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--- |
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license: apache-2.0 |
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base_model: openai/whisper-tiny |
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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-tiny-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-tiny-myanmar |
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This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) 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: 0.2353 |
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- Wer: 61.8878 |
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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-tiny-myanmar') |
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output = pipe(input_speech, generate_kwargs={"language": "myanmar", "task": "transcribe"}) |
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print(output['text']) # ကျွန်မ ပြည်ပ မှာ ပညာ သင် တော့ စာမြီးပွဲ ကို တပတ်တခါ စစ်တယ် |
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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: 128 |
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- eval_batch_size: 128 |
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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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| No log | 1.0 | 18 | 1.2679 | 357.6135 | |
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| 1.483 | 2.0 | 36 | 1.0660 | 102.5378 | |
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| 1.0703 | 3.0 | 54 | 0.9530 | 106.3669 | |
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| 1.0703 | 4.0 | 72 | 0.8399 | 100.5343 | |
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| 0.8951 | 5.0 | 90 | 0.7728 | 107.6581 | |
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| 0.7857 | 6.0 | 108 | 0.7143 | 107.5245 | |
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| 0.6614 | 7.0 | 126 | 0.5174 | 104.4078 | |
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| 0.6614 | 8.0 | 144 | 0.3004 | 90.3384 | |
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| 0.3519 | 9.0 | 162 | 0.2447 | 82.4577 | |
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| 0.2165 | 10.0 | 180 | 0.2333 | 83.8825 | |
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| 0.2165 | 11.0 | 198 | 0.2022 | 77.0258 | |
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| 0.1532 | 12.0 | 216 | 0.1759 | 73.0632 | |
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| 0.1039 | 13.0 | 234 | 0.1852 | 72.0837 | |
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| 0.0675 | 14.0 | 252 | 0.1902 | 71.2823 | |
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| 0.0675 | 15.0 | 270 | 0.1882 | 70.5254 | |
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| 0.0517 | 16.0 | 288 | 0.2002 | 69.7240 | |
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| 0.0522 | 17.0 | 306 | 0.1965 | 67.7649 | |
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| 0.0522 | 18.0 | 324 | 0.1935 | 68.2102 | |
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| 0.0404 | 19.0 | 342 | 0.2132 | 67.9430 | |
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| 0.0308 | 20.0 | 360 | 0.2110 | 66.6963 | |
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| 0.0236 | 21.0 | 378 | 0.2141 | 65.9394 | |
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| 0.0236 | 22.0 | 396 | 0.2200 | 64.4702 | |
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| 0.0116 | 23.0 | 414 | 0.2227 | 63.4016 | |
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| 0.0055 | 24.0 | 432 | 0.2244 | 64.1585 | |
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| 0.0025 | 25.0 | 450 | 0.2254 | 62.4666 | |
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| 0.0025 | 26.0 | 468 | 0.2282 | 63.1790 | |
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| 0.0006 | 27.0 | 486 | 0.2320 | 61.7097 | |
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| 0.0002 | 28.0 | 504 | 0.2342 | 62.0659 | |
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| 0.0002 | 29.0 | 522 | 0.2350 | 62.0214 | |
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| 0.0001 | 30.0 | 540 | 0.2353 | 61.8878 | |
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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 |
