readme: ---
  language: ko
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - robust-speech-event
  datasets:
  - kresnik/zeroth_korean
  model-index:
  - name: Wav2Vec2 XLS-R 300M Korean
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Zeroth Korean
        type: kresnik/zeroth_korean
        args: clean
      metrics:
      - name: Test WER
        type: wer
        value: 29.54
      - name: Test CER
        type: cer
        value: 9.53
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: ko
      metrics:
      - name: Test WER
        type: wer
        value: 76.26
      - name: Test CER
        type: cer
        value: 38.67
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: ko
      metrics:
      - name: Test WER
        type: wer
        value: 73.18
  ---
  
  # Wav2Vec2 XLS-R 300M Korean
  
  Wav2Vec2 XLS-R 300M Korean is an automatic speech recognition model based on the [XLS-R](https://arxiv.org/abs/2111.09296) architecture. This model is a fine-tuned version of [Wav2Vec2-XLS-R-300M](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the [Zeroth Korean](https://huggingface.co/datasets/kresnik/zeroth_korean) dataset.
  
  This model was trained using HuggingFace's PyTorch framework and is part of the [Robust Speech Challenge Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) organized by HuggingFace. All training was done on a Tesla V100, sponsored by OVH.
  
  All necessary scripts used for training could be found in the [Files and versions](https://huggingface.co/w11wo/wav2vec2-xls-r-300m-korean/tree/main) tab, as well as the [Training metrics](https://huggingface.co/w11wo/wav2vec2-xls-r-300m-korean/tensorboard) logged via Tensorboard.
  
  ## Model
  
  | Model                        | #params | Arch. | Training/Validation data (text) |
  | ---------------------------- | ------- | ----- | ------------------------------- |
  | `wav2vec2-xls-r-300m-korean` | 300M    | XLS-R | `Zeroth Korean` Dataset         |
  
  ## Evaluation Results
  
  The model achieves the following results on evaluation:
  
  | Dataset                          | Loss   | WER    | CER    |
  | -------------------------------- | ------ | ------ | ------ |
  | `Zeroth Korean`                  | 0.2089 | 29.54% | 9.53%  |
  | `Robust Speech Event - Dev Data` | N/A    | 76.26% | 38.67% |
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  
  - `learning_rate`: 7.5e-05
  - `train_batch_size`: 8
  - `eval_batch_size`: 8
  - `seed`: 42
  - `gradient_accumulation_steps`: 4
  - `total_train_batch_size`: 32
  - `optimizer`: Adam with `betas=(0.9, 0.999)` and `epsilon=1e-08`
  - `lr_scheduler_type`: linear
  - `lr_scheduler_warmup_steps`: 2000
  - `num_epochs`: 50.0
  - `mixed_precision_training`: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss |  Wer   |  Cer   |
  | :-----------: | :---: | :---: | :-------------: | :----: | :----: |
  |    19.7138    | 0.72  |  500  |     19.6427     |  1.0   |  1.0   |
  |    4.8039     | 1.44  | 1000  |     4.7842      |  1.0   |  1.0   |
  |    4.5619     | 2.16  | 1500  |     4.5608      | 0.9992 | 0.9598 |
  |     4.254     | 2.88  | 2000  |     4.2729      | 0.9955 | 0.9063 |
  |    4.1905     |  3.6  | 2500  |     4.2257      | 0.9903 | 0.8758 |
  |    4.0683     | 4.32  | 3000  |     3.9294      | 0.9937 | 0.7911 |
  |     3.486     | 5.04  | 3500  |     2.7045      | 1.0012 | 0.5934 |
  |     2.946     | 5.75  | 4000  |     1.9691      | 0.9425 | 0.4634 |
  |     2.634     | 6.47  | 4500  |     1.5212      | 0.8807 | 0.3850 |
  |    2.4066     | 7.19  | 5000  |     1.2551      | 0.8177 | 0.3601 |
  |    2.2651     | 7.91  | 5500  |     1.0423      | 0.7650 | 0.3039 |
  |    2.1828     | 8.63  | 6000  |     0.9599      | 0.7273 | 0.3106 |
  |    2.1023     | 9.35  | 6500  |     0.9482      | 0.7161 | 0.3063 |
  |    2.0536     | 10.07 | 7000  |     0.8242      | 0.6767 | 0.2860 |
  |    1.9803     | 10.79 | 7500  |     0.7643      | 0.6563 | 0.2637 |
  |    1.9468     | 11.51 | 8000  |     0.7319      | 0.6441 | 0.2505 |
  |    1.9178     | 12.23 | 8500  |     0.6937      | 0.6320 | 0.2489 |
  |    1.8515     | 12.95 | 9000  |     0.6443      | 0.6053 | 0.2196 |
  |    1.8083     | 13.67 | 9500  |     0.6286      | 0.6122 | 0.2148 |
  |     1.819     | 14.39 | 10000 |     0.6015      | 0.5986 | 0.2074 |
  |    1.7684     | 15.11 | 10500 |     0.5682      | 0.5741 | 0.1982 |
  |    1.7195     | 15.83 | 11000 |     0.5385      | 0.5592 | 0.2007 |
  |    1.7044     | 16.55 | 11500 |     0.5362      | 0.5524 | 0.2097 |
  |    1.6879     | 17.27 | 12000 |     0.5119  