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@@ -18,10 +18,10 @@ model-index:
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  args: ml
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  metrics:
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  - type: wer
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- value: 23.69
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  name: WER
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  - type: cer
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- value: 12.17
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  name: CER
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  library_name: transformers
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  ---
@@ -36,12 +36,27 @@ 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 - 23.69
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- CER - 12.17
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  **In SMC Malayalam Speech Corpus dataset:**
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- WER - 23.8
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- CER - 12.63
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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  **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
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+
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+
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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 and 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 during their open hack day](https://www.eventbrite.com/e/open-hack-day-tickets-783582435157)