Full Code of m3hrdadfi/soxan for AI

Repository: m3hrdadfi/soxan
Branch: main
Commit: 1cf82aa33892
Files: 15
Total size: 8.1 MB

Directory structure:
gitextract_xc5zf8sr/

├── .github/
│   └── workflows/
│       └── sync_streamlit_speech_text_emotion_to_space.yml
├── .gitignore
├── LICENSE
├── README.md
├── notebooks/
│   ├── Eating_Sound_Collection_using_Wav2Vec2.ipynb
│   ├── Emotion_recognition_in_Greek_speech_using_HuBERT.ipynb
│   ├── Emotion_recognition_in_Greek_speech_using_Wav2Vec2.ipynb
│   └── Regression_Example_Emotion_recognition_in_Greek_speech_using_Wav2Vec2.ipynb
├── requirements.txt
├── run_wav2vec_clf.py
└── src/
    ├── __init__.py
    ├── collator.py
    ├── modeling_outputs.py
    ├── models.py
    └── trainer.py

================================================
FILE CONTENTS
================================================

================================================
FILE: .github/workflows/sync_streamlit_speech_text_emotion_to_space.yml
================================================
name: Sync Streamlit to Hugging Face space [speech-text-emotion]

on:
  push:
    branches: [streamlit-speech-text-emotion]

  # to run this workflow manually from the Actions tab
  workflow_dispatch:

jobs:
  sync-streamlit-to-space:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
        with:
          fetch-depth: 0
      - name: Push to space
        env:
          HF_TOKEN: ${{ secrets.HF_TOKEN }}
          HF_USERNAME: m3hrdadfi
          HF_SPACE: huggingface.co/spaces/m3hrdadfi/speech-text-emotion
          TARGET_BRANCH: main
          GIT_USER_EMAIL: m3hrdadfi@gmail.com
          GIT_USER_NAME: Mehrdad Farahani
        run: |
          echo "Cloning destination git repository"
          git config --global user.email "$GIT_USER_EMAIL"
          git config --global user.name "$GIT_USER_NAME"
          CLONE_DIR=$(mktemp -d)
          git clone "https://$HF_USERNAME:$HF_TOKEN@$HF_SPACE" "$CLONE_DIR"
          ls -la "$CLONE_DIR"
          echo "Copy contents to clone git repository"
          cp -rf ./apps/streamlit-speech-text-emotion/* "$CLONE_DIR"
          COMMIT_MESSAGE=$(git log --oneline --format=%B -n 1 HEAD | head -n 1)
          COMM=${COMM:=Synch with Github}
          echo "commit message:"
          echo "$COMMIT_MESSAGE"
          cd "$CLONE_DIR"
          echo "Remove wav files"
          rm -f *.wav
          echo "Files that will be pushed:"
          ls -la
          echo "git add:"
          git add -A
          echo "git status:"
          git status
          echo "git diff-index:"
          git diff-index --quiet HEAD || git commit --message "$COMMIT_MESSAGE"
          echo "git push origin:"
          git push "https://$HF_USERNAME:$HF_TOKEN@$HF_SPACE" "$TARGET_BRANCH"


================================================
FILE: .gitignore
================================================
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
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parts/
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share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
#  Usually these files are written by a python script from a template
#  before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/

# Translations
*.mo
*.pot

# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal

# Flask stuff:
instance/
.webassets-cache

# Scrapy stuff:
.scrapy

# Sphinx documentation
docs/_build/

# PyBuilder
target/

# Jupyter Notebook
.ipynb_checkpoints

# IPython
profile_default/
ipython_config.py

# pyenv
.python-version

# pipenv
#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
#   However, in case of collaboration, if having platform-specific dependencies or dependencies
#   having no cross-platform support, pipenv may install dependencies that don't work, or not
#   install all needed dependencies.
#Pipfile.lock

# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/

# Celery stuff
celerybeat-schedule
celerybeat.pid

# SageMath parsed files
*.sage.py

# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/

# Spyder project settings
.spyderproject
.spyproject

# Rope project settings
.ropeproject

# mkdocs documentation
/site

# mypy
.mypy_cache/
.dmypy.json
dmypy.json

# Pyre type checker
.pyre/
.idea
*.tmp.py
.DS_Store
.DS_store

================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
# Soxan

> در زبان پارسی به نام سخن


This repository consists of models, scripts, and notebooks that help you to use all the benefits of Wav2Vec 2.0 in your
research. In the following, I'll show you how to train speech tasks in your dataset and how to use the pretrained
models.

## How to train

I'm just at the beginning of all the possible speech tasks. To start, we continue the training script with the speech
emotion recognition problem.

### Training - Notebook

| Task                                     | Notebook                                                                                                                                                                                                            |
|------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Speech Emotion Recognition (Wav2Vec 2.0) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/m3hrdadfi/soxan/blob/main/notebooks/Emotion_recognition_in_Greek_speech_using_Wav2Vec2.ipynb) |
| Speech Emotion Recognition (Hubert)      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/m3hrdadfi/soxan/blob/main/notebooks/Emotion_recognition_in_Greek_speech_using_HuBERT.ipynb)   |
| Audio Classification (Wav2Vec 2.0)       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/m3hrdadfi/soxan/blob/main/notebooks/Eating_Sound_Collection_using_Wav2Vec2.ipynb)             |

### Training - CMD

```bash
python3 run_wav2vec_clf.py \
    --pooling_mode="mean" \
    --model_name_or_path="lighteternal/wav2vec2-large-xlsr-53-greek" \
    --model_mode="wav2vec2" \ # or you can use hubert
    --output_dir=/path/to/output \
    --cache_dir=/path/to/cache/ \
    --train_file=/path/to/train.csv \
    --validation_file=/path/to/dev.csv \
    --test_file=/path/to/test.csv \
    --per_device_train_batch_size=4 \
    --per_device_eval_batch_size=4 \
    --gradient_accumulation_steps=2 \
    --learning_rate=1e-4 \
    --num_train_epochs=5.0 \
    --evaluation_strategy="steps"\
    --save_steps=100 \
    --eval_steps=100 \
    --logging_steps=100 \
    --save_total_limit=2 \
    --do_eval \
    --do_train \
    --fp16 \
    --freeze_feature_extractor
```

### Prediction

```python
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
from transformers import AutoConfig, Wav2Vec2FeatureExtractor
from src.models import Wav2Vec2ForSpeechClassification, HubertForSpeechClassification

model_name_or_path = "path/to/your-pretrained-model"

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
config = AutoConfig.from_pretrained(model_name_or_path)
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)
sampling_rate = feature_extractor.sampling_rate

# for wav2vec
model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)

# for hubert
model = HubertForSpeechClassification.from_pretrained(model_name_or_path).to(device)


def speech_file_to_array_fn(path, sampling_rate):
    speech_array, _sampling_rate = torchaudio.load(path)
    resampler = torchaudio.transforms.Resample(_sampling_rate, sampling_rate)
    speech = resampler(speech_array).squeeze().numpy()
    return speech


def predict(path, sampling_rate):
    speech = speech_file_to_array_fn(path, sampling_rate)
    inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
    inputs = {key: inputs[key].to(device) for key in inputs}

    with torch.no_grad():
        logits = model(**inputs).logits

    scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
    outputs = [{"Emotion": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in
               enumerate(scores)]
    return outputs


path = "/path/to/disgust.wav"
outputs = predict(path, sampling_rate)    
```

Output:

```bash
[
    {'Emotion': 'anger', 'Score': '0.0%'},
    {'Emotion': 'disgust', 'Score': '99.2%'},
    {'Emotion': 'fear', 'Score': '0.1%'},
    {'Emotion': 'happiness', 'Score': '0.3%'},
    {'Emotion': 'sadness', 'Score': '0.5%'}
]
```


## Demos

| Demo                                                     | Link                                                                                                               |
|----------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------|
| Speech To Text With Emotion Recognition (Persian) - soon | [huggingface.co/spaces/m3hrdadfi/speech-text-emotion](https://huggingface.co/spaces/m3hrdadfi/speech-text-emotion) |


## Models

| Dataset                                                                                                                      | Model                                                                                                                                           |
|------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------|
| [ShEMO: a large-scale validated database for Persian speech emotion detection](https://github.com/mansourehk/ShEMO)          | [m3hrdadfi/wav2vec2-xlsr-persian-speech-emotion-recognition](https://huggingface.co/m3hrdadfi/wav2vec2-xlsr-persian-speech-emotion-recognition) |
| [ShEMO: a large-scale validated database for Persian speech emotion detection](https://github.com/mansourehk/ShEMO)          | [m3hrdadfi/hubert-base-persian-speech-emotion-recognition](https://huggingface.co/m3hrdadfi/hubert-base-persian-speech-emotion-recognition)     |
| [ShEMO: a large-scale validated database for Persian speech emotion detection](https://github.com/mansourehk/ShEMO)          | [m3hrdadfi/hubert-base-persian-speech-gender-recognition](https://huggingface.co/m3hrdadfi/hubert-base-persian-speech-gender-recognition)       |
| [Speech Emotion Recognition (Greek) (AESDD)](http://m3c.web.auth.gr/research/aesdd-speech-emotion-recognition/)              | [m3hrdadfi/hubert-large-greek-speech-emotion-recognition](https://huggingface.co/m3hrdadfi/hubert-large-greek-speech-emotion-recognition)       |
| [Speech Emotion Recognition (Greek) (AESDD)](http://m3c.web.auth.gr/research/aesdd-speech-emotion-recognition/)              | [m3hrdadfi/hubert-base-greek-speech-emotion-recognition](https://huggingface.co/m3hrdadfi/hubert-base-greek-speech-emotion-recognition)         |
| [Speech Emotion Recognition (Greek) (AESDD)](http://m3c.web.auth.gr/research/aesdd-speech-emotion-recognition/)              | [m3hrdadfi/wav2vec2-xlsr-greek-speech-emotion-recognition](https://huggingface.co/m3hrdadfi/wav2vec2-xlsr-greek-speech-emotion-recognition)     |
| [Eating Sound Collection](https://www.kaggle.com/mashijie/eating-sound-collection)                                           | [m3hrdadfi/wav2vec2-base-100k-eating-sound-collection](https://huggingface.co/m3hrdadfi/wav2vec2-base-100k-eating-sound-collection)             |
| [GTZAN Dataset - Music Genre Classification](https://www.kaggle.com/andradaolteanu/gtzan-dataset-music-genre-classification) | [m3hrdadfi/wav2vec2-base-100k-gtzan-music-genres](https://huggingface.co/m3hrdadfi/wav2vec2-base-100k-gtzan-music-genres)                       |

================================================
FILE: notebooks/Eating_Sound_Collection_using_Wav2Vec2.ipynb
================================================
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5fbCls1d2yBs"
      },
      "source": [
        "# Eating Sound Collection using Wav2Vec 2.0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sp37lZOV2042"
      },
      "source": [
        "**Wav2Vec 2.0** is a pretrained model for Automatic Speech Recognition (ASR) and was released in [September 2020](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) by Alexei Baevski, Michael Auli, and Alex Conneau.  Soon after the superior performance of Wav2Vec2 was demonstrated on the English ASR dataset LibriSpeech, *Facebook AI* presented XLSR-Wav2Vec2 (click [here](https://arxiv.org/abs/2006.13979)). XLSR stands for *cross-lingual  speech representations* and refers to XLSR-Wav2Vec2`s ability to learn speech representations that are useful across multiple languages.\n",
        "\n",
        "Similar to Wav2Vec2, XLSR-Wav2Vec2 learns powerful speech representations from hundreds of thousands of hours of speech in more than 50 languages of unlabeled speech. Similar, to [BERT's masked language modeling](http://jalammar.github.io/illustrated-bert/), the model learns contextualized speech representations by randomly masking feature vectors before passing them to a transformer network.\n",
        "\n",
        "![wav2vec2_structure](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/xlsr_wav2vec2.png)\n",
        "\n",
        "The authors show for the first time that massively pretraining an ASR model on cross-lingual unlabeled speech data, followed by language-specific fine-tuning on very little labeled data achieves state-of-the-art results. See Table 1-5 of the official [paper](https://arxiv.org/pdf/2006.13979.pdf)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0xJwDkA3QQR"
      },
      "source": [
        "During fine-tuning week hosted by HuggingFace, more than 300 people participated in tuning XLSR-Wav2Vec2's pretrained on low-resources ASR dataset for more than 50 languages. This model is fine-tuned using [Connectionist Temporal Classification](https://distill.pub/2017/ctc/) (CTC), an algorithm used to train neural networks for sequence-to-sequence problems and mainly in Automatic Speech Recognition and handwriting recognition. Follow this [notebook](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_XLSR_Wav2Vec2_on_Turkish_ASR_with_%F0%9F%A4%97_Transformers.ipynb#scrollTo=Gx9OdDYrCtQ1) for more information about XLSR-Wav2Vec2 fine-tuning.\n",
        "\n",
        "This model was shown significant results in many low-resources languages. You can see the [competition board](https://paperswithcode.com/dataset/common-voice) or even testing the models from the [HuggingFace hub](https://huggingface.co/models?filter=xlsr-fine-tuning-week). \n",
        "\n",
        "\n",
        "In this notebook, we will go through how to use this model to recognize the emotional aspects of speech in a language (or even as a general view using for every classification problem). Before going any further, we need to install some handy packages and define some enviroment values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TYsw43ULuXC_"
      },
      "source": [
        "%%capture\n",
        "\n",
        "!pip install git+https://github.com/huggingface/datasets.git\n",
        "!pip install git+https://github.com/huggingface/transformers.git\n",
        "!pip install jiwer\n",
        "!pip install torchaudio\n",
        "!pip install librosa\n",
        "\n",
        "# Monitor the training process\n",
        "# !pip install wandb"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cp4-LTa2uphv",
        "outputId": "e32a0dbd-8cd5-4bb9-87f6-c48e01a765b6"
      },
      "source": [
        "%env LC_ALL=C.UTF-8\n",
        "%env LANG=C.UTF-8\n",
        "%env TRANSFORMERS_CACHE=/content/cache\n",
        "%env HF_DATASETS_CACHE=/content/cache\n",
        "%env CUDA_LAUNCH_BLOCKING=1"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "env: LC_ALL=C.UTF-8\n",
            "env: LANG=C.UTF-8\n",
            "env: TRANSFORMERS_CACHE=/content/cache\n",
            "env: HF_DATASETS_CACHE=/content/cache\n",
            "env: CUDA_LAUNCH_BLOCKING=1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A0B06FRmusKF"
      },
      "source": [
        "# # Uncomment this part if you want to setup your wandb project\n",
        "\n",
        "# %env WANDB_WATCH=all\n",
        "# %env WANDB_LOG_MODEL=1\n",
        "# %env WANDB_PROJECT=YOUR_PROJECT_NAME\n",
        "# !wandb login YOUR_API_KEY --relogin"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "krC50MmmvBWJ"
      },
      "source": [
        "## Prepare Data\n",
        "\n",
        "For this particular example, we use [Eating Sound Collection](https://www.kaggle.com/mashijie/eating-sound-collection) provided by [Jeannette Shijie Ma](https://www.kaggle.com/mashijie). \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "Pp1zdKtuu8xj"
      },
      "source": [
        "#@title Kaggle Credential\n",
        "\n",
        "import json\n",
        "\n",
        "username = \"\" #@param {type: \"string\"}\n",
        "api_key = \"\" #@param {type: \"string\"}\n",
        "\n",
        "\n",
        "if username and api_key:\n",
        "    token = {\"username\": username, \"key\": api_key}\n",
        "\n",
        "    !mkdir ~/.kaggle\n",
        "    !mkdir /content/.kaggle\n",
        "    with open('/content/.kaggle/kaggle.json', 'w') as f:\n",
        "        json.dump(token, f)\n",
        "\n",
        "    !cp /content/.kaggle/kaggle.json ~/.kaggle/kaggle.json\n",
        "    !chmod 600 /root/.kaggle/kaggle.json\n",
        "\n",
        "    print('Your are ready to use kaggle API!')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "r1JfdqZqvLwT",
        "outputId": "543a8648-7a00-43f8-a4b7-9b1b3ae2e69c"
      },
      "source": [
        "#@title Download the dataset\n",
        "\n",
        "!mkdir -p /content/data\n",
        "!kaggle datasets download mashijie/eating-sound-collection\n",
        "!unzip -q eating-sound-collection.zip -d /content/data\n",
        "!rm eating-sound-collection.zip"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading eating-sound-collection.zip to /content\n",
            "100% 5.63G/5.63G [01:59<00:00, 55.0MB/s]\n",
            "100% 5.63G/5.63G [01:59<00:00, 50.7MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pFSqZ0jwCMSv"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from pathlib import Path\n",
        "from tqdm import tqdm\n",
        "\n",
        "import torchaudio\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import os\n",
        "import sys"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7MXAESg_Dqr6",
        "outputId": "8bc4b0df-7a50-42ee-af4b-fdd52beae66a"
      },
      "source": [
        "data = []\n",
        "\n",
        "for path in tqdm(Path(\"/content/data/clips_rd/\").glob(\"**/*.wav\")):\n",
        "    name = str(path).split('/')[-1].split('.')[0]\n",
        "    label = str(path).split('/')[-2]\n",
        "    \n",
        "    try:\n",
        "        # There are some broken files\n",
        "        s = torchaudio.load(path)\n",
        "        data.append({\n",
        "            # \"name\": name,\n",
        "            \"path\": path,\n",
        "            \"label\": label\n",
        "        })\n",
        "    except Exception as e:\n",
        "        # print(str(path), e)\n",
        "        pass\n",
        "\n",
        "    # break"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "11140it [02:23, 77.90it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "10b89ZpLDqx9",
        "outputId": "9aaf95d6-b1d1-40c3-fff1-244b164b1e9b"
      },
      "source": [
        "df = pd.DataFrame(data)\n",
        "df.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>path</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>/content/data/clips_rd/grapes/grapes_3_10.wav</td>\n",
              "      <td>grapes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>/content/data/clips_rd/grapes/grapes_1_13.wav</td>\n",
              "      <td>grapes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>/content/data/clips_rd/grapes/grapes_3_39.wav</td>\n",
              "      <td>grapes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>/content/data/clips_rd/grapes/grapes_2_13.wav</td>\n",
              "      <td>grapes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>/content/data/clips_rd/grapes/grapes_2_77.wav</td>\n",
              "      <td>grapes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            path   label\n",
              "0  /content/data/clips_rd/grapes/grapes_3_10.wav  grapes\n",
              "1  /content/data/clips_rd/grapes/grapes_1_13.wav  grapes\n",
              "2  /content/data/clips_rd/grapes/grapes_3_39.wav  grapes\n",
              "3  /content/data/clips_rd/grapes/grapes_2_13.wav  grapes\n",
              "4  /content/data/clips_rd/grapes/grapes_2_77.wav  grapes"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iMhTHur8voOp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "2e4e80c1-b63a-4254-c728-758ce27cec84"
      },
      "source": [
        "# Filter broken and non-existed paths\n",
        "\n",
        "print(f\"Step 0: {len(df)}\")\n",
        "\n",
        "df[\"status\"] = df[\"path\"].apply(lambda path: True if os.path.exists(path) else None)\n",
        "df = df.dropna(subset=[\"path\"])\n",
        "df = df.drop(\"status\", 1)\n",
        "print(f\"Step 1: {len(df)}\")\n",
        "\n",
        "df = df.sample(frac=1)\n",
        "df = df.reset_index(drop=True)\n",
        "df.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Step 0: 11140\n",
            "Step 1: 11140\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>path</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>/content/data/clips_rd/jelly/jelly_13_09.wav</td>\n",
              "      <td>jelly</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>/content/data/clips_rd/candied_fruits/candied_...</td>\n",
              "      <td>candied_fruits</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>/content/data/clips_rd/pickles/pickles_11_44.wav</td>\n",
              "      <td>pickles</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>/content/data/clips_rd/fries/fries_5_04.wav</td>\n",
              "      <td>fries</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>/content/data/clips_rd/carrots/carrots_4_54.wav</td>\n",
              "      <td>carrots</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                path           label\n",
              "0       /content/data/clips_rd/jelly/jelly_13_09.wav           jelly\n",
              "1  /content/data/clips_rd/candied_fruits/candied_...  candied_fruits\n",
              "2   /content/data/clips_rd/pickles/pickles_11_44.wav         pickles\n",
              "3        /content/data/clips_rd/fries/fries_5_04.wav           fries\n",
              "4    /content/data/clips_rd/carrots/carrots_4_54.wav         carrots"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TWku4ra3Bp52"
      },
      "source": [
        "Let's explore how many labels (sounds) are in the dataset with what distribution."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "beNpKMh5xXmX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 768
        },
        "outputId": "bc1ba8fb-f105-4024-9c82-51898f9acc63"
      },
      "source": [
        "print(\"Labels: \", df[\"label\"].unique())\n",
        "print()\n",
        "df.groupby(\"label\").count()[[\"path\"]]"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Labels:  ['jelly' 'candied_fruits' 'pickles' 'fries' 'carrots' 'aloe' 'chips'\n",
            " 'pizza' 'grapes' 'soup' 'gummies' 'ribs' 'salmon' 'wings' 'noodles'\n",
            " 'burger' 'cabbage' 'ice-cream' 'chocolate' 'drinks']\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>path</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>label</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>aloe</th>\n",
              "      <td>546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>burger</th>\n",
              "      <td>595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cabbage</th>\n",
              "      <td>499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>candied_fruits</th>\n",
              "      <td>806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>carrots</th>\n",
              "      <td>660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chips</th>\n",
              "      <td>719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chocolate</th>\n",
              "      <td>290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>drinks</th>\n",
              "      <td>292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fries</th>\n",
              "      <td>644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>grapes</th>\n",
              "      <td>579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gummies</th>\n",
              "      <td>678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ice-cream</th>\n",
              "      <td>727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>jelly</th>\n",
              "      <td>442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>noodles</th>\n",
              "      <td>411</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pickles</th>\n",
              "      <td>872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pizza</th>\n",
              "      <td>609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ribs</th>\n",
              "      <td>488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>salmon</th>\n",
              "      <td>501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>soup</th>\n",
              "      <td>278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wings</th>\n",
              "      <td>504</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                path\n",
              "label               \n",
              "aloe             546\n",
              "burger           595\n",
              "cabbage          499\n",
              "candied_fruits   806\n",
              "carrots          660\n",
              "chips            719\n",
              "chocolate        290\n",
              "drinks           292\n",
              "fries            644\n",
              "grapes           579\n",
              "gummies          678\n",
              "ice-cream        727\n",
              "jelly            442\n",
              "noodles          411\n",
              "pickles          872\n",
              "pizza            609\n",
              "ribs             488\n",
              "salmon           501\n",
              "soup             278\n",
              "wings            504"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i2hwRai7BNrx"
      },
      "source": [
        "Let's display some random sample of the dataset and run it a couple of times to get a feeling for the audio and the sound label."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DZaQ_sP5xkIX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103
        },
        "outputId": "797d1522-d574-4208-83e0-99ccc73cda10"
      },
      "source": [
        "import torchaudio\n",
        "import librosa\n",
        "import IPython.display as ipd\n",
        "import numpy as np\n",
        "\n",
        "idx = np.random.randint(0, len(df))\n",
        "sample = df.iloc[idx]\n",
        "path = sample[\"path\"]\n",
        "label = sample[\"label\"]\n",
        "\n",
        "\n",
        "print(f\"ID Location: {idx}\")\n",
        "print(f\"      Label: {label}\")\n",
        "print()\n",
        "\n",
        "speech, sr = torchaudio.load(path)\n",
        "speech = speech[0].numpy().squeeze()\n",
        "speech = librosa.resample(np.asarray(speech), sr, 16_000)\n",
        "ipd.Audio(data=np.asarray(speech), autoplay=True, rate=16000)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ID Location: 1207\n",
            "      Label: chips\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" autoplay=\"autoplay\">\n",
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