readme: ---
  license: apache-2.0
  language:
  - vi
  tags:
  - automatic-speech-recognition
  - common-voice
  - hf-asr-leaderboard
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_7_0
  model-index:
  - name: xls-asr-vi-40h
    results:
    - task:
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 7.0
        type: mozilla-foundation/common_voice_7_0
        args: vi
      metrics:
      - name: Test WER (with Language model)
        type: wer
        value: 56.57
  ---
  
  # xls-asr-vi-40h
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common voice 7.0 vi & private dataset.
  It achieves the following results on the evaluation set (Without Language Model):
  - Loss: 1.1177
  - Wer: 60.58
  
  ## Evaluation
  Please run the eval.py file
  
  ```bash
  !python eval_custom.py --model_id geninhu/xls-asr-vi-40h --dataset mozilla-foundation/common_voice_7_0 --config vi --split test
  ```
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-06
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1500
  - num_epochs: 50.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 23.3878       | 0.93  | 1500  | 21.9179         | 1.0    |
  | 8.8862        | 1.85  | 3000  | 6.0599          | 1.0    |
  | 4.3701        | 2.78  | 4500  | 4.3837          | 1.0    |
  | 4.113         | 3.7   | 6000  | 4.2698          | 0.9982 |
  | 3.9666        | 4.63  | 7500  | 3.9726          | 0.9989 |
  | 3.5965        | 5.56  | 9000  | 3.7124          | 0.9975 |
  | 3.3944        | 6.48  | 10500 | 3.5005          | 1.0057 |
  | 3.304         | 7.41  | 12000 | 3.3710          | 1.0043 |
  | 3.2482        | 8.33  | 13500 | 3.4201          | 1.0155 |
  | 3.212         | 9.26  | 15000 | 3.3732          | 1.0151 |
  | 3.1778        | 10.19 | 16500 | 3.2763          | 1.0009 |
  | 3.1027        | 11.11 | 18000 | 3.1943          | 1.0025 |
  | 2.9905        | 12.04 | 19500 | 2.8082          | 0.9703 |
  | 2.7095        | 12.96 | 21000 | 2.4993          | 0.9302 |
  | 2.4862        | 13.89 | 22500 | 2.3072          | 0.9140 |
  | 2.3271        | 14.81 | 24000 | 2.1398          | 0.8949 |
  | 2.1968        | 15.74 | 25500 | 2.0594          | 0.8817 |
  | 2.111         | 16.67 | 27000 | 1.9404          | 0.8630 |
  | 2.0387        | 17.59 | 28500 | 1.8895          | 0.8497 |
  | 1.9504        | 18.52 | 30000 | 1.7961          | 0.8315 |
  | 1.9039        | 19.44 | 31500 | 1.7433          | 0.8213 |
  | 1.8342        | 20.37 | 33000 | 1.6790          | 0.7994 |
  | 1.7824        | 21.3  | 34500 | 1.6291          | 0.7825 |
  | 1.7359        | 22.22 | 36000 | 1.5783          | 0.7706 |
  | 1.7053        | 23.15 | 37500 | 1.5248          | 0.7492 |
  | 1.6504        | 24.07 | 39000 | 1.4930          | 0.7406 |
  | 1.6263        | 25.0  | 40500 | 1.4572          | 0.7348 |
  | 1.5893        | 25.93 | 42000 | 1.4202          | 0.7161 |
  | 1.5669        | 26.85 | 43500 | 1.3987          | 0.7143 |
  | 1.5277        | 27.78 | 45000 | 1.3512          | 0.6991 |
  | 1.501         | 28.7  | 46500 | 1.3320          | 0.6879 |
  | 1.4781        | 29.63 | 48000 | 1.3112          | 0.6788 |
  | 1.4477        | 30.56 | 49500 | 1.2850          | 0.6657 |
  | 1.4483        | 31.48 | 51000 | 1.2813          | 0.6633 |
  | 1.4065        | 32.41 | 52500 | 1.2475          | 0.6541 |
  | 1.3779        | 33.33 | 54000 | 1.2244          | 0.6503 |
  | 1.3788        | 34.26 | 55500 | 1.2116          | 0.6407 |
  | 1.3428        | 35.19 | 57000 | 1.1938          | 0.6352 |
  | 1.3453        | 36.11 | 58500 | 1.1927          | 0.6340 |
  | 1.3137        | 37.04 | 60000 | 1.1699          | 0.6252 |
  | 1.2984        | 37.96 | 61500 | 1.1666          | 0.6229 |
  | 1.2927        | 38.89 | 63000 | 1.1585          | 0.6188 |
  | 1.2919        | 39.81 | 64500 | 1.1618          | 0.6190 |
  | 1.293         | 40.74 | 66000 | 1.1479          | 0.6181 |
  | 1.2853        | 41.67 | 67500 | 1.1423          | 0.6202 |
  | 1.2687        | 42.59 | 69000 | 1.1315          | 0.6131 |
  | 1.2603        | 43.52 | 70500 | 1.1333          | 0.6128 |
  | 1.2577        | 44.44 | 72000 | 1.1191          | 0.6079 |
  | 1.2435        | 45.37 | 73500 | 1.1177          | 0.6079 |
  | 1.251         | 46.3  | 75000 | 1.1211          | 0.6092 |
  | 1.2482        | 47.22 | 76500 | 1.1177          | 0.6060 |
  | 1.2422        | 48.15 | 78000 | 1.1227          | 0.6097 |
  | 1.2485        | 49.07 | 79500 | 1.1187          | 0.6071 |
  | 1.2425        | 50.0  | 81000 | 1.1177          | 0.6058 |
  
  
  ### Framework versions
  
  - Transformers 4.16.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 1.17.1.dev0
  - Tokenizers 0.11.0
  