readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: wav2vec2-xls-r-300m-th-v7_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-v7_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: 3.4099
  - Wer: 0.9988
  - Cer: 0.7861
  - Clean Cer: 0.7617
  - 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: 4
  - total_train_batch_size: 64
  - 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   |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:---------:|:------:|
  | 8.5484        | 0.4   | 500   | 3.6234          | 1.0    | 1.0    | 1.0       | 0.0000 |
  | 3.2275        | 0.8   | 1000  | 2.2960          | 0.9998 | 0.7081 | 0.6540    | 0.0000 |
  | 0.9955        | 1.2   | 1500  | 1.2224          | 0.9549 | 0.4327 | 0.3756    | 0.0000 |
  | 0.66          | 1.61  | 2000  | 0.9559          | 0.9232 | 0.3651 | 0.3040    | 0.0000 |
  | 0.546         | 2.01  | 2500  | 0.9207          | 0.9481 | 0.3585 | 0.2826    | 0.0000 |
  | 0.4459        | 2.41  | 3000  | 0.7701          | 0.8693 | 0.2940 | 0.2383    | 0.0000 |
  | 0.4041        | 2.81  | 3500  | 0.7756          | 0.8224 | 0.2949 | 0.2634    | 0.0000 |
  | 0.3637        | 3.21  | 4000  | 0.6015          | 0.7015 | 0.2064 | 0.1807    | 0.0000 |
  | 0.334         | 3.61  | 4500  | 0.5615          | 0.6675 | 0.1907 | 0.1638    | 0.0000 |
  | 0.3283        | 4.02  | 5000  | 0.6205          | 0.7073 | 0.2092 | 0.1803    | 0.0000 |
  | 0.3762        | 4.42  | 5500  | 0.7517          | 0.6366 | 0.1778 | 0.1600    | 0.0000 |
  | 0.4954        | 4.82  | 6000  | 0.9374          | 0.7073 | 0.2023 | 0.1735    | 0.0000 |
  | 0.5568        | 5.22  | 6500  | 0.8859          | 0.7027 | 0.1982 | 0.1666    | 0.0000 |
  | 0.6756        | 5.62  | 7000  | 1.0252          | 0.6802 | 0.1920 | 0.1628    | 0.0000 |
  | 0.7752        | 6.02  | 7500  | 1.1259          | 0.7657 | 0.2309 | 0.1908    | 0.0000 |
  | 0.8305        | 6.43  | 8000  | 1.3857          | 0.9029 | 0.3252 | 0.2668    | 0.0000 |
  | 1.7385        | 6.83  | 8500  | 3.2320          | 0.9998 | 0.9234 | 0.9114    | 0.0000 |
  | 2.7839        | 7.23  | 9000  | 3.3238          | 0.9999 | 0.9400 | 0.9306    | 0.0000 |
  | 2.8307        | 7.63  | 9500  | 3.2678          | 0.9998 | 0.9167 | 0.9053    | 0.0000 |
  | 2.7672        | 8.03  | 10000 | 3.2435          | 0.9995 | 0.8992 | 0.8867    | 0.0000 |
  | 2.7426        | 8.43  | 10500 | 3.2396          | 0.9995 | 0.8720 | 0.8561    | 0.0000 |
  | 2.7608        | 8.84  | 11000 | 3.2689          | 0.9993 | 0.8399 | 0.8202    | 0.0000 |
  | 2.8195        | 9.24  | 11500 | 3.3283          | 0.9989 | 0.8084 | 0.7865    | 0.0000 |
  | 2.9044        | 9.64  | 12000 | 3.4099          | 0.9988 | 0.7861 | 0.7617    | 0.0000 |
  
  
  ### Framework versions
  
  - Transformers 4.27.0.dev0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  