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--- |
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license: mit |
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datasets: |
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- thennal/IMaSC |
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language: |
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- ml |
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model-index: |
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- name: Malwhisper-v1-medium |
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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: Common Voice 11.0 |
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type: mozilla-foundation/common_voice_11_0 |
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config: ml |
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split: test |
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args: ml |
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metrics: |
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- type: wer |
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value: 61.84 |
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name: WER |
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- type: cer |
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value: 15.41 |
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name: CER |
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library_name: transformers |
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--- |
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# Malwhisper-v1-medium |
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) fine-tuned on [IMASc dataset](https://www.kaggle.com/datasets/thennal/imasc). |
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## About Dataset |
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IMaSC is a Malayalam text and speech corpus made available by ICFOSS for the purpose of developing speech technology for Malayalam, particularly text-to-speech. The corpus contains 34,473 text-audio pairs of Malayalam sentences spoken by 8 speakers, totalling in approximately 50 hours of audio. |
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## Training |
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[Script Used for training](https://github.com/kurianbenoy/Keyword_generator_project/blob/main/Whisper_IMASC_final_e2eofficerun.ipynb) |
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[Training run](https://wandb.ai/hello34/wandb_whisper_e2e/runs/q2xlvbw5) |
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[Experiment Tracking with Weights and Biases](https://wandb.ai/hello34/wandb_whisper_e2e) |
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- GPUs used: A100 - 80 GB |
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- Training Time: 16 hours |
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- This project was build with A100 80GB GPU provided by [E2E Cloud during their open hack day](https://www.eventbrite.com/e/open-hack-day-tickets-783582435157) |
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## Evaluation |
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The fine-tuned model on evaluating in the following dataset: |
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**In Mozilla CommonVoice 11.0 dataset (Malayalam subset):** |
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WER - 61.84 |
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CER - 15.41 |
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**In SMC Malayalam Speech Corpus dataset:** |
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WER - 70.49 |
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CER - 17.0 |
