readme: ---
  language:
  - te
  license: apache-2.0
  tags:
  - hf-asr-leaderboard
  - generated_from_trainer
  datasets:
  - INDIC_SUPERB_MUCS_OPENSLR
  metrics:
  - wer
  model-index:
  - name: Whisper Small Telugu - Naga Budigam
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Crowed sourced dataset
        type: INDIC SUPERB, MUCS, OPENSLR
        config: None
        split: None
        args: 'config: te, split: test'
      metrics:
      - name: Wer
        type: wer
        value: 28.59758159493464
  ---
  
  <!-- 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 - Naga Budigam
  
  This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Crowed sourced dataset dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0768
  - Wer: 28.5976
  
  ## 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: 4
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 3000
  - training_steps: 10000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer     |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|
  | 0.0729        | 1.08  | 5000  | 0.0934          | 33.3306 |
  | 0.0519        | 2.16  | 10000 | 0.0768          | 28.5976 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  