readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: wav2vec2-xls-r-300m-th-v6_0
    results: []
  ---
  
  <!-- 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-xls-r-300m-th-v6_0
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.2428
  - Wer: 0.9676
  - Cer: 0.5675
  - Clean Cer: 0.6041
  - Learning Rate: 0.0000
  
  ## 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: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - 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: 500
  - num_epochs: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    | Clean Cer | Rate   |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:---------:|:------:|
  | 11.3276       | 0.2   | 500   | 4.4779          | 1.0    | 1.0    | 1.0       | 0.0000 |
  | 3.7719        | 0.4   | 1000  | 3.6245          | 1.0    | 0.9810 | 0.9772    | 0.0000 |
  | 3.5501        | 0.6   | 1500  | 3.5625          | 1.0000 | 0.9266 | 0.9124    | 0.0000 |
  | 2.5415        | 0.8   | 2000  | 2.1438          | 0.9982 | 0.6024 | 0.5525    | 0.0000 |
  | 1.2717        | 1.0   | 2500  | 1.4526          | 0.9479 | 0.4061 | 0.3663    | 0.0000 |
  | 0.9813        | 1.2   | 3000  | 1.3663          | 0.9263 | 0.4066 | 0.3809    | 0.0000 |
  | 0.8408        | 1.41  | 3500  | 1.1297          | 0.8834 | 0.3393 | 0.3068    | 0.0000 |
  | 0.7527        | 1.61  | 4000  | 0.9215          | 0.8106 | 0.2797 | 0.2527    | 0.0000 |
  | 0.6899        | 1.81  | 4500  | 0.8934          | 0.8148 | 0.2798 | 0.2483    | 0.0000 |
  | 0.6379        | 2.01  | 5000  | 0.7953          | 0.7446 | 0.2383 | 0.2165    | 0.0000 |
  | 0.5703        | 2.21  | 5500  | 0.7227          | 0.7047 | 0.2175 | 0.1970    | 0.0000 |
  | 0.5516        | 2.41  | 6000  | 0.7193          | 0.7074 | 0.2168 | 0.1949    | 0.0000 |
  | 0.5165        | 2.61  | 6500  | 0.6856          | 0.6735 | 0.2044 | 0.1886    | 0.0000 |
  | 0.4976        | 2.81  | 7000  | 0.6708          | 0.6733 | 0.2046 | 0.1853    | 0.0000 |
  | 0.4867        | 3.01  | 7500  | 0.6434          | 0.6413 | 0.1908 | 0.1783    | 0.0000 |
  | 0.4371        | 3.21  | 8000  | 0.6099          | 0.6234 | 0.1846 | 0.1706    | 0.0000 |
  | 0.4303        | 3.41  | 8500  | 0.5943          | 0.6056 | 0.1736 | 0.1596    | 0.0000 |
  | 0.4231        | 3.61  | 9000  | 0.5615          | 0.5895 | 0.1692 | 0.1555    | 0.0000 |
  | 0.4233        | 3.82  | 9500  | 0.5883          | 0.5966 | 0.1721 | 0.1599    | 0.0000 |
  | 0.4134        | 4.02  | 10000 | 0.6170          | 0.6094 | 0.1765 | 0.1647    | 0.0000 |
  | 0.4011        | 4.22  | 10500 | 0.6374          | 0.5986 | 0.1711 | 0.1589    | 0.0000 |
  | 0.4896        | 4.42  | 11000 | 0.7166          | 0.6267 | 0.1804 | 0.1618    | 0.0000 |
  | 0.5358        | 4.62  | 11500 | 0.7572          | 0.6151 | 0.1788 | 0.1651    | 0.0000 |
  | 0.5669        | 4.82  | 12000 | 0.7859          | 0.6111 | 0.1795 | 0.1717    | 0.0000 |
  | 0.6109        | 5.02  | 12500 | 0.7980          | 0.5910 | 0.1681 | 0.1601    | 0.0000 |
  | 0.538         | 5.22  | 13000 | 0.7520          | 0.5908 | 0.1663 | 0.1524    | 0.0000 |
  | 0.5886        | 5.42  | 13500 | 0.9358          | 0.6562 | 0.1934 | 0.1797    | 0.0000 |
  | 0.6967        | 5.62  | 14000 | 0.8481          | 0.5808 | 0.1618 | 0.1504    | 0.0000 |
  | 0.7136        | 5.82  | 14500 | 0.9073          | 0.5872 | 0.1624 | 0.1504    | 0.0000 |
  | 0.8836        | 6.02  | 15000 | 1.1423          | 0.6392 | 0.1764 | 0.1519    | 0.0000 |
  | 0.9568        | 6.22  | 15500 | 1.1805          | 0.6059 | 0.1703 | 0.1523    | 0.0000 |
  | 1.0651        | 6.43  | 16000 | 1.4273          | 0.6196 | 0.1750 | 0.1662    | 0.0000 |
  | 1.2183        | 6.63  | 16500 | 1.6106          | 0.8786 | 0.3178 | 0.2718    | 0.0000 |
  | 1.4574        | 6.83  | 17000 | 1.8468          | 0.8987 | 0.3667 | 0.3330    | 0.0000 |
  | 1.6748        | 7.03  | 17500 | 2.0536          | 0.9169 | 0.4167 | 0.4011    | 0.0000 |
  | 1.7574        | 7.23  | 18000 | 2.2253          | 0.9515 | 0.5439 | 0.5556    | 0.0000 |
  | 1.8764        | 7.43  | 18500 | 2.1131          | 0.9540 | 0.5569 | 0.5645    | 0.0000 |
  | 1.9001        | 7.63  | 19000 | 2.4071          | 0.9894 | 0.7434 | 0.7678    | 0.0000 |
  | 2.039         | 7.83  | 19500 | 2.3444          | 0.9814 | 0.6931 | 0.7218    | 0.0000 |
  | 2.0798    