--- language: - gu license: apache-2.0 tags: - whisper-event metrics: - wer model-index: - name: Whisper Gujarati Base - Vasista Sai Lodagala results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: google/fleurs type: google/fleurs config: gu_in split: test metrics: - type: wer value: 18.44 name: WER --- # Whisper Gujarati Base This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Gujarati 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 Training Data: ULCA ASR Corpus, OpenSLR, Microsoft Research Telugu Corpus (Train+Dev), Google/Fleurs Train+Dev set. Evaluation Data: Google/Fleurs Test set, Microsoft Research Telugu Corpus Test . ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3.3e-05 - train_batch_size: 80 - eval_batch_size: 88 - seed: 22 - optimizer: adamw_bnb_8bit - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 4000 - training_steps: 7225 (terminated upon convergence. Initially set to 21250 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.