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--- |
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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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metrics: |
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- wer |
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model-index: |
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- name: whisper-small-myanmar |
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results: [] |
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datasets: |
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- chuuhtetnaing/myanmar-speech-dataset-openslr-80 |
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language: |
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- my |
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pipeline_tag: automatic-speech-recognition |
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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-small-myanmar |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the [chuuhtetnaing/myanmar-speech-dataset-openslr-80](https://huggingface.co/datasets/chuuhtetnaing/myanmar-speech-dataset-openslr-80) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1904 |
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- Wer: 49.0650 |
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## Usage |
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```python |
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from datasets import Audio, load_dataset |
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from transformers import pipeline |
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# Load a sample audio |
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dataset = load_dataset("chuuhtetnaing/myanmar-speech-dataset-openslr-80") |
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dataset = dataset.cast_column("audio", Audio(sampling_rate=16000)) |
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test_dataset = dataset['test'] |
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input_speech = test_dataset[42]['audio'] |
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pipe = pipeline(model='chuuhtetnaing/whisper-small-myanmar') |
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output = pipe(input_speech, generate_kwargs={"language": "myanmar", "task": "transcribe"}) |
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print(output['text']) # αα»α½ααΊα ααΌααΊα ααΎα¬ ααα¬αααΊ αα±α¬α· α
α¬αα±αΈαα½α² ααα― ααααΊααα« α
α
αΊαααΊ |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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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: 200 |
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- num_epochs: 30 |
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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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| 1.2566 | 1.0 | 36 | 0.8893 | 215.0045 | |
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| 0.8862 | 2.0 | 72 | 0.6243 | 388.6465 | |
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| 0.3546 | 3.0 | 108 | 0.2046 | 316.8744 | |
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| 0.1839 | 4.0 | 144 | 0.1695 | 81.3001 | |
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| 0.1198 | 5.0 | 180 | 0.1385 | 63.8914 | |
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| 0.0969 | 6.0 | 216 | 0.1583 | 66.0285 | |
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| 0.084 | 7.0 | 252 | 0.1539 | 70.6589 | |
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| 0.0628 | 8.0 | 288 | 0.1603 | 61.3090 | |
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| 0.0565 | 9.0 | 324 | 0.1424 | 60.3295 | |
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| 0.0355 | 10.0 | 360 | 0.1457 | 58.1478 | |
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| 0.0299 | 11.0 | 396 | 0.1547 | 57.7916 | |
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| 0.0183 | 12.0 | 432 | 0.1543 | 54.3633 | |
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| 0.0131 | 13.0 | 468 | 0.1532 | 54.1407 | |
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| 0.011 | 14.0 | 504 | 0.1604 | 53.8736 | |
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| 0.0083 | 15.0 | 540 | 0.1630 | 54.0516 | |
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| 0.0042 | 16.0 | 576 | 0.1711 | 52.1371 | |
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| 0.0034 | 17.0 | 612 | 0.1670 | 52.5824 | |
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| 0.0022 | 18.0 | 648 | 0.1649 | 52.5378 | |
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| 0.0013 | 19.0 | 684 | 0.1802 | 52.1817 | |
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| 0.0014 | 20.0 | 720 | 0.1820 | 53.1612 | |
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| 0.002 | 21.0 | 756 | 0.1792 | 52.7159 | |
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| 0.0016 | 22.0 | 792 | 0.1796 | 50.7124 | |
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| 0.0004 | 23.0 | 828 | 0.1803 | 50.4007 | |
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| 0.0003 | 24.0 | 864 | 0.1804 | 49.4657 | |
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| 0.0001 | 25.0 | 900 | 0.1819 | 49.2431 | |
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| 0.0 | 26.0 | 936 | 0.1857 | 49.0205 | |
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| 0.0 | 27.0 | 972 | 0.1879 | 49.1541 | |
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| 0.0 | 28.0 | 1008 | 0.1893 | 49.1095 | |
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| 0.0 | 29.0 | 1044 | 0.1901 | 49.1095 | |
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| 0.0 | 30.0 | 1080 | 0.1904 | 49.0650 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.15.1 |
