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metadata
language:
  - ta
license: apache-2.0
tags:
  - whisper-event
metrics:
  - wer
model-index:
  - name: Whisper Tamil Large-v2 - Vasista Sai Lodagala
    results:
      - 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: 7.5
            name: WER
      - 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: 8.75
            name: WER

Whisper Tamil Large-v2

This model is a fine-tuned version of openai/whisper-large-v2 on the Tamil 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: MILE ASR Corpus, ULCA ASR Corpus, Shrutilipi ASR Corpus, Microsoft Research Tamil Corpus (Train+Dev), Babel ASR Corpus, Google/Fleurs (Train+Dev) set.

Evaluation Data: MILE ASR Corpus Test, Babel Test, Microsoft Research Tamil Corpus Test, Google/Fleurs Test set.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.75e-05
  • train_batch_size: 8
  • eval_batch_size: 24
  • seed: 22
  • optimizer: adamw_bnb_8bit
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 22000
  • training_steps: 52500 (Initially set to 76000 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.