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--- |
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license: apache-2.0 |
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datasets: |
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- thennal/IMaSC |
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- vrclc/openslr63 |
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- vrclc/festvox-iiith-ml |
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- smcproject/MSC |
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language: |
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- ml |
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- en |
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base_model: openai/whisper-medium |
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model-index: |
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- name: vrclc/Whisper-med-ml - Bajiyo Baiju |
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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 13 Malayalam |
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type: mozilla-foundation/common_voice_13_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: 63.64 |
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name: WER |
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- type: cer |
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value: 13.61 |
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name: CER |
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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 16 Malayalam |
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type: mozilla-foundation/common_voice_16_1 |
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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: 64.63 |
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name: WER |
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- type: cer |
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value: 14.07 |
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name: CER |
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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: OpenSLR Malayalam -Test |
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type: vrclc/openslr63 |
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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: 14.65 |
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name: WER |
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- type: cer |
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value: 2.59 |
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name: CER |
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library_name: transformers |
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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-med-ml |
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the datasets: [IMASC](https://huggingface.co/datasets/thennal/IMaSC), [MSC](https://huggingface.co/datasets/smcproject/MSC), [OpenSLR Malayalam Train split](https://huggingface.co/datasets/vrclc/openslr63), [Festvox Malayalam](https://huggingface.co/datasets/vrclc/openslr63) . |
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It achieves the following results on the validation set : [OpenSLR-Test](https://huggingface.co/vrclc/openslr63) |
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- Loss: 0.0318 |
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- Wer: 14.7300 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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: 1e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 1000 |
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- training_steps: 6000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:| |
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| 0.0599 | 0.4 | 1000 | 0.0910 | 42.4981 | |
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| 0.0341 | 0.79 | 2000 | 0.0584 | 30.0572 | |
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| 0.0183 | 1.19 | 3000 | 0.0439 | 23.1650 | |
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| 0.0147 | 1.58 | 4000 | 0.0363 | 18.7360 | |
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| 0.0107 | 1.98 | 5000 | 0.0322 | 16.4220 | |
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| 0.0032 | 2.37 | 6000 | 0.0318 | 14.7300 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
