readme: ---
  language:
  - et
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - et
  - robust-speech-event
  - generated_from_trainer
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: XLS-R-1B - Estonian
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: et
      metrics:
      - name: Test WER
        type: wer
        value: 52.47
      - name: Test CER
        type: cer
        value: 12.59
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: sv
      metrics:
      - name: Test WER
        type: wer
        value: 61.02
      - name: Test CER
        type: cer
        value: 21.08
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: et
      metrics:
      - name: Test WER
        type: wer
        value: 59.23
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: et
      metrics:
      - name: Test WER
        type: wer
        value: 69.08
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # 
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - ET dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8824
  - Wer: 0.5246
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 7e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - training_steps: 25000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step  | Validation Loss | Wer    |
  |:-------------:|:------:|:-----:|:---------------:|:------:|
  | 1.0296        | 2.79   | 500   | 0.8106          | 0.8029 |
  | 0.9339        | 5.59   | 1000  | 0.7419          | 0.7932 |
  | 0.8925        | 8.38   | 1500  | 0.7137          | 0.7706 |
  | 0.8484        | 11.17  | 2000  | 0.7020          | 0.7677 |
  | 0.7521        | 13.97  | 2500  | 0.7043          | 0.7375 |
  | 0.719         | 16.76  | 3000  | 0.6617          | 0.7428 |
  | 0.656         | 19.55  | 3500  | 0.6388          | 0.7202 |
  | 0.6085        | 22.35  | 4000  | 0.6211          | 0.6960 |
  | 0.5598        | 25.14  | 4500  | 0.6132          | 0.6644 |
  | 0.4969        | 27.93  | 5000  | 0.6065          | 0.6521 |
  | 0.4638        | 30.73  | 5500  | 0.6978          | 0.6577 |
  | 0.4385        | 33.52  | 6000  | 0.5994          | 0.6565 |
  | 0.396         | 36.31  | 6500  | 0.6170          | 0.6258 |
  | 0.3861        | 39.11  | 7000  | 0.6486          | 0.6217 |
  | 0.3602        | 41.9   | 7500  | 0.6508          | 0.6115 |
  | 0.3251        | 44.69  | 8000  | 0.7022          | 0.6253 |
  | 0.3197        | 47.49  | 8500  | 0.7706          | 0.6215 |
  | 0.3013        | 50.28  | 9000  | 0.6419          | 0.5999 |
  | 0.2813        | 53.07  | 9500  | 0.6908          | 0.5959 |
  | 0.286         | 55.87  | 10000 | 0.7151          | 0.5916 |
  | 0.2645        | 58.66  | 10500 | 0.7181          | 0.5860 |
  | 0.2535        | 61.45  | 11000 | 0.7877          | 0.5979 |
  | 0.247         | 64.25  | 11500 | 0.8199          | 0.6129 |
  | 0.2412        | 67.04  | 12000 | 0.7679          | 0.5884 |
  | 0.2404        | 69.83  | 12500 | 0.7266          | 0.5816 |
  | 0.2293        | 72.63  | 13000 | 0.7928          | 0.5795 |
  | 0.2176        | 75.42  | 13500 | 0.7916          | 0.5846 |
  | 0.2143        | 78.21  | 14000 | 0.7954          | 0.5765 |
  | 0.2185        | 81.01  | 14500 | 0.8317          | 0.5907 |
  | 0.2057        | 83.8   | 15000 | 0.8016          | 0.5851 |
  | 0.1895        | 86.59  | 15500 | 0.8080          | 0.5679 |
  | 0.1883        | 89.39  | 16000 | 0.8103          | 0.5712 |
  | 0.1802        | 92.18  | 16500 | 0.8383          | 0.5644 |
  | 0.1826        | 94.97  | 17000 | 0.8799          | 0.5657 |
  | 0.1717        | 97.77  | 17500 | 0.8620          | 0.5709 |
  | 0.1701        | 100.56 | 18000 | 0.8717          | 0.5662 |
  | 0.1623        | 103.35 | 18500 | 0.8534          | 0.5594 |
  | 0.158         | 106.15 | 19000 | 0.8595     