readme: ---
  license: apache-2.0
  language: tt
  tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
  - hf-asr-leaderboard
  - robust-speech-event
  - tt
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-large-xlsr-53-W2V2-TATAR-SMALL
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: tt
      metrics:
      - name: Test WER
        type: wer
        value: 53.16
  ---
  
  <!-- 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-large-xlsr-53-W2V2-TATAR-SMALL
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4714
  - Wer: 0.5316
  
  ## 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: 0.0003
  - train_batch_size: 16
  - eval_batch_size: 8
  - 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: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 6.2446        | 1.17  | 400   | 3.2621          | 1.0    |
  | 1.739         | 2.35  | 800   | 0.5832          | 0.7688 |
  | 0.4718        | 3.52  | 1200  | 0.4785          | 0.6824 |
  | 0.3574        | 4.69  | 1600  | 0.4814          | 0.6792 |
  | 0.2946        | 5.86  | 2000  | 0.4484          | 0.6506 |
  | 0.2674        | 7.04  | 2400  | 0.4612          | 0.6225 |
  | 0.2349        | 8.21  | 2800  | 0.4600          | 0.6050 |
  | 0.2206        | 9.38  | 3200  | 0.4772          | 0.6048 |
  | 0.2072        | 10.56 | 3600  | 0.4676          | 0.6106 |
  | 0.1984        | 11.73 | 4000  | 0.4816          | 0.6079 |
  | 0.1793        | 12.9  | 4400  | 0.4616          | 0.5836 |
  | 0.172         | 14.08 | 4800  | 0.4808          | 0.5860 |
  | 0.1624        | 15.25 | 5200  | 0.4854          | 0.5820 |
  | 0.156         | 16.42 | 5600  | 0.4609          | 0.5656 |
  | 0.1448        | 17.59 | 6000  | 0.4926          | 0.5817 |
  | 0.1406        | 18.77 | 6400  | 0.4638          | 0.5654 |
  | 0.1337        | 19.94 | 6800  | 0.4731          | 0.5652 |
  | 0.1317        | 21.11 | 7200  | 0.4861          | 0.5639 |
  | 0.1179        | 22.29 | 7600  | 0.4766          | 0.5521 |
  | 0.1197        | 23.46 | 8000  | 0.4824          | 0.5584 |
  | 0.1096        | 24.63 | 8400  | 0.5006          | 0.5559 |
  | 0.1038        | 25.81 | 8800  | 0.4994          | 0.5440 |
  | 0.0992        | 26.98 | 9200  | 0.4867          | 0.5405 |
  | 0.0984        | 28.15 | 9600  | 0.4798          | 0.5361 |
  | 0.0943        | 29.33 | 10000 | 0.4714          | 0.5316 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.10.0+cu111
  - Datasets 1.14.0
  - Tokenizers 0.10.3
  