readme: ---
  language:
  - ta
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: Whisper Base Ta - Bharat Ramanathan
    results:
    - task:
        type: automatic-speech-recognition
        name: Automatic Speech Recognition
      dataset:
        name: mozilla-foundation/common_voice_11_0
        type: mozilla-foundation/common_voice_11_0
        config: ta
        split: test
      metrics:
      - type: wer
        value: 15.78
        name: WER
    - task:
        type: automatic-speech-recognition
        name: Automatic Speech Recognition
      dataset:
        name: google/fleurs
        type: google/fleurs
        config: ta_in
        split: test
      metrics:
      - type: wer
        value: 20.41
        name: WER
  ---
  
  <!-- 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 Base Ta - Bharat Ramanathan
  
  This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2269
  - Wer: 21.7243
  
  ## 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: 64
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1000
  - training_steps: 10000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer     |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|
  | 0.5559        | 0.1   | 1000  | 0.3963          | 35.3308 |
  | 0.3891        | 0.2   | 2000  | 0.3146          | 29.1511 |
  | 0.3425        | 0.3   | 3000  | 0.2834          | 25.5930 |
  | 0.3108        | 0.1   | 4000  | 0.2669          | 24.7191 |
  | 0.2866        | 0.1   | 5000  | 0.2596          | 25.0936 |
  | 0.2697        | 0.2   | 6000  | 0.2507          | 24.5943 |
  | 0.2421        | 0.05  | 6500  | 0.2411          | 23.0395 |
  | 0.2425        | 0.1   | 7000  | 0.2370          | 23.3804 |
  | 0.2404        | 0.15  | 7500  | 0.2333          | 22.7959 |
  | 0.2381        | 0.2   | 8000  | 0.2311          | 22.9420 |
  | 0.2429        | 0.25  | 8500  | 0.2305          | 22.0166 |
  | 0.2402        | 0.3   | 9000  | 0.2284          | 22.1140 |
  | 0.2377        | 0.35  | 9500  | 0.2271          | 22.0653 |
  | 0.2389        | 0.4   | 10000 | 0.2269          | 21.7243 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1.dev0
  - Tokenizers 0.13.2
  