readme: ---
  language:
  - myv
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - myv
  - robust-speech-event
  - model_for_talk
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: wav2vec2-large-xls-r-300m-myv-v1
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: myv
      metrics:
      - name: Test WER
        type: wer
        value: 0.599548532731377
      - name: Test CER
        type: cer
        value: 0.12953851902597
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: myv
      metrics:
      - name: Test WER
        type: wer
        value: NA
      - name: Test CER
        type: cer
        value: NA
  ---
  
  <!-- 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. -->
  
  # wav2vec2-large-xls-r-300m-myv-v1
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - MYV dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8537
  - Wer: 0.6160
  
  ### Evaluation Commands
  
  1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
  
  python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-myv-v1 --dataset mozilla-foundation/common_voice_8_0 --config myv --split test --log_outputs
  
  2. To evaluate on speech-recognition-community-v2/dev_data
  
  Erzya language not found in speech-recognition-community-v2/dev_data!
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.000222
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - 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: 1000
  - num_epochs: 150
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Wer    |
  |:-------------:|:------:|:----:|:---------------:|:------:|
  | 19.453        | 1.92   | 50   | 16.4001         | 1.0    |
  | 9.6875        | 3.85   | 100  | 5.4468          | 1.0    |
  | 4.9988        | 5.77   | 150  | 4.3507          | 1.0    |
  | 4.1148        | 7.69   | 200  | 3.6753          | 1.0    |
  | 3.4922        | 9.62   | 250  | 3.3103          | 1.0    |
  | 3.2443        | 11.54  | 300  | 3.1741          | 1.0    |
  | 3.164         | 13.46  | 350  | 3.1346          | 1.0    |
  | 3.0954        | 15.38  | 400  | 3.0428          | 1.0    |
  | 3.0076        | 17.31  | 450  | 2.9137          | 1.0    |
  | 2.6883        | 19.23  | 500  | 2.1476          | 0.9978 |
  | 1.5124        | 21.15  | 550  | 0.8955          | 0.8225 |
  | 0.8711        | 23.08  | 600  | 0.6948          | 0.7591 |
  | 0.6695        | 25.0   | 650  | 0.6683          | 0.7636 |
  | 0.5606        | 26.92  | 700  | 0.6821          | 0.7435 |
  | 0.503         | 28.85  | 750  | 0.7220          | 0.7516 |
  | 0.4528        | 30.77  | 800  | 0.6638          | 0.7324 |
  | 0.4219        | 32.69  | 850  | 0.7120          | 0.7435 |
  | 0.4109        | 34.62  | 900  | 0.7122          | 0.7511 |
  | 0.3887        | 36.54  | 950  | 0.7179          | 0.7199 |
  | 0.3895        | 38.46  | 1000 | 0.7322          | 0.7525 |
  | 0.391         | 40.38  | 1050 | 0.6850          | 0.7364 |
  | 0.3537        | 42.31  | 1100 | 0.7571          | 0.7279 |
  | 0.3267        | 44.23  | 1150 | 0.7575          | 0.7257 |
  | 0.3195        | 46.15  | 1200 | 0.7580          | 0.6998 |
  | 0.2891        | 48.08  | 1250 | 0.7452          | 0.7101 |
  | 0.294         | 50.0   | 1300 | 0.7316          | 0.6945 |
  | 0.2854        | 51.92  | 1350 | 0.7241          | 0.6757 |
  | 0.2801        | 53.85  | 1400 | 0.7532          | 0.6887 |
  | 0.2502        | 55.77  | 1450 | 0.7587          | 0.6811 |
  | 0.2427        | 57.69  | 1500 | 0.7231          | 0.6851 |
  | 0.2311        | 59.62  | 1550 | 0.7288          | 0.6632 |
  | 0.2176        | 61.54  | 1600 | 0.7711          | 0.6664 |
  | 0.2117        | 63.46  | 1650 | 0.7914          | 0.6940 |
  | 0.2114        | 65.38  | 1700 | 0.8065          | 0.6918 |
  | 0.1913        | 67.31  | 1750 | 0.8372          | 0.6945 |
  | 0.1897        | 69.23  | 1800 | 0.8051          | 0.6869 |
  | 0.1865        | 71.15  | 1850 | 0.8076          | 0.6740 |
  | 0.1844        | 73.08  | 1900 | 0.7935          | 0.6708 |
  | 0.1757        | 75.0   | 1950 | 0.8015          | 0.6610 |
  | 0.1636        | 76.92  | 2000 | 0.7614          | 0.6414 |
  | 0.1637        | 78.85  | 2050 | 0.8123          | 0.6592 |
  | 0.1599        | 80.77  | 2100 | 0.7907          | 0.6566 |
  | 0