readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice_11_0
  metrics:
  - wer
  model-index:
  - name: firstcolab3
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: common_voice_11_0
        type: common_voice_11_0
        config: th
        split: train+validation
        args: th
      metrics:
      - name: Wer
        type: wer
        value: 0.6226224783861671
  ---
  
  <!-- 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. -->
  
  # firstcolab3
  
  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_11_0 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2756
  - Wer: 0.6226
  - Cer: 0.0535
  
  ## 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: 0.0001
  - train_batch_size: 32
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 20
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
  | 7.187         | 0.75  | 1000  | 3.7705          | 1.0    | 1.0    |
  | 2.0277        | 1.5   | 2000  | 0.6139          | 0.9202 | 0.1545 |
  | 0.8368        | 2.24  | 3000  | 0.4351          | 0.8589 | 0.1147 |
  | 0.6772        | 2.99  | 4000  | 0.3762          | 0.8200 | 0.0990 |
  | 0.5702        | 3.74  | 5000  | 0.3434          | 0.7889 | 0.0891 |
  | 0.5205        | 4.49  | 6000  | 0.3427          | 0.7726 | 0.0855 |
  | 0.4773        | 5.24  | 7000  | 0.3073          | 0.7408 | 0.0767 |
  | 0.4389        | 5.98  | 8000  | 0.2969          | 0.7421 | 0.0759 |
  | 0.4069        | 6.73  | 9000  | 0.2884          | 0.7134 | 0.0711 |
  | 0.3858        | 7.48  | 10000 | 0.2952          | 0.7066 | 0.0699 |
  | 0.36          | 8.23  | 11000 | 0.2846          | 0.6902 | 0.0662 |
  | 0.3517        | 8.98  | 12000 | 0.2729          | 0.6756 | 0.0638 |
  | 0.3265        | 9.72  | 13000 | 0.2844          | 0.6756 | 0.0645 |
  | 0.3127        | 10.47 | 14000 | 0.2769          | 0.6803 | 0.0640 |
  | 0.3016        | 11.22 | 15000 | 0.2772          | 0.6566 | 0.0618 |
  | 0.2855        | 11.97 | 16000 | 0.2791          | 0.6540 | 0.0598 |
  | 0.2699        | 12.72 | 17000 | 0.2714          | 0.6455 | 0.0589 |
  | 0.264         | 13.46 | 18000 | 0.2782          | 0.6472 | 0.0588 |
  | 0.2518        | 14.21 | 19000 | 0.2693          | 0.6398 | 0.0578 |
  | 0.2498        | 14.96 | 20000 | 0.2761          | 0.6300 | 0.0561 |
  | 0.2426        | 15.71 | 21000 | 0.2796          | 0.6366 | 0.0561 |
  | 0.2271        | 16.45 | 22000 | 0.2804          | 0.6336 | 0.0554 |
  | 0.2271        | 17.2  | 23000 | 0.2758          | 0.6347 | 0.0552 |
  | 0.22          | 17.95 | 24000 | 0.2785          | 0.6279 | 0.0544 |
  | 0.2143        | 18.7  | 25000 | 0.2783          | 0.6246 | 0.0538 |
  | 0.2134        | 19.45 | 26000 | 0.2756          | 0.6226 | 0.0535 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  