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--- |
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language: |
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- gu |
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license: apache-2.0 |
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tags: |
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- whisper-event |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Gujarati Base - Vasista Sai Lodagala |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: google/fleurs |
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type: google/fleurs |
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config: gu_in |
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split: test |
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metrics: |
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- type: wer |
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value: 30.0 |
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name: WER |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Whisper Gujarati Base |
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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. |
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It has been fine-tuned as a part of the Whisper fine-tuning sprint. |
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## Training and evaluation data |
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Training Data: ULCA ASR Corpus, OpenSLR, Microsoft Research Telugu Corpus (Train+Dev), Google/Fleurs Train+Dev set. |
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Evaluation Data: Google/Fleurs Test set, Microsoft Research Telugu Corpus Test . |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3.3e-05 |
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- train_batch_size: 80 |
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- eval_batch_size: 88 |
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- seed: 22 |
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- optimizer: adamw_bnb_8bit |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 4000 |
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- training_steps: 4250 (terminated upon convergence. Initially set to 21250 steps) |
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- mixed_precision_training: True |
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## Acknowledgement |
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This work was done at Speech Lab, IITM. |
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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. |
