readme: ---
  language:
  - te
  license: apache-2.0
  tags:
  - whisper-event
  metrics:
  - wer
  model-index:
  - name: Whisper Telugu Tiny - Vasista Sai Lodagala
    results:
    - task:
        type: automatic-speech-recognition
        name: Automatic Speech Recognition
      dataset:
        name: google/fleurs
        type: google/fleurs
        config: te_in
        split: test
      metrics:
      - type: wer
        value: 17.15
        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 Telugu Tiny
  
  This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Telugu data available from multiple publicly available ASR corpuses.
  It has been fine-tuned as a part of the Whisper fine-tuning sprint.
  
  ## Training and evaluation data at Speech Lab, IITM
  
  Training Data: CSTD IIIT-H ASR Corpus, ULCA ASR Corpus, Shrutilipi ASR Corpus, Microsoft Research Telugu Corpus (Train+Dev), Babel ASR Corpus, Google/Fleurs (Train+Dev) set.
  Evaluation Data: Babel Test, Microsoft Research Telugu Corpus Test, Google/Fleurs Test set, OpenSLR.
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 88
  - eval_batch_size: 88
  - seed: 22
  - optimizer: adamw_bnb_8bit
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 15000
  - training_steps: 14652 (terminated upon convergence. Initially set to 85952 steps)
  - mixed_precision_training: True
  
  ## Acknowledgement
  This work was done at Speech Lab, IITM. The compute resources for this work were funded by "Bhashini: National Language translation Mission" project of the Ministry of Electronics and Information Technology (MeitY), Government of India.
  