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--- |
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language: |
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- ml |
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license: apache-2.0 |
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base_model: openai/whisper-small |
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tags: |
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- generated_from_trainer |
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datasets: |
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- thennal/IMaSC |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Small Malayalam - Arjun Shaji |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: google/fleurs |
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type: thennal/IMaSC |
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args: 'config: ml, split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 9.54563571143882 |
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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 Small Malayalam - Arjun Shaji |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0209 |
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- Wer: 9.5456 |
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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: 500 |
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- training_steps: 8000 |
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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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| 0.0433 | 0.5800 | 1000 | 0.0434 | 27.1379 | |
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| 0.02 | 1.1601 | 2000 | 0.0312 | 20.3733 | |
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| 0.0169 | 1.7401 | 3000 | 0.0242 | 15.4975 | |
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| 0.0071 | 2.3202 | 4000 | 0.0217 | 12.3555 | |
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| 0.0058 | 2.9002 | 5000 | 0.0197 | 11.0646 | |
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| 0.0022 | 3.4803 | 6000 | 0.0202 | 10.0881 | |
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| 0.0008 | 4.0603 | 7000 | 0.0204 | 9.7006 | |
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| 0.0005 | 4.6404 | 8000 | 0.0209 | 9.5456 | |
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### Framework versions |
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- Transformers 4.41.0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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