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
================================================
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*.py[cod]
*$py.class

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*.so

# Distribution / packaging
.Python
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*.egg-info/
.installed.cfg
*.egg
MANIFEST

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#  before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
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# Installer logs
pip-log.txt
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# Unit test / coverage reports
htmlcov/
.tox/
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.coverage
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.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

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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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\" type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WNjWoWRCmPk"
      },
      "source": [
        "For training purposes, we need to split data into train test sets; in this specific example, we break with a `20%` rate for the test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mlim-044xtJN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c7da7f4-cbd9-439c-fe54-f293e88520f7"
      },
      "source": [
        "save_path = \"/content/data\"\n",
        "\n",
        "train_df, test_df = train_test_split(df, test_size=0.2, random_state=101, stratify=df[\"label\"])\n",
        "\n",
        "train_df = train_df.reset_index(drop=True)\n",
        "test_df = test_df.reset_index(drop=True)\n",
        "\n",
        "train_df.to_csv(f\"{save_path}/train.csv\", sep=\"\\t\", encoding=\"utf-8\", index=False)\n",
        "test_df.to_csv(f\"{save_path}/test.csv\", sep=\"\\t\", encoding=\"utf-8\", index=False)\n",
        "\n",
        "\n",
        "print(train_df.shape)\n",
        "print(test_df.shape)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(8912, 2)\n",
            "(2228, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kcnD-d_rDElt"
      },
      "source": [
        "## Prepare Data for Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nnVfxQYDDIc6",
        "outputId": "81fd9d63-1cf5-4dc0-9a29-385728f48cd1"
      },
      "source": [
        "# Loading the created dataset using datasets\n",
        "from datasets import load_dataset, load_metric\n",
        "\n",
        "\n",
        "data_files = {\n",
        "    \"train\": \"/content/data/train.csv\", \n",
        "    \"validation\": \"/content/data/test.csv\",\n",
        "}\n",
        "\n",
        "dataset = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\", )\n",
        "train_dataset = dataset[\"train\"]\n",
        "eval_dataset = dataset[\"validation\"]\n",
        "\n",
        "print(train_dataset)\n",
        "print(eval_dataset)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using custom data configuration default-9fcc95274763cd69\n",
            "Reusing dataset csv (/content/cache/csv/default-9fcc95274763cd69/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Dataset({\n",
            "    features: ['path', 'label'],\n",
            "    num_rows: 8912\n",
            "})\n",
            "Dataset({\n",
            "    features: ['label', 'path'],\n",
            "    num_rows: 2228\n",
            "})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsOrQPBOEVOy"
      },
      "source": [
        "# We need to specify the input and output column\n",
        "input_column = \"path\"\n",
        "output_column = \"label\""
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-gh7fQ1XEpC7",
        "outputId": "f109902c-765d-4185-a936-4a0e99a36da9"
      },
      "source": [
        "# we need to distinguish the unique labels in our SER dataset\n",
        "label_list = train_dataset.unique(output_column)\n",
        "label_list.sort()  # Let's sort it for determinism\n",
        "num_labels = len(label_list)\n",
        "print(f\"A classification problem with {num_labels} classes: {label_list}\")"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A classification problem with 20 classes: ['aloe', 'burger', 'cabbage', 'candied_fruits', 'carrots', 'chips', 'chocolate', 'drinks', 'fries', 'grapes', 'gummies', 'ice-cream', 'jelly', 'noodles', 'pickles', 'pizza', 'ribs', 'salmon', 'soup', 'wings']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4TkGYrVTFR6Y"
      },
      "source": [
        "In order to preprocess the audio into our classification model, we need to set up the relevant Wav2Vec2 assets regarding our language in this case `lighteternal/wav2vec2-large-xlsr-53-greek` fine-tuned by [Dimitris Papadopoulos](https://huggingface.co/lighteternal/wav2vec2-large-xlsr-53-greek). To handle the context representations in any audio length we use a merge strategy plan (pooling mode) to concatenate that 3D representations into 2D representations.\n",
        "\n",
        "There are three merge strategies `mean`, `sum`, and `max`. In this example, we achieved better results on the mean approach. In the following, we need to initiate the config and the feature extractor from the Dimitris model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQrGaFohGzVo"
      },
      "source": [
        "from transformers import AutoConfig, Wav2Vec2Processor, Wav2Vec2FeatureExtractor"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Y1adr7vFrq7"
      },
      "source": [
        "model_name_or_path = \"facebook/wav2vec2-base-100k-voxpopuli\"\n",
        "pooling_mode = \"mean\""
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AZjDSmBRGqr6"
      },
      "source": [
        "# config\n",
        "config = AutoConfig.from_pretrained(\n",
        "    model_name_or_path,\n",
        "    num_labels=num_labels,\n",
        "    label2id={label: i for i, label in enumerate(label_list)},\n",
        "    id2label={i: label for i, label in enumerate(label_list)},\n",
        "    finetuning_task=\"wav2vec2_clf\",\n",
        ")\n",
        "setattr(config, 'pooling_mode', pooling_mode)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZXVl9qW1Gw_-",
        "outputId": "e934261f-9e31-4041-c751-6425afcb4182"
      },
      "source": [
        "feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path,)\n",
        "target_sampling_rate = feature_extractor.sampling_rate\n",
        "print(f\"The target sampling rate: {target_sampling_rate}\")"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The target sampling rate: 16000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CbGuYgLqHXZg"
      },
      "source": [
        "# Preprocess Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qLk-eM1DFjtE"
      },
      "source": [
        "So far, we downloaded, loaded, and split the SER dataset into train and test sets. The instantiated our strategy configuration for using context representations in our classification problem SER. Now, we need to extract features from the audio path in context representation tensors and feed them into our classification model to determine the emotion in the speech.\n",
        "\n",
        "Since the audio file is saved in the `.wav` format, it is easy to use **[Librosa](https://librosa.org/doc/latest/index.html)** or others, but we suppose that the format may be in the `.mp3` format in case of generality. We found that the **[Torchaudio](https://pytorch.org/audio/stable/index.html)** library works best for reading in `.mp3` data.\n",
        "\n",
        "An audio file usually stores both its values and the sampling rate with which the speech signal was digitalized. We want to store both in the dataset and write a **map(...)** function accordingly. Also, we need to handle the string labels into integers for our specific classification task in this case, the **single-label classification** you may want to use for your **regression** or even **multi-label classification**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UqlIV3uGxDA"
      },
      "source": [
        "def speech_file_to_array_fn(path):\n",
        "    speech_array, sampling_rate = torchaudio.load(path)\n",
        "    resampler = torchaudio.transforms.Resample(sampling_rate, target_sampling_rate)\n",
        "    speech = resampler(speech_array).squeeze().numpy()\n",
        "    return speech\n",
        "\n",
        "def label_to_id(label, label_list):\n",
        "\n",
        "    if len(label_list) > 0:\n",
        "        return label_list.index(label) if label in label_list else -1\n",
        "\n",
        "    return label\n",
        "\n",
        "def preprocess_function(examples):\n",
        "    speech_list = [speech_file_to_array_fn(path) for path in examples[input_column]]\n",
        "    target_list = [label_to_id(label, label_list) for label in examples[output_column]]\n",
        "\n",
        "    result = feature_extractor(speech_list, sampling_rate=target_sampling_rate)\n",
        "    result[\"labels\"] = list(target_list)\n",
        "\n",
        "    return result"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vYL7Z6Pd-Hp8"
      },
      "source": [
        "# Remove this part\n",
        "max_samples = 100\n",
        "train_dataset = train_dataset.select(range(max_samples))\n",
        "eval_dataset = eval_dataset.select(range(max_samples))"
      ],
      "execution_count": 21,
      "outputs": []
    },
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        "id": "ioP8FfR2GxHi",
        "outputId": "e3e976b3-0f7f-4b29-99c9-2317b443e810"
      },
      "source": [
        "train_dataset = train_dataset.map(\n",
        "    preprocess_function,\n",
        "    batch_size=10,\n",
        "    batched=True,\n",
        "    num_proc=4\n",
        ")\n",
        "eval_dataset = eval_dataset.map(\n",
        "    preprocess_function,\n",
        "    batch_size=10,\n",
        "    batched=True,\n",
        "    num_proc=4\n",
        ")"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " "
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=0.0, description=' #0', max=3.0, style=ProgressStyle(description_width='ini…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "   "
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c3201cfc4e0b4d82add46e49fa63ba4b",
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          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n",
            "    "
          ],
          "name": "stdout"
        },
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              "HBox(children=(FloatProgress(value=0.0, description=' #3', max=3.0, style=ProgressStyle(description_width='ini…"
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          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eu1qcRucHk6d",
        "outputId": "9f3d2aaf-10d5-4ce3-d24b-061ae264a875"
      },
      "source": [
        "idx = 0\n",
        "print(f\"Training input_values: {train_dataset[idx]['input_values']}\")\n",
        "# print(f\"Training attention_mask: {train_dataset[idx]['attention_mask']}\")\n",
        "print(f\"Training labels: {train_dataset[idx]['labels']} - {train_dataset[idx]['label']}\")"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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0.1176101341843605, 0.18185588717460632, 0.2011384516954422, 0.17388059198856354, 0.16160251200199127, 0.18288028240203857, 0.19167619943618774, 0.15304040908813477, 0.21248656511306763, 0.2320164144039154, 0.1754094809293747, 0.19831474125385284, 0.23765772581100464, 0.13360939919948578, 0.12323462218046188, 0.2077205330133438, 0.13303492963314056, 0.12599775195121765, 0.2282721847295761, 0.2445976585149765, 0.18302232027053833, 0.21532614529132843, 0.17823798954486847, 0.20187276601791382, 0.15008112788200378, 0.10248958319425583, 0.053042929619550705, 0.03613066300749779, 0.09249254316091537, 0.08493267744779587, 0.12211950123310089, 0.0733577311038971, 0.049066945910453796, 0.09599065035581589, 0.0821748748421669, -6.720394594594836e-05, -0.024759547784924507, 0.06176005303859711, 0.10787288844585419, 0.044565826654434204, 0.10088987648487091, 0.05432308092713356, 0.032419588416814804, 0.09062901139259338, 0.12257885932922363, 0.06062883883714676, 0.015911906957626343, 0.06644667685031891, 0.12651874125003815, 0.0637129470705986, -0.00016153565957210958, 0.06758525967597961, 0.09760179370641708, 0.12389711290597916, 0.13262808322906494, 0.04561571404337883, 0.08599776029586792, 0.10778097808361053, 0.09044789522886276, 0.09217625111341476, 0.11722038686275482, 0.08243187516927719, 0.06661631911993027, 0.0822589248418808, 0.04340073838829994, 0.10290156304836273, 0.1934203803539276, 0.20091316103935242, 0.12749487161636353, 0.061483755707740784]\n",
            "Training labels: 4 - carrots\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HcrEgJO9Hmx7"
      },
      "source": [
        "Great, now we've successfully read all the audio files, resampled the audio files to 16kHz, and mapped each audio to the corresponding label."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QL7q6DfcH0Fs"
      },
      "source": [
        "## Model\n",
        "\n",
        "Before diving into the training part, we need to build our classification model based on the merge strategy. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uJZoC4T5HpuP"
      },
      "source": [
        "from dataclasses import dataclass\n",
        "from typing import Optional, Tuple\n",
        "import torch\n",
        "from transformers.file_utils import ModelOutput\n",
        "\n",
        "\n",
        "@dataclass\n",
        "class SpeechClassifierOutput(ModelOutput):\n",
        "    loss: Optional[torch.FloatTensor] = None\n",
        "    logits: torch.FloatTensor = None\n",
        "    hidden_states: Optional[Tuple[torch.FloatTensor]] = None\n",
        "    attentions: Optional[Tuple[torch.FloatTensor]] = None\n"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fv62ShDsH5DZ"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss\n",
        "\n",
        "from transformers.models.wav2vec2.modeling_wav2vec2 import (\n",
        "    Wav2Vec2PreTrainedModel,\n",
        "    Wav2Vec2Model\n",
        ")\n",
        "\n",
        "\n",
        "class Wav2Vec2ClassificationHead(nn.Module):\n",
        "    \"\"\"Head for wav2vec classification task.\"\"\"\n",
        "\n",
        "    def __init__(self, config):\n",
        "        super().__init__()\n",
        "        self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n",
        "        self.dropout = nn.Dropout(config.final_dropout)\n",
        "        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)\n",
        "\n",
        "    def forward(self, features, **kwargs):\n",
        "        x = features\n",
        "        x = self.dropout(x)\n",
        "        x = self.dense(x)\n",
        "        x = torch.tanh(x)\n",
        "        x = self.dropout(x)\n",
        "        x = self.out_proj(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "class Wav2Vec2ForSpeechClassification(Wav2Vec2PreTrainedModel):\n",
        "    def __init__(self, config):\n",
        "        super().__init__(config)\n",
        "        self.num_labels = config.num_labels\n",
        "        self.pooling_mode = config.pooling_mode\n",
        "        self.config = config\n",
        "\n",
        "        self.wav2vec2 = Wav2Vec2Model(config)\n",
        "        self.classifier = Wav2Vec2ClassificationHead(config)\n",
        "\n",
        "        self.init_weights()\n",
        "\n",
        "    def freeze_feature_extractor(self):\n",
        "        self.wav2vec2.feature_extractor._freeze_parameters()\n",
        "\n",
        "    def merged_strategy(\n",
        "            self,\n",
        "            hidden_states,\n",
        "            mode=\"mean\"\n",
        "    ):\n",
        "        if mode == \"mean\":\n",
        "            outputs = torch.mean(hidden_states, dim=1)\n",
        "        elif mode == \"sum\":\n",
        "            outputs = torch.sum(hidden_states, dim=1)\n",
        "        elif mode == \"max\":\n",
        "            outputs = torch.max(hidden_states, dim=1)[0]\n",
        "        else:\n",
        "            raise Exception(\n",
        "                \"The pooling method hasn't been defined! Your pooling mode must be one of these ['mean', 'sum', 'max']\")\n",
        "\n",
        "        return outputs\n",
        "\n",
        "    def forward(\n",
        "            self,\n",
        "            input_values,\n",
        "            attention_mask=None,\n",
        "            output_attentions=None,\n",
        "            output_hidden_states=None,\n",
        "            return_dict=None,\n",
        "            labels=None,\n",
        "    ):\n",
        "        return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n",
        "        outputs = self.wav2vec2(\n",
        "            input_values,\n",
        "            attention_mask=attention_mask,\n",
        "            output_attentions=output_attentions,\n",
        "            output_hidden_states=output_hidden_states,\n",
        "            return_dict=return_dict,\n",
        "        )\n",
        "        hidden_states = outputs[0]\n",
        "        hidden_states = self.merged_strategy(hidden_states, mode=self.pooling_mode)\n",
        "        logits = self.classifier(hidden_states)\n",
        "\n",
        "        loss = None\n",
        "        if labels is not None:\n",
        "            if self.config.problem_type is None:\n",
        "                if self.num_labels == 1:\n",
        "                    self.config.problem_type = \"regression\"\n",
        "                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):\n",
        "                    self.config.problem_type = \"single_label_classification\"\n",
        "                else:\n",
        "                    self.config.problem_type = \"multi_label_classification\"\n",
        "\n",
        "            if self.config.problem_type == \"regression\":\n",
        "                loss_fct = MSELoss()\n",
        "                loss = loss_fct(logits.view(-1, self.num_labels), labels)\n",
        "            elif self.config.problem_type == \"single_label_classification\":\n",
        "                loss_fct = CrossEntropyLoss()\n",
        "                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\n",
        "            elif self.config.problem_type == \"multi_label_classification\":\n",
        "                loss_fct = BCEWithLogitsLoss()\n",
        "                loss = loss_fct(logits, labels)\n",
        "\n",
        "        if not return_dict:\n",
        "            output = (logits,) + outputs[2:]\n",
        "            return ((loss,) + output) if loss is not None else output\n",
        "\n",
        "        return SpeechClassifierOutput(\n",
        "            loss=loss,\n",
        "            logits=logits,\n",
        "            hidden_states=outputs.hidden_states,\n",
        "            attentions=outputs.attentions,\n",
        "        )\n"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OrBrR1b7zvUL"
      },
      "source": [
        "## Training\n",
        "\n",
        "The data is processed so that we are ready to start setting up the training pipeline. We will make use of 🤗's [Trainer](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer) for which we essentially need to do the following:\n",
        "\n",
        "- Define a data collator. In contrast to most NLP models, XLSR-Wav2Vec2 has a much larger input length than output length. *E.g.*, a sample of input length 50000 has an output length of no more than 100. Given the large input sizes, it is much more efficient to pad the training batches dynamically meaning that all training samples should only be padded to the longest sample in their batch and not the overall longest sample. Therefore, fine-tuning XLSR-Wav2Vec2 requires a special padding data collator, which we will define below\n",
        "\n",
        "- Evaluation metric. During training, the model should be evaluated on the word error rate. We should define a `compute_metrics` function accordingly\n",
        "\n",
        "- Load a pretrained checkpoint. We need to load a pretrained checkpoint and configure it correctly for training.\n",
        "\n",
        "- Define the training configuration.\n",
        "\n",
        "After having fine-tuned the model, we will correctly evaluate it on the test data and verify that it has indeed learned to correctly transcribe speech."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ji9-n1eUIKZc"
      },
      "source": [
        "### Set-up Trainer\n",
        "\n",
        "Let's start by defining the data collator. The code for the data collator was copied from [this example](https://github.com/huggingface/transformers/blob/9a06b6b11bdfc42eea08fa91d0c737d1863c99e3/examples/research_projects/wav2vec2/run_asr.py#L81).\n",
        "\n",
        "Without going into too many details, in contrast to the common data collators, this data collator treats the `input_values` and `labels` differently and thus applies to separate padding functions on them (again making use of XLSR-Wav2Vec2's context manager). This is necessary because in speech input and output are of different modalities meaning that they should not be treated by the same padding function.\n",
        "Analogous to the common data collators, the padding tokens in the labels with `-100` so that those tokens are **not** taken into account when computing the loss."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rkM0VLIwy903"
      },
      "source": [
        "from dataclasses import dataclass\n",
        "from typing import Dict, List, Optional, Union\n",
        "import torch\n",
        "\n",
        "import transformers\n",
        "from transformers import Wav2Vec2Processor, Wav2Vec2FeatureExtractor\n",
        "\n",
        "\n",
        "@dataclass\n",
        "class DataCollatorCTCWithPadding:\n",
        "    \"\"\"\n",
        "    Data collator that will dynamically pad the inputs received.\n",
        "    Args:\n",
        "        feature_extractor (:class:`~transformers.Wav2Vec2FeatureExtractor`)\n",
        "            The feature_extractor used for proccessing the data.\n",
        "        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):\n",
        "            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)\n",
        "            among:\n",
        "            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single\n",
        "              sequence if provided).\n",
        "            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the\n",
        "              maximum acceptable input length for the model if that argument is not provided.\n",
        "            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of\n",
        "              different lengths).\n",
        "        max_length (:obj:`int`, `optional`):\n",
        "            Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).\n",
        "        max_length_labels (:obj:`int`, `optional`):\n",
        "            Maximum length of the ``labels`` returned list and optionally padding length (see above).\n",
        "        pad_to_multiple_of (:obj:`int`, `optional`):\n",
        "            If set will pad the sequence to a multiple of the provided value.\n",
        "            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=\n",
        "            7.5 (Volta).\n",
        "    \"\"\"\n",
        "\n",
        "    feature_extractor: Wav2Vec2FeatureExtractor\n",
        "    padding: Union[bool, str] = True\n",
        "    max_length: Optional[int] = None\n",
        "    max_length_labels: Optional[int] = None\n",
        "    pad_to_multiple_of: Optional[int] = None\n",
        "    pad_to_multiple_of_labels: Optional[int] = None\n",
        "\n",
        "    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
        "        input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n",
        "        label_features = [feature[\"labels\"] for feature in features]\n",
        "\n",
        "        d_type = torch.long if isinstance(label_features[0], int) else torch.float\n",
        "\n",
        "        batch = self.feature_extractor.pad(\n",
        "            input_features,\n",
        "            padding=self.padding,\n",
        "            max_length=self.max_length,\n",
        "            pad_to_multiple_of=self.pad_to_multiple_of,\n",
        "            return_tensors=\"pt\",\n",
        "        )\n",
        "\n",
        "        batch[\"labels\"] = torch.tensor(label_features, dtype=d_type)\n",
        "\n",
        "        return batch\n"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YhJ0OA4RxQe"
      },
      "source": [
        "data_collator = DataCollatorCTCWithPadding(feature_extractor=feature_extractor, padding=True)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mYxy2IR-KcU2"
      },
      "source": [
        "Next, the evaluation metric is defined. There are many pre-defined metrics for classification/regression problems, but in this case, we would continue with just **Accuracy** for classification and **MSE** for regression. You can define other metrics on your own."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LL8I5MKvPnth"
      },
      "source": [
        "is_regression = False"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XK26Z6IfR36K"
      },
      "source": [
        "import numpy as np\n",
        "from transformers import EvalPrediction\n",
        "\n",
        "\n",
        "def compute_metrics(p: EvalPrediction):\n",
        "    preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions\n",
        "    preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1)\n",
        "\n",
        "    if is_regression:\n",
        "        return {\"mse\": ((preds - p.label_ids) ** 2).mean().item()}\n",
        "    else:\n",
        "        return {\"accuracy\": (preds == p.label_ids).astype(np.float32).mean().item()}"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZsH_nKJdK28o"
      },
      "source": [
        "Now, we can load the pretrained XLSR-Wav2Vec2 checkpoint into our classification model with a pooling strategy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Tl6iKAUR4EL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120,
          "referenced_widgets": [
            "1dee66d422f94e97b5410bda7d630183",
            "1140dc0960f54148a12d30ef4345ee96",
            "36750aaa3f494df4984a6878f7fddec2",
            "6f80a3a28a964c6383530cdd03ea4e47",
            "b182c0e62ff14820903daee05b60dec3",
            "ea64efae2d8c4f8eb9f0475ebec11512",
            "ec4c4a4c9dd84e7a9bf33b1b87c491d0",
            "8e3472f819d1468984cefd82c1217f00"
          ]
        },
        "outputId": "1fb33baa-00cd-4208-c595-b20373d58c29"
      },
      "source": [
        "model = Wav2Vec2ForSpeechClassification.from_pretrained(\n",
        "    model_name_or_path,\n",
        "    config=config,\n",
        ")"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1dee66d422f94e97b5410bda7d630183",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=377569754.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of Wav2Vec2ForSpeechClassification were not initialized from the model checkpoint at facebook/wav2vec2-base-100k-voxpopuli and are newly initialized: ['classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bqF4rNMzI1M5"
      },
      "source": [
        "The first component of XLSR-Wav2Vec2 consists of a stack of CNN layers that are used to extract acoustically meaningful - but contextually independent - features from the raw speech signal. This part of the model has already been sufficiently trained during pretraining and as stated in the [paper](https://arxiv.org/pdf/2006.13979.pdf) does not need to be fine-tuned anymore. \n",
        "Thus, we can set the `requires_grad` to `False` for all parameters of the *feature extraction* part."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KHMhxFGoR4Hb"
      },
      "source": [
        "model.freeze_feature_extractor()"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n0HzBneBK84G"
      },
      "source": [
        "In a final step, we define all parameters related to training. \n",
        "To give more explanation on some of the parameters:\n",
        "- `learning_rate` and `weight_decay` were heuristically tuned until fine-tuning has become stable. Note that those parameters strongly depend on the Common Voice dataset and might be suboptimal for other speech datasets.\n",
        "\n",
        "For more explanations on other parameters, one can take a look at the [docs](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer#trainingarguments).\n",
        "\n",
        "**Note**: If one wants to save the trained models in his/her google drive the commented-out `output_dir` can be used instead."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3tPqZ12BLCJk"
      },
      "source": [
        "# from google.colab import drive\n",
        "\n",
        "# drive.mount('/gdrive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vUtWjldAI9-H"
      },
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"/content/wav2vec2-base-100k-eating-sound-collection\",\n",
        "    # output_dir=\"/content/gdrive/MyDrive/wav2vec2-base-100k-eating-sound-collection\"\n",
        "    per_device_train_batch_size=4,\n",
        "    per_device_eval_batch_size=4,\n",
        "    gradient_accumulation_steps=2,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    num_train_epochs=1.0,\n",
        "    fp16=True,\n",
        "    save_steps=10,\n",
        "    eval_steps=10,\n",
        "    logging_steps=10,\n",
        "    learning_rate=1e-4,\n",
        "    save_total_limit=2,\n",
        ")"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XAtuL0APLZSs"
      },
      "source": [
        "For future use we can create our training script, we do it in a simple way. You can add more on you own."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KfW6uDolJYZv"
      },
      "source": [
        "from typing import Any, Dict, Union\n",
        "\n",
        "import torch\n",
        "from packaging import version\n",
        "from torch import nn\n",
        "\n",
        "from transformers import (\n",
        "    Trainer,\n",
        "    is_apex_available,\n",
        ")\n",
        "\n",
        "if is_apex_available():\n",
        "    from apex import amp\n",
        "\n",
        "if version.parse(torch.__version__) >= version.parse(\"1.6\"):\n",
        "    _is_native_amp_available = True\n",
        "    from torch.cuda.amp import autocast\n",
        "\n",
        "\n",
        "class CTCTrainer(Trainer):\n",
        "    def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor:\n",
        "        \"\"\"\n",
        "        Perform a training step on a batch of inputs.\n",
        "\n",
        "        Subclass and override to inject custom behavior.\n",
        "\n",
        "        Args:\n",
        "            model (:obj:`nn.Module`):\n",
        "                The model to train.\n",
        "            inputs (:obj:`Dict[str, Union[torch.Tensor, Any]]`):\n",
        "                The inputs and targets of the model.\n",
        "\n",
        "                The dictionary will be unpacked before being fed to the model. Most models expect the targets under the\n",
        "                argument :obj:`labels`. Check your model's documentation for all accepted arguments.\n",
        "\n",
        "        Return:\n",
        "            :obj:`torch.Tensor`: The tensor with training loss on this batch.\n",
        "        \"\"\"\n",
        "\n",
        "        model.train()\n",
        "        inputs = self._prepare_inputs(inputs)\n",
        "\n",
        "        if self.use_amp:\n",
        "            with autocast():\n",
        "                loss = self.compute_loss(model, inputs)\n",
        "        else:\n",
        "            loss = self.compute_loss(model, inputs)\n",
        "\n",
        "        if self.args.gradient_accumulation_steps > 1:\n",
        "            loss = loss / self.args.gradient_accumulation_steps\n",
        "\n",
        "        if self.use_amp:\n",
        "            self.scaler.scale(loss).backward()\n",
        "        elif self.use_apex:\n",
        "            with amp.scale_loss(loss, self.optimizer) as scaled_loss:\n",
        "                scaled_loss.backward()\n",
        "        elif self.deepspeed:\n",
        "            self.deepspeed.backward(loss)\n",
        "        else:\n",
        "            loss.backward()\n",
        "\n",
        "        return loss.detach()\n"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qv7Ju3qYJeJn"
      },
      "source": [
        "Now, all instances can be passed to Trainer and we are ready to start training!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nEFkfK45JYiZ"
      },
      "source": [
        "trainer = CTCTrainer(\n",
        "    model=model,\n",
        "    data_collator=data_collator,\n",
        "    args=training_args,\n",
        "    compute_metrics=compute_metrics,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=eval_dataset,\n",
        "    tokenizer=feature_extractor,\n",
        ")"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0gGLwJAOLtDg"
      },
      "source": [
        "### Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UpN6xlWCLxJ7"
      },
      "source": [
        "Training will take between 10 and 60 minutes depending on the GPU allocated to this notebook. \n",
        "\n",
        "In case you want to use this google colab to fine-tune your model, you should make sure that your training doesn't stop due to inactivity. A simple hack to prevent this is to paste the following code into the console of this tab (right mouse click -> inspect -> Console tab and insert code)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zyYZH7YZL8a9"
      },
      "source": [
        "```javascript\n",
        "function ConnectButton(){\n",
        "    console.log(\"Connect pushed\"); \n",
        "    document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click() \n",
        "}\n",
        "setInterval(ConnectButton,60000);\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "6M8bNvLLJnG1",
        "outputId": "0ccbcac2-bcff-40fe-da79-475ff47b3401"
      },
      "source": [
        "trainer.train()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='12' max='12' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [12/12 00:18, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>2.983600</td>\n",
              "      <td>2.960002</td>\n",
              "      <td>0.080000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=12, training_loss=2.985006014506022, metrics={'train_runtime': 20.324, 'train_samples_per_second': 4.92, 'train_steps_per_second': 0.59, 'total_flos': 5184541517757696.0, 'train_loss': 2.985006014506022, 'epoch': 0.96})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3prIN9eiMBHo"
      },
      "source": [
        "The training loss goes down and we can see that the Acurracy on the test set also improves nicely. Because this notebook is just for demonstration purposes, we can stop here.\n",
        "\n",
        "The resulting model of this notebook has been saved to [m3hrdadfi/wav2vec2-base-100k-eating-sound-collection](https://huggingface.co/m3hrdadfi/wav2vec2-base-100k-eating-sound-collection)\n",
        "\n",
        "As a final check, let's load the model and verify that it indeed has learned to recognize the sound of your eating.\n",
        "\n",
        "Let's first load the pretrained checkpoint."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VsaOTx_FVm0i"
      },
      "source": [
        "## Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4tGNY7hRXO44"
      },
      "source": [
        "import librosa\n",
        "from sklearn.metrics import classification_report"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "c179789d1b5d45acbc5d6ac5bab0b832",
            "e9d9abef9b964776a9a021520ba4d992",
            "6c720dc5f44e4ea29514581abba8622e",
            "e76ee2831f374af7942b991c86f213d7",
            "5fd40b7c360143348d565be3737d2da2",
            "f83849e39ca84c3ab7cfd41db31f586f",
            "91df691fd8d64fc08e13918ab129b04d",
            "f9307f431af0483b8529085b869d2135"
          ]
        },
        "id": "IYxg1Tfo2VUw",
        "outputId": "d0154d9d-6280-4d15-fc82-9f80472bc60a"
      },
      "source": [
        "test_dataset = load_dataset(\"csv\", data_files={\"test\": \"/content/data/test.csv\"}, delimiter=\"\\t\")[\"test\"]\n",
        "test_dataset"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using custom data configuration default-a87e4969089cca91\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset csv/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /content/cache/csv/default-a87e4969089cca91/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c179789d1b5d45acbc5d6ac5bab0b832",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rDataset csv downloaded and prepared to /content/cache/csv/default-a87e4969089cca91/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0. Subsequent calls will reuse this data.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['path', 'label'],\n",
              "    num_rows: 2228\n",
              "})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QgZFkMDHW_Um",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65ebb730-9e31-44a2-e83b-135b58a08bd5"
      },
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(f\"Device: {device}\")"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Device: cuda\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ESFEXeaWgua",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164,
          "referenced_widgets": [
            "018dfa68a89c43c8b064050242e82181",
            "3877cc93cf12456097b0575959f25e87",
            "e7e6a8c03649466d875f7150e6ad5b72",
            "85c562df9b824ef8b415bda19e334b54",
            "bfb450ba48414582806803bce59c268e",
            "42ff79e2ae01425ba0134c58e2580cc0",
            "645a359bcf354e6d957dea3d0d0acf29",
            "9d6520c0051a43709fc2f3697cc80244",
            "c16f2f140d6b4183b74fab44b7d20b2b",
            "c3a064ce28534052b4b4abac7b395a81",
            "dc27704fed6e4a01b9a23f9d9b3dc3df",
            "d976392882e04bc99cc477d9520c7bcf",
            "8b853441e8c34071ba45b0f7191eb9f6",
            "48a593d89da84f649fab9e7f87f8f767",
            "ff209e55d853421a84770d8f9164f62c",
            "9c38ebf5864a4e8896cf002876fa7cec",
            "1fed03a5bc0e4333805eabf089b9b0ea",
            "a7ad0056c5f64423b29be2fd88c78719",
            "934b92e53b7d41d5b4fe32014e95cbed",
            "d06b74a1da0048dbb711c7b73b4c0af3",
            "b3288ffcd75841048ea74f0f75c6c6fd",
            "bbaa7594bded475abb8e0bdf92054c86",
            "3187736ad3714b8e81224b53f0687664",
            "404bd7661d9e43a6901a6bc12b5c6f93"
          ]
        },
        "outputId": "347f6268-b943-469a-9859-0c0ce45eaa63"
      },
      "source": [
        "model_name_or_path = \"m3hrdadfi/wav2vec2-base-100k-eating-sound-collection\"\n",
        "config = AutoConfig.from_pretrained(model_name_or_path)\n",
        "feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)\n",
        "model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "018dfa68a89c43c8b064050242e82181",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=2270.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c16f2f140d6b4183b74fab44b7d20b2b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=213.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1fed03a5bc0e4333805eabf089b9b0ea",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=380001726.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BkEd4w8IV7kZ"
      },
      "source": [
        "def speech_file_to_array_fn(batch):\n",
        "    speech_array, sampling_rate = torchaudio.load(batch[\"path\"])\n",
        "    speech_array = speech_array.squeeze().numpy()\n",
        "    speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, feature_extractor.sampling_rate)\n",
        "\n",
        "    batch[\"speech\"] = speech_array\n",
        "    return batch\n",
        "\n",
        "\n",
        "def predict(batch):\n",
        "    features = feature_extractor(batch[\"speech\"], sampling_rate=feature_extractor.sampling_rate, return_tensors=\"pt\", padding=True)\n",
        "\n",
        "    input_values = features.input_values.to(device)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        logits = model(input_values).logits \n",
        "\n",
        "    pred_ids = torch.argmax(logits, dim=-1).detach().cpu().numpy()\n",
        "    batch[\"predicted\"] = pred_ids\n",
        "    return batch"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "c8839901ba094c8ab415d65748d3af49",
            "245c287d72cb404f827736af171b8803",
            "f843e9c85ceb48fa8828986330993260",
            "9e9f79c923b94e90abccc36283252499",
            "6146d16c791242ed8415a8ef0478b420",
            "75e6d16ae7cb40c3bea7723921f94ae9",
            "e0d8f9cbacb64b8cbd82708309653734",
            "221f9e32a50d4d18b478da3df315b5fa"
          ]
        },
        "id": "S4P6P6XwW85p",
        "outputId": "f59e0a91-c409-422a-cff2-13ed14b11bfc"
      },
      "source": [
        "test_dataset = test_dataset.map(speech_file_to_array_fn)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c8839901ba094c8ab415d65748d3af49",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=2228.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "1cf3d36ba8b24757a48ea06239b8c595",
            "5b7655a0ec914ce285b4802a7265dd1b",
            "07e02f90c9474a9bac60af6e157c5635",
            "c6d50e8f952e455bae2bf13902960026",
            "4b725e6473e5477c9161eecf4af4113f",
            "fc2deb84c1124cf9b28005f8eafdcc5f",
            "d3561462d3224124a63ed50cf42709f9",
            "33189cc9393f494082c08f62c71f5d8d"
          ]
        },
        "id": "K_oZJzHsXKHv",
        "outputId": "c31ccfcc-416f-4b79-f916-21e6b3e27fb5"
      },
      "source": [
        "result = test_dataset.map(predict, batched=True, batch_size=8)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1cf3d36ba8b24757a48ea06239b8c595",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=279.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BnfJLZvAaxTo",
        "outputId": "810472e5-03e2-4448-ff87-2473918ef403"
      },
      "source": [
        "label_names = [config.id2label[i] for i in range(config.num_labels)]\n",
        "label_names"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['aloe',\n",
              " 'burger',\n",
              " 'cabbage',\n",
              " 'candied_fruits',\n",
              " 'carrots',\n",
              " 'chips',\n",
              " 'chocolate',\n",
              " 'drinks',\n",
              " 'fries',\n",
              " 'grapes',\n",
              " 'gummies',\n",
              " 'ice-cream',\n",
              " 'jelly',\n",
              " 'noodles',\n",
              " 'pickles',\n",
              " 'pizza',\n",
              " 'ribs',\n",
              " 'salmon',\n",
              " 'soup',\n",
              " 'wings']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vRtajzvTabeH",
        "outputId": "a9cfb08c-8766-4cab-c107-7922a8f07424"
      },
      "source": [
        "y_true = [config.label2id[name] for name in result[\"label\"]]\n",
        "y_pred = result[\"predicted\"]\n",
        "\n",
        "print(y_true[:5])\n",
        "print(y_pred[:5])"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[2, 15, 0, 3, 18]\n",
            "[2, 15, 0, 3, 10]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tUt5rIppXrzl",
        "outputId": "6f4e1502-b862-446c-c1e1-bc1d11a1fa9f"
      },
      "source": [
        "print(classification_report(y_true, y_pred, target_names=label_names))"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                precision    recall  f1-score   support\n",
            "\n",
            "          aloe       0.99      0.91      0.95       109\n",
            "        burger       1.00      0.57      0.73       119\n",
            "       cabbage       0.95      0.96      0.96       100\n",
            "candied_fruits       0.97      1.00      0.98       161\n",
            "       carrots       0.98      1.00      0.99       132\n",
            "         chips       0.99      0.99      0.99       144\n",
            "     chocolate       0.89      1.00      0.94        58\n",
            "        drinks       1.00      1.00      1.00        58\n",
            "         fries       0.97      0.94      0.95       129\n",
            "        grapes       1.00      0.99      1.00       116\n",
            "       gummies       0.96      0.97      0.96       136\n",
            "     ice-cream       0.97      1.00      0.99       145\n",
            "         jelly       0.99      0.90      0.94        88\n",
            "       noodles       0.83      0.95      0.89        82\n",
            "       pickles       0.99      0.98      0.99       174\n",
            "         pizza       0.81      0.98      0.89       122\n",
            "          ribs       0.89      0.86      0.88        98\n",
            "        salmon       0.79      1.00      0.88       100\n",
            "          soup       1.00      0.93      0.96        56\n",
            "         wings       0.89      0.85      0.87       101\n",
            "\n",
            "      accuracy                           0.94      2228\n",
            "     macro avg       0.94      0.94      0.94      2228\n",
            "  weighted avg       0.95      0.94      0.94      2228\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ylb2Z6Xke2ro"
      },
      "source": [
        "# Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EQzCioPhWIiX"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchaudio\n",
        "from transformers import AutoConfig, Wav2Vec2Processor\n",
        "\n",
        "import librosa\n",
        "import IPython.display as ipd\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DocavTvQWIr_"
      },
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model_name_or_path = \"m3hrdadfi/wav2vec2-base-100k-eating-sound-collection\"\n",
        "config = AutoConfig.from_pretrained(model_name_or_path)\n",
        "feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)\n",
        "sampling_rate = feature_extractor.sampling_rate\n",
        "model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1SSs95o9WIvK"
      },
      "source": [
        "def speech_file_to_array_fn(path, sampling_rate):\n",
        "    speech_array, _sampling_rate = torchaudio.load(path)\n",
        "    resampler = torchaudio.transforms.Resample(_sampling_rate)\n",
        "    speech = resampler(speech_array).squeeze().numpy()\n",
        "    return speech\n",
        "\n",
        "\n",
        "def predict(path, sampling_rate):\n",
        "    speech = speech_file_to_array_fn(path, sampling_rate)\n",
        "    features = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors=\"pt\", padding=True)\n",
        "\n",
        "    input_values = features.input_values.to(device)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        logits = model(input_values).logits\n",
        "\n",
        "    scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]\n",
        "    outputs = [{\"Label\": config.id2label[i], \"Score\": f\"{round(score * 100, 3):.1f}%\"} for i, score in enumerate(scores)]\n",
        "    return outputs\n",
        "\n",
        "\n",
        "STYLES = \"\"\"\n",
        "<style>\n",
        "div.display_data {\n",
        "    margin: 0 auto;\n",
        "    max-width: 500px;\n",
        "}\n",
        "table.xxx {\n",
        "    margin: 50px !important;\n",
        "    float: right !important;\n",
        "    clear: both !important;\n",
        "}\n",
        "table.xxx td {\n",
        "    min-width: 300px !important;\n",
        "    text-align: center !important;\n",
        "}\n",
        "</style>\n",
        "\"\"\".strip()\n",
        "\n",
        "def prediction(df_row):\n",
        "    path, label = df_row[\"path\"], df_row[\"label\"]\n",
        "    df = pd.DataFrame([{\"Label\": label, \"Sentence\": \"    \"}])\n",
        "    setup = {\n",
        "        'border': 2,\n",
        "        'show_dimensions': True,\n",
        "        'justify': 'center',\n",
        "        'classes': 'xxx',\n",
        "        'escape': False,\n",
        "    }\n",
        "    ipd.display(ipd.HTML(STYLES + df.to_html(**setup) + \"<br />\"))\n",
        "    speech, sr = torchaudio.load(path)\n",
        "    speech = speech[0].numpy().squeeze()\n",
        "    speech = librosa.resample(np.asarray(speech), sr, sampling_rate)\n",
        "    ipd.display(ipd.Audio(data=np.asarray(speech), autoplay=True, rate=sampling_rate))\n",
        "\n",
        "    outputs = predict(path, sampling_rate)\n",
        "    r = pd.DataFrame(outputs)\n",
        "    ipd.display(ipd.HTML(STYLES + r.to_html(**setup) + \"<br />\"))"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "UD7oUP20YwYT",
        "outputId": "82aac5b9-3b85-432d-8711-6ad7611e5f76"
      },
      "source": [
        "test = pd.read_csv(\"/content/data/test.csv\", sep=\"\\t\")\n",
        "test.head()"
      ],
      "execution_count": 68,
      "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/cabbage/cabbage_7_12.wav</td>\n",
              "      <td>cabbage</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>/content/data/clips_rd/pizza/pizza_3_30.wav</td>\n",
              "      <td>pizza</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>/content/data/clips_rd/aloe/aloe_10_21.wav</td>\n",
              "      <td>aloe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>/content/data/clips_rd/candied_fruits/candied_...</td>\n",
              "      <td>candied_fruits</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>/content/data/clips_rd/soup/soup_3_24.wav</td>\n",
              "      <td>soup</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                path           label\n",
              "0    /content/data/clips_rd/cabbage/cabbage_7_12.wav         cabbage\n",
              "1        /content/data/clips_rd/pizza/pizza_3_30.wav           pizza\n",
              "2         /content/data/clips_rd/aloe/aloe_10_21.wav            aloe\n",
              "3  /content/data/clips_rd/candied_fruits/candied_...  candied_fruits\n",
              "4          /content/data/clips_rd/soup/soup_3_24.wav            soup"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "FlJO2LfVWIyT",
        "outputId": "f917fea4-ac34-47b4-b0a8-baa2650b22be"
      },
      "source": [
        "prediction(test.iloc[0])"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              "div.display_data {\n",
              "    margin: 0 auto;\n",
              "    max-width: 500px;\n",
              "}\n",
              "table.xxx {\n",
              "    margin: 50px !important;\n",
              "    float: right !important;\n",
              "    clear: both !important;\n",
              "}\n",
              "table.xxx td {\n",
              "    min-width: 300px !important;\n",
              "    text-align: center !important;\n",
              "}\n",
              "</style><table border=\"2\" class=\"dataframe xxx\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: center;\">\n",
              "      <th></th>\n",
              "      <th>Label</th>\n",
              "      <th>Sentence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>cabbage</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 2 columns</p><br />"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" autoplay=\"autoplay\">\n",
              "                    <source 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\" 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              "                </audio>\n",
              "              "
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            "text/plain": [
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          "metadata": {
            "tags": []
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        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              "div.display_data {\n",
              "    margin: 0 auto;\n",
              "    max-width: 500px;\n",
              "}\n",
              "table.xxx {\n",
              "    margin: 50px !important;\n",
              "    float: right !important;\n",
              "    clear: both !important;\n",
              "}\n",
              "table.xxx td {\n",
              "    min-width: 300px !important;\n",
              "    text-align: center !important;\n",
              "}\n",
              "</style><table border=\"2\" class=\"dataframe xxx\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: center;\">\n",
              "      <th></th>\n",
              "      <th>Label</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>aloe</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>burger</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>cabbage</td>\n",
              "      <td>100.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>candied_fruits</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>carrots</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>chips</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>chocolate</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>drinks</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>fries</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>grapes</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>gummies</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>ice-cream</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>jelly</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>noodles</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>pickles</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>pizza</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>ribs</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>salmon</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>soup</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>wings</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20 rows × 2 columns</p><br />"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hzoKOgpoWI1K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "563cde17-2786-41c8-9a6e-79bfcd0cf992"
      },
      "source": [
        "prediction(test.iloc[1])"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              "div.display_data {\n",
              "    margin: 0 auto;\n",
              "    max-width: 500px;\n",
              "}\n",
              "table.xxx {\n",
              "    margin: 50px !important;\n",
              "    float: right !important;\n",
              "    clear: both !important;\n",
              "}\n",
              "table.xxx td {\n",
              "    min-width: 300px !important;\n",
              "    text-align: center !important;\n",
              "}\n",
              "</style><table border=\"2\" class=\"dataframe xxx\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: center;\">\n",
              "      <th></th>\n",
              "      <th>Label</th>\n",
              "      <th>Sentence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>pizza</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 2 columns</p><br />"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" autoplay=\"autoplay\">\n",
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\" type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              "div.display_data {\n",
              "    margin: 0 auto;\n",
              "    max-width: 500px;\n",
              "}\n",
              "table.xxx {\n",
              "    margin: 50px !important;\n",
              "    float: right !important;\n",
              "    clear: both !important;\n",
              "}\n",
              "table.xxx td {\n",
              "    min-width: 300px !important;\n",
              "    text-align: center !important;\n",
              "}\n",
              "</style><table border=\"2\" class=\"dataframe xxx\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: center;\">\n",
              "      <th></th>\n",
              "      <th>Label</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>aloe</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>burger</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>cabbage</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>candied_fruits</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>carrots</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>chips</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>chocolate</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>drinks</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>fries</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>grapes</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>gummies</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>ice-cream</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>jelly</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>noodles</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>pickles</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>pizza</td>\n",
              "      <td>99.9%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>ribs</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>salmon</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>soup</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>wings</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20 rows × 2 columns</p><br />"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7nqwbTTXSfMO",
        "outputId": "1da5a96e-4314-4850-b3cb-a459ee911c7d"
      },
      "source": [
        "prediction(test.iloc[2])"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              "div.display_data {\n",
              "    margin: 0 auto;\n",
              "    max-width: 500px;\n",
              "}\n",
              "table.xxx {\n",
              "    margin: 50px !important;\n",
              "    float: right !important;\n",
              "    clear: both !important;\n",
              "}\n",
              "table.xxx td {\n",
              "    min-width: 300px !important;\n",
              "    text-align: center !important;\n",
              "}\n",
              "</style><table border=\"2\" class=\"dataframe xxx\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: center;\">\n",
              "      <th></th>\n",
              "      <th>Label</th>\n",
              "      <th>Sentence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>aloe</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 2 columns</p><br />"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              "div.display_data {\n",
              "    margin: 0 auto;\n",
              "    max-width: 500px;\n",
              "}\n",
              "table.xxx {\n",
              "    margin: 50px !important;\n",
              "    float: right !important;\n",
              "    clear: both !important;\n",
              "}\n",
              "table.xxx td {\n",
              "    min-width: 300px !important;\n",
              "    text-align: center !important;\n",
              "}\n",
              "</style><table border=\"2\" class=\"dataframe xxx\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: center;\">\n",
              "      <th></th>\n",
              "      <th>Label</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>aloe</td>\n",
              "      <td>100.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>burger</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>cabbage</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>candied_fruits</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>carrots</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>chips</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>chocolate</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>drinks</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>fries</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>grapes</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>gummies</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>ice-cream</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>jelly</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>noodles</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>pickles</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>pizza</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>ribs</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>salmon</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>soup</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>wings</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20 rows × 2 columns</p><br />"
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}

================================================
FILE: notebooks/Emotion_recognition_in_Greek_speech_using_HuBERT.ipynb
================================================
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            "_model_name": "LayoutModel",
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          }
        }
      }
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5fbCls1d2yBs"
      },
      "source": [
        "# Emotion Recognition in Greek Speech Using HuBERT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sp37lZOV2042"
      },
      "source": [
        "**HuBERT** leverages the masked prediction loss over sequences, e.g., \n",
        "Google’s Bidirectional Encoder Representations from Transformers, or BERT, method, to represent the sequential structure of speech. HuBERT uses an offline clustering step to generate noisy labels for Masked Language Model pretraining. Concretely, HuBERT consumes masked continuous speech features to predict predetermined cluster assignments. The predictive loss is applied over only the masked regions, forcing the model to learn good high-level representations of unmasked inputs in order to infer the targets of masked ones correctly.\n",
        "\n",
        "<img src=\"https://scontent-arn2-1.xx.fbcdn.net/v/t39.2365-6/199774494_858205978461255_8774541146123618096_n.jpg?_nc_cat=102&ccb=1-3&_nc_sid=ad8a9d&_nc_ohc=ijlx7hJyyDYAX9kLFAg&_nc_ht=scontent-arn2-1.xx&oh=7eed4335bf838d8372c50cae730c13a8&oe=60F2042B\" alt=\"HuBERT\" height=\"700\" />\n",
        "\n",
        "The official [paper](https://arxiv.org/pdf/2106.07447.pdf)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0xJwDkA3QQR"
      },
      "source": [
        "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "44tiWBh0FFcJ",
        "outputId": "48de2ab5-c2be-43c7-e88e-b4ead315a745"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Thu Jun 17 16:21:06 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 465.27       Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   50C    P8    10W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hirl5Co6GW4P"
      },
      "source": [
        "# from google.colab import drive\n",
        "\n",
        "# drive.mount('/content/drive', force_remount=True)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "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 torchaudio\n",
        "!pip install librosa\n",
        "\n",
        "# Monitor the training process\n",
        "# !pip install wandb"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cp4-LTa2uphv",
        "outputId": "ecd9b4f4-ca03-4085-b2eb-fb1b5299aa50"
      },
      "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": 4,
      "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": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "krC50MmmvBWJ"
      },
      "source": [
        "## Prepare Data\n",
        "\n",
        "For this particular example, we use [Acted Emotional Speech Dynamic Database – AESDD](http://m3c.web.auth.gr/research/aesdd-speech-emotion-recognition/) provided by Multidisciplinary Media & Mediated Communication Research Group ([M3C](http://m3c.web.auth.gr/)). \n",
        "\n",
        "The Acted Emotional Speech Dynamic Database (AESDD) is a publically available speech emotion recognition dataset that contains utterances of acted emotional speech in the Greek language for five different emotions `sadness`, `disgust`, `happiness`, `anger`, and `fear`.\n",
        "\n",
        "The dataset consists of directories of emotions; each folder includes specific emotions. We need to loop over directories and save the paths related to each class based on the directory name.\n",
        "\n",
        "```bash\n",
        ".\n",
        "├── Tools\\ and\\ Documentation\n",
        "│   ├── ESTrainer.mlapp\n",
        "│   ├── Speech\\ Emotion\\ Recognition\\ Adapted\\ to\\ Multimodal\\ Semantic\\ Repositories_documentation.pdf\n",
        "│   ├── Speech\\ Emotion\\ Recognition\\ for\\ Performance\\ Interaction.pdf\n",
        "│   └── readme.txt\n",
        "├── anger\n",
        "│   ├── a01\\ (1).wav\n",
        "│   ├── a01\\ (2).wav\n",
        "│   ├── ...\n",
        "├── disgust\n",
        "│   ├── d01\\ (1).wav\n",
        "│   ├── d01\\ (2).wav\n",
        "│   ├── ...\n",
        "├── fear\n",
        "│   ├── f01\\ (1).wav\n",
        "│   ├── f01\\ (2).wav\n",
        "│   ├── ...\n",
        "├── happiness\n",
        "│   ├── h01\\ (1).wav\n",
        "│   ├── h01\\ (2).wav\n",
        "│   ├── ...\n",
        "└── sadness\n",
        "    ├── s01\\ (1).wav\n",
        "    ├── s01\\ (2).wav\n",
        "    ├── ...\n",
        "\n",
        "6 directories, 609 files\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4UVGPPOVCl-Y"
      },
      "source": [
        "Since the dataset is hosted on [Mega](https://mega.nz/#F!0ShVXY7C!-73kVoK05OjTPEA95UUvMw) and unable to access it directly, we upload the dataset on the G-drive to make this tutorial accessible."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nEKDAy2bCGFM",
        "outputId": "86a6cd25-b129-4ac3-d414-06b7708ee258"
      },
      "source": [
        "# Download the dataset from \n",
        "\n",
        "!mkdir -p /content/data\n",
        "!gdown https://drive.google.com/uc?id=1_IAWexEWpH-ly_JaA5EGfZDp-_3flkN1\n",
        "!unzip -q aesdd.zip -d /content/data/\n",
        "!mv \"/content/data/Acted Emotional Speech Dynamic Database/\" /content/data/aesdd/"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1_IAWexEWpH-ly_JaA5EGfZDp-_3flkN1\n",
            "To: /content/aesdd.zip\n",
            "410MB [00:02, 175MB/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": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7MXAESg_Dqr6",
        "outputId": "c72c65c7-4532-432a-8c55-c8b5eaff689c"
      },
      "source": [
        "data = []\n",
        "\n",
        "for path in tqdm(Path(\"/content/data/aesdd\").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",
        "            \"emotion\": 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": [
            "605it [00:01, 530.15it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "10b89ZpLDqx9",
        "outputId": "1a89eb3f-5a2b-41fc-acf6-0ac82d702090"
      },
      "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>name</th>\n",
              "      <th>path</th>\n",
              "      <th>emotion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>d19 (4)</td>\n",
              "      <td>/content/data/aesdd/disgust/d19 (4).wav</td>\n",
              "      <td>disgust</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>d01 (5)</td>\n",
              "      <td>/content/data/aesdd/disgust/d01 (5).wav</td>\n",
              "      <td>disgust</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>d12 (5)</td>\n",
              "      <td>/content/data/aesdd/disgust/d12 (5).wav</td>\n",
              "      <td>disgust</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>d16 (1)</td>\n",
              "      <td>/content/data/aesdd/disgust/d16 (1).wav</td>\n",
              "      <td>disgust</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>d07 (3)</td>\n",
              "      <td>/content/data/aesdd/disgust/d07 (3).wav</td>\n",
              "      <td>disgust</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      name                                     path  emotion\n",
              "0  d19 (4)  /content/data/aesdd/disgust/d19 (4).wav  disgust\n",
              "1  d01 (5)  /content/data/aesdd/disgust/d01 (5).wav  disgust\n",
              "2  d12 (5)  /content/data/aesdd/disgust/d12 (5).wav  disgust\n",
              "3  d16 (1)  /content/data/aesdd/disgust/d16 (1).wav  disgust\n",
              "4  d07 (3)  /content/data/aesdd/disgust/d07 (3).wav  disgust"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iMhTHur8voOp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "6a213a0c-c904-47b6-bc23-6e83884564c4"
      },
      "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: 604\n",
            "Step 1: 604\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>name</th>\n",
              "      <th>path</th>\n",
              "      <th>emotion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>d10 (6)</td>\n",
              "      <td>/content/data/aesdd/disgust/d10 (6).wav</td>\n",
              "      <td>disgust</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>a19 (2)</td>\n",
              "      <td>/content/data/aesdd/anger/a19 (2).wav</td>\n",
              "      <td>anger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>s20 (2)</td>\n",
              "      <td>/content/data/aesdd/sadness/s20 (2).wav</td>\n",
              "      <td>sadness</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>s08 (3)</td>\n",
              "      <td>/content/data/aesdd/sadness/s08 (3).wav</td>\n",
              "      <td>sadness</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>h02 (5)</td>\n",
              "      <td>/content/data/aesdd/happiness/h02 (5).wav</td>\n",
              "      <td>happiness</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      name                                       path    emotion\n",
              "0  d10 (6)    /content/data/aesdd/disgust/d10 (6).wav    disgust\n",
              "1  a19 (2)      /content/data/aesdd/anger/a19 (2).wav      anger\n",
              "2  s20 (2)    /content/data/aesdd/sadness/s20 (2).wav    sadness\n",
              "3  s08 (3)    /content/data/aesdd/sadness/s08 (3).wav    sadness\n",
              "4  h02 (5)  /content/data/aesdd/happiness/h02 (5).wav  happiness"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TWku4ra3Bp52"
      },
      "source": [
        "Let's explore how many labels (emotions) are in the dataset with what distribution."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "beNpKMh5xXmX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "0ef49574-07b5-4d0a-828a-cfecff87c451"
      },
      "source": [
        "print(\"Labels: \", df[\"emotion\"].unique())\n",
        "print()\n",
        "df.groupby(\"emotion\").count()[[\"path\"]]"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Labels:  ['disgust' 'anger' 'sadness' 'happiness' 'fear']\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",
              "    }\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>emotion</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>anger</th>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>disgust</th>\n",
              "      <td>122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fear</th>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>happiness</th>\n",
              "      <td>119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sadness</th>\n",
              "      <td>122</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           path\n",
              "emotion        \n",
              "anger       121\n",
              "disgust     122\n",
              "fear        120\n",
              "happiness   119\n",
              "sadness     122"
            ]
          },
          "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 emotional label."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DZaQ_sP5xkIX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "449a4c88-7258-4eae-d285-548e8f8180a7"
      },
      "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[\"emotion\"]\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: 256\n",
            "      Label: anger\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" autoplay=\"autoplay\">\n",
              "                    <source 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\" type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WNjWoWRCmPk"
      },
      "source": [
        "For training purposes, we need to split data into train test sets; in this specific example, we break with a `20%` rate for the test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mlim-044xtJN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e1410b59-5486-413f-d596-246f109710ac"
      },
      "source": [
        "save_path = \"/content/data\"\n",
        "\n",
        "train_df, test_df = train_test_split(df, test_size=0.2, random_state=101, stratify=df[\"emotion\"])\n",
        "\n",
        "train_df = train_df.reset_index(drop=True)\n",
        "test_df = test_df.reset_index(drop=True)\n",
        "\n",
        "train_df.to_csv(f\"{save_path}/train.csv\", sep=\"\\t\", encoding=\"utf-8\", index=False)\n",
        "test_df.to_csv(f\"{save_path}/test.csv\", sep=\"\\t\", encoding=\"utf-8\", index=False)\n",
        "\n",
        "\n",
        "print(train_df.shape)\n",
        "print(test_df.shape)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(483, 3)\n",
            "(121, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kcnD-d_rDElt"
      },
      "source": [
        "## Prepare Data for Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224,
          "referenced_widgets": [
            "6cb7ead7dd60467cbc296ced68b5a887",
            "c0fb3fdd04154c1ebd7011ee32591235",
            "09b6b1d6115e409dbd97d152d5ea66e3",
            "717522e52f344f02961b83c47a70fe24",
            "1773f9e45a93467a84bc76874c4a3d08",
            "37fb8f6c3a5748ebb12d5ffe79728ee1",
            "37ffe40f40ed41f0b37e7b28bb2c8dee",
            "3ebf2586b21c424b915fecaa21a6f6a2",
            "7ff66e8680d84452919faf047d92fbf0",
            "17eee8cde17e4079985222014f55af6b",
            "28c7fee188a44bad9f98edb52b916cbc",
            "0ae42e9388904eae8dcd5433604f98c9",
            "c2598c40a44040a784806d0a086e07bb",
            "1cd796c9cbe948299bc30c23637e29ed",
            "8eff94b7ddfc49bf985c29ddca4868d6",
            "27b2d578565d427bb3fefa1b7dadbeb9"
          ]
        },
        "id": "nnVfxQYDDIc6",
        "outputId": "c3a5d757-2137-4ff9-f9d9-44f9ca460b02"
      },
      "source": [
        "# Loading the created dataset using datasets\n",
        "from datasets import load_dataset, load_metric\n",
        "\n",
        "\n",
        "data_files = {\n",
        "    \"train\": \"/content/data/train.csv\", \n",
        "    \"validation\": \"/content/data/test.csv\",\n",
        "}\n",
        "\n",
        "dataset = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\", )\n",
        "train_dataset = dataset[\"train\"]\n",
        "eval_dataset = dataset[\"validation\"]\n",
        "\n",
        "print(train_dataset)\n",
        "print(eval_dataset)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using custom data configuration default-5e9243b4c54d19a7\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset csv/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /content/cache/csv/default-5e9243b4c54d19a7/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6cb7ead7dd60467cbc296ced68b5a887",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7ff66e8680d84452919faf047d92fbf0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rDataset csv downloaded and prepared to /content/cache/csv/default-5e9243b4c54d19a7/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0. Subsequent calls will reuse this data.\n",
            "Dataset({\n",
            "    features: ['name', 'path', 'emotion'],\n",
            "    num_rows: 483\n",
            "})\n",
            "Dataset({\n",
            "    features: ['name', 'path', 'emotion'],\n",
            "    num_rows: 121\n",
            "})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsOrQPBOEVOy"
      },
      "source": [
        "# We need to specify the input and output column\n",
        "input_column = \"path\"\n",
        "output_column = \"emotion\""
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-gh7fQ1XEpC7",
        "outputId": "dae27202-09f2-4afd-e6a8-edf2b46b29d8"
      },
      "source": [
        "# we need to distinguish the unique labels in our SER dataset\n",
        "label_list = train_dataset.unique(output_column)\n",
        "label_list.sort()  # Let's sort it for determinism\n",
        "num_labels = len(label_list)\n",
        "print(f\"A classification problem with {num_labels} classes: {label_list}\")"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A classification problem with 5 classes: ['anger', 'disgust', 'fear', 'happiness', 'sadness']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4TkGYrVTFR6Y"
      },
      "source": [
        "In order to preprocess the audio into our classification model, we need to set up the relevant HuBERT scale in this case `facebook/hubert-base-ls960` by [FaceBook HuBERT-Base](https://huggingface.co/facebook/hubert-base-ls960). To handle the context representations in any audio length we use a merge strategy plan (pooling mode) to concatenate that 3D representations into 2D representations.\n",
        "\n",
        "There are three merge strategies `mean`, `sum`, and `max`. In this example, we achieved better results on the mean approach. In the following, we need to initiate the config and the feature extractor from the Dimitris model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQrGaFohGzVo"
      },
      "source": [
        "from transformers import AutoConfig, Wav2Vec2Processor, Wav2Vec2FeatureExtractor"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Y1adr7vFrq7"
      },
      "source": [
        "model_name_or_path = \"facebook/hubert-base-ls960\"\n",
        "pooling_mode = \"mean\""
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AZjDSmBRGqr6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "762a2c3e3399495c86430ff5e59c6cbc",
            "db88bd4eacca42b7a4bf9f744769e524",
            "7193382b9aee4709bce34d7b53022298",
            "d170546207d440e5a0121ede9373f5ca",
            "f81b4897f16e481695347b23923ea2d1",
            "8687ac97aad24559a9e8adf5de9158d5",
            "b68872f9575a45e88e8f97d2e489adb6",
            "098d064849e94863ba86dc167758f2a7"
          ]
        },
        "outputId": "3c68b6d1-db0b-46e7-b34f-7cdfac661774"
      },
      "source": [
        "# config\n",
        "config = AutoConfig.from_pretrained(\n",
        "    model_name_or_path,\n",
        "    num_labels=num_labels,\n",
        "    label2id={label: i for i, label in enumerate(label_list)},\n",
        "    id2label={i: label for i, label in enumerate(label_list)},\n",
        "    finetuning_task=\"wav2vec2_clf\",\n",
        ")\n",
        "setattr(config, 'pooling_mode', pooling_mode)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "762a2c3e3399495c86430ff5e59c6cbc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1290.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83,
          "referenced_widgets": [
            "abc0d68f7aa2464bacc5b3ff77d9da5e",
            "b8391a98fe8747e6ad7a5b0ef6668775",
            "b39dea8e89e040a484199c2061fcb81f",
            "a07a6a7098df40d09720066d460b3c00",
            "ca96a9a041334c019f13390eeb46e297",
            "e60c0df059c14522b3a77e05881014ae",
            "f308b3d597954aec9ea244bfe6eea962",
            "96aee8b43e5f44418d04e0a7bcbb7cb8"
          ]
        },
        "id": "ZXVl9qW1Gw_-",
        "outputId": "baefbb75-935f-4a4b-d62d-2b33e7a6e9b4"
      },
      "source": [
        "feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path,)\n",
        "target_sampling_rate = feature_extractor.sampling_rate\n",
        "print(f\"The target sampling rate: {target_sampling_rate}\")"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "abc0d68f7aa2464bacc5b3ff77d9da5e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=213.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "The target sampling rate: 16000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CbGuYgLqHXZg"
      },
      "source": [
        "# Preprocess Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qLk-eM1DFjtE"
      },
      "source": [
        "So far, we downloaded, loaded, and split the SER dataset into train and test sets. The instantiated our strategy configuration for using context representations in our classification problem SER. Now, we need to extract features from the audio path in context representation tensors and feed them into our classification model to determine the emotion in the speech.\n",
        "\n",
        "Since the audio file is saved in the `.wav` format, it is easy to use **[Librosa](https://librosa.org/doc/latest/index.html)** or others, but we suppose that the format may be in the `.mp3` format in case of generality. We found that the **[Torchaudio](https://pytorch.org/audio/stable/index.html)** library works best for reading in `.mp3` data.\n",
        "\n",
        "An audio file usually stores both its values and the sampling rate with which the speech signal was digitalized. We want to store both in the dataset and write a **map(...)** function accordingly. Also, we need to handle the string labels into integers for our specific classification task in this case, the **single-label classification** you may want to use for your **regression** or even **multi-label classification**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UqlIV3uGxDA"
      },
      "source": [
        "def speech_file_to_array_fn(path):\n",
        "    speech_array, sampling_rate = torchaudio.load(path)\n",
        "    resampler = torchaudio.transforms.Resample(sampling_rate, target_sampling_rate)\n",
        "    speech = resampler(speech_array).squeeze().numpy()\n",
        "    return speech\n",
        "\n",
        "def label_to_id(label, label_list):\n",
        "\n",
        "    if len(label_list) > 0:\n",
        "        return label_list.index(label) if label in label_list else -1\n",
        "\n",
        "    return label\n",
        "\n",
        "def preprocess_function(examples):\n",
        "    speech_list = [speech_file_to_array_fn(path) for path in examples[input_column]]\n",
        "    target_list = [label_to_id(label, label_list) for label in examples[output_column]]\n",
        "\n",
        "    result = feature_extractor(speech_list, sampling_rate=target_sampling_rate)\n",
        "    result[\"labels\"] = list(target_list)\n",
        "\n",
        "    return result"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
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        },
        "id": "ioP8FfR2GxHi",
        "outputId": "2f2ec2fd-ee95-4e0d-f3fe-ed5a458e909a"
      },
      "source": [
        "train_dataset = train_dataset.map(\n",
        "    preprocess_function,\n",
        "    batch_size=100,\n",
        "    batched=True,\n",
        "    num_proc=4\n",
        ")\n",
        "eval_dataset = eval_dataset.map(\n",
        "    preprocess_function,\n",
        "    batch_size=100,\n",
        "    batched=True,\n",
        "    num_proc=4\n",
        ")"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    "
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2bbf2108af9c443ea3fbb4a9b2e4ae99",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description=' #0', max=2.0, style=ProgressStyle(description_width='ini…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
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          "data": {
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              "model_id": "989d41c4a10e4355b476a87ecfde27b0",
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              "HBox(children=(FloatProgress(value=0.0, description=' #1', max=2.0, style=ProgressStyle(description_width='ini…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=0.0, description=' #2', max=2.0, style=ProgressStyle(description_width='ini…"
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        {
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              "HBox(children=(FloatProgress(value=0.0, description=' #3', max=2.0, style=ProgressStyle(description_width='ini…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n",
            "  "
          ],
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            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eu1qcRucHk6d",
        "outputId": "2fd6ddb8-75e2-4199-8205-c829118c1409"
      },
      "source": [
        "idx = 0\n",
        "print(f\"Training input_values: {train_dataset[idx]['input_values']}\")\n",
        "# print(f\"Training attention_mask: {train_dataset[idx]['attention_mask']}\")\n",
        "print(f\"Training labels: {train_dataset[idx]['labels']} - {train_dataset[idx]['emotion']}\")"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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0.00025643614935688674, 0.0006744723068550229, 5.7817705965135247e-05, 0.006360385566949844, -0.00036270165583118796, -0.002103216480463743, 0.0007726048352196813, 0.0003364754084032029, -0.0006126652588136494, 0.006455965340137482, -0.0007876614108681679, -0.00026514087221585214, 0.006620623171329498, -0.0019012920092791319, 0.00702050281688571, 0.002820705994963646, -0.004739703610539436, 0.012028885073959827, 0.0013756705448031425, -0.0011558537371456623, 0.0044310614466667175, 0.00613448629155755, 0.0010208847234025598, 0.010412256233394146, 0.0007329682703129947, 0.00869072787463665, 0.016268761828541756, 0.0006878339918330312, 0.0003278923104517162, 0.014811179600656033, 0.0038666354957967997, 0.004003255162388086, 0.0064684743992984295, -0.0018984455382451415, 0.00344687607139349, 0.004463857971131802, -0.0016785678453743458, 0.003943781368434429, 0.004039251711219549, -0.0012362201232463121, 0.00470334617421031, 0.004008159041404724, -0.005532151088118553, -0.001118989079259336, 0.001798219163902104, -0.0039046918973326683, -0.0031094590667635202, 0.0010998416692018509, 9.493989637121558e-05, 6.545082578668371e-05, -0.007249128073453903, -0.0012046275660395622, 0.0013897418975830078, -0.006901478860527277, -0.0025050260592252016, -0.003451647935435176, -0.00967329926788807, -0.0006040117586962879, -0.004596145823597908, -0.017500950023531914, -0.006148552522063255, -0.010341549292206764, -0.022737527266144753, -0.004694323055446148, -0.009294955059885979]\n",
            "Training labels: 3 - happiness\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HcrEgJO9Hmx7"
      },
      "source": [
        "Great, now we've successfully read all the audio files, resampled the audio files to 16kHz, and mapped each audio to the corresponding label."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QL7q6DfcH0Fs"
      },
      "source": [
        "## Model\n",
        "\n",
        "Before diving into the training part, we need to build our classification model based on the merge strategy. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uJZoC4T5HpuP"
      },
      "source": [
        "from dataclasses import dataclass\n",
        "from typing import Optional, Tuple\n",
        "import torch\n",
        "from transformers.file_utils import ModelOutput\n",
        "\n",
        "\n",
        "@dataclass\n",
        "class SpeechClassifierOutput(ModelOutput):\n",
        "    loss: Optional[torch.FloatTensor] = None\n",
        "    logits: torch.FloatTensor = None\n",
        "    hidden_states: Optional[Tuple[torch.FloatTensor]] = None\n",
        "    attentions: Optional[Tuple[torch.FloatTensor]] = None\n"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fv62ShDsH5DZ"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss\n",
        "\n",
        "# from transformers.models.wav2vec2.modeling_wav2vec2 import (\n",
        "#     Wav2Vec2PreTrainedModel,\n",
        "#     Wav2Vec2Model\n",
        "# )\n",
        "from transformers.models.hubert.modeling_hubert import (\n",
        "    HubertPreTrainedModel,\n",
        "    HubertModel\n",
        ")\n",
        "\n",
        "\n",
        "class HubertClassificationHead(nn.Module):\n",
        "    \"\"\"Head for hubert classification task.\"\"\"\n",
        "\n",
        "    def __init__(self, config):\n",
        "        super().__init__()\n",
        "        self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n",
        "        self.dropout = nn.Dropout(config.final_dropout)\n",
        "        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)\n",
        "\n",
        "    def forward(self, features, **kwargs):\n",
        "        x = features\n",
        "        x = self.dropout(x)\n",
        "        x = self.dense(x)\n",
        "        x = torch.tanh(x)\n",
        "        x = self.dropout(x)\n",
        "        x = self.out_proj(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "class HubertForSpeechClassification(HubertPreTrainedModel):\n",
        "    def __init__(self, config):\n",
        "        super().__init__(config)\n",
        "        self.num_labels = config.num_labels\n",
        "        self.pooling_mode = config.pooling_mode\n",
        "        self.config = config\n",
        "\n",
        "        self.hubert = HubertModel(config)\n",
        "        self.classifier = HubertClassificationHead(config)\n",
        "\n",
        "        self.init_weights()\n",
        "\n",
        "    def freeze_feature_extractor(self):\n",
        "        self.hubert.feature_extractor._freeze_parameters()\n",
        "\n",
        "    def merged_strategy(\n",
        "            self,\n",
        "            hidden_states,\n",
        "            mode=\"mean\"\n",
        "    ):\n",
        "        if mode == \"mean\":\n",
        "            outputs = torch.mean(hidden_states, dim=1)\n",
        "        elif mode == \"sum\":\n",
        "            outputs = torch.sum(hidden_states, dim=1)\n",
        "        elif mode == \"max\":\n",
        "            outputs = torch.max(hidden_states, dim=1)[0]\n",
        "        else:\n",
        "            raise Exception(\n",
        "                \"The pooling method hasn't been defined! Your pooling mode must be one of these ['mean', 'sum', 'max']\")\n",
        "\n",
        "        return outputs\n",
        "\n",
        "    def forward(\n",
        "            self,\n",
        "            input_values,\n",
        "            attention_mask=None,\n",
        "            output_attentions=None,\n",
        "            output_hidden_states=None,\n",
        "            return_dict=None,\n",
        "            labels=None,\n",
        "    ):\n",
        "        return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n",
        "        outputs = self.hubert(\n",
        "            input_values,\n",
        "            attention_mask=attention_mask,\n",
        "            output_attentions=output_attentions,\n",
        "            output_hidden_states=output_hidden_states,\n",
        "            return_dict=return_dict,\n",
        "        )\n",
        "        hidden_states = outputs[0]\n",
        "        hidden_states = self.merged_strategy(hidden_states, mode=self.pooling_mode)\n",
        "        logits = self.classifier(hidden_states)\n",
        "\n",
        "        loss = None\n",
        "        if labels is not None:\n",
        "            if self.config.problem_type is None:\n",
        "                if self.num_labels == 1:\n",
        "                    self.config.problem_type = \"regression\"\n",
        "                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):\n",
        "                    self.config.problem_type = \"single_label_classification\"\n",
        "                else:\n",
        "                    self.config.problem_type = \"multi_label_classification\"\n",
        "\n",
        "            if self.config.problem_type == \"regression\":\n",
        "                loss_fct = MSELoss()\n",
        "                loss = loss_fct(logits.view(-1, self.num_labels), labels)\n",
        "            elif self.config.problem_type == \"single_label_classification\":\n",
        "                loss_fct = CrossEntropyLoss()\n",
        "                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\n",
        "            elif self.config.problem_type == \"multi_label_classification\":\n",
        "                loss_fct = BCEWithLogitsLoss()\n",
        "                loss = loss_fct(logits, labels)\n",
        "\n",
        "        if not return_dict:\n",
        "            output = (logits,) + outputs[2:]\n",
        "            return ((loss,) + output) if loss is not None else output\n",
        "\n",
        "        return SpeechClassifierOutput(\n",
        "            loss=loss,\n",
        "            logits=logits,\n",
        "            hidden_states=outputs.hidden_states,\n",
        "            attentions=outputs.attentions,\n",
        "        )\n"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OrBrR1b7zvUL"
      },
      "source": [
        "## Training\n",
        "\n",
        "The data is processed so that we are ready to start setting up the training pipeline. We will make use of 🤗's [Trainer](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer) for which we essentially need to do the following:\n",
        "\n",
        "- Define a data collator. In contrast to most NLP models, XLSR-Wav2Vec2 has a much larger input length than output length. *E.g.*, a sample of input length 50000 has an output length of no more than 100. Given the large input sizes, it is much more efficient to pad the training batches dynamically meaning that all training samples should only be padded to the longest sample in their batch and not the overall longest sample. Therefore, fine-tuning XLSR-Wav2Vec2 requires a special padding data collator, which we will define below\n",
        "\n",
        "- Evaluation metric. During training, the model should be evaluated on the word error rate. We should define a `compute_metrics` function accordingly\n",
        "\n",
        "- Load a pretrained checkpoint. We need to load a pretrained checkpoint and configure it correctly for training.\n",
        "\n",
        "- Define the training configuration.\n",
        "\n",
        "After having fine-tuned the model, we will correctly evaluate it on the test data and verify that it has indeed learned to correctly transcribe speech."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ji9-n1eUIKZc"
      },
      "source": [
        "### Set-up Trainer\n",
        "\n",
        "Let's start by defining the data collator. The code for the data collator was copied from [this example](https://github.com/huggingface/transformers/blob/9a06b6b11bdfc42eea08fa91d0c737d1863c99e3/examples/research_projects/wav2vec2/run_asr.py#L81).\n",
        "\n",
        "Without going into too many details, in contrast to the common data collators, this data collator treats the `input_values` and `labels` differently and thus applies to separate padding functions on them (again making use of XLSR-Wav2Vec2's context manager). This is necessary because in speech input and output are of different modalities meaning that they should not be treated by the same padding function.\n",
        "Analogous to the common data collators, the padding tokens in the labels with `-100` so that those tokens are **not** taken into account when computing the loss."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rkM0VLIwy903"
      },
      "source": [
        "from dataclasses import dataclass\n",
        "from typing import Dict, List, Optional, Union\n",
        "import torch\n",
        "\n",
        "import transformers\n",
        "from transformers import Wav2Vec2FeatureExtractor\n",
        "\n",
        "\n",
        "@dataclass\n",
        "class DataCollatorCTCWithPadding:\n",
        "    \"\"\"\n",
        "    Data collator that will dynamically pad the inputs received.\n",
        "    Args:\n",
        "        feature_extractor (:class:`~transformers.Wav2Vec2FeatureExtractor`)\n",
        "            The feature_extractor used for proccessing the data.\n",
        "        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):\n",
        "            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)\n",
        "            among:\n",
        "            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single\n",
        "              sequence if provided).\n",
        "            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the\n",
        "              maximum acceptable input length for the model if that argument is not provided.\n",
        "            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of\n",
        "              different lengths).\n",
        "        max_length (:obj:`int`, `optional`):\n",
        "            Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).\n",
        "        max_length_labels (:obj:`int`, `optional`):\n",
        "            Maximum length of the ``labels`` returned list and optionally padding length (see above).\n",
        "        pad_to_multiple_of (:obj:`int`, `optional`):\n",
        "            If set will pad the sequence to a multiple of the provided value.\n",
        "            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=\n",
        "            7.5 (Volta).\n",
        "    \"\"\"\n",
        "\n",
        "    feature_extractor: Wav2Vec2FeatureExtractor\n",
        "    padding: Union[bool, str] = True\n",
        "    max_length: Optional[int] = None\n",
        "    max_length_labels: Optional[int] = None\n",
        "    pad_to_multiple_of: Optional[int] = None\n",
        "    pad_to_multiple_of_labels: Optional[int] = None\n",
        "\n",
        "    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
        "        input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n",
        "        label_features = [feature[\"labels\"] for feature in features]\n",
        "\n",
        "        d_type = torch.long if isinstance(label_features[0], int) else torch.float\n",
        "\n",
        "        batch = self.feature_extractor.pad(\n",
        "            input_features,\n",
        "            padding=self.padding,\n",
        "            max_length=self.max_length,\n",
        "            pad_to_multiple_of=self.pad_to_multiple_of,\n",
        "            return_tensors=\"pt\",\n",
        "        )\n",
        "\n",
        "        batch[\"labels\"] = torch.tensor(label_features, dtype=d_type)\n",
        "\n",
        "        return batch"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YhJ0OA4RxQe"
      },
      "source": [
        "data_collator = DataCollatorCTCWithPadding(feature_extractor=feature_extractor, padding=True)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mYxy2IR-KcU2"
      },
      "source": [
        "Next, the evaluation metric is defined. There are many pre-defined metrics for classification/regression problems, but in this case, we would continue with just **Accuracy** for classification and **MSE** for regression. You can define other metrics on your own."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LL8I5MKvPnth"
      },
      "source": [
        "is_regression = False"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XK26Z6IfR36K"
      },
      "source": [
        "import numpy as np\n",
        "from transformers import EvalPrediction\n",
        "\n",
        "\n",
        "def compute_metrics(p: EvalPrediction):\n",
        "    preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions\n",
        "    preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1)\n",
        "\n",
        "    if is_regression:\n",
        "        return {\"mse\": ((preds - p.label_ids) ** 2).mean().item()}\n",
        "    else:\n",
        "        return {\"accuracy\": (preds == p.label_ids).astype(np.float32).mean().item()}"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZsH_nKJdK28o"
      },
      "source": [
        "Now, we can load the pretrained checkpoint into our classification model with a pooling strategy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qOsSrY2ujmMI",
        "outputId": "f3cdfc65-a61b-4510-bea1-b470b568fe88"
      },
      "source": [
        "drive_dir = \"/content\"\n",
        "!ls {drive_dir}\n",
        "output_dir = os.path.join(drive_dir, \"ckpts\", \"hubert-base-greek-ser\")\n",
        "!ls {output_dir}"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "aesdd.zip  cache  data\tsample_data\n",
            "ls: cannot access '/content/ckpts/hubert-base-greek-ser': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RDuk0PjmjeZC"
      },
      "source": [
        "import os\n",
        "import shutil\n",
        "\n",
        "last_checkpoint = None\n",
        "checkpoints = []\n",
        "if os.path.exists(output_dir):\n",
        "    for subdir in os.scandir(output_dir):\n",
        "        if subdir.is_dir():\n",
        "            checkpoints.append(subdir.path)\n",
        "            \n",
        "            \n",
        "if len(checkpoints) > 0:\n",
        "    checkpoints = list(sorted(checkpoints, key=lambda ckpt: ckpt.split('/')[-1].split('-')[-1], reverse=True))\n",
        "    model_name_or_path = os.path.join(\"/content\", checkpoints[0].split(\"/\")[-1])\n",
        "    last_checkpoint = model_name_or_path\n",
        "    shutil.copytree(checkpoints[0], model_name_or_path)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sGES8XwDjv55",
        "outputId": "6c6a846a-92b4-4416-f7d4-41cd8cac2510"
      },
      "source": [
        "print(f\"model_name_or_path: {model_name_or_path}\")\n",
        "print(f\"last_checkpoint: {last_checkpoint}\")"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "model_name_or_path: facebook/hubert-base-ls960\n",
            "last_checkpoint: None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Tl6iKAUR4EL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120,
          "referenced_widgets": [
            "4e444cf9dea24335b6d627b09d79d55c",
            "959be65e7a074e00a4ac7c426f1767c1",
            "0f661004203c42a0bcd8f4cf4b7da9b2",
            "ab449bed23eb428eb430af60ac1f63b0",
            "55fafb88a302427ab40db3ced046a12a",
            "25f5e6bb91214a92827ad926e453efb4",
            "32312709917d4f1d9ba106dab4997552",
            "03d357f3273f4e88ba3b14f3f8a3f866"
          ]
        },
        "outputId": "8651c9da-4af5-462e-9912-4d23417bd5f8"
      },
      "source": [
        "model = HubertForSpeechClassification.from_pretrained(\n",
        "    model_name_or_path,\n",
        "    config=config,\n",
        ")"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4e444cf9dea24335b6d627b09d79d55c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=377569754.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of HubertForSpeechClassification were not initialized from the model checkpoint at facebook/hubert-base-ls960 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.dense.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bqF4rNMzI1M5"
      },
      "source": [
        "The first component of XLSR-Wav2Vec2 consists of a stack of CNN layers that are used to extract acoustically meaningful - but contextually independent - features from the raw speech signal. This part of the model has already been sufficiently trained during pretraining and as stated in the [paper](https://arxiv.org/pdf/2006.13979.pdf) does not need to be fine-tuned anymore. \n",
        "Thus, we can set the `requires_grad` to `False` for all parameters of the *feature extraction* part."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KHMhxFGoR4Hb"
      },
      "source": [
        "model.freeze_feature_extractor()"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n0HzBneBK84G"
      },
      "source": [
        "In a final step, we define all parameters related to training. \n",
        "To give more explanation on some of the parameters:\n",
        "- `learning_rate` and `weight_decay` were heuristically tuned until fine-tuning has become stable. Note that those parameters strongly depend on the Common Voice dataset and might be suboptimal for other speech datasets.\n",
        "\n",
        "For more explanations on other parameters, one can take a look at the [docs](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer#trainingarguments).\n",
        "\n",
        "**Note**: If one wants to save the trained models in his/her google drive the commented-out `output_dir` can be used instead."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vUtWjldAI9-H"
      },
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=output_dir,\n",
        "    per_device_train_batch_size=4,\n",
        "    per_device_eval_batch_size=4,\n",
        "    gradient_accumulation_steps=2,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    num_train_epochs=1.0,\n",
        "    fp16=True,\n",
        "    save_steps=10,\n",
        "    eval_steps=10,\n",
        "    logging_steps=10,\n",
        "    learning_rate=1e-4,\n",
        "    save_total_limit=2,\n",
        "    do_train=True,\n",
        "    do_eval=True,\n",
        "    do_predict=True\n",
        ")"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XAtuL0APLZSs"
      },
      "source": [
        "For future use we can create our training script, we do it in a simple way. You can add more on you own."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KfW6uDolJYZv"
      },
      "source": [
        "from typing import Any, Dict, Union\n",
        "\n",
        "import torch\n",
        "from packaging import version\n",
        "from torch import nn\n",
        "\n",
        "from transformers import (\n",
        "    Trainer,\n",
        "    is_apex_available,\n",
        ")\n",
        "\n",
        "if is_apex_available():\n",
        "    from apex import amp\n",
        "\n",
        "if version.parse(torch.__version__) >= version.parse(\"1.6\"):\n",
        "    _is_native_amp_available = True\n",
        "    from torch.cuda.amp import autocast\n",
        "\n",
        "\n",
        "class CTCTrainer(Trainer):\n",
        "    def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor:\n",
        "        \"\"\"\n",
        "        Perform a training step on a batch of inputs.\n",
        "\n",
        "        Subclass and override to inject custom behavior.\n",
        "\n",
        "        Args:\n",
        "            model (:obj:`nn.Module`):\n",
        "                The model to train.\n",
        "            inputs (:obj:`Dict[str, Union[torch.Tensor, Any]]`):\n",
        "                The inputs and targets of the model.\n",
        "\n",
        "                The dictionary will be unpacked before being fed to the model. Most models expect the targets under the\n",
        "                argument :obj:`labels`. Check your model's documentation for all accepted arguments.\n",
        "\n",
        "        Return:\n",
        "            :obj:`torch.Tensor`: The tensor with training loss on this batch.\n",
        "        \"\"\"\n",
        "\n",
        "        model.train()\n",
        "        inputs = self._prepare_inputs(inputs)\n",
        "\n",
        "        if self.use_amp:\n",
        "            with autocast():\n",
        "                loss = self.compute_loss(model, inputs)\n",
        "        else:\n",
        "            loss = self.compute_loss(model, inputs)\n",
        "\n",
        "        if self.args.gradient_accumulation_steps > 1:\n",
        "            loss = loss / self.args.gradient_accumulation_steps\n",
        "\n",
        "        if self.use_amp:\n",
        "            self.scaler.scale(loss).backward()\n",
        "        elif self.use_apex:\n",
        "            with amp.scale_loss(loss, self.optimizer) as scaled_loss:\n",
        "                scaled_loss.backward()\n",
        "        elif self.deepspeed:\n",
        "            self.deepspeed.backward(loss)\n",
        "        else:\n",
        "            loss.backward()\n",
        "\n",
        "        return loss.detach()\n"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qv7Ju3qYJeJn"
      },
      "source": [
        "Now, all instances can be passed to Trainer and we are ready to start training!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nEFkfK45JYiZ"
      },
      "source": [
        "trainer = CTCTrainer(\n",
        "    model=model,\n",
        "    data_collator=data_collator,\n",
        "    args=training_args,\n",
        "    compute_metrics=compute_metrics,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=eval_dataset,\n",
        "    tokenizer=feature_extractor,\n",
        ")"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0gGLwJAOLtDg"
      },
      "source": [
        "### Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UpN6xlWCLxJ7"
      },
      "source": [
        "Training will take between 10 and 60 minutes depending on the GPU allocated to this notebook. \n",
        "\n",
        "In case you want to use this google colab to fine-tune your model, you should make sure that your training doesn't stop due to inactivity. A simple hack to prevent this is to paste the following code into the console of this tab (right mouse click -> inspect -> Console tab and insert code)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zyYZH7YZL8a9"
      },
      "source": [
        "```javascript\n",
        "function ConnectButton(){\n",
        "    console.log(\"Connect pushed\"); \n",
        "    document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click() \n",
        "}\n",
        "setInterval(ConnectButton,60000);\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "6M8bNvLLJnG1",
        "outputId": "19b29abd-0305-47d3-c12e-910d57cc7399"
      },
      "source": [
        "if training_args.do_train:\n",
        "    print(f\"last_checkpoint: {last_checkpoint}\")\n",
        "    train_result = trainer.train(resume_from_checkpoint=last_checkpoint)\n",
        "    trainer.save_model()\n",
        "    feature_extractor.save_pretrained(training_args.output_dir)\n",
        "    metrics = train_result.metrics\n",
        "    metrics[\"train_samples\"] = len(train_dataset)\n",
        "\n",
        "    trainer.log_metrics(\"train\", metrics)\n",
        "    trainer.save_metrics(\"train\", metrics)\n",
        "    trainer.save_state()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "last_checkpoint: None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='60' max='60' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [60/60 02:30, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.640200</td>\n",
              "      <td>1.593208</td>\n",
              "      <td>0.223140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>1.565500</td>\n",
              "      <td>1.487835</td>\n",
              "      <td>0.380165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>1.451700</td>\n",
              "      <td>1.311041</td>\n",
              "      <td>0.404959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.335400</td>\n",
              "      <td>1.469661</td>\n",
              "      <td>0.429752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.373700</td>\n",
              "      <td>1.197774</td>\n",
              "      <td>0.454545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.214400</td>\n",
              "      <td>1.171175</td>\n",
              "      <td>0.487603</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3prIN9eiMBHo"
      },
      "source": [
        "The training loss goes down and we can see that the Acurracy on the test set also improves nicely. Because this notebook is just for demonstration purposes, we can stop here.\n",
        "\n",
        "The resulting model of this notebook has been saved to [m3hrdadfi/hubert-base-greek-speech-emotion-recognition](https://huggingface.co/m3hrdadfi/hubert-base-greek-speech-emotion-recognition)\n",
        "\n",
        "As a final check, let's load the model and verify that it indeed has learned to recognize the emotion in the speech.\n",
        "\n",
        "Let's first load the pretrained checkpoint."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bw6Sv7R2qASn"
      },
      "source": [
        "## Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKzaYkJ7qASo"
      },
      "source": [
        "import librosa\n",
        "from datasets import load_dataset, load_metric\n",
        "from sklearn.metrics import classification_report\n",
        "from transformers import AutoConfig, Wav2Vec2Processor, Wav2Vec2FeatureExtractor"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "5a57a6a4204e45f2aca5c166a893911b",
            "388febc688f041cca057b361ed2ddbe8",
            "d4025daadd75496cafe9d5a55a22aef5",
            "0bb4070be76245988251d8c54f68acbf",
            "6dede346f4d84f03a731de2f7c162d4c",
            "5f22bc5c4fd04c66ad02ded97fa8534f",
            "96cd3fc4e6cd4423b51ec2e9c5b2ef23",
            "20af16772e7b4284a80cac396445d2cd"
          ]
        },
        "id": "Cmt9qo1YqASp",
        "outputId": "ba2b3790-d4c2-4b72-c1cf-17c51428ede6"
      },
      "source": [
        "test_dataset = load_dataset(\"csv\", data_files={\"test\": \"/content/data/test.csv\"}, delimiter=\"\\t\")[\"test\"]\n",
        "test_dataset"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using custom data configuration default-c0949ad448731ab8\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset csv/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /content/cache/csv/default-c0949ad448731ab8/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5a57a6a4204e45f2aca5c166a893911b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rDataset csv downloaded and prepared to /content/cache/csv/default-c0949ad448731ab8/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0. Subsequent calls will reuse this data.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['name', 'path', 'emotion'],\n",
              "    num_rows: 121\n",
              "})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tce7k6XCqASr",
        "outputId": "f67ab2b6-d2ab-4568-8a88-4e1994775b6b"
      },
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(f\"Device: {device}\")"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Device: cuda\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CD-OVFfOqASs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164,
          "referenced_widgets": [
            "1d9225ce04cc4aa49576cbf38d4ed9d1",
            "01822370fbc34a11aeee06b38ac8414c",
            "fcf24adbec544854bfef5cc1864d9b73",
            "b4bc0613ff334e868d7125f9f9be1256",
            "4c248590dadc4c2e96112640c79e86b6",
            "f3a309dcc6f94259b03682ba97fa47ca",
            "9b273af69f704ac695e226ae847e2f66",
            "9072ea0038444dc1b53014ca761c71f8",
            "5f2a6a93760d4e0cba08eacfd92236c2",
            "87d8a2b23a34459faf647080f2b3146b",
            "f361babc67cc4d3ca3fcdbd58f5f8748",
            "6e46942596a5426ab69aaefe0476e4bd",
            "e823c515ad66421dbec2b73c75dfbdc7",
            "db85385bd3954e7e91864c8dce7b2e09",
            "9e45c94b836e456d82faddfc46488b6f",
            "0176005366bf4363947319e5e8ac0854",
            "3b82795606904318bd6cb47c07c11aea",
            "8cfed54f7a71441db85f40dddb6a0290",
            "10fa0b164b0546a5bb5d6a1ddf549c84",
            "cd3199d548ba464aa5af2caa270928f3",
            "8baa3e2190f94a288a5a12d7c3ce0c02",
            "9a9154607c764a57b76592f5db2dc06e",
            "6e601b969fdc4387a8ec9666f54b3808",
            "83fddd0627e24fb380ddf58a77bffe06"
          ]
        },
        "outputId": "3301c511-4044-487a-c9b4-f54c5fb4fdc1"
      },
      "source": [
        "model_name_or_path = \"m3hrdadfi/hubert-base-greek-speech-emotion-recognition\"\n",
        "config = AutoConfig.from_pretrained(model_name_or_path)\n",
        "feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)\n",
        "model = HubertForSpeechClassification.from_pretrained(model_name_or_path).to(device)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1d9225ce04cc4aa49576cbf38d4ed9d1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1693.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5f2a6a93760d4e0cba08eacfd92236c2",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=213.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3b82795606904318bd6cb47c07c11aea",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=379949267.0, style=ProgressStyle(descri…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xFqSnJS2qASt"
      },
      "source": [
        "def speech_file_to_array_fn(batch):\n",
        "    speech_array, sampling_rate = torchaudio.load(batch[\"path\"])\n",
        "    speech_array = speech_array.squeeze().numpy()\n",
        "    speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, feature_extractor.sampling_rate)\n",
        "\n",
        "    batch[\"speech\"] = speech_array\n",
        "    return batch\n",
        "\n",
        "\n",
        "def predict(batch):\n",
        "    features = feature_extractor(batch[\"speech\"], sampling_rate=feature_extractor.sampling_rate, return_tensors=\"pt\", padding=True)\n",
        "\n",
        "    input_values = features.input_values.to(device)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        logits = model(input_values).logits \n",
        "\n",
        "    pred_ids = torch.argmax(logits, dim=-1).detach().cpu().numpy()\n",
        "    batch[\"predicted\"] = pred_ids\n",
        "    return batch"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "19d129fceaf147fc876b192ccb3c82c1",
            "eb532ff6a91d42b0a457badf26f114d8",
            "76bf15dfbca440b7b86c883949baf1cf",
            "19447b997cef4917aff0928d54fb6c87",
            "9fecd0186d5747f0831da783bd76dfd4",
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            "5cd44a2f821b4e3aae46796d5c169df7",
            "327a70c980e342d88dad7a0e23524ba5"
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        },
        "id": "O8BPF2CbqASt",
        "outputId": "9da996a6-6515-4349-fd52-ddfd3e5a0923"
      },
      "source": [
        "test_dataset = test_dataset.map(speech_file_to_array_fn)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "19d129fceaf147fc876b192ccb3c82c1",
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              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0, max=121.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "b788cc15aca2473193507ce298df1e79",
            "b489484843fb4858bd1e87f6d64d654b",
            "039ff1e800af4ad0b1aed45a5dc8916f",
            "a39ffe587bbd4cb399c041bd88977763",
            "b89948691b4a48a9b7001bc6e1ded073",
            "45fb2ce2d1dd42f5985549cfa14ff889",
            "307d60713eef4111b5d3aa8720cdef66",
            "2301d2d93d064db6869e68161ae2c3b8"
          ]
        },
        "id": "_eT8k5JxqASu",
        "outputId": "3f782abd-3f88-4c93-e58a-ff67de7e7ddf"
      },
      "source": [
        "result = test_dataset.map(predict, batched=True, batch_size=2)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b788cc15aca2473193507ce298df1e79",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=61.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ogvOEnfqASu",
        "outputId": "c9a19153-d1b0-41bd-ff38-aaba0d6879ff"
      },
      "source": [
        "label_names = [config.id2label[i] for i in range(config.num_labels)]\n",
        "label_names"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['anger', 'disgust', 'fear', 'happiness', 'sadness']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NYjUGDssqASv",
        "outputId": "4c438d3f-6a15-457c-9d0f-dadf00778445"
      },
      "source": [
        "y_true = [config.label2id[name] for name in result[\"emotion\"]]\n",
        "y_pred = result[\"predicted\"]\n",
        "\n",
        "print(y_true[:5])\n",
        "print(y_pred[:5])"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1, 4, 0, 4, 3]\n",
            "[1, 4, 0, 4, 3]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4jSDoRQcqASv",
        "outputId": "0fedee43-88cd-44cc-b75d-2ee30239dc98"
      },
      "source": [
        "print(classification_report(y_true, y_pred, target_names=label_names))"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       anger       1.00      0.96      0.98        24\n",
            "     disgust       0.89      1.00      0.94        24\n",
            "        fear       1.00      1.00      1.00        24\n",
            "   happiness       0.95      0.88      0.91        24\n",
            "     sadness       1.00      1.00      1.00        25\n",
            "\n",
            "    accuracy                           0.97       121\n",
            "   macro avg       0.97      0.97      0.97       121\n",
            "weighted avg       0.97      0.97      0.97       121\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ylb2Z6Xke2ro"
      },
      "source": [
        "# Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EQzCioPhWIiX"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchaudio\n",
        "from transformers import AutoConfig, Wav2Vec2Processor\n",
        "\n",
        "import librosa\n",
        "import IPython.display as ipd\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DocavTvQWIr_"
      },
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model_name_or_path = \"m3hrdadfi/hubert-base-greek-speech-emotion-recognition\"\n",
        "config = AutoConfig.from_pretrained(model_name_or_path)\n",
        "feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)\n",
        "sampling_rate = feature_extractor.sampling_rate\n",
        "model = HubertForSpeechClassification.from_pretrained(model_name_or_path).to(device)"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1SSs95o9WIvK"
      },
      "source": [
        "def speech_file_to_array_fn(path, sampling_rate):\n",
        "    speech_array, _sampling_rate = torchaudio.load(path)\n",
        "    resampler = torchaudio.transforms.Resample(_sampling_rate)\n",
        "    speech = resampler(speech_array).squeeze().numpy()\n",
        "    return speech\n",
        "\n",
        "\n",
        "def predict(path, sampling_rate):\n",
        "    speech = speech_file_to_array_fn(path, sampling_rate)\n",
        "    features = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors=\"pt\", padding=True)\n",
        "\n",
        "    input_values = features.input_values.to(device)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        logits = model(input_values).logits\n",
        "\n",
        "    scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]\n",
        "    outputs = [{\"Label\": config.id2label[i], \"Score\": f\"{round(score * 100, 3):.1f}%\"} for i, score in enumerate(scores)]\n",
        "    return outputs\n",
        "\n",
        "\n",
        "STYLES = \"\"\"\n",
        "<style>\n",
        "div.display_data {\n",
        "    margin: 0 auto;\n",
        "    max-width: 500px;\n",
        "}\n",
        "table.xxx {\n",
        "    margin: 50px !important;\n",
        "    float: right !important;\n",
        "    clear: both !important;\n",
        "}\n",
        "table.xxx td {\n",
        "    min-width: 300px !important;\n",
        "    text-align: center !important;\n",
        "}\n",
        "</style>\n",
        "\"\"\".strip()\n",
        "\n",
        "def prediction(df_row):\n",
        "    path, label = df_row[\"path\"], df_row[\"emotion\"]\n",
        "    df = pd.DataFrame([{\"Emotion\": label, \"Sentence\": \"    \"}])\n",
        "    setup = {\n",
        "        'border': 2,\n",
        "        'show_dimensions': True,\n",
        "        'justify': 'center',\n",
        "        'classes': 'xxx',\n",
        "        'escape': False,\n",
        "    }\n",
        "    ipd.display(ipd.HTML(STYLES + df.to_html(**setup) + \"<br />\"))\n",
        "    speech, sr = torchaudio.load(path)\n",
        "    speech = speech[0].numpy().squeeze()\n",
        "    speech = librosa.resample(np.asarray(speech), sr, sampling_rate)\n",
        "    ipd.display(ipd.Audio(data=np.asarray(speech), autoplay=True, rate=sampling_rate))\n",
        "\n",
        "    outputs = predict(path, sampling_rate)\n",
        "    r = pd.DataFrame(outputs)\n",
        "    ipd.display(ipd.HTML(STYLES + r.to_html(**setup) + \"<br />\"))"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "UD7oUP20YwYT",
        "outputId": "01171817-ce32-4fdf-9216-ff586a77b55e"
      },
      "source": [
        "test = pd.read_csv(\"/content/data/test.csv\", sep=\"\\t\")\n",
        "test.head()"
      ],
      "execution_count": 55,
      "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>name</th>\n",
              "      <th>path</th>\n",
              "      <th>emotion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>d11 (4)</td>\n",
              "      <td>/content/data/aesdd/disgust/d11 (4).wav</td>\n",
              "      <td>disgust</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>s10 (4)</td>\n",
              "      <td>/content/data/aesdd/sadness/s10 (4).wav</td>\n",
              "      <td>sadness</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>a13 (5)</td>\n",
              "      <td>/content/data/aesdd/anger/a13 (5).wav</td>\n",
              "      <td>anger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>s05 (2)</td>\n",
              "      <td>/content/data/aesdd/sadness/s05 (2).wav</td>\n",
              "      <td>sadness</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>h03 (4)</td>\n",
              "      <td>/content/data/aesdd/happiness/h03 (4).wav</td>\n",
              "      <td>happiness</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      name                                       path    emotion\n",
              "0  d11 (4)    /content/data/aesdd/disgust/d11 (4).wav    disgust\n",
              "1  s10 (4)    /content/data/aesdd/sadness/s10 (4).wav    sadness\n",
              "2  a13 (5)      /content/data/aesdd/anger/a13 (5).wav      anger\n",
              "3  s05 (2)    /content/data/aesdd/sadness/s05 (2).wav    sadness\n",
              "4  h03 (4)  /content/data/aesdd/happiness/h03 (4).wav  happiness"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "FlJO2LfVWIyT",
        "outputId": "8b66a4b4-afe2-411f-c843-763928795675"
      },
      "source": [
        "prediction(test.iloc[0])"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              "div.display_data {\n",
              "    margin: 0 auto;\n",
              "    max-width: 500px;\n",
              "}\n",
              "table.xxx {\n",
              "    margin: 50px !important;\n",
              "    float: right !important;\n",
              "    clear: both !important;\n",
              "}\n",
              "table.xxx td {\n",
              "    min-width: 300px !important;\n",
              "    text-align: center !important;\n",
              "}\n",
              "</style><table border=\"2\" class=\"dataframe xxx\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: center;\">\n",
              "      <th></th>\n",
              "      <th>Emotion</th>\n",
              "      <th>Sentence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>disgust</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 2 columns</p><br />"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" autoplay=\"autoplay\">\n",
              "                    <source 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type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              "div.display_data {\n",
              "    margin: 0 auto;\n",
              "    max-width: 500px;\n",
              "}\n",
              "table.xxx {\n",
              "    margin: 50px !important;\n",
              "    float: right !important;\n",
              "    clear: both !important;\n",
              "}\n",
              "table.xxx td {\n",
              "    min-width: 300px !important;\n",
              "    text-align: center !important;\n",
              "}\n",
              "</style><table border=\"2\" class=\"dataframe xxx\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: center;\">\n",
              "      <th></th>\n",
              "      <th>Label</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>anger</td>\n",
              "      <td>0.1%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>disgust</td>\n",
              "      <td>98.9%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>fear</td>\n",
              "      <td>0.2%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>happiness</td>\n",
              "      <td>0.5%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>sadness</td>\n",
              "      <td>0.3%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 2 columns</p><br />"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hzoKOgpoWI1K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "daae8f76-78b5-40e8-cadd-3d2b571f8e0b"
      },
      "source": [
        "prediction(test.iloc[1])"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              "div.display_data {\n",
              "    margin: 0 auto;\n",
              "    max-width: 500px;\n",
              "}\n",
              "table.xxx {\n",
              "    margin: 50px !important;\n",
              "    float: right !important;\n",
              "    clear: both !important;\n",
              "}\n",
              "table.xxx td {\n",
              "    min-width: 300px !important;\n",
              "    text-align: center !important;\n",
              "}\n",
              "</style><table border=\"2\" class=\"dataframe xxx\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: center;\">\n",
              "      <th></th>\n",
              "      <th>Emotion</th>\n",
              "      <th>Sentence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>sadness</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 2 columns</p><br />"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" autoplay=\"autoplay\">\n",
              "                    <source 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type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              "div.display_data {\n",
              "    margin: 0 auto;\n",
              "    max-width: 500px;\n",
              "}\n",
              "table.xxx {\n",
              "    margin: 50px !important;\n",
              "    float: right !important;\n",
              "    clear: both !important;\n",
              "}\n",
              "table.xxx td {\n",
              "    min-width: 300px !important;\n",
              "    text-align: center !important;\n",
              "}\n",
              "</style><table border=\"2\" class=\"dataframe xxx\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: center;\">\n",
              "      <th></th>\n",
              "      <th>Label</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>anger</td>\n",
              "      <td>0.1%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>disgust</td>\n",
              "      <td>0.3%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>fear</td>\n",
              "      <td>0.3%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>happiness</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>sadness</td>\n",
              "      <td>99.3%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 2 columns</p><br />"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "7nqwbTTXSfMO",
        "outputId": "b1046381-bfe0-4a96-f9c6-77468c0ec6c2"
      },
      "source": [
        "prediction(test.iloc[2])"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              "div.display_data {\n",
              "    margin: 0 auto;\n",
              "    max-width: 500px;\n",
              "}\n",
              "table.xxx {\n",
              "    margin: 50px !important;\n",
              "    float: right !important;\n",
              "    clear: both !important;\n",
              "}\n",
              "table.xxx td {\n",
              "    min-width: 300px !important;\n",
              "    text-align: center !important;\n",
              "}\n",
              "</style><table border=\"2\" class=\"dataframe xxx\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: center;\">\n",
              "      <th></th>\n",
              "      <th>Emotion</th>\n",
              "      <th>Sentence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>anger</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 2 columns</p><br />"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" autoplay=\"autoplay\">\n",
              "                    <source 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FILE: notebooks/Emotion_recognition_in_Greek_speech_using_Wav2Vec2.ipynb
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5fbCls1d2yBs"
      },
      "source": [
        "# Emotion Recognition in Greek Speech 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": "ddd8d0a1-7513-4b5a-c4ca-5627e1f6aa14"
      },
      "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": 2,
      "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 [Acted Emotional Speech Dynamic Database – AESDD](http://m3c.web.auth.gr/research/aesdd-speech-emotion-recognition/) provided by Multidisciplinary Media & Mediated Communication Research Group ([M3C](http://m3c.web.auth.gr/)). \n",
        "\n",
        "The Acted Emotional Speech Dynamic Database (AESDD) is a publically available speech emotion recognition dataset that contains utterances of acted emotional speech in the Greek language for five different emotions `sadness`, `disgust`, `happiness`, `anger`, and `fear`.\n",
        "\n",
        "The dataset consists of directories of emotions; each folder includes specific emotions. We need to loop over directories and save the paths related to each class based on the directory name.\n",
        "\n",
        "```bash\n",
        ".\n",
        "├── Tools\\ and\\ Documentation\n",
        "│   ├── ESTrainer.mlapp\n",
        "│   ├── Speech\\ Emotion\\ Recognition\\ Adapted\\ to\\ Multimodal\\ Semantic\\ Repositories_documentation.pdf\n",
        "│   ├── Speech\\ Emotion\\ Recognition\\ for\\ Performance\\ Interaction.pdf\n",
        "│   └── readme.txt\n",
        "├── anger\n",
        "│   ├── a01\\ (1).wav\n",
        "│   ├── a01\\ (2).wav\n",
        "│   ├── ...\n",
        "├── disgust\n",
        "│   ├── d01\\ (1).wav\n",
        "│   ├── d01\\ (2).wav\n",
        "│   ├── ...\n",
        "├── fear\n",
        "│   ├── f01\\ (1).wav\n",
        "│   ├── f01\\ (2).wav\n",
        "│   ├── ...\n",
        "├── happiness\n",
        "│   ├── h01\\ (1).wav\n",
        "│   ├── h01\\ (2).wav\n",
        "│   ├── ...\n",
        "└── sadness\n",
        "    ├── s01\\ (1).wav\n",
        "    ├── s01\\ (2).wav\n",
        "    ├── ...\n",
        "\n",
        "6 directories, 609 files\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4UVGPPOVCl-Y"
      },
      "source": [
        "Since the dataset is hosted on [Mega](https://mega.nz/#F!0ShVXY7C!-73kVoK05OjTPEA95UUvMw) and unable to access it directly, we upload the dataset on the G-drive to make this tutorial accessible."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nEKDAy2bCGFM",
        "outputId": "6d658021-9f4d-458e-8e3b-53d961a03caf"
      },
      "source": [
        "# Download the dataset from \n",
        "\n",
        "!mkdir -p /content/data\n",
        "!gdown https://drive.google.com/uc?id=1_IAWexEWpH-ly_JaA5EGfZDp-_3flkN1\n",
        "!unzip -q aesdd.zip -d /content/data/\n",
        "!mv \"/content/data/Acted Emotional Speech Dynamic Database/\" /content/data/aesdd/"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1_IAWexEWpH-ly_JaA5EGfZDp-_3flkN1\n",
            "To: /content/aesdd.zip\n",
            "410MB [00:04, 87.4MB/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": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7MXAESg_Dqr6",
        "outputId": "93cd9a9a-a47d-4c7b-c3dd-79b3016e2976"
      },
      "source": [
        "data = []\n",
        "\n",
        "for path in tqdm(Path(\"/content/data/aesdd\").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",
        "            \"emotion\": label\n",
        "        })\n",
        "    except Exception as e:\n",
        "        # print(str(path), e)\n",
        "        pass\n",
        "\n",
        "    # break"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "605it [00:01, 563.84it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "10b89ZpLDqx9",
        "outputId": "4228d11b-e1cb-4f91-b8e0-1994167c0b22"
      },
      "source": [
        "df = pd.DataFrame(data)\n",
        "df.head()"
      ],
      "execution_count": 6,
      "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>name</th>\n",
              "      <th>path</th>\n",
              "      <th>emotion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>f03 (5)</td>\n",
              "      <td>/content/data/aesdd/fear/f03 (5).wav</td>\n",
              "      <td>fear</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>f13 (4)</td>\n",
              "      <td>/content/data/aesdd/fear/f13 (4).wav</td>\n",
              "      <td>fear</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>f12 (3)</td>\n",
              "      <td>/content/data/aesdd/fear/f12 (3).wav</td>\n",
              "      <td>fear</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>f01 (5)</td>\n",
              "      <td>/content/data/aesdd/fear/f01 (5).wav</td>\n",
              "      <td>fear</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>f11 (5)</td>\n",
              "      <td>/content/data/aesdd/fear/f11 (5).wav</td>\n",
              "      <td>fear</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      name                                  path emotion\n",
              "0  f03 (5)  /content/data/aesdd/fear/f03 (5).wav    fear\n",
              "1  f13 (4)  /content/data/aesdd/fear/f13 (4).wav    fear\n",
              "2  f12 (3)  /content/data/aesdd/fear/f12 (3).wav    fear\n",
              "3  f01 (5)  /content/data/aesdd/fear/f01 (5).wav    fear\n",
              "4  f11 (5)  /content/data/aesdd/fear/f11 (5).wav    fear"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iMhTHur8voOp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "06eab027-7912-4bf5-e781-2aa7bc220c43"
      },
      "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": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Step 0: 604\n",
            "Step 1: 604\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>name</th>\n",
              "      <th>path</th>\n",
              "      <th>emotion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>h12 (2)</td>\n",
              "      <td>/content/data/aesdd/happiness/h12 (2).wav</td>\n",
              "      <td>happiness</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>d16 (2)</td>\n",
              "      <td>/content/data/aesdd/disgust/d16 (2).wav</td>\n",
              "      <td>disgust</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>f07 (2)</td>\n",
              "      <td>/content/data/aesdd/fear/f07 (2).wav</td>\n",
              "      <td>fear</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>a13 (1)</td>\n",
              "      <td>/content/data/aesdd/anger/a13 (1).wav</td>\n",
              "      <td>anger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>h14 (6)</td>\n",
              "      <td>/content/data/aesdd/happiness/h14 (6).wav</td>\n",
              "      <td>happiness</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      name                                       path    emotion\n",
              "0  h12 (2)  /content/data/aesdd/happiness/h12 (2).wav  happiness\n",
              "1  d16 (2)    /content/data/aesdd/disgust/d16 (2).wav    disgust\n",
              "2  f07 (2)       /content/data/aesdd/fear/f07 (2).wav       fear\n",
              "3  a13 (1)      /content/data/aesdd/anger/a13 (1).wav      anger\n",
              "4  h14 (6)  /content/data/aesdd/happiness/h14 (6).wav  happiness"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TWku4ra3Bp52"
      },
      "source": [
        "Let's explore how many labels (emotions) are in the dataset with what distribution."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "beNpKMh5xXmX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "171d276b-8be2-45fc-943c-3610a5c4e1b4"
      },
      "source": [
        "print(\"Labels: \", df[\"emotion\"].unique())\n",
        "print()\n",
        "df.groupby(\"emotion\").count()[[\"path\"]]"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Labels:  ['happiness' 'disgust' 'fear' 'anger' 'sadness']\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",
              "    }\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>emotion</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>anger</th>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>disgust</th>\n",
              "      <td>122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fear</th>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>happiness</th>\n",
              "      <td>119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sadness</th>\n",
              "      <td>122</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           path\n",
              "emotion        \n",
              "anger       121\n",
              "disgust     122\n",
              "fear        120\n",
              "happiness   119\n",
              "sadness     122"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "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 emotional label."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DZaQ_sP5xkIX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "aa27d8ae-d95e-475f-eee7-e9e05f4cbe1b"
      },
      "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[\"emotion\"]\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": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ID Location: 286\n",
            "      Label: sadness\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" autoplay=\"autoplay\">\n",
              "                    <source 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\" type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WNjWoWRCmPk"
      },
      "source": [
        "For training purposes, we need to split data into train test sets; in this specific example, we break with a `20%` rate for the test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mlim-044xtJN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8b2530e-91bd-40e1-e60c-086f1ff8431e"
      },
      "source": [
        "save_path = \"/content/data\"\n",
        "\n",
        "train_df, test_df = train_test_split(df, test_size=0.2, random_state=101, stratify=df[\"emotion\"])\n",
        "\n",
        "train_df = train_df.reset_index(drop=True)\n",
        "test_df = test_df.reset_index(drop=True)\n",
        "\n",
        "train_df.to_csv(f\"{save_path}/train.csv\", sep=\"\\t\", encoding=\"utf-8\", index=False)\n",
        "test_df.to_csv(f\"{save_path}/test.csv\", sep=\"\\t\", encoding=\"utf-8\", index=False)\n",
        "\n",
        "\n",
        "print(train_df.shape)\n",
        "print(test_df.shape)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(483, 3)\n",
            "(121, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kcnD-d_rDElt"
      },
      "source": [
        "## Prepare Data for Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224,
          "referenced_widgets": [
            "d1ae7ec1a2dd4d028278527973c89163",
            "09bfc3d75b104740a264fc44b054a06e",
            "80763823702748ae9de2a8ef1acfb636",
            "111fff43f1b34a3eae32a7b40eed8e23",
            "992262ea2f2943d7bf1fa3ef12763cab",
            "38994cac02c54294a02e488a47134391",
            "641c87d13435440194be2be48c46dd14",
            "48a65bdc0f554a53872e4586158ff894",
            "270777ba713d47e1aa73965657310975",
            "4132700b3c19413fad9e01dd28b4c56e",
            "000e0546e7b945a8ab548c00475186c3",
            "d912a0a16c494349b81fe8dddcedb1f9",
            "9bf8b5186a96425892c1fe5fa4fcabe3",
            "80c3a9d360a74d27b0c2a403c47460fa",
            "71a9af276402485c9739b0e9b1739ba6",
            "025aacc6cd7a48df9888c16869d73722"
          ]
        },
        "id": "nnVfxQYDDIc6",
        "outputId": "813a7f17-b823-4d2a-e298-734646242912"
      },
      "source": [
        "# Loading the created dataset using datasets\n",
        "from datasets import load_dataset, load_metric\n",
        "\n",
        "\n",
        "data_files = {\n",
        "    \"train\": \"/content/data/train.csv\", \n",
        "    \"validation\": \"/content/data/test.csv\",\n",
        "}\n",
        "\n",
        "dataset = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\", )\n",
        "train_dataset = dataset[\"train\"]\n",
        "eval_dataset = dataset[\"validation\"]\n",
        "\n",
        "print(train_dataset)\n",
        "print(eval_dataset)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using custom data configuration default-5c24106fd6acf37d\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset csv/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /content/cache/csv/default-5c24106fd6acf37d/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d1ae7ec1a2dd4d028278527973c89163",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "270777ba713d47e1aa73965657310975",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rDataset csv downloaded and prepared to /content/cache/csv/default-5c24106fd6acf37d/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0. Subsequent calls will reuse this data.\n",
            "Dataset({\n",
            "    features: ['name', 'path', 'emotion'],\n",
            "    num_rows: 483\n",
            "})\n",
            "Dataset({\n",
            "    features: ['name', 'path', 'emotion'],\n",
            "    num_rows: 121\n",
            "})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsOrQPBOEVOy"
      },
      "source": [
        "# We need to specify the input and output column\n",
        "input_column = \"path\"\n",
        "output_column = \"emotion\""
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-gh7fQ1XEpC7",
        "outputId": "354be843-a3ed-4dbf-af47-df935c90e9d2"
      },
      "source": [
        "# we need to distinguish the unique labels in our SER dataset\n",
        "label_list = train_dataset.unique(output_column)\n",
        "label_list.sort()  # Let's sort it for determinism\n",
        "num_labels = len(label_list)\n",
        "print(f\"A classification problem with {num_labels} classes: {label_list}\")"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A classification problem with 5 classes: ['anger', 'disgust', 'fear', 'happiness', 'sadness']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4TkGYrVTFR6Y"
      },
      "source": [
        "In order to preprocess the audio into our classification model, we need to set up the relevant Wav2Vec2 assets regarding our language in this case `lighteternal/wav2vec2-large-xlsr-53-greek` fine-tuned by [Dimitris Papadopoulos](https://huggingface.co/lighteternal/wav2vec2-large-xlsr-53-greek). To handle the context representations in any audio length we use a merge strategy plan (pooling mode) to concatenate that 3D representations into 2D representations.\n",
        "\n",
        "There are three merge strategies `mean`, `sum`, and `max`. In this example, we achieved better results on the mean approach. In the following, we need to initiate the config and the feature extractor from the Dimitris model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQrGaFohGzVo"
      },
      "source": [
        "from transformers import AutoConfig, Wav2Vec2Processor"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Y1adr7vFrq7"
      },
      "source": [
        "model_name_or_path = \"lighteternal/wav2vec2-large-xlsr-53-greek\"\n",
        "pooling_mode = \"mean\""
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "748b930dc33b476c95d534372061c518",
            "0d5bcc52dd114f9b901061aafb9d4f9c",
            "08c07035aac44299a3fccc1c99eb1906",
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            "05aed6d4dbc1470ba6cbd8eb414a6a86",
            "875e2fab8fb04b02a13b5d2fb0bbcb48"
          ]
        },
        "id": "AZjDSmBRGqr6",
        "outputId": "2286c25e-6d93-4355-8f69-e592fe15d5d4"
      },
      "source": [
        "# config\n",
        "config = AutoConfig.from_pretrained(\n",
        "    model_name_or_path,\n",
        "    num_labels=num_labels,\n",
        "    label2id={label: i for i, label in enumerate(label_list)},\n",
        "    id2label={i: label for i, label in enumerate(label_list)},\n",
        "    finetuning_task=\"wav2vec2_clf\",\n",
        ")\n",
        "setattr(config, 'pooling_mode', pooling_mode)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "748b930dc33b476c95d534372061c518",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1562.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247,
          "referenced_widgets": [
            "cc160e510d894b698abe915f95f3735b",
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        },
        "id": "ZXVl9qW1Gw_-",
        "outputId": "9dd51ab8-e5ef-4911-9a82-a0fa413ec33c"
      },
      "source": [
        "processor = Wav2Vec2Processor.from_pretrained(model_name_or_path,)\n",
        "target_sampling_rate = processor.feature_extractor.sampling_rate\n",
        "print(f\"The target sampling rate: {target_sampling_rate}\")"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cc160e510d894b698abe915f95f3735b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=158.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3b6b84cb7c2e46c7ba2b2d4c771cc789",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=535.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "df2139a7dbc949e79111412f42df47ec",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=138.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "208850889b55497ab1444a61f9442d2d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=85.0, style=ProgressStyle(description_w…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The target sampling rate: 16000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CbGuYgLqHXZg"
      },
      "source": [
        "# Preprocess Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qLk-eM1DFjtE"
      },
      "source": [
        "So far, we downloaded, loaded, and split the SER dataset into train and test sets. The instantiated our strategy configuration for using context representations in our classification problem SER. Now, we need to extract features from the audio path in context representation tensors and feed them into our classification model to determine the emotion in the speech.\n",
        "\n",
        "Since the audio file is saved in the `.wav` format, it is easy to use **[Librosa](https://librosa.org/doc/latest/index.html)** or others, but we suppose that the format may be in the `.mp3` format in case of generality. We found that the **[Torchaudio](https://pytorch.org/audio/stable/index.html)** library works best for reading in `.mp3` data.\n",
        "\n",
        "An audio file usually stores both its values and the sampling rate with which the speech signal was digitalized. We want to store both in the dataset and write a **map(...)** function accordingly. Also, we need to handle the string labels into integers for our specific classification task in this case, the **single-label classification** you may want to use for your **regression** or even **multi-label classification**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UqlIV3uGxDA"
      },
      "source": [
        "def speech_file_to_array_fn(path):\n",
        "    speech_array, sampling_rate = torchaudio.load(path)\n",
        "    resampler = torchaudio.transforms.Resample(sampling_rate, target_sampling_rate)\n",
        "    speech = resampler(speech_array).squeeze().numpy()\n",
        "    return speech\n",
        "\n",
        "def label_to_id(label, label_list):\n",
        "\n",
        "    if len(label_list) > 0:\n",
        "        return label_list.index(label) if label in label_list else -1\n",
        "\n",
        "    return label\n",
        "\n",
        "def preprocess_function(examples):\n",
        "    speech_list = [speech_file_to_array_fn(path) for path in examples[input_column]]\n",
        "    target_list = [label_to_id(label, label_list) for label in examples[output_column]]\n",
        "\n",
        "    result = processor(speech_list, sampling_rate=target_sampling_rate)\n",
        "    result[\"labels\"] = list(target_list)\n",
        "\n",
        "    return result"
      ],
      "execution_count": 19,
      "outputs": []
    },
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        "id": "ioP8FfR2GxHi",
        "outputId": "2bea4ed0-33d6-4183-9e6c-cbdf14072453"
      },
      "source": [
        "train_dataset = train_dataset.map(\n",
        "    preprocess_function,\n",
        "    batch_size=100,\n",
        "    batched=True,\n",
        "    num_proc=4\n",
        ")\n",
        "eval_dataset = eval_dataset.map(\n",
        "    preprocess_function,\n",
        "    batch_size=100,\n",
        "    batched=True,\n",
        "    num_proc=4\n",
        ")"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    "
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "37a315c7c54c4b37861155ca7db9a8b5",
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            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='#0', max=2.0, style=ProgressStyle(description_width='init…"
            ]
          },
          "metadata": {
            "tags": []
          }
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        {
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              "HBox(children=(FloatProgress(value=0.0, description='#2', max=2.0, style=ProgressStyle(description_width='init…"
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        {
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              "HBox(children=(FloatProgress(value=0.0, description='#1', max=2.0, style=ProgressStyle(description_width='init…"
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        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n",
            "  "
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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        },
        {
          "output_type": "stream",
          "text": [
            "  "
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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              "model_id": "82640fdfc9ee49249812dbcdcf05e2c1",
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              "model_id": "40b5f7de561b4f5b86a2d4b04decedd4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='#3', max=1.0, style=ProgressStyle(description_width='init…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eu1qcRucHk6d",
        "outputId": "b3007ace-f4f3-443a-f31e-0afa09e5eacb"
      },
      "source": [
        "idx = 0\n",
        "print(f\"Training input_values: {train_dataset[idx]['input_values']}\")\n",
        "print(f\"Training attention_mask: {train_dataset[idx]['attention_mask']}\")\n",
        "print(f\"Training labels: {train_dataset[idx]['labels']} - {train_dataset[idx]['emotion']}\")"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training input_values: [-0.0007524748798459768, 0.004419753327965736, -0.012662916444242, 0.0016497393371537328, 0.01925051212310791, -0.013010337948799133, -0.018743593245744705, 0.030535006895661354, -0.007420312147587538, -0.008200610987842083, -0.005278450902551413, 0.01008838415145874, 0.01753123104572296, -0.03521161153912544, 0.008765327744185925, 0.021295689046382904, -0.001567490748129785, -0.040061283856630325, 0.030490050092339516, 0.031789425760507584, -0.056860245764255524, -0.004201843868941069, 0.06782747805118561, -0.03282438963651657, -0.05081942677497864, 0.052758101373910904, 0.024646123871207237, -0.051771849393844604, -0.005826461128890514, 0.04431638494133949, -0.014967367053031921, -0.006660561077296734, -0.01957547478377819, 0.03018687479197979, 0.01206017192453146, -0.042734295129776, 0.00607768539339304, 0.052774202078580856, -0.05650204420089722, 0.007801816798746586, 0.0277846846729517, -0.022254042327404022, 0.008400841616094112, 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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "Training labels: 3 - happiness\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HcrEgJO9Hmx7"
      },
      "source": [
        "Great, now we've successfully read all the audio files, resampled the audio files to 16kHz, and mapped each audio to the corresponding label."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QL7q6DfcH0Fs"
      },
      "source": [
        "## Model\n",
        "\n",
        "Before diving into the training part, we need to build our classification model based on the merge strategy. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uJZoC4T5HpuP"
      },
      "source": [
        "from dataclasses import dataclass\n",
        "from typing import Optional, Tuple\n",
        "import torch\n",
        "from transformers.file_utils import ModelOutput\n",
        "\n",
        "\n",
        "@dataclass\n",
        "class SpeechClassifierOutput(ModelOutput):\n",
        "    loss: Optional[torch.FloatTensor] = None\n",
        "    logits: torch.FloatTensor = None\n",
        "    hidden_states: Optional[Tuple[torch.FloatTensor]] = None\n",
        "    attentions: Optional[Tuple[torch.FloatTensor]] = None\n"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fv62ShDsH5DZ"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss\n",
        "\n",
        "from transformers.models.wav2vec2.modeling_wav2vec2 import (\n",
        "    Wav2Vec2PreTrainedModel,\n",
        "    Wav2Vec2Model\n",
        ")\n",
        "\n",
        "\n",
        "class Wav2Vec2ClassificationHead(nn.Module):\n",
        "    \"\"\"Head for wav2vec classification task.\"\"\"\n",
        "\n",
        "    def __init__(self, config):\n",
        "        super().__init__()\n",
        "        self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n",
        "        self.dropout = nn.Dropout(config.final_dropout)\n",
        "        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)\n",
        "\n",
        "    def forward(self, features, **kwargs):\n",
        "        x = features\n",
        "        x = self.dropout(x)\n",
        "        x = self.dense(x)\n",
        "        x = torch.tanh(x)\n",
        "        x = self.dropout(x)\n",
        "        x = self.out_proj(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "class Wav2Vec2ForSpeechClassification(Wav2Vec2PreTrainedModel):\n",
        "    def __init__(self, config):\n",
        "        super().__init__(config)\n",
        "        self.num_labels = config.num_labels\n",
        "        self.pooling_mode = config.pooling_mode\n",
        "        self.config = config\n",
        "\n",
        "        self.wav2vec2 = Wav2Vec2Model(config)\n",
        "        self.classifier = Wav2Vec2ClassificationHead(config)\n",
        "\n",
        "        self.init_weights()\n",
        "\n",
        "    def freeze_feature_extractor(self):\n",
        "        self.wav2vec2.feature_extractor._freeze_parameters()\n",
        "\n",
        "    def merged_strategy(\n",
        "            self,\n",
        "            hidden_states,\n",
        "            mode=\"mean\"\n",
        "    ):\n",
        "        if mode == \"mean\":\n",
        "            outputs = torch.mean(hidden_states, dim=1)\n",
        "        elif mode == \"sum\":\n",
        "            outputs = torch.sum(hidden_states, dim=1)\n",
        "        elif mode == \"max\":\n",
        "            outputs = torch.max(hidden_states, dim=1)[0]\n",
        "        else:\n",
        "            raise Exception(\n",
        "                \"The pooling method hasn't been defined! Your pooling mode must be one of these ['mean', 'sum', 'max']\")\n",
        "\n",
        "        return outputs\n",
        "\n",
        "    def forward(\n",
        "            self,\n",
        "            input_values,\n",
        "            attention_mask=None,\n",
        "            output_attentions=None,\n",
        "            output_hidden_states=None,\n",
        "            return_dict=None,\n",
        "            labels=None,\n",
        "    ):\n",
        "        return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n",
        "        outputs = self.wav2vec2(\n",
        "            input_values,\n",
        "            attention_mask=attention_mask,\n",
        "            output_attentions=output_attentions,\n",
        "            output_hidden_states=output_hidden_states,\n",
        "            return_dict=return_dict,\n",
        "        )\n",
        "        hidden_states = outputs[0]\n",
        "        hidden_states = self.merged_strategy(hidden_states, mode=self.pooling_mode)\n",
        "        logits = self.classifier(hidden_states)\n",
        "\n",
        "        loss = None\n",
        "        if labels is not None:\n",
        "            if self.config.problem_type is None:\n",
        "                if self.num_labels == 1:\n",
        "                    self.config.problem_type = \"regression\"\n",
        "                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):\n",
        "                    self.config.problem_type = \"single_label_classification\"\n",
        "                else:\n",
        "                    self.config.problem_type = \"multi_label_classification\"\n",
        "\n",
        "            if self.config.problem_type == \"regression\":\n",
        "                loss_fct = MSELoss()\n",
        "                loss = loss_fct(logits.view(-1, self.num_labels), labels)\n",
        "            elif self.config.problem_type == \"single_label_classification\":\n",
        "                loss_fct = CrossEntropyLoss()\n",
        "                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\n",
        "            elif self.config.problem_type == \"multi_label_classification\":\n",
        "                loss_fct = BCEWithLogitsLoss()\n",
        "                loss = loss_fct(logits, labels)\n",
        "\n",
        "        if not return_dict:\n",
        "            output = (logits,) + outputs[2:]\n",
        "            return ((loss,) + output) if loss is not None else output\n",
        "\n",
        "        return SpeechClassifierOutput(\n",
        "            loss=loss,\n",
        "            logits=logits,\n",
        "            hidden_states=outputs.hidden_states,\n",
        "            attentions=outputs.attentions,\n",
        "        )\n"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OrBrR1b7zvUL"
      },
      "source": [
        "## Training\n",
        "\n",
        "The data is processed so that we are ready to start setting up the training pipeline. We will make use of 🤗's [Trainer](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer) for which we essentially need to do the following:\n",
        "\n",
        "- Define a data collator. In contrast to most NLP models, XLSR-Wav2Vec2 has a much larger input length than output length. *E.g.*, a sample of input length 50000 has an output length of no more than 100. Given the large input sizes, it is much more efficient to pad the training batches dynamically meaning that all training samples should only be padded to the longest sample in their batch and not the overall longest sample. Therefore, fine-tuning XLSR-Wav2Vec2 requires a special padding data collator, which we will define below\n",
        "\n",
        "- Evaluation metric. During training, the model should be evaluated on the word error rate. We should define a `compute_metrics` function accordingly\n",
        "\n",
        "- Load a pretrained checkpoint. We need to load a pretrained checkpoint and configure it correctly for training.\n",
        "\n",
        "- Define the training configuration.\n",
        "\n",
        "After having fine-tuned the model, we will correctly evaluate it on the test data and verify that it has indeed learned to correctly transcribe speech."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ji9-n1eUIKZc"
      },
      "source": [
        "### Set-up Trainer\n",
        "\n",
        "Let's start by defining the data collator. The code for the data collator was copied from [this example](https://github.com/huggingface/transformers/blob/9a06b6b11bdfc42eea08fa91d0c737d1863c99e3/examples/research_projects/wav2vec2/run_asr.py#L81).\n",
        "\n",
        "Without going into too many details, in contrast to the common data collators, this data collator treats the `input_values` and `labels` differently and thus applies to separate padding functions on them (again making use of XLSR-Wav2Vec2's context manager). This is necessary because in speech input and output are of different modalities meaning that they should not be treated by the same padding function.\n",
        "Analogous to the common data collators, the padding tokens in the labels with `-100` so that those tokens are **not** taken into account when computing the loss."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rkM0VLIwy903"
      },
      "source": [
        "from dataclasses import dataclass\n",
        "from typing import Dict, List, Optional, Union\n",
        "import torch\n",
        "\n",
        "import transformers\n",
        "from transformers import Wav2Vec2Processor\n",
        "\n",
        "\n",
        "@dataclass\n",
        "class DataCollatorCTCWithPadding:\n",
        "    \"\"\"\n",
        "    Data collator that will dynamically pad the inputs received.\n",
        "    Args:\n",
        "        processor (:class:`~transformers.Wav2Vec2Processor`)\n",
        "            The processor used for proccessing the data.\n",
        "        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):\n",
        "            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)\n",
        "            among:\n",
        "            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single\n",
        "              sequence if provided).\n",
        "            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the\n",
        "              maximum acceptable input length for the model if that argument is not provided.\n",
        "            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of\n",
        "              different lengths).\n",
        "        max_length (:obj:`int`, `optional`):\n",
        "            Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).\n",
        "        max_length_labels (:obj:`int`, `optional`):\n",
        "            Maximum length of the ``labels`` returned list and optionally padding length (see above).\n",
        "        pad_to_multiple_of (:obj:`int`, `optional`):\n",
        "            If set will pad the sequence to a multiple of the provided value.\n",
        "            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=\n",
        "            7.5 (Volta).\n",
        "    \"\"\"\n",
        "\n",
        "    processor: Wav2Vec2Processor\n",
        "    padding: Union[bool, str] = True\n",
        "    max_length: Optional[int] = None\n",
        "    max_length_labels: Optional[int] = None\n",
        "    pad_to_multiple_of: Optional[int] = None\n",
        "    pad_to_multiple_of_labels: Optional[int] = None\n",
        "\n",
        "    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
        "        input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n",
        "        label_features = [feature[\"labels\"] for feature in features]\n",
        "\n",
        "        d_type = torch.long if isinstance(label_features[0], int) else torch.float\n",
        "\n",
        "        batch = self.processor.pad(\n",
        "            input_features,\n",
        "            padding=self.padding,\n",
        "            max_length=self.max_length,\n",
        "            pad_to_multiple_of=self.pad_to_multiple_of,\n",
        "            return_tensors=\"pt\",\n",
        "        )\n",
        "\n",
        "        batch[\"labels\"] = torch.tensor(label_features, dtype=d_type)\n",
        "\n",
        "        return batch"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YhJ0OA4RxQe"
      },
      "source": [
        "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mYxy2IR-KcU2"
      },
      "source": [
        "Next, the evaluation metric is defined. There are many pre-defined metrics for classification/regression problems, but in this case, we would continue with just **Accuracy** for classification and **MSE** for regression. You can define other metrics on your own."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LL8I5MKvPnth"
      },
      "source": [
        "is_regression = False"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XK26Z6IfR36K"
      },
      "source": [
        "import numpy as np\n",
        "from transformers import EvalPrediction\n",
        "\n",
        "\n",
        "def compute_metrics(p: EvalPrediction):\n",
        "    preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions\n",
        "    preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1)\n",
        "\n",
        "    if is_regression:\n",
        "        return {\"mse\": ((preds - p.label_ids) ** 2).mean().item()}\n",
        "    else:\n",
        "        return {\"accuracy\": (preds == p.label_ids).astype(np.float32).mean().item()}"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZsH_nKJdK28o"
      },
      "source": [
        "Now, we can load the pretrained XLSR-Wav2Vec2 checkpoint into our classification model with a pooling strategy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Tl6iKAUR4EL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171,
          "referenced_widgets": [
            "0561dae888b24853bf6bd12b36b9f58c",
            "147983c81ea54adf9a9fcc94be46e53c",
            "149ebd49f5b047c0869941fda06c1136",
            "1e356a9dd0c6482ca56e83cb600a49ab",
            "0eb6c74ddd614b0284cc8fcb7e9749b9",
            "d5e84bc6021746ac834f95b6850e397e",
            "a68fdcd0b0ef4349a2fd46d5d10ad4fb",
            "32863857edff47a6894d619eef3aa72a"
          ]
        },
        "outputId": "7af3b6e2-dfc2-4e78-b326-010e32f86a94"
      },
      "source": [
        "model = Wav2Vec2ForSpeechClassification.from_pretrained(\n",
        "    model_name_or_path,\n",
        "    config=config,\n",
        ")"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0561dae888b24853bf6bd12b36b9f58c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1262159319.0, style=ProgressStyle(descr…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at lighteternal/wav2vec2-large-xlsr-53-greek were not used when initializing Wav2Vec2ForSpeechClassification: ['lm_head.weight', 'lm_head.bias']\n",
            "- This IS expected if you are initializing Wav2Vec2ForSpeechClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing Wav2Vec2ForSpeechClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of Wav2Vec2ForSpeechClassification were not initialized from the model checkpoint at lighteternal/wav2vec2-large-xlsr-53-greek and are newly initialized: ['classifier.out_proj.bias', 'classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bqF4rNMzI1M5"
      },
      "source": [
        "The first component of XLSR-Wav2Vec2 consists of a stack of CNN layers that are used to extract acoustically meaningful - but contextually independent - features from the raw speech signal. This part of the model has already been sufficiently trained during pretraining and as stated in the [paper](https://arxiv.org/pdf/2006.13979.pdf) does not need to be fine-tuned anymore. \n",
        "Thus, we can set the `requires_grad` to `False` for all parameters of the *feature extraction* part."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KHMhxFGoR4Hb"
      },
      "source": [
        "model.freeze_feature_extractor()"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n0HzBneBK84G"
      },
      "source": [
        "In a final step, we define all parameters related to training. \n",
        "To give more explanation on some of the parameters:\n",
        "- `learning_rate` and `weight_decay` were heuristically tuned until fine-tuning has become stable. Note that those parameters strongly depend on the Common Voice dataset and might be suboptimal for other speech datasets.\n",
        "\n",
        "For more explanations on other parameters, one can take a look at the [docs](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer#trainingarguments).\n",
        "\n",
        "**Note**: If one wants to save the trained models in his/her google drive the commented-out `output_dir` can be used instead."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3tPqZ12BLCJk"
      },
      "source": [
        "# from google.colab import drive\n",
        "\n",
        "# drive.mount('/gdrive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vUtWjldAI9-H"
      },
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"/content/wav2vec2-xlsr-greek-speech-emotion-recognition\",\n",
        "    # output_dir=\"/content/gdrive/MyDrive/wav2vec2-xlsr-greek-speech-emotion-recognition\"\n",
        "    per_device_train_batch_size=4,\n",
        "    per_device_eval_batch_size=4,\n",
        "    gradient_accumulation_steps=2,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    num_train_epochs=1.0,\n",
        "    fp16=True,\n",
        "    save_steps=10,\n",
        "    eval_steps=10,\n",
        "    logging_steps=10,\n",
        "    learning_rate=1e-4,\n",
        "    save_total_limit=2,\n",
        ")"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XAtuL0APLZSs"
      },
      "source": [
        "For future use we can create our training script, we do it in a simple way. You can add more on you own."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KfW6uDolJYZv"
      },
      "source": [
        "from typing import Any, Dict, Union\n",
        "\n",
        "import torch\n",
        "from packaging import version\n",
        "from torch import nn\n",
        "\n",
        "from transformers import (\n",
        "    Trainer,\n",
        "    is_apex_available,\n",
        ")\n",
        "\n",
        "if is_apex_available():\n",
        "    from apex import amp\n",
        "\n",
        "if version.parse(torch.__version__) >= version.parse(\"1.6\"):\n",
        "    _is_native_amp_available = True\n",
        "    from torch.cuda.amp import autocast\n",
        "\n",
        "\n",
        "class CTCTrainer(Trainer):\n",
        "    def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor:\n",
        "        \"\"\"\n",
        "        Perform a training step on a batch of inputs.\n",
        "\n",
        "        Subclass and override to inject custom behavior.\n",
        "\n",
        "        Args:\n",
        "            model (:obj:`nn.Module`):\n",
        "                The model to train.\n",
        "            inputs (:obj:`Dict[str, Union[torch.Tensor, Any]]`):\n",
        "                The inputs and targets of the model.\n",
        "\n",
        "                The dictionary will be unpacked before being fed to the model. Most models expect the targets under the\n",
        "                argument :obj:`labels`. Check your model's documentation for all accepted arguments.\n",
        "\n",
        "        Return:\n",
        "            :obj:`torch.Tensor`: The tensor with training loss on this batch.\n",
        "        \"\"\"\n",
        "\n",
        "        model.train()\n",
        "        inputs = self._prepare_inputs(inputs)\n",
        "\n",
        "        if self.use_amp:\n",
        "            with autocast():\n",
        "                loss = self.compute_loss(model, inputs)\n",
        "        else:\n",
        "            loss = self.compute_loss(model, inputs)\n",
        "\n",
        "        if self.args.gradient_accumulation_steps > 1:\n",
        "            loss = loss / self.args.gradient_accumulation_steps\n",
        "\n",
        "        if self.use_amp:\n",
        "            self.scaler.scale(loss).backward()\n",
        "        elif self.use_apex:\n",
        "            with amp.scale_loss(loss, self.optimizer) as scaled_loss:\n",
        "                scaled_loss.backward()\n",
        "        elif self.deepspeed:\n",
        "            self.deepspeed.backward(loss)\n",
        "        else:\n",
        "            loss.backward()\n",
        "\n",
        "        return loss.detach()\n"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qv7Ju3qYJeJn"
      },
      "source": [
        "Now, all instances can be passed to Trainer and we are ready to start training!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nEFkfK45JYiZ"
      },
      "source": [
        "trainer = CTCTrainer(\n",
        "    model=model,\n",
        "    data_collator=data_collator,\n",
        "    args=training_args,\n",
        "    compute_metrics=compute_metrics,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=eval_dataset,\n",
        "    tokenizer=processor.feature_extractor,\n",
        ")"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0gGLwJAOLtDg"
      },
      "source": [
        "### Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UpN6xlWCLxJ7"
      },
      "source": [
        "Training will take between 10 and 60 minutes depending on the GPU allocated to this notebook. \n",
        "\n",
        "In case you want to use this google colab to fine-tune your model, you should make sure that your training doesn't stop due to inactivity. A simple hack to prevent this is to paste the following code into the console of this tab (right mouse click -> inspect -> Console tab and insert code)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zyYZH7YZL8a9"
      },
      "source": [
        "```javascript\n",
        "function ConnectButton(){\n",
        "    console.log(\"Connect pushed\"); \n",
        "    document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click() \n",
        "}\n",
        "setInterval(ConnectButton,60000);\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "6M8bNvLLJnG1",
        "outputId": "819249df-b341-4007-d6de-b8137dfa1651"
      },
      "source": [
        "trainer.train()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='60' max='60' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [60/60 09:55, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.531200</td>\n",
              "      <td>1.542005</td>\n",
              "      <td>0.280992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>1.545000</td>\n",
              "      <td>1.437195</td>\n",
              "      <td>0.388430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>1.519600</td>\n",
              "      <td>1.392583</td>\n",
              "      <td>0.396694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.425600</td>\n",
              "      <td>1.290362</td>\n",
              "      <td>0.429752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.408200</td>\n",
              "      <td>1.239589</td>\n",
              "      <td>0.438017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.287800</td>\n",
              "      <td>1.215581</td>\n",
              "      <td>0.487603</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=60, training_loss=1.4529215176900228, metrics={'train_runtime': 594.5685, 'train_samples_per_second': 0.812, 'train_steps_per_second': 0.101, 'total_flos': 8.45847594771822e+16, 'epoch': 0.99})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3prIN9eiMBHo"
      },
      "source": [
        "The training loss goes down and we can see that the Acurracy on the test set also improves nicely. Because this notebook is just for demonstration purposes, we can stop here.\n",
        "\n",
        "The resulting model of this notebook has been saved to [m3hrdadfi/wav2vec2-xlsr-greek-speech-emotion-recognition](https://huggingface.co/m3hrdadfi/wav2vec2-xlsr-greek-speech-emotion-recognition)\n",
        "\n",
        "As a final check, let's load the model and verify that it indeed has learned to recognize the emotion in the speech.\n",
        "\n",
        "Let's first load the pretrained checkpoint."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VsaOTx_FVm0i"
      },
      "source": [
        "## Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4tGNY7hRXO44"
      },
      "source": [
        "import librosa\n",
        "from sklearn.metrics import classification_report"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "8a5be3ab74704643a36dd219fa3eb5a3",
            "0094521e8cf64ac098cfe122cf7021c4",
            "969ace95cb9742258efb81f517fccbe9",
            "ae3d53de5af147e98ee12966054cf41c",
            "5a4c57e5833840e19700a650781a95db",
            "ee8702b9d94c4b869e0601d42df0519c",
            "5c8945a46d2748c9b50c939e178c9d97",
            "654a83f92aec429a823f89cfadd618cb"
          ]
        },
        "id": "IYxg1Tfo2VUw",
        "outputId": "1bdf2b50-7663-4c63-eb16-bcd97614f285"
      },
      "source": [
        "test_dataset = load_dataset(\"csv\", data_files={\"test\": \"/content/data/test.csv\"}, delimiter=\"\\t\")[\"test\"]\n",
        "test_dataset"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using custom data configuration default-557d7b43c54d3362\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset csv/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /content/cache/csv/default-557d7b43c54d3362/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8a5be3ab74704643a36dd219fa3eb5a3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rDataset csv downloaded and prepared to /content/cache/csv/default-557d7b43c54d3362/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0. Subsequent calls will reuse this data.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['name', 'path', 'emotion'],\n",
              "    num_rows: 121\n",
              "})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QgZFkMDHW_Um",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8efb8a2c-2021-4b76-9ba6-d6a0d7c20dde"
      },
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(f\"Device: {device}\")"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Device: cuda\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ESFEXeaWgua",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360,
          "referenced_widgets": [
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            "482acb885cfc409ab154abceeb15a436",
            "6ba7834558484368a9cb78f03d1afc25",
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          ]
        },
        "outputId": "1ece0dda-8d00-4b24-d227-d787b2dcbb61"
      },
      "source": [
        "model_name_or_path = \"m3hrdadfi/wav2vec2-xlsr-greek-speech-emotion-recognition\"\n",
        "config = AutoConfig.from_pretrained(model_name_or_path)\n",
        "processor = Wav2Vec2Processor.from_pretrained(model_name_or_path)\n",
        "model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "735b283d128b417d93ee1060f4863aad",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1925.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "bca57f08a63a47b8897dbc4cf7eccae8",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=214.0, style=ProgressStyle(description_…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a6508c4c275547669f0f3646e9d6e599",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=535.0, style=ProgressStyle(description_…"
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "821ee8ae573a4e7092c810dfa5317802",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=398.0, style=ProgressStyle(description_…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "86b895aedb3446bdb5f7380a7677b03b",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=23.0, style=ProgressStyle(description_w…"
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          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c4e0188a78504385b5e3849f621a2660",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=309.0, style=ProgressStyle(description_…"
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          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9d76f30b2da34f95adc14d65a5db42be",
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              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1266153513.0, style=ProgressStyle(descr…"
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          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BkEd4w8IV7kZ"
      },
      "source": [
        "def speech_file_to_array_fn(batch):\n",
        "    speech_array, sampling_rate = torchaudio.load(batch[\"path\"])\n",
        "    speech_array = speech_array.squeeze().numpy()\n",
        "    speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, processor.feature_extractor.sampling_rate)\n",
        "\n",
        "    batch[\"speech\"] = speech_array\n",
        "    return batch\n",
        "\n",
        "\n",
        "def predict(batch):\n",
        "    features = processor(batch[\"speech\"], sampling_rate=processor.feature_extractor.sampling_rate, return_tensors=\"pt\", padding=True)\n",
        "\n",
        "    input_values = features.input_values.to(device)\n",
        "    attention_mask = features.attention_mask.to(device)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        logits = model(input_values, attention_mask=attention_mask).logits \n",
        "\n",
        "    pred_ids = torch.argmax(logits, dim=-1).detach().cpu().numpy()\n",
        "    batch[\"predicted\"] = pred_ids\n",
        "    return batch"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "c3d1935d11764b65aa03c1b8abd431cc",
            "56318e1f4b4a41f5bfcd9239ba2442b9",
            "fc3498912378423b856ae47d79a37cc3",
            "7438096ce4f34d1e97d217a9ef53f375",
            "66120bcdfe194074b51c204be44a6bf5",
            "92636f7a713147e19a5a7af33a654db0",
            "f06d687a08cc4b9ba7aa738ff475e928",
            "21a3955a06b44d6bbf9b0676a999aa8e"
          ]
        },
        "id": "S4P6P6XwW85p",
        "outputId": "59cd6b44-182f-438a-f32d-a604d96132c5"
      },
      "source": [
        "test_dataset = test_dataset.map(speech_file_to_array_fn)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c3d1935d11764b65aa03c1b8abd431cc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=121.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "7bcc8e538ff34d4196055461811097ac",
            "8f144001036e4ffb845cb2d5575bb86e",
            "9da77be834fa48e2a416844aae7ee58a",
            "101d52fef40d426fa713c385304499bf",
            "b280d16336ce4cca95c8577025fd4764",
            "2f21becfde73488ea26aeb8d338fa332",
            "3abc327d585e4840b51a12e5efb2281f",
            "992a468bf6d54c2d98837e10a562cfab"
          ]
        },
        "id": "K_oZJzHsXKHv",
        "outputId": "6bf8dd80-e5db-4415-810b-bfa2fa76e60f"
      },
      "source": [
        "result = test_dataset.map(predict, batched=True, batch_size=8)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7bcc8e538ff34d4196055461811097ac",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BnfJLZvAaxTo",
        "outputId": "dfea7223-1211-471e-8653-5fef695e58fb"
      },
      "source": [
        "label_names = [config.id2label[i] for i in range(config.num_labels)]\n",
        "label_names"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['anger', 'disgust', 'fear', 'happiness', 'sadness']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vRtajzvTabeH",
        "outputId": "6a3ea32f-bc88-4b50-de82-ef96feb5d6be"
      },
      "source": [
        "y_true = [config.label2id[name] for name in result[\"emotion\"]]\n",
        "y_pred = result[\"predicted\"]\n",
        "\n",
        "print(y_true[:5])\n",
        "print(y_pred[:5])"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1, 4, 0, 4, 3]\n",
            "[1, 4, 0, 4, 3]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tUt5rIppXrzl",
        "outputId": "b434aa90-93f5-45bc-80bf-a821c9dff7c7"
      },
      "source": [
        "print(classification_report(y_true, y_pred, target_names=label_names))"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       anger       0.96      0.96      0.96        24\n",
            "     disgust       0.96      1.00      0.98        24\n",
            "        fear       0.96      0.96      0.96        24\n",
            "   happiness       1.00      0.88      0.93        24\n",
            "     sadness       0.93      1.00      0.96        25\n",
            "\n",
            "    accuracy                           0.96       121\n",
            "   macro avg       0.96      0.96      0.96       121\n",
            "weighted avg       0.96      0.96      0.96       121\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ylb2Z6Xke2ro"
      },
      "source": [
        "# Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EQzCioPhWIiX"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchaudio\n",
        "from transformers import AutoConfig, Wav2Vec2Processor\n",
        "\n",
        "import librosa\n",
        "import IPython.display as ipd\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DocavTvQWIr_"
      },
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model_name_or_path = \"m3hrdadfi/wav2vec2-xlsr-greek-speech-emotion-recognition\"\n",
        "config = AutoConfig.from_pretrained(model_name_or_path)\n",
        "processor = Wav2Vec2Processor.from_pretrained(model_name_or_path)\n",
        "sampling_rate = processor.feature_extractor.sampling_rate\n",
        "model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1SSs95o9WIvK"
      },
      "source": [
        "def speech_file_to_array_fn(path, sampling_rate):\n",
        "    speech_array, _sampling_rate = torchaudio.load(path)\n",
        "    resampler = torchaudio.transforms.Resample(_sampling_rate)\n",
        "    speech = resampler(speech_array).squeeze().numpy()\n",
        "    return speech\n",
        "\n",
        "\n",
        "def predict(path, sampling_rate):\n",
        "    speech = speech_file_to_array_fn(path, sampling_rate)\n",
        "    features = processor(speech, sampling_rate=sampling_rate, return_tensors=\"pt\", padding=True)\n",
        "\n",
        "    input_values = features.input_values.to(device)\n",
        "    attention_mask = features.attention_mask.to(device)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        logits = model(input_values, attention_mask=attention_mask).logits\n",
        "\n",
        "    scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]\n",
        "    outputs = [{\"Emotion\": config.id2label[i], \"Score\": f\"{round(score * 100, 3):.1f}%\"} for i, score in enumerate(scores)]\n",
        "    return outputs\n",
        "\n",
        "\n",
        "STYLES = \"\"\"\n",
        "<style>\n",
        "div.display_data {\n",
        "    margin: 0 auto;\n",
        "    max-width: 500px;\n",
        "}\n",
        "table.xxx {\n",
        "    margin: 50px !important;\n",
        "    float: right !important;\n",
        "    clear: both !important;\n",
        "}\n",
        "table.xxx td {\n",
        "    min-width: 300px !important;\n",
        "    text-align: center !important;\n",
        "}\n",
        "</style>\n",
        "\"\"\".strip()\n",
        "\n",
        "def prediction(df_row):\n",
        "    path, emotion = df_row[\"path\"], df_row[\"emotion\"]\n",
        "    df = pd.DataFrame([{\"Emotion\": emotion, \"Sentence\": \"    \"}])\n",
        "    setup = {\n",
        "        'border': 2,\n",
        "        'show_dimensions': True,\n",
        "        'justify': 'center',\n",
        "        'classes': 'xxx',\n",
        "        'escape': False,\n",
        "    }\n",
        "    ipd.display(ipd.HTML(STYLES + df.to_html(**setup) + \"<br />\"))\n",
        "    speech, sr = torchaudio.load(path)\n",
        "    speech = speech[0].numpy().squeeze()\n",
        "    speech = librosa.resample(np.asarray(speech), sr, sampling_rate)\n",
        "    ipd.display(ipd.Audio(data=np.asarray(speech), autoplay=True, rate=sampling_rate))\n",
        "\n",
        "    outputs = predict(path, sampling_rate)\n",
        "    r = pd.DataFrame(outputs)\n",
        "    ipd.display(ipd.HTML(STYLES + r.to_html(**setup) + \"<br />\"))"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "UD7oUP20YwYT",
        "outputId": "ec5251d3-4e27-42e7-f8de-54544c3beb2c"
      },
      "source": [
        "test = pd.read_csv(\"/content/data/test.csv\", sep=\"\\t\")\n",
        "test.head()"
      ],
      "execution_count": 56,
      "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>name</th>\n",
              "      <th>path</th>\n",
              "      <th>emotion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>d09 (5)</td>\n",
              "      <td>/content/data/aesdd/disgust/d09 (5).wav</td>\n",
              "      <td>disgust</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>s20 (2)</td>\n",
              "      <td>/content/data/aesdd/sadness/s20 (2).wav</td>\n",
              "      <td>sadness</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>a17 (5)</td>\n",
              "      <td>/content/data/aesdd/anger/a17 (5).wav</td>\n",
              "      <td>anger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>s16 (6)</td>\n",
              "      <td>/content/data/aesdd/sadness/s16 (6).wav</td>\n",
              "      <td>sadness</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>h14 (4)</td>\n",
              "      <td>/content/data/aesdd/happiness/h14 (4).wav</td>\n",
              "      <td>happiness</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      name                                       path    emotion\n",
              "0  d09 (5)    /content/data/aesdd/disgust/d09 (5).wav    disgust\n",
              "1  s20 (2)    /content/data/aesdd/sadness/s20 (2).wav    sadness\n",
              "2  a17 (5)      /content/data/aesdd/anger/a17 (5).wav      anger\n",
              "3  s16 (6)    /content/data/aesdd/sadness/s16 (6).wav    sadness\n",
              "4  h14 (4)  /content/data/aesdd/happiness/h14 (4).wav  happiness"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "FlJO2LfVWIyT",
        "outputId": "74a524ce-f4b5-46a9-df20-dc8be43a0a4b"
      },
      "source": [
        "prediction(test.iloc[0])"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              "div.display_data {\n",
              "    margin: 0 auto;\n",
              "    max-width: 500px;\n",
              "}\n",
              "table.xxx {\n",
              "    margin: 50px !important;\n",
              "    float: right !important;\n",
              "    clear: both !important;\n",
              "}\n",
              "table.xxx td {\n",
              "    min-width: 300px !important;\n",
              "    text-align: center !important;\n",
              "}\n",
              "</style><table border=\"2\" class=\"dataframe xxx\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: center;\">\n",
              "      <th></th>\n",
              "      <th>Emotion</th>\n",
              "      <th>Sentence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>disgust</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 2 columns</p><br />"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" autoplay=\"autoplay\">\n",
              "                    <source 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          "output_type": "display_data",
          "data": {
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              "div.display_data {\n",
              "    margin: 0 auto;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: center;\">\n",
              "      <th></th>\n",
              "      <th>Emotion</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>anger</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>disgust</td>\n",
              "      <td>99.4%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>fear</td>\n",
              "      <td>0.1%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>happiness</td>\n",
              "      <td>0.1%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>sadness</td>\n",
              "      <td>0.4%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 2 columns</p><br />"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hzoKOgpoWI1K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "0fad7fac-2f09-455f-c5b3-30316911cf54"
      },
      "source": [
        "prediction(test.iloc[1])"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              "div.display_data {\n",
              "    margin: 0 auto;\n",
              "    max-width: 500px;\n",
              "}\n",
              "table.xxx {\n",
              "    margin: 50px !important;\n",
              "    float: right !important;\n",
              "    clear: both !important;\n",
              "}\n",
              "table.xxx td {\n",
              "    min-width: 300px !important;\n",
              "    text-align: center !important;\n",
              "}\n",
              "</style><table border=\"2\" class=\"dataframe xxx\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: center;\">\n",
              "      <th></th>\n",
              "      <th>Emotion</th>\n",
              "      <th>Sentence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>sadness</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 2 columns</p><br />"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" autoplay=\"autoplay\">\n",
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\" type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              "div.display_data {\n",
              "    margin: 0 auto;\n",
              "    max-width: 500px;\n",
              "}\n",
              "table.xxx {\n",
              "    margin: 50px !important;\n",
              "    float: right !important;\n",
              "    clear: both !important;\n",
              "}\n",
              "table.xxx td {\n",
              "    min-width: 300px !important;\n",
              "    text-align: center !important;\n",
              "}\n",
              "</style><table border=\"2\" class=\"dataframe xxx\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: center;\">\n",
              "      <th></th>\n",
              "      <th>Emotion</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>anger</td>\n",
              "      <td>0.5%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>disgust</td>\n",
              "      <td>0.2%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>fear</td>\n",
              "      <td>0.1%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>happiness</td>\n",
              "      <td>0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>sadness</td>\n",
              "      <td>99.2%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 2 columns</p><br />"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "7nqwbTTXSfMO",
        "outputId": "ab767a32-1c73-4eed-f388-d12e5f4a0fcf"
      },
      "source": [
        "prediction(test.iloc[2])"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              "div.display_data {\n",
              "    margin: 0 auto;\n",
              "    max-width: 500px;\n",
              "}\n",
              "table.xxx {\n",
              "    margin: 50px !important;\n",
              "    float: right !important;\n",
              "    clear: both !important;\n",
              "}\n",
              "table.xxx td {\n",
              "    min-width: 300px !important;\n",
              "    text-align: center !important;\n",
              "}\n",
              "</style><table border=\"2\" class=\"dataframe xxx\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: center;\">\n",
              "      <th></th>\n",
              "      <th>Emotion</th>\n",
              "      <th>Sentence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>anger</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 2 columns</p><br />"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" autoplay=\"autoplay\">\n",
              "                    <source 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================================================
FILE: notebooks/Regression_Example_Emotion_recognition_in_Greek_speech_using_Wav2Vec2.ipynb
================================================
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      "name": "Multi Variable Regression - Emotion recognition in Greek speech using Wav2Vec2.ipynb",
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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "5fbCls1d2yBs"
      },
      "source": [
        "# Emotion Recognition in Greek Speech 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": "652dcdfd-477d-425a-b5a7-c2c16ad786aa"
      },
      "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": 2,
      "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 [Acted Emotional Speech Dynamic Database – AESDD](http://m3c.web.auth.gr/research/aesdd-speech-emotion-recognition/) provided by Multidisciplinary Media & Mediated Communication Research Group ([M3C](http://m3c.web.auth.gr/)). \n",
        "\n",
        "The Acted Emotional Speech Dynamic Database (AESDD) is a publically available speech emotion recognition dataset that contains utterances of acted emotional speech in the Greek language for five different emotions `sadness`, `disgust`, `happiness`, `anger`, and `fear`.\n",
        "\n",
        "The dataset consists of directories of emotions; each folder includes specific emotions. We need to loop over directories and save the paths related to each class based on the directory name.\n",
        "\n",
        "```bash\n",
        ".\n",
        "├── Tools\\ and\\ Documentation\n",
        "│   ├── ESTrainer.mlapp\n",
        "│   ├── Speech\\ Emotion\\ Recognition\\ Adapted\\ to\\ Multimodal\\ Semantic\\ Repositories_documentation.pdf\n",
        "│   ├── Speech\\ Emotion\\ Recognition\\ for\\ Performance\\ Interaction.pdf\n",
        "│   └── readme.txt\n",
        "├── anger\n",
        "│   ├── a01\\ (1).wav\n",
        "│   ├── a01\\ (2).wav\n",
        "│   ├── ...\n",
        "├── disgust\n",
        "│   ├── d01\\ (1).wav\n",
        "│   ├── d01\\ (2).wav\n",
        "│   ├── ...\n",
        "├── fear\n",
        "│   ├── f01\\ (1).wav\n",
        "│   ├── f01\\ (2).wav\n",
        "│   ├── ...\n",
        "├── happiness\n",
        "│   ├── h01\\ (1).wav\n",
        "│   ├── h01\\ (2).wav\n",
        "│   ├── ...\n",
        "└── sadness\n",
        "    ├── s01\\ (1).wav\n",
        "    ├── s01\\ (2).wav\n",
        "    ├── ...\n",
        "\n",
        "6 directories, 609 files\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4UVGPPOVCl-Y"
      },
      "source": [
        "Since the dataset is hosted on [Mega](https://mega.nz/#F!0ShVXY7C!-73kVoK05OjTPEA95UUvMw) and unable to access it directly, we upload the dataset on the G-drive to make this tutorial accessible."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nEKDAy2bCGFM",
        "outputId": "d492adcb-7507-4389-d2c5-21922724e1ef"
      },
      "source": [
        "# Download the dataset from \n",
        "\n",
        "!mkdir -p /content/data\n",
        "!gdown https://drive.google.com/uc?id=1_IAWexEWpH-ly_JaA5EGfZDp-_3flkN1\n",
        "!unzip -q aesdd.zip -d /content/data/\n",
        "!mv \"/content/data/Acted Emotional Speech Dynamic Database/\" /content/data/aesdd/"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1_IAWexEWpH-ly_JaA5EGfZDp-_3flkN1\n",
            "To: /content/aesdd.zip\n",
            "410MB [00:03, 111MB/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": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tLu6DVtJj4PI"
      },
      "source": [
        "sample_label = {\n",
        "    'happiness': [2/100, 4/100, 6/100],\n",
        "    'sadness': [3/100, 6/100, 9/100],\n",
        "    'anger': [5/100, 10/100, 15/100],\n",
        "    'disgust': [7/100, 14/100, 28/100],\n",
        "    'fear': [11/100, 22/100, 33/100],\n",
        "}"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7MXAESg_Dqr6",
        "outputId": "d6cbd397-c53b-44ed-d9a0-013ae6f4d89f"
      },
      "source": [
        "data = []\n",
        "\n",
        "for path in tqdm(Path(\"/content/data/aesdd\").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",
        "            \"emotion\": sample_label[label]\n",
        "        })\n",
        "    except Exception as e:\n",
        "        # print(str(path), e)\n",
        "        pass\n",
        "\n",
        "    # break"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "605it [00:01, 501.41it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "10b89ZpLDqx9",
        "outputId": "765f457c-d451-4d6e-bdb9-46e4efdf06b7"
      },
      "source": [
        "df = pd.DataFrame(data)\n",
        "df.head()"
      ],
      "execution_count": 41,
      "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>name</th>\n",
              "      <th>path</th>\n",
              "      <th>emotion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>d15 (3)</td>\n",
              "      <td>/content/data/aesdd/disgust/d15 (3).wav</td>\n",
              "      <td>[0.07, 0.14, 0.28]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>d16 (1)</td>\n",
              "      <td>/content/data/aesdd/disgust/d16 (1).wav</td>\n",
              "      <td>[0.07, 0.14, 0.28]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>d20 (5)</td>\n",
              "      <td>/content/data/aesdd/disgust/d20 (5).wav</td>\n",
              "      <td>[0.07, 0.14, 0.28]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>d11 (1)</td>\n",
              "      <td>/content/data/aesdd/disgust/d11 (1).wav</td>\n",
              "      <td>[0.07, 0.14, 0.28]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>d01 (2)</td>\n",
              "      <td>/content/data/aesdd/disgust/d01 (2).wav</td>\n",
              "      <td>[0.07, 0.14, 0.28]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      name                                     path             emotion\n",
              "0  d15 (3)  /content/data/aesdd/disgust/d15 (3).wav  [0.07, 0.14, 0.28]\n",
              "1  d16 (1)  /content/data/aesdd/disgust/d16 (1).wav  [0.07, 0.14, 0.28]\n",
              "2  d20 (5)  /content/data/aesdd/disgust/d20 (5).wav  [0.07, 0.14, 0.28]\n",
              "3  d11 (1)  /content/data/aesdd/disgust/d11 (1).wav  [0.07, 0.14, 0.28]\n",
              "4  d01 (2)  /content/data/aesdd/disgust/d01 (2).wav  [0.07, 0.14, 0.28]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iMhTHur8voOp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "49ece9a6-51c4-40e1-fce2-0254687947da"
      },
      "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": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Step 0: 604\n",
            "Step 1: 604\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>name</th>\n",
              "      <th>path</th>\n",
              "      <th>emotion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>s13 (5)</td>\n",
              "      <td>/content/data/aesdd/sadness/s13 (5).wav</td>\n",
              "      <td>[0.03, 0.06, 0.09]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>s05 (4)</td>\n",
              "      <td>/content/data/aesdd/sadness/s05 (4).wav</td>\n",
              "      <td>[0.03, 0.06, 0.09]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>d13 (6)</td>\n",
              "      <td>/content/data/aesdd/disgust/d13 (6).wav</td>\n",
              "      <td>[0.07, 0.14, 0.28]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>s16 (2)</td>\n",
              "      <td>/content/data/aesdd/sadness/s16 (2).wav</td>\n",
              "      <td>[0.03, 0.06, 0.09]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>f15 (4)</td>\n",
              "      <td>/content/data/aesdd/fear/f15 (4).wav</td>\n",
              "      <td>[0.11, 0.22, 0.33]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      name                                     path             emotion\n",
              "0  s13 (5)  /content/data/aesdd/sadness/s13 (5).wav  [0.03, 0.06, 0.09]\n",
              "1  s05 (4)  /content/data/aesdd/sadness/s05 (4).wav  [0.03, 0.06, 0.09]\n",
              "2  d13 (6)  /content/data/aesdd/disgust/d13 (6).wav  [0.07, 0.14, 0.28]\n",
              "3  s16 (2)  /content/data/aesdd/sadness/s16 (2).wav  [0.03, 0.06, 0.09]\n",
              "4  f15 (4)     /content/data/aesdd/fear/f15 (4).wav  [0.11, 0.22, 0.33]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TWku4ra3Bp52"
      },
      "source": [
        "Let's explore how many labels (emotions) are in the dataset with what distribution."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "beNpKMh5xXmX"
      },
      "source": [
        "# print(\"Labels: \", df[\"emotion\"].unique())\n",
        "# print()\n",
        "# df.groupby(\"emotion\").count()[[\"path\"]]"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "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 emotional label."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DZaQ_sP5xkIX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "ab38a3fa-886e-47da-de19-cde1eeb41f95"
      },
      "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[\"emotion\"]\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": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ID Location: 410\n",
            "      Label: anger\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" autoplay=\"autoplay\">\n",
              "                    <source 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A0cEPgSwA/UCvQPwA8oDJgR5A84CWwISAvMCLwMBAvYAsQCmACwA0wDUAb0BHQHM/wIATQGAAfIBAQM8A2QCIAKpAqID4gRFBZ0EMgQaBBIELQW+BZ4EjwM9A1cDIgMMAmYBswH1AMj/Cv9S/j7+w/06/QL9Ufwv/MH7Vvsh+8L6L/vC+zT8sfuv+pv6cfuJ/LX9Y/65/Q/9+/yz/Qz/sv+WABkBz/++/oP/IAEmAkYCegG1AJAAgwALAeYBfQGTANL/2v4V/5P/vf+L/3b+2f1i/SP9QP28/Uv+/v1g/cH8Zfwo/Yr+Xf9q/5P+Ef6e/uL/KwHIAXYBjAB3ABcBOgJtAyEDbgITAp8B/gGyAn8DAwQwA/gBZQHCAYsC9wI4A8UC3wGdAY8BDQJbAlsC1AJ7AsQBqgGeAaoBpQGVAeYBnwETAaAAOQBDAFMAsACxAAAAQv9m/nD+Jv9T/xD/d/7f/Zj9sf3b/T/+cv7s/Rv91PxA/br9AP7F/fT90f0v/UH9yf1t/pT+hP5L/iv+WP6X/hj/Zf9F/yn/+f4e/6X/BgDg/5P/oP99/6f/JgCIAIAA9f+Y/7f/zv/p/5YA+gChAEAAEABWANgALQGOAcsBrAEiAZcAxACyAaUCpgLEAToBHQEqAXIB0QESAo8BHgG0ABQA+P9UAPAA8wBfAOv/Of+6/jz/LgDkADYAA//H/ur+df8ZAHYAfwDT/0z/af/5/54AngCXAJoAfQBsAEUAagDTAAsBAQHCAHwAaAAgAGEA1QCTAD8A+f/4//D/iP+B/+X/0f82/+b+3f7X/gD/Jf82/03/EP9m/nf+Cf9C/47/iv8l/wb/HP9j/8b/5f/2//P/zv+t/8b/GAAnAAkA+f/y/+X/4f/n//n/KwAEAMb/vf+t/7//0//t//r/j/8+/0v/qf/0/4z/Qv9I/0b/Tf9K/5H/qv8X/+/+TP+J/4j/Sf9V/6n/8v8CAOj/6/8oAEsAbADHACgBQQH5AAwBIAE/AasBJAJUAs8BZwFqAbUBBwI3AlgCFQJJAcwA/gB0AdABhQH5AHQAFAAcACkAUQBoAB8Au/8s//T+Mf99/3H/H/8L///+Rv+R/1f/+v71/lb/7f83AO7/nv+E/8v/NwB0AHgAZABTAEAAIQDj/xIAigBmALP/N/8p/xz/K/9a/zb/xf4f/vb9X/57/kP+9/3c/fv9SP5I/ij+M/5w/rP+nv65/tP+Qf+i/5f/7f8MAO7/GAB+ANkA/wAPAR4BQQFVAVABMQFpAc0B+wHeAXIBqwHyAcgByAGwAV0BMwFdAWYBEgGyAJUA6gDqADQAwf/K//X/HQDy/8H/nv+F/3n/Sv96/6n/AAD5/5D/jP9c/5P/GABJACgA+f/Y/+L/IQA2ADgACwDN//z/2v9l/3P/p/+v/5z/f/9O/xX/EP9Q/0X/Jv8y/0H/hP9G/97+9v5J/43/pv+c/3P/W/+C/8D/r/+y/wAADADg/wgAMgA+AAMA1v8UABwAPwBoAGgAVABBAE8ATQBVAH4AogCOAH8AfQCGAJgArQC7ALsAwwCXAIsAogDAAMMAngCXAI0AbABUAEwAQgBHAEcAPQAEALv/nv+I/6r/4f/s/9//u/+E/3L/qP/f/xcAKQDj/4f/eP8MAIMAbQBMAAgA6P/Z/+f/FQAsACgA9P+o/3L/qP+w/4n/k/+i/3L/IP8R/zX/av9x/2L/M/8L/yP/Tf+e/8H/i/9s/13/a/+O/7z/LwBFAB8A/f/u/xsAQwCVAKoAnwCxAI4AjgDIAOkAGAETAfMA7QDaAM4A4QD1APAA4gCiAGwAUgBzAI4AjQBrABQA6v/S/+T/5P/2/yYA+v+//8X/zv+6/6f/u/+5/4r/sf/2/zcAIADH/4D/m//2/wQAEwABAOj/pv99/4b/mP+h/5z/pv90/zL/DP8S/yL/Iv8v/xr/3/62/qb+Av9Z/z3/Lv8g/xj/Qv9W/4n/GwBiAHAAaQB1AKQAqQDuABkBTwFTAT8BiwFPARQBaAHaAckBWQEgAfwAIAEtASABFAHDAHQAQgA1AEEAPQAEAMH/mf+r/6j/oP+X/2X/NP8g/1X/gf+B/0v/Cv8G/xT/Sv+O/6f/tv+5/6f/uP+4/7r/zv/a/yYAKgDg/6L/t/8DADIANQAVAOj/1P/d/+L/AQAiACgAFQAHAN7/4P/q/zEAZwBTAE0AIgAdADQAtADhALwAjwBEACQA+f/v/+j/6v/g/9L/0v/B/8D/3P8AAP//yP+V/4f/mv+e/6n/3//c/8n/xf/N/9//1f/U/9n/xf/A/7X/kf9R/yT/Qv86/yj/GP8a/yT/JP9f/6T/wf+2/93/SwBUAHIApAB8AF0ASwA0AA8A+v/d/8//qP/L/9f/0//Z/9r/cgDfAMYAxQDYAK8AwgCwALsAoACPAMYAwgDIAJIArADNANQA9gAnAV4BhQGwAcIBygG/AaEBbAFFASEB5QCbAF8AKAAjAA0A5v/1/ywAQQAiAEIASwBeAEoAYQBOAEsANQDK/53/jf/k/8n/CwDw/0MAjf82/0P+K/5x/TT8RftU+Fz6JfpI+o35xPnl+xD8/vzJ/Lf9Gv58/rT+2/4u/1cAFACQAOsBxwIxBF8DTQM1BGEEdAToA5MDVgN3Al4C1QL0AugC+AK6AsgCfALmAh0DJwIvAvgB/gHKAbQAQwCJAB8An/8Y/17/5v/C/2AAGwDiAIUBSQHGARIBkADr/5b+Z/46/tL91f2b/dj9//1h/R79M/3//Pv8Avt1+qX7/ftV/Pf7lvyQ/P38T/2P/SH+o/2j/qL/XP+X/xkA+wDxAnADYgN/AgcC9gJEA04DtgIqArIB0AFvASICzgIpA+IDVgNvA5kDXQQrBWsFVgQcAz4CvAGJAaYAJgA4/4j/Yv9Y/x0AkwBKAfUAIAAk/73+P/7u/ZH8PPvc+rb6afw8/Rb+Df/z/hv/Cf/A/SH9GvyC+R34ffUX9dL1qPXc9cn1dfd7+fb7Zv1b/+EA6QGDAmoCfgNxBQsINAoGDKMOSRHhFKoYUB40IxYcgRPaBwAA2Pz+8K7nZN4313zZ0N065Xnz4P0JCtgQrRE2F70Yrhn+FVMM0gOL/qP88P9mAXoC0AQNBc4FUgRGA88Bhv8O+Bnu/eaK47Hmmupu7aTyE/m4AOQJeA/lFNQYUhZCEJ4Ee/it8OHoHeNW3izbXt/z58fz6P/eCRcRMRayFrMUXBPvEKIQqw7FC1ULxg+iGPgleijGIMEXFQqQ/+rvDt6t0mrIjMBQwlvGgdUH7osC2RaLICgonDGsMhgvySjMHAMSsQYA/HH5S/ev+Mz7Evsd+ZD3rPZF9WrxYOqc5PvfqdzM3efiUupN95kE5g9NGxgjeizPMIgsQySvFsID1vDK3KfLlcOowFHIsdON4wX60xAqIicsuy+rK68l1xhnDQkDMv0I/S4B2wpHGh8xIzPHLocjjQ/+/xvmiMhft5qmU6Ozrt+1YdVQ9fUSVTB0OjNEPko/QuA3nicLEWMEEfdc7+7teewS9NX8vP5X/Ub7xPbE8VDoPtok1DzR49IE3nzn5/elDtIeVC4/N+05YjwgNLUk1BKu+/vjhNFSvrO1IrnXwuzWD+rfAJoXryccMYkxOymfGzsPVwF19+H02PnoAvsT/yWFPntT6UboN3AY3/H72B61mpvnk0iPa6SEvm7Z1Qy1LSpJ3lc5TndDxDKnHd0MPPp17ObsB+5K+UIHERKmGAkYIwyt+U3m+9D2xdi7zbdqwX7PXecvBf4cFjSLQglFRkJuNf8kZxObAn7yh+f43DzWtdRu0GzWdtzs4g3r8/FR+lwE2A1dE30XbBY7E6MRIg/aDqESwRdXH7ooZC9xPHZIHDK4GH/279AMv8OkV5mtnZKlrMOK4ZT5mCboP/VLbU2IOdEpkhjOB6n+7vdN930ElgkkEoccvhtdFekDsei+zxm/SbUPuDK7nsqr5TP+zxgbLkw7PUL5P2Axbh+rDswCD/1/9ibxAvOV8q32efUj5HfaT8yowgbCtcZ/2C3vCwuwIQswXzVjOe8ytiOPFgwJ1QF8A0IL2xPNIxQ0bkshPJUY2Plcyvq1ZJ1MidCTJqPQxz/0FA0pPs9bW1/LXiE7qiNSDyj5vvFA6+/sfwETCdER6B2+F8oPMPfe2DbA5a9PsO66ucf1307/9xsUMpVB90YYQNY1bB6HCFj6xfDA8mnyRvOy+3z8+Pzl+3vpE9l8wySvOq59tqDUJvhvF/04SEqHT5dMvz61KPcOdfl37B3pSfKeBrgZxDKpQ9pUcT8EDsPpLLK8m3OLAYBGnWy36uIDG2ozWmAic6deBVHbHEb9guz41kTdVOGl6WEHpA9hG5kpcxvmDTzuccyQtcWrhbcQzLjjrf0IHJEzC0GhSVtBQDA3Hw0B7e/z6F3nu/Ri92f9MwXG/mr7IfXH4HnSh73/rGe0G8Wa7iITZC+9RytMrkU2Oa8k7wxn95rlAeI45vn5GhbrKS0/cUozUFVG3A9Y41WzzZJllmWNBag3zqfwFiiLP4tRnWJtRxwxrwbW217YZ8/X20Xv4Pk/GNAnWi0VN1Mk+w7a7x/J77Qgqv25atIz7BgHbx1eNJ47pj1FMi8bkAl38W3jhuRN6mn6CAVACsYQvgpcBL/65evI2f/JN7nJuJ3LTeWACwEgRzaiNxUxLysjG6kMKvqF8Bfpo/DG/P4UqyN5Mzw5YjjXNcA0fiLF6lDMPaPJoLyu0Lbf2QzwSguzLKwnbC3VLxUQegmj5ZfYZeOW6eoHZhWJHHUv5i5pLOoo+gxd963W5cMKugS9HtUY7PsFfhAMHYEjCCDnHaENp/qp8XLnju1Z+ZQE7BPxGbobsxYiClT9avTE3PnKtbyDtSLGDt77++UVqiUQLnwr9iBxHMIOLwCi96ruhvIAABgR8SM/LVo3ojYGNkkyozPUIoXqm8xpovGcc7UrvsTfJO2t82IWmReoIpUppwXTBhzsUN6O8aL54BtlLcEqVDcCMb4tsC9tESr6JtYDv/u4er1u1g/oI/vgAYQJ6A6RDVkPMwbX+KLwFO2C9zsHMhXoHTkiox/KFewKTPrX75XZVMOwt561TspQ5joCmRnwITAi9R1EE1YQEAMl+ez2mPXqA04QCyP0MFA0ljfPMeUsWSflHtAZt/iG09O9TKyav27MpNc867vnrPMMA5YJghcgC1r8RPzx9TkFoRR1IjIzjjI2MHcspycaI9UVLgCN4d7Od8fGxTXXn9/h7QD2tvDg/fn+RwD/Ao73q/aO9mL7bQzzGe4hCycWIoIW2w6y/cbs3No/xPm98cB7z9XrXgFuFFwdihiKE7AJtQK//Ar5NfxoA/sNNxt8IxQrsSwkKg8pUB/yHCEUWxLbE8/+g+jl1crNBtTjz/zQadtq1qDfU+iM+JUQ5A7QEBcROxDhG+saXB13JJ8eUSFYHFUZaxzAFZsLoPkT6ojgkNUW0PDVqNum4ozhveb18qr2VgD9/9ICmwfnBV4L1AuPDjURnQ2BCAUCOffa74TrA+cj6+XuwPOz+tr9twQPBLwAZf9b/T4BUwE7CQcRxhWHGeIXjhl4G50aTxpjGCAYnhl+HDsY+Q0KBAb0X+0r4NLXVtdF0pjYD+DS6PP5QAALB+0NnQqgD2sKrApdDbEKThDWEnIXKB7xHEgbWxOJBi4AfPF46kXjpeFU4szjJ+up7OTzlfNy9//16fEb9Tb00ffO9zX6Lv9uAW4EqAGnAIT9ovwj+h75pPwB/Iv/IP4wAKb/k/++AFYBvQOZBuAKmguAD/MNfQ9dDW8L+gtSCNgJkQd9CiIPPRN3Fa4R7xDYD0EMvQAE9F/s+emJ6DLnUOo085T8NQCnBfoIXQuIB84ANv/o/a7/2QFBBxMOBRKME5sPcwy4CFkAFPoK8xvuCe4n7fbvcfZa+HP7Avym+Jn4wfMR8aTuhesl7HPvF/CX8zX3bPnNANf+lQMhBXID4wNdAJcBCwCSABwCRgXjB3EHSQjoCH8JiAjWBN0FyQVkBV8EnAEDAWYA9gMqA0kGxQazCcMLbBJEFOAQghPXB+4LmAFl/54BKfdv/DX5pPrUALP9XgDcAeH9HgSj/QD/ygHI/nACBP5n/ej9kf3v/Jb7EPxy/RcAzv8rAfoBsf/9/ET6b/lo+jz4Q/PB8qXxH/QC8q3yGfBC80L05/QG+ob52AHR/L7+f//5AEYC2/7KANEDRgUABdkDuAJ8BiAFegRXBm4IPgQSAt7+TAEN/rD8pftI+7v7L/tnAqECvQbIBXwIGQbgCYoIRghDDNcO3hAVD8oQIQ/kEzwOxQvfB/oEpwcVAJb9rwAO/2j71/Zb9gf7ifjd9C72EvS49zb3b/lx/Sn95Py1+xsA4ACfAyUAz/8PAon/gv6l+3T4J/qy+Qv43vkE+Pj6ffle94f6Ffoy+372BPhl+hf8Z/5S+Qn6OfklAq0B0AJ1ALwBtQdxAtADxPpL/xsDDvg1+HQAdgA5AM389APpBF8CVAQw/83/aACcB6sCm//NBtAMxgntCjQKhA/2EmgL4gcUC7wTtA1zBTsASAf/BdAEU/qM+1kEi/8T/C75kANYA+36Fffn+5//IQES+9T88v2w+qv9R/r8/3v/sfqE+T366wHKAST6KPxY/ET+rv5n/an8yfd5+sn0IPrW/VD5Hfoz/ln9TwEY/IT7RQKE+BP6Ofg1/az6Ifj1+R8DWADz9KMCwP6EBXUHyff8/yv+QgLwAKD8YgF4BLAJvPuf/qgVqQUP+IcDxw6oB7n2Cw9MBNwOVf52/WMMIPkRFpD0mf0uB7UIqgyF6EsOrQm5AzYEnfg6CRv0Ug/6Bq/wfgehCtv9RveRAgYDEQgS7xL6vwZqATwFtvVeAx4EXP5h9OgAtwZL+xfzu/qMA278BP+18wL9uAR1/3TzF/fFA0cMT+98/W0Egv7G/6Pu/w2B8/kIlfcj97AHu/sVA3/3tQsX+KcEB/oIBhQHPffuCajy1AMZDxvxXwaM/oUETQMk9/AJxPeLDj3xrRC69RT8MhRp7EcTnvSnBWP4MgZnC0QGu/tD9ZsehflO/UUJAOvMEff+pPZTATT2Lxwh8lb9/PbJEboCt+t0DvnsJwkwBdXtGAVj7AoYz/2+6jIFQwI1D23lLBPI98oJef4T/Pj/Y/8kBVwAi/xKAqQMe+5dDDz/Uwdn9tUKEfQpCY0AxvAYEk7sMhYg84rxwRk99MMMte/JBdAP7ufmEyLtJA8aANDxXBGB9QUTZOAAF7ADE+jWG9Tc5x+n9p73R/459rcaY/BXAUjrABHTEl7pUvTOCCsT9fIk9S0EPAAeFHPxjPbcDKL1Lw+r8tjrzh+B7aH1SSLX04cZmAAF9bgQE91hJXLkSAG/FC/l5ARsDb/17RG678r7Uh6m6jEFzPqIAvgCZf768xUIRwW2CFHzNP5fE233nQ618S/yPimg62vvzxTt9zQdLO1T8+EQViCa2MkSzv059QIouM6zJ53gliPR99XhyiLM5g8l3tonApkGDP6vBRLvVQBBBdMAGwEB/ITwfSax23IOafOz+kUN4Pzz9zrt8SWf4xYbH9tpC0MWM+zN+tr3rQ3r/ln6r/mG+UoRGwnnyis/Kdq3BOoNGd9GG1LvdAqQ96r38QmnCJL0Rg2c7fgQzPdhDDbyHAru/rn4ESKcyeY4x+UY+WUtCNI9JJ3gUBOUE+XV0izo2VgeWv9I7dYbaeiAFRT4J/boEyD36wVh9lT8Vg9t84gDvPfI/OkGmgHf9n373P9mFTvlCQt0Aq3wryk7zhkgc/uJ7IcrrMYBL+7uTu3TKWvSriD+7sD6Uxt+1wsX//2J/vf5KvECGP/uDwyi8KEI2PNWF/r0Qfc0GZbbwS2D3R0Isg346QoWKu+MAekPzu4XAs0K6vpHBzvyAQedB2H8v/YDDc//GP4fAnf1HhHu8iAJuf0C+oADTgXJAOf7rgdw68Ug1ern8iEepOUvEMP39/P3F07tQgPqBG/vmBco8ZcJ5PQf/coQv+rBFbTipBjg8Qn49R1G3+wc1OufCAwJgOiAIXHXQx7l8e/8XAv86PQeX+DeFiTs1RT48W0DMgdR9GsU2O3uBtr8wwCfDurvCAIND1rxmBBm8dIHVAJl81QMSP7V8fcP+e99FUrr7f4rFRPwQQcQ+qYHJ/wkBp75BgXg/SIJDvH5Ez3xkgjP+zABFAWa87kQqu26FCvqMA4O+owBk/vU+1EJivgqBlD5ygR0+pwEefwdA777k/rEBEwBd/6T+UMHtv1u/NUHPfJOC6H7wvdIEJPpww+b9MsD3wEL+fsIFvz7APH/fwkr73MYiOyrDp353P2vDTH1HwfK9/8Npfh8AhH/QwJyBDv8//uKBlb8DwVn/CsARvv9AwIE0flC/dgEuAC6/uT9FP5hBWEAaPzZ/4sGMv7jAfr4PQZk/9QEgfWZCbX8JgJIAhb2iQlj+YkCHfuwAiL7X/4PBsf3AAT//Qn9VgeX9SkEEPwu/UoCmfzoA3X8JP8U/zECSf/F/uf84AEGAqIAVfrvBPL/CgFLA0D5UQhV/MgEAACf/IYEuQLD+qcDPwAdA1v95PwNBF7+qgfK9ysC8QOiAXQAcAE5/DQFcQGs/KICFv0ZA8L+4wE8/FkEaP++/pj+nQMO/yUAmALv+f8F+/xHAXD+Pf9aAML/4P4T/TEFUv0l/FgCX/2xAWj+jv5O/wgAzv14/SoGavsUAY7/YP+9/0z+bwC7/av/2P+eAFb9fwB9AG7+nf/t/Z//RgD1/akCsQCl/RACb/7YARIAcgDFAAQAWQAtAMYAgf2eA77/zP/nAjz/9ACNAVj+PQBCAogAXgFuAFIA+AA8APUBOP5CAWsBtP4H/wABzgDpAMcBbv/XA+L9MQLyAFj+mAINAWX+xP7fAFb+UQH1/m3/KQJx/QcAeQCZ/lkAo/+E/+P+OQAL/QoBFv99/u//h/7xAZz+Yv2yAcn/5v65AJb+zQAw/8j+BgBJ/0UAbABB/uv+WwGC/7f9tgDIAKsAKf6rAA3+HQBK/wP/GwFP/sIDKv6pAMQBzf7A/2ECa/52AWABvf0hAVn+cgLjADv/YP4YAKICmf+O/k7+NAEFACMAuv6kALQCp//RAln/FwK9AaP/+gBw/oQBbgDA/q/+AwBeAeH/JP1MAUz/Uf2sAj/+zv7IAHL/8P6JAr/9cf+EAu39NgIY/43/pv8TALf/4P+c//7/bv/s/yQD3f5bAen/rACAA6T++AFMAYH+8wLG/5z/QADXAHMA8v9xAG//dgA/APr/S/8LAHf93/+w/1X9QACP/7v+KQG2/0UAHwGZ/6ABcv7+/4z/lQDV/wn+1wDh/g0AdP/PAIIAvgAaAMz+sAHI/6n+3P/U/kcATf8//xH/HP//AMn/hwCu/5n/u//rACQAQf94/tf+owGI/cX+9P8f/iwB8/4eAc4A9P5aAWEAUACm/n3/cwF7/xgAjgCfAPwBkgA/APoA9gELARYAHgBfAbsAdP9GALf/6//o/xD/rACYAFX/QQCvANUAhQCdANf/sgDh/3H/dwC2/tT/4v/M/zsAAgDAAHMATgB0AMMAIAAY/1T/fP+Y/5D/WP5Q/17/6P9tAN3/sgDLAKL/NgCvAE/+cQBU/3//fwBc/uP/pf9T/08AJQCNAF0ABQC8AGf/mf/g/6D+aABI/+39+QBT/u7/ywCU/60Ax/9dADYAqf+J/6n/oP4eAEL/zACt/+X+SwGc/0YAyQDe/4cAOwECAGkA9gA7AKEA5v88APMABAATAXv/fwAjAPH+gwDK/kcAlQBq/5z/9f/0/9//EQBO/2oA+f/6//kAkgFJAGAAegGKAEAAXgCsAEwArAB+/+P/5v/j/jcAmf/P/tT/v//T/3P/3v8sAOD/NwC0/yYAFADc/wcABgAeABAAVQDx//n/FwDp/vf/Yf80/+L/L/8pAP7/AQDo/1f/6//W/7n/WgA6AH0AKABWAEUAWABNAO7/RwB1//7/4P9c/7//9//p/0IAAgBwACIBfQCDAG8A/P8SAKP/8P/I/3P/JwBQ/xYAv/8eAPj/W/9KAIP/jv9n/5P/sP9t/4//Zv8QACsA6v8mADcAYgA9AP3/BABjAP//swBr/y0AlP97/0oBvf80ASQAnv81AAcAPADr/zr/1P8SAO//9/95/+b/fwAYAPD/MwATANwAl/8oAAwAEwDKAFX/uv9ZAKP/z//w//3/5gCm/1kALwA/AI4A3v+sALb/awBCAIX/cP/f/xEAUf92/3L/WwCVAID/u/9hAEMAgQDe/6T/oQBmAKX/WwBMAIMAAwETAKQAvQDKAJwASQBWAPP/HwCu/yMAjwAfAA4AJAAMAOH/RwBwAFwAQgAuAN7/a/+I/zj/LP+z/8//AAC0/8L/LwAHACEAtP9DAG0ALAAR/w//OACP/4MAhv9Q/zP/6P/T/5b+rv7+/YL+5f6o/ir+IwBO/qn/vv8W/zMBi/+IAa4AdAECAcb/pP/s/8IBpwAQAX8AMgInAvr/Zf+U/5AAFf/m/R8AtgGkAB4A6v1j/7QDl/+Q+7z/LAMbA5H+b/2vAH4CTgHf/ZD+GAFAAUEAff/Y/OMASwMlALIA3/3DAEADBQAgAH3+LgE0AjT/lABhAOkAlQEc/1EAdwCZ/9L/Uv8m/y7/AAAWAFb/zv4VABwBSQG8AEEA8/+KAMUAsQDJ/wP/+Pww/uH/bP8z/df9cwG1APT9Af6y/ggBmgAT/eUBGv4DAaj/Pv2y/gv/9gBO/+f/1P4LA2v/tv9h/vv+nQIpAL/++P6fANQBsf81/4cDPwNbBIMAcwIXBEED6AGZ/3MABQB2AcwA//6Q/QH/CP/D/n387fkm/Rj+5PyZ/Un7lf4//qj+T/2m/UQAZf1lACr/nwH1AOwA9wNwAeYC1ALfASkEBwHpAj4DtAPsA6wEQggmBoMHAggPCK4DnQFiAKUAUgE1+nH5CfoM+j/8pvt2/bT7K/43/+r+4/9g+0792ftC/Qv95fzp/wH9OwC+AMgC5wWYBEsFMARdBIYDlAA0/Vr3YPXj8/7xOvHC78Tx3/S1+CD5Yvuh/RH+OQAKAOoAdQJEAdUBDQOnBU4JPQoUDhERRhR4FkEYjBqnHdEZvhIXDugIJgiZAFX1a+1b7GPvBPCd7ejqlPCD96D7GPon+Ur8APzX+zz3/fZd+Qv7hvxT/poF0goIEYgR4hEXFCYWOxXEDSAGcgL/AHH7a/Tz7GDtsOua7ODq4OvR61fm4+c95NLpIudK53LqffGS/PkAEAdmCR4R8hTkFwUXaBdaFmgX2BjOGm0fgSPtKNMtCCzkKpMlLBcDFZb8uPN44fbRndPGxB3NDcjO0YLdneSn8TL4JAK6CPkJKAmGCbUISgtOCO4IdgnDEscVIBtKHV8fcCfgIQwhcha0D5gGE/lp7NbfMth60DnN28qLz+rSwdda4NrluvBf8lL2X/3w/lwGFQPqBMsJ9AptEWEP9hGiFEIW6BdHFg8XwhjyGfUY1hvAG0ojUyLEIm8ZjhkaD2r++QDG3v/lrtNFyDjUvcOv1n3ZZd/c8G72PQQ6DlAMbBYcEfsSyxIeC7UN8wuTDPoM8BEmEWAb5BizGXAcHBYtFnoK8v//9nvpQOKL10vMEs35xzvOHtZO2g/rl/LT/ZcI5giOE50S0w/RDWAE0ARmAVr9fv3j+tb94ACgAdMGNgkZDWMPnQ8jEF8RNhG0E6sR4xRhEwsbgRhAE/gZLgKrERH/uPb5+JTetO2S3XLeo+Lu3ezq4epD7m/3l/hxBFEHjARMC9QIJw/cD9UO/RCjFl4TaBbjFP8S3BmzDsIN6QQh/kT9j/Ai6w3jPdtg3ujYRNoU3/3emuuK7nj0FAAoAm8KPA04DTMR3g0yCnMIDQKvAAr8zffQ9r7yfPYz9XD4cfwH/zsE0QXeCMsMig6yEa0S8BI1FpQYAx0LH5YqYCeOIDUZ3A8lEJUEzPZE4ija2NcD1gnT4s2q2LnkMOu57sL1eARIDz4NhQiHCcQSuxZ0E3wNgg99GBEYIRSJD7ISkRZIEHcGy/78/Xz8de+h5FjivOLv4QfbX9ex3trnruoE7UPz7f14BjEIqggvEV4UyhKHDPsEtwdaAl/87/Io7UHv4eyp7Q7vdfVU/sEEcggZDwQWBBrPGSUXaBgUFigW7xB1ELEVMRaVI9IdZA/PGFoRshH+DtbuZOWK3/7Wm9cTyDfF4tOL1x/gi+ba9BgL1xDRESkU8RoYJ9Yl8hk+Fm4W7xmjEhQJrQnaDFYPiwRx/OUBjQTDAe3zSOXd523kI93y0tzLUtXO1svY392w6gP9qAYrCogRIR/qJUIoExw7FtAT3AvxAP3yY+5V6lbpEOXi6CjzFPuMAfUFsw0eFhsaIRjFFcQSHhAKDc0ImwVTBtEKEgrWFOUhaxrBGUIWFREwGGwHfu6p5FTYOdq10TzBecra1JDhSuUd7DoG1Rm2IgwgTx5YLBAzxijwHCQS/RKNEBEAwvd391T+dPza72rv5vZy/1/7VeyK6I3rQOmn4Q7X1dUV3G7cg9yj5BzzHgMFChcPbxu7JSMugCkLIBQcCBXxC43/FPPP6//nkeIk4gznn+6I9kD6uwDPB48PQhKSDnANBwoxB9sDzf/C/hwBxwGZAy8MGxe4JlopbSSKIjsggyNDG98FjfFa5ufhyNvp0MLM8tO/3cjltup8+5wPfRloGRwU8xhwIuUfYhGcBNgAKALM/VD0bPMi+QD+Vfs2+XoAYgneC40Bqfi39TP34vHf5GLda9uu3BTd7d5M5130AvzMAwwLaBb6IGgiaB8XF+IVhhG6CPP+9fNe7+Lp4eZf5lHoV+zF72TyhPc2/lsEkweFBiAIJgo6DJgOIg5jEIMSDRdcHCkkbSmDK34wsSF+JrEhSxY+E8r1d+6C57TaINl1znnLM9RO0i7dA+UE8Kb/Nf7iB7kL4hK3HUgW0BO2E1wPLhQwDWoIwgqJBQEG5QJ2At8G+gKMANb5KvSp9kXvnuqk5ALcNN5w21XcPuE74gnqFvGa9+v/jgd4DywTtBFpESkQqg8uDCoEW//M+UH5FfjC9BX1nfXw+Mj8j/0YAQ0EswbwCe4I4wtxDjoQ2BDqEGMThBh1HBogQCE5I1gmHh8rIIoX/hC9DJX8aPPr6ILiq+Cz2iXWctYI2qDiGuWO7RzzgPoaBTIFzAudESEUsRXUEtsPKhL5EFMOZQrvBT4GqgNbAWn/g/zN/L/4o/Qm8rfu9Owb6TjlgOOV4/HjDOUT5qrpBe8M9KL4T/0WAuwGjwnLCvAL4AwuDcMLMwkqBjEF5gLXAXH/Rf72/ej92/1F/XT+sf5EAN//igBAAu0DFQZhB48IWAwaD6QSnhYWGvodCiBKIfwi5CCzHFgZdA+0DMEDU/pi9Nfq2uoT5mXirOEx4TPmLegu6k7u/PFc+CD6avzmAJEEzQeWB+4GrggNCjYIwQcFBjUHmAZxA1MD2ABrAV8Bw/1k/Fn68PiX+Ir0IPPo8YLwIfEQ74XwGPEe8crzOvQP+LT7G/5AAtQDvgdHCRoIUQeFAy0CJQEg/8j+7/6u/2YBywFOA3EEhgT0BTYFSQVpBMwDRARdA18FoQRHA1MECwQOBSoFWgRdBPcDbwPnA5ED3wNRBHkDBgNVArQChQJwATwA7/8JAIb/T/9C/9X/BgFoAfgA+QH2AssDNQQlAykDKgR7BA8EAwPDAuMD8wO1A30DQgPmA5gD7ALZASoBhQHb/4P+zv25/MD8wfs++g75evje92n3rvYb9qT2jfaH9uf2XfcC+Hf4Gfhj+E350foA/C78vvy6/Er9lv0B/UL9af3Z/ML85vz0/LH92/1D/oj+nv4cAJgAggDCAPcApwECAkMCgwLVAnEDkgOUA2YDBgP5ApsCNAIHAmQCdwI6AkoCMALZAv8CZgOrA5QDigSOBOwEZgVtBSIGLgZiBs0GGQdTBx8HVwchB/QGpAbHBTEFgQTZA6MDHwPBAv0CuAJjAuEBbwHRAD0Aov97/qv9/PyX/IH8Kvyr+277V/to+7H7KfxF/Ir8tPzV/C797vwe/FT7ufvl+9L7ZPuY+jr6p/k1+Ur5pfnv+er59vll+s/6UPuk+9v7VPwv/Sf+Af+L/10AxwAkAUUBEAFHAdYAmwDV/+X+df5E/q79Ef2r/C38lPzj/D390/0Y/r3+tf+3AKIBOwLkAqkD9gP4A4AExwQjBf0EmQT6BJoFBwadBVAFgQW/BeYF1wWQBakFzwVKBbUEjwTWBGAF6wQQBE8EZwQ9BC4EmwOQA60DkANEA3sCIwJ7AeAAmgC0/1H/ef+p/zAAOgDG/2r/QP8x/5/+zf08/ZP8ZvwN/A77nPrO+pP7t/vr+2f8dfxW/d39wf3o/c39Bv3A/OD7i/on+rX5Y/l9+Gb44fgY+fP5iPoK+xH8rvxG/c/9i/3Y/fz9Gf6g/oX+iv6+/u/+gP8XAPEAQgEwAXcBYAFxAVEBzgDWAMsAXwA1ACEAIACYACgBpgESApUCewP+A2MEdQRIBNgEDgWSBCsEDAQEBAcE8gOaAzwDEAMFA9wCdALzAeEBMgKDAr0C0gIJA10DaANFA1ADcANfA2UDTAMIA9MC6QIqAyAD2wKlAqMCMwLgAZIBAAGVAPT/hv9w//z+dP4o/j3+nv7D/pz+g/5r/pz+B//b/sP+cv4V/v79cf2M/Ab8//vt+037E/sj+0r72fsV/F/8bPx//Jr8ovzW/Pb8Tf2i/cD9+P0d/lv+sf4M/3D/cv93/z3/nv65/r7+Q/4Y/uP99/3f/bT9c/39/NT8uPwD/U79jP27/QX+if4C/2X/U/+0/x0AUQCqAM8ATwHFATUCTwJDAq4CjAJ9Ah8CXgGaAbcBxgGSAe4A7gBIAZQBhgFVAWUBvwHkAa8BngHEAWQChwJZAtACRQOPA8kDQASNBKgEXQQOBBMEEASKA+UCHAMLA5ICJgIJAt4BDgI2AgcC9QGNAaABvAHEAZQBUQFsAXoBcgEMAa0AmQBbAMX/Ov/D/mn+/P1d/df8Xfwg/Or7nvuE+1X7bPuA+3v74/v2+/z79/tm/PL8Fv1j/VT9R/16/ZL9Y/04/Ub9T/1m/Wj9X/2F/Yj9s/3I/a/90/3p/ez9CP6B/gz/gf8EAGcAyABlAecBFQI5AkcCWAJmAooCowKHApsCdQKKAooCNALzAWABsQA6APb/oP9y/3b/Y/8n/2//vf8LAK4A5wAdATQBKwF/AeIBHAJxAqoC5gIvA0ADTwMfA/QCnwL+AX8B6wBiAA0A2//G/7v/r/+6/7T/r/+0/7H/yf/8/wIAAAAxAFsAsAAVATYBNwFHAUIBUAFCASABBAHiALYAjwB+ADoAIADU/3j/Ov8h/yr//f7w/s/+A/9N/4b/nf+F/6H/lP8e/+j+AP/f/sr+ff5Y/lb+jP68/oj+PP79/cX9jP14/Tn9Rv1g/WH9Z/2c/df94/3+/UT+ZP5X/r/+Rv+e/6n/rP/B/9D/5v8lAOoAbQFOAfIArgDaANkAgABwAFUAYACjAH8AaACqANEAMwDm/+z/QAB8ABQADQAgAGYAjgDJABQBPQFzAbABcwHsAM8A+wACAXEAZgCWAHMAjwDoAAwBHAE+ARABNwH6AF8AWAB9AIQAKQA3AIQAkQDrAFYBZgF4AU8BZgF9AQ8BxQB3AKEAjwAsAEUAfgCZANAAJwE2AVABOAEXAfkApgB6ADkAVgCsAJoASwBNAEQAagC+AKsAfwAvABsA3v91/yb/0P7+/gD/nf6r/uz+6P4l/4z/mf+w/4r/f/9b/+T+kv44/kf+Pv7r/cz95P34/TL+e/6F/nX+P/5F/h7+4/3A/Wf9of0I/nP+wv77/lD/ov/6/xkAKwAUANL/+//M/zH/Xv92/6v/kP92/8//8/9ZAKoAxQDgAPIABwEeAeUAoQA8AFcAVgDp/wUAJgAcAF4AzwDcAPwA0gDMAMIAhgBnAAkAewDlAPYAGQEzAWEBbAF3AT8BCgH9ANMAuADHAL8AzQDlALkApQCNAGwAaABIABgAAgAVAAQA/f8GAAsAMgAmACUAHQAkAEkAWwBBADUAQAAfABEA+v/N/7T/rv+Q/27/bP+d/8n/yP/L//L/BgANAPj/1f/i/wkAKwA7ACsABgDi/7z/pv+N/3//qf+Z/5D/4v8IADkAVABNAD0ALAAbAOH/o//H//X/9f/b/9z/0P+i/13/8P65/hL+BP4K/sv9CP6P/hL/Ev8x/x//I/8H/9/+U/+q/4D/5P+aAKYAqwDuABoBBQHpAOcAzwBxADQAFgDv//H/IABDAF4AawBtAG8AUgBAAFkAeABXAIAAvADVAOwA1wDKANEAxACHAIAAiwCiAI4AlADOANQA4gDcAO0AxAB+AIMAagAeABcAOAAdAD0AUgA3ADMA/v/2/wIA9//k/9//1P++/7X/m/+F/2r/Xf9H/0T/Nv8l/yX/Ev8S/xH/BP8S/1D/cP+v/93/wP/J/7n/0P+n/3X/fP9a/0f/P/9w/5H/nf/c/xEAKwDSAAoBugC/AJgAkwCXAGgAZgBgAD0AdACoANAA5ADZAOoA3gDIAMUAzAC4ALwAywCvAJMAgABAAA0A7f/a/6r/lf+a/2r/Uv9O/1T/Nf8Q/+/+5v7s/gD/Dv/7/ub+yP7D/rr+r/7T/uv+AP80/2T/j/+f/5b/r//U/+H/8f8KAH4A6QDXAJUAWABSAG8ATQAuAAwABAArACoAbwDLAOMA4wDhAOMA+wAtAT4BFAGeAIAApgCZAJoArAAAARQB/gDPAMEA0QC7AKUAewBpAFEANgApABgA6//c/9n/rf+j/4T/Wv9l/3H/Yv9g/3n/eP91/3P/ZP9T/2n/Zf9a/3//kP+M/3L/bf9u/3n/fv9R/1T/S/9P/7H/z//5/xwAJgA3ADcAOABLAJ4AswC8AJ4AiAC7APQA+ADkAPkAqwBdADoAQQAcAGUACgE5AT4BCgHfAIMALwADACsAy//8/tv+DP9T/2b/g/+v/3D/VP+i/6v/vv+j/3T/gf8o/77+k/7l/un+ff6T/rT+zv4c/57/z//z/wAAFwAkANr/rP9s/4v/mP9E/2v/wP/l/1EAqQDJAOIA3QAFARoBgABiAGMAeADkAP0AEAEUATUBRQFWAUgBQwEJAe8AeQDv/7H/Xf9Q/0j/LP/7/i7/Yv/A/wUAKAAxAD4AUQDHAJMAAADl/zAAUwASAEYAPABHAEgAUgDc/wcAff9J/9j9q/sI/Jr9Bf7e/Rz9rP0k/xD/hP9I/wAAnv8JANT/ogB0ATUBEwERAaYCsQLMAk8CPgL5Ao0DVgNVA9sDrwO+A9sC3QEKAr0BUAF+AP7/SgA4AOcATAEMATMBkwHuAccBkAGrAL8AaQAGANX/Vf9r/8f+of48/or+rf06/WT8f/oq+zL6TvqT+gv6YvqX+vr6NPv6+7r7qfya/BD83Pya/Jv85/sd/OH8UPy7/A39Lf3E/Kr9rf6+/qz+oP4LAAQAgwApAXkB3wG1AhkDPwOaBB0FPwb9BZcG8geECMUJjQnhC8cJRwmZCmIJ5AoxCX8H3gVjBAIEwwMmAusAtgCDAAEBhAFRAt8C7QHjAZMBgQH5AbgBLAD5/sL9FvzA/Av7uPkw+Gv3hvce+Bn56vgl+mf6CPv5+8L8Tf3T/G38b/sm/B/8c/o1+mL5vvjx+Iv4u/hL+Qz5EvmG+Rv67Pqh+3P7cvpB+nb7BfxI/On7ePt8/Hv90f4MAEIA7ACGAYQC/gI7A/kDcgS4BMAEngXcB/kIgglcC0sMJg/gDxwS+xJxFGwWHxb8FCkThBMUEOcOZQl4A/IApfz9+e32dPKZ8Frv1e9v8Ivy/PQq9xf7X/2kAG0DkgayBoQGRAYfBLoDKAKv/rL8XfnZ+Hf4svc390X3JvlQ+nX8Cf1+/Rn+Bv7y/FL8tPrV+MH3NfWb80fzNvI88ivx0vFP8qfzv/U29z75CfoO/AD9w/5s/zEAcQDp//4AmwFCA0gENwVyBosH3AlgCoQM8wxODnsQyRGsExEW4xjyG24gqh5lIqIdixoHHRYUwxE4B3r93vjp8LbtE+XO4aLcf90q3wveWOWw5evupPG5+D0AtASvDcsMRBEjEFESgxL0DjoNWAaFBykEdgKLATT9RP9L/b7+Mv0H/b/9B/sV/KT1BPf38izxL+6y5jHo+eJJ5sPhgOKv47vlFexL7VH0Xfb8/VYBAwfECvAOdhKEETAUtRI6FgMVlRPPEbEPfBALEDMQgw6YED4SehWjFiIeYiD2HzUliRd+HWUaRw9iDFv3v++K5wzkYtkc093MfslX1YjRENxa3xbq6vRn/HgKKw+oHa8gliLdI6AkNyXmH+sZ+wzTCYUHqP2g+y/yMe8T8m/wku9A7hbxQu8t9EHwCfEH8/rx8PFQ6oruzOns7D7rhOVU6iPsl+/g8tL1S/hPAcwEnAeUDOAOGBS6FDoU/hUqFj8XuhOTEIAQtA4zD8sOjAuUDewPBhUFGCAiiB2RIUUmLRNZJhkSvApxA0/qXuph3vna9clHzfS/ZcxQ1OjR1eVC5W75Ff/aC3cYtByGKF4oECmbK68r6SVjHbUWkAg6C3QAQ/QG9BTn5eo250vnZORI5p3ozOad64fppO5z7lntz+q17X3yo/Am86Ly4/Vm/8T/SwJ4A1kFkgo2CgMJkwenB/sGNgaaAocFBgUJBgkHswNHB90J7gnHCkILoQx9E38VUhiyGswhlCt7K7gfSh6fIS8a3xjyACzuDe3Q57XgItRvxt3HKNS1zyHTwNwc5yL1avxYA/wPqB+JJ28lbyS4KJMslSqmHHEPbQ0nDPcChvrd7tbp3e8R7BblOeMp5Drm6udb4xzj0Onh63Lpmufk6rLwwPi09lT0QP5wB6kILwo+B+8KqRJ3C3YIkANpBIwGQQLp+/z77QEbAr8A5f6iA7gHjwgmCY8IFAwWFPIT+BbrG2sezSwILDUX5iEzIioZHxuJ+mHtefAf6izhFdfqw/DK9tx/0lnaluHg7JL7bADEBxoU8SKhJSYhkyI8KOgpFiZWFrMHVw1AC3D9zPVC6Avn2u7r513fFeFU4QLjbOhb47blne0d7oHsUO6c8+76wP8T+1r9LwrUDokMJQ6NCBwOghFTCbYE2/5pAFT+1foX9SX4Xf3d+kv6Uf02BXMHJAjBCTgMWRSZFx4ashwaIOgpvjKIIVgXuybMGJ4b3gp569Tul+zl5NvbQ83YwvbYgtkw1BfgZ+Vj9Sr/PQF/DSMcSiQWIpMeDSROKJ0m5hq5ChgHtA0KAkL37e7A5Yru1u0q43DiQOTy5E/oL+ZA5V/ss+5C7pLuzfE2+hcAlv4i/cgFbxDnEAILPQojC8AObgqSAd/7Tfqb+jz39vMo8pH3s/qZ+U37MAM8CC0JKQnlDZMTKRqEGkMc1yHqJhssYTd2H44XFC16Et4bkQNy50nusee63n7XPMuDv5PcidUR1BXkNedC+HcBiAJEEtkfcSMPIfof2SVvJ4UjpBi0CrkG5Q6u/jP3xfBK5qbwJe0Y5BDlQOZ44n7pk+cD5EvsBet+7LLta/EW+D8Alf6j/sUIExJzENsMJg7uB6UPTwgkAM35X/Ve94j0yPFe71X3SPkL+l3/IAZXCogLyQ/9EoAYsx3UGxEimiNaJjMzbTJdENEj/R8YEGQexe5m5hvrHuQA3ErW17yjx5zgwc/l2vDjbOsR/xcCAgWcGc0igyb/HgsiBSmfKo8kQRVRBkkN1Q65+bD3yufd56ryoOaE3+rjBeIR4s7oNeAq5snsQuvS7OHs2vXH/o0DaP8lAmsSLBoCESEQ0AxCDs0P9QQG+w/0GPXU8YPwpem97mP2jvfo+I0AKQvEDA8OBxTwFisdzh/tHiAhqyV8JXsxxDFNBxEnJRqsEP4Y0+fD5hDpwuQv2OnTY7zyzQnjZNDH3DTpBfAWA4cBNgn3HoklZSWzHZglAyqUKiwfDhPJBR8OSQxv9T313eU152zvP+EI27bhx9sX3Vzlv9sP5Urqoekq7cLwlvpGBXMIvgOtCzkbWh/xEyoVnA5BETAOVwDT9tDuou926nPqDeM27N/yYvR++fsDjQ5kDzQTMRmtHAok2SJ5I/Im9ycCKkk2aSlNCFUrNxKxD4kPBN+p5bPnZNup0zbOerrD08zd9M634YfuH/RoBbsDLhIwJSsoPiQSIJgrQSvUJ04bEhAjBtQO4wM08JXy+OIy5VDpMtn02GfiBNd024ripNws6qHs7epf8b75LgLuDMQLfwvRFTEiYhsAFYQVsApQDycBiPgJ75Xo/OmZ40nlA+WB8MH03PdAAhYMxBX9E+Ubeh6YI08ncSYMJi0qPCrjM34rZwe8JRUTugv9CJ7gCN3I5W/bAcuhzZC8mNJW3jjOa+Kk9DD20giDBkcUwCmxLkgp+CJXLDsxcSvnGaUSZgWvDqQCJ+2g7ZDh8eCZ5KvWCdJ24KbXadql3MXZLe2j7zvtc/EK/HYIGxFHEcAQchm+JQ4fMRe9EeIMrg1n+hHxuuar5RrkydoD4OjjwvE/9sT6CQVuEfEahRyOIFgiiyq8KqApuCQuLo8sazc7GLoJLSQ4CAkQk/Gx1QHc0uPX1dLFBcBhw5PenNp40Bbsg/waBJ4PKg4YJUMyETjjKO8nqi8FNgMnnRCBCOkCEQsj8pDlOeE42yzgVtvdzCXRd9l21yrcNdVI32jxz/Ep8TT4rQbYFBwYHxcvHNklqyhqII0VnQ0cEukCzO+j5QffPuVc2ozTyNkE6QbxdvcZAPMIGx1fHkklaCQNKwcwvjEyLN0pey6UPKYi+QMoI/sDUA6i+gPNAtSk2pbWbMinvLW3Qtp+4Q7SVel2/Y4FhRYDEUQlbzaUPOUx+isjL/U1hjFlFisIdf1mCkz2GeKy33XUitpy2+zKHMvh1C3QPNs813vVavCH8+nw0frBCM0ZUyOkG6odAy6iLNAn+xpBCYkNqAQ17iTgndbm3bbZUM9P1z7lhfSD/FcAZxG9HAonCi0GK3wrKjZNMmg0ByvaKa48ogSyF5ENM/jpCu/ZWsxyzz3Rl8w8xwC3Q8R+4gHeIOF2+vEDAhOYHXYcqS+3Qdw7gzG1MDQvdzTsKRMP6v05/cz9kesM23XTrs1B1g/MY8OHy4TQU9KW1lrXTuBU/n78P/tKCgYXrix1MC0l0ydwMFcuXSbwEyoDRQG181XfydITziLVMs6Ozf/as+dd/l0FKww2HNMkZDNONdowazA6Nhgz/DJ7KZc2gRt+Bmcb5PdcBlHsYMtEy7nGy8kzxLS557cb0sja8uCc8IAIOBOMHB4qxi0RRJhOyzurOI024y4CMH0cUv6Z+UjwNOgt2/3KAclAyHjKGMDGwvPPHdEW1cXYgdsW8zQFlgC3CMYXLSQjON8z8ipVNAcv/ClUHHQICwDX7+jfrc4Pxq7NmcsFyGbST9+U9JwIFg69HK4j5jDhOGM49TPbNTo29DAqLzY1+C4qB1gauwSP/N392s+LyYzDfb/lvIS5+a/Ix6DX0Niw7VoFHR2pILMtWDUnQv1X1EjZPpo5Si/xLGwhGAQ29nfvteIF2ebJP8mhx8zEzMJKuZbK3tXcz5rZcdhQ6XkFzAXqCo8b6yb9ONA8rjb+ONo2FTDfGQ8L9v2Q7aPdfcjvubzAV8goxE7Pgdmc7Y0IbBMYHuEonzBSNyE3TDQXMIQ1mC7YKosqvDhTI0MK+R03+x8EAvBlyYrEZLc2tpasQK3nqYnLDNQc3QL8aBd/MYIzl0VSRLVV61/5SYJBpjGiIHogYgvU75/rvdx62j7NmsRyyjTK1MlAxT696c3W1UPSl9wB1WrvGAabBpUYKyThNA5IyUNaRxRAoDmqMZAQKwHW6eXXzMnFs2qrxLTGuzHH39j65TkAgRXRJR8tQTBsN3E2+jMILJoj6S2YJW0q/zApN2st4BYRJqgJlwf+8OfI6riyp5mknZnVn+GdHsMK0VHkBhAnJ6JGmEvzVXVVmVq8XSdI0zWwIIIMoQ0q/DLqY+dd3gvhUNcv1S3VLtWR0efEbrq5wRDFicct0N7NVesrBPsQ6CscOttKv1sZVrJWm0WAMqokPP/h6TvOZb2CtoWm4KzqtfbEBtlN6vD6ggydHHQnpyjOIxsnbCSJIZodHxssJ14nATO1PxFIk1CKJ8EpkRgH/Hj4r7uYmZONGozRipeXDptrwuTrlvoxKP83DF0mYWJY+E01OftGhTVJHP0KsPhuBoAMpPymAY/7vQFp+HPgIdn2zOzGvrJymMiZXKpduK/PXdWN9UAfRDNETQ9YJF39Y75ThUF5KzsNEwfx5qXQpsTuv17L6MdBzvTWFuGh7Hz1ZvWM/awBDQnqCa0EQRVaGTUjBiZiKj47MULHSdNQOk+pVSwtdQwUDa3bS9/0uuaKJZA6jyqhu7QcwwLaXgeqDhwnZjJ7PL5G7S6GKmURsB7zKsMdaSNdGJ8i0DRZI8EfzQ+m/ODxscGMsqeooJ7jotaSLZyRvGXUQ/haCXcVQDOlMy4+jzlcMF0x5R9RFhIPpAMEDgUMZQFrAJHy5/OB6WDbStJQx0TBq8Soxqrawu/q//IZCR0zNGo71D6JQLQyQjD0KVUmsia8LLA07Se2B4sTXP5W/IP2xLsWupmrQ6sitsq0psK14IHszPkhEvUgGzzlOEszvigmLR48AzZ1MCMiKyCqIL4TjAiqAl75W+5l0gm/hMPfwtHGlLy/tNfH7dLP4vDw5PPOByoLdQ9SGVUggC/OL3UsWij7LeIm4CQQGO39LPgm44DS0sMOwMnAT8SdyKTQeuzqA6QTTCBfI64i6SYPHmsYQRH6EL4TlxmcJEs2hktwWpdFoityKHwCGv5l1k6p8pyijvCdxqbRv9PXzvZcCJgWjCXMMIczziZnG+kL9A9TFXwdAyIBKo8weS3LKZMk/R64DqzzWtJgwqa4h7RDtwO12b6FytrUBujx9XkDTgW4APwElQdJFXMbZSAGKUctgyu/KuoqUB2EFXb9wuJa0UzJZ8dQyA/R89WT6ND5SwfVGYsdIxj3EsEECgTiAd8EMw0ZGPosbjyAU89fek/NOCoqLP5F8WbTla9Wr2ma/q+1wwbcIvouBEAQdBQaE+MMWv9N+pz17/Br+yoFoyJuOK9GWk2iQ905/iohGNL/Ief6yQfBUrwVw67V6d2P6iPuV+2S7cDpieIf3f7X7Nj/3nzvlQnyIjI2iD1dN6c2hS2iGVsPh/Cg31vQGMtl2ajjEPmsAHELSxEMEugUkAwyA7v8BexQ7ZLzGgBjGPwnsD1HSdJUPy4cMLwO+/OcArLEj8+MvwzGlees6qcHoQk0B7oKWf2n+C7yB+K/56Lmq/TBACEXZi4oP7hE+zFGJWgXLwpz+sHvtt2Q3X7jLexc/+QHIQhmBPjz9eHx3AfMTsp0yD7JdNuV6m0C6xmPKNYzOCntFeAKlfiY9fzvreiE6WDplPZZBQYV+xqwFY0QPgZB/i/60P3g/bIAMf4ZAYAPSxvpJUkqWyxNLKgpTvy9AFbnkesGA7jUNvMY5L/u6wR5/PcJMgCu8evnjOTz7kL5pPrb/MAENxF1F7AogB9nI9AXagZd/4Hw2viB+jQGRv86/osDMwOCDPMFGfl77DHbQtVc1w3eQuz07kT3afqE/NkHbwuDBgQAVvMW7Wrwze229UMAkwceB60Cbf+OAOoF9AFg/iX/x/0SAq4K/hXXHtUcYRZ5DbkNMwuuCwMOKgy4EGIW5xGhBy8E2ACZBTvzdO6S7ybscvmx76b45Ao0CBkFFQGw+0sD3APT/Vb7Xvog+S/9EwJSBkgFIwP8AKT1KPZP85T4y/zP99f2v/uQBB8JfgloCWAGKAElAjf8HvmmABYA8gEsAxT5Iv/NA7T+wvjn70TsRuk/5QbkU+fN6rXrBeiM76f1jfJD+p7/BAM4CBwEwQpHFcUSihKdENcSHRBADJ4RERVkGvIbwR7eJMsczAkLCmoRMQmJ/jX4/vhvBMgGdv+ZBTIPlQkJ/+v45/KH9LPyBOqY4zHnTPAl+B8Cu/rf8mb3Xe7F65Dtn+yd89jwSvO+/6oNtBqqGJQTmRLQCUgLUxCNBeUDQwNeAYML8grhDRcT1Akd/Vnt4+i+6+XmQN+/2gXaMt8w4nDqePpp9FfuL++F84D+hf7fAsYHvAVmAhsGdhEWGaUZkBgNG1Qc2yAuK3otFCBBDrIKeBMXEsIMogxQDOkNMAcwBJoOqhBMBCj05+vy6nTsAezu6u3mv+Uc6f3vovi28TXrCuc34LfgKuQx8cz4kPTS980AkQ8uGAIU5xILDqQEKwZWC5cPhBD0C+MO7BBPD14SfhIXDRr7TumS6xfwNevV5XTkV+mF5z7ioewf+Dz4eu3C6MHyKPhi+dv//gU5B34F/wdnFRsdGBr0Gh4jOiZkHEQP7hS3ID8YyQ6CCf4NOxYZFpsOCA2tDWsF5vsd9Jr0DPaI9Qfv0OV16f3y5vLO9u7veOys7IThnONL6aPwn++l7iz0hPrjAgoIzQxaDNkE+f78Ax4N8gvnCGgLxQtXC3cKOA8LEdkKOP0P8X72/vXP76Tsuuz97HPpA+qm7Sj1wvJc7TDxgvWW+XH7rgCPBJMFUwqfDVEUghf+GKofsCOqHcMRaBZEH5AYtw7jCtYN4A9sEEkNmgu3Dh0EZfuE+ID1JvPz7tXsM+iY6SXvKfBf9iH3j/JN9K7vkO6r8e/0xfU79f35Hf2QAtIHFAmUC+wJnQETA3wKSQrkCI8JZAkuCekFVQYhC8MGpvuY8THwT/EK7ZzpGusT7D/p+eQx6tz0OvL+66btSvLd99P6qP6tBqcKFwnkC7cWsBv+HZYg9Ca2JtIagxgIH2kkrRu9Dh0OTBD3EWkRsAo9CYIFTfop9G/y5vF/7yHpHuSI35jk2uvS7Fvxdu2W7PXsfOoO8Ef1CvlM9374cv6UAw8MaA82ETAQLAnLB7IN9RMbEbENbQ32CxQKYAgyDLUJrf6C8aLq0usV6YPl8eMw5d/kHeCK42TvXfJ27OPpJO1h8kn1CPnNAlsKXQw1D3EYISIQJVorKDDGKowd+BfWIN8jPxvsEIgQEhTaELcN3w0BDrYD3/LH7Gbu3O286TzlfONO4drhQuiZ7+zwSutG6Nnod+qq7nP1gfn++UT7SgAFCbwOcBBBEdEOAAkAB24NEhQeEgYQ5BBxEEcP9wvuDPwK1/4D8vTr/etp6b/kVOWK58Tk0uDh49ztg+806eTnVu6E84j0gPf4ALgKhAuMDk4ZUSIoJ9kqKjKOMdsjKx9sIigkPx3BEEMQaBJhDhALLwkeCRUBSPNg7EzpJeae45PgHN/y3QfeI+Rl68TuJOzg6p7rb+sC7pzzh/my/cQAWgRUCv0PthNOFkAUEQ56CkEMHA/vDu4OSBCKD9MMEQpuC4oK3P/o8SPraukN5ubjHeXq59jljeOO5onux/Bo6oHpqu2G8Z/yoPjKA0MMzw9yEWccTCbpKokvei9JLJEkACGgJIkjFB3OFE4SqBOhENwNLgxzBaL5Nu+u6Nvny+QT4Dvd/toW3D3ezuSd6jzpz+ZA5nPo0Oye76f1d/vs/UoBzge8DxcUkBQbFCcTrA8ADyYQ4hLvEvcP0w/HDVIL2QoiCYEC/vXs6y3rNer65Y/jheR+5rPlOeb+6sPuUOws6T/rHvE19e/3Jf+mBf0LZhH6FuUiaikYKngq4yfKKLQm7iIKI6MeuBmvFXcUxBQnFFoO6gaQ/qD26fEU64Tn9uFV3JLbRdl72wPi5uOn5bzj6+ML5wjqSe7J8W32cvg6/GwDpwy7EbwSAxNpEsET+RN+FIoXKReVE5oQAxACEOAN6wk9ADX2qu8E7p3sxedS5nfk2OOr5evnROyQ7GvpTeh26pPvLfWV/OICDgbrCJQQYBm8Io4odSYvJ6klpSavKL0k2SJ3HRwZ4RZpFEoVFxIsDAwFHvta9Tjzte3j6LDj5N1Z3ezceN7R4hzjcuPQ4Zjjyeer6B3uEPGg9ML4e/vjAl0KnQ5fDyIQWBKJEvUSoRVYFnAV7xKeEA4Q0A88DGgGdP7798P01O/O7XPqf+hg5y/m/eeo6WLrD+td6dPqZu3Z8ej1G/u+AaMGXguDEDsbNyAmJPAjGSPyJbcmVyeEJEcj7B1oGA4X/xV+FFgQGAk8Am786fYN8R/veurg46ndG9w94HbgWuOH4MDh4eOP46PpluvG7unwaPMw+UH+cwVjCsILBg2bDikRRxOaEzMSJBN/En8RrxGBENkPawrdBCT/Jvpi917zKe9X6wHpQugG6ivrRezz66frQe3a7prxjPSr+NH72P3OA2MLhhMOGHca2h+5ItwjSiOgJFUlKSGwHrEb8hhSF+QUrRKHDmoJhAPh/hb6d/NK7VnojeWS4B/fhN/13zfjguGm43fk7+Ri6DnqHu/X8HfzVfe5/OwCkQd6DFoOtQ/fD0kRkBOjEWwSGBKYELkOCQzcDBkLOgdmAC37w/gJ9erxNO+Q7mHsIuqk6rHrSO3x6xHsXu5Y8ejzO/bY+3T/JgR5B9cMLRP5FakauB3KIdoiYCHsI28k7yDOHRsbDBmeFjQS/A0oC3MHJAJO/U36B/Xv7hzpYuUH42zg/ODj307ji+MH5Dbosei864jsoe6x8gD2i/pj/qoCrgfbCyIPCRFdEjASXBKXELEPGRE2D0sNjAq6CIsHkAP2/8H7V/Ut8fHuB+7n7IPqs+nu6rrr+OuW7OPtxvAF8RnzJfdB/MIBOQSNCckOjxIaFckXjxw4H2IguCBIILQgVCGpH1ocYRnsFBwRsw2RCdQFPAHa/D/3PvKP707tDeos6LrlD+Ri5Tflk+dA6kfqgusJ7ejv9vKX9TT5a/z+/nIBpwUyCoEONA80D1AQ/A7DDrwOTg7sDOII4QXfBOoEPwOx/vD64/bu8mHv3u3o7VXsUeqA6cLrPe5i74/wYvLx9D72L/hY+1cADgbtB2AKDw5KEkMWfBcCGnMcih1xHTEdGx3eHCccSBnJFekQgg5jCyQHBQTT/hX7Yfc79IzxQu6U64zoQ+dm6JzpsOk36mTrjO0J8QbzHPWB9hP4Dvve/VgC3AUxCLQJiQrxDCIP3w9IDrwLOQoCCfEHKwZyA+IAO//E/O342PVg9Ojyge9P69rpWesV7frsiO3j7w/yh/MA9cH4zvv5/b//4AJDB8kKQg65ECET2BPNFD0XdRnpGW4YSBhqGCUYOxb7EjYROQ7LCd0EfgGw/7T8HPll9kD1xPQp88zxc/Cw78vuau7h7zTxePN49I72//gH/Kf++v9dAeMCggQ+BhQIRwqPC+wKZgrBCdUJSAgkBiwEFAHN/QH7Tvk2+cj2vfOZ8ePw0fAH8PnvyO/471TvCfA+8jv1nPY59wj5LPvS/U8AvAKnBcoHuQg6Cn8MmQ/OEJUR9hGFEbQRIhIsEgwRjg+oDVYM0gpJCgoJ0gdWBTgBsf/F/Dn7cPrn96X3RPVX9Ov1Uvbv9wf3gPcw+PD4cvsD/b3/gv+SAJoCJATUBl4H2AlTCggIzQcIB3YHuAa8AjMAuv46/mH9oPvL+Eb2gfOk8Evw6+/O7yzwRu9G8Lby8PSR91H3h/e/+Kz59vvs/fz/NQIoA9kD8gUwCM0JQAoBCQQJNQoECpYKHwvDCvQJEAhRB+wGWwZYBZQCCwFuADEAuwA6AZIBZgE4AoQARQANAuwBKwK3/7z+VQBXAXwCSwJCAoIChAIIA2cEogWpBcgEDANzAgADVANrA4gCnwDf//D/V/9Q/wf+4vtj+g745vft98X3RPeX9CT0UfMN9Fj2gPX89K/1Fvbi9yH6bPsc/Y/9bv3n/b7/XgIFA14CfQJ7AjEDfwRPBHoF+gRBA8cChwIJA2MCKgH5/wX/a/5+/j7/EQCT/2b+Q/4//vr+h//M/0oAXQD7AagDtgXuB3II1Ai3CawJcwnYCmIKeAqsCksJGApSCfkH0wYRBG8DHwJRAScBef/3/qn9D/0n/Cr7lvp9+E34Ffgw+DP51vgC+Wf5Ifk2+lf7l/u5/KL7tftV/T3+GP/k/Y7+Qf/3/kT/Pv81ANH/kv6U/VH9AP6W/Xv97Px6/Kn8G/x//Ab9YPxy+zH7svuS/F39ov0Q/lH+ff5K/4b/UAAGAe0AiwHzAQ0DLgRWBDMFcAU5BYkFxwVfBrMGZwY8BhIG/AX+BdQFmwXQBEUEXARrBD4EgQN1AoYBqAAEAET/u/6+/un9S/1C/Tf9ff2M/XH96f3p/pj/XQA8AfsB/gEVAhMCQQE1Aa4AsQDQAPr/lv+e/nz+vv7//Vr9ZPyG+zz7Sft0+8H7n/s1+0X7IPuY+yH8Jfwr/Af8f/w7/dr9a/6l/h//j/+l/4sAMQJMA0EDOwNgA4kDuwOIA4YD+gIqAtsBxgGDASkB0wCUAMEArwC9AK0AhACFAPf/z/+Z/2L/rP+R/+b/FwBLAM0A3wDqABYBSAF+AYoBUAF8AZsBqgHQAbgBpAGLAUgBAAHWAIMAAADB/2H/BP/E/mH+UP7b/WX9cP1M/TP9Lv0k/Yv9sP3C/S/+ZP7D/hD/Hv9J/0b/af/4/y8A7//y/yAA2wDzAeoB2gHqAZUBewFCARsBEgHrANEAtQDjAC0BAgHPAIEA6v+R/6D/uf+2/3X/WP9w/4n/o/8YAFgASwBPAPP/OgBgAEYAYQBVAGkApgDaAPcAKgERAfYA7ACbAIQAbwAfAL//b/8//9D+j/5K/vb9r/2S/YT9lv0v/n3+tf4K/zD/Jf86/3r/kP+l/9H/3f8AADsAdQCZAL4A7wDpACkBSAFQAVUBEAHKAIIAaABiAEUAOQBFAHcAlACLAJ8AgAB3AHEAMQBEAE0AMwA9ABUAMgA7ADwAkACLAL0AEwEsAUsBZgFtAWUBQgEuAesAmgCOAG8ALwDy/87/nv+N/0T/4P7g/oP+Pv4M/p39Wf0u/Rr9R/18/ab96/0r/h3/zP82AG8AQQBdAAwAm/+h/+T/CQADAFoA+gASASUBogE1AhcCogGCAVkBNQHIAEkAxv9v/13/XP/d/+X/sv9+/4b/hP89/zj/YP+L/5D/yP/u/2AAaABvAKcAtADcANkAJQFhAVoBYQGAAU8BCgGpAGsAcwBcAFgAKACIAAgBpwBlAEcAFQD3/4b/cv9q/z3/Mf/a/sH+0/7E/p7+mf6K/qb+3P4J/xH/Dv9S/23/dv+L/3H/f/+3/6r/1//D/+P/FgDt/+//zf/1/24AuADIAMMAkgDJAMEAlQCqABIA1P/Q/6r/zv+z/53/of+y/8L/5P8WAEIAdgBrAH8AsADIAMAA0wD1AA0BOwFmAXQBbwFYAdwAOgC7/6D/l/9H/0n/gP/q/zoAWgCLAEwADQDv/6v/fv84/7b+q/6Y/nz+rP7I/iX/mv+x/2z/l//i//T/0f+h//T/VACEACcAsP+z/7X/mv9p/2//bP+2/wAAAwAKAPX/4v8vAHYA5/9t/37/5P9DAHgAogDUACABbQFsAUsBCAHeAH0AUABbAB8AYwBeAG4AqgCGAF8AcgB5AHQAWABQAEgAjgCHAN3/kv+U/+7/EwCn/1j/hf+//zAA9P/H/6n/rv+x/xH/9v6Y/o7+vf4g/2j/jP+K/33/zP/n//H/p/90/13/d/9X/0n/rv/o/7r/kf/A/8//IQDhABgBMQEYAYoAKAAIABIAxv+x/13/Pv+T/83/8v83AFcAZQBcAEUAngCdAKwAZwBGAF4AkgCYAJgAlACmAK4AOgAXAO7/KQA8AFEAjwCjAO0AHAHzALcAJQCL/4T/cv9Q/zn/Xf+C/77/2//U/wEA6/+t/3j/WP/8/2gA7v8AAJ4AagAEAFIAewCVAEUAGAAzABcAyP92/0T/cP9+/yT/8P7j/iH/4/5B/4T/4v88AKH/nP/e/83/lv99/4D/zv/F/8j/DAAAACgAQABLAHYAywAzAVMB7gC+AJoACwC6/5v/rP+m/wAA4//T//7/yf+Q/9r/IQBPAOYA5wAhAcQAPwAVAAkAn/+9/+D/nv8NAIsAIwH1ANsARgAdABEARwC/ADEAJgBq/2X/Xv98/yUAzP+T/x7/dP9k/wn/9f73/iP/uf7l/tb/MQFoAYAA6v/M/+L/CQCT/2//g/9e/2b/x/+oACIBuwD0/93/BwCuADoANACzAGEAuP9d/3sATQASAM3/x/9jADsA8gB/AFMAzQDu/3D/m/+RAE0AuP8lAEUAWABGACMAEAC4/5j/Wf/1/mb/PAD//yIAJwFZAYcBuQCBAC0APP+i/z//jP/a/5//1f88//z/IwCM/yP/tf7i/tb+H/9B/03/Cv8U/93+ef+NAJsAaQAXACYARABYAFcANQDX/9b/s/9EALoAxwABAVIARQB4AEIAegBOAMr/Nv/F/g//ZP+P/+b/CwB5AMEBNgEaAb8APAAnABv/QABUAIAAiQAxAO0A2wDOAC8Aqv+D/0b/Yv88AGcBiAEFAWMAEQCnAOkA0gD4/0z/Gv+s/u/+n//N/wX/tf6S/ij/EAAaAIAAEABK/3b/cP+Q/00AJAE7AX8AFABJAOIAHQEdAY4A/P+7//r/nABEACUA2P9k/zH/IP9U/+f/kf8j/zf/sf8e/xb/zf+O/8D/6P4z/0T/gv88/zL/BP8Z/y4Aj/9kAAwAfQCwALX/NgBE/0X/uP60/44AkwB6AX8A9AD/AOgAvgBWAMD/Yv9T/7T/bgDQ/x0AaAC8/73/EgBjACABmQBHAFUA0P+vAK8Avv/B/xEAewBpAEIAmABgATsBMQG/AVgBmwEaAbEA9AABAHj/bf8d/+D+l/+B/8v+8v6f/rr/wv/2/kcAVwA7AEz/+P2m/hP/CP+g/o7+TP+p/wwAxf87ACwAJwDR/9P/vQBiAJQAVf9n/8L/kP9t/6//vQDfAJIAGAAqACsAcADm/y4AbgCEAGYA/P/0AFEAAACe/6//LQABAJwAEgHdAH3/ev/K/xMAUQAjAGQAbwDuAKcA+wABANT/FwCv/ob/VP9hAGgA1v8mABUA4f9h/yMAQf9j/8j+rv1k/rv+h/9N/47+Cv8K/+//RgDTANsAeAAGAUUAqQDkAOEAlABy/w0AJAHeANQAmgDNAMEAlwB2ANsAOAFoAfMAv/93AAcAmP+n/7X/9P8l/yL/Ov+A/0QA8f8aACj/o//DAI4AXwCI/7D/rP8aAFwA6gCeAFEAsgAIAM8APADe/08A2v6A/6r/pP5z//T+VP/N/mb9Tv51/g7/Ov5t/oP/YQDJAMYAtQG3AN0AAQBA/5//of/h/17/Tv9FAOcAfwCcAKEA0ACW/0v/BwCmAEoBeQADAfkA1ACvAF4ARAD5/7D/f/9JACYB0QFDATAB8ACwAEMAT//o//P/Xf8c/2n/PQDbACIAxf/V/9L/rADx/8b/ov8AAFYAlf/e/zz/lAA5ALH/4v/K/zIA2v7m/uD/uwA3AAAAk//j/uL+Pv/7/m/+0P3Z/gUAY///APUAmAC0/6/+w//q/1cAhgBZAF7/rP6A/yMAJwA5ABkANwBlACQAeQGOAQYBgQAdAKQAhgBjAWEBBQG9/73/kQBSAD0ATwC9AAkA//9J/7v/CQCB/zAAe/85/5b/Lv+B/6P/1//k/zT/Xf9R/00AAQDe/7b/2f9wAEn//v92AHkAVgDm/4r/Zv/L/7L/Qf89/23/q/+X/+3/bwGMAboAgADb/wAAyP8n/0MAzQC/ALwAGQBoAIYA0//b/5f/KQDoAFUA/P8sANMAhwDA/zMARwEDAbf//v9pAOIAWQCi/2cArABSAZ4ATgAbAAUAxP8C/nT+p/58ADcACP/NAEn/df9E/nb+gf86/sf+Xf6q/8v/xP+E/7n+yf7I/tn/4P9uABgAmv/P/34A2QEZAUIAoP///j3/Cv8q/x8AG/8l/6//OAFYAowBfQG0APD/uv6p/uT/2gAsAWAAAAAzAXABywDkAIsBbAHuAD8AiwFaAvIBxgFcAKUAqQCUAJcAmABVAA4ACwAA/5z/z/+J/5f/6/4UAAP/tv5k/9L+5v44/fD+p/9jAJkAcP9wAE//KP/7/uz/QgDS/oX+7f2B/hT+Kv5v/kn+cf9d/+D/XABpAeIBbQB6/73/JQAjAGUAMwFnASUAsP/i/08A5QCrAPn/5v+//+P/WwDv/y0B5wDnAEoAnP/CALAA/wAaALQAvQDAABsBTgGcAVcB1wDW/yUAsQBxAVABGAA8AOv//v9HALr/uwAkAN7/hf9I/6P/Sv+A/zH/1/5K/mv+V/+9/5n/of7l/gb/f/+l/9b+QwCo/lD+l/1K/gkAn/86ACb/E/80/9P/0/+K/9z/jP/V/20ATABCAXQAigBHAA8AqQBHAF4BcQA+ABAAlwDeAEEATAD0AOwBeQHxAL4AMwHRAPz/JwBEAAYBvAD0/xYAiQAJAT4AfwBcAXMB8gBHAMYAwgCqAK7/I/+p/0sAPAAWAPD/bABPAIL/dP8E/kj/oP5E/2f+8P22/lz+d/+J/tT/Z/5//hz+mv7U/xb/f/8h/+X/R/+L/0j/MP99/1b+oP50/g7/fwBqAA4AxP9KAXUBJAGPASoBQgHg/0cABQEsASIBWgCHAFz/Rv8AAFUA+gDiANYAfAAVAYcB3gF8ARkAhgDHAMgA6gDuAIgB2gDbAGgBzgGNAlgByQE4AVAA6/+E/v3+Yv+9/2P/Vv+L/vL+fv8m/87/tf4M/1//5P8BALX+G/+c/jH+hv3G/eH9H/4U/zr/OwCj/2QA6wCrAEAAmP90/9P+7f4P/8j/Nv9w/1IA1gBYABQA8P/8/nb/VP4Q/9n/g/9XAIIAUgBZAGQAWwC3AEEAagAaAY8AswA5AX8BnQGRAWIBUQFVARMBlwEEAd8ABAHqAHMBZgH+AWQBmQApAHz/EwDA/y4A9QBeAO4AwwAmAO3/lv9+AHsApP5t/n7/1P8IALL+mf5H/jj97f1e/vX9pv2Z/rz+Gf+1/u/+Jf9r/rr+c/8sAJr/1v+j/9P+RP75/Tb/eP8x/83/EQCI/5H/zf8dAHj/+v7C/0gA1wDSAB0CSQEFAHMAiADVAUwCnwKpAkUBTwGmAfYBdwIlAggC7AAiAPwAXAE1AW8Awv9RAKsAVgGZARABVwB8/7L/zv8QAMUALAErAbUAuwCLAEQA/f9eAKEAEv+8/jf/l/+1/07+eP41/h7+VP6N/vD+wf1B/VH82fwf/if/q//2/ob/2v84/+f+pv3U/f/9+/38/jz/gv8S/+7+s/5//mr+gP5L//3/mwAyAYIBWgGDAboBiAFCARcBegGlAR4BWgEyAZcAjwE9ApUC9AHkALsBYwFUAYEBFQFaAUQBuAGtAcgBugEzAYAB8wDnACcBMwB8AD0AZgDdAPj/zP95AMcA8QBgAEgArQASAJj/tP4P/3z/9v5Q/jn+bP55/jr+QP7l/vD9Af1n/Kn8XP75/c39z/2R/ar+LP7V/br+kv/l/xz/If+0/9gANACX/qr+xv7B/y7/af9jAKQAswDD/5cAuQCKAbwBAgFTAef/rf8z/+//KAGsAN8AzQAKArYCiQLoATkBfgH4ABsBQwFpAeUBHAFLAAsAvgBDAZABBQLgAbcBsQC3APgA3wDAAKD/wP8HAJcAAAEFAfAARgA6AF4AHAEUAa4AVgCD/2T/wv46/4f/5/50/xH/df8r/9/+Yv58/dj9e/1t/vP9kf0w/bL8zf2D/bD95P3B/Yj+2/7J/rD+N/4f/eb9XP4v/kv/rf76/6IANwDr/6//bAAiAU4CXwIxAmgBswDZAEABfwEEAZMAgQDYAHkBGgEwAaQAXwCHAGcAYwFBAVIBfAGNAa4B5wAFAZ4BIwI4ApkBoQE2AVMBUAEuAZsBGAE7AS8BQgFLAboATgCY/xMARgDe//D/gf+d/7L/yP/x/7b/0f/R/8z/mP8i/7/+rf7i/vn+S/+4/4n/tv8qAHoAVAC2/7T+E/4D/nf9df1X/ZD9A/1L/cb9pv4OAIj/uP9O/hL+av7n/lEAyf8kANb/OQCSAFMAgQCs/77/6v4L/4//k/8MANb/4P+a/6P/DwDAAGUBLgHuAGABlwHrAcMBOQH8ANEA2gClAI4AeADlAMMA/wAuAfgACAF/ALsArACRAEMA/v9jAK4A7wDAAJIAkABhALcAzgCwAJAAGgDY/6n/BQAxAJ0ARQDL/5z/Nv/F/93/1/+v/13/9/8oABoARgDj/2f/1/59/rX+7f7P/uD+0/6g/rX+8f40/0v/Wf+M/6n/mv+n/+D/zP+o/8D/1P8wAEwAawB4AE8AWQBgAFAARwA0ABYAAgAQAB4ALABQAE4AeQCWAJIAfAB0ADQAJAAfAAIAOgBRADUACAAGAPv/4P/N/7z/nv+n/5b/c/+F/4v/nP/B/6L/lv/e/zQADQDX/+j/3v/5/w4AHgAxAFoAagCEAIwAawBIAFwAUwAWAPD/wf+0/6v/sP+l/+b/EgAfADIAMwBAADgAQQBCAEcAbABfAHMAuACNAI8ApACSAJoAxACmAIQAiwCvANoAmABlAD0AHQDu/8f/d/96/4//Sf9c/43/sP+Z/1n/Iv9w/6n/jf9s/zL/PP+p/2n/0f7V/tv+Qv+P/7n/uP+k/87/6v/X/6L/uf+x/7//q/+l/5n/fP9//1j/a/+3/+j/6f8QAPD/DgDj/6H/FQBdAJEAoQDBACABSQEnAQEBtACtALkAuQCkAJMArgD4AOcAZwBPAGcAhABeAGkAmQCPAJMAxQDSAKYAigBNAFoALQCZ/33/c/+2/57/Y/+F/4P/qf8CADQAFwApAAQALwAfAH//Sv9I/1H/7v7s/kX/Wf+r/xsAKwAKABAA5P/u/8L/M/8a/yL/Nf/v/u3+V/9f/5v/AwATABQAAADj/+n/nP9+/1D/UP+p/+3/NABTAHMAlgCLAHEAdQCAAIQAawBmAEoAUgArAPP/DwDZ/93/BwAmAL4AcQGLASoBEAEJAe0AtwAlAOL/vP+Y/7f/xf+8/8r/5f/s/+P/0f+6/6//nf+U/4r/df+3/+D/9P8BAAQAHAAOAP//+f/h//f//P/2/xEACwBMAEIAGwD8/+3/7f/H/6b/av92/2D/iP/D/7z/3/8OAAwACwBLAEcASgBZAEcAHgD+/+n/4//Q/9L/5v/3/y0AOABQAEcAJQAEAOH/3v/z//7/EAD//yAAPQBwAMEAfwCGAGYATQAEANT/CQAhAD8AKgAUAFsA1wChAFsAKgAAAOT/r/+6/+v/CgAUACkARABcAFAAOgD8/9X/xv+8/87/vf/N/8j/mP9w/1j/Nv8g/xL/5/7a/g3/c/+e/5X/gv9v/1X/Nf8o/zf/YP+p/7b/4P+gABYBXAFaAeUAfwA6AO3/sv+R/4n/t//l/zMAeACRAHMAKwA3AOH/lv+Y/1L/XP+A/9j/4P+o/6//9v87ADcAEgD1//X/NQAHAIL/qv/N/0cAowC4ABEBUwFnAesBYwI1AgcC0QHTAZ4BNAHyAHkACQDa/7X/sv+R/7r/j/9l/4r/df+m/5f/kf+P/7z/tf+u/7n/sP8SAPb/3/8IAPb/2v+Y/0n/8/7y/vn+yv74/kD/af+K/8L/6f8gAEQARQA8ACcA+//a/8H/rv+h/3n/hv9x/3L/ef+B/5n/wv+3/6P/pf+Z/6v/mP+c/5D/nv/A/7X/wf/F/97/7v/Q/9f/xv++/9L/2//X/+r/AAAGABUA/P8QACcAIABGAF8AcgCWAHsAWwBJADIAPgBNAC8AEgBcAHMAnAC3AKUAoACfAH8AXwCcAM4ADAG5AHMAwAApAVIBZgGXAasBvgG8AdEBrQGfAXoB1gC7AKMAdgBvAGUAVAAeAAcA6v8KACAABgDR/57/Sv9X/0H/k/59/nX+lv71/gL/Hv9I/3v/kf+A/0T/0P6N/oP+o/40/sf9z/0E/lT+ev61/sz+uP6Q/pH+if5W/lf+Zf5Z/l/+ff6W/rD+4f7+/iH/ZP+P/5j/ef9z/1X/Nv8Y/xv/Ff/6/hH/Z//x/wAA5v8RAIEA8gBAAYYBswEMAn4CmALrAioDawOgA+kDFQQkBMoEKgV9BaEFHwVkBOEDMAPmAq8CdAJBAuEBvQH1AdgBaAGdATQBmgA1AA4A/f8cALb/kP4F/rz99f3z/fr9D/7t/Rn+Nf6I/gP/JP/I/nf+6P11/bD90f3E/Tv9z/yZ/Iv87/xn/dv9/v3z/er9z/1l/WP9YP1A/T/95vzW/CP9hv0V/pr+u/6q/m/+oP4E/wD/9v7I/rn+z/69/rf++v74/uj+Lv9m/8T/BwBXAC4Awv+q/3z/vv94AGAB3QFCAmoCKwJxAsICDAN+AwIEfQSrBIIENgSWBJkFrgaHBoYF6wT4BKwFRgXqBRoGrQSUA1ADbwOXAioDWgKaAPj/1f8CAPz/OAA+/1H+gP1+/fj9Gf7f/Tb9C/1u/Hv8Gf1T/Sn97fyU/B38kPxS/UD9+vzg/H78DPx0/O/88PwX/Qn98fz8/C39lP1r/pT+9v2+/cr9jf1+/ZL9if1H/db88vxF/Y/+If8K/kz9V/y6/M39EP6I/k3+/fw+/cH+lP/m//H+tv8GAdQCPgTnBDQG/wWZBRgFQAboB30JDgpkCr8LLw0xDvoOzA1wCAoDAv4YAIMEOARWBAADvgDw/aT8n/wp/HT6QfjT+IL79//8/4z9fvxs+mL6EPup/QMB2gETASYCNgRfBHgD5/+j/TT8GvtA/RP/uP9T/qX7EfpP+pn5U/hj+Cf4afk9+7X9H/+h/NL5FflA++X+6wB5AjUE9gMVA1ADKgMDAsv9avkT+fD6/fxf/Fb7hvrO92T11vSC90H7bvxV/UsAMARMBjMFqQOqBOkFnQdbC6sPDRJhEFwPfBDIE4kSbRDsD6IQXhT0EVQNqf+48hLq6el39d34Jfnw9SH0pvRM9gz3x/Se80XykviFA1gOvRHoDCgIdwShBZ8IWAwJDjQLyAb4AggCEf/W+eLyEe4J74Pxy/R39hz1ZPLW7z/wlPR++eH7cv7iAagFyQp0DB4K7gb5AxoEpQeAC+ILJgjqAtP9E/oS98P0ie3y4+Dh2eRV7SH0EvUd9nH1xPYn/qMG6guKDPwKLwv1DZIQPRIVEdwOXg48ECMTdhi0G6Ia/xVQDdAKPAhPC40LQgZe9Qrj+d4Z4FTvCfQw9Gv1BfRP+Jj9dAB+/tb8E/z4BLgQtxbCF1QQ3Qk4BvoFGQnNCW8H2wA4+4T3yfQW8cvr3+je52/sI/Ma+TP8gfnD+Kb65f22Ar8HowsSDbkNQQ0fDjgMiQQb/sD62vvk/TT/LP4P+7zy0+qN5H/cjN3j25niQvJA/S4FgQKCAMwCRAXEBoULBhI2Ff8SBhHXEr8QHAzgCIwOJRj1HJ0bNB17GsAXhxAFCgINIgog+tbdFdXx1vXlt/Fa8bj4sveo8h70I/06BVQIxAOWBGATQhyYGoATgQq1BUACLgGJBPgH9wI499Ltf+iQ6HfnI+W46I3vuvX1+T/++wBxAogD/AOTBwoNnhG4EoYQxAxlCVwFXf1E9r3z4/YM+oL6p/kf+l727+1p5NXckOBs3kTlN/U7AlkMpQajBE4F8AN2A9wFbQyJEiEUAxD9DoAO9AxOC+QOHBvEI9MfOB8uIGwhkR6TEHMOE/Wz0z7JLsvb5Fv1ffXP9df25PMc8hn3Qv1NB4QLiA0AHC0nESOKFl4GNf14/U39swJjChAE1/VZ56rhIeTc4ZnfM+XD76z4gf/zBIMGxQIk/SL+wAWIEPoYVxgwFBIPdQlAAnv4r/Li79fvtPId+dH+Hf3J8dre9dbz2QLfHOya+mECEAZ9BWwFPgdSBF8CRAPjBloPzRhLGZcTQQ81CioGsgqgFvYhviBTHmck3CQeIIIewh5b9NbM9b9/yEbt9vbg+Z/8A/Qu7nPw5fUf/8MGmwOyC24guymDKUcaXQWg++rzFvb6BEwN/AUD9hzlY+IO4+DbPdz55K3u3vdBAnwLrguv/xX1GffJAD8QcxtCG7EYchIPCREAwvb+8mnvQOuZ71b9iAT2/+rwXNgI0YjN/dRk8TQC9AymDmcMog94CtIBX/yh+h4CghGCHo4gVR15EpgE7/7yAmcRVBqXHskmVS9DMuk3ijlvBGvGFa2WtLHhlPibAmILIwBg8c/p5OmM8HX3rvQZAtki0TYsN4cl+gcM8xPoV+1KBpEXdxTuBeXwhuV14FLVDtJG2vDmX/blBKMSUxV8ANXrRumk88cHfhegHoQlQR9FEvQEVfNs6BnePtpn58b7LAOg+0bue97B2BrXNeBq9boDyg7MErkTtBTsDkcA1vgm/XQJVhf0IXMo+CaOHe0SJxEJGT4lwSfULKIhQRzq+evRGcpYxtHbVOd28hP9UAA1/376ovZD9Qf6Sv/9Bb0ZZSdmK7kkBRXRCccB5//gAf4D1v/2+Rzv/uRb4yHeOtjf2lXg/eyj9wL+5QOTAcX60vgW+dwCdxEDFTEXQBZAEowLPvx58K7o2+G94+vplvKD8evq0Oee50Lt9/E++RoBEQiWDP4OUhC0EfQOzQkxC1MS7hpUIRkkBiBjIiwdUR+uIFskSiviHxkGudgxxvvJQt2H6/7r2u8j8wnzw/Es9TD7Vf6L/6kF9xYJLCkxOCnlF2MKLQMrAs4EKAklCWkB4/Tc6wnpg+Rx3b7Z69qm5P7vbfaZ+w35X/SI8KXyufy5CMQNzBCxFa8W3BR0DGsCBvna7a/oNux88tnwW+gJ4rjgi+W/6UTudPi3/lkDjAncEKwVhRPPDJELgA6QFGEamB/DI5kifSQWIVsmkyjqKnYsfSK7/kzW6Mcbyz3gg+kI62Hw1PEF7ebr8ey/89b3/fU/+1YQViUSLxEpbxpsEqMLGgrID7wU4xIMCaL3A/CL8MTtQeb63gvbUd9H4xHq2fCh8Ontoeo/7v75vgRLCTgPuxJ6FQAWFxPUD+cFevqT9GDzyfSA7xHo0OGv393g5eHy6ZzxF/Xi9yn/ngfpD6gQ6Q/BEQ8TbhiBHwAjFiRCJc4jLCmqLYAzeTVjNWoPgt/dyXrGZOA265/rOfX89h3u9+nB56HtXvS063jor/50FhcpsSjtHcUYjQ5HBp0JMxKkFkMVNgWc+3n/ff5596XuE+I9347dR+AR7GPx7+za6M3lK+oV9IP3Yf97BaUGNAwREXQW0xdgCtf/b/4l/pD74fi982zy5O35577uPfUP+Q/6jfqbACwF1wT4A1UDngNYBQwHCAtyEQ8TlBL5EtsUdBgtHOQZJxDzBmH/kP2B/9YAIwIlAmgB5/4b/vYAoQRCCBwHBQIv+xP2YfY2+PP7vgD0AmMC+wH4AX8B7AA3/en3mfQU82X2k/6qBg4MxArRA1L9OviF9GryiPIa8/XxQ/L593r/3QNnA8z+6vmW9E3xm/Ki9RT5Kvt3+zz7d/3mALgApQG8AtL/SP2a/d//ZAJ+AmoBLwAW/RX9QP4C/qn9PPyc+538aACKBmYKOglwBZoDaQUaB60M+xIwFoIYehblEgoSjRAqD1QPhgyyCFUFlQN4BQcH/ARdAlv+K/kb99n39fit/A39cfm+9kDzevO79zf7Kf3//QT9u/yj/EL8UPzj+mz3ufMg8rT0Lfmn+578q/xn+wX7sfnP+Cn51vdv92T3Cvje+e37A/xA/Zz9DvyN/M376PoA+5/6JPq8+F/3Avmb+j38Cf9hATsDTgU1BrEHxQg9B9oGOQmgDa0RYRXxGSUasxVXEl0P6Q9HErMSbhHFDfAI+wQVBA4GYAfHBRgCgv/j/Uv9Dv9i/3P9XvhM8uPx3vX5+cr+rQAw/dH5/PfZ9rH4afl490/1T/Kb8h/2TPkq+876XPiS9bv0jfVR9zv4Zvan8+Py+fRv90r6Cf03/Fn6rvgA+XP7evx8/YT+rv2g/HL9RP9rAjsDsQGcApIEdQcMC/ALHwwHDDkMVgz8DDoS5hOhE1IUjhLREv4SixOhEZkMkQgiBU4F6wYBCWwJZwV1Aeb9Vfqt+hP76foq+sj29fTj9R33Bvq+/XL8v/oC+jP56Psu/nL+hv2596Hyn/I89Wv55fuF+yv5Z/V18yH0RvWU9Srzw+/L7hLw/PKA90L6Bfow+Mf3G/r+/Gf/PAFqAHb9xvyi/r4CBQc3CeQKrAuDCo0Jkgl1CkMNWw60DvoTfxbPFN4UaRJwEAEOmwwbDjwNHwuHCWcIvwY9BnMFlAPMAcb/5fwx/BT8IfyK+w/4UfYz9vD2Nvli+1v7Ivof+gP65frT/OL8WPsr9x71UfZd+En7Ufwg++r4C/bY9Oz11/b29pD1MPOU8q7zT/X+9p33afZs82nxivK89fT43fsf/cT9WP6JAOADLQeoCQoLVwsaCsEJigt9D8oQ8hHAFCoV6xNKFOoTqBSVE7sQMg9cCxUI1AUVBcoEcwW7BTAEvAKFAlQB/P9n/k/9efuv+Iz4L/l4+jf9of4B/V/7FPov+UX5nPqB+wb7RvhT95v4iPkJ+6v63/jL9n70P/R/9ZP2+Pay9L3yzPLn8lf0HPeB92T24vQH9Pr1jPc9+Kf5nflc+Tv6z/1cA7YG9AiTCjQKlgizCFUMSQ8cEBQTChUaFGsUXhQkFT8VhBJJECYMdwgKB1gGGAfRBw4HkQS3AMP+if53/vr+tf55/d361vnU+uj7+P3L/yD+gfuw+rX6Mvze/XP+m/2V+oH3UfcV+V/7avyb++z5Q/eo9an2+Pfw96v1B/Iv8H7wgfGd9Cv3vfbJ9M/yLPPB9KH2j/hE+qX6b/sr/noCegZPCUQKtAnAB0UItwzfDuYQnhRWFT0SERL6EaoTbhSTEicQNwuxB4YGPwcCCNgJDAjaA3sA3/69/ub/9/9I/l76Cvbc9v749/qX/Zr/hvzC+bX5lPpn/YT/z/8C/nL62fiu+nf8+f1g/X37wvk/95j2Sfho+bD4J/as8s7xMPJt8+P2IPe69FLy0vHG88X1d/hi+777P/tn/MD/uQNtBhcIsQd5BiAHRAulDrwRoBVyFS4RkQ9NEDERKhK9Ee0PfwoWB5gGtwapBh8HRgbEAa7+9v3M/k8AWAC9/1z9L/ne+ED7vP19/5D/V/y8+CL4LPlE+678S/1A/AP6Q/mv+uz8M/6d/YL7jPhx9gn3Wvmb+jv6JvlR9wL2zvXM9UT3S/dm9oL1CfUj9zP4KPrs/Gn9Yv20/iUBhQT3BdIFZAUVB/4IWAq7DrYSPhMJEXAQbxB/EWoRoRGrEPULfQcPBT8GPAf2BgkF8AH0/hv9ev2b/in+pPwy+cf2IPff+Jb7Q/0W/ZH6YfiB9yb5LfxE/uP+Bf0m+8n7Nf3F/vH+f/yK+QL3SPV09hv5JfqV+Zz3i/Z+9t/22viE+gL5D/dN9q33nvrJ/Nn+Tv8s/5H/bAKoBY0HOAcDB6sI7AekCeEOrhE+ECkP4A39DaAOrQ/FEKENvwnyBb0EwwV7CDsJFAZKA8sAx/42/3z/8P8g/i/6T/kE+b36G/3J/Qz7nPdR9fDz5/Wd+Pb66Ppr+MH3Sviw+ZH7G/w2+yb4wPVM9vn4tvoW+4P6SPh89tz2Q/kd/FX8Lvuk+Vj4/PlA/HL/BALgAYIBJgLeA+cFbQbkB6UJKwmJCfYMsxDJECoQmQ/yDocNfA00DrQM4gqNB5MFMQVEBh0HRAXRA6QCbQBG/xb/mf8m/+v8FPxN+4H7x/xx/Z37Mvku+Ob2yvbG9335kPkq90j22PZ+9+j3wfdU9yn1jPOL9IL2ffgI+ej3NPbP9Z72aveU+ez6WvqQ+QL7SP6AAHMCVQSRBB4EygQ+BlAHPQeECcUKXQrcDLEQFxJdEB8P2Q7rDUsMeA0VDXMKXAgVBrUFTgYQCOYH8gRjA4cCcgF1AZkBegFV/rH7cvst+xT9kv7m/Vf7lflQ+A/4D/ki+i770PjP9QL2e/bt9qv2lvWu9BnybfHB8rP0tPXa9H3zIvM89Ff1mvfx+cr6o/nL+cz82/7q/xUCswOSBDEEqAXKB8gIEwuKC78M5g5TECwQ8w/sD7oPXA7fDUMPRw00C9wJdghzB4kGeQalBdkCFAJkArICfQJKAj4CfP99/rn+Rv8z//P+/f3A+9T6uvqc+wX8gvuL+vf3bvZT9oT2Nvea9R704fFI8PXw3vFE8zPzVfJz8WvxnfJf9Kj22PfS9/H3DPol/S7/tQE7AzoEfQPIAhIEFQchCvcKow1aEFUQ3g4lD9cPFxD+DpkOjg5oDPkKrwlVCRkJrAfCBVsEUQNaA+MCZQPLA2wCegDW/rv/eQAqASkBIgBa/nH9xP1J/l//Yv8T/jT8/fmW+ef5nPnk+SX4o/XG8pvxTfJ28tDys/El8GjvBPBk8dny1PSF9eX0KfUY+GD7fP1aAH8CSgL9ALkAJAScB6II1wuUDpsONA2rDcIPPBA5D6kOWw67CxoKXQlCCUoJqAczBYICdgGKAn4DXgSXBEQDpwBK/iP/pQDXAboBlQCd/9j+//4NAF4C2QKYANT9+/sb/IT8e/wW/Ir5t/bb80Xye/Mc9LLzrPLv8XDxsPCm8bH0XPbt9W71uvXF9w36efxCADABxP8t/+T/9wMWBiQIegyyDZIMhws8DdgPDBAlDzIPcg3TCrcJiwmFCtgJNgf8BLwCIgKsAlsCAgOPAUX/Jf3N+2z9eP/WAB4ASv6l/Ef8GP3A/gYBOwFD/0/9Mfya/EP9kf35/PP6Y/hl9Wb1fvdl+C73VPTD8sLxEvF+86b2qfbR9Lj0f/ax+BT78P5SAYb/3v3z/QABTQRHB8MLQg2XC9YKYAy1DlIQkxAqEAEONgtRCrYKvQvTC0oJbwVuAusB8QKZA5IEugN7APr82ft//XT/WQD1/0X+FPyZ+5b8U/7i/6j/0f2d+6/6X/yK/af9d/wK+pP3nvTy9BX3w/d49oD0sfO58nfyz/R09zr3vPX99T/3xPjb+xwAFQEN/5/9v/7VAXoE4whpDL8Logl1CacLKQ4hD+APSA8PDNEJTAnBCkYMgwthCKMEqQIdA2AEugV4BgUECgAe/fr8SP+eAIIAJv92/KD6tPqz/Pj+p/8V/jz7Fvmv+eD7If33/Nj6l/gA9sb0sfZy+AL4hfXY87DzvfM19Zr4mfnD9632ofdn+qz8RQAxAtz/YP3E/aIA5QNACLgLfwuxCEkITgqQDEgO2w4rDYIJDwe6BzoKcgyvDEcJtQTSAUcCEwQ7BuEG1AT2AFf9k/0CALgBMAE+/5/8ffqE+jL9+P8ZAG79OPo4+KH4ivuE/ar81Pki9wH1qfSa9rj4mPgK9svzs/L88mb17vgY+kL4x/a295r6A/6kAesCiQAU/hD/8AFOBRsKsAwdC3sIywg2C6cNbBA0EfYNfQnSBs4GiwiACpwKWwc1AxUBmQHSAzUGVgbLAxv/3ft3/Lr/pgJqAvL/cfwH+iL6HP1mABcBc/6Q+l34xvj0+qz8nvw5+mT2zfMw9F/2rPiW+PD1qvN/8kjz7/bI+uL76Pla+IT5+Ptf/0ECjwL3/23+af+XAhcIrgwrDfIJ4AcaCLkJyQyKD5YOogpWBgUEiwWQCBAKggh/BAEBrP/aAHkEnwa8BeABNf2U+1T9yAANAzoCJv/b+6v6ZPx1/7cBAgFu/ZP5U/ib+U/84v21/CD58PQS83f0yfeI+bv4svaK9KLzrvVD+QX8MPsz+ab5Nfv//XIBAwS1AuoAyQBHAhAH6gvTDTAMVgpLCckI0gpbDt8OiQsHB7IDnwP9BZII9Qi0BaUBTv9D/+wBjgRJBeIC3/2B+vP6Uv6cAVICVgDG/Nv5DPoq/fT/bgAN/vz5Lfe29gj51/v1+8D59vUD8yjznPVN+O74s/ef9WHzFfSm+Jb8CP36++b72Pz+/aIA4gMjBE4D3ALmA/oH6QyoD8YOOA22C94Jkwr4DWoPYg1ACWEFDwRRBWUH6QeiBfoB6v63/ff/2QJCBOoCTv8f/CH7Xv19AI4B2/+E/FT5hfj7+qn+fQBP/zH88vid9zX5eftu/OD68vaJ80/yyfPz9Qz3Uveh9e/ys/Kj9iX7wfzl/DD9qf1f/nsAzwKpA90DIAOeAz8Hwws7DnsOGg7GDBMLcgtJDsMPDw6ACq4GmATbBHkGbQcRBj4DMAD8/q4AMAPfBPMDuQD+/NT6LPyt/joAy/84/WX61/hw+pz9QP9n/kv8NPns9gL3PPi4+QX5APaO8k7xVvLh89n1hPfK9hX0h/NR9s75l/uu/K79+f1E/sz/1gHyA3YFRwVtBqIJMQ3NDlQPGBDLDn0MCgxmDTYOlQ3bCy4J3QZbBiwH+wa6BdsD4ABb/+D/cwG9AuYB8v9w/XH7EPzB/ef+9f7r/SX8rfpl+3L9j/69/VL7U/hL9hb2a/e6+B74wPUG86vxBvLu8qv0e/b29arzUPPS9a/43foZ/Q7/q/+H/1YAhAGnA5oFhQaSCMQLFg5oDkQPjRAQEKQOyA6EDyoPmg21C/MJNgifB+MHzgbsBNUChgDW/38AuQEkAj0Bu/+0/aP8yPye/fP91/zz+jD5zfit+Yj7I/xq+7v5KPeS9WH1avYK9wP2/vNm8vvxQ/JH8yb1VPZr9ePzYvTV9vD4WfpI/HP+Kf+r/5wALALnBIEGpQf3CasNKg8WD6cPkxAzEPcO4g//EMMPzg2cDK4KsQlqCawJGAkMB9QDSAHmAHwBoAKEAigBV/+j/ej8qv2C/kL+jfzc+QH4r/fj+Ob6yPtF+1z5tPYo9RD11PUK9vj0uPIH8fDwkPEN89j0hPXu8yzyyPJV9Yv3Ofrf/JD+O/+U/9gA4gGlA2QFXAb6BwUMmg60DxER+BHyEawQyBCSEWYQdw6kDLkK1wmjCT8K4QkvCKcFSQM5AvABbQKLAjQBI/+E/fz84P0f/yn/sv2f+p/3m/Zj96v5Wvt5+6n5svaS9OjzDvXw9fz0sPKz8CDwr/A18lP0/fU59Vnzy/Jl9D32u/jW+6T9C/95/30B5AK9A+8FYgceCNkKOQ8vEMkQqBHrEasR6xDwERUSYxA9DicLIgkUCRMKCQtKCjMHGwOfAPr/VwA6AScBcP9w/UP8Jvxb/YX+pv7n/CT5dva89ST3xvlD+8j63fiv9h/1ZPVB9mD3gPa38xHyL/HY8Ery3vSs9bP0h/My9NH1ZPcq+gz9fv7e/4AAhQL9Av4CBgS7BVsG9QhaDoESjBSBFXAW+BMXFVkUTRVOETQNhAxTDbAMgQ5OD8QIOAaS+QP1X/bp+bH7S/al80/10f2Z/P/7RPtI96z2Wezc6wj1cAEQB2kEsv7q9jH3uu1e7JTp1uhv6iflPulu51rrW+2371/sN/QZBj8TVx2WHfwgsiT3LmM8PlIJNSMT0gC+73L5Iwj5FFIRzgmu5M/RI9ON1wLm8uM15X/n3+t9+GkI6AviAGsD/P+vDq4rtT1NRSktJhlWCLsBGwOkFW0fxQz1+4bnnOik4SjQdsgnx5TFGsk635DyBv1T6tXQbNHG2Kbpw/u5C74UYAvFAqwDNAsHDegZ0h9ELJAzDz6hRIc78T7fPPZPSRKg5fneHtlb6fDmRPXb8KHrvcIKvQTNv9gK6DXjX/4UCdcSVhi/J7sjlAx4GBIaxi8iQThLQj9wFLT5Qe4c6/Hkdfiq+P7qqNdyxbfPU8xTuXSx4cfl1b7pyfr1Cj0OcO5E43r0LQkeFPAc5SAkHoMLvPvrAhIFdwceEoMdKy24Ljs1SzHCLggoYkPbKhTkW+DG3F3u5u638WT0HPnZ2KG/6NRb5OH7RO8n8ScH1BMfEP8YESRZE4wQqw3VFg8vcTvVM4MQdfO57anrjN546v37G/B03IrNnNWL4DPVqcMjylLdfPBZ/VkGIBfXCWHsaOsz//IK+w15D8ENZQp9AET85ftV/0wJoA9tFrEoMTGWNO0mICnSKjhLL1t6DR3siNyP3UveUObc9NIDKOpfwK7Lz9Wa6cTe+OKG+E8MzBSaIJExrB11DjoB6wbzIxQ/YUt4MYEJwvOQ65TXTtoP7zT0M+omzfrRKeWE2Km1IbVPzBfiZPnKByohcRcz9orlyPHwBWsOcRVCE7UUEwib/Xf6KvvQAQUGHRMrIbU2CjmTObUqYCiWMPBO6U7BCWvwo9ZG02/bu+f9+CQC4OVmwqnDFM/Q3q/dwOdI9lYFzRCnHycorxqJDUUGgQn1IgdDCU9+MpEM4voJ7Q7bMN+19S0Cpe790LHZReNZzpC43bwNzGnhUOpc//gSKgOi6qnbR+iI/W4KehN0IDgelhGGBiMDiQaoDOcTLx4MKAM5ujYoLwwoxS0pNbhFYFs8INDraMaDwtXRZN366xL25+arwK+9FMry4mDlLuOX7Y8CyhewIuArCSZmFUwFXgTAHmtGAld3O7ERpfrx77zgNeDD+QEIffH0zUzN6trz0iu7bLCfv17Xtulh/icXNRBY8C/V+N7M/eUSQCF8IQ4d4BHEBBz+oAlCFAoTZBBRF3EwXzR5KMkiVx5oGRkknD2yVPtGqwV20nS9QMS32obul/5N/EzkqMlezFLYU+N34rPdOu8aDJ0haCalIhAS7/7N9bgI+TGISKdD8iWFC6r9W/Hg8XYAxghQ99Phu9T/2pTbGM50wze5hcDZy9zbIe4J+X7tKN4Y4NruZwNaETofxyO+HyQUCBQGGqIccxvYGu8k9S1HLtgpAiovIrIc6hv6MUA+rzFFD7nfB8sIyF7XuefJ+Qn8Ze8p2bDN1dlP5FrqjOz19vMEaxEhFdYTBxC3AWH5Uf8PGGQxDzQWKbAZlwoZ/k8AXwZ2C/gFY/Vd65rkR+Jn2x7P/sQpwXa+Wcs63c7jSuRz3KTaTeDx8XMHexaMHD0aShbqFdoboB9bJf0ooiY5IxohAiFcH90ZOxL8EwIbVB8dKIE0BS57DSbixMyL1SflOvNFAI0Mwwer8pzeZ+GC7hfxTu1C7ST6agJQBCMCLAAc/Dbys+7k++QWVSgeJT4ZGQ+nA/D6rfuDB/oRqwYP8Hjk7eOp45/awNKR0bfQU9Nr373xiv93/v/z9O/59AkAswiKEAMVnRNOEcEP+BMVGXIWAQ5iCE8HfAr9DisRAhJLDH0CS/za+lQAyQIBAhIElwLD/Ob93gN2CJcMqAlKCBoNdxGIErcSHxBlDH4IBgEZ/2b/LPzv+fH5D/zH/nMABP/8/cD7YvkZ+m75Pvpw+xv6Gfi0+E73uPWv9BzxDPDw7hbt8u6l81H1qPYk+bn6fPzK+5b8LwCQAE7+7QD9BcgGoAVcAnABif/L+Pv1WPlW+7P56Pmv+df6afl59CD1R/j7/CADJQgmCxcNYQpjCagNGhBgEncSbxF8D2sNFgtkCzYLlAfMAyQAyADwAQgC5QJZA+IBMgEnAiUELwgHCegHVwifB3EHswYeBOgCAQObAWIAsACFANX9bPiH9h33gPe0+Dr6Hvzf/Hr7LvvJ/bP/L/9+/b/8oftf+Nz1xfVY9r32U/Zn9XH0YfKH70Pt8+zP7yD01fdt+uL7b/tj+0n8Tv1aALYC9AOVBZ0HMAkBC6UKYQjZBgcE1QFrAkkEFQUDBRQDJQHmAJIB/QM/BSsG4AaIBs4G/AZrBjMFigRVAr8AgQHXAiICvACfAC3/kP5d/owAKATyBY4F0QUZCBMJsQiICJ0JLQrLCHMHpAcfB4wEgwDY/n7+tf1Z/IX6mfcD9LDxRvAF8p3zg/T59Nf0O/VG9vL3UvkK+zP8+v0OAEcCEwUIBoMEVgI7ASAB6wB0ALMApwC5/5b/IACKAFwAngABATkBTwIaAtsBoAHT/9L9tPyP/CH9dv3//Fv8bPrl+OD4OPni+Uj64/pG/Kj+GQGCAt4DtwVWB28IsQlgCzQMyAswCwMLqgqBCZIHUgWJA60Bsv+3/o3+Mf7M/O/6u/ln+Rv5T/iK+Av6G/xl/dX+FwGRAuUCxAFFAbICogQQBRYFpQR9A04CEgF3ARsCBwKdAXABRgJsAwcDiwEHAbL/rP2t/Ij71Ptj/Ob6SPlp+Fb3IvaZ9Vj1BPZ+9nf3pvng+1n9x/0X/gj/ggAmAYUCiQSfBT4H2QjtCKwI/QfqBhsGvwSAA7gCmAGWARICRAGo/3/9Mfvs+bv57fkM+5T8Fv55/48AMwFkAQQB2AACAZ8BjgMEBVYFIAX6A6kCcAIVAwUEPgVtBbcE/wQCBfIDUwKHANr+l/3N/N78QP3H/K77JPq3+On30/fj9wv4WviN+ET53vpV/FL9Vf0J/QP+bf+YABICBQOQA00EaQQVBf4FLAbABSUF+AQPBdwEjwR6BEEDtAEHAPv+PP8X/8X+YP7W/YX9NP4t/w8AOwAG/z3+j/4e/4X/1v+e/9X/SwBnAOcAogHCARsB+AAxAicEegRIAzACVgGTAFcArACnAGv/Ov2q+3v75fvc+8X7ifsx+4b6tfna+Tb7VfyS/Eb9O/68/gL/Mv+l/4gARQEGAhoDvQNsA/wChAJoAmMCggIfAygDnQIEAroBVgH7AI8Avv9D/37/GQDgALgB1AENAX8AGADG/x0AUgBXAJAAkwCYAL4AlQAMAOP/7P9yAHkBCgKTApECxwHQACEAvP8sAKoAtQDQAFMAyv8C/9P95Py9/I38rvyf/Tn+SP58/a78svwx/cL9vP51ADoBOQErAVoBXQG2ABMApf/n/1EAKwG3AacB2ACp/7r+YP7x/pr/OgBEABcAvf/G/z4A+QBgAS4BFQHRAOsA8wDXAL8AWQD+/9v/CgA3ACsAZ/+Z/nD+bP4+/4AAdAGAAeMAPwDP/9T/QAAEAVcBQQHNABcA2f/O/2r/Nf9S/3n/zP/l/6X/JP9y/jD+r/6A/1cA3AClACoAvP9j/3T/0f/6/83/8f/I/43/Ov8S/33/lv+2//L/egCMADsAif8J/27/9P+6ANwB0AK1Ai0CuAE/AekA6AAkAXABoAFFAZIAyP/c/uT9VP1y/en9mv4R/wr/zP5S/h/+JP56/i3/6P9yAJ0AjQBUAFcAeQCPAMUAeQHSAYkBSgHsALAAHgAIAKgADgFEAWMBbQEtAcUAHQC4/4//bv+O/5j/nv98/9/+Zf4v/gH+R/6r/t7+2P7w/hT/O/91/03/nv/4/w8AVgC7AB4BIgEcAUYBcwFiAVsB/gBTAIL/cv4k/gb+2v0y/qf+Uv+k/2P/Q/+H/6b/7P9mAO0AlwEfAkICngEjAeMAAgH/AO0ARQGDAdQB4wGcAVoBCQHnABEB3QC4AIYAIgDi/37/AP8J/zj/Yv+l/5j/pP+S/0//WP8Z/1P+F/4e/rv+TP8Z/xn/1/7v/m3/3/9HAJYAaAANAGb/Zf5d/rn+Uv/c/+7/3/+0/3X/NP8e/w//+/4U/1v/vv/t/7f/tv+e/3f/n//w/4EAGQFLATQB8wBRAAgASADbAGkBrQGkAW4BbgFiAYUBtgGyAWYBHgEBARkBbQFRAT8BIwEaAf8AvwCBADAA0P9X/4P/y/8WAEcAIwC4/0b/5/7m/k//Zf90/5X/k/+E/3z/NP/R/pP+Vf50/gf/ev+e/8D/LP+7/uX+BP9+/6b/uP/G/7z/wf/a/6L/Uf8Y/0L/yf/4//3/wv96/wL/yv6T/n7+t/72/mX/pf///4kAtwCEAIYAnADnAKcBTQJJAoAB/QD5AAMBBQH2AAQBCQHxAC4AsP/T/zMAjwCGAIMAZQA/ADgAPwBKAEEA5//o/w4AIAA0ABgAIgAFAPz/OgCVAOMA5gCpAK0AywBVAIEATwF4AXMBFgG8AKMARAC1/1T/O/9j/3P/9/5t/lD+Qv4J/vz9Fv5Z/lr+Qf5X/pP+tf7h/jD/d//O/+z/1P+Y/3//XP+g/83/QgD1AMgAdwAjAOr/0//m/0EAjAB2AKMAowBpAGoAWwAXAOH/5P/o/yEASQBpAF4APAANAAoAUgCTAKcAqAB7AEUAIAAJAF8AnAAVATYBFQHxAHQAeQCTADkAIQAjAN//9v8+AGUASgBBAFAAOQA6ADEAFQDV/6X/vP/k/zAATwAYAAEA7v+u/13/SP90/6X/uf/K/9j/2v/g/7P/dP9O/0//V/9+/7v/z/+t/3X/Z/9d/2v/qv/V/+r/DwAqADMAEwDo/9//4v/2/z8AegCLAI8AagBCACkABwD8//H///8SAAgABgAAABcALwAUAN3/1v/U/8r/0P8FACEAGgACANb/3//H/6z/h/9n/zj/C//3/hD/Uv+J/2f/Jv8Q/x7/I/8u/3f/CgA7APT/6v++/53/h/+6/wcAIgBcALgANQGDAXwBigFOAegA4QDGAMkAwQCNAGsAbACzAAEBtgGrAjMDTQMZA7ACZAItAgwC7wH3AdoBBQIHAosBXQEEAfsAGQF2AYUBUwGYAHcAs/9a/tD9VP0Z/mL+EP3e++X7Ofyu/O/83/zp+rr3hfU99q/3pvdq+VX6e/uF/Kv61PqP+/H9M/91/y0BaQBQAXICmAHUAXMCSgOEBFQFtAbJBRgE5AK/AegBzQKtAxwFlQbCBo0G5QXnBqgHigfZB9gHSwihCBYIaAYdBdEDewLeAYQB6wA1ADH/Uf22+yH6Tfpy+6P7wfvZ+c/31PZS90j4WvkV+V/4JfjR+OX5Qvfi+Or4afkJ+fP48/vA+mH9uf5x/8AAZP8xAKEA0gANAhsACQEFA1YEOAbTBscHIwgXCkwLEw3oDIkHrgKp/xEAKALfBHAIJgl4CPEFVgOlA9EDEgWaA1kCnwKKA3EE/wGbAkkB9f4I/bn7FP6I/yL+k/pr+Rn5Ufmm+OT4oPqm9N/vJu2p7TbxaPKC9aTy3fHr8ivyh/P49JH4//UY9R74e/pF/hb+/f1l/R4AlwNYBjkK+gw6DZEKOQ1bEXgW2Rv1IKgozCybKokjkRjPEE0LbgcJBOYE4AYGAtT7LvZ48u7tPunk5Rzng+ux7tvy/PUy+DD6nfdt+cMAhApuEfIStBEJDdAMEglxB4oIAQdxBK78Fvpn9ZnwP+cu2DDTmdEF1yHctt8/4jvgAt8Q3lrkmO+i+i//YwLJCa8PiRIqEcgUYBgpGbsb5x0QJJQmlyepI2AmAyuNLxQ2oDF1LWwiOhM1+2DrdOUu4sfl8OKD4pnjNuGJ3HzXgtlq4EzqWPAP+5IJeBD7ErER6hQpGhkh7SLDJI4qIyhFHqIO+wIK/5L2fu086J7mKOFy0UnHxMT5xS3FBMTex0DTYt5j4Yvow/Ph/HwA0gTeEDkdmSBvHesd7h0aG1YYbxbyF44aAxzGGTAbtRxdITshWh/eI1soZSueIFIZdwzv+L/kX9Z/2pjl4uw/6IDokudM4Wrf6N9H6lD0I/uLBO4RERn0FV0TWw/iFMUciCPlKOUpWyQDFVIEH/MR7jfpReJR3fTdIt8D1bfJa8MRxpPFuMv+2MvpN/e09aj25fuJAmoG4Qr1FYgfHh/XF74WJBJmCwYGgQVFCZYLDg42DScPkA0sEX8TShnrHr4kgC+oORw58CoBHVL2g9qp17DmrPpu//r63O5H4drM7sxI2sLjKfGK+P8GVBMkEbkHygXFBy0OCyQANuVBkTn+JUoPOfi+7Ojt+O6X7ZDuvOI92JzJrr3Tu+m758Ll1EfnYfUlAD35S/cH+Mz50AdvGRYn+iftItgZPRGEB6wC2QKfAX8EWwWfAK37Kvk39kf4qv/GCj8a4B3XIVwoTytXPW1AojjoItP56+Fx6N/6IwJIAdftVOCU1JfJBdSI3UvfteJY7Lj7HQ1JD4sKVQurCg4ayzFwQKxFKjv8I+kL4vzK9177BfJc6cXm1Nz00/7JWsAxuSK0l7rk2M3xtgA1BLz98/mb9VX9HBB+I2UngicYJH8bJQ9KAk78vfqi+Fj3Q/nZ8sHqT+Ht4qbsmPv3AhIRjxz6KNIxRzADOFNFB1UnUNc/WAsf7Rfm3/Ds/nn48+nQ2eLP3rx7xEXMMNCv1UrdTfZ2EFEXPBRzE+QTGSAfNJRDQ1JSRlstZxTYArr7wPdT7nXlo+WR1T7Lu79jt0a1pLOlviTfYPfq/tcDSQLRCQwM6wnwFHgdYRrMGLcVxxIsC6f7avNS8dHt2uzP6sDkSd9b3gjjQfIZA1QOLxWSGb4jRi7/Mu43RkFgTFRoDnSoZW8s19mztwPFfuX5+PP9FOsyzTiplJ4Sua7JldP74+oA0R1EKeoeVxNoC/IKRiXTSq5oYWi1Q0kUcfAd4SThzOSO59LtZuZZz0C8FLN3rcSqr7jr3ScFRBIgE+URUArKBFII1B2ZMXMyGiKCE4ILwfnG53LcvN0X4g3mVuob51/f3dIPzzPeXPpZE94f/SI2I+MoaycqMPA4fkiKUelc+1/xYrhmTUSaDW6u/ZVXtTjmpPgg6+XQ0qiFjQ6ASrCo2c3vQf7XEo8lDCVSFO4JZxfzJ1NHgWoiddtmGDf6/RvZM9bl5VL0RPPA7C3fobhvoD6fpq8Qv6XS3fDuEMASMwKR/4ICmQ1DHUI2REZZQKUmKxDo/eXqHeBf3nvqau/76aTggtUTyu3GqdUY7CsJByG+JSkd9RILEaEYRyP9OXtMwFfMVbxYF1FoR69QgUO2CxyrcZSXtsbgVOL+yom4x5gVgDuK/sZv7Qzy4e+/BmIjwCnZItEpwzhwQoNWbG9/dolhZTHgALro7+qK84DujeTz3aTH9aEVlTij2rMYucTJt+38BN39DvZNAlgRZh6IL7RI0VZjQJUfzBcGEgL+c+xX5kjl5uHK2lPWnM+fwiHA9c+p7AwLShlzE0YKEQtME0ki2jGUQYhKOUkASEtNs1epTKNO6EhUKGbM9IMQlCi9etXGw9m7s64Tlv+BNqr04pH3Qv1HDCwx00JdOTA3pEhSU/ha8mhYdS9uqEQ/DlXqS93f3q3aCNJezfnAoZ9CipqTZql8tke/BN17/ZUFswCGCnscsizhN2NF1V2XXyI/sBqgDxIHd/cP6svjD98PzNvEh8HIvfe/YsLu1Njz2RH9FmYN+wz8E5UcQiQpOThF3EXBP29AcEDlTDdNgUcTQzIeYNOWhzWN1rPY1j7T186/xsaoW5VLrdbj/APZDykcZD6oSko+xDUEQPRGBUYDT29jR1+/NlMERODyznHH8sVFyPPMc8iftxmm+aTMsKm8O8bm4CQAzBGPEkgWbSQeMdAyaTUfSl1XKkptJrEPMQKx61HTzdGo2xrRecC3u23BGcH0w+rTAvFaDcwXKxbFFukapRlQGtkkPTiuPa859jiXO5NAdD1fOPI42zuQJIjcQJk6mmC+kdpa37Dhb9nEtA+d9L579sYNRQowE2g4hD9kLiYpGC7KKn4kMDR8VuVWiivEAU/ni9jQzh/SVt+46hDn09TQwji5CrrkvefFVdyk+54MXgn8Bl0OnROvETUdWDyxS8k99yDADQYGTO5m36/kP+lC4CrOPNEq2iDTV8051oXr3gDyCbUPmhCCDAAHZQknFbEhwCU8I8wqITZmOVE3uzyhP+FClzvsIsHp6LrutlzWqv9wAxDsFsVUs7K7YNEG4cPwQPsHBggNqA+PFvoVBRGhEDIgdjjtSmJGHTbWFsr2Xuqt97UHIgqaAwr8YuzbzTa7+b8FyfDGRchw2XDrGO6G60/uw/Nx+qwKGSEtMZArESISFm0K7AW/AqQB9fxG98zzQPSF8Czs0OHw29femuqP/asEsv+c+bz4DPjH/m0HjBPHHYsk4zH4Nk9Ac0XiRvNSbkdbGJbsVM3J4x4ZqB98CbTkXsZYwJHBrszJ47vgCtnI22rutgWj/FD0YfiK+pYLpSq/QwRKLzNQFNsKXRIGGJgc9iI+H7sQi/6t683iatv1zGS/U8Byy4rag+S64AbZUdfP1iLeyfQIB+4OWwmmCdQKZA1kCd0E8g/0EpIRrRHOFGQSFwUR98f4lP/9ANP+bwA8Buf/3/rN+7H/MQPRBWYMJRrZJ/0xEUHWMQIO+eA10Jv1Ch2UMhwuyxdo/vvrSuFt65bydu3d5rbkL/YQBMAE0vf54crSEdkE9JUZ8ilBIAsMDvnW994A+hBbGkkXPQ+GCNUIHxCGDbb9ueuk3VPile7a9DD6VPXE5wTgqt5c5yH0RPK+6jHrC+229ZT7pwQGCmUDpf5mAAMNHBOeEpUTRxjQFX4OtgTo/qH/zfw7/dcBiQTIAGv8+PhD+BL0tPHY9pT8GQJFC4oRNw/8BmH5jvv3BKgOdxmZGJYW+BVbE3UTPBekE24NrgWk+8P5zfrW/g4CvgB0/jb52/N28X/wpfHy8NTvDvTY+6UBCwOtAhv9X/fV89/0LP53BDgE/AJ5ACL8LPuD/psE0AYAAd/3n/PJ9aD6hwC+A0UAMPse+bv56v5Y/lz4JfU+9Oz0zviw/nYD9gGt+vz0NfZ8+Z36t/lV+kL8rPq2+CH6Pv8L//MBLgDMAYADGgQDCo0K7Q34DYwTFROXEwIVuA9DCJIDIQmiDKMStxEwElgPwQfmBBAFtAkkBjECkvwI+g75Tvo2AaoFFQRN/SH3EfOd80Xzw/TB9B/1tPX1+BH/wwL1AX/6T/Wy9Pr4A/2Z/4MAGAHUAeUCHAT8BHkCeP0q/N386f/1APIAgf/m/Wz6J/a69I7zGvU69WX0FfXc9Gj1QPlN+1f8a/zn+cP4H/qH++f8ogC8Aj0DPwPFAYUEvQhMCogMKgkWCGwIaQWyCuMM2Qp1C98IfQtmCeUCSAF/AQYHDgbAB0kKTAxmDA4JqwYHCAgJXAQiA6D+Sf6X+7793QLvBQUH7wD+/Ef7KvqK9tD1ePU39jj3HffL+aj9Ev5O/G34NPYX9136egG7B+AITwPF/Fr6N/xX+676Fvxl+4n6c/ow+oD7mPrY9+P2FPZE+Kb6cvoy+/b3h/U79v76UwM6B70GogP3A90CWwMFAlQEBQWv/wb9KPsAAdYCMAE0Aub/2/tp+1YAhwZuBloDUgXsCcsQBBCYCLoBTf88BnIMPxJkFw8aORfgDQYHtwjrDRML/ALw/+sBPQMV/6v/xgE0/ITx1+gV7W70R/kj+WP3oPVo70nsZe9o9/b6tPnD9qL3Uvwc/nsAO/96/cf6nPjE/XwG6gy6C4kFZgC//Sb9l/xXAUkF/gRzAhj+uwAd/8T5N/LB74bzffX4+vz9gf+a+WTyuvLU+AsA2wCIABj/kv4s/A/9sQIXBjwHRQXkBkgJnwxFD3sRMRNoF7kYIhxpEv8IgABK/qoOfBG0F8cUuxBlBzL9d/r8+1cCkvxp+Pv11/Oa9oLxN/MV8+PtVOp/6x31Lfr3/CL1cvI98MTuWfYB/78J+QksBGMB/QCDA2ICHwbTB/gITwRyAMUG4QjDB3YAHv1u/HH60Pqc/uUFkwST/d31XfJW8qTssO2M8kr3a/lj99z6gP5a/dD4v/fu/NABqAQTBQIJ2AnZAmf8+P4FBRYK7QsoDnUSDxRWFNoVJhhwDiwGgftV/q0KBxENF98VzhAgA8z37vSY+icCJQHX/r79IvrT+Dr1BfYq9sDxbuwj7w75I/7x/wD71fcm9Bnx7/Ry/TQHqggkBSkCYwDR/t39JwAFAiwDfAAuAE8F7wetBc3/YPsJ+df2g/i6/zkIfQjBAg377PQy82LxLfaf/On+XP3M+rL7h/2N/qD8L/uQ+jL7jP7IAcsFLQQl/hX58viq/Cz/dgM3BYUHTgcQCAIK5w0RD5wIdgRc//EEIwpsDZIR1BPEEUYJPgOiAMoDWwbdBKEEYgSQATEAyv0B/6L9gPlr9oX3PvsZ/a3+E/6N/ST63/Y092j7gwD9ACH/EP0W+if5Yfnj+hP8B/x4+k76tfxo/ZT+yf5c/i79r/vv/DAABwQcBWgEfAHJ/en7cPo0/U8AQAHQACb/Cv8j/5X+G/z3+Qz4zvbY9mP3C/l2+eX4pvfd91z4Rfp5/Cv/RQLxA6EEGAY0CTkMzQ0ECsEIOQe8CVEO7xGCFooX5BQRDn4Jxwf9CDUJ+AbZBAoEagJRAV4ApAH7ADv9lfkv+Dr6vvxM/nb9WP2w+nr24vRw92P7W/3J+5/6qPpa+hL5QPkj+7b73/no98752fxT/53/EQCw/2f9mftX/HD/7AIxAzoB2f+I/Y76hvi6+aH89f2C/Ej7JPux+kj6Mvnm+Dz5Lfjg96359/p8+7/7wvuB/LH8EP4bAHEDGQZ3B1YI8wrKDfoP6A9VDGELTQgXC7IOgRGlFIkT5A4LCYgGlwVnCFMKBgmfB/wFaAFeAFgAygCO//T8W/s4+/T80/0r//f8o/pg90r0zvTd9xH7Bf02/D36vfel9j33avmm/HD98vsF+sT6j/r1+ij7H/t2+3b6m/k6+z3+agBDADz+FPzD+pP57vmT/DL9KP0U+z36kvq8+8r7lvtc/Pv8T/2J/eX+Xf+0/4D+h/00/Vj/4ADRAnkE0geYCWcJYwneC0QNTAkGCbcIdQ7+DoMOTxHJEOEMHAgbCNAInAtwDEoLqAlGCPgCnQBFATMDDAKT/jj9vPtG/Iv7HPwz/OP7fPhC9dX1uPgU+yn7NPpe+fb3ivaO9tX3zvpj+yf64/hx+vD6V/qz+Qv5MfmX+Pb4kPuw/38ALv+U/Ej6Gfni+AD7mf0K/57+Dv1T+6P6d/tq/KH8O/3n/X79Kv2G/kD/uP71/dD9/P4o/53/pQKtBzIJdggACnUOUwyyCLIJowuzD4cNJxAaE80RWw3tCAsJjQiiCnQLEQpzCJoGkAPgAVsAPwA1/xX+yvwu+1L8Bv5L/uH7HfsF+pH4X/jZ+Wz7mPsG+sb32va597L4hPkX+8/6DvkP+Cn5QPpd+sj5afoY+t34h/kD/NL9kv0H/Q77oPlb+SP6PPtu/D/9n/wM+6z6nvu2/Gb8Zvvh+2H82PzP/Z//cAB2//T91v2e/9sCjQTCBREIDwtPDB8NtgvEDJUOLQ0vEOwQahL4EhIRBQ3tCkAKEgjaCEoJpgiAB7EFhAJCAWUA9P+2/pb9R/yl+6X8YfyB+1/6Ifr1+On30PdA+mr8svvF+Wr4Efha+Lr4e/my+n/6Mfnm+L351/n9+X/5HfnB+IX49fga+wf9ff0v/fz7Cfqi+Nf5EfsN++76b/r9+Xn6vPr2+zn8F/zb/P/8M/3w/uQA2wHEAYsA8f+MAVoE8gbOCbALLwtdCxMO4g2mDXUMdQ9qEukPQBAIEUEPtQquCNcHZwj5CBAIeQfVBgIF9wGJANEAp//Y/bX8T/tI+x77Dvty+rP51fgV+JT42Pnn+6n8r/s1+jb5rvgH+XX6yvvB+yn6S/gF+LD41/nt+UL5gPm3+Xv50/kI/C79UfxX+nz5cPl6+d/5dvqz+1v7l/n1+Ar6hfs1/Ar8+/s8/Fr8ofy3/nQAEQEGATQATABUAuMEHQf6Cf8LlwzxDIsNEQ7JDgIPYRCVEWMRRxJTEnkPqAujCUQI6wfQCAgJQQi9BoEEzwH9ABQBAgCh/nb8bPqi+Rn6O/vN+wH7Zfn69x33SPiY+sP7b/sw+nH4kPf69+n4oPmb+X74Kfec9vT2CPlt+pr6UPq1+WH5UPrO/Pj9JP5n/QX8MfqG+OP4bvo9+7D6T/rx+JL4AfpT+9v7wPtx+037j/tX/Hb+SAC3Ad0BsQFvAjwEOgaLCXkMCA5KD38QSRIDErwRUxF8EvoSzxKkE0MTuA/WCkwJwAdGB9kI4AjsBsgE8QFF//D+c/+E/vD8Fvus+GX3dvhz+Vj5D/lM9zX12PSt9gP5n/r7+on53Pe09u72Y/g4+gH7g/lF+NL3OPie+Rb7JPwS/FX7bPpt+7D9Zv7k/uD9Dvv49zr3VvhO+Uv62/qb+u34KPhR+fn6hPu6+8D7wvtj+8j8Ov90AZ0CcwJ7AlkDkQSfBlcKwQzMDdYNZg+DEMQRXhFAEQUSmBGkEWgStxJSEAgNAgowCO0GJgiACQQJtAa9A2AAbf4w/pT+WP4P/Rr7zvjz9//3RPhR+Pz3+fXT9L71/vcJ+uj6sfqZ+fD3Cvde+HT6WvsA+/f51fh7+GL5/PqH/Cb90fum+v/6I/w5/Wv+lf5T/Bn5ivdz+Mv4yPlJ+3r6P/j+9p332vi8+mf7RPtS+yj7nPu//eAAUwIwAmwBywGQAogEugdtCo8L+QqFC/IN/RCOEhQT4BEUEBYP1A7PD+wR2xF0DiwKewd0BqkHgAqzCukIfAaOAl7/KwDRAWEB1P5w+zT4ufYz93j4y/mF+Qv3L/SK8+71Lvka+9P7yvox+L/2+/da+lL8KPxq+i/40vaF97n5Fvxi/CH7r/kT+d35K/u9/Bb9Kfua+Mb2DfcV+Uf6Hvrh+Sn5gvd19/T5QfwG/af8kftT+1j8vv6fARsD4AKnAT8BHAOuBSQIRwoiC2ILTAtNDIoPohIPFDUTfxDJDtgNHg8uEYISoREJDoAKgAhfCDgJ/Ao1CioHmwNdAJ/+B/9QAPX/qv1k+oT3rfbz9yT5WvnH9yH1b/P/8tH0q/eM+Yn5nve59UT1h/b9+A/7efrZ+Ar3BPYA93z5zPvm+5D6ffnE+Tn76vyN/WX9lfu8+Mv2MPff+Zj7u/vh+y37XPns+UX9Sf80/x//lf7Q/bf+4wAyApMCtAKEAn0CTAOYBR0JMAslCq8IGgt8Dk4OHQ8QEgcTsQ84DEEMfw6yD6IO/g4YD2wMfwkDCTsK6grlCYsGzgKoAMr/3v/H/9j+qfx8+q/4cPfw9435KPqO+EH21PNB81X0EfbI9+v38PbE9LnzqfVf+O/5l/rH+an3JPYT9kf4c/r++tj6Y/rO+Rn5Q/oF/Gf8gPuw+V/5JPq3+sb70vyO/O77ofvT++j8hv7B/9H/Gv8D//r/mwCAAecB9AKNA8cDsQQ3BnwHhQekCT8LkgrQCBcLiQ7RDs8N+Q0EEB0PsQ2SDr4QBhHUDg0MxgvjC/MIowfeCBoJ2AYNBLwBNgBn/xL/Zf78/GX7YPml+C353vmC+Xf49PYV9Wz0xfTB9fH1IfaC9jX2APZx9sf3KfmA+XP4Pff79mX3ffjq+Wj50/cC93/3XPix+AD5Z/mA+b/4tfgZ+if87/zt/Aj9OP10/YH+QgBrAJQAHAERAfQAFQI9AwYE5AP4AnMDDQQLBcsFNQdlCKMHMga4BkYJIAorCx8LzAtIDGwLkQtjC/cMJg2KDK8M+QvpCsIKvgqiCXoICwjrBg4FhgSZAyYCFQEKAFT+avyG/Az9Dv3f/PL7uvpr+ef3Fvdo+FP5t/iQ9yb3Dfed95j4FPkf+U342fdE91P3vPeD+Ob4hfjd99H3rPgi+Wn5kPn5+br5SflZ+X36X/st+/X6VfxI/Ur9cv2P/Yz+C/+5/1T/2v9xAHsAMQA4AKIB0wARAMwCpQT8ApoBEgTrB3EFoALYBCgJCgnmBZsHmwn8CMUHgweTCbYKQAlZCKsIxQkICXsIXQmZCaEIoQdnBxUH6wYvBrIFbwRRA0YCiQHBAfEAsf+J/kP+gv2C/F38OPyw+8n6uPoa+x/7V/v/++H7Avud+vb6aPsc+wP7a/uZ+z77u/q5+uL6LPvk+hL6EPqQ+mv6Avpa+p768PkK+Q75zPlh+mT6p/rc+hz7N/vn+yX8vvym/tX9uvyz/Wf/MP8h/qv/jwHSABkA3QA9AscCTQKFAr0CVwMEBOYEnQX1BTkFUwTlBFAEmgTNBT0GsAXPBMwF+QWHBtUGEgYDB58GNQW3BNcE3wSCBBIF6gSKA2MDagSQBBMERgOqAqcCMwK0Ad4BNwKfAbcA4/8X/5n+2/7k/qP+xv5h/v396/34/VX9cfyw/PD8UPwr/Dj8T/xF/Lv70ft+/Ln8J/zI++/7Cvzb+8D72Ptq+3/7gvx3/B/8Sfyg/Jv8NvxJ/Gv8h/yp/Kn8Vfxf/KT8gvxT/Jb8Yf1W/o/+ff6H/gv/jv+D/0YARgHAAfMBeQHGAEQBoQFVAvsB4gGUAm8CYAIqAvAC+QP8A4EC3QC/ACsBKQEHAtABigAnALgA8QEDA+ADaAT0A7ECqQDMAK0BsgGWAckAGQGPAFEBmAI+Ax0EcQRJBEMESQS6A4sDJgNTArkB1gEkAosCtALoAr4CugIEA5wCiAJLAoABlQCB/xf/Lv8Q/33+1/2//R3+jv6x/nv+Tv79/Uj9yPx0/Jr8Wv0u/rL9jfxI/LT8i/3e/fL98f3K/Z79Jv3m/Ab9K/2N/RX+L/5L/kn+Pf6q/r/+mf6N/m/+a/55/j/+Bf4E/g/+Yv7v/nX/uv+8/8v/9/8RAAIA4v8eAAgAxf+c/6j/7f9DAJoAMAAPAHgAuQC+AMsAdQESArcBewGlAfUBGgKvAbsBrQEyATEB/ADiAHIB0AHPAWgBBgGgABgAuf+w/7//zf/2/wAAHQARADgAbQB5AGsAdwArAdMBVAJcAgIC9wF3Ab4AkQB0AIoAyADxAIABjwFrAdABPAIUAnYBCAHKANkA1QBoABAATABmAE8ABgCl/5n/ev8s/yb/LP8M/6r+cv7H/uz+z/7I/kH/c/9U/w//1v69/lH+IP4H/hH+MP4o/v392P32/Sz+f/6A/nP+qP5y/hv+A/4b/i/+Pv6l/ub+4v4P/0j/a/+y//T/xv+p/53/bf+P/8r/8P8kAFQASgBBAEAANQBZAGgAqADMAMkA1QDYAPUA9QD3AOIA2wDSAL4A4wD6AA8B9ADVAOwACgFIAXMBiQGIAZ4BnQGtAc4B4gHYAbQBxwG7Aa4BpgGaAYIBRwElAd8AowBnABUA2v+4/7r/Yf90/6v/sf+z/7H/mP9g/2f/kv+W/47/EgA9AAoA3v8IACIA2v+V/1z/a/+b/5D/cv9y/5P/if9I/w7/G/+K/4r/Xv9F/yb/5/7h/h3/Vf+E/1b/If/6/tX+qP6W/qP+r/7b/gv/Ov9V/0z/Of82/0H/L/8y/1D/X/9o/4r/pv+//7b/uf/B/6L/sf/k/xYAHwAaADwAcwCEAIgAkwCeAJcA+QBCAQ4B4ADBAN4A6gDVAL0AsACOAGEAMwAsAGkADAFAAdkAnwBgACgAKwAqAGwAXQHgAfoB/AEQAtAB2gAHAMn/AgAYAPH/9P9PAIcANgDd/9P/i/9V/zj/5/61/pT+yP4X/zj/PP9n/6P/xv98/8v+sf7W/hP/lP/z//z/CgA2AFwAiQBfACAA0v+W/4//vv8hAOf/qf+g/8n/GwAxAEwAiADKAMkA2AClAIMAlACgAJgAhAClAKoAswCXAIAAbQBXACgAEgA5ACUABQAYAEIAJAAOAAEA/v/8/+3/5//0/wIA9//0//f/2/+W/43/gf97/2r/of+j/3r/nv+F/3//LP8s/zH/H/9x/4f/rf/B/+H/7//f/97/y////9//kv94/43/e/9w/7//FwBkABMAYP88/63/RP9Y/9b/nf+A/4f/AAAiAOj/9f9dAMMAPwDd/+n/+v8fAEUARgABAPH/kQAdASUA7/+8APsARgBJ/+P/fwCAAGMAegDbAKoAmAAMAVkBUAEbASsBEwG+ALAAMgAYAKUAlgAPAN///f9JAJwAcgByAHMAWAAOAKT/l/9W/yP/Vf/W/1MAdQBBAFMAjQCHAEQA7f+8/8L/4/8jAMb/L/8t/zL/IP/o/vH+B/8t/1f/UP9Z/3H/k/9t/+H+4f7t/in/X/8a/0r/Qv9M/6H/0P/s/+X/0f+4/1H/B//L/uD+//5z/xwAUwCNAHwAhQB4AFIASAAuAOX/GwB6/9j/bwB/AJ4ALwC5AIcA+gAsAdcA0gAyAUoBfAHqAAAAtf+a/+T/LgCvAH8AJADg/4wAUQBTAKH/5v/fAKIAnAAw/8L/RADg/08ABAAZACYAtwAeAcoAkgCs/swANgHRAKEBmP/3/23/JQDQ/zr/KACIAnoBDgJQAYgDDQIVATIDx/9kBHf+PQAuAJf88P6K/KT9DQNaApsAyv9p/HH+pQBh/4j84fyo/5j8j/kD+sn9gQEJ+5T88Pp0AFMBj/u6/Pr9TwCH/sr/o/5KAe7/+vor/yQCtwAuAqr9Dv3zADf/qQAj/vn/ggO1AGr/8P8pAG0B/gACApcCNgG3AZr/oQLnAXgCSgPiAtMDAAPkAv0BQQPoAMcB1wLLAkMETQL0AY8CDgGTAlEB7wOMAoACUgSUAHoBgQBAAXQBJwJDAbL/3ACOANL+wQF9/ef/PP/2/wv/sfsH/yX70/3J/Gj7Q/0d/NL7Ofzc+bj8Avqh/nX7wf0R/Br7Kf6j+1v/JPyS/Cf92/+K+wIBMfqx/tz9pP4DAmv8LQDo/eT+nAEHAGH/WQIe/yMCL/5XAuT+YgG5AIsDegOuANoDPv5CBtADsgNiAloBZAKOBd0CBgbPArgEowaSAwcI/QG6BZoDUgIaBFsFFQSXBm0DuAHNAdAA8QI/AboBfwK9/w0A9/1q/Wj/yP2I/xj9QP6h/e39Dfwn/Qn80/zE/dn8H/6y/SP8Xft+/D394v5S/df9yvux+pz5QPtC/CH8HP3i+w/9ef0+/RT81/sD/Vr9JP4p/Jr8dfuu/Br8Nvxs/j39Xf++/c38A/0a/pj9ev2O/hkAJACBAQ8Aw/7aAmUDHQSPAyoC3QTPApcEVwV/BUMHzwV9BScF/wgrCgMLsAmaCRUJxAiWB1II9Qc3CQQJaggdCEkGWgfPBToGNwXUAzEFiAM8AYkAnv3O/ZX9Nf5J/Pj7hfpo+hD6rvnY+b34lvpR+Zn5TPlI+cb4XfhP+IX4zvg9+kD61/iL+W74UvgG+vf4lfgs+Tz4KPc+9ib3nfaL+BT46PZq+on5UPoO+6H40vol+wr9KP6V/d7+d/0mAP/+CgJqAqAFuQVrBfQHUgfWCU8IQQsbC30MQQ7BDrgPrRHdD6oSPxG1D9sPTg5TD68PZhB8DX0MYAj6BjMHaQc7CF4ITgNCAMv7Ivok+2X7R/se+RH4m/SH9IXz/fSy9Fr0APUj9Rz1+PZh9Qf1pvZC9r/5TfoA++v5D/p3+YT7cPzt+3v8C/sh+GP2TPiH9or43vj89bP1VfRO9Sn3lPdx+CT4Ffj1+NX5G/tj/Pr9p//9AM4BJAPuBEQHcQnkCY4N9w+mEdcSmRPlFtsVdBNMFScWDxa0FkMSlhC6ELoRzhGQEV4QnQziCMgFaQW6BXoEvf7R+1T6Yfhg+BL2jvSc9IzxyO9E8DLxd/HG7+fvHPGS8jD0QfQk9Rf1I/S99AX2Kflp+jn6j/gu+Yb7lfv0/FD8I/x8+EX4jfmW+Bj8u/la98b1mvXx9/L5VPui+sz5xfij+bP6Hv/dAN0BTQPHAtMEpwciCbIMkA/LEIUT+RMYF5QZIRrtF1sX2hZRFXATUBSwFYIXghX/D6YOywxuDJgK3QjzCNIEa/9a/IH5dPpD+Oz2x/U/9KnxH+/s7XruQfDX7uXt1e3174nvi++e7xLyXPPx8r/0QvYg+Ab3y/VM97v5Xvsj/AX9ovwG/Nf5nPea+Ub6wvvW+3b6xvhh9nX2tPi1/DH/k/9i/VH7efv9/f4BcwSXBowGmgWHBhoIawu2D1MR5hCVEswTdhbWF04VtBQ5EZsLLw1FEYsUdxYbE70MiAeSA0UFigmxC/cLWQZ2/nT6t/nu+RD89PzP+9f5tPU18y30efTS9JDz0PKJ9XL2LPaz9hX1+fIj8g7zWPcV/N/8kPrL95T1gfZM+fb87/9A/8D7b/mJ+Jf4RvlI+VX5Dvnl+JP5lvwv/zD/Fv31+1D9fwMEBTEF0QX/ANwAawEXA68DqQTuBHcFbgXHBOIFiAO/BYMG9QV4BjsFbQfXB2IF/gJcA7kEhQWTB2kIkAmJCa4HfweUB2sHgwhGB3AFZAYXBQUE8QR3AwQCNwFe/xMAbwFPAUUBAP/f/dz9n/ux/P3+1/6x/kb7svf891r43/iF+t35Afk2+G728PdY+k76bPoq+hn5Svqg+v/6afxb/AT72/kw+j771f2b/mv9ov2I/TT+rv7J/nD/nv7m/IP8mfzA/Uf+jf33/PP70vyY+//7gv36/XP+1/wT/gABowJxA88DvAPoBAgHYwcPBrwGxwgEChYLMAyeDlkPXA7wDgoP4w5CD3gNkQvvC+8KegioBg4HBAjUBT0Cu/4e/vL/Fv+n/KP7/Pvl+kf3mPVn95j40/eJ9Vz1ZPie+GD2VfZK+DP5A/jq9Z/4hf1M/Vr3ifWa+13+Mfxz+g79kv8Y/rP7qvtp/pX+4/tt+mD7Tfw/+4f7xvto/hP9bvmh+yD+v/1M+Jz5Mf5M/pv83vnn+UIAKQX7AFb+5AEoC4AKv//hBQcOHhFHEkgJrAksCcgOwBf3C4IIXg5aFAMXuA1wCckLLw/qDNcABgE2CWkHHAGO/af/V/8H+Tn2+Pdn/NT8DfS58cT4gvwU96fvr/SQ/Pf4k/Fk8hf9OAFw+D/z3Pht/kj7R/YR+hcDngBC9kf2zP45Apz8qvg0/WIBuP2m+IL7SQF8/yP3mPRA+hf8g/uF+/H50vre/BP8lPtA//oBef88+wX9cwArARMDFAWXBdQC7ASDBqsIQQzkDvsRshGHFswSnQsgCAoTtBvwDpgHSAZREXoW/xLwCIICnQPu99T6Dg2jDYj5r/Ih/AkBw/t18+ztOPVC/w74dOim5h/3e/7M9qvz8vcB+LHyt+5Z9+/+Ufgk8X71YwFuACf5dP1LBZYDu/by9E4CUwgUAib9OgLeBioBQfi1+okAAP9J+PryLfQA9xv6KP97/YL6jflP9kP6+/+BAs3/Pv4+ATECOANECBYQxxJmEbESQhdTHBQjYxhrBksT/yZRHUoIcAf5E10XfRB5CgMEOAcOBcnwqvEw/tT5U+327sz3IPTA69zn4O4d9avuduHC47L1xfkW8VPxWvyW+8LyNfFh/tcM7wC+86f8VQlvBzYAagOYDKUKTv6h+/UFTQ63Bk39JwRfCeP9hfNV9ST6nvlR7gPm1OxI8BrtEe/u9W731fA977f6SAd9BgMCoQaJDqwP+A0RE5MbRR5JG2ob3yH8KF8mXhBgCIMgHSr4Ebv96wIdEZoRDAXz/aT5Bf0X8brkkfOB9vnrXOVa7mTydel34g3qzvl89XPlq+CC8gYBTP49+X3+nQRB/iX6iwGuEhEROP42/ToKjQ/tCEYGFA1gECACb/Z8/5QLVAla+pP3bQA1/LruBup47xzypekJ3YDime7v6YDmyPFC+0n0veze9jkHhQiuB8cM+hO8Fb8RGhiRH7YjdCPPIoQmxivXKREXWwnHGyYmUQ/I+p74mgVlDKwC0fc98crxX+nm3IXrI/NP5tbfGuri74bpHeZ67yv96vfT6+7ox/qoCakKcQmgCloMggapBXsMhxfGFPED9wFWDGMP7wp3CVsK1gi1/BTx0/cRAXf/B/R08Jf18/DD52HneOtl7Orj7tje4F3s+er17Df5gvww8oXw9P4XD9kRyg6ZE7UU0hV8G4EiOyhmIwshQSUsLLkwAiikD7IF2RvlH8AEyfF39QEDSwOw9tzup+yI6+zdmtgK6hrsj94c38XuOvFN6vLpSPq6Bp351+81+AcJig67DvQRSBXsER0JFAtQFQodKhH6APcDyAusCQsEcAOQAfH8ge5f5wrz1fb67nbnhuwi8iXo0t8X6JPuu+i23t/cQevG8p/vyfjQBfcBB/sKAyAShRc+EPcS6R6OHsccRCErJ+UsGipwJCwn6SyjLS4ZAffeALAd8Q2k8uPmpu3n+v30neze6y3n69oQ0kDiY++c393Yte1B/7j2xuxr+hQSuA/E+zf+1BBkFn8SrhfMHjgbwQz8CdkVJhtDEuH/Pv7EBj0A7fSg9Tz5wvAX44PaI+Qp7tHled7e5Q3vS+mV4anpWvT57PfnauwT9s7/8/tV/ewKxxA4CCYKThinHTcXJxanH/YkJyD4HTcj2SW4JPEh4SErKh0qngpz6K7w/gkj+BTcs+Gx9iL3ud8k2KfhdubT2PTO2uPN8+bsw/PjDFsSW/+D+IYRrCj/HoUTrB7WLL0kuRE6EhQczRWtBIsAOQYQA9r2j+yS7N3oZNe7zDbZ7+Wh4YfZzNr65l3ruuQv54z1dQHZAHkA7g9YFc4EhPziASUF5QOAA1YNfBfZDgAFIAVXChYMcwiVDVsZahmHFvkbBhwVGREWBhvWI3gxOigy+tzixPHU9jHiN+Fj92H/t+B0yEXSvOGI49XSBt+5+r74bfjsEocgbg5eAMQQdjJcOG8t0DJsN1QkVAl2BxAX6BlkBlz06PFI7rvkDdcvz//NJ8QGvWnPCOdB6CzeuNnP4i3tcPPcAvMWoR78FyMTtx5UIQEMCP70/8oBNARFCd0JkQM39Zrn4Og/+OUDKAXzDJsSWw0iDGsWpxrwFtYaXyn/N3JEVEx0FWfb2dkI7tTx4+/aBjAIWt8qtFi7DNMB3tLV/9YB9gIC6/vNCrwdXgzG+woNAjuoWElNG0NcOFQWBP01B88cuyAtDfbz6ekW4fnTh8fZu/S6F7+HwRLRi+y57TvaJ84V2bX1jgveHbkrCCwaID0axxfUFTsLovsJAvMH4QchBjr40uqk4onatuiQBGEP2At2BqMI0AvaC7cbyiKVHxYpYz6qSnFU4D8t7WTHH9Pp8oz/gf5aCYvvwLDXmca7wtOX47relOmZA5EExwDUE7kZJQi8C4guU2CDao1Ouje/G4f9LP12D2ggYRxH+2bdW8+7wNS9yrmasvq+vcrZzcDfcfAN5n3V/NYD+2Ee/ioINVw7azDDHLcVUBcuGnwDGPV5Be4EL/mg6n3XndJl0oXYLfgjDPoIbf+x/csEXwm0Frkpjy7oLRY57kQZUJ9SOVKYCgLBbspd5QcDlQLY8ink07uZiRehM9XF5EngOOWF/QEJiQZdEAAkDB5vFTY0WWCKcAJe1Tu8Ie8HYPoCBO8UexCl+NLVlb8LszOsBbV0spezecdM1HHYeOmu72Dt6u1D/g8m+T4AQT8+bTejK9UmQhfUDI4N5fjb7GTynu8S49DPncIGyZnS0eOu96X6bfh2AZX/XwO7GJUrwTF9L3k54kevTTxLt067SKc83O5avjnah+RD507t9eKUy/WssY8wt4jaX9to5dz7Wgp3E0EUOyRYOR4oJSbFR4Fecl6KTqo0uRmv9sboJ/QG+dDzgubhyG62Pakkqg63o7QZuvXTheBh56H7HwdvD8sI+A3IMbJMeUbLPvA/qTg+F6zvXPq9/Y/fsNhn5h7iwc7CuZ7AOdeb2CbhTfshCIMDSwJNCUkY6iA9H3coODl2Pg5HGkcgTFlOCjoHO9FAJfdpvcHFXcDM107oRN5S3THFXZxsrTnQiu3sAsn/ChM8KMkdzRpXNH82Zy23M9xB8k7NTJIxlwrp7znhRdNQ0lDuXvti3dK++K/EreCyvrHnyEjsNe/s72cFvRVRG4QRphDFLek5njONOllCRTtMEfLdgtfI02nIrtb35mfwk99Iv3DFVtU93jju8wHRFN8VGg9mD9UWNBgnE+8YoylUNaA4hDxwQPA06CxOIfooFUY3Qc36ibzcs46x+s2g6vn5aQG557m1TL5b2nztY/9ZBModxiVGFn8e+TQ2IoYMCgwJIsUynTZZLkUap/vX1KrFZtWK9Mf+0PKh4JDO4roTu0zCI9Ip5tzsePhSD64asBxhFhsDaAaIDnMZtSo6QPcmTfIJ1lLBys3C5nX2rwJh/THsXuCj2n/wOvaA+MoF1AJECi4QmhNhEIEDwf/9CREOEiT8PKo1wibAGrYPfw6uKWVLwlf+Rmghpex3zOPDx8ze7K4ClwZ39bzhANiMzWzK8dtR6eX4mAakDdYW+xVpDCP/4P0xDvoftTCVPtc5PiNJABDqbOc47OHy5vnh9i7pn9EOvou2Y7rWx5jUEuR88JD5Gf50/0T2Muz17IX2gwrjHwAtBC3LGmsGg/q3+Q0E4xF5GN8QggSC+J7zyPIe8xfyyvN39o72+PrUBEsJGwlZB7oJyQ/CEwUfTSYjLwg36TkUR2BP0EigNJ4Ugv1t6LTbAeMs8fr96/oW7iLirdD9xBPH8s9k6LT2kPkjAHUE1wTpBB8KHxHKGIEbmR1VJJYjPRfRCbr6G+8i6b3mo+vp7NfeQdDZwKS297lMwXPPF+N/8a/5V/6DBHAIcQIlAvgGvApdESgcQyNHIRAZ0g4nAgQBHgXiBM8HsQoDB5D+vfcE9o32yPRT9UP6awNDB8cReBaoFLUNKALIAXcEIwigCnEM1BFNFIAM+Q/4FG8TMg+bB98HbQfIBNEG8wZHCAwLcwGD/t8C0wRTCR8Ljg/4E6UW+xlnEncDgvfL6RHlNeYF6rXxK/Vp80HuAOcZ5TPnkOjb7KjvPfK48nX2FQAuCrQO6QxUBjcD9AHeACsFsAb4A2b7QO4P5xrlC+Sy5ZnnvevT7nbxAvk0/4kBrAQBAZr94/9SAGIDTAN+AooD8wETAX0F9QhqCcsLTwzTCT8G7wQKAzsETAfSB8YJwQiLBVIEhAJXB/sHHwR4CWsMMg5DDn4Oyg7YCaMDPgLCAvgCTQGW/+H/sAN1AwoDdATPAcf+bfjl+EH8xAGJBOkF3gFW/k3+yP2LAcoD5wShAjkBNAIeAjsBegOSASAAR/xu+FT51Pjm+Pb5pPrY+8P7APmg9yP0aPZd96320vlz+437JPyj/kQAUP+y/bsAlP/a/V394/2y/4H+gP0A/tr73Ph2+k38zv9P/9IA4v7f/kkADfr9/Xb/Sf1s/oz/2/8e//n8ff80AVQAtwSpArwACQAYAOwAv/2e/eb+g/+wAzoGwgRsBcIDnwFb/wYC7QW9BxAHUQQMAp0A2AFDAh8D+wAgBPIFngQDCFkJuAbkArr/kf4f/vb+CAHRACkCSAG9/S7+8v7pAGP/Cf05/vAA4ARbBKoEDwJNAr0CNwLsBBkHxAbUBe8GIQY7BgEEFgQLASgA3/+zAJ4BH/7S/Mn6N/2o+Rr66Pqd+xD8DvrR+/r4z/dh8xn0y/ao9rL5fffY+I/7p/te/HP99f+r/xv/IP40AOkBEgWaA4EBswF3AHv+PPuD+yP9w/2W+838b/vs+wr64vks+qP4Fv4E/rgAoQNfAEkBBf6Q/akBQgLmAykD9gQ3BpwEgQEUAUUCtgBxASQAlAS2BWgDRwNhAsED0gLhA5YF+AecBcYE8ARgBSkH9wV/BlQDP/8R/X3+9AEdAdMAa/0D/mwAIQFlAOH7SfoS/d/9wv2lAP4DEAd2AS8CGQLFA1gDnQPgBjoFBATX/5wA/ADW/lf7ZfpA+sn8mfz0+uX64Pnc+Xf65Pni/Lv/kv4TACwAcP+v/1n/x//0AjUCgwLfAVb/swIiAwUC3P6S/+D+2v9+AFz8Dv/e/I79dP0SABgBwwGrAgIA7gH1/gsC9QFoAbsBnv/AAfP8M/6DAM/8cf1t/S3/av37/I3/DQC6/h35efph/SUAogL4/6oCdwH/AQoDpf9CAl8AxgAwA9UAUQA2ABgBMAIn/+f72ftJ/B79wACz/kT9of20/339f/ri/GL+0QBs/pz/rwLdAKADmgOAAv0CZP8NA3cEBQikBe4CxgH2AhAEMgEmAysBNQFAAOn+YwC3/+0AHgHx/vAB7/pc/97+OQDtBAv96QCL/pb/IAIHAiYEVgB+AuAEQf5iAuQDIgOwAq7/8wIuAvD76PrI/gkAGv2J+9r+mv2h/PL9Sv4T/ub9n/rS//v7UP3A/q38FgJF/rsCo/zb/50Bj/zEAGb8Ivvi/Kr9+/yP/fD+mf+b/pP6A/p+/F0BRwB3AEwCwQEgBbQBhwF+AWcDCwPEATsFBwbSBZAECgPb/SP+Q/4fAHQDOwHo/un+L/3G/mn/pf2l/4T+AwBrANgDPARoAEsBpQFVAgMDCQLVA5ECpQKVAFD/PwEc/owANv8R/xIA3P2P/AH+Qfoq/EP8i/pj/4n+WgDe/Pb+PABL/wj/9/v7ANcFEwJaAKcBwAKuAeL9qwDLASICzgDa/6D/bv5k/Y/97v3K/UT7MQC2APb+LgFG/20C2/6oAI0BHwPNAV4CrQKyAtIDfwPNAdAABwMQAl4CA/4JAWL/KAEe/lL+AAHfAfsCwvxw/gn/sQCg/zv8HgH/AKP/KgFMAA4B7/7g/jv+gf+fAPcBHACLACACRv+O/av/Yv6G/nkA/f6E/jD8TP0+/v3/EgCOAQj/K/0Z/X8AEf8H//f+W/5vAk4CTQMH/1sAJv6N/J792/3WANgAXQHf/z39sv/F/iL+av3m/T0A+v9w/Qj/Cv8//WMCAP9sAF8D/ALQAU7/3/5ZAZH+SQH0AT3/0gGv/lkCHQEY/6UCw/+2AUICFP/9/3z9RP+M/nQBZQHO/x4CugBB/5b/YQBPAjv/u/90AdsAfwPi/k//EP+x/u3+5AC//xMBgwBD/47/PwDCAWQARQGIAvwAeAAUARMA1QOj/0EBlQBW/2gBNQDdAQYAn//u/2n/Ev6rAXkAAwGO/5L/6AKQ/yMBsAC8AtUBfgEZAaIBcgB2/+EBegFYA/b/4QBnAfIA/P9F/nL/Ef8s/43+a/+e/gv/A/2V+xj/9v4M/9v97P7n/7n9yf7u/vv+D/+n/Qz/5P3e/NX9c/2n/BP9bv3Z/Mj85Pw5/hH+/f1d/RT9tvwT/XX9/v1v/V/8n/37/ID/rP6U/6EAfP8n/wX+hQHn/5wAGQHvAPgAaAGEARgDUQJ6AowFbgKwBCADOwT5BHwC1wRdBMQDzAQbBZUEUQcuB+EFRAb4BBIGDgVCBFQDqwIUAxsBzQIbAh8A9QD6/6IA+gBuAdcAxP+W/ywAvv64/m3+n/0Y/yj+7v9n/9L9zP5Z/qr8ofzo+yn80/wc/aP7z/oQ+0v7FPzk+ln7afrB+2L8OPwr/H/7pPzG+237SvvU+8b72vui+836qvr++u/7cv0J/3L9cf12/j/9wvyg/sr/BAE8/2P/SgK6AacBMwG9AmkC8APcAsgCRwIcAa0DsQTZByUH5whXCXgJcgr3CQwKeAkDCmgIxQcxB18GzwTLAScBVwBbADYAMgDG/wf/OP/r/nr/l/9nANAA2AAd/2v+2/6//+L+l/3w/Q79MP54/iL+r/2Q/Wz9EP1r/Wb+fv6O/ar8Ifwa/MT7avsk/Lf8WfvW++H8J/38+3T7C/uo+qr6Qfm394f22/b49mr4xPjw+XL7Hfyq/Qb/XQAHAQEBGgGrAGkBXgS0BAYE8AKXBfoHSAgwCZAJAQxjDoIPjQ4HDx8QSxAFEPQNvgrXCEcGqwFy/Lj3EvVb8wzyGvEz8/v1cPk+/Z4AuALaBG4F9gODAvIA2QCS/wMAbf/p/wIBVwI4AxIC9AGGAc4AFv4w/F76ufiI90D2M/Vr9RX27fYZ9w34//ib+ar7f/wS/eL8kP3g/Y39LvyI+hb56Pc39w/3zPeu+Qj8Rf5AACMC1gSfBkcHdAfECAcJ6AmuCqILVQyiDsgRWhXQF9kYeRogGZAX/xK5DvYIUgPi+hTzVeyw5qTkZ+Ox5FPnqu5P9Tb+BgWMC2YP/BBOEN8McwrJBh4FRQITATL/kP8EAIAArQFdAn0CygDD/vf6OPiG9EvxXu6f6ynqCup962rtovB19Mz4N/2HAZsFSAhKCSwJNwh5BYIBdP3E+dr1iPM48uzxqPMK9yr7uv9LBOwHywv9DSgQ/g+WDnUOkQ48EJ0RYhQwGIMZ0RqbGqAWvxB4CmkDQvvr8krp0OLE3wvekt8c5VzrBvOS+5oB2wfdDfQQMxOeE/AQsQ4xDWULQwkdCLQGUgVCBA4D1gINA84A+f2i+uP1GvEU7WXp0uVu5HfitOPp5inr5vFP+XkArwUxC8gN5A5RD7wLEQdZAg39J/hj9A7x++6c7p/vU/L19mP8kwJ7CGcNPBDWE28VsBXeFU0T8RIBE34VxxjUHZ8fOx0CFZ0N6gZ3/gz3AOyT4fbYxtQT0oHWxt116IXzyPo1A3AM5xN/GOgb0RoBGNAU0RDjDroPWg+/DaUKpgcKBjME8wC8/QP6i/Mk7D3l/OCS3vPdiN1R3sTgH+aK7iv5gANLC6YR2xQnFh0XxhV4EWYLKAE9+OfwY+uw6lzr6et+7EXu/fLt+qwCKglODPwNtw78DtIQrRFZE5sTpBRwFiobZiNuLYEtkyNUGMEHOf7u9aTpddyX0ifJVsZgy/XRMOKv8Yr8ygaQDM8PUhfQHeMd5xs9FgcPTA9UEqYVshbwEzwRvw5GCmMFxwA1+dLwQuUY2oLSWs4l0UTWa9vi4mPpUfLZAIkNEhfrG0YbtBhtFRMSmQ8WDH8Fl/909uzuBuy56xXw6vBr8Czuee7n8fX38P7wAjQFtAVNCXUNTBPHF7AbLxwGHgEgryQEKuYyDS8EHSoOmPUF7Ffnt9/p1TTNXcbYx8/We+EH8+oA9QO2CqgM5AsrEcUUrBT8FfkSxQ76FGkbQyQtKKYgBxcxDesC//vJ83fm6dvyzxnJ/coWz/HYl+TH7Oz1mPs0AMYIWg9OFEYVmBAcCxUK1QqPEFMUaxRKEnkLxAUd/5X4nu/l5WHcu9NJ0iHWsd+i7Qj7AgguEeQZ2h4dIoshPR3eFAgOIQlRCfMMSRUEHeknIi7NHmUb2AEr+X/1NOHI17jFrbzjvbDPCd6z8fgE6QYiFbcc8x9eJ3cfWBlhE30M2AjDClkNuRSWGXMT0hHqCfMAUv6B9njsmeJc1GHLhc2z0gfd/OZy6WDzr/z7Am0QFBZ+Fo0XtxGKDqcPAg6uDx0SrRGJD34MrAb0AfL4bu+g5YbYWdJEzRjS3d1I6Jb1PQH8C4cWLhxIHo8cGBRaCX0Ew//bAToGLgzvFCYfmiktNqlAfjPrJ04D4+lP4m7NlM5QyMTA4cXe1E/oBQbJH5MfmiMuG8MRbxNjB0sEIf6O9a73evzeBvYW7R8ZH8Ug7xl/DAEFP/Va5ybfI9G/y2vNR9E33J/opPQ4AykLDg7LEr0PKg7kDOwFPwQAAan+kQNZCWMPRBVnFX0UCBOPDHIF3vnx6Dfals81y5rS+9rN4/Xx7vmwBi4QjRTnGEYTmwoABSL/Y/9nBJoH2Aw+FBwcNCdBMz46/UEjNe4nNQxQ8irpy9jK2qrWyNPZ1I3YKuJW75cGDQ5DEQ4NHv9T/rT1xvYx+Gz2Z/ug/nsE5w9EHqIiCSa8I+0YhRPkCSL8PvJh5l7fmdrz2yLhPOhs7cPw0PU9+zMD6QQLBf4CAv2q+dL2H/j7/Z4CdQa1CpcP4BJfF1kX+hQSEWsEIvpx7U/jS+BQ3CPdhOKu6Szz+/9KCBwPJRP3DekIzgJH/Yf9bABdBKYKzBCJFkAa7R7lItYoJiniItcVNgTG+VLsnOrw6QLpgOxa8Hzzy/Ze/8b/nP/U/+v9SgAMAmMDzgCD+kv55PYv+l0BMAhUC4ILrQoCBXMFGgZ+BHQDowDC++z1LvTn8SDyovRl85D2rvmJ/H8AlwD4/oj+Bf+q/WwAzwD3/j/+//lz+Kb5+PsA/0IBmgHYAJj+4vpw+l360Pnb+l/5AfkE+lb6mfvm/cD/6P+yAVAB6gFNAxUEYwYzB0QIfgiPCGEJigpRDGMPVQ5FDDIIuwRXA4sBQgjcC0MMzgs+B74AUf77/U76u/wi/qr+GAKBAYUAYf6b+8368Pml+lz9I/7E+s33y/V99d37vACBBJ0I8waEBOwBmf8r/vj9hvyn+7H89vsv/ab8hftJ/EH9mAArA40EkQKh/nT6nfaB90X4RPm2+gP6Uvry+qf7UPyC/cz8FP2I/W/8+f5l/UT7DfvX+cb6xPwe/zgBDAPHAlUEZwQwBN8GQwYVByoIKgcBCQ4L8w4/EB4PuwpLBA8GTgW+Cf4NyQm9BXYCqP42+/X7TPiB97n60PoyABYCGwFJAEH5k/cu9ij1jfk3/Nj72fkg+6P6Z//oB9QJew4uDi8JbAUVAar9rvtU+pb3V/gP+Av4Wft//I7+qP81AMYAGQCk/x/9DvpC92n2KPdu+JD8W/+9APgCHQQeBYgFEwSn/tn6QfYw8v3z3PTP+Pr8DgDjAaMCjASeBBwDlQKiAI//BAH+AiIFgQcrCv8LixFuFaMbQxqvFiUQ1wQKASD2BPUp+P/1/Pdr+a35pPppAMv/q/3bAAz+PAChAMn9v/3t98P17fX2+JYB5wcfCUwIZQlnB+kFIQjGBTIFEwFO+GH1IfOG8vXyIfOU9EP43vvn/BIB8AKKAHn/EfzT+339zf0Z/mv9fv/zAeAE3QcJC14NvgsNCAwFnP9u+9P0x+lV547j0uJE6U7tjPam/YwChgbbBzEMjQoRCNYGpwUlBSgH2wh9DPMRAhdMHOoixCdOLHsu1h1LFJv/suvV5zDW1dXA3FrbdOQP6pHuL/rmBfQHQAuNDh8KhgvXAr35n/1b+ib/2wk8DWsZMx2eF0sUUw4fBq4AS/l/7UzoeuC412LaJd385anyrfh5AQIIUAkkCy4L+wcDB2gFJgF7An4EbAQhCK8K3QxIENMP7w2GCoME2vwt9ernmuCY233VStsT3aHmH/I/+EcFuAp9EGsT2w5ADk8JSwajBRoDTgWzBhQKGQ4pFckbxiHgJJAlqCSUIXMfIg7x/Evw8OHP29PXBNMB2qDh2OkA9BH8ugQ3DbwNPgoUDEoJgQQcA/b/QACiAsQDNQ6BFTQaqRwVFzYQBguX/0HyUOlY4drcu9uP2sPhuezV8Qb58v7gBYEO5g7UC0MLAQhdBVADoADNA4EICQkWC+oNJg7BDkcKNwNP/Hr2ue9+6svgdtrU34fZU97Q5fvs5vzU/NcF7A7BDR4UjQ5OC80LSAd8B/EETAXQCjIJAQxID/wTyBlCGHgc0xenGDMYRhuwEYID0fg77h7t7ear4jzgzuPX5Yzqk/D//awIvA39ByMFvgRDB5MHywELAYADkgZeCTcNIxLdGXQWiBCmCD0Dw/0x9uvqLOO73i7equN06H/uEPaA+/j/+wO+BVQKWQquBjADpgG0AZwE2QRNBZ8H4wmQCg8MBws1CFUDz/us9ZTu7+jp2znfT9kM2krkn+Qn+Nz6RwR6EIoLuBN8EREN6Q8lBtgGjgXGARkHzwRXB9kLww3XEtYUMBagFsISlhHHEK4OOBXbGmARWgih+vX0P/m88Lnr/ea95v/qQOzp7pD22AAoBZUAbvyV+1gB4AVsAgIDfwNyCn4SKBSMF8AZchfmEuMGKP7d+wj3Wu+H6B/kpOQ16bXr2O8r8r/yQfUv9yv2/vi8+rH7c/1I/aMBzAnFDlgUxBVKFF4U4Q9GCzUGpf3j9Ify1dvl2MDWhdHa4iPazOl384r4tw1SCEIQWhTNDKsSzgejB+0OlwhmD/ILUAxqEZwQyxSJE9APrQ5FC2AFJwVn+h0ENARnCh8Q7w/+HdkYmRkECrkFffou+mnw0eVf4OndoeiQ6y3zzfZrAhoDjgbBADIEjAPcBKkBPf3c/lYDZg2WDz0SQBIgFG4QKA5sBDsDuvr69DHu1eXp5qLns+qp5lvpbOlL7b3ygPMm9mb5hvlm/6L+PgJ4CAYHCA2ZBsoFCgdN/5j+3vhu8KfzOe657uvxpPAR+d75Kv3RAOIBGgeOCNgHMwloB0oK9gyODKQOkA+TERMRwQudCVEIqQdrBY0DOgGm/oQGDwBkBk8GngtHGMkMxhXhDKkSUhUhCywK/f6//2v68vj99M70vPio+O72EvnC9+/5XPyT9l/2S/Mu9wv64Pvi/XwCgQdvCkUNLAzTDTsNcQg/BOL8bPku+Nnyc+/q7RfsQO1W7mbsG/D+78/x4fSM85/2Sfmj+Pf8sfsF+zL+P/v4/aD6fPj0+g36hvox/O77Vv8SAXMCMwTIAuMDqAVSBXIFpAakBmwHPAV5CSAJsQwUDqMJ0go8Cd8HAgebBcsCyAUdBKIAdv58/jAD8QWzBUII3wnTC9sQSg1cE94Qew8lFNgNeg6vB9EFEAL3AML7j/bj9JbzR/Rv8yn1wPHH94/1DP0h+qf9gv+Y/I0ClvrKALH/yf8eACwB6gAQBIADDwPxAgz9n/tV9kb53fVO9S31//LF7qzx0/EP87v39PIX+eT1f/mX+3T6Jv1D+sr8JfyG+1n/xAB4Ak4CjP+//fj/fwSaAxwELQC1A0QJiQeHCAgJrQhHDPIGzwkUCdQEPQix/xgFcvxs+qYBJP5zABf9svwoAGf/8ABM/SMCWQSoAngIM//MCSAHSgvkBPL78gmlA5UJMftU/7gAx/3J/Zz2s/m5+TH9xf6h/rD2rAW/ARwFvAT/AF0JUQbeBNkEewn6AmgJiQfoAhYCEgPY/TUAVf4D9mz9xfMc+uP7pPZ2/Vf8mf87AdH9gP0iAZsCLALe/5v+u/+1AYH6RgFn/rz8aQAw9wb8Dvg0/1oAlPmt9Y/9mgGm/av3kAPs+7H7+P9E/oUCAvZjFWPpUAiGAxDyIRDE76kEPvJpEILvefjwCl/qmwxI8DMDLP9+/msLmvGJDTQAAATpBub6+QE1BzQCRgGU+MsAExHF81ALe+y/DKQVKes8Dx/9oQFdEwP56wsQ+J0UrQk49XUWTe0aIO/xCAgD/BH/CguJ8J8N6OqzDuT5fvarDHXxmAPMC8zx2BF99DgIFA8U88cKpQu9+e4P6Pj8AtULaO2wFgPmeA3X/bDwxRAL6XoF0wUN56IWhO1U+tgNquwaERLyEgEP+uUHV/p5BErzi/2aCU/yiggj8tD87wZY9BD5hwbD6Q8Taeg4+0sJ6OkMEGfyLfq2FWHn1wobBfHk0CnM1xoNjQgc6EogjOAlEk38SAP0BTT5qwzb8GMTivIcCar6+QSb9k0HsAR+8NYPK+vjHEzsDwyq9osHtgMl9QsO+/JKEoX0Swok/csFKgrP/NUEEghT9uAZ2u4kD2v5MQX4ClfupRor6VwflunjEB38CwOo//j8OQ4u9ZEK4u8WF8nsyhGn8z4CrQE2+XQLAfBoEmflKxzh5a8OVfuF+ooTMuSNHuTiEBcM+Dr4tgwk94X/PfaACCv3eQVk/MrwkhNU9KMDsvxF+pgMAfgRBWT2hP2ICv72Kf6u/mT30A/E73EHoPXbCyD6hfteBlnuOBT56DAK/vP69koSp+jjBv0CBfE1E1LonROp/Tz4hQ3673EWU+o7EiXvhwueBDLyvBNa6Ngh2OYLEIEA5fmFEarxZg1e8ysLp/wjBWjyVwpD+pMHRgEZ8d4ZNefBGHnv6P88C4Ly5wmy8KoH4P8qAMT6FQL8/iUJh/oNA2v/hgi//3kDmf8V+xIQM/K/D87upg1l/E3+AgO296EMCfP0BAv4pwtE9uMDIPfoBtr95fp/BOj3pQud++kGnPaoDHn6vwie9JIFqf/p/oUCnfToBuf4QQT/9ZYE5/kNBDD8bfyjAAb+AACJ/CcD9fluBCcAvfvfBlb9XgOQ//z/YARO/74FzPpHCBn8iQXN/XT+IwWY+BsGJPl5Atj/s/zqAn//UAAH/2r90wSR+6IDnv35/twAF/20//X8SAMP/lUDW/o6A/r/UP/bAUn9LQcB/d0CzAKy/tkFl/3jBTv9CwSx/Xf/gALb9+YDzvrPASn/+/1e/k8AXf5kAw/+3P1lAR4AMALe/Xz+MQH7AKr9rwG0++oEzvxMAiD/j/5oBL36KQMu/F7/AP0X/779vf/H/nP/mQP1/TADFf90AFkD6f/X/roBuf/DA67++P8OAUoB1gON/c4B0/8tAn8ATwEQAMYAdAO+/xUCO/7XAigDhP7DAHH+HgKcAC3/Uv4F/+L+BgDA/8X8EADp/gAA7P/c/Uz/Zv9lALEBIv95AQH/1v8YAOr/+QBoABb/2QBx/3v+lgIV/BUBbP7c/f//u/1h/1P+2/9w/DoAhvzx/x8ATP15/+7+GAKH/6j/rf7CAHAAP/8H/9j+awB+/zoA2QCu/yYCN/8nAuj/MQAfAbL/yAJB/80BqwBjAVYB6f7KAgwCsf9nAm//cgLGAJ3/uwD8/gcChwAmAb3/JgDJACsAXwCw//L/pAODAGoAkAAI/4IBsf7VAAn+ogCy/mYANwHo/VcBh/4XAUUAbf4kACsAdf/qAOL+VwBY/+P/3wCOAJD/Rf++Aaf9iAHC/QgA+/7q/qb/v/1eALP8yADj/S7/5f4Z/f3+nADM/6f9n/0n/iABCv+j/Y7/1/7gANYAov9g/ysEFgJDAtP/D/8lAv3+cAHQ/AAEIAHpAoH+sQLdA6r/zABC+wEDpf8dA7b+gP/1/8AAdgDs/3H+1f+9/0n/BwHh/VEAdf0YBEf+hQK7/sP9vv9v/0gCev2d/2L9SwLp/sQCuP6pAeL/Yf4NA939HwIY/3gANAEp/5//qQCZ//YCCP+0AP4Biv6TAu79dwFAAav+ev+Q/RkAI/6MAUEArAAU/5/9nQH9/kQDTP9dANT/2P+OAdj/P/96/z7/nf9GACH+qwFVAEYEDADgAUP/qPxjAXP/5QGV/Sn8ov5kAh0DZPz/+0b+ZQIlA9n8Yv6wAJAC+v4p/Wj9QAFfAgn/vP+V/ZICvwBs/aX/MP0lA1oAN/4EAEb7dAOn+mEBAADO/4YDWPgzBFMBxgPUAMz7UwUYBFP8vQD2/iQH1f+aAUj9JAHRB239tAQf+QkHrP15AXsA+/lgA1T5Av1w/Uz7qwBA/kYB//9s/vUEnv7CAWL9lv3H/k8BDP9bAUAA7v40Am8CiQS4/fgF/QCHA+0Bdv4EBDT+x/8AAHAAygL5/lX+bgFE/qP+4f2aAQYBr/+W/4sCXAHm/6X/Y/8aAST8UQCF/0IDYAAJAYz/9QJkAzAAZQKi/bwB5v5z/m7+jP0R/kMAcfmG/Kz+wQLB/lT7XQBa+sYAVvpH/Rb/2/v1/1r8If3+/Ob9egFg+5f9d/oG/DX+c/wHAfb7dQCQAWX/PAO2AFz/hwOy/VIASgFgAewDAAGDAbgCjwCGAT8C/QMNBDcDoAW4AuEEVAQ/BQcGmAktCOcIbQimBp8IUgQOA5YDhgT9AXMBgf+2ABEC1QC3/m39Yv9B/9YBY//d/Dz/Ff4j/YP7avsz/pL+efyV/Lv+Ov8+/lL76/oa/ZP7i/xg+0j7KvyW+mn37fV6+Wz0NPbi9hPzlPMW9Zb59frn+T760fwq/qb9cfs6/hP/2vwx/gn8VP91A6gAqQGVAncBSwX4CZcGMwl6CKkKTA5xCNMMgA7wDaMTsBCVDlgVlhIzEvMMTQm3CWoGywNH/y3+3Ps8/E73wfbe+qH6rvvG9oD1JPjO9zD2CvcQ9/T2FPzK+9D/tgIhAksG6QTCA8YFhQSTBB0DnP6KAWQBXf/4/+r8Xvyy/Kz5mfnR9zX0YPUL86nph+gI6nXkruhd6F/rsfLP9VH9qAPCCD4KjAv3CmAKxwmjCRgIXQbtBnMILgoNCvAKOw3cDIYMBw+nD5AVShcrFRsX9xB1E1AWdRC0CwIBqPxI9zfyzPCl7F7touzl60vxz/R2+UwAXf3k+sr7rP0mAaIBHgD9/30B5QIUBC8IzAxODZ8LDAk2CEUHsQdWA6v9qvjK8V3vte8F7u/qRetl6sHp7+t57lXyqvbS9N3ybPQe90v4O/pc+2X5/fq//kECfgVNB1kJlwqCCc8IWAjsCzIJXAb2BMcClAWmBp4HRgiKC4ENRA1vEIwU7hrYHEodzhsbG74cTBSODZQFp/uw8TPtxuX73VDjl+Mi5rTtHvJt+cQDLwXKBjsKjQc1BwIIuASeApADMASBBkgIdArkDAcPDBDcCyQHDQX0/Wz1ae6L527fp9xf2szYaeAP4n7nTPIG9m777QBGA1EGcAQbAu4CxQAG/+P9Tf9LADz83/+tAN8AvQHKAD8E7wPXAxAEOwUABf0Dcwa3BlIF6gaZBuwIJgkLCnYMLA+7EIwasB1SHWggqh3jIjMZbg5XBm77R/MB7BzgT9x33nvgZubX6vXxmPwKCCMLOAwxDZMMvgsRCBwBoP+1/gQAPwGeARAG4QqnDRYPeAy7CYQHWwCV+JztmOU53t3Z3dYK1SLalOBG6AHymPqTAj8KjQ2vDrYOlAssCdgGGgJA/NT6OPwN+3f7hfyB/iwB8QFyAV4DYQNRA5oCsAF1AiUDWwLrAKIBmgFWApYBNwJoBGAGRQpDDxUQYheMHAoiqya6IKokBh7UFM4NUgEW9y3uPumx4zniBuNH6NvvJvSS+XgAKQYnCd4GpQSDAsP98PpS+Yb3QPh6/JP/SgQICFQLTBB/D/AL9AcbARz55vJ56KniwN/821HgVeOh6DHxpPd/ADcGMQgDDYoLMwntBYMA2v3n+9n3EfZv+D/4UPzf/eT+hQBSAYAENQMBA/oCCQGDAcb+SADa/q78kf63/WD/8gGvA0oGewgJCbgORRBlF1gYIRm6HbceSSX3H0Qejh3cFmISDgo++yn3tfNN7G/nqeKb5ozp+/Dk8Gby2PnE+t/9PPwF+cn6U/t6+Sj6UPulAa4FBwnlDMgOyRPiFZ4RuQw+Bif/hPqg8fDph+Sy4Z3h+uFu5EfqA/H79g/7zP5iAnYDVQXpAVv9rf3z+kr5YfZd9dX31ffK+2r9Y//aAzYFVQc2BqQFCgg0Bm4EegFhAGYABwAsAS///f8DBV4GWAj4CssLaxCiD34QaA7RDnMRZw6PEIgLHRBzFUASUBBfDp0N7AfsA3/9Lfb89erw7+tS6/Lr6O908k72R/ao/LIBiwHQAQ0AXP+4AJX+tPxE/ib/1gEfA8wFxgakCsEK6geiBU0BQf1l+kX05O7D65Tp/+kO6s/sgO8l9cz5OPxUAEwBjwLBAqz+jP1b+3r6SPmy9mT5kfpZ/X0AjwLiBwMJkQmxChsIiwe3BFECSgG3/Ev9Sf1E/CP/+v7pANMC2QEoCBcIVgfFCBcIEQqeCLcKnwhzCZQLSApCCmgLegkhCekL7gxWDK4J8grRBrICNgBu/W/6ZPcO9fLxD/Ip83rz1/Mf+IL6d/rz+yv7fvyi/ej88PsK/Zv+PQBoAZ0CAwVyBtEH/QbgBikF8AN8AXb93Pql+ff3y/Zz9cv0O/Yy9rr31vaX94T5xvn8+LT5O/jd+OH6DvsJ/yD/QwLUApMDbgUlBG8H/ATcAf0BTACC/wX/nQDzAEIBJAOHBf4EOgVtBjAHxggvBQYD6AEPAFUA1AEw/in+8v6XASoFFwQdA3ECTQUrAV//Mv6h//ABD/8dAkcFXwroDJcO1Q2iCWsLIAkRA6v/Nfv89x/3PPSO85f3wPoL++L6zfoa+1z8BvyY+nL7FPud+yj+u/7iAQ4GfQhPCrUKrgpgCykJAQbZAjYAGv5I+lz4j/cG+Dj4l/ej93L3Q/fj9iT3WPi79+H2XvY091/5zfwk/u39bwLZAUEDyQNFAmUFjAR4BE0A7P7L/5v/IP1Q/pj/Ef+zBIsDJAgwCicIxwcbBokDZwLl/0L8r/pv+TX5sffh++L8nv70ArwAvP8pAacBfASvAVsDsQO0A3kIpgjDDCEPRxO1EyUN/gm1B9kFFAMS/g/7uflJ+Wn2NPfw+kL/DwFu/q79vP0BADn9YPv6+nX6FfvU+eT6S/+kAzYE9ARHBcwG3gVhBM8Bzv/T/fP5B/g6+LL5Bvvr+pv5R/tf+sT5CfdE9zb3rfVJ9dD0Cvey+ZL89f3RAKgCOwM4A1kE0wHMAoEAFgCa/ScAqQDL/g8DzQEVCJAGpASXBNgECQQjA978OP0g/icA6fwl+00Bfv4i/tT78/5H/sEAHgOyArMDYgexBk8IYAh4CWkL8AlVCs0JFg0eDhUS8AqBCaYGEAbdA8wAqv6f+cH9sfY09a31iPnX/BL6QfXZ9lb7B/gM96j48fvP/bX83fybAhoHwgWBBEgF+wQ9BJwArP/sACX/Yf0Y/Mz7BP5V/Tr6Yvm49wf4/fT88jL0W/XT9nj23/cb/A3/OwBmARMC4QUzAzYD8QIRApcCVQBIAJ//VgJDAs0DCgSOA5AD1QPpA3oBUgBBAIb9ZAD8/Lv8NP+6/Gj/kvw3/6f9KP90AeoEUgYhBisJ9whhCcUHRAdcB+wHHQi4CLkMyA/REgERAQthCSUF5gL/+6T2H/VD9a3z6fG+9XL79P4X/V36Z/rY+hT3APTN8qP1C/i4+I/8sgSKCiwLVwl6CLYIWAYbA48A+f49/ar6HPv0/eb/LgG4/5X9KPu99r7zB/My8XrxvfGQ8hT3Sftb/wACfgOJBO8DjwIeALIA0v8R/bD9X/4oACAB0gSuBMQFWQYoBNsETQKrApsBLv9iACIACQDOA/z/+AMqA4EBAwTAARwGmgU8CFQGzAcYBusIdQjiCpYK6AtDEgsOUA9gA9ICCAFl/5T7L/gG+TP5N/mo9hn5+/zg/ib4l/Xz8yv1kPMS8vr0wvoq/Gz9HgNeCQwM0QlfB4gFsQJU/5v+xv+pASwAoP+e/yYDywP2Adb+UPo99ljyAu//7zrzRPVJ+Ar5qfx+AA8BkP+c/977Avkm+N/4ePpW/U4AXwF6A48ETwbcBd0FsAO7Ad7+0/s+/eb+1f6zAWwDdAU5B5kF+AebBhoE1gNFARABbgMqBu8HgQtwDX8SZRL5FfMUYxjrHLMVLw3N/tX8jvYW9dPyJPJQ8z/3jfbS9oD53fjb93/u6uml6ezrM+4f9cD76wHbBXMMSxP8Fm8VjhABC30F6/9F/WL/TgN/BFwDlASvBJsD2v5I+Izx7upF5U3kJecu7PfwkPP/92j7FP/9AZ0CaAMKAvD9dvpg+sn5vfvx/P/9mP9C/k0BIgShBIMEHQHq/kD+mvxH/mv/KQAKAr0BQAKtAywHuQgTCuYKSAofCtsMtw6nEScU4BTGGE4YIh/GJHkjfxeVBUf4dO6I6AvlV+NJ6Cvx9/Ew9Qf7jwCX/mfyAerU55voS+4a9sT+sQd/DTkSKRgWHxUiyhvyERsJCQBV+HX3Q/vl/UT/Iv/V/wMA0/sO9XXt4OMo3XXax90A6c3yJPoIAeYE5Af7CSALygtlCDIDsv2z9X3yGvSo9ln6t/sc/gIDTwW1BYgFwgHl/JL2GfNI9VL5U/x4/u4AYAPzBM4GKwtrD4YRvQ8iEL8SNBfeGIgdJyFGJt4p+CgzJT8TrwMK9B/nXd2+1KrUOeAc6oPwxvYt/QICvPup8RjtY++Y7/7uefOj/nsJzg+rFXUhBSu9KHAgIBjVEAMGYPjT8Bfw3vCn7wLxX/cP++v40/IL7tnqzOaI483kj+rl8Gz0Mfn5AdYJlg5NEfES1RKyC3gC8vqc8+Dw5uz+7fjyKPdy/uwDIQoTDMoIJAXA/6f5YvWE87H1U/gi+0YBJAcSDU0Q+RHRE7gSNhGVEbYSiBY4F6wY6hwIIBogMxdBDY4DqvpJ71Lk6+Ba46znJOvs7032ev2l/p76bvkS/Bv88ve79EH2VPp1+2r82AOVDvQUTRSwEekRQg9FBx4AoP5Q/+L7Tvfx9mn5wPgO9SP0FPdg+aP4SfiW+qP88vnR9fv10/gT+lH6jfyIADwBxv5+/v/+U/+m/fH7dPzn+3b78/sS/2sBjwIKAskBjwHQ/+/+Of1J/i3/Kf5e/mIBFwFqA3cFcAQdBxAHIAqyC5YLjQuDCuwLcAycDZER8BacF6MS4wioAf/9Ovfx78XtwPLY+Nr6MP3vAj4IeAYu/rz7NP6b/nz7qfh/+kP8nfeO8ij2a//yBcIFIAaoCj8L4gPR/Y/+lgAY/rD4dvl2/+kA//xG/CABKgW8A+ABFgbEBxcB5/Yn8eLwBfHC7kXxOPlE/W39EvxIAH8CHP2i9Mzu3OtB5svj9ecu8if8BAR5DMoT1RZZERUHuP9E+z34lveI+4IDHwm5DEcOyhD4Ey8TexGsD20PTw0aCy4NNROkHa0osykCIVASlwOZ+Ljo7tVVzzjVc9kc34fp2vkrCUQJIgGCAsYFh/4T9DbwS/WN+dD5LAP7GBcrUy8CK2AnFyNHFKv/V/S27pfmydxu2Z7jMvCE9JT2Bfwl/3z7YPOI71/wU+7/6HrqC/WhAmMNuhaNIP4kuCDxF8cQ+wRu+Ezl2NVR0N/LJ9OY3wLw2f/pCUkPVhLREucN+wdbATP86/hR+FT8sAEdB6YNOhB4E+IWHBqEHYYcHxvQGpMeeSGLJOov0S21GNr+0+RQ27nQz743wmzYteD+5P/wRPyZD34OrvjN+SsB4fWv8Ij2eARJFi0aMh/MNONBiTZHJHcUPQTY72LWo83I1tzYlNcA26znDvmo+ZD1Hvs8+znz6Owf7qP8rQgGC14VXyVpKmcnrSKsHr4Z6AgO9jbugulB29rRAMnry/DTA9NO54D4IAfrECUPkBaUF0IS+AkiA2sCSfqa8lL0cwCyCPINmxcsHe8hlyAWHK8d0Bm+E+YPRg1gEnIYCCsiJ3gWAwQS6xfhr8uavtLHnta41t/baO9A+20LRg4aAlYMXQys+qv6RAFsDKUY5RtlI3w1qDhBJ9oXIQqN9qXfPMrfxn/TAtXQ1Dng8PLgAb/+gf0eBlAGUf2T9SL7OQy6ExoSqRunJ2okvBkvEP8NVgnF9J7fd93b1sPLD8ozy9be8OUe6uv7lgUEFMcUFQ9FFGAS1AqK/qf67v9e/6P69f6lDpwWhxkhHlogkiG3HDIRKg8FDtUOuQo9ECQdnzDjMswSAwJz5lXSCrzPrFHB99Of1jjaPPClBc4VhRqWFVAePBcF+Kf2QgQzEwAlSiVyKCA2hTLfHogS5ARc7czQbLcQt2DJptJA2cbszQHxDFYJ8wdbD/gLcwAA92X9fxBhFFkS7B0kJEQe0BLnBrX/avY13trNCtLp12vYbtgp407rqfWM+Jf/yQ+LDvoLqwM3A4QKgQbvAd398gIGAav4NwFODokV6hULFo0VnhX4Em4OiQ0pEHsMDQqBDLUYOStzQjc83RJz/sbZw8BatY6tCsU+117RQ9fG+8QT5ikcMfobhRlvC8npivLFCtMXNiGrHPwgtzEILZYY/Qwv+VnbzbzUqYa3A9GB3M/nef7lE3sbkxGEEvQZHQ+JALj2n/9yEkoSnBJiIbImZRknBQL2sPGa507PVshnzonRkNe83nfyGgnSDFUIegneDBkKO/6M9pv7Q/7z9ED0qf/9DL4QoAzaEWQa9BXCEicUaxBqEGMKAQL0BmcS6xAqFDkaTydZOYBL5C3y/8jtSsCTrcOoRawmybDXvNBW6N0VtSbtNGYscRURF7kA5eXx+zoRSBd/G1wbkydJM6Yh/gp7AAjqYcsoryuowMGw2oblkfX0DfQfwR29En4XERxDD/78WvbBBdgSnRDNFDgg3R9nDab0+edb5p/cbskAx/rMjtOD40rsYQXFG1YVCQ3+B0QLAQhw+sjyOvcY+vHzX/sIBy8SKRivEFgRvhXeERMROgpIBnIJiwbB/zgG1BTuEkkXdBnTJ3g/J1IzK/r7qOlgvJ6t36rfr8LJQNxj1fbxLSDAKtg1/yvdGXkZ0PvT4Qv5nw0ZFSkXMhc6Igsseh6NC9r+NOlMy7uufKy+xCDe4O3F+3YRviHWHcETSxb6FZAJ0vtA85gAUBFGElEYqyADGW8KDPYp5e/h2tKJwfO7nr+O0lbsKP66EyQmNCB3FToMAAVI/w/ys+mW7ArvOvZHBIIMExoCIWIaxBQBFn0TahP2C+8CtQLjAToABgVWEO0PYRwvFk8mPjdhVD47Vvy+7s+/Xq2FrRWvcMBy4F3cKO0TGn8tLj2PNNoc7hFN+b7cPeyr/8gKCBTBFPAaPS4lKwoZKwph8wTVN7hJrLG8MNsU7mD7agsXGskf/hbOE9cRDwUi+53ttfRQDJYPAxg8IlEXag6O9zDcatHPw8+3wbhAxNbe6gNsFoUn5jOnJjIXSAfF9RjvSOn24R/pyfIn/XgOexU/HogmASFyGrgabxCvDk4J5voI+Uz9O/9NBOoTjxJTH0AeZyISLWpISDRSBo/1rcNnuQSznq0pxP3g7uSG+CERWiMFOEYw8RfsCsn47+CI6GfyWwRRFusXpBn/J5op6SCLEOP36+LUyQq59r7/1RPtJfmL/9IN7BXOEGsPmAnAAVj+GOxY66EAUgK/BpYCJfBC7SziM9j24V3t4fUj/ZABtwwcHAAc3BZQDzX+qfGj5eTe4OYL8Bnz+/rLAesLmho/HnMhZCQTHI8T0RD0CIcLdA4eBmb/LAafBboTxyBOIUAqEiwkLKMhxhsT+nXmMs8htYG7hb75zkbp6/ecCNgZ/B6OJfkfSxVtBl/6Tu0S7dT1KQBPDl0WZhpfHDUZYhG6B3P0PuVv0pXIbs0X0qri9/Qu+h8B3gbzAxIGggXzAcf+lPl49UD0CPpN/F35h/kK9S30oPiI+9oFLw8SEDQS3g9ADRwN7wXRAUX6+fTn8KzuZ/LA+EUC1QoXEhEYRR7tH2QhPh7BF/YKZf1v9AHsh+q37QnuJ/VA/YIBjgp7C24F5APd/JvzmfNY707yLfa19rv1N/qa+nH7WAdqBGcJ7RBKDgISTA9CCuAM3Qi/BEsBuf3v+3z8C/06/Tb/uAD7ATcCJQAkAH79wPeP9p3zl/bl+1v+IQXjCgQL6AlmB+IFzgQ9AXH7l/YA9Nvwze6f8dj3Mv4FBH8HEgmPCzUKdAgaBVoAU/6r9yP3xffR9ub5v/mI+on+yAAqBFgH/wQEBHYBnvsg/H37Zf2mAdcCFAXjBg0JsgiSCaIIXwTWBF//z/j690/xXe4k7+Xs//Gi+TD42v3CA4IEKQc0CH0Hegf1B98DlAS7A7H/AgDG/6b8R/43+y74DPtw+kX7XPvJ/JT8d/4Z/0EBKgQrBJsFlARPA4gErAUuBZ4JJwyKCxoKHgl0BREEBwB9/DT7Vfl2+TH6Lv9XA84IMwroCrMJCAkyCH8DMwN6AjEAEwIVAHX/sQIGAfH/yQDD/vj9hf2x+cD6g/oN+mT8M/z//OD9Nf82/3kAVwB5/i4AJf1n/N37G/lC9EfzRe8D7r7xPe/b8qb3h/1n/R7/FAFcAuoCOQFeAcUAm/91/zEBqQJXBeAFNwXdAqMCCwBy/nz+9/u8/XD9vP0ZAfYCmAVmBb4FhwR1A44BgAKzBlUH0QlOCQ4HqAixByMFPwPk/hj9Rfyp+wn+PwGDA6oE+wa8BpcEmgTpA2QFkQOIAesChAN/BWQEzQE1BLj/tPsF/dX6Nfrv+e74tvro+4z7Kf57/7cAo/8p/mL9sPzr+7z7QPut/DH+lP7r/xb9Wf3j+xb6wft4+fv7w/qy/Sj/0f5cAdP/VASi/4L/ev7I/TAAnwDAA5gFWgg4BmIFlgXBAmD/9f3o+tD7qfxQ/Ib+Wv4sAoQBn/49/6r+cv9gAOQA1ANeBNQDnQLEBPcFkwNwBTAAgv+Y/7v9n/4/APT/oP+3/QL8q/1k/iX9xvsJ/Vr+nAA9/1wBdAHUBIoCvACE/xD+fwD9/tEAOgGxAk4CVgJ4AogCeAFsANL/J/5C/kD9nPwD/xz/KQDXAR4CLQB5/yf+Uvza/Aj8Zv6J/5r9cP7g/7P+vP0Z/Yb9cvuu+938mPy6/+0BrgNhBSgFQAb+A7MDSQUvAjABw/4f/4UCIwLKAQ4CJwJmA8/+8P9bAGb/k/75/JwBpwCOAQsAAAKBAkUCAwF8/JkA1/5p/6f+0v3FAD3+8f3F+sn8r/7H/PT9GfwA/xz/ef7T///+0v/e/2gAjgAtAsoDSAIzAn8CxQGwAVMBh/8ZAJb/XP1J/Rz8hP0y/iH/o/5BAe8D4wCmABABvv4h/gr+2f30/xIBwgHIACkC7QJ2AL0A9v/G/kX+afwCAJMBvwL9ASACDgIkA5EDwAGXAhYAFADG/Tv+CQD+ADYBQQGoAP3/mP3O/Pz8BP1w/jL+1v2N/dD/1QGCAZ0BywHk/9D/tP8y/2sA1QCQACwASv7p/sX9YP6R/zL+BAA1/Wj9UADd/sgAuP4XALoBUgCjAMsA7wCnAM0BzgFrBL4DGgEj/kUA/gB8/9D/3/+IAKoA8P8XAGwC3AD8AdIALAFZAZH/sv6H/5T/7f/m/9L8dAG0AQMC1P9k/L399/xp/jf+n/+TAEn/2v/2/5sCCALqAf0BRP89/i//Dv9T/jP/tf6h/9j+yv6H/y0AuP2D/Vb91vy3/6r/QP4aAa8CVgLjAb8AxABn/4UAJABMAbMCCwI6/48B4f9A/0YAB/6dAIz9cf8fAP7+Yv5q/XgACwATAUkBbQHHApgAgAEfASsApQGI/xMARAFVANUAr/7b/ZD/SADS/yIBAwJYAe0A8gBRARQB5P8tAN8A0v7GABUB/v9uASsD+AL+APP+i/4R/Wr9qvyW/QD/5v/4Ae/+ZQFNADgBSf8X/3P/3/4PABz+jP/B/QoA0/+S/W//SQEF//L+0f8/AJj/xf4LAXkAogDiANoAGwIMASIAzv8n/wMAT/+UAHX/+P41/1T+PQAS/2j/if7aANH/D/6dAP39AwIOAOb+6QDF/ykBCwCZAN8ARAIQAMYAd/8fASEBkP4MAYP+cwBN/Zn+0wBJAG8AzwC8ACkArwAM/+/+Jv8uAUIAggDIAY4CgAHj/loAov/6/tz+K/5xARAA//98/oL/hQAcAOz/GQB5AXv9if/U/37/DAF9AZABQADHAAICI/+DAD8Aaf6UAEf98/9w/+X/KQFp/0oB6QBPAU8Apf/7/1gCnACBAA4AZf+XADn/H/9LAJwA5f8FAfP9HAADAR0ASwDy/7EBMAAH/5L/5wC8/+MAd/0mARICMQIZA7z9NQB3/kEAWf7X/nH+0/40AFz/c/5z/l8AXf7n/o/+Xv+X/sv/Jv9N/sn+wf89ANP/5AGIAKf+7/32/Pv9vP1U/v7/cf+0ADMAxv/NAYcCPgIdAAgB2QGVAc0BtP9i/9QAgQGLAMH/yQCHAEwBYf/R/3ABo//DASgAUAFcAWkAjAAUAHIBEgAvABkAgADZADIB0gDo/wUBwgAU/+gA7QD+/+P/CwCw/4H/jv82/joBif88AI7/gQBqALoAzwDs/wIBKACO/9r+WwAc/ZcAr/2J/8EAPP4J/sr9Av/N/kP+B/3Z/2L/bgFM/zP/QQAe/9QAjf+p/+AA1/04/7L9w/6pAd7+WwG4//cA0wEcAbT/HQAnAYD/BwAX/i8B6f/R/0oBq/5NAMMAywFcABQCnAAUAFcANgBbAAkAJAEo/tkBt/yJAU4BqQKRAS/9lgGDAHgACP2I/uH9BgPy/V3/ov9wAfQAK/57Ac7+8ANAACEBigIlAGkAHv5z/S0AegB2AJQA8f5xAND+7P+O/zQARQHB/97/5v2NAGT/PgBv/xD/9ADf/QQALAC/AA8CMQD7AMQAvQCRAMv+tP4r/lX+Uf+L/23/dACn/2QAdAAFAYUBOwAAAOT+LQC4/p/+IwC//1gBNAH//qQArgATAnv/1v8gAf0AxQJiAJcBF/0A/9/+FAAWAS0AnwHtANj+O/3J/qD/ywDo/jD/4/4y/3f9q//J/db+hgBb//T/OgFc/+P/BwCG/pABEP76//P9Mf/+/yIA0f9nAB4BbgB+AK/+jAHg/4YAJ//M/uMA7gCbAvn+JAHiAAr+5wL//7EAj/9VAIICS/91AOH/QAJoAQAA9QByAH4Bv//NAEgAAAKaALMAqACm/TUBz/7bAeL+lf8AAPT+zv8i/kQANQLaAQj/EwCD/A0Bwv/c/9sApP2GANT+/v0E/ub/6wHCAaL9Ev+9AJP/dv8L/0j9n/7v/xoARQAxAAEDuf9Q/rr++AAABVcBEALz/1b9ewI4/8QAvf1o/24Baf70/5D/hwBZAQUAnvw0Afb+AwFBANn+PQA6/SIA9P9CAdX+ogCVAfr9u/61/34AmADx/joAEwDDAIIB7wAr/w3+ngAgANT+xv8aAMAAqQAAAJ4BJ/0DApj+XwJyAVn/ZwIz/yoDVQAcAsoCcAO0/0wAof7fAIwCOwBB/k8AcwEWASEAzPthAcIB7gDg/A/8E/ww/hkA2f+T/8L+CgAw/xgAqf5s/ygBYv/D/lj/8gJvARv80/42/dr/1P40/UIBlf69ATr/w/1O/a3+9QAQ/qz8uP4gASj/fgAZ/tUAAACo/yIAR/6LAWcBVgDHATUBRwDEADQAhwE9ALcD5v/8ASUCwgGdAoH/PgKSAJIBKwO0A7sDuQHbAcQD7QAsA+8EdAOWAskDXAJzBH0BFAEvAXP+fgP0/34CoADV/u4Avf7q//n9jv4L/S/9sfzU+pj8/vnk+8b69Poc/Cn7Qv71+ef7yfyG+Fj8zfug/Iz73vp7/J37uP3C/t/8Ov2w+638GvwU/cAAtf0HAG/6S/+KAHkAXQX4/ggDFQJaAmMDYQKTBT0B2wMMBL4EXgjbBuoHMQeECGAIsgqfA4gEjgUlBR4JaAZdCSAHmwQfBbQEfwQ2BfoBDQOCATMA0wCiABL/CP4L/6P9Gv2p/kz+yPzf/HL7Dv1m+sz6IPtW/dz9UfpY+yv7DPyN+g/5wPp5+WD49vZT9Zb2PPak+UP5u/cd+vj5Ffjz+Gb8sf0B+5n5jPyi/Tb+uPxE/H79p/7N/6UALf+aARAElgMEBP4FFggzCIkI8gewCtkMyAsFCk8K8QnSCeoHJwnkCv4MGw6VCooJjwZ7BmoEnwSLBE0EFQV7AdT/+v10/Vn9N/4n/bj8zPwm+0r6Wvle+YP6jvkv+Sz68fqt/OT88/zv+pL6v/oJ/W/8h/0g/tb69fqv+Tf88PnC+Fn2U/b/9pn3DPhR9yj4wfe499b2zvvp+j0ADv4Q/TD+sPwIAtoAiwLSAvcBHQSgBWkGjAiZBmQLPQsADi4Rlg7QEHkL5AoGCdAJAgyxDAUNzAqDCIAFywZXBEEHWgXIBHgDqP53/8v95v2w+xD9Efzr/JH5l/oC+ob5iPr19lv5Yfhn+hb6c/qE+R76Ifmq+cT5cPqP+0/6rfsl+R765fnW+9P9UvtN+Tv3/fMq9PvzIfdU+gL5QfkB99X27/l4/YH88v4S/xT/VP9PAMsEoQdBCFsI+gr7DJcS3BCgFKcSohGkEM4MgA9IDTMQ3A4IENcLPgqIB9IE2gOtAoEF1QHlAib+Lv0T+c/2nfdO+Cj6BfkK+v/1I/ZF9J/36/b8+ET7p/oH/Kb6M/xX/Hr9K/vO+237cf02/hv/7/76/AL8Q/pa+3r7zPtr+1z24PKu8hT05PdS92z5u/gp95P2/vex+ub+FgEPAZEBNQAGBGQGDAtcDuQQvxMnFb8ScROXFOMTlhLQDtUQFxHnEo8QHw3bClQGrgSYAxAEIQXYAp/9X/n89BDzrPOg9Xz3H/bk82fx6u8B8OXxbvQ2+M74Yvfj9wj4+vkO+1z8WP2q/h/+sv47AHsB8wILAjEBOf/Y/7IApwDF/7b+/vY19Mry8fO4+a35yvrN+Ab3l/UL+bb7RwH/AZMBLwDM/7sEOQiqD44QixRlFIUScREbD/cQrBKFFRwWTBXJEbsO+wrjBmUHWgjLCjYIggJC/U/3mfSy88H1Efie+K31PvHV7c/sou2c8NXzofXq9vD11/U+9S72//i8+1/+Zv4z/kX+t/4g/4z/HAEpA9kD0wGDAtEBqv+n/xr8ivr/+TL5I/oE+cT49/dE+JL40PrE/Jz/0AFfAP8ApwBzAyIG/QrnDZgURRN8DxkMGgY6DcsRERqrGZUWTBDrB+IEmAKrCicRgBFiCIn9NfQL8gP1APmT/8X+H/tu8dPrnOtK7rH0UPcI9uryNfHJ72ryc/SA9wj6QvvG+8D7i/4H/xH/FP/9/yID2gTUBSMGpgT1AIH/EP9RAw8E3QEbAMH5ePgv9kL53v0M/8X+5vzZ+Bb4Vvt/AMMDXwPjBPEFowfzCnUOahCjC6cEfQkkD1QXihd0FEsQeQhABCYF3Q5bFUMVVwt4AGH1SfQr+RAAzgWFADT5dO5M6P/pjPGr+F/6GPWf7v3qn+oO8EX03/ie+Tz3z/Rb9Cv4svyRAHEBgAKiAq0DPQM0BRwGJQbqBfQCzgUlBqEFlf8Z/UP7HfsZ/fD8OQDN/Kr7x/hp+Iz55P5eAl0ATf8D/CoAawLUCDQOaBRZCxD/EwHzB0MX+BZwFtcQKAqhA8ABdAv9FDoZzBAKBrv4m/do/FYD6Qf/A1z+KvPO7WrssPN/+1D64/Ws70fr1uv68fj0Dfgy9lb0mfPC8QX1Sfrm/vv8Dvyc/sUB2QIZBK4FcwaEBYsC0QT8CIwJ3QZvArL85fsS/oUBDwOe/yD8L/gq+Cj67P1WAksBhfzW+sL4ivy2AxgI7QtKCnkORgB5/CkCHQ2AHFUT0g+SCbMHjQM6BkkPMRdaFbsJgAFP+vT+3//UBpAHjP8w+Zrxx/Ag8aX34vrl99Ty3+1n6zbvPPTz8x71q/Iw9H3zufKG9xr8WP6b+5j9LQEoA0IDCgOCA2sDKgUPBmsK/QplCZIGSwMSAvj6PAF5A7oBDwFD+3T5H/VG95H+/gECAWb/vf0Z+8b3mvrUA+sGZwuGCo0NjwjD+HwACggDGLMZ1BV5Es0FTwHE/38KHBS2GcUTmgkz+FHyavlR/iIIngXCAFX0N+mj6FLtQfjm+xX7X/RH673mqe009Lb2kfkT+cH4+vFv85r4Hv5+ANj/qQOPAu8CYARDBVkECgefB28IbAgeCF4IAwWHAeoC7wSBAHMAFf06/n37+vYt+QH61/u7+cf5F/wy+6j8jPy1/an97v6AA9sIDAu6EEsOwAAHAwwFTRfqGyYZ4xZsCOsCAP/0BpMRGBjCE+kIovXl7TH0WPzDBzsF3P8e82/nauUV6pj3mfxl+7H14uuS5/Lun/aq+mL81vtu/WX3mPam+t8AZgN+AUEEOwT+A4MEwgUQA7cD3wXeBwcJQgeDB5wFwwBO/pAAbgjXASP69ftF9Dz1e/e7+2b9JPhr9wT3//SI+Ov+XQYDA5r9r//1/3YEDAvDEhQXBhFEAE0D+QbSENoaLRp4FSQDK/8KAIwDzgt6EnoRHgU+8W/syfTu+Y8B5AOe/s3y+ek869XvuPdV/S39DPnG76ztovYU/Mj8vP72/Dv7N/p9/DkBsgFpAsQB9wDn/1ID6gklCFUBXP/pAB0ACQNMBf0HdAX7/Vn7P/wyAE0CTAdr/fv1K/KP8S/8OfrG/gf9Zviw9m/zh/vcA+YHugi0AoT91v4PA4YLig5KEUgOZxIsBeH6nwe5C9MWLBGIDLsFXP/XAAL/mAXOCLIKuAiY/WTzg/V0+yv8AQCs/MD65fm/9J/1oPbF+zX7h/zN/Cr2H/kY/rH7UfqI+Q766v8T/hD9Bv6f/Aj8Xfxt/o7/rwRVBxACavtp+3P91QDYAqQDEQfSAkL8k/va/lcB6QaFCdIHavqG9Ib4UPaL/2X/3wKxAA70P/dn9rT55wBJBYsFMP2S+F38pvx5/v8E+Qa0D5EJtgwVBen6mQQJBWAWaxMtEggT/wdKA237OAR0DtkR8hAKCu38HPWP+Xb7qAS8A6sDuAHK80TxGfCA96r3yvfh+sD0J/Ro9b/2Bvh59m/20/sJ+lv4K/yz/sL9PPl3+pn78v7YBNUFpQRzA1gAawBzArAC4An4DWoJdQQeAu0BmAMyBrcJgwhLCMn8j/Qa98TvOfxK/3D6IvgU7q7xJ+7e7h34hfvP/On44/bU+bP2wPvFBfAGoAn2C5sSKxGRE84JqAejDuYMLxxuGxscixRODBIKLf97CKwPZxWOEtsDjvuc9vn0XvTa/lP9UfmQ9Qvue+xd6snxafAk8e7wu+xu8+30WPVa+ez0XvF49q74pP2zArQFqwavAGf8vfztBFMMIw2jD9AMRQblBAsF2gUHDA0PqwtEB/8B/wAbAs8EBQGPAXQA5foJ+LjrgPCB7yfwqfQv7dXylu0L73nzSe+A9Gr3lPns+hf6w/yNAbMGiApdCekN3RDFFNsZCxw0Hz0gWx/vDtYMng35EiofnRgpGR8PAgfq/3H3Yvz9/OsDdwNE9wvtFuum6aHoweuJ6WTuae5R7QXuNuu97C3pb+1K8FLwqPvzAcsDogHm+wn8bQCHAhMHbg34D1cQIA6CCu8GMgp5Dz4PkQ0cDW4MnQzaCeMEFgU6A7X/eP6x/Vn8Bf00/TT5B/OZ8PfvhvK88Bbpte/I6vroCPBy7cjzJfQs93T6ofX698L9DATOAywD6whFC1kMRxAwE0UWSReMF/gcrx2SHkwhKyG1HwwMBgQxCxoH9Q7kEKQMMAl/+bHxge3F6arsi/KH9dzt/eRW5vHjKuEN54jmA+qy7ZDtBPVe9xr4mfrz+zL8fvsLBVEN8A/mEwUSHw9+D5YOExHbFIYTThTMEyoP/go9Ck0L0Qd5A34AKvxj+1r62/du+rf3pPFi8WPtWulO7KrvhvCn8E/yAPGh8mv0HvdU+bf1Qvh/94z7vgB8AsoIuwdQByQFVgKqBT8JYQv5DLoORw7kDXIPthAoERMSThKWERASlhOVE9YUuA4SA3P7p/hR+8j+BQEAANL9sfaI72PqY+c+6F3r2e6s6lbml+q27kXy8PVr9qn2Rfa59+D8qwP9B8EMcw9xDskKlwtfEv4U5RWjFB0SUhGfDtcN/Q3fC0gKCQeOAkf8TvmD+xT8jfiv9ajynO+w7UXsae7o71nvFfDc8XDwi/Aw9qT6ZPsd/Ov9OAAGAqMFPQqHDaEKAwjoCZME5QMVBcwFrQdbAgUAtf8B/sD8+/5EAXYA7v14/Rv+7PyeAL4E5QelB6AGUQgyC2AL5A+eFbEQJgbvALYAAQB5A2oCEwQsA1n9Y/gw9MHwMvDZ87fy8+xb61zuG/HU9lP60fu+/H77j/qS/gUB/gJ+CbEPLg+KDbcPUxICFaIU+RF5D3YMNQiCBhoGAwU2BckE0/8C+a/08vJR8fDwu++T7tHtIOuy6zvvge9J8O7zT/Sy8970hPfF/PsC2QUSCIMJGAp9CyYP6BC6EUkShBIHDg8H+AfvACICBwJe+g/9a/iG9aL3OvTD8sjyGvLm8pPz6fa3+cv8WwGJAb4DuQZJB8gNohGxE/8XfBotHswaXQ8lCYADcQGkAwP+Gf6p/lP8RPya9/rz0fLG8HPu/OnN50fqfO2N9cv7pf1WAQEEcgfGCwoMVAudC40Nsw1YDMENJxHcFBkWeBEZC9cH9AHs+4r3PPNb8J/ucuyU66/snex67L3suepT59TlUuZd6d7vufXH+gMCtQhQDd8PmRAREegSLBNQEmsTNhb0F+YYTRjHFT8R3gzTCGcEjf5U8eTsDec73v7fF95Z4ETlfuUU6rbsA+2p8QT2FPqj+7D/nQW/CNQOtBOZF2YbVh0WH9kg/CCYIW0i9yLIIQQg7x6PG8gMUvwI9QjraOmX5ajgnuI44Erg8+Mf5SXpk+0E71PtsOm06+rxY/ueBXUPXhfJG9gd3COuJvIjyx/1GlEWyg1TBbYDgwR+BGEBbvwx+AbxSOpj5NzeNdo315/Vktcz3Erks+5R9pH8NAP7BwIKzQz3EHIUPxWjFZAYohxMHqcf7CDyHscZohMADj0H7P+y+Qb0Me/K6sDnUOdl58Pnweiw6BLoE+Ro4jPl8eVe7MnyMvc3/2IF5gtqEQQTahVLFL4P3Qs1CCgHwgedCAgLoAttDJgOPhEtFNsS7hDkCwsHgQO2AZsBwQEWACv75/J67q/uJvDM9JHzr/LC8jXy7PJs9jD7WwAaAvEB/wByASYG3ApDEEETcRI3EfIQ7hCQEgESbg7fCBIEs/2W+Lj2P/Zl9tr0rvCI7W/sDexz7mbve/Do8KDwKfKC9Zb68AAGBlgJ5AsPDdgNCQ8cEOMQvQ+GDJwJNAetBf0DBwP7AeL/Rv3Q+qP4Mfdi9uj08fIy8TXw9vC68yb33voI/sMAjQOGBsIIzgrjC3wMUAiCAc79FfkV96r1OPM68uTxE/Mn9cz48fzx/hD/xf7O/N78Lv/YAPMDoARGBSAIiAzSEc0WBxvfGzEdhx7wHWQeYB7vGYIKd/oz8abqb+gJ5YjiiuML5/HnNOwO9Bj84QFYARD+j/nD+b/6Xf5YAx8HAQs1D4kUehpvIMkhOhxUFKoLIAF7+F3xXOym6ELkm+GD45bnPevy7zD0avXV8xnyP/QF+WL9ygBSBHgInAy/EYUX5ByyHxgfuxtyFZoOVQneBHT/Nvi78RvtS+rR6f/rje/w8eHyRPPl83X0MPZb+Sv8tP3j/jEB/wQ+CtcO9RLsFWkXyBWBE/oN3gXf/kz0+Ou15Qvh3t6U4Dvk4urX8bL41gG+COcNgg+mDwwODgudCBAIBAcYCDkK6g1zE4UYoBwTIfYjeCMrH9IZWBNBDEcC6+un3tTWs9Ht0U7SyNHi3K3o0O2o+Hf/zQlqELAPqQmgCHIK6gswDJYNTxF5FvYcGyD1IuwjaSFKGFQP8wKS9UXskeSX2xfUfc8+0LPYtN825qvtkvSh+y4A2wFABHMH5AmYCRQInQjSDCITERj4GX0agRoOGHAT+Q0aB1H/Q/YY7PbkruCX3/ri8eeT7ADyDfhP//gGrgtmDgMQwg+cDfoKyQn9CUAK1wk6CUUJWgpxClwKZQmhA+H7S/It6Ing29pt10nW0tdk3ajmxvEP/4cLqxVVG7wc3RsJGTsVGg9HCBUCVP1p+1z9CgH/BrwMnREsFa0WdxZkFaARkAoLAzL7BPX88P3yq+576CPq2O7+85H57/hU+z8DZAK/ALcAEAJmBOwFDgJEAKwCVQhcDXQQ5BGHEgYVdhM7DrEI8wQ+/W31c+6L5x/kbeT35P7mFukB6X/u5vYo+vX6ZP5XAiEGbQdbCBwMbxCEEmkT0xS2FXwWRxf4FRER9grcBF//N/rb88fuYOwd6brmYedg6bvsEvG29JP34fmi/DQBGAWuBmcH/QjBCp4LgAyNDocQMRFsEKgOgQ3VCkIHXATq/7P59vTK8cXuLexk6rjpZemA6zfudfEV9q76aP8OA6QEhwbjB4kHTwbSA7IBDAA0/67+N/6o/nAALgLhBDMH4AhvCpAMag1LDeQNhQ/HDzMPFA/VDhoP4w5tD8APDAvM/on5QPZt8irvyOiS4wzmn+eE6ITulvOP+/kBsAPiAtMGuQsjEFUOFwpACC0IwwivBxIIyghgB/QC8QGJ/3H8rfp2+Mn0z+/t6djoA+0C7p/usfCM9Ez67f91AxAIfQyBDt0PBg+qDD8LbQsKCrsFawI9AfUAzgDc/9z+Gv5z/Lv6fvpy+T33Rfba9Rf18fT79jn7LP/jAWUEdwf2CqENiw+7EKUPCw2WCU0GuQPpACn+Tvwr+tP35/b59qn3ufjx+Nb3Evbw88LzWvMH87/0JvYi+Ir76/5EA9kH+QmRC18LBwrLB1MEHAEt/an5n/fl9ov3/vnC/SoCZgZ9CS0NnhAAEocRwBD2DyoNkgzRC7kKzQp+CuUL1w4qD7gN6QwPB4r9vfYM8vrqs+U64JTcJ99n4tHmqu8G+dwA8gjKDccQSxQ4FuQUcBArC2sHDwWJAxkBNP+1/iD9m/vz+2T76fjR9tfzC/CY7Crpnugw6/XrF+1p8T74PwHICb8PrxSyGFca7BptGcwVgBF8DOMF6/7S+Qr39/U19Xz02PMp9FH1tfbJ90D39/V99Uf1EvUT9oj4dPy5AFMEcAiYDdASzBYpGLcWRxNiDwQLhQX+/xH74PbJ86zxlvDu8Xb01fbr+NL6Jfs6/Cz9+Psl+uX3XPcT+D/5gvpP/UsA8QP1Bn0IYAktCSYIAgbHAgP/nvx4+wX7evqW+1b+SALXBVYISQrZCk4LzQrPCkYJ6AaiBc8E6AOKBNMFggdJCTwJVQpbCQoMeQrpCvcIzwH4+OPxvu7c6wHrzeWO5r/oPO+u80P7/wHiCQUPMA6PDOoLNA5qCzUIigH2//z+wACBAZ8DHwXHA4IDQQIEAZH9pvp59X/w2+ka5kDnDOsm7ujvTPMN+QYBRwieDckQxxK/EhARIQ5hC/4JLggmBEf/TPz7++38Xv4s/l/8jfoT+Yn4qvfk9WP0OfR+80vzbPXL+Xz/MASeB+0KRg5JEVcUFxW1Eu0OqQpzBqUCNf++/HP6G/gc9jz1mvXz9p74hflY+WX43fgj+hz7mPkA+vb5xflU/EP+DQFmA6EEZQYzCJEH1AfgBkkFVwJn/iD95/xf/QL+qP4y/6oA9AFwA3EE6AQxBAQDgQKaAU8CrgNfBGUFRAdACAsKwAuADK8MGQsFCUEGFQX5AnQCJwGBAecAcgE8Ae77mfgT9en0rvEH7fvnq+lx7BLwO/PU98P/1AU5C44N1RDPEi4VuBGNDRgIQwWsA2kBuP6m+yL6vfd4+Jv3O/Zj9JzzH/Lt7m/r+uog7jTwXPHQ8t32af0PBFMJug0mEfITOBWuFIQSjg+hDN0IWQO9/Qj6UPhV+Br3VvZh9+n3RfkN+2L7oPz+/Qb+Cv+z/ysBPAQZBxQJlAqxC6ANiQ/DD00PuwyHCe0FnwGu/Rr6xvao9KPyzvBa8NHxLPQH9qP4Q/rw/IP+g//6/5AAEwHTADQCiQJ+A1gEKwWEBZIFjgQvBDcDOAEn/+r80vvT+qL6WfoR+/P7r/2b/pX/kQAjAToCFgJuAogCpALsAmYDGQS3BAgFQAW7BS4GwgWoBcsFBgW7BNIDXAPcAiEDIANOA5MD7AJPBDEEPgWvBSYH7AUyAw3/N/mh92f16PO68WzwWvCd9PP31/siADsE3QdqCAgIhAecB5sFCAOu/kz8qPom+2b8CP1f/Vr9k/0u/ff8Hfxp+5D5vfaq87/yxvMw9rz4dvp0/Gn/RgOYBpIJZgv4C4gLfgnrBvMEpQMlAs0AFP+4/Yn+//+FAYQCCQI1AccA4//L/hn+Lv1d/NP7XPu8/P3+9AGCBNYFvwY0B4cHTwc/BsUDUgEm/nH8TfuL+pT74/y+/fD9af2k/Kz9bP2j/M77SvpU+ZP5Tfqd+8z9vf8wAkMDoQSiBR8GnAZ8BRAEZQLHAD//o/73/cP9DP6s/if/uf9AABIA6f8r//v9Nv2H/Hn7Mvtw+m776PyA/voAxQKMBCsGqAaGBtIG4AWWBQEFwgNiAp4ATQBWAPb/BgDU/8n/TgAiAEwApgBOAMf/Tv8s/m79D/66/vL/cAFBAs4DRAUWB6sHAwdGB6MF0gMoAggAQf6t/Bj6efja+A/4c/nh+s77nP1k/mf/uwAvAT4BZgGNAL//Ef+v/r/+3/7f/qH/YP+E/zwANQAZAFz/VP6n/cj82/si/ET8uvxI/fz94/51AFUB5AGJAjICEQK7AXgBTwEMASoAtP8S/6n+S/+P/+P/RQAiAFwAmQAGAFkAAACV/5v/EP/m/jb/nP/o/2UASQC5AA0BSwF4AUcBoQBWAOb/Lv+x/9D/9/+CAKEA7QDiAccBFwIxAscBuwEQAVkAPwAWAPL/+/+o/5v/Vf81/0r/Qv8e//L+if5q/hD+Jv6M/n3+jv7o/mj/xP+xAC0BWgHHAZ4BMgHZAHAABwCa/zv/1v6C/i3+/f0z/mj+a/5+/sf+0f6x/v3+Xf+d/9r/GgCeAPsAdwHbAaACWgMvAzYDwQLtAl4DbQMdA30C7QE/ASoB2AAvAWIBIQEaASEBPQHcAeMBVQJqAuwBkAGBAd8BugHrAQoBZAFnAOX/0v+0/mD+1f15/H/8i/wD/Pn80/wq/ZL90/3N/Q//Nv98/wsAaP+7/83/xf///97/sP+u/xv/Bf+e/lv+zP1f/Qb9r/y1/Jb8/vwO/VD9of2o/fz9nf4f/4z/o/+Y/ycA1AAdAaIBZwJPAu4BsgGfAaYBTgEsAbEApwCvAKUAOgFaAXABpwF6AVMBugHTAdcBoAEpAdYA2QCJALMArgDJAIgB8QDLAIYAMAAzANn/ef9f/5r/qP/S/8z/uv8PAAEA5f/u/+P/8f/V/4T/Qf8g/zv/bP9Z/zX/Mv98/3j/Xf8p/+f+Gf/g/sj+qP7i/oT/yv/+/xMANgBPAGYAOgDb/4j/ff9y/1//Vf+T/8X/6v8rAEwAxQBWAcEBngFfAR4B0QCWAGUALgDz/wwAEQD2/8P/sf/G/63/af9h/2P/kf/D/9L///8IAE8AmgDbAFMB0wEhAlgCIAIMAt0BjgFSARIBjACz/2D/3v53/kH+bf7C/tP+1v76/iT/Uv+X/5z/XP+V/67/fP96/zT/Uf9+/+X/s/9v/6X/4v8bAO//CQAaAD0AlP/m/rf+h/7R/uX+if6U/mb/EgC1APoA/QAhATABOwFCAVYBOwHbAIoAdwBZAFMAZQB+AEUA+P/A/4X/o/+b/4L/9v+GAOgAMAEpAQgBwAB1ABUA3v/a/7n/9P98/yn/Hf/D/s/+2/4u/5j/4v+y/5b/yf/f/+T/1f/D/7D/cf+5/kz+k/6k/u7+ZP9F/8P/IQCbAPEAzwASAXEBVwHOAPoALgGmAZgBcgHhAd8BogE7AecAlgBDALT/U/9N/zP/Fv8B///+A//7/v3+Av8u/0b/QP9f/47/s//H//T/JwB0AJoAqAC4AOwAFwFjAbMBvQH/AagBJQHOAFgAIgDv/+H/nf8k/wz/DP8G/xn/Qf83/2H/h/9P/1L/mv8IADMAOABFACsAAQD1//j//v8QAAYA0v+o/3j/Vv9X/0j/Zf+E/3r/cf9Z/1L/T/8//1P/cf+A/5X/sv+k/7D/6/86AIYAaQBOAEUAPgAIANv/3//Z/87/yv/h/xAAqACzAHgAeABIACIAJQAuAFIAcwBlAI0ArwDNAMwA1ADFAKUAlgCOAIUAfQB/AHcAbAA0ADcAEADm/7r/j/91/3L/iP96/57/q//C/7b/x//l/7n/ov+i/8L/1f/X/87/nv+S/4r/kv+4/6v/uv/J/+H/EQA0AGYAhgCpALQAmQCFAFIANQA2ABAA5//E/wAAIAAEABgAXwB3AEgATgAYANX/Cv+V/qz+dv6m/iT/hv+0/+H/3f/n/+P/1P/h//H//P/X/9H/KACMAJYAXQAZANz/t/+E/3j/0P/t/wsA8P8SADgASQC6AJAAuAC2AL4A7gAcAQ0BfgBYADkALwAAAAEAKAApAIT/D/9B/xv/fv/j/w0AEwD3/zUAcABxAI4AqABhADkAmv9I/3f/Ov+X//b/xv+c/5b/CwBwAIwAywCuAKMAegDo/7D/3v/m/zAAkQBqAFEAZgC+AAwBLgE8AToB4gCHAGUAWADb/0//Wv9V/0r/KP85/4j/rv+8/8b/y//b/5v/Z/9D/zv/Tf9l/+b/HQAFAP7/TACEAIcAkwB+AGEAFgDW/47/bP92/4L/9//1/8H/wv/4/zEAOAAwAAgA9v+b/1//GP8c/yn/YP+5/5n/hP+N/wYAUgBbAGsAVgA0AAAAe/9B/2X/Tf9c/7n/MwCNAKYA4gAHAQkBJAEYARQBCAH6AAgBrwAsABMAJQA6ABgABwAxAEwAOgAnAAoADQD9/2P/Gf8r/xD/Iv+C/4r/df9x/8r/DgAZACEA/f/8/6z/Xf8j/xn/Hv8p/3D/2v8UAAgALQAQAPn/8v+3/7P/sf+l/6T/hf9p/5f/sP/F//T/9//x//D/7f/e/0cAlwCbALkASwHNAZgBgwFlAUIBFgHHAIIAXABAAPz/2//I/9//+//a/+L/8P/g/9b/0v/s//7/9v8MAPH/8P8fABAACgDv/9P/vP+k/5z/dP+S/83/ov+C/3r/b/9z/4D/g/+q/7v/yP+QACYBjAG1AYYBRwHNAIEAFwDN/3X/jf/C/8r/CQAQADkAGgDo/9j/gP8U/w7/Fv8b/x7/Dv9x/5X/3P+W/wT/EP/U/ur+hv8GAEYAWABjALkADgEJARQBuAH4AcwBNQGHAD4AGQArABUAQwAvABgAJwAXABsACADf/8v/xP+r/5n/j/+F/3v/ev/I/y4AYgCIAFoAJwALAA8A9f/z/xIADwAbAND/sf+z/57/lP9n/2b/V/9W/3j/Uf90/2b/UP9u/3b/wP/q/xwAEQAlACcA///v/9H/zf/c/8//r//+/9H/nf/P/8r//P8jADYATwBSAEsAIgAEABYA/f9DAFgAVgCKAGoAbwBcAFAAPABIAEEAYwCeAJ0AjAByAHIAggBfAGUAZwAbABIA2f/W/9f/vv/w/9T/3v/o/9X/7//O/73/q/+4/7//n/+j/63/wv+V/4b/lP+F/4H/af+c/5T/o/+5/+//UQBPAGYATgBnAEYAHQD4/+b/tv+b/5b/Z/+C/3n/tP8RACEAVgBtAE4ATwDz//P/1/8OABEAJwCTAJkA2wCrAJQAYwBbAC0AEQD7/9z/EAApAG0AggCoAKoAXgAnAAsADAATAPz/FgArAEYAXgBDAEcABgDv/83/vv+3/6X/xv8LABQA3f+7/zD/LP/z/tv+Gv86/2r/ff+l/7j/5P/5/xYA8P/c/93/AgAmAEQAQQAnAEwABwAGAPb/0f/7/9n/4v8LAD8AZgBEAEMA5f+i/7X/i/9//7D/6P8rAJ8ATQGrAWwBTwEoAeAAuABgACgAlwC6AJ0AlACEAG0A8P/I/+7/4v/f/8H/y//B/8//w/+J/1n/+f5B/wD///4H/+3+bf+I/7D/5f8oAEsAXAA/AFkAcQA+AIz/Hf9S/x7/Qv+Q/8X/xP+e/+H//P8UABIA5//5/5j/bf9Z/zv/cf9w/+D/TgB6AK8A8AALAf4ACAHxAM8AiwBzADoAov+E/5n/u//E/4T/1P/4/ygASQAQAEsAQQA8AO//uP/V/+H/zf/y/9X/u/8HADUAgQCeALgAsACRAB0AFQCp/5//kf90/9P/p//r//T/WACwAJoAwwC3AF4AOACX/2X/hv9d/63/0v8rABwABQBpAH4AngC3AGwAZgA1AMD/kP+Y/6b/sf/O/6P/of/O/y4AWQB0AHAAYABWAPD/1/+f/5X/bf92/7n/mv/A/8D/9v89ADkARwA/ABsA7P+z/4f/c/9h/47/w//Q/7P/sv8fAE0ARwBNACkACQDZ/3D/NP9K/1L/ev/F/yEAgACXAG0ARwBIAAAA7f/u/9L/9v/w/+f/8v/f/7f/y//c//X/2f+d/6X/hf9//43/mv+H/5X/tv8cAH4ADQFqAUkBPwEWAR8B5ACUADsACgDC/3L/Kf8X/0L/Sf99/5H/t//X/7r/sv+v/5r/tf++/1QA4wDrACEBAAHFAFsA7/8TAB0A6v/T//n/PAATAKr//f9yAHQAogBJABoAUwB/AKYAVgBrAGUAZwBYACoAQwD9/7r/pP+e/7f/x/+0//X/5v/a/wwARwBhACwAPwD3/9r/1P/h/yUAFgAPAPz/3//U/87/vP+6/6z/pf+8/7v/zf/Q/8T/zv/K//D/GAA6ACwAKgASAOD/0/+s/8P/x//C/9H/6P+0/7f/4f/N/83/8v8gACwAOQA3AEMAHQAAAO7/8v8EAAwAFAATAPb/+/8SACYAOgA6AE0APAAqAAUADAADABEA+v/y/wcA6f/m/9z/xf+e/6v/mv+w/8H/zf/T/8b/oP96/2n/e/+y/73/zP/h//X/+f/w/9z////6//L/EAAKABcAHAAGACoAMQAsAGEAmQCaAGUASQAuADgALAAVABsAHQA2AE8AUAA9ACoAFQABAPH/6v/u/+//8f8jAEIAPwBKABsAJQAUACgApACWAIkAaQA9ABsA/P8MAAYAFwAaAAcACgAMAP///v/2/woABAD6//f/4/8EABwACADr/93/4//y/+j/9v/5/wAACQAAAAgA6P+j/4P/Kv/j/v/+8f4Y/x//NP9s/6T/5//Z/+b/zv/C/7L/m/+7/7v/8/8fACoAYACRAJgAZQBHADAAFgD3/93/BAAPABgAbwDWANYAuwCqAJMArACUAFsAIQAqAEAAQgBNAFcAlQCSAFwAPQAuAAAAwv+i/4f/af+A/7f/4//0/+P/3P/G/8H/dv9c/3//b/9//7L///82AFgAYABtAHoAbwBmAFoAMQA6ABMAuP+S/4j/sv/L/7n/w//1/wQAEgAJAPv/BgD7/6z/ff+Z/6X/wf/k/9X/yv/s/xUAMgBGAFIARAAtABAAxP+o/7z/uP/X//z/+P/t//L/EAA5AFAAbQBZAEEAMADr/9L/6f/X/+j/HAAgAAUA7v8RAFEATwBVAFYARQA6AOD/wP/R/8L/1P/Y/8n/z//d/xoAOgBFAEcAOgBHAAoA9P/g/9L/1//a//z/6//m//b/JQBEAEwARgBCADAA8P/X/8X/vP+y/7D/1v/n/+b/9P8bAEMAUgBDAEQASABFAEwADgDm/9n/z//l//z/HwAkADoARAA9AC8ADADW/7H/sP+j/6T/tf/C/8z/6v/m/+T/BAAFAAgA+P/1/wEA3//M/7f/uP+9/7n/5P8bAEsAaAB2AH0AeACAAIAAcQBuAGUAUAALAM//yP/J/73/tP+7/+L//f/4//v//f/+//j/w/+c/6n/kv+e/7v/sf+w/7n/6f8IABEAFwAJAAMA1P+p/5D/iP+Q/4z/uv/n/xQAKQA8AE0APgAvAB0ABAD2/wEACAATABoAIwAfABwAPABXAG0AdABzAHIAZQBdAE8AVgA8APr/zP+0/7b/zP/D/8f/6P/4//v/9P/7/wAAAQDN/6L/q/+s/63/xP/G//P/MgBVAIEAoQC2AJoAcwBBACkAHgAUAAsABwAPAP//9v/v/+H/zP/J/8b/w//E/7//3f/n/+T/7P/3/wcAAQD1//X/AAAPABIADgAFAPr/+P/+//v//P8HAAMA+//8//3/9P/m/9T/yP+//8L/1f/X/9v/5f/u//n/EQBIAFgASwA9ACIAGgAMAAcADQAVABYA///1//z/AAAFAP7/9v/y/+v/5//j/+L/4v/h/9z/3P/i/+X/7f/5/wkAHQAbABkAKQAaAP7/8P/b/+r/8f/d/+z/9//r//H/BQARAD0AOQAiACIABQD1/+3/7v/u/+3/8P/8/wYADgAQABQAFgAVABYAHQAkACIAJQAiACMAFwATABEAEQATAA4AGgAaABMACAD5//P/7//r/+//8f/6//7/AAACAPj/+//4/+r/4//s//z/AwAGAAYABQD9//j//P8CAA0AFwAUABAACwAOAAUA/P/3/+3/7f/v//L/6//2/wMA/v/8/+//6//2//X//v8GAAUABgAJAA8AIQAqACMAJAAVAP3/7P/j/+P/4f/e/9//2//S/9H/zv/I/8n/zP/M/9T/6v/4//P/8//v/+v/7f/t/+3/7f/w/+7/8P/y//f/AAAAAAAA9P/v//v/AAD9/wUAHwAnACMAKwAcAA8AEgAGAA4ANgBSAGgAbwBaAFAAPQAtACEAJwAaAPD/2v/Z//z/DwAVAC8AVgBPADkAOQA4ADsANAAsABwADAAFAP3/+P/y//b/9P/z/+r/2//k/+b/5v/m/+T/4v/W/8v/wP/A/7//zf/a/+f/7f/p//X//P/+//v/9f/q/+P/zf/C/8r/xv/L/+D/7f/0//n/+/8AAAAA/P/5/wAAAgAIAAwABwAFAAIABAAAAAAABwAPABEAEAAQAAwABQD+//r/+P/3//j//v8IAAwADgAWABYAEwARAAsADAALAAcABQAGAAYADAANAAsADQAOAA4ACgAKAAkABgAEAAgACAAFAAUAAwACAAIACAALABAAEwANAAsABwAGAAoADAALABAAEQD///H/6//y//3/BgAIAAYABwADAAEA+//w/+r/7v/o/+j/5//r//L/+f////7/AgAFAAkABAAHAAkADAAGAPf/+P/z//b/+P/2//X/9/8AAAYACgAKAAkABgADAP//9P/2//P/8v/6//n/9v/6/wEABgAGAAQAAQAAAP7/8f/r/+7/7f/u//T/+v8AAAMACQANAA4ADwAMAAoABgACAAQAAwABAAEAAAAAAAEAAAABAAQAAgD9//z///////7//f/+/////P/7//v//f8AAP//BgARABUAGQAVABEADAAHAAIAAgAAAP7////8//v//v/+/////////wAA///5//b/9v/1//b/+P/9/////v///wAAAAAAAP///v/9//r/+P/5//r//v8DAAUABwAKAAwACgAKAAkACQAHAAUABQACAAAAAAD//////v/+//7//v/+//7//v/+/////////wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\" type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WNjWoWRCmPk"
      },
      "source": [
        "For training purposes, we need to split data into train test sets; in this specific example, we break with a `20%` rate for the test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mlim-044xtJN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91552b87-9444-4211-ebe2-4d5ce98f7e10"
      },
      "source": [
        "save_path = \"/content/data\"\n",
        "\n",
        "train_df, test_df = train_test_split(df, test_size=0.2, random_state=101)\n",
        "\n",
        "train_df = train_df.reset_index(drop=True)\n",
        "test_df = test_df.reset_index(drop=True)\n",
        "\n",
        "train_df.to_csv(f\"{save_path}/train.csv\", sep=\"\\t\", encoding=\"utf-8\", index=False)\n",
        "test_df.to_csv(f\"{save_path}/test.csv\", sep=\"\\t\", encoding=\"utf-8\", index=False)\n",
        "\n",
        "\n",
        "print(train_df.shape)\n",
        "print(test_df.shape)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(483, 3)\n",
            "(121, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kcnD-d_rDElt"
      },
      "source": [
        "## Prepare Data for Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hr4md2ywo3eK"
      },
      "source": [
        "import ast\n",
        "\n",
        "def str2list(batch):\n",
        "    batch[\"emotion\"] = ast.literal_eval(batch[\"emotion\"])\n",
        "    return batch"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285,
          "referenced_widgets": [
            "dcb3759e82ea4fdea6d6348d62a59e69",
            "1f08307342a74e8f839182e6918c2eee",
            "92d31d73f0be45e59f02a606dd9ba4bc",
            "97386307594a4a74a804f45720b23096",
            "7fd7c3ef7f29430a8558ea2cc2301e4b",
            "7d65a6ca925a4267b2682c9afe9e3071",
            "81574abedcd5492aaedd597a7308997e",
            "19a153f2925b42dc86c3f8cc94efc2b1",
            "23cab63510b54331a45c531512b1220b",
            "2f9e2eb66ac243ee947c5e0e90ba0963",
            "73ba3c43267b48d8bb03dd125f994564",
            "08804f4be2fd401da39dbe6b09372b89",
            "f53da1384c604aaa8dece6bb4094efc9",
            "5c027df6f15d48f4b7e195c3b5ca10d1",
            "15d48f43109c49b08639d99ef5c00af8",
            "e22ffc20153b4ab7b541c0d63a2fa1db"
          ]
        },
        "id": "nnVfxQYDDIc6",
        "outputId": "5dfd9965-6181-4b93-a3e5-a0b4afa8ee60"
      },
      "source": [
        "# Loading the created dataset using datasets\n",
        "from datasets import load_dataset, load_metric\n",
        "\n",
        "\n",
        "data_files = {\n",
        "    \"train\": \"/content/data/train.csv\", \n",
        "    \"validation\": \"/content/data/test.csv\",\n",
        "}\n",
        "\n",
        "dataset = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")\n",
        "dataset = dataset.map(str2list)\n",
        "train_dataset = dataset[\"train\"]\n",
        "eval_dataset = dataset[\"validation\"]\n",
        "\n",
        "print(train_dataset)\n",
        "print(eval_dataset)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using custom data configuration default-b009d1261915c83d\n",
            "Reusing dataset csv (/content/cache/csv/default-b009d1261915c83d/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dcb3759e82ea4fdea6d6348d62a59e69",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=483.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "23cab63510b54331a45c531512b1220b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=121.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Dataset({\n",
            "    features: ['emotion', 'name', 'path'],\n",
            "    num_rows: 483\n",
            "})\n",
            "Dataset({\n",
            "    features: ['emotion', 'name', 'path'],\n",
            "    num_rows: 121\n",
            "})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsOrQPBOEVOy"
      },
      "source": [
        "# We need to specify the input and output column\n",
        "input_column = \"path\"\n",
        "output_column = \"emotion\""
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OWHA4xtpoUC-",
        "outputId": "01c2e7f2-d104-4a69-8987-94bc722d6579"
      },
      "source": [
        "train_dataset[0][output_column]"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.05, 0.1, 0.15]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-gh7fQ1XEpC7",
        "outputId": "65dc9f63-6de3-4df5-88b8-24456c75f024"
      },
      "source": [
        "num_labels = len(train_dataset[0][output_column])\n",
        "label_list = list(range(num_labels))\n",
        "print(f\"A regression problem with {num_labels} items: {label_list}\")\n",
        "is_regression = True"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A regression problem with 3 items: [0, 1, 2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4TkGYrVTFR6Y"
      },
      "source": [
        "In order to preprocess the audio into our classification model, we need to set up the relevant Wav2Vec2 assets regarding our language in this case `lighteternal/wav2vec2-large-xlsr-53-greek` fine-tuned by [Dimitris Papadopoulos](https://huggingface.co/lighteternal/wav2vec2-large-xlsr-53-greek). To handle the context representations in any audio length we use a merge strategy plan (pooling mode) to concatenate that 3D representations into 2D representations.\n",
        "\n",
        "There are three merge strategies `mean`, `sum`, and `max`. In this example, we achieved better results on the mean approach. In the following, we need to initiate the config and the feature extractor from the Dimitris model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQrGaFohGzVo"
      },
      "source": [
        "from transformers import AutoConfig, Wav2Vec2Processor"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Y1adr7vFrq7"
      },
      "source": [
        "model_name_or_path = \"lighteternal/wav2vec2-large-xlsr-53-greek\"\n",
        "pooling_mode = \"mean\""
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AZjDSmBRGqr6"
      },
      "source": [
        "# config\n",
        "config = AutoConfig.from_pretrained(\n",
        "    model_name_or_path,\n",
        "    num_labels=num_labels,\n",
        "    label2id={label: i for i, label in enumerate(label_list)},\n",
        "    id2label={i: label for i, label in enumerate(label_list)},\n",
        "    finetuning_task=\"wav2vec2_clf\",\n",
        "    problem_type=\"regression\"\n",
        ")\n",
        "setattr(config, 'pooling_mode', pooling_mode)"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "J4wRa6YdjRVU",
        "outputId": "c6d3d43f-054c-45a0-c0ae-b87319cac048"
      },
      "source": [
        "config.problem_type"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'regression'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZXVl9qW1Gw_-",
        "outputId": "e33efaca-6f5d-4981-88ef-a06e6966005c"
      },
      "source": [
        "processor = Wav2Vec2Processor.from_pretrained(model_name_or_path,)\n",
        "target_sampling_rate = processor.feature_extractor.sampling_rate\n",
        "print(f\"The target sampling rate: {target_sampling_rate}\")"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The target sampling rate: 16000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CbGuYgLqHXZg"
      },
      "source": [
        "# Preprocess Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qLk-eM1DFjtE"
      },
      "source": [
        "So far, we downloaded, loaded, and split the SER dataset into train and test sets. The instantiated our strategy configuration for using context representations in our classification problem SER. Now, we need to extract features from the audio path in context representation tensors and feed them into our classification model to determine the emotion in the speech.\n",
        "\n",
        "Since the audio file is saved in the `.wav` format, it is easy to use **[Librosa](https://librosa.org/doc/latest/index.html)** or others, but we suppose that the format may be in the `.mp3` format in case of generality. We found that the **[Torchaudio](https://pytorch.org/audio/stable/index.html)** library works best for reading in `.mp3` data.\n",
        "\n",
        "An audio file usually stores both its values and the sampling rate with which the speech signal was digitalized. We want to store both in the dataset and write a **map(...)** function accordingly. Also, we need to handle the string labels into integers for our specific classification task in this case, the **single-label classification** you may want to use for your **regression** or even **multi-label classification**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UqlIV3uGxDA"
      },
      "source": [
        "def speech_file_to_array_fn(path):\n",
        "    speech_array, sampling_rate = torchaudio.load(path)\n",
        "    resampler = torchaudio.transforms.Resample(sampling_rate, target_sampling_rate)\n",
        "    speech = resampler(speech_array).squeeze().numpy()\n",
        "    return speech\n",
        "\n",
        "def preprocess_function(examples):\n",
        "    speech_list = [speech_file_to_array_fn(path) for path in examples[input_column]]\n",
        "    target_list = [label for label in examples[output_column]] # Do any preprocessing on your float/integer data\n",
        "\n",
        "    result = processor(speech_list, sampling_rate=target_sampling_rate)\n",
        "    result[\"labels\"] = list(target_list)\n",
        "\n",
        "    return result"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "onJ2ichkpl5q"
      },
      "source": [
        "train_dataset = train_dataset.select(range(100))\n",
        "eval_dataset = eval_dataset.select(range(100))"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "ioP8FfR2GxHi",
        "outputId": "3e75660e-7517-4958-f38e-3e19195edaa6"
      },
      "source": [
        "train_dataset = train_dataset.map(\n",
        "    preprocess_function,\n",
        "    batch_size=100,\n",
        "    batched=True,\n",
        "    num_proc=4\n",
        ")\n",
        "eval_dataset = eval_dataset.map(\n",
        "    preprocess_function,\n",
        "    batch_size=100,\n",
        "    batched=True,\n",
        "    num_proc=4\n",
        ")"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  "
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0c5871b4f88d4165b63d6d313d8e7386",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description=' #0', max=1.0, style=ProgressStyle(description_width='ini…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "  "
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a1dc9bf4f3d64370bbdc444cf0c4bbad",
              "version_minor": 0,
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              "HBox(children=(FloatProgress(value=0.0, description=' #1', max=1.0, style=ProgressStyle(description_width='ini…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1954ab8f32ca47548345073fe3acefc3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description=' #2', max=1.0, style=ProgressStyle(description_width='ini…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dc422c120c4748f48ff0c5f0fc253f2a",
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              "HBox(children=(FloatProgress(value=0.0, description=' #3', max=1.0, style=ProgressStyle(description_width='ini…"
            ]
          },
          "metadata": {
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        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n",
            "   "
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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              "model_id": "906fc829b3494228b00603b035d83616",
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              "HBox(children=(FloatProgress(value=0.0, description=' #0', max=1.0, style=ProgressStyle(description_width='ini…"
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        {
          "output_type": "stream",
          "text": [
            " "
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8759bf0c1dc24342b5f8e9568461a1a6",
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              "HBox(children=(FloatProgress(value=0.0, description=' #1', max=1.0, style=ProgressStyle(description_width='ini…"
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            "tags": []
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        {
          "output_type": "display_data",
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              "model_id": "9a66d0d5774e47fb86f7109d3d3b411f",
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              "HBox(children=(FloatProgress(value=0.0, description=' #2', max=1.0, style=ProgressStyle(description_width='ini…"
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            "tags": []
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        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7b07eab52b7f4c8f8ba43c476a0c3ecb",
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              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description=' #3', max=1.0, style=ProgressStyle(description_width='ini…"
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          "metadata": {
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          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eu1qcRucHk6d",
        "outputId": "cacb1608-5219-4b7e-99db-58871cfd4278"
      },
      "source": [
        "idx = 0\n",
        "print(f\"Training input_values: {train_dataset[idx]['input_values']}\")\n",
        "print(f\"Training attention_mask: {train_dataset[idx]['attention_mask']}\")\n",
        "print(f\"Training labels: {train_dataset[idx]['labels']} - {train_dataset[idx]['emotion']}\")\n",
        "print(f\"Type of label: {type(train_dataset[idx]['labels'])}\")"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training input_values: [-0.010774079710245132, -0.01810072548687458, -0.014727980829775333, -0.008076385594904423, -0.008145168423652649, -0.007798978593200445, -0.007959483191370964, -0.008001809008419514, -0.007976813241839409, -0.007508280221372843, -0.00824645534157753, -0.007159586995840073, 0.00020136195234954357, 0.0007507744594477117, 0.0008653859258629382, 0.0006440710858441889, 0.0004495307512115687, 0.00042356998892500997, 0.0007293993257917464, 0.0005440274835564196, 0.0010411323746666312, 0.0008375509059987962, 0.000538927037268877, 0.0006388535257428885, 0.0005701392656192183, 0.0006645595422014594, 0.000773670501075685, 0.0005853964248672128, 0.0005868952139280736, 0.0007286491454578936, 0.0007448248798027635, 0.0007389641250483692, 0.00040652835741639137, 0.0005920393159613013, 0.00029072293546050787, 0.0005208543734624982, 0.000625554530415684, 0.0005883093108423054, 0.0006173308938741684, 0.0005788262351416051, 0.0006759322714060545, 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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "Training labels: [0.05, 0.1, 0.15] - [0.05, 0.1, 0.15]\n",
            "Type of label: <class 'list'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HcrEgJO9Hmx7"
      },
      "source": [
        "Great, now we've successfully read all the audio files, resampled the audio files to 16kHz, and mapped each audio to the corresponding label."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QL7q6DfcH0Fs"
      },
      "source": [
        "## Model\n",
        "\n",
        "Before diving into the training part, we need to build our classification model based on the merge strategy. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uJZoC4T5HpuP"
      },
      "source": [
        "from dataclasses import dataclass\n",
        "from typing import Optional, Tuple\n",
        "import torch\n",
        "from transformers.file_utils import ModelOutput\n",
        "\n",
        "\n",
        "@dataclass\n",
        "class SpeechClassifierOutput(ModelOutput):\n",
        "    loss: Optional[torch.FloatTensor] = None\n",
        "    logits: torch.FloatTensor = None\n",
        "    hidden_states: Optional[Tuple[torch.FloatTensor]] = None\n",
        "    attentions: Optional[Tuple[torch.FloatTensor]] = None\n"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fv62ShDsH5DZ"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss\n",
        "\n",
        "from transformers.models.wav2vec2.modeling_wav2vec2 import (\n",
        "    Wav2Vec2PreTrainedModel,\n",
        "    Wav2Vec2Model\n",
        ")\n",
        "\n",
        "\n",
        "class Wav2Vec2ClassificationHead(nn.Module):\n",
        "    \"\"\"Head for wav2vec classification task.\"\"\"\n",
        "\n",
        "    def __init__(self, config):\n",
        "        super().__init__()\n",
        "        self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n",
        "        self.dropout = nn.Dropout(config.final_dropout)\n",
        "        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)\n",
        "\n",
        "    def forward(self, features, **kwargs):\n",
        "        x = features\n",
        "        x = self.dropout(x)\n",
        "        x = self.dense(x)\n",
        "        x = torch.tanh(x)\n",
        "        x = self.dropout(x)\n",
        "        x = self.out_proj(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "class Wav2Vec2ForSpeechClassification(Wav2Vec2PreTrainedModel):\n",
        "    def __init__(self, config):\n",
        "        super().__init__(config)\n",
        "        self.num_labels = config.num_labels\n",
        "        self.pooling_mode = config.pooling_mode\n",
        "        self.config = config\n",
        "\n",
        "        self.wav2vec2 = Wav2Vec2Model(config)\n",
        "        self.classifier = Wav2Vec2ClassificationHead(config)\n",
        "\n",
        "        self.init_weights()\n",
        "\n",
        "    def freeze_feature_extractor(self):\n",
        "        self.wav2vec2.feature_extractor._freeze_parameters()\n",
        "\n",
        "    def merged_strategy(\n",
        "            self,\n",
        "            hidden_states,\n",
        "            mode=\"mean\"\n",
        "    ):\n",
        "        if mode == \"mean\":\n",
        "            outputs = torch.mean(hidden_states, dim=1)\n",
        "        elif mode == \"sum\":\n",
        "            outputs = torch.sum(hidden_states, dim=1)\n",
        "        elif mode == \"max\":\n",
        "            outputs = torch.max(hidden_states, dim=1)[0]\n",
        "        else:\n",
        "            raise Exception(\n",
        "                \"The pooling method hasn't been defined! Your pooling mode must be one of these ['mean', 'sum', 'max']\")\n",
        "\n",
        "        return outputs\n",
        "\n",
        "    def forward(\n",
        "            self,\n",
        "            input_values,\n",
        "            attention_mask=None,\n",
        "            output_attentions=None,\n",
        "            output_hidden_states=None,\n",
        "            return_dict=None,\n",
        "            labels=None,\n",
        "    ):\n",
        "        return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n",
        "        outputs = self.wav2vec2(\n",
        "            input_values,\n",
        "            attention_mask=attention_mask,\n",
        "            output_attentions=output_attentions,\n",
        "            output_hidden_states=output_hidden_states,\n",
        "            return_dict=return_dict,\n",
        "        )\n",
        "        hidden_states = outputs[0]\n",
        "        hidden_states = self.merged_strategy(hidden_states, mode=self.pooling_mode)\n",
        "        logits = self.classifier(hidden_states)\n",
        "\n",
        "        loss = None\n",
        "        if labels is not None:\n",
        "            if self.config.problem_type is None:\n",
        "                if self.num_labels == 1:\n",
        "                    self.config.problem_type = \"regression\"\n",
        "                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):\n",
        "                    self.config.problem_type = \"single_label_classification\"\n",
        "                else:\n",
        "                    self.config.problem_type = \"multi_label_classification\"\n",
        "\n",
        "            if self.config.problem_type == \"regression\":\n",
        "                loss_fct = MSELoss()\n",
        "                loss = loss_fct(logits.view(-1, self.num_labels), labels)\n",
        "            elif self.config.problem_type == \"single_label_classification\":\n",
        "                loss_fct = CrossEntropyLoss()\n",
        "                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\n",
        "            elif self.config.problem_type == \"multi_label_classification\":\n",
        "                loss_fct = BCEWithLogitsLoss()\n",
        "                loss = loss_fct(logits, labels)\n",
        "\n",
        "        if not return_dict:\n",
        "            output = (logits,) + outputs[2:]\n",
        "            return ((loss,) + output) if loss is not None else output\n",
        "\n",
        "        return SpeechClassifierOutput(\n",
        "            loss=loss,\n",
        "            logits=logits,\n",
        "            hidden_states=outputs.hidden_states,\n",
        "            attentions=outputs.attentions,\n",
        "        )\n"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OrBrR1b7zvUL"
      },
      "source": [
        "## Training\n",
        "\n",
        "The data is processed so that we are ready to start setting up the training pipeline. We will make use of 🤗's [Trainer](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer) for which we essentially need to do the following:\n",
        "\n",
        "- Define a data collator. In contrast to most NLP models, XLSR-Wav2Vec2 has a much larger input length than output length. *E.g.*, a sample of input length 50000 has an output length of no more than 100. Given the large input sizes, it is much more efficient to pad the training batches dynamically meaning that all training samples should only be padded to the longest sample in their batch and not the overall longest sample. Therefore, fine-tuning XLSR-Wav2Vec2 requires a special padding data collator, which we will define below\n",
        "\n",
        "- Evaluation metric. During training, the model should be evaluated on the word error rate. We should define a `compute_metrics` function accordingly\n",
        "\n",
        "- Load a pretrained checkpoint. We need to load a pretrained checkpoint and configure it correctly for training.\n",
        "\n",
        "- Define the training configuration.\n",
        "\n",
        "After having fine-tuned the model, we will correctly evaluate it on the test data and verify that it has indeed learned to correctly transcribe speech."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ji9-n1eUIKZc"
      },
      "source": [
        "### Set-up Trainer\n",
        "\n",
        "Let's start by defining the data collator. The code for the data collator was copied from [this example](https://github.com/huggingface/transformers/blob/9a06b6b11bdfc42eea08fa91d0c737d1863c99e3/examples/research_projects/wav2vec2/run_asr.py#L81).\n",
        "\n",
        "Without going into too many details, in contrast to the common data collators, this data collator treats the `input_values` and `labels` differently and thus applies to separate padding functions on them (again making use of XLSR-Wav2Vec2's context manager). This is necessary because in speech input and output are of different modalities meaning that they should not be treated by the same padding function.\n",
        "Analogous to the common data collators, the padding tokens in the labels with `-100` so that those tokens are **not** taken into account when computing the loss."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rkM0VLIwy903"
      },
      "source": [
        "from dataclasses import dataclass\n",
        "from typing import Dict, List, Optional, Union\n",
        "import torch\n",
        "\n",
        "import transformers\n",
        "from transformers import Wav2Vec2Processor\n",
        "\n",
        "\n",
        "@dataclass\n",
        "class DataCollatorCTCWithPadding:\n",
        "    \"\"\"\n",
        "    Data collator that will dynamically pad the inputs received.\n",
        "    Args:\n",
        "        processor (:class:`~transformers.Wav2Vec2Processor`)\n",
        "            The processor used for proccessing the data.\n",
        "        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):\n",
        "            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)\n",
        "            among:\n",
        "            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single\n",
        "              sequence if provided).\n",
        "            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the\n",
        "              maximum acceptable input length for the model if that argument is not provided.\n",
        "            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of\n",
        "              different lengths).\n",
        "        max_length (:obj:`int`, `optional`):\n",
        "            Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).\n",
        "        max_length_labels (:obj:`int`, `optional`):\n",
        "            Maximum length of the ``labels`` returned list and optionally padding length (see above).\n",
        "        pad_to_multiple_of (:obj:`int`, `optional`):\n",
        "            If set will pad the sequence to a multiple of the provided value.\n",
        "            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=\n",
        "            7.5 (Volta).\n",
        "    \"\"\"\n",
        "\n",
        "    processor: Wav2Vec2Processor\n",
        "    padding: Union[bool, str] = True\n",
        "    max_length: Optional[int] = None\n",
        "    max_length_labels: Optional[int] = None\n",
        "    pad_to_multiple_of: Optional[int] = None\n",
        "    pad_to_multiple_of_labels: Optional[int] = None\n",
        "\n",
        "    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
        "        input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n",
        "        label_features = [feature[\"labels\"] for feature in features]\n",
        "\n",
        "        # d_type = torch.long if isinstance(label_features[0], int) else torch.float\n",
        "        d_type = torch.float\n",
        "\n",
        "        batch = self.processor.pad(\n",
        "            input_features,\n",
        "            padding=self.padding,\n",
        "            max_length=self.max_length,\n",
        "            pad_to_multiple_of=self.pad_to_multiple_of,\n",
        "            return_tensors=\"pt\",\n",
        "        )\n",
        "\n",
        "        batch[\"labels\"] = torch.tensor(label_features, dtype=d_type)\n",
        "\n",
        "        return batch"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YhJ0OA4RxQe"
      },
      "source": [
        "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mYxy2IR-KcU2"
      },
      "source": [
        "Next, the evaluation metric is defined. There are many pre-defined metrics for classification/regression problems, but in this case, we would continue with just **Accuracy** for classification and **MSE** for regression. You can define other metrics on your own."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XK26Z6IfR36K"
      },
      "source": [
        "import numpy as np\n",
        "from transformers import EvalPrediction\n",
        "\n",
        "\n",
        "def compute_metrics(p: EvalPrediction):\n",
        "    preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions\n",
        "    preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1)\n",
        "\n",
        "    if is_regression:\n",
        "        return {\"mse\": ((preds - p.label_ids) ** 2).mean().item()}\n",
        "    else:\n",
        "        return {\"accuracy\": (preds == p.label_ids).astype(np.float32).mean().item()}"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZsH_nKJdK28o"
      },
      "source": [
        "Now, we can load the pretrained XLSR-Wav2Vec2 checkpoint into our classification model with a pooling strategy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Tl6iKAUR4EL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171,
          "referenced_widgets": [
            "2d6ea197b69c4ebc8cbabdab871d098d",
            "83d41ca78151477390f0b898ccb2f484",
            "5469b185c4d74e4c85f8c7f9fa4f4f6c",
            "5e68b078d0034e35b762273e4779f76c",
            "7f3119c9e7e64ec4895ef6a40941eba9",
            "33dd4bb4d7054975b6f9801e6c3edf2a",
            "2adc2a1a8a744bd483ab815072a84e25",
            "a13a5e263a8f4d1cae5f8707a52d735d"
          ]
        },
        "outputId": "39c82387-3f3b-4318-8131-b88289e23dab"
      },
      "source": [
        "model = Wav2Vec2ForSpeechClassification.from_pretrained(\n",
        "    model_name_or_path,\n",
        "    config=config,\n",
        ")"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2d6ea197b69c4ebc8cbabdab871d098d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1262159319.0, style=ProgressStyle(descr…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at lighteternal/wav2vec2-large-xlsr-53-greek were not used when initializing Wav2Vec2ForSpeechClassification: ['lm_head.weight', 'lm_head.bias']\n",
            "- This IS expected if you are initializing Wav2Vec2ForSpeechClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing Wav2Vec2ForSpeechClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of Wav2Vec2ForSpeechClassification were not initialized from the model checkpoint at lighteternal/wav2vec2-large-xlsr-53-greek and are newly initialized: ['classifier.dense.weight', 'classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bqF4rNMzI1M5"
      },
      "source": [
        "The first component of XLSR-Wav2Vec2 consists of a stack of CNN layers that are used to extract acoustically meaningful - but contextually independent - features from the raw speech signal. This part of the model has already been sufficiently trained during pretraining and as stated in the [paper](https://arxiv.org/pdf/2006.13979.pdf) does not need to be fine-tuned anymore. \n",
        "Thus, we can set the `requires_grad` to `False` for all parameters of the *feature extraction* part."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KHMhxFGoR4Hb"
      },
      "source": [
        "model.freeze_feature_extractor()"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n0HzBneBK84G"
      },
      "source": [
        "In a final step, we define all parameters related to training. \n",
        "To give more explanation on some of the parameters:\n",
        "- `learning_rate` and `weight_decay` were heuristically tuned until fine-tuning has become stable. Note that those parameters strongly depend on the Common Voice dataset and might be suboptimal for other speech datasets.\n",
        "\n",
        "For more explanations on other parameters, one can take a look at the [docs](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer#trainingarguments).\n",
        "\n",
        "**Note**: If one wants to save the trained models in his/her google drive the commented-out `output_dir` can be used instead."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3tPqZ12BLCJk"
      },
      "source": [
        "# from google.colab import drive\n",
        "\n",
        "# drive.mount('/gdrive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vUtWjldAI9-H"
      },
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"/content/wav2vec2-xlsr-greek-speech-emotion-recognition\",\n",
        "    # output_dir=\"/content/gdrive/MyDrive/wav2vec2-xlsr-greek-speech-emotion-recognition\"\n",
        "    per_device_train_batch_size=4,\n",
        "    per_device_eval_batch_size=4,\n",
        "    gradient_accumulation_steps=2,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    num_train_epochs=1.0,\n",
        "    fp16=True,\n",
        "    save_steps=10,\n",
        "    eval_steps=10,\n",
        "    logging_steps=10,\n",
        "    learning_rate=1e-4,\n",
        "    save_total_limit=2,\n",
        ")"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XAtuL0APLZSs"
      },
      "source": [
        "For future use we can create our training script, we do it in a simple way. You can add more on you own."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KfW6uDolJYZv"
      },
      "source": [
        "from typing import Any, Dict, Union\n",
        "\n",
        "import torch\n",
        "from packaging import version\n",
        "from torch import nn\n",
        "\n",
        "from transformers import (\n",
        "    Trainer,\n",
        "    is_apex_available,\n",
        ")\n",
        "\n",
        "if is_apex_available():\n",
        "    from apex import amp\n",
        "\n",
        "if version.parse(torch.__version__) >= version.parse(\"1.6\"):\n",
        "    _is_native_amp_available = True\n",
        "    from torch.cuda.amp import autocast\n",
        "\n",
        "\n",
        "class CTCTrainer(Trainer):\n",
        "    def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor:\n",
        "        \"\"\"\n",
        "        Perform a training step on a batch of inputs.\n",
        "\n",
        "        Subclass and override to inject custom behavior.\n",
        "\n",
        "        Args:\n",
        "            model (:obj:`nn.Module`):\n",
        "                The model to train.\n",
        "            inputs (:obj:`Dict[str, Union[torch.Tensor, Any]]`):\n",
        "                The inputs and targets of the model.\n",
        "\n",
        "                The dictionary will be unpacked before being fed to the model. Most models expect the targets under the\n",
        "                argument :obj:`labels`. Check your model's documentation for all accepted arguments.\n",
        "\n",
        "        Return:\n",
        "            :obj:`torch.Tensor`: The tensor with training loss on this batch.\n",
        "        \"\"\"\n",
        "\n",
        "        model.train()\n",
        "        inputs = self._prepare_inputs(inputs)\n",
        "\n",
        "        if self.use_amp:\n",
        "            with autocast():\n",
        "                loss = self.compute_loss(model, inputs)\n",
        "        else:\n",
        "            loss = self.compute_loss(model, inputs)\n",
        "\n",
        "        if self.args.gradient_accumulation_steps > 1:\n",
        "            loss = loss / self.args.gradient_accumulation_steps\n",
        "\n",
        "        if self.use_amp:\n",
        "            self.scaler.scale(loss).backward()\n",
        "        elif self.use_apex:\n",
        "            with amp.scale_loss(loss, self.optimizer) as scaled_loss:\n",
        "                scaled_loss.backward()\n",
        "        elif self.deepspeed:\n",
        "            self.deepspeed.backward(loss)\n",
        "        else:\n",
        "            loss.backward()\n",
        "\n",
        "        return loss.detach()\n"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qv7Ju3qYJeJn"
      },
      "source": [
        "Now, all instances can be passed to Trainer and we are ready to start training!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nEFkfK45JYiZ"
      },
      "source": [
        "trainer = CTCTrainer(\n",
        "    model=model,\n",
        "    data_collator=data_collator,\n",
        "    args=training_args,\n",
        "    compute_metrics=compute_metrics,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=eval_dataset,\n",
        "    tokenizer=processor.feature_extractor,\n",
        ")"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0gGLwJAOLtDg"
      },
      "source": [
        "### Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UpN6xlWCLxJ7"
      },
      "source": [
        "Training will take between 10 and 60 minutes depending on the GPU allocated to this notebook. \n",
        "\n",
        "In case you want to use this google colab to fine-tune your model, you should make sure that your training doesn't stop due to inactivity. A simple hack to prevent this is to paste the following code into the console of this tab (right mouse click -> inspect -> Console tab and insert code)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zyYZH7YZL8a9"
      },
      "source": [
        "```javascript\n",
        "function ConnectButton(){\n",
        "    console.log(\"Connect pushed\"); \n",
        "    document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click() \n",
        "}\n",
        "setInterval(ConnectButton,60000);\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "6M8bNvLLJnG1",
        "outputId": "e75b5145-bb3a-4063-ee74-74184eaaf3f6"
      },
      "source": [
        "trainer.train()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='12' max='12' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [12/12 27:16, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Mse</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.017500</td>\n",
              "      <td>0.008415</td>\n",
              "      <td>0.008415</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=12, training_loss=0.01614018203690648, metrics={'train_runtime': 1749.5188, 'train_samples_per_second': 0.057, 'train_steps_per_second': 0.007, 'total_flos': 1.657459865366448e+16, 'train_loss': 0.01614018203690648, 'epoch': 0.96})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jfqtBOeWxoGN"
      },
      "source": [
        "trainer.save_model()"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PV1qjFvbx55s"
      },
      "source": [
        "processor.save_pretrained(training_args.output_dir)"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3prIN9eiMBHo"
      },
      "source": [
        "The training loss goes down and we can see that the Acurracy on the test set also improves nicely. Because this notebook is just for demonstration purposes, we can stop here.\n",
        "\n",
        "The resulting model of this notebook has been saved to [m3hrdadfi/wav2vec2-xlsr-greek-speech-emotion-recognition](https://huggingface.co/m3hrdadfi/wav2vec2-xlsr-greek-speech-emotion-recognition)\n",
        "\n",
        "As a final check, let's load the model and verify that it indeed has learned to recognize the emotion in the speech.\n",
        "\n",
        "Let's first load the pretrained checkpoint."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VsaOTx_FVm0i"
      },
      "source": [
        "## Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4tGNY7hRXO44"
      },
      "source": [
        "import librosa\n",
        "from sklearn.metrics import classification_report, mean_squared_error"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IYxg1Tfo2VUw",
        "outputId": "e80bdd47-1b1e-4977-a9dc-cbb4fd09d93d"
      },
      "source": [
        "test_dataset = load_dataset(\"csv\", data_files={\"test\": \"/content/data/test.csv\"}, delimiter=\"\\t\")[\"test\"]\n",
        "test_dataset = test_dataset.select(range(5))\n",
        "test_dataset"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using custom data configuration default-cd7dc870b524cbdd\n",
            "Reusing dataset csv (/content/cache/csv/default-cd7dc870b524cbdd/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['name', 'path', 'emotion'],\n",
              "    num_rows: 5\n",
              "})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QgZFkMDHW_Um",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66e2e804-ec8c-47b3-a368-6a23301dd089"
      },
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(f\"Device: {device}\")"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Device: cpu\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ESFEXeaWgua"
      },
      "source": [
        "# model_name_or_path = \"m3hrdadfi/wav2vec2-xlsr-greek-speech-emotion-recognition\"\n",
        "model_name_or_path = \"/content/wav2vec2-xlsr-greek-speech-emotion-recognition\"\n",
        "config = AutoConfig.from_pretrained(model_name_or_path)\n",
        "processor = Wav2Vec2Processor.from_pretrained(model_name_or_path)\n",
        "model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BkEd4w8IV7kZ"
      },
      "source": [
        "def speech_file_to_array_fn(batch):\n",
        "    batch[\"emotion\"] = ast.literal_eval(batch[\"emotion\"])\n",
        "    speech_array, sampling_rate = torchaudio.load(batch[\"path\"])\n",
        "    speech_array = speech_array.squeeze().numpy()\n",
        "    speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, processor.feature_extractor.sampling_rate)\n",
        "\n",
        "    batch[\"speech\"] = speech_array\n",
        "    return batch\n",
        "\n",
        "\n",
        "def predict(batch):\n",
        "    features = processor(batch[\"speech\"], sampling_rate=processor.feature_extractor.sampling_rate, return_tensors=\"pt\", padding=True)\n",
        "\n",
        "    input_values = features.input_values.to(device)\n",
        "    attention_mask = features.attention_mask.to(device)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        logits = model(input_values, attention_mask=attention_mask).logits \n",
        "\n",
        "\n",
        "    batch[\"predicted\"] = logits.detach().cpu().numpy()\n",
        "    return batch"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "41f82099f80b4a03845844f8489a5826",
            "af99577c37ff4dc287a2e00443e4374a",
            "c4512334385347ab9fd76247018c5c30",
            "861c05c80e064570b106159ab3110e2e",
            "51ba184c85054a7eb833c582d8b86ee7",
            "53f0ed181dff46459d22bb80ab0ab883",
            "cd36a032ac5343ccba1520344d576cdd",
            "ca21585f728b46cbadffa84d5d8c5ffb"
          ]
        },
        "id": "S4P6P6XwW85p",
        "outputId": "8b45697e-e456-4dd8-b090-3d04ac41cf20"
      },
      "source": [
        "test_dataset = test_dataset.map(speech_file_to_array_fn)"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "41f82099f80b4a03845844f8489a5826",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=5.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "0ad22c80ab7d4864b0ef2658f5b4aaae",
            "c5584297a5c94a67bb59266981066c9a",
            "ad70de4f8b92424eb93c737b5e8e23d7",
            "4a744770e8804b4e8b926852c59eea84",
            "67f2f69515a9415fb6692863e4733dd9",
            "22b0dd4e93cc4551965023a2eca6ce6e",
            "5ec4ed9f02584f9f94556c016be24bff",
            "0699318c176f45218f481b803a086d2d"
          ]
        },
        "id": "K_oZJzHsXKHv",
        "outputId": "570dd14c-1412-4fa8-fc82-67468f25a33d"
      },
      "source": [
        "result = test_dataset.map(predict, batched=True, batch_size=2)"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0ad22c80ab7d4864b0ef2658f5b4aaae",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RdF1FK22zSkh",
        "outputId": "ccb5677f-e181-431c-b5ce-15226f6a1e06"
      },
      "source": [
        "result[\"emotion\"]"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[0.07, 0.14, 0.28],\n",
              " [0.07, 0.14, 0.28],\n",
              " [0.02, 0.04, 0.06],\n",
              " [0.05, 0.1, 0.15],\n",
              " [0.02, 0.04, 0.06]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BnfJLZvAaxTo",
        "outputId": "19398a3e-8b5e-48e9-bd6a-3a2a0390a798"
      },
      "source": [
        "result[\"predicted\"]"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[0.06850039213895798, 0.17856287956237793, 0.255866676568985],\n",
              " [0.07049965858459473, 0.16333678364753723, 0.194563627243042],\n",
              " [0.09779226779937744, 0.19064779579639435, 0.26533204317092896],\n",
              " [0.06363093852996826, 0.10668247193098068, 0.22417190670967102],\n",
              " [0.09723929315805435, 0.1277279555797577, 0.24414333701133728]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tUt5rIppXrzl",
        "outputId": "56c027e9-51fe-4f28-9f55-8b8367c9694d"
      },
      "source": [
        "print(mean_squared_error(result[\"emotion\"], result[\"predicted\"]))"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.008941762320560648\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}

================================================
FILE: requirements.txt
================================================
torch
numpy
git+https://github.com/huggingface/datasets.git
git+https://github.com/huggingface/transformers.git
torchaudio
librosa

================================================
FILE: run_wav2vec_clf.py
================================================
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

from datasets import load_dataset, load_metric
import numpy as np
import torch
import torchaudio

import transformers
from transformers import (
    HfArgumentParser,
    TrainingArguments,
    EvalPrediction,
    AutoConfig,
    Wav2Vec2Processor,
    Wav2Vec2FeatureExtractor,
    is_apex_available,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process

from src.models import Wav2Vec2ForSpeechClassification, HubertForSpeechClassification
from src.collator import DataCollatorCTCWithPadding
from src.trainer import CTCTrainer

logger = logging.getLogger(__name__)
MODEL_MODES = ["wav2vec", "hubert"]
POOLING_MODES = ["mean", "sum", "max"]
DELIMITERS = {"tab": "\t", "comma": ",", "pipe": "|"}


@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
    """

    model_name_or_path: str = field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
    )
    model_mode: str = field(
        default="wav2vec",
        metadata={
            "help": "Specifies the base model and must be from the following: " + ", ".join(MODEL_MODES)
        },
    )
    pooling_mode: str = field(
        default="mean",
        metadata={
            "help": "Specifies the reduction to apply to the output of Wav2Vec2 model and must be from the following: " + ", ".join(
                POOLING_MODES)
        },
    )
    config_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
    )
    feature_extractor_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained feature_extractor name or path if not the same as model_name"}
    )
    cache_dir: Optional[str] = field(
        default=None,
        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
    )
    freeze_feature_extractor: Optional[bool] = field(
        default=True, metadata={"help": "Whether to freeze the feature extractor layers of the model."}
    )
    model_revision: str = field(
        default="main",
        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
    )
    use_auth_token: bool = field(
        default=False,
        metadata={
            "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
                    "with private models)."
        },
    )


@dataclass
class DataTrainingArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.

    Using `HfArgumentParser` we can turn this class
    into argparse arguments to be able to specify them on
    the command line.
    """
    train_file: Optional[str] = field(
        default=None, metadata={"help": "The input training data file (a csv or JSON file)."}
    )
    validation_file: Optional[str] = field(
        default=None,
        metadata={"help": "An optional input evaluation data file to evaluate on (a csv or JSON file)."},
    )
    test_file: Optional[str] = field(
        default=None,
        metadata={"help": "An optional input evaluation data file to test on (a csv or JSON file)."},
    )
    input_column: Optional[str] = field(
        default="path",
        metadata={"help": "The name of the column in the datasets containing the audio path."},
    )
    target_column: Optional[str] = field(
        default="emotion",
        metadata={"help": "The name of the column in the datasets containing the labels."},
    )
    delimiter: Optional[str] = field(
        default="tab",
        metadata={
            "help": "Specifies the character delimiting individual cells in the CSV data and must be from the following: " + ", ".join(
                DELIMITERS.keys())
        },
    )
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
    )
    preprocessing_num_workers: Optional[int] = field(
        default=None,
        metadata={"help": "The number of processes to use for the preprocessing."},
    )
    max_train_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
                    "value if set."
        },
    )
    max_eval_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": "For debugging purposes or quicker training, truncate the number of validation examples to this "
                    "value if set."
        },
    )
    max_predict_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
                    "value if set."
        },
    )
    min_duration_in_seconds: Optional[float] = field(
        default=None,
        metadata={"help": "Filters out examples less than specified. Defaults to no filtering."},
    )
    max_duration_in_seconds: Optional[float] = field(
        default=None,
        metadata={"help": "Filters out examples longer than specified. Defaults to no filtering."},
    )

    def __post_init__(self):
        if self.train_file is None and self.validation_file is None:
            raise ValueError("Need either a dataset name or a training/validation file.")
        else:
            extension = self.train_file.split(".")[-1]
            assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
            extension = self.validation_file.split(".")[-1]
            assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."


def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        elif last_checkpoint is not None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )
    logger.info(f"last_checkpoint: {last_checkpoint}")

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )
    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank):
        transformers.utils.logging.set_verbosity_info()
    logger.info("Training/evaluation parameters %s", training_args)

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Loading a dataset from your local files.
    # CSV/JSON training and evaluation files are needed.
    data_files = {"train": data_args.train_file, "validation": data_args.validation_file}

    # Get the test dataset: you can provide your own CSV/JSON test file (see below)
    # when you use `do_predict` without specifying a GLUE benchmark task.
    if training_args.do_predict:
        if data_args.test_file is not None:
            train_extension = data_args.train_file.split(".")[-1]
            test_extension = data_args.test_file.split(".")[-1]
            assert (
                    test_extension == train_extension
            ), "`test_file` should have the same extension (csv or json) as `train_file`."
            data_files["test"] = data_args.test_file
        else:
            raise ValueError("Need a test file for `do_predict`.")

    for key in data_files.keys():
        logger.info(f"load a local file for {key}: {data_files[key]}")

    if data_args.train_file.endswith(".csv"):
        # Loading a dataset from local csv files
        datasets = load_dataset(
            "csv",
            data_files=data_files,
            delimiter=DELIMITERS.get(data_args.delimiter, "\t"),
            cache_dir=model_args.cache_dir
        )
    else:
        # Loading a dataset from local json files
        datasets = load_dataset("json", data_files=data_files, cache_dir=model_args.cache_dir)

    input_column_name = data_args.input_column
    output_column_name = data_args.target_column

    # Trying to have good defaults here, don't hesitate to tweak to your needs.
    is_regression = datasets["train"].features[output_column_name].dtype in ["float32", "float64"]
    if is_regression:
        num_labels = 1
        label_list = []
        logger.info(f"*** A regression problem ***")
    else:
        # A useful fast method:
        # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique
        label_list = datasets["train"].unique(output_column_name)
        label_list.sort()  # Let's sort it for determinism
        num_labels = len(label_list)

        logger.info(f"*** A classification problem with {num_labels} classes ***")

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    config = AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
        num_labels=num_labels,
        label2id={label: i for i, label in enumerate(label_list)},
        id2label={i: label for i, label in enumerate(label_list)},
        finetuning_task="wav2vec2_clf",
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )
    setattr(config, 'pooling_mode', model_args.pooling_mode)

    # tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(model_args.model_name_or_path)
    # feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_args.model_name_or_path)
    # processor = Wav2Vec2Processor.from_pretrained(
    #     model_args.processor_name if model_args.processor_name else model_args.model_name_or_path,
    #     cache_dir=model_args.cache_dir,
    #     revision=model_args.model_revision,
    #     use_auth_token=True if model_args.use_auth_token else None,
    # )
    feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
        model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )
    target_sampling_rate = feature_extractor.feature_extractor.sampling_rate

    if model_args.model_mode == "wav2vec":
        model = Wav2Vec2ForSpeechClassification.from_pretrained(
            model_args.model_name_or_path,
            from_tf=bool(".ckpt" in model_args.model_name_or_path),
            config=config,
            cache_dir=model_args.cache_dir,
            revision=model_args.model_revision,
            use_auth_token=True if model_args.use_auth_token else None,
        )
    elif model_args.model_mode == "hubert":
        model = HubertForSpeechClassification.from_pretrained(
            model_args.model_name_or_path,
            from_tf=bool(".ckpt" in model_args.model_name_or_path),
            config=config,
            cache_dir=model_args.cache_dir,
            revision=model_args.model_revision,
            use_auth_token=True if model_args.use_auth_token else None,
        )
    else:
        raise ValueError("--model_mode does not exist in predefined modes: " + ",".join(MODEL_MODES))

    if model_args.freeze_feature_extractor:
        model.freeze_feature_extractor()

    # NOTE: Duration controller for the future `min_duration_in_seconds` `max_duration_in_seconds`
    # data_args.min_duration_in_seconds, data_args.max_duration_in_seconds

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

    def label_to_id(label, label_list):

        if len(label_list) > 0:
            return label_list.index(label) if label in label_list else -1

        return label

    def preprocess_function(examples):
        speech_list = [speech_file_to_array_fn(path) for path in examples[input_column_name]]
        target_list = [label_to_id(label, label_list) for label in examples[output_column_name]]

        result = feature_extractor(speech_list, sampling_rate=target_sampling_rate)
        result["labels"] = list(target_list)

        return result

    if training_args.do_train:
        if "train" not in datasets:
            raise ValueError("--do_train requires a train dataset")

        train_dataset = datasets["train"]

        if data_args.max_train_samples is not None:
            train_dataset = train_dataset.select(range(data_args.max_train_samples))

        train_dataset = train_dataset.map(
            preprocess_function,
            batched=True,
            load_from_cache_file=not data_args.overwrite_cache
        )
        logger.info(f"Split sizes: {len(train_dataset)} train")

    if training_args.do_eval:
        if "validation" not in datasets:
            raise ValueError("--do_eval requires a validation dataset")

        eval_dataset = datasets["validation"]

        if data_args.max_eval_samples is not None:
            eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))

        eval_dataset = eval_dataset.map(
            preprocess_function,
            batched=True,
            load_from_cache_file=not data_args.overwrite_cache
        )
        logger.info(f"Split sizes: {len(eval_dataset)} validation")

    if training_args.do_predict:
        if "test" not in datasets:
            raise ValueError("--do_predict requires a test dataset")

        predict_dataset = datasets["test"]

        if data_args.max_predict_samples is not None:
            predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))

        predict_dataset = predict_dataset.map(
            preprocess_function,
            batched=True,
            load_from_cache_file=not data_args.overwrite_cache
        )
        logger.info(f"Split sizes: {len(predict_dataset)} test.")

    # Metric
    # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
    # predictions and label_ids field) and has to return a dictionary string to float.
    def compute_metrics(p: EvalPrediction):
        preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
        preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1)

        if is_regression:
            return {"mse": ((preds - p.label_ids) ** 2).mean().item()}
        else:
            return {"accuracy": (preds == p.label_ids).astype(np.float32).mean().item()}

    # Data collator
    data_collator = DataCollatorCTCWithPadding(feature_extractor=feature_extractor, padding=True)

    # Initialize our Trainer
    trainer = CTCTrainer(
        model=model,
        data_collator=data_collator,
        args=training_args,
        compute_metrics=compute_metrics,
        train_dataset=train_dataset if training_args.do_train else None,
        eval_dataset=eval_dataset if training_args.do_eval else None,
        tokenizer=feature_extractor,
    )

    # Training
    if training_args.do_train:
        if last_checkpoint is not None:
            checkpoint = last_checkpoint
        elif os.path.isdir(model_args.model_name_or_path):
            checkpoint = model_args.model_name_or_path
        else:
            checkpoint = None

        logger.info(f"*** Training from: {checkpoint} ***")
        train_result = trainer.train(resume_from_checkpoint=checkpoint)
        trainer.save_model()

        # save the feature_extractor and the tokenizer
        if is_main_process(training_args.local_rank):
            feature_extractor.save_pretrained(training_args.output_dir)

        metrics = train_result.metrics
        max_train_samples = (
            data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
        )
        metrics["train_samples"] = min(max_train_samples, len(train_dataset))

        trainer.log_metrics("train", metrics)
        trainer.save_metrics("train", metrics)
        trainer.save_state()

    # Evaluation
    results = {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***")
        metrics = trainer.evaluate()
        max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
        metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))

        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)

    # Final test metrics
    if training_args.do_predict:
        logger.info("*** Test ***")

        predict_dataset.remove_columns_(output_column_name)
        predictions = trainer.predict(predict_dataset, metric_key_prefix="predict").predictions
        predictions = np.squeeze(predictions) if is_regression else np.argmax(predictions, axis=1)

        output_predict_file = os.path.join(training_args.output_dir, f"predict_results.txt")
        if trainer.is_world_process_zero():
            with open(output_predict_file, "w", encoding="utf-8") as writer:
                logger.info(f"***** Predict results *****")
                writer.write("index\tprediction\n")
                for index, item in enumerate(predictions):
                    if is_regression:
                        writer.write(f"{index}\t{item:3.3f}\n")
                    else:
                        item = label_list[item]
                        writer.write(f"{index}\t{item}\n")

    # NOTE: Pushing to hub for future
    # training_args.push_to_hub

    return results


def _mp_fn(index):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
    main()


================================================
FILE: src/__init__.py
================================================


================================================
FILE: src/collator.py
================================================
from dataclasses import dataclass
from typing import Dict, List, Optional, Union
import torch

import transformers
from transformers import Wav2Vec2Processor, Wav2Vec2FeatureExtractor


@dataclass
class DataCollatorCTCWithPadding:
    """
    Data collator that will dynamically pad the inputs received.
    Args:
        feature_extractor (:class:`~transformers.Wav2Vec2FeatureExtractor`)
            The feature_extractor used for proccessing the data.
        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
            among:
            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
              sequence if provided).
            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
              maximum acceptable input length for the model if that argument is not provided.
            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
              different lengths).
        max_length (:obj:`int`, `optional`):
            Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).
        max_length_labels (:obj:`int`, `optional`):
            Maximum length of the ``labels`` returned list and optionally padding length (see above).
        pad_to_multiple_of (:obj:`int`, `optional`):
            If set will pad the sequence to a multiple of the provided value.
            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
            7.5 (Volta).
    """

    feature_extractor: Wav2Vec2FeatureExtractor
    padding: Union[bool, str] = True
    max_length: Optional[int] = None
    max_length_labels: Optional[int] = None
    pad_to_multiple_of: Optional[int] = None
    pad_to_multiple_of_labels: Optional[int] = None

    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
        input_features = [{"input_values": feature["input_values"]} for feature in features]
        label_features = [feature["labels"] for feature in features]

        d_type = torch.long if isinstance(label_features[0], int) else torch.float

        batch = self.feature_extractor.pad(
            input_features,
            padding=self.padding,
            max_length=self.max_length,
            pad_to_multiple_of=self.pad_to_multiple_of,
            return_tensors="pt",
        )

        batch["labels"] = torch.tensor(label_features, dtype=d_type)

        return batch


================================================
FILE: src/modeling_outputs.py
================================================
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from transformers.file_utils import ModelOutput


@dataclass
class SpeechClassifierOutput(ModelOutput):
    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None


================================================
FILE: src/models.py
================================================
import torch
import torch.nn as nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from transformers.models.wav2vec2.modeling_wav2vec2 import (
    Wav2Vec2PreTrainedModel,
    Wav2Vec2Model
)
from transformers.models.hubert.modeling_hubert import (
    HubertPreTrainedModel,
    HubertModel
)

from src.modeling_outputs import SpeechClassifierOutput


class Wav2Vec2ClassificationHead(nn.Module):
    """Head for wav2vec classification task."""

    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.dropout = nn.Dropout(config.final_dropout)
        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)

    def forward(self, features, **kwargs):
        x = features
        x = self.dropout(x)
        x = self.dense(x)
        x = torch.tanh(x)
        x = self.dropout(x)
        x = self.out_proj(x)
        return x


class Wav2Vec2ForSpeechClassification(Wav2Vec2PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.pooling_mode = config.pooling_mode
        self.config = config

        self.wav2vec2 = Wav2Vec2Model(config)
        self.classifier = Wav2Vec2ClassificationHead(config)

        self.init_weights()

    def freeze_feature_extractor(self):
        self.wav2vec2.feature_extractor._freeze_parameters()

    def merged_strategy(
            self,
            hidden_states,
            mode="mean"
    ):
        if mode == "mean":
            outputs = torch.mean(hidden_states, dim=1)
        elif mode == "sum":
            outputs = torch.sum(hidden_states, dim=1)
        elif mode == "max":
            outputs = torch.max(hidden_states, dim=1)[0]
        else:
            raise Exception(
                "The pooling method hasn't been defined! Your pooling mode must be one of these ['mean', 'sum', 'max']")

        return outputs

    def forward(
            self,
            input_values,
            attention_mask=None,
            output_attentions=None,
            output_hidden_states=None,
            return_dict=None,
            labels=None,
    ):
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.wav2vec2(
            input_values,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = outputs[0]
        hidden_states = self.merged_strategy(hidden_states, mode=self.pooling_mode)
        logits = self.classifier(hidden_states)

        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return SpeechClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


class HubertClassificationHead(nn.Module):
    """Head for hubert classification task."""

    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.dropout = nn.Dropout(config.final_dropout)
        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)

    def forward(self, features, **kwargs):
        x = features
        x = self.dropout(x)
        x = self.dense(x)
        x = torch.tanh(x)
        x = self.dropout(x)
        x = self.out_proj(x)
        return x


class HubertForSpeechClassification(HubertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.pooling_mode = config.pooling_mode
        self.config = config

        self.hubert = HubertModel(config)
        self.classifier = HubertClassificationHead(config)

        self.init_weights()

    def freeze_feature_extractor(self):
        self.hubert.feature_extractor._freeze_parameters()

    def merged_strategy(
            self,
            hidden_states,
            mode="mean"
    ):
        if mode == "mean":
            outputs = torch.mean(hidden_states, dim=1)
        elif mode == "sum":
            outputs = torch.sum(hidden_states, dim=1)
        elif mode == "max":
            outputs = torch.max(hidden_states, dim=1)[0]
        else:
            raise Exception(
                "The pooling method hasn't been defined! Your pooling mode must be one of these ['mean', 'sum', 'max']")

        return outputs

    def forward(
            self,
            input_values,
            attention_mask=None,
            output_attentions=None,
            output_hidden_states=None,
            return_dict=None,
            labels=None,
    ):
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.hubert(
            input_values,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = outputs[0]
        hidden_states = self.merged_strategy(hidden_states, mode=self.pooling_mode)
        logits = self.classifier(hidden_states)

        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return SpeechClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


================================================
FILE: src/trainer.py
================================================
from typing import Any, Dict, Union

import torch
from packaging import version
from torch import nn

from transformers import (
    Trainer,
    is_apex_available,
)

if is_apex_available():
    from apex import amp

if version.parse(torch.__version__) >= version.parse("1.6"):
    _is_native_amp_available = True
    from torch.cuda.amp import autocast


class CTCTrainer(Trainer):
    def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor:
        """
        Perform a training step on a batch of inputs.

        Subclass and override to inject custom behavior.

        Args:
            model (:obj:`nn.Module`):
                The model to train.
            inputs (:obj:`Dict[str, Union[torch.Tensor, Any]]`):
                The inputs and targets of the model.

                The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
                argument :obj:`labels`. Check your model's documentation for all accepted arguments.

        Return:
            :obj:`torch.Tensor`: The tensor with training loss on this batch.
        """

        model.train()
        inputs = self._prepare_inputs(inputs)

        if self.use_amp:
            with autocast():
                loss = self.compute_loss(model, inputs)
        else:
            loss = self.compute_loss(model, inputs)

        if self.args.gradient_accumulation_steps > 1:
            loss = loss / self.args.gradient_accumulation_steps

        if self.use_amp:
            self.scaler.scale(loss).backward()
        elif self.use_apex:
            with amp.scale_loss(loss, self.optimizer) as scaled_loss:
                scaled_loss.backward()
        elif self.deepspeed:
            self.deepspeed.backward(loss)
        else:
            loss.backward()

        return loss.detach()