readme: ---
  language:
  - te
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - google/fleurs
  metrics:
  - wer
  model-index:
  - name: whisper-small-telugu
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: google/fleurs
        type: google/fleurs
        config: te_in
        split: test
      metrics:
      - name: Wer
        type: wer
        value: 39.67740444608772
  ---
  
  <!-- 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. -->
  
  # whisper-small-telugu
  
  This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs dataset.
  It achieves the following results on the evaluation set (google/flerus telugu test set):
  - Loss: 0.3622
  - Wer: 39.6774
  
  [openai/whisper-small](https://huggingface.co/openai/whisper-small) has the following zero shot performance on google/fleurs test set:
  
  - Wer: 117.91
  
  ## 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: 1e-05
  - train_batch_size: 4
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 8
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - training_steps: 10000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer     |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|
  | 0.2623        | 1.55  | 500   | 0.2733          | 65.9750 |
  | 0.0859        | 3.1   | 1000  | 0.2045          | 39.7652 |
  | 0.0538        | 4.64  | 1500  | 0.2220          | 42.3811 |
  | 0.0265        | 6.19  | 2000  | 0.2526          | 42.3626 |
  | 0.0179        | 7.74  | 2500  | 0.2754          | 42.1685 |
  | 0.008         | 9.29  | 3000  | 0.2966          | 41.2257 |
  | 0.0061        | 10.83 | 3500  | 0.2950          | 40.6202 |
  | 0.0034        | 12.38 | 4000  | 0.3049          | 40.3198 |
  | 0.004         | 13.93 | 4500  | 0.3106          | 40.5879 |
  | 0.0018        | 15.48 | 5000  | 0.3199          | 40.1812 |
  | 0.0016        | 17.03 | 5500  | 0.3346          | 39.8345 |
  | 0.0006        | 18.57 | 6000  | 0.3337          | 40.2274 |
  | 0.0003        | 20.12 | 6500  | 0.3396          | 40.2597 |
  | 0.0005        | 21.67 | 7000  | 0.3465          | 40.1072 |
  | 0.0002        | 23.22 | 7500  | 0.3485          | 39.7282 |
  | 0.0002        | 24.77 | 8000  | 0.3519          | 39.7837 |
  | 0.0001        | 26.32 | 8500  | 0.3567          | 39.7560 |
  | 0.0001        | 27.86 | 9000  | 0.3614          | 39.8068 |
  | 0.0           | 29.41 | 9500  | 0.3609          | 39.4925 |
  | 0.0           | 30.96 | 10000 | 0.3622          | 39.6774 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  