readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - openslr
  metrics:
  - wer
  model-index:
  - name: wav2vec2-telugu
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: openslr
        type: openslr
        config: SLR66
        split: train
        args: SLR66
      metrics:
      - name: Wer
        type: wer
        value: 0.2884547694473777
  ---
  
  <!-- 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-telugu
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the openslr dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2982
  - Wer: 0.2885
  
  ## 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    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 6.6905        | 3.84  | 400  | 0.7109          | 0.7800 |
  | 0.4532        | 7.69  | 800  | 0.2972          | 0.3977 |
  | 0.1957        | 11.54 | 1200 | 0.2907          | 0.3522 |
  | 0.1284        | 15.38 | 1600 | 0.3117          | 0.3317 |
  | 0.0979        | 19.23 | 2000 | 0.3000          | 0.3353 |
  | 0.0749        | 23.08 | 2400 | 0.2823          | 0.3045 |
  | 0.0584        | 26.92 | 2800 | 0.2982          | 0.2885 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.13.0+cu117
  - Datasets 2.6.1
  - Tokenizers 0.13.2
  