--- 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: 6.61 name: WER --- # Whisper Tamil Large-v2 This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/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.