readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: wav2vec2-xls-r-300m-th-v5_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-v5_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: 2.4759
  - Wer: 0.9773
  - Cer: 0.5015
  - Clean Cer: 0.4737
  - 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: 5e-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   |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:---------:|:------:|
  | 8.9148        | 0.2   | 500   | 3.6934          | 1.0    | 1.0    | 1.0       | 0.0000 |
  | 3.5822        | 0.4   | 1000  | 3.6039          | 1.0000 | 0.9619 | 0.9544    | 0.0000 |
  | 2.7881        | 0.6   | 1500  | 1.3687          | 0.9913 | 0.4301 | 0.3477    | 0.0000 |
  | 1.8645        | 0.8   | 2000  | 0.9112          | 0.8704 | 0.3190 | 0.2739    | 0.0000 |
  | 1.6778        | 1.0   | 2500  | 0.7188          | 0.7284 | 0.2531 | 0.2405    | 0.0000 |
  | 1.5745        | 1.2   | 3000  | 0.6317          | 0.6847 | 0.2265 | 0.2156    | 0.0000 |
  | 1.499         | 1.41  | 3500  | 0.5829          | 0.6652 | 0.2193 | 0.2064    | 0.0000 |
  | 1.4473        | 1.61  | 4000  | 0.5348          | 0.6486 | 0.2060 | 0.1905    | 0.0000 |
  | 1.4049        | 1.81  | 4500  | 0.5086          | 0.6178 | 0.1905 | 0.1758    | 0.0000 |
  | 1.3661        | 2.01  | 5000  | 0.4858          | 0.6005 | 0.1887 | 0.1788    | 0.0000 |
  | 1.3244        | 2.21  | 5500  | 0.4548          | 0.5932 | 0.1829 | 0.1686    | 0.0000 |
  | 1.3088        | 2.41  | 6000  | 0.4406          | 0.5983 | 0.1784 | 0.1589    | 0.0000 |
  | 1.2746        | 2.61  | 6500  | 0.4253          | 0.5598 | 0.1661 | 0.1529    | 0.0000 |
  | 1.2535        | 2.81  | 7000  | 0.4131          | 0.5686 | 0.1668 | 0.1490    | 0.0000 |
  | 1.2472        | 3.01  | 7500  | 0.3988          | 0.5322 | 0.1541 | 0.1435    | 0.0000 |
  | 1.2025        | 3.21  | 8000  | 0.3843          | 0.5261 | 0.1549 | 0.1419    | 0.0000 |
  | 1.1932        | 3.41  | 8500  | 0.3785          | 0.5089 | 0.1455 | 0.1329    | 0.0000 |
  | 1.1722        | 3.61  | 9000  | 0.3673          | 0.4979 | 0.1405 | 0.1287    | 0.0000 |
  | 1.165         | 3.82  | 9500  | 0.3593          | 0.4964 | 0.1426 | 0.1307    | 0.0000 |
  | 1.1463        | 4.02  | 10000 | 0.3589          | 0.4973 | 0.1413 | 0.1284    | 0.0000 |
  | 1.128         | 4.22  | 10500 | 0.3834          | 0.4870 | 0.1359 | 0.1255    | 0.0000 |
  | 1.1792        | 4.42  | 11000 | 0.4313          | 0.4992 | 0.1405 | 0.1305    | 0.0000 |
  | 1.2958        | 4.62  | 11500 | 0.5493          | 0.5089 | 0.1431 | 0.1320    | 0.0000 |
  | 1.45          | 4.82  | 12000 | 0.7329          | 0.5845 | 0.1682 | 0.1563    | 0.0000 |
  | 1.539         | 5.02  | 12500 | 0.7063          | 0.5761 | 0.1656 | 0.1542    | 0.0000 |
  | 1.4903        | 5.22  | 13000 | 0.6155          | 0.5583 | 0.1631 | 0.1512    | 0.0000 |
  | 1.5217        | 5.42  | 13500 | 0.6886          | 0.5705 | 0.1632 | 0.1455    | 0.0000 |
  | 1.581         | 5.62  | 14000 | 0.7627          | 0.5699 | 0.1592 | 0.1466    | 0.0000 |
  | 1.6124        | 5.82  | 14500 | 0.7973          | 0.6106 | 0.1708 | 0.1502    | 0.0000 |
  | 1.6359        | 6.02  | 15000 | 0.7396          | 0.5906 | 0.1663 | 0.1435    | 0.0000 |
  | 1.6401        | 6.22  | 15500 | 0.9021          | 0.9429 | 0.2413 | 0.1377    | 0.0000 |
  | 1.7744        | 6.43  | 16000 | 1.0785          | 0.9960 | 0.2758 | 0.1415    | 0.0000 |
  | 1.9555        | 6.63  | 16500 | 1.3109          | 0.9980 | 0.3312 | 0.2032    | 0.0000 |
  | 2.2272        | 6.83  | 17000 | 1.6380          | 0.9943 | 0.3630 | 0.2467    | 0.0000 |
  | 2.4526        | 7.03  | 17500 | 2.0785          | 0.9964 | 0.5836 | 0.5109    | 0.0000 |
  | 2.5589        | 7.23  | 18000 | 2.0779          | 0.9834 | 0.6224 | 0.5848    | 0.0000 |
  | 2.8278        | 7.43  | 18500 | 2.7439          | 0.9990 | 0.8328 | 0.8113    | 0.0000 |
  | 2.9381        | 7.63  | 19000 | 2.6689          | 0.9987 | 0.7868 | 0.7622    | 0.0000 |
  | 2.8839        | 7.83  | 19500 | 2.5937          | 0.9983 | 0.7558 | 0.7294    | 0.0000 |
  | 2.8665    