demo.\n\n\nYou can also run the demo locally, by cloning the space from [here](https://huggingface.co/spaces/facebook/seamless-streaming/tree/main). See the [README](https://huggingface.co/spaces/facebook/seamless-streaming/blob/main/README.md) of the SeamlessStreaming HF repo for more details on installation.\n\n## Running SeamlessM4T & SeamlessExpressive [Gradio](https://github.com/gradio-app/gradio) demos locally\n\nTo launch the same demo Space we host on Hugging Face locally:\n\n```bash\ncd demo\npip install -r requirements.txt\npython app.py\n```\n\n# Resources and usage\n## Model\n### SeamlessM4T models\n| Model Name | #params | checkpoint | metrics |\n| ----------------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------- |\n| SeamlessM4T-Large v2 | 2.3B | [🤗 Model card](https://huggingface.co/facebook/seamless-m4t-v2-large) - [checkpoint](https://huggingface.co/facebook/seamless-m4t-v2-large/resolve/main/seamlessM4T_v2_large.pt ) | [metrics](https://dl.fbaipublicfiles.com/seamless/metrics/seamlessM4T_large_v2.zip) |\n| SeamlessM4T-Large (v1) | 2.3B | [🤗 Model card](https://huggingface.co/facebook/seamless-m4t-large) - [checkpoint](https://huggingface.co/facebook/seamless-m4t-large/resolve/main/multitask_unity_large.pt) | [metrics](https://dl.fbaipublicfiles.com/seamless/metrics/seamlessM4T_large.zip) |\n| SeamlessM4T-Medium (v1) | 1.2B | [🤗 Model card](https://huggingface.co/facebook/seamless-m4t-medium) - [checkpoint](https://huggingface.co/facebook/seamless-m4t-medium/resolve/main/multitask_unity_medium.pt) | [metrics](https://dl.fbaipublicfiles.com/seamless/metrics/seamlessM4T_medium.zip) |\n\n### SeamlessExpressive models\n\n[🤗 Model card](https://huggingface.co/facebook/seamless-expressive)\n\nTo access and download SeamlessExpressive, please request the model artifacts through [this request form](https://ai.meta.com/resources/models-and-libraries/seamless-downloads/). Upon approval, you will then receive an email with download links to each model artifact.\n\nPlease note that SeamlessExpressive is made available under its own [License](SEAMLESS_LICENSE) and [Acceptable Use Policy](ACCEPTABLE_USE_POLICY).\n\n### SeamlessStreaming models\n| Model Name | #params | checkpoint | metrics |\n| ----------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- |\n| SeamlessStreaming | 2.5B | [🤗 Model card](https://huggingface.co/facebook/seamless-streaming) - [monotonic decoder checkpoint](https://huggingface.co/facebook/seamless-streaming/resolve/main/seamless_streaming_monotonic_decoder.pt) - [streaming UnitY2 checkpoint](https://huggingface.co/facebook/seamless-streaming/resolve/main/seamless_streaming_unity.pt) | [metrics](https://dl.fbaipublicfiles.com/seamless/metrics/streaming/seamless_streaming.zip) |\n\n### Seamless models\nSeamless model is simply the SeamlessStreaming model with the non-expressive `vocoder_v2` swapped out with the expressive `vocoder_pretssel`.\nPlease check out above [section](#seamlessexpressive-models) on how to acquire `vocoder_pretssel` checkpoint.\n\n### W2v-BERT 2.0 speech encoder\n| Model Name | #params | checkpoint |\n| ----------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| W2v-BERT 2.0 | 600M | [🤗 Model card](https://huggingface.co/facebook/conformer-shaw) - [checkpoint](https://huggingface.co/facebook/conformer-shaw/resolve/main/conformer_shaw.pt)\n\nHere's how you should do a foward pass through the speech encoder:\n\n```python\nimport torch\n\nfrom fairseq2.data.audio import AudioDecoder, WaveformToFbankConverter\nfrom fairseq2.memory import MemoryBlock\nfrom fairseq2.nn.padding import get_seqs_and_padding_mask\nfrom fairseq2.data import Collater\nfrom pathlib import Path\nfrom seamless_communication.models.conformer_shaw import load_conformer_shaw_model\n\n\naudio_wav_path, device, dtype = ...\naudio_decoder = AudioDecoder(dtype=torch.float32, device=device)\nfbank_converter = WaveformToFbankConverter(\n num_mel_bins=80,\n waveform_scale=2**15,\n channel_last=True,\n standardize=True,\n device=device,\n dtype=dtype,\n)\ncollater = Collater(pad_value=1)\n\nmodel = load_conformer_shaw_model(\"conformer_shaw\", device=device, dtype=dtype)\nmodel.eval()\n\nwith Path(audio_wav_path).open(\"rb\") as fb:\n block = MemoryBlock(fb.read())\n\ndecoded_audio = audio_decoder(block)\nsrc = collater(fbank_converter(decoded_audio))[\"fbank\"]\nseqs, padding_mask = get_seqs_and_padding_mask(src)\n\nwith torch.inference_mode():\n seqs, padding_mask = model.encoder_frontend(seqs, padding_mask)\n seqs, padding_mask = model.encoder(seqs, padding_mask)\n```\n\n## Evaluation\n\n### SeamlessM4T Evaluation\nTo reproduce our results, or to evaluate using the same metrics over your own test sets, please check out the [README here](src/seamless_communication/cli/m4t/evaluate).\n### SeamlessExpressive Evaluation\n\nBelow is the script for efficient batched evaluation.\n\n```bash\nexport MODEL_DIR=\"/path/to/SeamlessExpressive/model\"\nexport TEST_SET_TSV=\"input.tsv\" # Your dataset in a TSV file, with headers \"id\", \"audio\"\nexport TGT_LANG=\"spa\" # Target language to translate into, options including \"fra\", \"deu\", \"eng\" (\"cmn\" and \"ita\" are experimental)\nexport OUTPUT_DIR=\"tmp/\" # Output directory for generated text/unit/waveform\nexport TGT_TEXT_COL=\"tgt_text\" # The column in your ${TEST_SET_TSV} for reference target text to calcuate BLEU score. You can skip this argument.\nexport DFACTOR=\"1.0\" # Duration factor for model inference to tune predicted duration (preddur=DFACTOR*preddur) per each position which affects output speech rate. Greater value means slower speech rate (default to 1.0). See expressive evaluation README for details on duration factor we used.\nexpressivity_evaluate ${TEST_SET_TSV} \\\n --gated-model-dir ${MODEL_DIR} --task s2st --tgt_lang ${TGT_LANG} \\\n --audio_root_dir \"\" --output_path ${OUTPUT_DIR} --ref_field ${TGT_TEXT_COL} \\\n --model_name seamless_expressivity --vocoder_name vocoder_pretssel \\\n --text_unk_blocking True --duration_factor ${DFACTOR}\n```\n\nPlease check out this [README section](docs/expressive/README.md#automatic-evaluation)\n\n### SeamlessStreaming and Seamless Evaluation\n\n[Streaming Evaluation README](src/seamless_communication/cli/streaming) has detailed instructions for running evaluations on the SeamlessStreaming and Seamless models.\n\n## Unity.cpp\nTo enable Seamless Communication Everywhere, we implemented unity.cpp so users could run SeamlessM4T models in GGML - a C tensor library allowing easier integration on verbose platforms.\n\nTo transcribe/translte a given audio,\n\n```\n./ggml/bin/unity --model seamlessM4T_medium.ggml input.wav\n```\n\nFor details of build and more usage please check out [unity.cpp](ggml)\n\n## Expressive Datasets\n\nWe created two expressive speech-to-speech translation datasets, mExpresso and mDRAL, between English and five other languages -- French, German, Italian, Mandarin and Spanish. We currently open source the speech-to-text of mExpresso for out-of-English directions, and we will open source the remaining part of the datasets soon. For details, please check out [README](docs/expressive/README.md#benchmark-datasets)\n\n### SeamlessAlignExpressive\nWe’re introducing the first expressive speech alignment procedure. Starting with raw data, the expressive alignment procedure automatically discovers pairs of audio segments sharing not only the same meaning, but the same overall expressivity. To showcase this procedure, we are making metadata available to create a benchmarking dataset called SeamlessAlignExpressive, that can be used to validate the quality of our alignment method. SeamlessAlignExpressive is the first large-scale (11k+ hours) collection of multilingual audio alignments for expressive translation. More details can be found on the [SeamlessAlignExpressive README](docs/expressive/seamless_align_expressive_README.md).\n\n\n## Converting raw audio to units\nPlease check out the [README here](src/seamless_communication/cli/m4t/audio_to_units). Note that SeamlessM4T v1 model uses reduced units and other models use non-reduced units.\n\n# Libraries\n\nSeamless Communication depends on 4 libraries developed by Meta.\n\n## [fairseq2](https://github.com/facebookresearch/fairseq2)\nfairseq2 is our next-generation open-source library of sequence modeling components that provides researchers and developers with building blocks for machine translation, language modeling, and other sequence generation tasks. All SeamlessM4T models in this repository are powered by fairseq2.\n\n## [SONAR and BLASER 2.0](https://github.com/facebookresearch/SONAR)\nSONAR, Sentence-level multimOdal and laNguage-Agnostic Representations is a new multilingual and -modal sentence embedding space which outperforms existing sentence embeddings such as LASER3 and LabSE on the xsim and xsim++ multilingual similarity search tasks. SONAR provides text and speech encoders for many languages. SeamlessAlign was mined based on SONAR embeddings.\n\nBLASER 2.0 is our latest model-based evaluation metric for multimodal translation. It is an extension of BLASER, supporting both speech and text. It operates directly on the source signal, and as such, does not require any intermediate ASR system like ASR-BLEU. As in the first version, BLASER 2.0 leverages the similarity between input and output sentence embeddings. SONAR is the underlying embedding space for BLASER 2.0. Scripts to run evaluation with BLASER 2.0 can be found in the [SONAR repo](https://github.com/facebookresearch/SONAR).\n\n## [stopes](https://github.com/facebookresearch/stopes)\nAs part of the seamless communication project, we've extended the stopes library. Version 1 provided a text-to-text mining tool to build training dataset for translation models. Version 2 has been extended thanks to SONAR, to support tasks around training large speech translation models. In particular, we provide tools to read/write the fairseq audiozip datasets and a new mining pipeline that can do speech-to-speech, text-to-speech, speech-to-text and text-to-text mining, all based on the new SONAR embedding space.\n\n## [SimulEval](https://github.com/facebookresearch/SimulEval)\nSimulEval is a library used for evaluating simulaneous translation models. SimulEval also provides a backend for generation using partial/incremental inputs with flexible/extensible states, which is used to implement streaming inference. Users define agents which implement SimulEval's interface, which can be connected together in a pipeline. You can find agents implemented for SeamlessStreaming [here](src/seamless_communication/streaming/agents).\n\n## [Legacy] SeamlessM4T v1 instructions\n#### Finetuning SeamlessM4T v1 models\nPlease check out the [README here](src/seamless_communication/cli/m4t/finetune).\n\n#### On-device models\nApart from Seamless-M4T large (2.3B) and medium (1.2B) models, we are also releasing a small model (281M) targeted for on-device inference. To learn more about the usage and model details check out the [README here](docs/m4t/on_device_README.md).\n\n#### SeamlessAlign mined dataset\nWe open-source the metadata to SeamlessAlign, the largest open dataset for multimodal translation, totaling 270k+ hours of aligned Speech and Text data. The dataset can be rebuilt by the community based on the [SeamlessAlign readme](docs/m4t/seamless_align_README.md).\n\n\n# Citation\nIf you use Seamless in your work or any models/datasets/artifacts published in Seamless, please cite :\n\n```bibtex\n@inproceedings{seamless2023,\n title=\"Seamless: Multilingual Expressive and Streaming Speech Translation\",\n author=\"{Seamless Communication}, Lo{\\\"i}c Barrault, Yu-An Chung, Mariano Coria Meglioli, David Dale, Ning Dong, Mark Duppenthaler, Paul-Ambroise Duquenne, Brian Ellis, Hady Elsahar, Justin Haaheim, John Hoffman, Min-Jae Hwang, Hirofumi Inaguma, Christopher Klaiber, Ilia Kulikov, Pengwei Li, Daniel Licht, Jean Maillard, Ruslan Mavlyutov, Alice Rakotoarison, Kaushik Ram Sadagopan, Abinesh Ramakrishnan, Tuan Tran, Guillaume Wenzek, Yilin Yang, Ethan Ye, Ivan Evtimov, Pierre Fernandez, Cynthia Gao, Prangthip Hansanti, Elahe Kalbassi, Amanda Kallet, Artyom Kozhevnikov, Gabriel Mejia, Robin San Roman, Christophe Touret, Corinne Wong, Carleigh Wood, Bokai Yu, Pierre Andrews, Can Balioglu, Peng-Jen Chen, Marta R. Costa-juss{\\`a}, Maha Elbayad, Hongyu Gong, Francisco Guzm{\\'a}n, Kevin Heffernan, Somya Jain, Justine Kao, Ann Lee, Xutai Ma, Alex Mourachko, Benjamin Peloquin, Juan Pino, Sravya Popuri, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Anna Sun, Paden Tomasello, Changhan Wang, Jeff Wang, Skyler Wang, Mary Williamson\",\n journal={ArXiv},\n year={2023}\n}\n```\n\n# License\n\nWe have three license categories.\n\nThe following non-generative components are MIT licensed as found in [MIT_LICENSE](MIT_LICENSE):\n- [W2v-BERT 2.0 speech encoder](#w2v-bert-20-speech-encoder)\n- Code\n- Text only part of the mExpresso dataset found in the [SeamlessExpressive README](docs/expressive/README.md).\n- UnitY2 forced alignment extractor found in the [UnitY2 Aligner README](docs/m4t/unity2_aligner_README.md).\n- Speech toxicity tool with the etox dataset found in the [ETOX README](src/seamless_communication/cli/toxicity/etox).\n- MuTox: Universal MUltilingual Audio-based TOXicity Dataset and Zero-shot Detector [Mutox README](src/seamless_communication/cli/toxicity/mutox)\n\nThe following models are CC-BY-NC 4.0 licensed as found in the [LICENSE](LICENSE):\n- SeamlessM4T models (v1 and v2).\n- SeamlessStreaming models.\n\nThe following models are Seamless licensed as found in [SEAMLESS_LICENSE](SEAMLESS_LICENSE):\n- Seamless models.\n- SeamlessExpressive models.\n" }, { "path": "SEAMLESS_LICENSE", "content": "Seamless Licensing Agreement\n\n“Agreement” means this “Seamless Licensing Agreement”, including, the terms and conditions for use, reproduction, distribution and modification of the Seamless Materials set forth herein.\n\n“Documentation” means the specifications, manuals and documentation accompanying Seamless distributed by Meta at [https://ai.meta.com/resources/models-and-libraries/seamless-downloads](https://ai.meta.com/resources/models-and-libraries/seamless-downloads).\n\n“Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.\n\n“Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).\n\n“Noncommercial Research Uses” means noncommercial research use cases related to research, development, education, processing, or analysis in each case with no direct or indirect commercial gain to you or others.\n\n“Seamless” means the foundational translation and transcription models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code, demonstration materials and other elements of the foregoing distributed by Meta at [https://ai.meta.com/resources/models-and-libraries/seamless-downloads](https://ai.meta.com/resources/models-and-libraries/seamless-downloads).\n\n“Seamless Materials” means, collectively, Meta’s proprietary Seamless and Documentation (and any portion thereof) made available under this Agreement.\n\n“Trade Control Laws” means any applicable U.S. and non-U.S. export control and trade sanctions laws and regulations.\n\nBy clicking “I Accept” below or by using or distributing any portion or element of the Seamless Materials, you agree to be bound by this Agreement.\n\n1. License Rights and Redistribution.\n a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Seamless Materials to use, reproduce, distribute, copy, create derivative works of, translate speech and text, and make modifications to the Seamless Materials solely for Noncommercial Research Uses.\n b. Redistribution and Use.\n i. Distribution of Seamless Materials, and any derivative works thereof, are subject to the terms of this Agreement. If you distribute or make the Seamless Materials, or any derivative works thereof, available to a third party, you may only do so under this Agreement. You shall also provide a copy of this Agreement to such third party.\n ii. If you submit for publication the results of research you perform on, using, or otherwise in connection with Seamless Materials, you must acknowledge the use of Seamless Materials in your publication as follows (or an equivalent acknowledgement of your choosing): “This material is based on work supported by the Seamless Licensing Agreement, Copyright © Meta Platforms, Inc. All Rights Reserved.”\n iii. If you receive Seamless Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you.\n iv. You must retain in all copies of the Seamless Materials that you distribute the following attribution notice within a “Notice” text file distributed as a part of such copies: “Seamless is licensed under the Seamless Licensing Agreement, Copyright © Meta Platforms, Inc. All Rights Reserved.”\n v. Your use of the Seamless Materials must comply with applicable laws and regulations (including Trade Control Laws)) and adhere to the Acceptable Use Policy for the Seamless Materials [https://ai.meta.com/resources/models-and-libraries/seamless-use-policy](https://ai.meta.com/resources/models-and-libraries/seamless-use-policy), which is hereby incorporated by reference into this Agreement.\n2. Restrictions. 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Except as provided in this Agreement, no modification or addition to any provision of this Agreement will be binding unless it is in writing and signed by an authorized representative of both you and Meta.\n" }, { "path": "Seamless_Tutorial.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"view-in-github\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"\\\"Open\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"# Seamless Tutorial\\n\"\n ],\n \"metadata\": {\n \"id\": \"SbI8G4-0V1OG\"\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"1p2d9R1LHJL2\"\n },\n \"source\": [\n \"## Quick Links\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"nLlZgJvBpWxT\"\n },\n \"source\": [\n \"1. seamless_communication GitHub repository: https://github.com/facebookresearch/seamless_communication\\n\",\n \"2. fairseq2 Github repository: https://github.com/facebookresearch/fairseq2\\n\",\n \"3. HuggingFace: https://huggingface.co/collections/facebook/seamless-communication-6568d486ef451c6ba62c7724\\n\",\n \"4. Seamless demos: https://seamless.metademolab.com/\\n\",\n \"5. Fleurs datasets for evaluation: https://huggingface.co/datasets/google/fleurs/tree/main/data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"YICcqOErh-om\"\n },\n \"source\": [\n \"### Set up seamless_communication, fairseq2 and some utilities.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"1Ei8HSHamsBG\"\n },\n \"outputs\": [],\n \"source\": [\n \"%%capture\\n\",\n \"!pip install fairseq2\\n\",\n \"!pip install pydub sentencepiece\\n\",\n \"!pip install git+https://github.com/facebookresearch/seamless_communication.git\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"TWlkq20jms6V\"\n },\n \"outputs\": [],\n \"source\": [\n \"import io\\n\",\n \"import json\\n\",\n \"import matplotlib as mpl\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import mmap\\n\",\n \"import numpy\\n\",\n \"import soundfile\\n\",\n \"import torchaudio\\n\",\n \"import torch\\n\",\n \"\\n\",\n \"from collections import defaultdict\\n\",\n \"from IPython.display import Audio, display\\n\",\n \"from pathlib import Path\\n\",\n \"from pydub import AudioSegment\\n\",\n \"\\n\",\n \"from seamless_communication.inference import Translator\\n\",\n \"from seamless_communication.streaming.dataloaders.s2tt import SileroVADSilenceRemover\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"j25uCSvKHRKu\"\n },\n \"source\": [\n \"# SeamlessM4T Inference:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"06JLP7rIEzfP\"\n },\n \"source\": [\n \"## Initialize the models:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"fA4iPYnoMLkK\",\n \"outputId\": \"c19ae7c7-c0c9-4b85-be2c-561f57d279f1\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stderr\",\n \"text\": [\n \"Downloading the checkpoint of seamlessM4T_v2_large...\\n\",\n \"100%|██████████| 8.45G/8.45G [01:14<00:00, 122MB/s]\\n\",\n \"Downloading the tokenizer of seamlessM4T_v2_large...\\n\",\n \"100%|██████████| 360k/360k [00:00<00:00, 10.7MB/s]\\n\",\n \"Downloading the tokenizer of seamlessM4T_v2_large...\\n\",\n \"100%|██████████| 4.93M/4.93M [00:00<00:00, 63.2MB/s]\\n\",\n \"Using the cached tokenizer of seamlessM4T_v2_large. Set `force` to `True` to download again.\\n\",\n \"Downloading the checkpoint of vocoder_v2...\\n\",\n \"100%|██████████| 160M/160M [00:00<00:00, 168MB/s]\\n\",\n \"/usr/local/lib/python3.10/dist-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\\n\",\n \" warnings.warn(\\\"torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\\\")\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Initialize a Translator object with a multitask model, vocoder on the GPU.\\n\",\n \"\\n\",\n \"model_name = \\\"seamlessM4T_v2_large\\\"\\n\",\n \"vocoder_name = \\\"vocoder_v2\\\" if model_name == \\\"seamlessM4T_v2_large\\\" else \\\"vocoder_36langs\\\"\\n\",\n \"\\n\",\n \"translator = Translator(\\n\",\n \" model_name,\\n\",\n \" vocoder_name,\\n\",\n \" device=torch.device(\\\"cuda:0\\\"),\\n\",\n \" dtype=torch.float16,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"BU4xoNRpqEey\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Download an english audio sample from the LJ speech dataset for testing purposes.\\n\",\n \"%%capture\\n\",\n \"!wget https://dl.fbaipublicfiles.com/seamlessM4T/LJ037-0171_sr16k.wav -O /content/LJ_eng.wav\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"PoWClYZ6FP1a\"\n },\n \"source\": [\n \"## S2ST inference:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"J_qeX25RnTr_\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 880\n },\n \"outputId\": \"0828c902-5ae3-49be-ffef-4e73751aaccb\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"English audio:\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"Translated text in spa: El examen y testimonio de los expertos permitieron a la comisión concluir que cinco disparos pueden haber sido disparados.\\n\",\n \"\\n\",\n \"Translated audio in spa:\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"\\n\",\n \"Translated text in fra: L'examen et le témoignage des experts ont permis à la commission de conclure que cinq coups de feu ont pu être tirés.\\n\",\n \"\\n\",\n \"Translated audio in fra:\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"\\n\",\n \"Translated text in deu: Die Prüfung und das Zeugnis der Experten ermöglichten es der Kommission, zu dem Schluss zu kommen, dass fünf Schüsse abgefeuert wurden.\\n\",\n \"\\n\",\n \"Translated audio in deu:\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"\\n\",\n \"Translated text in ita: L'esame e la testimonianza degli esperti hanno permesso alla commissione di concludere che cinque colpi possono essere stati sparati.\\n\",\n \"\\n\",\n \"Translated audio in ita:\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"\\n\",\n \"Translated text in hin: विशेषज्ञों की जांच और गवाही ने आयोग को यह निष्कर्ष निकालने में सक्षम बनाया कि पांच गोलीबारी की गई हो सकती है।\\n\",\n \"\\n\",\n \"Translated audio in hin:\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"\\n\",\n \"Translated text in cmn: 专家的检查和证词使委员会得出结论,可能有五次枪击.\\n\",\n \"\\n\",\n \"Translated audio in cmn:\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# README: https://github.com/facebookresearch/seamless_communication/tree/main/src/seamless_communication/cli/m4t/predict\\n\",\n \"# Please use audios with duration under 20 seconds for optimal performance.\\n\",\n \"\\n\",\n \"# Resample the audio in 16khz if sample rate is not 16khz already.\\n\",\n \"# torchaudio.functional.resample(audio, orig_freq=orig_freq, new_freq=16_000)\\n\",\n \"\\n\",\n \"print(\\\"English audio:\\\")\\n\",\n \"in_file = \\\"/content/LJ_eng.wav\\\"\\n\",\n \"display(Audio(in_file, rate=16000, autoplay=False, normalize=True))\\n\",\n \"\\n\",\n \"tgt_langs = (\\\"spa\\\", \\\"fra\\\", \\\"deu\\\", \\\"ita\\\", \\\"hin\\\", \\\"cmn\\\")\\n\",\n \"\\n\",\n \"for tgt_lang in tgt_langs:\\n\",\n \" text_output, speech_output = translator.predict(\\n\",\n \" input=in_file,\\n\",\n \" task_str=\\\"s2st\\\",\\n\",\n \" tgt_lang=tgt_lang,\\n\",\n \" )\\n\",\n \"\\n\",\n \" print(f\\\"Translated text in {tgt_lang}: {text_output[0]}\\\")\\n\",\n \" print()\\n\",\n \"\\n\",\n \" out_file = f\\\"/content/translated_LJ_{tgt_lang}.wav\\\"\\n\",\n \"\\n\",\n \" torchaudio.save(out_file, speech_output.audio_wavs[0][0].to(torch.float32).cpu(), speech_output.sample_rate)\\n\",\n \"\\n\",\n \" print(f\\\"Translated audio in {tgt_lang}:\\\")\\n\",\n \" audio_play = Audio(out_file, rate=speech_output.sample_rate, autoplay=False, normalize=True)\\n\",\n \" display(audio_play)\\n\",\n \" print()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"VTD8l_vXFX5x\"\n },\n \"source\": [\n \"## S2TT inference:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"xpvz3VdcFbYA\",\n \"outputId\": \"b91d60ca-fa55-414d-c56a-67da1d5eb691\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"Translated text in arb: فحص وشهادة الخبراء مكنت اللجنة من الاستنتاج بأن خمس طلقات قد تم إطلاقها\\n\",\n \"\\n\",\n \"Translated text in rus: Исследование и свидетельские показания экспертов позволили комиссии заключить, что пять выстрелов, возможно, были сделаны\\n\",\n \"\\n\",\n \"Translated text in tgl: Ang pagsusuri at patotoo ng mga eksperto ay nagpahintulot sa komisyon na magtapos na limang pagbaril ang maaaring binaril.\\n\",\n \"\\n\",\n \"Translated text in ind: pemeriksaan dan kesaksian para ahli memungkinkan komisi untuk menyimpulkan bahwa lima tembakan mungkin telah ditembakkan\\n\",\n \"\\n\",\n \"Translated text in tam: நிபுணர்களின் பரிசோதனை மற்றும் சாட்சியம் ஐந்து துப்பாக்கிச் சூடுகள் நடத்தப்பட்டிருக்கலாம் என்று முடிவு செய்ய ஆணையத்திற்கு உதவியது.\\n\",\n \"\\n\",\n \"Translated text in kor: 전문가들의 조사와 증언은 위원회가 5발의 총격이 발사됐을 수 있다는 결론을 내릴 수 있게 해 ⁇ 습니다.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"tgt_langs = (\\\"arb\\\", \\\"rus\\\", \\\"tgl\\\", \\\"ind\\\", \\\"tam\\\", \\\"kor\\\")\\n\",\n \"in_file = \\\"/content/LJ_eng.wav\\\"\\n\",\n \"\\n\",\n \"for tgt_lang in tgt_langs:\\n\",\n \"\\n\",\n \" text_output, _ = translator.predict(\\n\",\n \" input=in_file,\\n\",\n \" task_str=\\\"s2tt\\\",\\n\",\n \" tgt_lang=tgt_lang,\\n\",\n \" )\\n\",\n \"\\n\",\n \" print(f\\\"Translated text in {tgt_lang}: {text_output[0]}\\\")\\n\",\n \" print()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"IBfkgdQlFcRV\"\n },\n \"source\": [\n \"## ASR inference:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"E-GkJ-GsFjwM\",\n \"outputId\": \"191e3d49-ff61-4d3b-c235-1978ff522521\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"Transcribed text in spa: El examen y testimonio de los expertos permitieron a la comisión concluir que cinco disparos pueden haber sido disparados.\\n\",\n \"\\n\",\n \"Transcribed text in fra: L'examen et le témoignage des experts ont permis à la commission de conclure que cinq coups de feu ont pu être tirés.\\n\",\n \"\\n\",\n \"Transcribed text in deu: Die Prüfung und das Zeugnis der Experten ermöglichten es der Kommission, zu dem Schluss zu kommen, dass fünf Schüsse abgefeuert wurden.\\n\",\n \"\\n\",\n \"Transcribed text in ita: L'esame e la testimonianza degli esperti hanno permesso alla commissione di concludere che cinque colpi possono essere stati sparati.\\n\",\n \"\\n\",\n \"Transcribed text in hin: विशेषज्ञों की जांच और गवाही ने आयोग को यह निष्कर्ष निकालने में सक्षम बनाया कि पांच गोलीबारी की जा सकती है।\\n\",\n \"\\n\",\n \"Transcribed text in cmn: 专家的检查和证词史委员会的出结论可能有五次枪击\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"tgt_langs = (\\\"spa\\\", \\\"fra\\\", \\\"deu\\\", \\\"ita\\\", \\\"hin\\\", \\\"cmn\\\")\\n\",\n \"\\n\",\n \"for tgt_lang in tgt_langs:\\n\",\n \" in_file = f\\\"/content/translated_LJ_{tgt_lang}.wav\\\"\\n\",\n \"\\n\",\n \" text_output, _ = translator.predict(\\n\",\n \" input=in_file,\\n\",\n \" task_str=\\\"asr\\\",\\n\",\n \" tgt_lang=tgt_lang,\\n\",\n \" )\\n\",\n \"\\n\",\n \" print(f\\\"Transcribed text in {tgt_lang}: {text_output[0]}\\\")\\n\",\n \" print()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"1g3oeNp_Fj_m\"\n },\n \"source\": [\n \"## T2ST inference:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 784\n },\n \"id\": \"KivvCtS9FnH8\",\n \"outputId\": \"34392c09-40d0-4da6-95b1-9639a9abe960\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"Translated text in spa: Hola a todos, espero que todos estéis bien, gracias por asistir a nuestro taller.\\n\",\n \"\\n\",\n \"Translated audio in spa:\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"\\n\",\n \"Translated text in fra: Bonjour à tous ! J'espère que vous allez bien. Merci d'avoir assisté à notre atelier.\\n\",\n \"\\n\",\n \"Translated audio in fra:\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"\\n\",\n \"Translated text in deu: Hallo alle! Ich hoffe, dass es euch allen gut geht. Danke, dass ihr an unserem Workshop teilgenommen habt.\\n\",\n \"\\n\",\n \"Translated audio in deu:\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"\\n\",\n \"Translated text in ita: Salve a tutti! Spero che stiate tutti bene. Grazie per aver partecipato al nostro workshop.\\n\",\n \"\\n\",\n \"Translated audio in ita:\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"\\n\",\n \"Translated text in hin: हैलो सभी! मुझे आशा है कि आप सभी अच्छा कर रहे हैं। हमारी कार्यशाला में भाग लेने के लिए धन्यवाद।\\n\",\n \"\\n\",\n \"Translated audio in hin:\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"\\n\",\n \"Translated text in cmn: 大家好!我希望你们都很好.谢谢你们参加我们的研讨会.\\n\",\n \"\\n\",\n \"Translated audio in cmn:\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"tgt_langs = (\\\"spa\\\", \\\"fra\\\", \\\"deu\\\", \\\"ita\\\", \\\"hin\\\", \\\"cmn\\\")\\n\",\n \"\\n\",\n \"for tgt_lang in tgt_langs:\\n\",\n \"\\n\",\n \" text_output, speech_output = translator.predict(\\n\",\n \" input=\\\"Hey everyone! I hope you're all doing well. Thank you for attending our workshop.\\\",\\n\",\n \" task_str=\\\"t2st\\\",\\n\",\n \" tgt_lang=tgt_lang,\\n\",\n \" src_lang=\\\"eng\\\",\\n\",\n \" )\\n\",\n \"\\n\",\n \" print(f\\\"Translated text in {tgt_lang}: {text_output[0]}\\\")\\n\",\n \" print()\\n\",\n \"\\n\",\n \" out_file = f\\\"/content/{tgt_lang}.wav\\\"\\n\",\n \"\\n\",\n \" torchaudio.save(out_file, speech_output.audio_wavs[0][0].to(torch.float32).cpu(), speech_output.sample_rate)\\n\",\n \"\\n\",\n \" print(f\\\"Translated audio in {tgt_lang}:\\\")\\n\",\n \" audio_play = Audio(out_file, rate=speech_output.sample_rate, autoplay=False, normalize=True)\\n\",\n \" display(audio_play)\\n\",\n \" print()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"F_hA66F4Fnjk\"\n },\n \"source\": [\n \"## T2TT (MT) inference:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"wSrZZCIXFtFp\",\n \"outputId\": \"fe1accd0-d038-455f-9781-bdc6893df7b0\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"Translated text in arb: مرحباً للجميع! آمل أن تكونوا جميعاً بخير. شكراً لحضور ورشتنا.\\n\",\n \"\\n\",\n \"Translated text in rus: Привет всем! Надеюсь, вы все в порядке. Спасибо, что присутствовали на нашем семинаре.\\n\",\n \"\\n\",\n \"Translated text in ind: Hai semua orang! Saya harap kalian semua baik-baik saja. Terima kasih telah menghadiri lokakarya kami.\\n\",\n \"\\n\",\n \"Translated text in tam: எல்லோருக்கும் வணக்கம், நீங்கள் அனைவரும் நன்றாக இருக்கிறீர்கள் என்று நம்புகிறேன், எங்கள் பட்டறையில் கலந்து கொண்டதற்கு நன்றி.\\n\",\n \"\\n\",\n \"Translated text in kor: 안 ⁇ 하세요! 모두들 잘 지내셨으면 좋겠습니다. 워크 ⁇ 에 참석해 주셔서 감사합니다.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"tgt_langs = (\\\"arb\\\", \\\"rus\\\", \\\"ind\\\", \\\"tam\\\", \\\"kor\\\")\\n\",\n \"\\n\",\n \"for tgt_lang in tgt_langs:\\n\",\n \"\\n\",\n \" text_output, speech_output = translator.predict(\\n\",\n \" input=\\\"Hey everyone! I hope you're all doing well. Thank you for attending our workshop.\\\",\\n\",\n \" task_str=\\\"t2tt\\\",\\n\",\n \" tgt_lang=tgt_lang,\\n\",\n \" src_lang=\\\"eng\\\",\\n\",\n \" )\\n\",\n \"\\n\",\n \" print(f\\\"Translated text in {tgt_lang}: {text_output[0]}\\\")\\n\",\n \" print()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## UnitY2 aligner usage\"\n ],\n \"metadata\": {\n \"id\": \"N_q-Ek9M9M36\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"from seamless_communication.models.aligner.alignment_extractor import AlignmentExtractor\\n\",\n \"from fairseq2.typing import Device\\n\",\n \"import torch\"\n ],\n \"metadata\": {\n \"id\": \"ttDrZ9nh9LhH\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"alignment_extractor = AlignmentExtractor(\\n\",\n \" aligner_model_name_or_card=\\\"nar_t2u_aligner\\\",\\n\",\n \" unit_extractor_model_name_or_card=\\\"xlsr2_1b_v2\\\",\\n\",\n \" unit_extractor_output_layer=35,\\n\",\n \" unit_extractor_kmeans_model_uri=\\\"https://dl.fbaipublicfiles.com/seamlessM4T/models/unit_extraction/kmeans_10k.npy\\\",\\n\",\n \")\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"D-yY4y129WFD\",\n \"outputId\": \"a3678919-f1e4-45e2-bd62-272d99df5424\"\n },\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stderr\",\n \"text\": [\n \"Using the cached checkpoint of nar_t2u_aligner. Set `force` to `True` to download again.\\n\",\n \"Using the cached tokenizer of nar_t2u_aligner. Set `force` to `True` to download again.\\n\",\n \"Using the cached checkpoint of xlsr2_1b_v2. Set `force` to `True` to download again.\\n\",\n \"/usr/local/lib/python3.10/dist-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\\n\",\n \" warnings.warn(\\\"torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\\\")\\n\",\n \"WARNING:fairseq2.models:One or more operators in xlsr2_1b_v2 constructor do not support meta device. Skipping lazy initialization.\\n\",\n \"Using the cached checkpoint of https://dl.fbaipublicfiles.com/seamlessM4T/models/unit_extraction/kmeans_10k.npy. Set `force` to `True` to download again.\\n\"\n ]\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"# downloading en audio\\n\",\n \"! wget https://dl.fbaipublicfiles.com/seamlessM4T/LJ037-0171_sr16k.wav\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"N8FYd-Fg-YaE\",\n \"outputId\": \"5ad68739-cabf-4561-acde-64fc5ff01be5\"\n },\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"--2023-12-14 01:40:01-- https://dl.fbaipublicfiles.com/seamlessM4T/LJ037-0171_sr16k.wav\\n\",\n \"Resolving dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)... 3.162.163.19, 3.162.163.34, 3.162.163.51, ...\\n\",\n \"Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|3.162.163.19|:443... connected.\\n\",\n \"HTTP request sent, awaiting response... 200 OK\\n\",\n \"Length: 485430 (474K) [audio/x-wav]\\n\",\n \"Saving to: ‘LJ037-0171_sr16k.wav’\\n\",\n \"\\n\",\n \"\\rLJ037-0171_sr16k.wa 0%[ ] 0 --.-KB/s \\rLJ037-0171_sr16k.wa 100%[===================>] 474.05K --.-KB/s in 0.04s \\n\",\n \"\\n\",\n \"2023-12-14 01:40:02 (10.8 MB/s) - ‘LJ037-0171_sr16k.wav’ saved [485430/485430]\\n\",\n \"\\n\"\n ]\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"# listen to the audio\\n\",\n \"en_transcription = \\\"the examination and testimony of the experts enabled the commision to conclude that five shots may have been fired.\\\"\\n\",\n \"audio_play = Audio(\\\"LJ037-0171_sr16k.wav\\\", rate=16_000, autoplay=False, normalize=True)\\n\",\n \"display(audio_play)\\n\",\n \"print(en_transcription)\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 93\n },\n \"id\": \"v5k0B1An_DOd\",\n \"outputId\": \"706daf4a-a07b-4977-9a68-a09cd395a1c6\"\n },\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"the examination and testimony of the experts enabled the commision to conclude that five shots may have been fired.\\n\"\n ]\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"alignment_durations, _, tokenized_text_tokens = alignment_extractor.extract_alignment(\\\"LJ037-0171_sr16k.wav\\\", en_transcription, plot=True, add_trailing_silence=False)\\n\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 221\n },\n \"id\": \"GpmN4Ofs-ySY\",\n \"outputId\": \"681d840e-a0c1-4724-aac0-704dfd6ec17d\"\n },\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {}\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## HF transformers:\"\n ],\n \"metadata\": {\n \"id\": \"eQh3GBo_hb5g\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"# Refer to README: https://github.com/facebookresearch/seamless_communication/tree/main/docs/m4t#transformers-usage\\n\",\n \"# HF space: https://huggingface.co/spaces/facebook/seamless-m4t-v2-large\"\n ],\n \"metadata\": {\n \"id\": \"6jSyZHFihel5\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## m4t_evaluate\"\n ],\n \"metadata\": {\n \"id\": \"10CG60YSw4QB\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"# Refer to README: https://github.com/facebookresearch/seamless_communication/tree/main/src/seamless_communication/cli/m4t/evaluate\"\n ],\n \"metadata\": {\n \"id\": \"oQ5GuaQ7w7K8\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"GIPdJ3x9tstZ\"\n },\n \"source\": [\n \"# SeamlessExpressive Inference:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"l76dn3mRtwxK\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Please follow instructions to download SeamlessExpressive here: https://ai.meta.com/resources/models-and-libraries/seamless-downloads/\\n\",\n \"\\n\",\n \"!wget \\\"\\\" -O /content/SeamlessExpressive.tar.gz\\n\",\n \"\\n\",\n \"!tar -xzvf /content/SeamlessExpressive.tar.gz\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"wqkH-Js91cLX\",\n \"outputId\": \"09919807-5a69-4639-cd7d-5110f6f5f023\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"--2023-12-14 21:55:03-- https://dl.fbaipublicfiles.com/seamless/data/samples/expressivity_data.tar.gz\\n\",\n \"Resolving dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)... 3.162.163.51, 3.162.163.11, 3.162.163.34, ...\\n\",\n \"Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|3.162.163.51|:443... connected.\\n\",\n \"HTTP request sent, awaiting response... 200 OK\\n\",\n \"Length: 571233 (558K) [application/x-tar]\\n\",\n \"Saving to: ‘/content/expressivity_data.tar.gz’\\n\",\n \"\\n\",\n \"/content/expressivi 100%[===================>] 557.84K 2.53MB/s in 0.2s \\n\",\n \"\\n\",\n \"2023-12-14 21:55:03 (2.53 MB/s) - ‘/content/expressivity_data.tar.gz’ saved [571233/571233]\\n\",\n \"\\n\",\n \"./\\n\",\n \"./ex01_whisper_00367.wav\\n\",\n \"./ex01_confused_00367.wav\\n\",\n \"./ex01_enunciated_00367.wav\\n\",\n \"./ex01_happy_00367.wav\\n\",\n \"./ex01_sad_00367.wav\\n\",\n \"./ex01_laughing_00367.wav\\n\",\n \"./ex01_default_00367.wav\\n\"\n ]\n }\n ],\n \"source\": [\n \"!wget https://dl.fbaipublicfiles.com/seamless/data/samples/expressivity_data.tar.gz -O /content/expressivity_data.tar.gz\\n\",\n \"!tar -xzvf /content/expressivity_data.tar.gz\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"L1o7JgU2xiHV\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 1000\n },\n \"outputId\": \"3b51ce48-e7ca-4f6c-f8db-5d19dfa1e10f\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"English default audio:\\n\",\n \"\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"2023-12-14 21:55:07,810 INFO -- seamless_communication.cli.expressivity.predict.predict: Running inference on device=device(type='cuda', index=0) with dtype=torch.float16.\\n\",\n \"Downloading the tokenizer of seamless_expressivity...\\n\",\n \"100% 360k/360k [00:00<00:00, 10.3MB/s]\\n\",\n \"Using the cached tokenizer of seamless_expressivity. Set `force` to `True` to download again.\\n\",\n \"Using the cached tokenizer of seamless_expressivity. Set `force` to `True` to download again.\\n\",\n \"/usr/local/lib/python3.10/dist-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\\n\",\n \" warnings.warn(\\\"torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\\\")\\n\",\n \"2023-12-14 21:55:20,569 INFO -- seamless_communication.cli.expressivity.predict.predict: text_generation_opts=SequenceGeneratorOptions(beam_size=5, soft_max_seq_len=(1, 200), hard_max_seq_len=1024, step_processor=None, unk_penalty=0.0, len_penalty=1.0)\\n\",\n \"2023-12-14 21:55:20,569 INFO -- seamless_communication.cli.expressivity.predict.predict: unit_generation_opts=SequenceGeneratorOptions(beam_size=5, soft_max_seq_len=(25, 50), hard_max_seq_len=1024, step_processor=None, unk_penalty=0.0, len_penalty=1.0)\\n\",\n \"2023-12-14 21:55:20,569 INFO -- seamless_communication.cli.expressivity.predict.predict: unit_generation_ngram_filtering=False\\n\",\n \"2023-12-14 21:55:23,534 INFO -- seamless_communication.cli.expressivity.predict.predict: Saving expressive translated audio in spa\\n\",\n \"2023-12-14 21:55:23,538 INFO -- seamless_communication.cli.expressivity.predict.predict: Translated text in spa: Entonces, ¿qué estaba realmente haciendo?\\n\",\n \"\\n\",\n \"Translated default audio in spa:\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"English whisper audio:\\n\",\n \"\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"2023-12-14 21:55:28,309 INFO -- seamless_communication.cli.expressivity.predict.predict: Running inference on device=device(type='cuda', index=0) with dtype=torch.float16.\\n\",\n \"Using the cached tokenizer of seamless_expressivity. Set `force` to `True` to download again.\\n\",\n \"Using the cached tokenizer of seamless_expressivity. Set `force` to `True` to download again.\\n\",\n \"Using the cached tokenizer of seamless_expressivity. Set `force` to `True` to download again.\\n\",\n \"/usr/local/lib/python3.10/dist-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\\n\",\n \" warnings.warn(\\\"torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\\\")\\n\",\n \"2023-12-14 21:55:54,236 INFO -- seamless_communication.cli.expressivity.predict.predict: text_generation_opts=SequenceGeneratorOptions(beam_size=5, soft_max_seq_len=(1, 200), hard_max_seq_len=1024, step_processor=None, unk_penalty=0.0, len_penalty=1.0)\\n\",\n \"2023-12-14 21:55:54,236 INFO -- seamless_communication.cli.expressivity.predict.predict: unit_generation_opts=SequenceGeneratorOptions(beam_size=5, soft_max_seq_len=(25, 50), hard_max_seq_len=1024, step_processor=None, unk_penalty=0.0, len_penalty=1.0)\\n\",\n \"2023-12-14 21:55:54,236 INFO -- seamless_communication.cli.expressivity.predict.predict: unit_generation_ngram_filtering=False\\n\",\n \"2023-12-14 21:55:57,161 INFO -- seamless_communication.cli.expressivity.predict.predict: Saving expressive translated audio in spa\\n\",\n \"2023-12-14 21:55:57,166 INFO -- seamless_communication.cli.expressivity.predict.predict: Translated text in spa: Entonces, ¿qué estaba haciendo realmente?\\n\",\n \"\\n\",\n \"Translated whisper audio in spa:\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"English confused audio:\\n\",\n \"\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"2023-12-14 21:56:01,903 INFO -- seamless_communication.cli.expressivity.predict.predict: Running inference on device=device(type='cuda', index=0) with dtype=torch.float16.\\n\",\n \"Using the cached tokenizer of seamless_expressivity. Set `force` to `True` to download again.\\n\",\n \"Using the cached tokenizer of seamless_expressivity. Set `force` to `True` to download again.\\n\",\n \"Using the cached tokenizer of seamless_expressivity. Set `force` to `True` to download again.\\n\",\n \"/usr/local/lib/python3.10/dist-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\\n\",\n \" warnings.warn(\\\"torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\\\")\\n\",\n \"2023-12-14 21:56:10,642 INFO -- seamless_communication.cli.expressivity.predict.predict: text_generation_opts=SequenceGeneratorOptions(beam_size=5, soft_max_seq_len=(1, 200), hard_max_seq_len=1024, step_processor=None, unk_penalty=0.0, len_penalty=1.0)\\n\",\n \"2023-12-14 21:56:10,642 INFO -- seamless_communication.cli.expressivity.predict.predict: unit_generation_opts=SequenceGeneratorOptions(beam_size=5, soft_max_seq_len=(25, 50), hard_max_seq_len=1024, step_processor=None, unk_penalty=0.0, len_penalty=1.0)\\n\",\n \"2023-12-14 21:56:10,643 INFO -- seamless_communication.cli.expressivity.predict.predict: unit_generation_ngram_filtering=False\\n\",\n \"2023-12-14 21:56:12,360 INFO -- seamless_communication.cli.expressivity.predict.predict: Saving expressive translated audio in spa\\n\",\n \"2023-12-14 21:56:12,365 INFO -- seamless_communication.cli.expressivity.predict.predict: Translated text in spa: Entonces, ¿qué estaba haciendo en realidad?\\n\",\n \"\\n\",\n \"Translated confused audio in spa:\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"English enunciated audio:\\n\",\n \"\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"2023-12-14 21:56:15,985 INFO -- seamless_communication.cli.expressivity.predict.predict: Running inference on device=device(type='cuda', index=0) with dtype=torch.float16.\\n\",\n \"Using the cached tokenizer of seamless_expressivity. Set `force` to `True` to download again.\\n\",\n \"Using the cached tokenizer of seamless_expressivity. Set `force` to `True` to download again.\\n\",\n \"Using the cached tokenizer of seamless_expressivity. Set `force` to `True` to download again.\\n\",\n \"/usr/local/lib/python3.10/dist-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\\n\",\n \" warnings.warn(\\\"torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\\\")\\n\",\n \"2023-12-14 21:56:23,186 INFO -- seamless_communication.cli.expressivity.predict.predict: text_generation_opts=SequenceGeneratorOptions(beam_size=5, soft_max_seq_len=(1, 200), hard_max_seq_len=1024, step_processor=None, unk_penalty=0.0, len_penalty=1.0)\\n\",\n \"2023-12-14 21:56:23,186 INFO -- seamless_communication.cli.expressivity.predict.predict: unit_generation_opts=SequenceGeneratorOptions(beam_size=5, soft_max_seq_len=(25, 50), hard_max_seq_len=1024, step_processor=None, unk_penalty=0.0, len_penalty=1.0)\\n\",\n \"2023-12-14 21:56:23,186 INFO -- seamless_communication.cli.expressivity.predict.predict: unit_generation_ngram_filtering=False\\n\",\n \"2023-12-14 21:56:24,911 INFO -- seamless_communication.cli.expressivity.predict.predict: Saving expressive translated audio in spa\\n\",\n \"2023-12-14 21:56:24,916 INFO -- seamless_communication.cli.expressivity.predict.predict: Translated text in spa: Entonces, ¿qué estaba haciendo en realidad?\\n\",\n \"\\n\",\n \"Translated enunciated audio in spa:\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"English happy audio:\\n\",\n \"\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"2023-12-14 21:56:27,815 INFO -- seamless_communication.cli.expressivity.predict.predict: Running inference on device=device(type='cuda', index=0) with dtype=torch.float16.\\n\",\n \"Using the cached tokenizer of seamless_expressivity. Set `force` to `True` to download again.\\n\",\n \"Using the cached tokenizer of seamless_expressivity. Set `force` to `True` to download again.\\n\",\n \"Using the cached tokenizer of seamless_expressivity. Set `force` to `True` to download again.\\n\",\n \"/usr/local/lib/python3.10/dist-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\\n\",\n \" warnings.warn(\\\"torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\\\")\\n\",\n \"2023-12-14 21:56:34,259 INFO -- seamless_communication.cli.expressivity.predict.predict: text_generation_opts=SequenceGeneratorOptions(beam_size=5, soft_max_seq_len=(1, 200), hard_max_seq_len=1024, step_processor=None, unk_penalty=0.0, len_penalty=1.0)\\n\",\n \"2023-12-14 21:56:34,259 INFO -- seamless_communication.cli.expressivity.predict.predict: unit_generation_opts=SequenceGeneratorOptions(beam_size=5, soft_max_seq_len=(25, 50), hard_max_seq_len=1024, step_processor=None, unk_penalty=0.0, len_penalty=1.0)\\n\",\n \"2023-12-14 21:56:34,259 INFO -- seamless_communication.cli.expressivity.predict.predict: unit_generation_ngram_filtering=False\\n\",\n \"2023-12-14 21:56:36,072 INFO -- seamless_communication.cli.expressivity.predict.predict: Saving expressive translated audio in spa\\n\",\n \"2023-12-14 21:56:36,077 INFO -- seamless_communication.cli.expressivity.predict.predict: Translated text in spa: Entonces, ¿qué estaba haciendo en realidad?\\n\",\n \"\\n\",\n \"Translated happy audio in spa:\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"English sad audio:\\n\",\n \"\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"2023-12-14 21:56:38,964 INFO -- seamless_communication.cli.expressivity.predict.predict: Running inference on device=device(type='cuda', index=0) with dtype=torch.float16.\\n\",\n \"Using the cached tokenizer of seamless_expressivity. Set `force` to `True` to download again.\\n\",\n \"Using the cached tokenizer of seamless_expressivity. Set `force` to `True` to download again.\\n\",\n \"Using the cached tokenizer of seamless_expressivity. Set `force` to `True` to download again.\\n\",\n \"/usr/local/lib/python3.10/dist-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\\n\",\n \" warnings.warn(\\\"torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\\\")\\n\",\n \"2023-12-14 21:56:45,496 INFO -- seamless_communication.cli.expressivity.predict.predict: text_generation_opts=SequenceGeneratorOptions(beam_size=5, soft_max_seq_len=(1, 200), hard_max_seq_len=1024, step_processor=None, unk_penalty=0.0, len_penalty=1.0)\\n\",\n \"2023-12-14 21:56:45,496 INFO -- seamless_communication.cli.expressivity.predict.predict: unit_generation_opts=SequenceGeneratorOptions(beam_size=5, soft_max_seq_len=(25, 50), hard_max_seq_len=1024, step_processor=None, unk_penalty=0.0, len_penalty=1.0)\\n\",\n \"2023-12-14 21:56:45,496 INFO -- seamless_communication.cli.expressivity.predict.predict: unit_generation_ngram_filtering=False\\n\",\n \"2023-12-14 21:56:47,359 INFO -- seamless_communication.cli.expressivity.predict.predict: Saving expressive translated audio in spa\\n\",\n \"2023-12-14 21:56:47,362 INFO -- seamless_communication.cli.expressivity.predict.predict: Translated text in spa: Entonces, ¿qué estaba haciendo realmente?\\n\",\n \"\\n\",\n \"Translated sad audio in spa:\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"English laughing audio:\\n\",\n \"\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"2023-12-14 21:56:50,183 INFO -- seamless_communication.cli.expressivity.predict.predict: Running inference on device=device(type='cuda', index=0) with dtype=torch.float16.\\n\",\n \"Using the cached tokenizer of seamless_expressivity. Set `force` to `True` to download again.\\n\",\n \"Using the cached tokenizer of seamless_expressivity. Set `force` to `True` to download again.\\n\",\n \"Using the cached tokenizer of seamless_expressivity. Set `force` to `True` to download again.\\n\",\n \"/usr/local/lib/python3.10/dist-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\\n\",\n \" warnings.warn(\\\"torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\\\")\\n\",\n \"2023-12-14 21:56:56,380 INFO -- seamless_communication.cli.expressivity.predict.predict: text_generation_opts=SequenceGeneratorOptions(beam_size=5, soft_max_seq_len=(1, 200), hard_max_seq_len=1024, step_processor=None, unk_penalty=0.0, len_penalty=1.0)\\n\",\n \"2023-12-14 21:56:56,381 INFO -- seamless_communication.cli.expressivity.predict.predict: unit_generation_opts=SequenceGeneratorOptions(beam_size=5, soft_max_seq_len=(25, 50), hard_max_seq_len=1024, step_processor=None, unk_penalty=0.0, len_penalty=1.0)\\n\",\n \"2023-12-14 21:56:56,381 INFO -- seamless_communication.cli.expressivity.predict.predict: unit_generation_ngram_filtering=False\\n\",\n \"2023-12-14 21:56:58,865 INFO -- seamless_communication.cli.expressivity.predict.predict: Saving expressive translated audio in spa\\n\",\n \"2023-12-14 21:56:58,869 INFO -- seamless_communication.cli.expressivity.predict.predict: Translated text in spa: ¿Entonces qué estaba haciendo realmente?\\n\",\n \"\\n\",\n \"Translated laughing audio in spa:\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n }\n ],\n \"source\": [\n \"expressions = (\\\"default\\\", \\\"whisper\\\", \\\"confused\\\", \\\"enunciated\\\", \\\"happy\\\", \\\"sad\\\", \\\"laughing\\\")\\n\",\n \"\\n\",\n \"for expression in expressions:\\n\",\n \" print(f\\\"English {expression} audio:\\\")\\n\",\n \" print()\\n\",\n \"\\n\",\n \" in_file = f\\\"ex01_{expression}_00367.wav\\\"\\n\",\n \"\\n\",\n \" audio_play = Audio(in_file, rate=16000, autoplay=False, normalize=True)\\n\",\n \" display(audio_play)\\n\",\n \"\\n\",\n \" out_file = f\\\"spa_{expression}.wav\\\"\\n\",\n \"\\n\",\n \" !expressivity_predict {in_file} --tgt_lang spa \\\\\\n\",\n \" --model_name seamless_expressivity --vocoder_name vocoder_pretssel \\\\\\n\",\n \" --gated-model-dir SeamlessExpressive --output_path {out_file}\\n\",\n \"\\n\",\n \" print()\\n\",\n \" print(f\\\"Translated {expression} audio in spa:\\\")\\n\",\n \"\\n\",\n \" audio_play = Audio(out_file, rate=16000, autoplay=False, normalize=True)\\n\",\n \" display(audio_play)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Automatic Expressive Evaluation:\"\n ],\n \"metadata\": {\n \"id\": \"-qo85CRkgVSW\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"# Refer to README: https://github.com/facebookresearch/seamless_communication/blob/main/docs/expressive/README.md#automatic-evaluation\\n\",\n \"\\n\",\n \"# AutoPCP: https://github.com/facebookresearch/stopes/tree/main/stopes/eval/auto_pcp\\n\",\n \"\\n\",\n \"# VSim: https://github.com/facebookresearch/stopes/tree/main/stopes/eval/vocal_style_similarity\\n\",\n \"\\n\",\n \"# expressivity_evaluate: https://github.com/facebookresearch/seamless_communication#seamlessexpressive-evaluation\\n\",\n \"\\n\",\n \"# HF space: https://huggingface.co/spaces/facebook/seamless-expressive\"\n ],\n \"metadata\": {\n \"id\": \"gGg6R8zogfn1\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"4PNlRLsloKWo\"\n },\n \"source\": [\n \"# Streaming Standalone Inference\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Utility classes + functions\"\n ],\n \"metadata\": {\n \"id\": \"dvM68NSZGK8o\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"# Download an the LJ speech dataset sample if you didn't already run it above\\n\",\n \"# %%capture\\n\",\n \"!wget https://dl.fbaipublicfiles.com/seamlessM4T/LJ037-0171_sr16k.wav -O /content/LJ_eng.wav\"\n ],\n \"metadata\": {\n \"id\": \"ihWc_q0lGcnl\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"outputId\": \"05696d30-a68b-494f-e2c8-146b00673aa8\"\n },\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"--2023-12-13 06:11:00-- https://dl.fbaipublicfiles.com/seamlessM4T/LJ037-0171_sr16k.wav\\n\",\n \"Resolving dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)... 3.163.189.51, 3.163.189.108, 3.163.189.96, ...\\n\",\n \"Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|3.163.189.51|:443... connected.\\n\",\n \"HTTP request sent, awaiting response... 200 OK\\n\",\n \"Length: 485430 (474K) [audio/x-wav]\\n\",\n \"Saving to: ‘/content/LJ_eng.wav’\\n\",\n \"\\n\",\n \"\\r/content/LJ_eng.wav 0%[ ] 0 --.-KB/s \\r/content/LJ_eng.wav 100%[===================>] 474.05K --.-KB/s in 0.04s \\n\",\n \"\\n\",\n \"2023-12-13 06:11:00 (13.0 MB/s) - ‘/content/LJ_eng.wav’ saved [485430/485430]\\n\",\n \"\\n\"\n ]\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"R5PPqT9boJ9e\"\n },\n \"outputs\": [],\n \"source\": [\n \"import math\\n\",\n \"from simuleval.data.segments import SpeechSegment, EmptySegment\\n\",\n \"from seamless_communication.streaming.agents.seamless_streaming_s2st import (\\n\",\n \" SeamlessStreamingS2STVADAgent,\\n\",\n \")\\n\",\n \"\\n\",\n \"from simuleval.utils.arguments import cli_argument_list\\n\",\n \"from simuleval import options\\n\",\n \"\\n\",\n \"\\n\",\n \"from typing import Union, List\\n\",\n \"from simuleval.data.segments import Segment, TextSegment\\n\",\n \"from simuleval.agents.pipeline import TreeAgentPipeline\\n\",\n \"from simuleval.agents.states import AgentStates\\n\",\n \"\\n\",\n \"\\n\",\n \"SAMPLE_RATE = 16000\\n\",\n \"\\n\",\n \"\\n\",\n \"class AudioFrontEnd:\\n\",\n \" def __init__(self, wav_file, segment_size) -> None:\\n\",\n \" self.samples, self.sample_rate = soundfile.read(wav_file)\\n\",\n \" assert self.sample_rate == SAMPLE_RATE\\n\",\n \" # print(len(self.samples), self.samples[:100])\\n\",\n \" self.samples = self.samples # .tolist()\\n\",\n \" self.segment_size = segment_size\\n\",\n \" self.step = 0\\n\",\n \"\\n\",\n \" def send_segment(self):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" This is the front-end logic in simuleval instance.py\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" num_samples = math.ceil(self.segment_size / 1000 * self.sample_rate)\\n\",\n \"\\n\",\n \" if self.step < len(self.samples):\\n\",\n \" if self.step + num_samples >= len(self.samples):\\n\",\n \" samples = self.samples[self.step :]\\n\",\n \" is_finished = True\\n\",\n \" else:\\n\",\n \" samples = self.samples[self.step : self.step + num_samples]\\n\",\n \" is_finished = False\\n\",\n \" self.step = min(self.step + num_samples, len(self.samples))\\n\",\n \"\\n\",\n \" segment = SpeechSegment(\\n\",\n \" content=samples,\\n\",\n \" sample_rate=self.sample_rate,\\n\",\n \" finished=is_finished,\\n\",\n \" )\\n\",\n \" else:\\n\",\n \" # Finish reading this audio\\n\",\n \" segment = EmptySegment(\\n\",\n \" finished=True,\\n\",\n \" )\\n\",\n \" return segment\\n\",\n \"\\n\",\n \"\\n\",\n \"class OutputSegments:\\n\",\n \" def __init__(self, segments: Union[List[Segment], Segment]):\\n\",\n \" if isinstance(segments, Segment):\\n\",\n \" segments = [segments]\\n\",\n \" self.segments: List[Segment] = [s for s in segments]\\n\",\n \"\\n\",\n \" @property\\n\",\n \" def is_empty(self):\\n\",\n \" return all(segment.is_empty for segment in self.segments)\\n\",\n \"\\n\",\n \" @property\\n\",\n \" def finished(self):\\n\",\n \" return all(segment.finished for segment in self.segments)\\n\",\n \"\\n\",\n \"\\n\",\n \"def get_audiosegment(samples, sr):\\n\",\n \" b = io.BytesIO()\\n\",\n \" soundfile.write(b, samples, samplerate=sr, format=\\\"wav\\\")\\n\",\n \" b.seek(0)\\n\",\n \" return AudioSegment.from_file(b)\\n\",\n \"\\n\",\n \"\\n\",\n \"def reset_states(system, states):\\n\",\n \" if isinstance(system, TreeAgentPipeline):\\n\",\n \" states_iter = states.values()\\n\",\n \" else:\\n\",\n \" states_iter = states\\n\",\n \" for state in states_iter:\\n\",\n \" state.reset()\\n\",\n \"\\n\",\n \"\\n\",\n \"def get_states_root(system, states) -> AgentStates:\\n\",\n \" if isinstance(system, TreeAgentPipeline):\\n\",\n \" # self.states is a dict\\n\",\n \" return states[system.source_module]\\n\",\n \" else:\\n\",\n \" # self.states is a list\\n\",\n \" return system.states[0]\\n\",\n \"\\n\",\n \"\\n\",\n \"def plot_s2st(source_file, target_samples, target_fs, intervals, delays, prediction_lists):\\n\",\n \" mpl.rcParams[\\\"axes.spines.left\\\"] = False\\n\",\n \" mpl.rcParams[\\\"axes.spines.right\\\"] = False\\n\",\n \" mpl.rcParams[\\\"axes.spines.top\\\"] = False\\n\",\n \" mpl.rcParams[\\\"axes.spines.bottom\\\"] = False\\n\",\n \"\\n\",\n \" source_samples, source_fs = soundfile.read(source_file)\\n\",\n \"\\n\",\n \" _, axes = plt.subplots(5, sharex=True, figsize=(25, 5))\\n\",\n \" for ax in axes:\\n\",\n \" ax.set_yticks([])\\n\",\n \"\\n\",\n \" axes[0].plot(\\n\",\n \" numpy.linspace(0, len(source_samples) / source_fs, len(source_samples)),\\n\",\n \" source_samples,\\n\",\n \" )\\n\",\n \"\\n\",\n \" axes[1].plot(\\n\",\n \" numpy.linspace(0, len(target_samples) / target_fs, len(target_samples)),\\n\",\n \" target_samples,\\n\",\n \" )\\n\",\n \"\\n\",\n \" start = 0\\n\",\n \" for seg_index in range(len(intervals)):\\n\",\n \" start, duration = intervals[seg_index]\\n\",\n \" offset = delays[\\\"s2st\\\"][seg_index]\\n\",\n \"\\n\",\n \" samples = target_samples[\\n\",\n \" int((start) / 1000 * target_fs) : int(\\n\",\n \" (start + duration) / 1000 * target_fs\\n\",\n \" )\\n\",\n \" ]\\n\",\n \"\\n\",\n \" # Uncomment this if you want to see the segments without speech playback delay\\n\",\n \" axes[2].plot(\\n\",\n \" offset / 1000 + numpy.linspace(0, len(samples) / target_fs, len(samples)),\\n\",\n \" -seg_index * 0.05 + numpy.array(samples),\\n\",\n \" )\\n\",\n \" axes[4].plot(\\n\",\n \" start / 1000 + numpy.linspace(0, len(samples) / target_fs, len(samples)),\\n\",\n \" numpy.array(samples),\\n\",\n \" )\\n\",\n \"\\n\",\n \" from pydub import AudioSegment\\n\",\n \" print(\\\"Output translation (without input)\\\")\\n\",\n \" display(Audio(target_samples, rate=target_fs))\\n\",\n \" print(\\\"Output translation (overlay with input)\\\")\\n\",\n \" source_seg = get_audiosegment(source_samples, source_fs) + AudioSegment.silent(duration=3000)\\n\",\n \" target_seg = get_audiosegment(target_samples, target_fs)\\n\",\n \" output_seg = source_seg.overlay(target_seg)\\n\",\n \" display(output_seg)\\n\",\n \"\\n\",\n \" delay_token = defaultdict(list)\\n\",\n \" d = delays[\\\"s2tt\\\"][0]\\n\",\n \" for token, delay in zip(prediction_lists[\\\"s2tt\\\"], delays[\\\"s2tt\\\"]):\\n\",\n \" if delay != d:\\n\",\n \" d = delay\\n\",\n \" delay_token[d].append(token)\\n\",\n \" for key, value in delay_token.items():\\n\",\n \" axes[3].text(\\n\",\n \" key / 1000, 0.2, \\\" \\\".join(value), rotation=40\\n\",\n \" )\\n\",\n \"\\n\",\n \"def build_streaming_system(model_configs, agent_class):\\n\",\n \" parser = options.general_parser()\\n\",\n \" parser.add_argument(\\\"-f\\\", \\\"--f\\\", help=\\\"a dummy argument to fool ipython\\\", default=\\\"1\\\")\\n\",\n \"\\n\",\n \" agent_class.add_args(parser)\\n\",\n \" args, _ = parser.parse_known_args(cli_argument_list(model_configs))\\n\",\n \" system = agent_class.from_args(args)\\n\",\n \" return system\\n\",\n \"\\n\",\n \"\\n\",\n \"def run_streaming_inference(system, audio_frontend, system_states, tgt_lang):\\n\",\n \" # NOTE: Here for visualization, we calculate delays offset from audio\\n\",\n \" # *BEFORE* VAD segmentation.\\n\",\n \" # In contrast for SimulEval evaluation, we assume audios are pre-segmented,\\n\",\n \" # and Average Lagging, End Offset metrics are based on those pre-segmented audios.\\n\",\n \" # Thus, delays here are *NOT* comparable to SimulEval per-segment delays\\n\",\n \" delays = {\\\"s2st\\\": [], \\\"s2tt\\\": []}\\n\",\n \" prediction_lists = {\\\"s2st\\\": [], \\\"s2tt\\\": []}\\n\",\n \" speech_durations = []\\n\",\n \" curr_delay = 0\\n\",\n \" target_sample_rate = None\\n\",\n \"\\n\",\n \" while True:\\n\",\n \" input_segment = audio_frontend.send_segment()\\n\",\n \" input_segment.tgt_lang = tgt_lang\\n\",\n \" curr_delay += len(input_segment.content) / SAMPLE_RATE * 1000\\n\",\n \" if input_segment.finished:\\n\",\n \" # a hack, we expect a real stream to end with silence\\n\",\n \" get_states_root(system, system_states).source_finished = True\\n\",\n \" # Translation happens here\\n\",\n \" output_segments = OutputSegments(system.pushpop(input_segment, system_states))\\n\",\n \" if not output_segments.is_empty:\\n\",\n \" for segment in output_segments.segments:\\n\",\n \" # NOTE: another difference from SimulEval evaluation -\\n\",\n \" # delays are accumulated per-token\\n\",\n \" if isinstance(segment, SpeechSegment):\\n\",\n \" pred_duration = 1000 * len(segment.content) / segment.sample_rate\\n\",\n \" speech_durations.append(pred_duration)\\n\",\n \" delays[\\\"s2st\\\"].append(curr_delay)\\n\",\n \" prediction_lists[\\\"s2st\\\"].append(segment.content)\\n\",\n \" target_sample_rate = segment.sample_rate\\n\",\n \" elif isinstance(segment, TextSegment):\\n\",\n \" delays[\\\"s2tt\\\"].append(curr_delay)\\n\",\n \" prediction_lists[\\\"s2tt\\\"].append(segment.content)\\n\",\n \" print(curr_delay, segment.content)\\n\",\n \" if output_segments.finished:\\n\",\n \" print(\\\"End of VAD segment\\\")\\n\",\n \" reset_states(system, system_states)\\n\",\n \" if input_segment.finished:\\n\",\n \" # an assumption of SimulEval agents -\\n\",\n \" # once source_finished=True, generate until output translation is finished\\n\",\n \" assert output_segments.finished\\n\",\n \" break\\n\",\n \" return delays, prediction_lists, speech_durations, target_sample_rate\\n\",\n \"\\n\",\n \"\\n\",\n \"def get_s2st_delayed_targets(delays, target_sample_rate, prediction_lists, speech_durations):\\n\",\n \" # get calculate intervals + durations for s2st\\n\",\n \" intervals = []\\n\",\n \"\\n\",\n \" start = prev_end = prediction_offset = delays[\\\"s2st\\\"][0]\\n\",\n \" target_samples = [0.0] * int(target_sample_rate * prediction_offset / 1000)\\n\",\n \"\\n\",\n \" for i, delay in enumerate(delays[\\\"s2st\\\"]):\\n\",\n \" start = max(prev_end, delay)\\n\",\n \"\\n\",\n \" if start > prev_end:\\n\",\n \" # Wait source speech, add discontinuity with silence\\n\",\n \" target_samples += [0.0] * int(\\n\",\n \" target_sample_rate * (start - prev_end) / 1000\\n\",\n \" )\\n\",\n \"\\n\",\n \" target_samples += prediction_lists[\\\"s2st\\\"][i]\\n\",\n \" duration = speech_durations[i]\\n\",\n \" prev_end = start + duration\\n\",\n \" intervals.append([start, duration])\\n\",\n \" return target_samples, intervals\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Build SeamlessStreaming S2ST + S2TT agent\"\n ],\n \"metadata\": {\n \"id\": \"wGHmMwIPGWgm\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"TZPg2tm3oXGR\",\n \"outputId\": \"6c9b3f55-e50f-46f9-8d39-4e43d3b8251a\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"building system from dir\\n\"\n ]\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stderr\",\n \"text\": [\n \"Downloading the tokenizer of seamless_streaming_unity...\\n\",\n \"100%|██████████| 4.93M/4.93M [00:00<00:00, 18.8MB/s]\\n\",\n \"Downloading the checkpoint of seamless_streaming_unity...\\n\",\n \"100%|██████████| 3.34G/3.34G [00:29<00:00, 122MB/s]\\n\",\n \"Downloading the tokenizer of seamlessM4T_v2_large...\\n\",\n \"100%|██████████| 360k/360k [00:00<00:00, 13.8MB/s]\\n\",\n \"Using the cached tokenizer of seamlessM4T_v2_large. Set `force` to `True` to download again.\\n\",\n \"Downloading the checkpoint of seamless_streaming_monotonic_decoder...\\n\",\n \"100%|██████████| 3.98G/3.98G [00:35<00:00, 121MB/s]\\n\",\n \"/usr/local/lib/python3.10/dist-packages/torch/hub.py:294: UserWarning: You are about to download and run code from an untrusted repository. In a future release, this won't be allowed. To add the repository to your trusted list, change the command to {calling_fn}(..., trust_repo=False) and a command prompt will appear asking for an explicit confirmation of trust, or load(..., trust_repo=True), which will assume that the prompt is to be answered with 'yes'. You can also use load(..., trust_repo='check') which will only prompt for confirmation if the repo is not already trusted. This will eventually be the default behaviour\\n\",\n \" warnings.warn(\\n\",\n \"Downloading: \\\"https://github.com/snakers4/silero-vad/zipball/master\\\" to /root/.cache/torch/hub/master.zip\\n\",\n \"Downloading the checkpoint of vocoder_v2...\\n\",\n \"100%|██████████| 160M/160M [00:01<00:00, 119MB/s]\\n\",\n \"/usr/local/lib/python3.10/dist-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\\n\",\n \" warnings.warn(\\\"torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\\\")\\n\"\n ]\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"finished building system\\n\"\n ]\n }\n ],\n \"source\": [\n \"from seamless_communication.streaming.agents.seamless_streaming_s2st import (\\n\",\n \" SeamlessStreamingS2STJointVADAgent,\\n\",\n \")\\n\",\n \"\\n\",\n \"\\n\",\n \"print(\\\"building system from dir\\\")\\n\",\n \"\\n\",\n \"agent_class = SeamlessStreamingS2STJointVADAgent\\n\",\n \"tgt_lang = \\\"spa\\\"\\n\",\n \"\\n\",\n \"model_configs = dict(\\n\",\n \" source_segment_size=320,\\n\",\n \" device=\\\"cuda:0\\\",\\n\",\n \" dtype=\\\"fp16\\\",\\n\",\n \" min_starting_wait_w2vbert=192,\\n\",\n \" decision_threshold=0.5,\\n\",\n \" min_unit_chunk_size=50,\\n\",\n \" no_early_stop=True,\\n\",\n \" max_len_a=0,\\n\",\n \" max_len_b=100,\\n\",\n \" task=\\\"s2st\\\",\\n\",\n \" tgt_lang=tgt_lang,\\n\",\n \" block_ngrams=True,\\n\",\n \" detokenize_only=True,\\n\",\n \")\\n\",\n \"system = build_streaming_system(model_configs, agent_class)\\n\",\n \"print(\\\"finished building system\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Initialize states + run inference\"\n ],\n \"metadata\": {\n \"id\": \"rWAgPoUlGaQ0\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"izpe5S-rom8A\",\n \"outputId\": \"be5433bb-258f-4950-a599-61a73577ab15\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stderr\",\n \"text\": [\n \"Using cache found in /root/.cache/torch/hub/snakers4_silero-vad_master\\n\"\n ]\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"3200.0 El examen y el testimonio de los expertos\\n\",\n \"4160.0 permitieron\\n\",\n \"4800.0 a la Comisión\\n\",\n \"5120.0 concluir\\n\",\n \"7040.0 que\\n\",\n \"7360.0 cinco disparos pudieron\\n\",\n \"7583.9375 haber sido disparados,\\n\",\n \"End of VAD segment\\n\"\n ]\n }\n ],\n \"source\": [\n \"source_segment_size = 320 # milliseconds\\n\",\n \"audio_frontend = AudioFrontEnd(\\n\",\n \" wav_file=\\\"/content/LJ_eng.wav\\\",\\n\",\n \" segment_size=source_segment_size,\\n\",\n \")\\n\",\n \"\\n\",\n \"system_states = system.build_states()\\n\",\n \"\\n\",\n \"# you can pass tgt_lang at inference time to change the output lang.\\n\",\n \"# SeamlessStreaming supports 36 speech output languages, see https://github.com/facebookresearch/seamless_communication/blob/main/docs/m4t/README.md#supported-languages\\n\",\n \"# in the Target column for `Sp` outputs.\\n\",\n \"delays, prediction_lists, speech_durations, target_sample_rate = run_streaming_inference(\\n\",\n \" system, audio_frontend, system_states, tgt_lang\\n\",\n \")\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Visualize streaming outputs\"\n ],\n \"metadata\": {\n \"id\": \"WnHddD4KGgPr\"\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"The top row is the input audio, while the later rows are the output audio (in chunks), as well as output text, offset by the corresponding delays.\"\n ],\n \"metadata\": {\n \"id\": \"Ac3YKDJwISWJ\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 329\n },\n \"id\": \"x08NFlRzoxdT\",\n \"outputId\": \"565b921f-1797-44b8-c85a-476d9a1bcc6d\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"Output translation (without input)\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"Output translation (overlay with input)\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {}\n }\n ],\n \"source\": [\n \"target_samples, intervals = get_s2st_delayed_targets(delays, target_sample_rate, prediction_lists, speech_durations)\\n\",\n \"\\n\",\n \"plot_s2st(\\\"/content/LJ_eng.wav\\\", target_samples, target_sample_rate, intervals, delays, prediction_lists)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Seamless Unified Inference\"\n ],\n \"metadata\": {\n \"id\": \"Yy12VEzvJ1zo\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"# If you haven't already above, please follow instructions to download\\n\",\n \"# SeamlessExpressive here: https://ai.meta.com/resources/models-and-libraries/seamless-downloads/\\n\",\n \"\\n\",\n \"!wget \\\"https://d11ywzt2xtszji.cloudfront.net/SeamlessExpressive.tar.gz?Policy=eyJTdGF0ZW1lbnQiOlt7InVuaXF1ZV9oYXNoIjoiZ2sxMzhuZnNkNDQ0dmM2dDhhazgxbWluIiwiUmVzb3VyY2UiOiJodHRwczpcL1wvZDExeXd6dDJ4dHN6amkuY2xvdWRmcm9udC5uZXRcLyoiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE3MDI1NzIxMjl9fX1dfQ__&Signature=npTULjeiKp9U8hUng4f9Njb6QKpK52Rl9pQjRpamsQSNzWgYeshMABRUNjWQJrw5givbbdGhaa6mW2l3UYHi66x3rBLazIS7d7npHu6aTElyNRZtFgjKMlNWSRfZOXh7NsQSZOFwWy0VxJwVZ%7EKtJnBWvgh7Mov3SKeJFeJEdAESDVO%7EWCHO1Z2zIWl%7EIkfpX5OnMqz7ntU9SpzsVpEHgefcyktm5NZ2xIr%7EoOml3YUXwNEUDj5PhLUkeoSHpFXHSzI0S0GHlxp48C162gUS8qK1HtaXalk7GUDem%7ErAGpx-Bo9oPBe33PdSsvpqngT9E32eS33oJoU1am4RGKFysg__&Key-Pair-Id=K15QRJLYKIFSLZ&Download-Request-ID=1024805765443779\\\" -O /content/SeamlessExpressive.tar.gz\\n\",\n \"!tar -xzvf /content/SeamlessExpressive.tar.gz\"\n ],\n \"metadata\": {\n \"id\": \"smeOkMUSyLRk\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"outputId\": \"061652e9-2ba0-481a-9ebe-d3b09fd00968\"\n },\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"--2023-12-13 21:45:45-- https://d11ywzt2xtszji.cloudfront.net/SeamlessExpressive.tar.gz?Policy=eyJTdGF0ZW1lbnQiOlt7InVuaXF1ZV9oYXNoIjoiZ2sxMzhuZnNkNDQ0dmM2dDhhazgxbWluIiwiUmVzb3VyY2UiOiJodHRwczpcL1wvZDExeXd6dDJ4dHN6amkuY2xvdWRmcm9udC5uZXRcLyoiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE3MDI1NzIxMjl9fX1dfQ__&Signature=npTULjeiKp9U8hUng4f9Njb6QKpK52Rl9pQjRpamsQSNzWgYeshMABRUNjWQJrw5givbbdGhaa6mW2l3UYHi66x3rBLazIS7d7npHu6aTElyNRZtFgjKMlNWSRfZOXh7NsQSZOFwWy0VxJwVZ%7EKtJnBWvgh7Mov3SKeJFeJEdAESDVO%7EWCHO1Z2zIWl%7EIkfpX5OnMqz7ntU9SpzsVpEHgefcyktm5NZ2xIr%7EoOml3YUXwNEUDj5PhLUkeoSHpFXHSzI0S0GHlxp48C162gUS8qK1HtaXalk7GUDem%7ErAGpx-Bo9oPBe33PdSsvpqngT9E32eS33oJoU1am4RGKFysg__&Key-Pair-Id=K15QRJLYKIFSLZ&Download-Request-ID=1024805765443779\\n\",\n \"Resolving d11ywzt2xtszji.cloudfront.net (d11ywzt2xtszji.cloudfront.net)... 65.8.49.128, 65.8.49.90, 65.8.49.107, ...\\n\",\n \"Connecting to d11ywzt2xtszji.cloudfront.net (d11ywzt2xtszji.cloudfront.net)|65.8.49.128|:443... connected.\\n\",\n \"HTTP request sent, awaiting response... 200 OK\\n\",\n \"Length: 3808363189 (3.5G) [application/x-tar]\\n\",\n \"Saving to: ‘/content/SeamlessExpressive.tar.gz’\\n\",\n \"\\n\",\n \"/content/SeamlessEx 100%[===================>] 3.55G 40.1MB/s in 81s \\n\",\n \"\\n\",\n \"2023-12-13 21:47:08 (44.7 MB/s) - ‘/content/SeamlessExpressive.tar.gz’ saved [3808363189/3808363189]\\n\",\n \"\\n\",\n \"SeamlessExpressive/m2m_expressive_unity.pt\\n\",\n \"SeamlessExpressive/pretssel_melhifigan_wm-16khz.pt\\n\",\n \"SeamlessExpressive/pretssel_melhifigan_wm.pt\\n\"\n ]\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"# You may need to delete earlier loaded model to free memory\\n\",\n \"# del system, system_states\\n\",\n \"# import gc\\n\",\n \"\\n\",\n \"# gc.collect()\\n\",\n \"# torch.cuda.empty_cache()\"\n ],\n \"metadata\": {\n \"id\": \"Q_hyGuCgMy6O\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"# TODO: to run Seamless unified inference, need to download gated model\\n\",\n \"# and specify gated_model_dir (here we use `SeamlessExpressive`)\\n\",\n \"from seamless_communication.streaming.agents.seamless_s2st import (\\n\",\n \" SeamlessS2STJointVADAgent,\\n\",\n \")\\n\",\n \"\\n\",\n \"print(\\\"building system from dir\\\")\\n\",\n \"\\n\",\n \"agent_class = SeamlessS2STJointVADAgent\\n\",\n \"tgt_lang = \\\"spa\\\"\\n\",\n \"\\n\",\n \"model_configs = dict(\\n\",\n \" source_segment_size=320,\\n\",\n \" device=\\\"cuda:0\\\",\\n\",\n \" dtype=\\\"fp16\\\",\\n\",\n \" min_starting_wait_w2vbert=192,\\n\",\n \" decision_threshold=0.5,\\n\",\n \" min_unit_chunk_size=50,\\n\",\n \" no_early_stop=True,\\n\",\n \" max_len_a=0,\\n\",\n \" max_len_b=100,\\n\",\n \" task=\\\"s2st\\\",\\n\",\n \" tgt_lang=tgt_lang,\\n\",\n \" block_ngrams=True,\\n\",\n \" upstream_idx=1,\\n\",\n \" detokenize_only=True,\\n\",\n \" gated_model_dir=\\\"SeamlessExpressive\\\",\\n\",\n \")\\n\",\n \"system = build_streaming_system(model_configs, agent_class)\\n\",\n \"print(\\\"finished building system\\\")\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"7LATtfmGJ5hW\",\n \"outputId\": \"c1cd5a69-5366-4f99-84f1-e5fa75970878\"\n },\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"building system from dir\\n\"\n ]\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stderr\",\n \"text\": [\n \"Using the cached tokenizer of seamless_streaming_unity. Set `force` to `True` to download again.\\n\",\n \"Using the cached checkpoint of seamless_streaming_unity. Set `force` to `True` to download again.\\n\",\n \"Using the cached tokenizer of seamlessM4T_v2_large. Set `force` to `True` to download again.\\n\",\n \"Using the cached tokenizer of seamlessM4T_v2_large. Set `force` to `True` to download again.\\n\",\n \"Using the cached checkpoint of seamless_streaming_monotonic_decoder. Set `force` to `True` to download again.\\n\",\n \"Using cache found in /root/.cache/torch/hub/snakers4_silero-vad_master\\n\",\n \"/usr/local/lib/python3.10/dist-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\\n\",\n \" warnings.warn(\\\"torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\\\")\\n\"\n ]\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"finished building system\\n\"\n ]\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"source_segment_size = 320 # milliseconds\\n\",\n \"audio_frontend = AudioFrontEnd(\\n\",\n \" wav_file=\\\"/content/LJ_eng.wav\\\",\\n\",\n \" segment_size=source_segment_size,\\n\",\n \")\\n\",\n \"\\n\",\n \"system_states = system.build_states()\\n\",\n \"# you can pass tgt_lang at inference time to change the output lang.\\n\",\n \"# Seamless unified supports 6 output languages (eng, spa, fra, cmn, deu, ita)\\n\",\n \"delays, prediction_lists, speech_durations, target_sample_rate = run_streaming_inference(\\n\",\n \" system, audio_frontend, system_states, tgt_lang\\n\",\n \")\\n\"\n ],\n \"metadata\": {\n \"id\": \"1Go-cO6OKS3q\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"outputId\": \"80323e57-3849-44e9-b125-2edb57e563e9\"\n },\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stderr\",\n \"text\": [\n \"Using cache found in /root/.cache/torch/hub/snakers4_silero-vad_master\\n\"\n ]\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"3200.0 El examen y el testimonio de los expertos\\n\",\n \"4160.0 permitieron\\n\",\n \"4800.0 a la Comisión\\n\",\n \"5120.0 concluir\\n\",\n \"7040.0 que\\n\",\n \"7360.0 cinco disparos pudieron\\n\",\n \"7583.9375 haber sido disparados,\\n\",\n \"End of VAD segment\\n\"\n ]\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"target_samples, intervals = get_s2st_delayed_targets(delays, target_sample_rate, prediction_lists, speech_durations)\\n\",\n \"\\n\",\n \"plot_s2st(\\\"/content/LJ_eng.wav\\\", target_samples, target_sample_rate, intervals, delays, prediction_lists)\"\n ],\n \"metadata\": {\n \"id\": \"ptr3nXlQKYed\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 341\n },\n \"outputId\": \"e3c91ea0-9f46-4aa4-86fb-25bc1125eae8\"\n },\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"Output translation (without input)\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"Output translation (overlay with input)\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
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},\n \"metadata\": {}\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Streaming HF space:\\n\",\n \"Try out the streaming HuggingFace space at: https://huggingface.co/spaces/facebook/seamless-streaming\"\n ],\n \"metadata\": {\n \"id\": \"Jr0kcQ_mGj8s\"\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"# Unity.cpp\"\n ],\n \"metadata\": {\n \"id\": \"OzNNvD5aGr8i\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"# unity.cpp\\n\",\n \"%mkdir -p ggml/build\\n\",\n \"%cd ggml/build\\n\",\n \"!cmake -DGGML_OPENBLAS=ON -DBUILD_SHARED_LIBS=On -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS=\\\"-g2 -fno-omit-frame-pointer\\\" ..\\n\",\n \"!make -j4 unity\\n\",\n \"# Download seamless_M4T_medium model, converted to ggml format\\n\",\n \"# Conversion script: https://github.com/facebookresearch/seamless_communication/blob/main/ggml/ggml_convert.py\\n\",\n \"!wget https://dl.fbaipublicfiles.com/seamless/models/seamlessM4T_medium.ggml\\n\"\n ],\n \"metadata\": {\n \"id\": \"FFGHgLbaKQ00\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"#Launching the console. But google colab doesn't support a console in C program\\n\",\n \"./bin/unity --model seamlessM4T_medium.ggml -t 8\"\n ],\n \"metadata\": {\n \"id\": \"ktkvmng1KTKE\"\n },\n \"execution_count\": null,\n \"outputs\": []\n }\n ],\n \"metadata\": {\n \"accelerator\": \"GPU\",\n \"colab\": {\n \"provenance\": [],\n \"gpuType\": \"T4\",\n \"include_colab_link\": true\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"name\": \"python\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}" }, { "path": "demo/.gitignore", "content": "assets\n" }, { "path": "demo/dino_pretssel/index.html", "content": "\n\n\n\n \n Textless Acoustic Model with Self-Supervised Distillation for Noise-Robust Expressive Speech-to-Speech Translation\n \n \n\n\n\n
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Textless Acoustic Model with Self-Supervised Distillation for Noise-Robust Expressive Speech-to-Speech Translation
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\n Min-Jae Hwang, Ilia Kulikov, Benjamin Peloquin, Hongyu Gong, Peng-Jen Chen, and Ann Lee\n

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\n Abstract\n
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\n In this paper, we propose a textless acoustic model with a self-supervised distillation strategy for noise-robust expressive speech-to-speech translation (S2ST).\n Recently proposed expressive S2ST systems have achieved impressive expressivity preservation performances by cascading unit-to-speech (U2S) generator to the speech-to-unit translation model. \n However, these systems are vulnerable to the presence of noise in input speech, which is an assumption in real-world translation scenarios. \n To address this limitation, we propose a U2S generator that incorporates a DINO self-supervised training strategy into it's pretraining process.\n Because the proposed method captures noise-agnostic expressivity representation, it can generate qualified speech even in noisy environment.\n Objective and subjective evaluation results verified that the proposed method significantly improved the performance of the expressive S2ST system in noisy environments while maintaining competitive performance in clean environments.\n

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\n Systems\n
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We provide source speech as well as audio samples from four systems:
\n \n (1) PRETSSEL: We combined a Prosody UnitY2 and PRETSSEL model [1].\n
\n (2) PRETSSEL + Denoiser: We combined a Prosody UnitY2 and PRETSSEL with high-quality speech enhancement model.\n Specifically, we applied MetricGAN+ denoiser [3] to the input of PRETSSEL for removing noise components.\n
\n (3) DINO-PRETSSEL (proposed): We combined a Prosody UnitY2 and proposed DINO-PRETSSEL.\n

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\n S2ST using Benchmarking Dataset\n
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mExpresso English-to-Spanish [1]\n
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SourcePRETSSEL (conventional)PRETSSEL + DenoiserDINO-PRETSSEL (proposed)
Sample 1
Clean environment\n
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Noisy environment\n
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Sample 2
Clean environment\n
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Noisy environment\n
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mDRAL Spanish-to-English [1]\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
SourcePRETSSEL (conventional)PRETSSEL + DenoiserDINO-PRETSSEL (proposed)
Sample 1
Clean environment\n
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Noisy environment\n
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Sample 2
Clean environment\n
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Noisy environment\n
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\n
\n \n
\n S2ST using Authors' Speech\n
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\n
English-to-Spanish\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
SourcePRETSSEL (conventional)PRETSSEL + DenoiserDINO-PRETSSEL (proposed)
Sample 1
Clean environment\n
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Noisy environment\n
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Sample 2
Clean environment\n
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Noisy environment\n
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Spanish-to-English\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
SourcePRETSSEL (conventional)PRETSSEL + DenoiserDINO-PRETSSEL (proposed)
Sample 1
Clean environment\n
\n \n \n 		\n
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\n \n \n 		\n
\n \n \n
Noisy environment\n
\n \n \n 		\n
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\n \n \n
Sample 2
Clean environment\n
\n \n \n 		\n
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\n \n \n
Noisy environment\n
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\n \n \n
\n
\n
\n Template based on Textless NLP and HiFi-GAN\n pages.\n
\n
\n References\n
\n [1] Seamless Communication, “Seamless: Multilingual Expressive and Streaming Speech Translation,” arXiv, 2023.\n
\n [2] Jörgen Valk and Tanel Alumäe, “VoxLingua107: a dataset for spoken language recognition,” In Proc. IEEE SLT Workshop, 2021.\n
\n [3] Szu-Wei Fu et al., \"MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement,\" arXiv, 2021.\n
\n\n\n" }, { "path": "demo/dino_pretssel/jquery-3.5.js", "content": "/*! jQuery v3.5.0 | (c) JS Foundation and other contributors | jquery.org/license */\n!function(e,t){\"use strict\";\"object\"==typeof module&&\"object\"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error(\"jQuery requires a window with a document\");return t(e)}:t(e)}(\"undefined\"!=typeof window?window:this,function(C,e){\"use strict\";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return\"function\"==typeof e&&\"number\"!=typeof e.nodeType},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement(\"script\");if(o.text=e,t)for(r in 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obj : { default: obj }; }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\n/**\n * The `CanvasEntry` class represents an element consisting of a wave `canvas`\n * and an (optional) progress wave `canvas`.\n *\n * The `MultiCanvas` renderer uses one or more `CanvasEntry` instances to\n * render a waveform, depending on the zoom level.\n */\nvar CanvasEntry = /*#__PURE__*/function () {\n function CanvasEntry() {\n _classCallCheck(this, CanvasEntry);\n\n /**\n * The wave node\n *\n * @type {HTMLCanvasElement}\n */\n this.wave = null;\n /**\n * The wave canvas rendering context\n *\n * @type {CanvasRenderingContext2D}\n */\n\n this.waveCtx = null;\n /**\n * The (optional) progress wave node\n *\n * @type {HTMLCanvasElement}\n */\n\n this.progress = null;\n /**\n * The (optional) progress wave canvas rendering context\n *\n * @type {CanvasRenderingContext2D}\n */\n\n this.progressCtx = null;\n /**\n * Start of the area the canvas should render, between 0 and 1\n *\n * @type {number}\n */\n\n this.start = 0;\n /**\n * End of the area the canvas should render, between 0 and 1\n *\n * @type {number}\n */\n\n this.end = 1;\n /**\n * Unique identifier for this entry\n *\n * @type {string}\n */\n\n this.id = (0, _getId.default)(typeof this.constructor.name !== 'undefined' ? this.constructor.name.toLowerCase() + '_' : 'canvasentry_');\n /**\n * Canvas 2d context attributes\n *\n * @type {object}\n */\n\n this.canvasContextAttributes = {};\n }\n /**\n * Store the wave canvas element and create the 2D rendering context\n *\n * @param {HTMLCanvasElement} element The wave `canvas` element.\n */\n\n\n _createClass(CanvasEntry, [{\n key: \"initWave\",\n value: function initWave(element) {\n this.wave = element;\n this.waveCtx = this.wave.getContext('2d', this.canvasContextAttributes);\n }\n /**\n * Store the progress wave canvas element and create the 2D rendering\n * context\n *\n * @param {HTMLCanvasElement} element The progress wave `canvas` element.\n */\n\n }, {\n key: \"initProgress\",\n value: function initProgress(element) {\n this.progress = element;\n this.progressCtx = this.progress.getContext('2d', this.canvasContextAttributes);\n }\n /**\n * Update the dimensions\n *\n * @param {number} elementWidth Width of the entry\n * @param {number} totalWidth Total width of the multi canvas renderer\n * @param {number} width The new width of the element\n * @param {number} height The new height of the element\n */\n\n }, {\n key: \"updateDimensions\",\n value: function updateDimensions(elementWidth, totalWidth, width, height) {\n // where the canvas starts and ends in the waveform, represented as a\n // decimal between 0 and 1\n this.start = this.wave.offsetLeft / totalWidth || 0;\n this.end = this.start + elementWidth / totalWidth; // set wave canvas dimensions\n\n this.wave.width = width;\n this.wave.height = height;\n var elementSize = {\n width: elementWidth + 'px'\n };\n (0, _style.default)(this.wave, elementSize);\n\n if (this.hasProgressCanvas) {\n // set progress canvas dimensions\n this.progress.width = width;\n this.progress.height = height;\n (0, _style.default)(this.progress, elementSize);\n }\n }\n /**\n * Clear the wave and progress rendering contexts\n */\n\n }, {\n key: \"clearWave\",\n value: function clearWave() {\n // wave\n this.waveCtx.clearRect(0, 0, this.waveCtx.canvas.width, this.waveCtx.canvas.height); // progress\n\n if (this.hasProgressCanvas) {\n this.progressCtx.clearRect(0, 0, this.progressCtx.canvas.width, this.progressCtx.canvas.height);\n }\n }\n /**\n * Set the fill styles for wave and progress\n *\n * @param {string} waveColor Fill color for the wave canvas\n * @param {?string} progressColor Fill color for the progress canvas\n */\n\n }, {\n key: \"setFillStyles\",\n value: function setFillStyles(waveColor, progressColor) {\n this.waveCtx.fillStyle = waveColor;\n\n if (this.hasProgressCanvas) {\n this.progressCtx.fillStyle = progressColor;\n }\n }\n /**\n * Draw a rectangle for wave and progress\n *\n * @param {number} x X start position\n * @param {number} y Y start position\n * @param {number} width Width of the rectangle\n * @param {number} height Height of the rectangle\n * @param {number} radius Radius of the rectangle\n */\n\n }, {\n key: \"fillRects\",\n value: function fillRects(x, y, width, height, radius) {\n this.fillRectToContext(this.waveCtx, x, y, width, height, radius);\n\n if (this.hasProgressCanvas) {\n this.fillRectToContext(this.progressCtx, x, y, width, height, radius);\n }\n }\n /**\n * Draw the actual rectangle on a `canvas` element\n *\n * @param {CanvasRenderingContext2D} ctx Rendering context of target canvas\n * @param {number} x X start position\n * @param {number} y Y start position\n * @param {number} width Width of the rectangle\n * @param {number} height Height of the rectangle\n * @param {number} radius Radius of the rectangle\n */\n\n }, {\n key: \"fillRectToContext\",\n value: function fillRectToContext(ctx, x, y, width, height, radius) {\n if (!ctx) {\n return;\n }\n\n if (radius) {\n this.drawRoundedRect(ctx, x, y, width, height, radius);\n } else {\n ctx.fillRect(x, y, width, height);\n }\n }\n /**\n * Draw a rounded rectangle on Canvas\n *\n * @param {CanvasRenderingContext2D} ctx Canvas context\n * @param {number} x X-position of the rectangle\n * @param {number} y Y-position of the rectangle\n * @param {number} width Width of the rectangle\n * @param {number} height Height of the rectangle\n * @param {number} radius Radius of the rectangle\n *\n * @return {void}\n * @example drawRoundedRect(ctx, 50, 50, 5, 10, 3)\n */\n\n }, {\n key: \"drawRoundedRect\",\n value: function drawRoundedRect(ctx, x, y, width, height, radius) {\n if (height === 0) {\n return;\n } // peaks are float values from -1 to 1. Use absolute height values in\n // order to correctly calculate rounded rectangle coordinates\n\n\n if (height < 0) {\n height *= -1;\n y -= height;\n }\n\n ctx.beginPath();\n ctx.moveTo(x + radius, y);\n ctx.lineTo(x + width - radius, y);\n ctx.quadraticCurveTo(x + width, y, x + width, y + radius);\n ctx.lineTo(x + width, y + height - radius);\n ctx.quadraticCurveTo(x + width, y + height, x + width - radius, y + height);\n ctx.lineTo(x + radius, y + height);\n ctx.quadraticCurveTo(x, y + height, x, y + height - radius);\n ctx.lineTo(x, y + radius);\n ctx.quadraticCurveTo(x, y, x + radius, y);\n ctx.closePath();\n ctx.fill();\n }\n /**\n * Render the actual wave and progress lines\n *\n * @param {number[]} peaks Array with peaks data\n * @param {number} absmax Maximum peak value (absolute)\n * @param {number} halfH Half the height of the waveform\n * @param {number} offsetY Offset to the top\n * @param {number} start The x-offset of the beginning of the area that\n * should be rendered\n * @param {number} end The x-offset of the end of the area that\n * should be rendered\n */\n\n }, {\n key: \"drawLines\",\n value: function drawLines(peaks, absmax, halfH, offsetY, start, end) {\n this.drawLineToContext(this.waveCtx, peaks, absmax, halfH, offsetY, start, end);\n\n if (this.hasProgressCanvas) {\n this.drawLineToContext(this.progressCtx, peaks, absmax, halfH, offsetY, start, end);\n }\n }\n /**\n * Render the actual waveform line on a `canvas` element\n *\n * @param {CanvasRenderingContext2D} ctx Rendering context of target canvas\n * @param {number[]} peaks Array with peaks data\n * @param {number} absmax Maximum peak value (absolute)\n * @param {number} halfH Half the height of the waveform\n * @param {number} offsetY Offset to the top\n * @param {number} start The x-offset of the beginning of the area that\n * should be rendered\n * @param {number} end The x-offset of the end of the area that\n * should be rendered\n */\n\n }, {\n key: \"drawLineToContext\",\n value: function drawLineToContext(ctx, peaks, absmax, halfH, offsetY, start, end) {\n if (!ctx) {\n return;\n }\n\n var length = peaks.length / 2;\n var first = Math.round(length * this.start); // use one more peak value to make sure we join peaks at ends -- unless,\n // of course, this is the last canvas\n\n var last = Math.round(length * this.end) + 1;\n var canvasStart = first;\n var canvasEnd = last;\n var scale = this.wave.width / (canvasEnd - canvasStart - 1); // optimization\n\n var halfOffset = halfH + offsetY;\n var absmaxHalf = absmax / halfH;\n ctx.beginPath();\n ctx.moveTo((canvasStart - first) * scale, halfOffset);\n ctx.lineTo((canvasStart - first) * scale, halfOffset - Math.round((peaks[2 * canvasStart] || 0) / absmaxHalf));\n var i, peak, h;\n\n for (i = canvasStart; i < canvasEnd; i++) {\n peak = peaks[2 * i] || 0;\n h = Math.round(peak / absmaxHalf);\n ctx.lineTo((i - first) * scale + this.halfPixel, halfOffset - h);\n } // draw the bottom edge going backwards, to make a single\n // closed hull to fill\n\n\n var j = canvasEnd - 1;\n\n for (j; j >= canvasStart; j--) {\n peak = peaks[2 * j + 1] || 0;\n h = Math.round(peak / absmaxHalf);\n ctx.lineTo((j - first) * scale + this.halfPixel, halfOffset - h);\n }\n\n ctx.lineTo((canvasStart - first) * scale, halfOffset - Math.round((peaks[2 * canvasStart + 1] || 0) / absmaxHalf));\n ctx.closePath();\n ctx.fill();\n }\n /**\n * Destroys this entry\n */\n\n }, {\n key: \"destroy\",\n value: function destroy() {\n this.waveCtx = null;\n this.wave = null;\n this.progressCtx = null;\n this.progress = null;\n }\n /**\n * Return image data of the wave `canvas` element\n *\n * When using a `type` of `'blob'`, this will return a `Promise` that\n * resolves with a `Blob` instance.\n *\n * @param {string} format='image/png' An optional value of a format type.\n * @param {number} quality=0.92 An optional value between 0 and 1.\n * @param {string} type='dataURL' Either 'dataURL' or 'blob'.\n * @return {string|Promise} When using the default `'dataURL'` `type` this\n * returns a data URL. When using the `'blob'` `type` this returns a\n * `Promise` that resolves with a `Blob` instance.\n */\n\n }, {\n key: \"getImage\",\n value: function getImage(format, quality, type) {\n var _this = this;\n\n if (type === 'blob') {\n return new Promise(function (resolve) {\n _this.wave.toBlob(resolve, format, quality);\n });\n } else if (type === 'dataURL') {\n return this.wave.toDataURL(format, quality);\n }\n }\n }]);\n\n return CanvasEntry;\n}();\n\nexports.default = CanvasEntry;\nmodule.exports = exports.default;\n\n/***/ }),\n\n/***/ \"./src/drawer.js\":\n/*!***********************!*\\\n !*** ./src/drawer.js ***!\n \\***********************/\n/*! no static exports found */\n/***/ (function(module, exports, __webpack_require__) {\n\n\"use strict\";\n\n\nObject.defineProperty(exports, \"__esModule\", {\n value: true\n});\nexports.default = void 0;\n\nvar util = _interopRequireWildcard(__webpack_require__(/*! ./util */ \"./src/util/index.js\"));\n\nfunction _getRequireWildcardCache() { if (typeof WeakMap !== \"function\") return null; var cache = new WeakMap(); _getRequireWildcardCache = function _getRequireWildcardCache() { return cache; }; return cache; }\n\nfunction _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } if (obj === null || _typeof(obj) !== \"object\" && typeof obj !== \"function\") { return { default: obj }; } var cache = _getRequireWildcardCache(); if (cache && cache.has(obj)) { return cache.get(obj); } var newObj = {}; var hasPropertyDescriptor = Object.defineProperty && Object.getOwnPropertyDescriptor; for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) { var desc = hasPropertyDescriptor ? Object.getOwnPropertyDescriptor(obj, key) : null; if (desc && (desc.get || desc.set)) { Object.defineProperty(newObj, key, desc); } else { newObj[key] = obj[key]; } } } newObj.default = obj; if (cache) { cache.set(obj, newObj); } return newObj; }\n\nfunction _typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return _typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n/**\n * Parent class for renderers\n *\n * @extends {Observer}\n */\nvar Drawer = /*#__PURE__*/function (_util$Observer) {\n _inherits(Drawer, _util$Observer);\n\n var _super = _createSuper(Drawer);\n\n /**\n * @param {HTMLElement} container The container node of the wavesurfer instance\n * @param {WavesurferParams} params The wavesurfer initialisation options\n */\n function Drawer(container, params) {\n var _this;\n\n _classCallCheck(this, Drawer);\n\n _this = _super.call(this);\n _this.container = container;\n /**\n * @type {WavesurferParams}\n */\n\n _this.params = params;\n /**\n * The width of the renderer\n * @type {number}\n */\n\n _this.width = 0;\n /**\n * The height of the renderer\n * @type {number}\n */\n\n _this.height = params.height * _this.params.pixelRatio;\n _this.lastPos = 0;\n /**\n * The `` element which is added to the container\n * @type {HTMLElement}\n */\n\n _this.wrapper = null;\n return _this;\n }\n /**\n * Alias of `util.style`\n *\n * @param {HTMLElement} el The element that the styles will be applied to\n * @param {Object} styles The map of propName: attribute, both are used as-is\n * @return {HTMLElement} el\n */\n\n\n _createClass(Drawer, [{\n key: \"style\",\n value: function style(el, styles) {\n return util.style(el, styles);\n }\n /**\n * Create the wrapper `` element, style it and set up the events for\n * interaction\n */\n\n }, {\n key: \"createWrapper\",\n value: function createWrapper() {\n this.wrapper = this.container.appendChild(document.createElement('wave'));\n this.style(this.wrapper, {\n display: 'block',\n position: 'relative',\n userSelect: 'none',\n webkitUserSelect: 'none',\n height: this.params.height + 'px'\n });\n\n if (this.params.fillParent || this.params.scrollParent) {\n this.style(this.wrapper, {\n width: '100%',\n overflowX: this.params.hideScrollbar ? 'hidden' : 'auto',\n overflowY: 'hidden'\n });\n }\n\n this.setupWrapperEvents();\n }\n /**\n * Handle click event\n *\n * @param {Event} e Click event\n * @param {?boolean} noPrevent Set to true to not call `e.preventDefault()`\n * @return {number} Playback position from 0 to 1\n */\n\n }, {\n key: \"handleEvent\",\n value: function handleEvent(e, noPrevent) {\n !noPrevent && e.preventDefault();\n var clientX = e.targetTouches ? e.targetTouches[0].clientX : e.clientX;\n var bbox = this.wrapper.getBoundingClientRect();\n var nominalWidth = this.width;\n var parentWidth = this.getWidth();\n var progress;\n\n if (!this.params.fillParent && nominalWidth < parentWidth) {\n progress = (this.params.rtl ? bbox.right - clientX : clientX - bbox.left) * (this.params.pixelRatio / nominalWidth) || 0;\n } else {\n progress = ((this.params.rtl ? bbox.right - clientX : clientX - bbox.left) + this.wrapper.scrollLeft) / this.wrapper.scrollWidth || 0;\n }\n\n return util.clamp(progress, 0, 1);\n }\n }, {\n key: \"setupWrapperEvents\",\n value: function setupWrapperEvents() {\n var _this2 = this;\n\n this.wrapper.addEventListener('click', function (e) {\n var scrollbarHeight = _this2.wrapper.offsetHeight - _this2.wrapper.clientHeight;\n\n if (scrollbarHeight !== 0) {\n // scrollbar is visible. Check if click was on it\n var bbox = _this2.wrapper.getBoundingClientRect();\n\n if (e.clientY >= bbox.bottom - scrollbarHeight) {\n // ignore mousedown as it was on the scrollbar\n return;\n }\n }\n\n if (_this2.params.interact) {\n _this2.fireEvent('click', e, _this2.handleEvent(e));\n }\n });\n this.wrapper.addEventListener('dblclick', function (e) {\n if (_this2.params.interact) {\n _this2.fireEvent('dblclick', e, _this2.handleEvent(e));\n }\n });\n this.wrapper.addEventListener('scroll', function (e) {\n return _this2.fireEvent('scroll', e);\n });\n }\n /**\n * Draw peaks on the canvas\n *\n * @param {number[]|Number.} peaks Can also be an array of arrays\n * for split channel rendering\n * @param {number} length The width of the area that should be drawn\n * @param {number} start The x-offset of the beginning of the area that\n * should be rendered\n * @param {number} end The x-offset of the end of the area that should be\n * rendered\n */\n\n }, {\n key: \"drawPeaks\",\n value: function drawPeaks(peaks, length, start, end) {\n if (!this.setWidth(length)) {\n this.clearWave();\n }\n\n this.params.barWidth ? this.drawBars(peaks, 0, start, end) : this.drawWave(peaks, 0, start, end);\n }\n /**\n * Scroll to the beginning\n */\n\n }, {\n key: \"resetScroll\",\n value: function resetScroll() {\n if (this.wrapper !== null) {\n this.wrapper.scrollLeft = 0;\n }\n }\n /**\n * Recenter the view-port at a certain percent of the waveform\n *\n * @param {number} percent Value from 0 to 1 on the waveform\n */\n\n }, {\n key: \"recenter\",\n value: function recenter(percent) {\n var position = this.wrapper.scrollWidth * percent;\n this.recenterOnPosition(position, true);\n }\n /**\n * Recenter the view-port on a position, either scroll there immediately or\n * in steps of 5 pixels\n *\n * @param {number} position X-offset in pixels\n * @param {boolean} immediate Set to true to immediately scroll somewhere\n */\n\n }, {\n key: \"recenterOnPosition\",\n value: function recenterOnPosition(position, immediate) {\n var scrollLeft = this.wrapper.scrollLeft;\n var half = ~~(this.wrapper.clientWidth / 2);\n var maxScroll = this.wrapper.scrollWidth - this.wrapper.clientWidth;\n var target = position - half;\n var offset = target - scrollLeft;\n\n if (maxScroll == 0) {\n // no need to continue if scrollbar is not there\n return;\n } // if the cursor is currently visible...\n\n\n if (!immediate && -half <= offset && offset < half) {\n // set rate at which waveform is centered\n var rate = this.params.autoCenterRate; // make rate depend on width of view and length of waveform\n\n rate /= half;\n rate *= maxScroll;\n offset = Math.max(-rate, Math.min(rate, offset));\n target = scrollLeft + offset;\n } // limit target to valid range (0 to maxScroll)\n\n\n target = Math.max(0, Math.min(maxScroll, target)); // no use attempting to scroll if we're not moving\n\n if (target != scrollLeft) {\n this.wrapper.scrollLeft = target;\n }\n }\n /**\n * Get the current scroll position in pixels\n *\n * @return {number} Horizontal scroll position in pixels\n */\n\n }, {\n key: \"getScrollX\",\n value: function getScrollX() {\n var x = 0;\n\n if (this.wrapper) {\n var pixelRatio = this.params.pixelRatio;\n x = Math.round(this.wrapper.scrollLeft * pixelRatio); // In cases of elastic scroll (safari with mouse wheel) you can\n // scroll beyond the limits of the container\n // Calculate and floor the scrollable extent to make sure an out\n // of bounds value is not returned\n // Ticket #1312\n\n if (this.params.scrollParent) {\n var maxScroll = ~~(this.wrapper.scrollWidth * pixelRatio - this.getWidth());\n x = Math.min(maxScroll, Math.max(0, x));\n }\n }\n\n return x;\n }\n /**\n * Get the width of the container\n *\n * @return {number} The width of the container\n */\n\n }, {\n key: \"getWidth\",\n value: function getWidth() {\n return Math.round(this.container.clientWidth * this.params.pixelRatio);\n }\n /**\n * Set the width of the container\n *\n * @param {number} width The new width of the container\n * @return {boolean} Whether the width of the container was updated or not\n */\n\n }, {\n key: \"setWidth\",\n value: function setWidth(width) {\n if (this.width == width) {\n return false;\n }\n\n this.width = width;\n\n if (this.params.fillParent || this.params.scrollParent) {\n this.style(this.wrapper, {\n width: ''\n });\n } else {\n this.style(this.wrapper, {\n width: ~~(this.width / this.params.pixelRatio) + 'px'\n });\n }\n\n this.updateSize();\n return true;\n }\n /**\n * Set the height of the container\n *\n * @param {number} height The new height of the container.\n * @return {boolean} Whether the height of the container was updated or not\n */\n\n }, {\n key: \"setHeight\",\n value: function setHeight(height) {\n if (height == this.height) {\n return false;\n }\n\n this.height = height;\n this.style(this.wrapper, {\n height: ~~(this.height / this.params.pixelRatio) + 'px'\n });\n this.updateSize();\n return true;\n }\n /**\n * Called by wavesurfer when progress should be rendered\n *\n * @param {number} progress From 0 to 1\n */\n\n }, {\n key: \"progress\",\n value: function progress(_progress) {\n var minPxDelta = 1 / this.params.pixelRatio;\n var pos = Math.round(_progress * this.width) * minPxDelta;\n\n if (pos < this.lastPos || pos - this.lastPos >= minPxDelta) {\n this.lastPos = pos;\n\n if (this.params.scrollParent && this.params.autoCenter) {\n var newPos = ~~(this.wrapper.scrollWidth * _progress);\n this.recenterOnPosition(newPos, this.params.autoCenterImmediately);\n }\n\n this.updateProgress(pos);\n }\n }\n /**\n * This is called when wavesurfer is destroyed\n */\n\n }, {\n key: \"destroy\",\n value: function destroy() {\n this.unAll();\n\n if (this.wrapper) {\n if (this.wrapper.parentNode == this.container) {\n this.container.removeChild(this.wrapper);\n }\n\n this.wrapper = null;\n }\n }\n /* Renderer-specific methods */\n\n /**\n * Called after cursor related params have changed.\n *\n * @abstract\n */\n\n }, {\n key: \"updateCursor\",\n value: function updateCursor() {}\n /**\n * Called when the size of the container changes so the renderer can adjust\n *\n * @abstract\n */\n\n }, {\n key: \"updateSize\",\n value: function updateSize() {}\n /**\n * Draw a waveform with bars\n *\n * @abstract\n * @param {number[]|Number.} peaks Can also be an array of arrays for split channel\n * rendering\n * @param {number} channelIndex The index of the current channel. Normally\n * should be 0\n * @param {number} start The x-offset of the beginning of the area that\n * should be rendered\n * @param {number} end The x-offset of the end of the area that should be\n * rendered\n */\n\n }, {\n key: \"drawBars\",\n value: function drawBars(peaks, channelIndex, start, end) {}\n /**\n * Draw a waveform\n *\n * @abstract\n * @param {number[]|Number.} peaks Can also be an array of arrays for split channel\n * rendering\n * @param {number} channelIndex The index of the current channel. Normally\n * should be 0\n * @param {number} start The x-offset of the beginning of the area that\n * should be rendered\n * @param {number} end The x-offset of the end of the area that should be\n * rendered\n */\n\n }, {\n key: \"drawWave\",\n value: function drawWave(peaks, channelIndex, start, end) {}\n /**\n * Clear the waveform\n *\n * @abstract\n */\n\n }, {\n key: \"clearWave\",\n value: function clearWave() {}\n /**\n * Render the new progress\n *\n * @abstract\n * @param {number} position X-Offset of progress position in pixels\n */\n\n }, {\n key: \"updateProgress\",\n value: function updateProgress(position) {}\n }]);\n\n return Drawer;\n}(util.Observer);\n\nexports.default = Drawer;\nmodule.exports = exports.default;\n\n/***/ }),\n\n/***/ \"./src/drawer.multicanvas.js\":\n/*!***********************************!*\\\n !*** ./src/drawer.multicanvas.js ***!\n \\***********************************/\n/*! no static exports found */\n/***/ (function(module, exports, __webpack_require__) {\n\n\"use strict\";\n\n\nObject.defineProperty(exports, \"__esModule\", {\n value: true\n});\nexports.default = void 0;\n\nvar _drawer = _interopRequireDefault(__webpack_require__(/*! ./drawer */ \"./src/drawer.js\"));\n\nvar util = _interopRequireWildcard(__webpack_require__(/*! ./util */ \"./src/util/index.js\"));\n\nvar _drawer2 = _interopRequireDefault(__webpack_require__(/*! ./drawer.canvasentry */ \"./src/drawer.canvasentry.js\"));\n\nfunction _getRequireWildcardCache() { if (typeof WeakMap !== \"function\") return null; var cache = new WeakMap(); _getRequireWildcardCache = function _getRequireWildcardCache() { return cache; }; return cache; }\n\nfunction _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } if (obj === null || _typeof(obj) !== \"object\" && typeof obj !== \"function\") { return { default: obj }; } var cache = _getRequireWildcardCache(); if (cache && cache.has(obj)) { return cache.get(obj); } var newObj = {}; var hasPropertyDescriptor = Object.defineProperty && Object.getOwnPropertyDescriptor; for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) { var desc = hasPropertyDescriptor ? Object.getOwnPropertyDescriptor(obj, key) : null; if (desc && (desc.get || desc.set)) { Object.defineProperty(newObj, key, desc); } else { newObj[key] = obj[key]; } } } newObj.default = obj; if (cache) { cache.set(obj, newObj); } return newObj; }\n\nfunction _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }\n\nfunction _typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return _typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n/**\n * MultiCanvas renderer for wavesurfer. Is currently the default and sole\n * builtin renderer.\n *\n * A `MultiCanvas` consists of one or more `CanvasEntry` instances, depending\n * on the zoom level.\n */\nvar MultiCanvas = /*#__PURE__*/function (_Drawer) {\n _inherits(MultiCanvas, _Drawer);\n\n var _super = _createSuper(MultiCanvas);\n\n /**\n * @param {HTMLElement} container The container node of the wavesurfer instance\n * @param {WavesurferParams} params The wavesurfer initialisation options\n */\n function MultiCanvas(container, params) {\n var _this;\n\n _classCallCheck(this, MultiCanvas);\n\n _this = _super.call(this, container, params);\n /**\n * @type {number}\n */\n\n _this.maxCanvasWidth = params.maxCanvasWidth;\n /**\n * @type {number}\n */\n\n _this.maxCanvasElementWidth = Math.round(params.maxCanvasWidth / params.pixelRatio);\n /**\n * Whether or not the progress wave is rendered. If the `waveColor`\n * and `progressColor` are the same color it is not.\n *\n * @type {boolean}\n */\n\n _this.hasProgressCanvas = params.waveColor != params.progressColor;\n /**\n * @type {number}\n */\n\n _this.halfPixel = 0.5 / params.pixelRatio;\n /**\n * List of `CanvasEntry` instances.\n *\n * @type {Array}\n */\n\n _this.canvases = [];\n /**\n * @type {HTMLElement}\n */\n\n _this.progressWave = null;\n /**\n * Class used to generate entries.\n *\n * @type {function}\n */\n\n _this.EntryClass = _drawer2.default;\n /**\n * Canvas 2d context attributes.\n *\n * @type {object}\n */\n\n _this.canvasContextAttributes = params.drawingContextAttributes;\n /**\n * Overlap added between entries to prevent vertical white stripes\n * between `canvas` elements.\n *\n * @type {number}\n */\n\n _this.overlap = 2 * Math.ceil(params.pixelRatio / 2);\n /**\n * The radius of the wave bars. Makes bars rounded\n *\n * @type {number}\n */\n\n _this.barRadius = params.barRadius || 0;\n return _this;\n }\n /**\n * Initialize the drawer\n */\n\n\n _createClass(MultiCanvas, [{\n key: \"init\",\n value: function init() {\n this.createWrapper();\n this.createElements();\n }\n /**\n * Create the canvas elements and style them\n *\n */\n\n }, {\n key: \"createElements\",\n value: function createElements() {\n this.progressWave = this.wrapper.appendChild(this.style(document.createElement('wave'), {\n position: 'absolute',\n zIndex: 3,\n left: 0,\n top: 0,\n bottom: 0,\n overflow: 'hidden',\n width: '0',\n display: 'none',\n boxSizing: 'border-box',\n borderRightStyle: 'solid',\n pointerEvents: 'none'\n }));\n this.addCanvas();\n this.updateCursor();\n }\n /**\n * Update cursor style\n */\n\n }, {\n key: \"updateCursor\",\n value: function updateCursor() {\n this.style(this.progressWave, {\n borderRightWidth: this.params.cursorWidth + 'px',\n borderRightColor: this.params.cursorColor\n });\n }\n /**\n * Adjust to the updated size by adding or removing canvases\n */\n\n }, {\n key: \"updateSize\",\n value: function updateSize() {\n var _this2 = this;\n\n var totalWidth = Math.round(this.width / this.params.pixelRatio);\n var requiredCanvases = Math.ceil(totalWidth / (this.maxCanvasElementWidth + this.overlap)); // add required canvases\n\n while (this.canvases.length < requiredCanvases) {\n this.addCanvas();\n } // remove older existing canvases, if any\n\n\n while (this.canvases.length > requiredCanvases) {\n this.removeCanvas();\n }\n\n var canvasWidth = this.maxCanvasWidth + this.overlap;\n var lastCanvas = this.canvases.length - 1;\n this.canvases.forEach(function (entry, i) {\n if (i == lastCanvas) {\n canvasWidth = _this2.width - _this2.maxCanvasWidth * lastCanvas;\n }\n\n _this2.updateDimensions(entry, canvasWidth, _this2.height);\n\n entry.clearWave();\n });\n }\n /**\n * Add a canvas to the canvas list\n *\n */\n\n }, {\n key: \"addCanvas\",\n value: function addCanvas() {\n var entry = new this.EntryClass();\n entry.canvasContextAttributes = this.canvasContextAttributes;\n entry.hasProgressCanvas = this.hasProgressCanvas;\n entry.halfPixel = this.halfPixel;\n var leftOffset = this.maxCanvasElementWidth * this.canvases.length; // wave\n\n entry.initWave(this.wrapper.appendChild(this.style(document.createElement('canvas'), {\n position: 'absolute',\n zIndex: 2,\n left: leftOffset + 'px',\n top: 0,\n bottom: 0,\n height: '100%',\n pointerEvents: 'none'\n }))); // progress\n\n if (this.hasProgressCanvas) {\n entry.initProgress(this.progressWave.appendChild(this.style(document.createElement('canvas'), {\n position: 'absolute',\n left: leftOffset + 'px',\n top: 0,\n bottom: 0,\n height: '100%'\n })));\n }\n\n this.canvases.push(entry);\n }\n /**\n * Pop single canvas from the list\n *\n */\n\n }, {\n key: \"removeCanvas\",\n value: function removeCanvas() {\n var lastEntry = this.canvases[this.canvases.length - 1]; // wave\n\n lastEntry.wave.parentElement.removeChild(lastEntry.wave); // progress\n\n if (this.hasProgressCanvas) {\n lastEntry.progress.parentElement.removeChild(lastEntry.progress);\n } // cleanup\n\n\n if (lastEntry) {\n lastEntry.destroy();\n lastEntry = null;\n }\n\n this.canvases.pop();\n }\n /**\n * Update the dimensions of a canvas element\n *\n * @param {CanvasEntry} entry Target entry\n * @param {number} width The new width of the element\n * @param {number} height The new height of the element\n */\n\n }, {\n key: \"updateDimensions\",\n value: function updateDimensions(entry, width, height) {\n var elementWidth = Math.round(width / this.params.pixelRatio);\n var totalWidth = Math.round(this.width / this.params.pixelRatio); // update canvas dimensions\n\n entry.updateDimensions(elementWidth, totalWidth, width, height); // style element\n\n this.style(this.progressWave, {\n display: 'block'\n });\n }\n /**\n * Clear the whole multi-canvas\n */\n\n }, {\n key: \"clearWave\",\n value: function clearWave() {\n var _this3 = this;\n\n util.frame(function () {\n _this3.canvases.forEach(function (entry) {\n return entry.clearWave();\n });\n })();\n }\n /**\n * Draw a waveform with bars\n *\n * @param {number[]|Number.} peaks Can also be an array of arrays\n * for split channel rendering\n * @param {number} channelIndex The index of the current channel. Normally\n * should be 0. Must be an integer.\n * @param {number} start The x-offset of the beginning of the area that\n * should be rendered\n * @param {number} end The x-offset of the end of the area that should be\n * rendered\n * @returns {void}\n */\n\n }, {\n key: \"drawBars\",\n value: function drawBars(peaks, channelIndex, start, end) {\n var _this4 = this;\n\n return this.prepareDraw(peaks, channelIndex, start, end, function (_ref) {\n var absmax = _ref.absmax,\n hasMinVals = _ref.hasMinVals,\n height = _ref.height,\n offsetY = _ref.offsetY,\n halfH = _ref.halfH,\n peaks = _ref.peaks;\n\n // if drawBars was called within ws.empty we don't pass a start and\n // don't want anything to happen\n if (start === undefined) {\n return;\n } // Skip every other value if there are negatives.\n\n\n var peakIndexScale = hasMinVals ? 2 : 1;\n var length = peaks.length / peakIndexScale;\n var bar = _this4.params.barWidth * _this4.params.pixelRatio;\n var gap = _this4.params.barGap === null ? Math.max(_this4.params.pixelRatio, ~~(bar / 2)) : Math.max(_this4.params.pixelRatio, _this4.params.barGap * _this4.params.pixelRatio);\n var step = bar + gap;\n var scale = length / _this4.width;\n var first = start;\n var last = end;\n var i = first;\n\n for (i; i < last; i += step) {\n var peak = peaks[Math.floor(i * scale * peakIndexScale)] || 0;\n var h = Math.round(peak / absmax * halfH);\n /* in case of silences, allow the user to specify that we\n * always draw *something* (normally a 1px high bar) */\n\n if (h == 0 && _this4.params.barMinHeight) h = _this4.params.barMinHeight;\n\n _this4.fillRect(i + _this4.halfPixel, halfH - h + offsetY, bar + _this4.halfPixel, h * 2, _this4.barRadius);\n }\n });\n }\n /**\n * Draw a waveform\n *\n * @param {number[]|Number.} peaks Can also be an array of arrays\n * for split channel rendering\n * @param {number} channelIndex The index of the current channel. Normally\n * should be 0\n * @param {number?} start The x-offset of the beginning of the area that\n * should be rendered (If this isn't set only a flat line is rendered)\n * @param {number?} end The x-offset of the end of the area that should be\n * rendered\n * @returns {void}\n */\n\n }, {\n key: \"drawWave\",\n value: function drawWave(peaks, channelIndex, start, end) {\n var _this5 = this;\n\n return this.prepareDraw(peaks, channelIndex, start, end, function (_ref2) {\n var absmax = _ref2.absmax,\n hasMinVals = _ref2.hasMinVals,\n height = _ref2.height,\n offsetY = _ref2.offsetY,\n halfH = _ref2.halfH,\n peaks = _ref2.peaks,\n channelIndex = _ref2.channelIndex;\n\n if (!hasMinVals) {\n var reflectedPeaks = [];\n var len = peaks.length;\n var i = 0;\n\n for (i; i < len; i++) {\n reflectedPeaks[2 * i] = peaks[i];\n reflectedPeaks[2 * i + 1] = -peaks[i];\n }\n\n peaks = reflectedPeaks;\n } // if drawWave was called within ws.empty we don't pass a start and\n // end and simply want a flat line\n\n\n if (start !== undefined) {\n _this5.drawLine(peaks, absmax, halfH, offsetY, start, end, channelIndex);\n } // always draw a median line\n\n\n _this5.fillRect(0, halfH + offsetY - _this5.halfPixel, _this5.width, _this5.halfPixel, _this5.barRadius);\n });\n }\n /**\n * Tell the canvas entries to render their portion of the waveform\n *\n * @param {number[]} peaks Peaks data\n * @param {number} absmax Maximum peak value (absolute)\n * @param {number} halfH Half the height of the waveform\n * @param {number} offsetY Offset to the top\n * @param {number} start The x-offset of the beginning of the area that\n * should be rendered\n * @param {number} end The x-offset of the end of the area that\n * should be rendered\n * @param {channelIndex} channelIndex The channel index of the line drawn\n */\n\n }, {\n key: \"drawLine\",\n value: function drawLine(peaks, absmax, halfH, offsetY, start, end, channelIndex) {\n var _this6 = this;\n\n var _ref3 = this.params.splitChannelsOptions.channelColors[channelIndex] || {},\n waveColor = _ref3.waveColor,\n progressColor = _ref3.progressColor;\n\n this.canvases.forEach(function (entry, i) {\n _this6.setFillStyles(entry, waveColor, progressColor);\n\n entry.drawLines(peaks, absmax, halfH, offsetY, start, end);\n });\n }\n /**\n * Draw a rectangle on the multi-canvas\n *\n * @param {number} x X-position of the rectangle\n * @param {number} y Y-position of the rectangle\n * @param {number} width Width of the rectangle\n * @param {number} height Height of the rectangle\n * @param {number} radius Radius of the rectangle\n */\n\n }, {\n key: \"fillRect\",\n value: function fillRect(x, y, width, height, radius) {\n var startCanvas = Math.floor(x / this.maxCanvasWidth);\n var endCanvas = Math.min(Math.ceil((x + width) / this.maxCanvasWidth) + 1, this.canvases.length);\n var i = startCanvas;\n\n for (i; i < endCanvas; i++) {\n var entry = this.canvases[i];\n var leftOffset = i * this.maxCanvasWidth;\n var intersection = {\n x1: Math.max(x, i * this.maxCanvasWidth),\n y1: y,\n x2: Math.min(x + width, i * this.maxCanvasWidth + entry.wave.width),\n y2: y + height\n };\n\n if (intersection.x1 < intersection.x2) {\n this.setFillStyles(entry);\n entry.fillRects(intersection.x1 - leftOffset, intersection.y1, intersection.x2 - intersection.x1, intersection.y2 - intersection.y1, radius);\n }\n }\n }\n /**\n * Returns whether to hide the channel from being drawn based on params.\n *\n * @param {number} channelIndex The index of the current channel.\n * @returns {bool} True to hide the channel, false to draw.\n */\n\n }, {\n key: \"hideChannel\",\n value: function hideChannel(channelIndex) {\n return this.params.splitChannels && this.params.splitChannelsOptions.filterChannels.includes(channelIndex);\n }\n /**\n * Performs preparation tasks and calculations which are shared by `drawBars`\n * and `drawWave`\n *\n * @param {number[]|Number.} peaks Can also be an array of arrays for\n * split channel rendering\n * @param {number} channelIndex The index of the current channel. Normally\n * should be 0\n * @param {number?} start The x-offset of the beginning of the area that\n * should be rendered. If this isn't set only a flat line is rendered\n * @param {number?} end The x-offset of the end of the area that should be\n * rendered\n * @param {function} fn The render function to call, e.g. `drawWave`\n * @param {number} drawIndex The index of the current channel after filtering.\n * @returns {void}\n */\n\n }, {\n key: \"prepareDraw\",\n value: function prepareDraw(peaks, channelIndex, start, end, fn, drawIndex) {\n var _this7 = this;\n\n return util.frame(function () {\n // Split channels and call this function with the channelIndex set\n if (peaks[0] instanceof Array) {\n var channels = peaks;\n\n if (_this7.params.splitChannels) {\n var filteredChannels = channels.filter(function (c, i) {\n return !_this7.hideChannel(i);\n });\n\n if (!_this7.params.splitChannelsOptions.overlay) {\n _this7.setHeight(Math.max(filteredChannels.length, 1) * _this7.params.height * _this7.params.pixelRatio);\n }\n\n return channels.forEach(function (channelPeaks, i) {\n return _this7.prepareDraw(channelPeaks, i, start, end, fn, filteredChannels.indexOf(channelPeaks));\n });\n }\n\n peaks = channels[0];\n } // Return and do not draw channel peaks if hidden.\n\n\n if (_this7.hideChannel(channelIndex)) {\n return;\n } // calculate maximum modulation value, either from the barHeight\n // parameter or if normalize=true from the largest value in the peak\n // set\n\n\n var absmax = 1 / _this7.params.barHeight;\n\n if (_this7.params.normalize) {\n var max = util.max(peaks);\n var min = util.min(peaks);\n absmax = -min > max ? -min : max;\n } // Bar wave draws the bottom only as a reflection of the top,\n // so we don't need negative values\n\n\n var hasMinVals = [].some.call(peaks, function (val) {\n return val < 0;\n });\n var height = _this7.params.height * _this7.params.pixelRatio;\n var offsetY = height * drawIndex || 0;\n var halfH = height / 2;\n return fn({\n absmax: absmax,\n hasMinVals: hasMinVals,\n height: height,\n offsetY: offsetY,\n halfH: halfH,\n peaks: peaks,\n channelIndex: channelIndex\n });\n })();\n }\n /**\n * Set the fill styles for a certain entry (wave and progress)\n *\n * @param {CanvasEntry} entry Target entry\n * @param {string} waveColor Wave color to draw this entry\n * @param {string} progressColor Progress color to draw this entry\n */\n\n }, {\n key: \"setFillStyles\",\n value: function setFillStyles(entry) {\n var waveColor = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : this.params.waveColor;\n var progressColor = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : this.params.progressColor;\n entry.setFillStyles(waveColor, progressColor);\n }\n /**\n * Return image data of the multi-canvas\n *\n * When using a `type` of `'blob'`, this will return a `Promise`.\n *\n * @param {string} format='image/png' An optional value of a format type.\n * @param {number} quality=0.92 An optional value between 0 and 1.\n * @param {string} type='dataURL' Either 'dataURL' or 'blob'.\n * @return {string|string[]|Promise} When using the default `'dataURL'`\n * `type` this returns a single data URL or an array of data URLs,\n * one for each canvas. When using the `'blob'` `type` this returns a\n * `Promise` that resolves with an array of `Blob` instances, one for each\n * canvas.\n */\n\n }, {\n key: \"getImage\",\n value: function getImage(format, quality, type) {\n if (type === 'blob') {\n return Promise.all(this.canvases.map(function (entry) {\n return entry.getImage(format, quality, type);\n }));\n } else if (type === 'dataURL') {\n var images = this.canvases.map(function (entry) {\n return entry.getImage(format, quality, type);\n });\n return images.length > 1 ? images : images[0];\n }\n }\n /**\n * Render the new progress\n *\n * @param {number} position X-offset of progress position in pixels\n */\n\n }, {\n key: \"updateProgress\",\n value: function updateProgress(position) {\n this.style(this.progressWave, {\n width: position + 'px'\n });\n }\n }]);\n\n return MultiCanvas;\n}(_drawer.default);\n\nexports.default = MultiCanvas;\nmodule.exports = exports.default;\n\n/***/ }),\n\n/***/ \"./src/mediaelement-webaudio.js\":\n/*!**************************************!*\\\n !*** ./src/mediaelement-webaudio.js ***!\n \\**************************************/\n/*! no static exports found */\n/***/ (function(module, exports, __webpack_require__) {\n\n\"use strict\";\n\n\nObject.defineProperty(exports, \"__esModule\", {\n value: true\n});\nexports.default = void 0;\n\nvar _mediaelement = _interopRequireDefault(__webpack_require__(/*! ./mediaelement */ \"./src/mediaelement.js\"));\n\nfunction _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }\n\nfunction _typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return _typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _get(target, property, receiver) { if (typeof Reflect !== \"undefined\" && Reflect.get) { _get = Reflect.get; } else { _get = function _get(target, property, receiver) { var base = _superPropBase(target, property); if (!base) return; var desc = Object.getOwnPropertyDescriptor(base, property); if (desc.get) { return desc.get.call(receiver); } return desc.value; }; } return _get(target, property, receiver || target); }\n\nfunction _superPropBase(object, property) { while (!Object.prototype.hasOwnProperty.call(object, property)) { object = _getPrototypeOf(object); if (object === null) break; } return object; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n/**\n * MediaElementWebAudio backend: load audio via an HTML5 audio tag, but playback with the WebAudio API.\n * The advantage here is that the html5