whisper-small-myanmar
This model is a fine-tuned version of openai/whisper-small on the chuuhtetnaing/myanmar-speech-dataset-openslr-80 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1904
- Wer: 49.0650
Usage
from datasets import Audio, load_dataset
from transformers import pipeline
# Load a sample audio
dataset = load_dataset("chuuhtetnaing/myanmar-speech-dataset-openslr-80")
dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
test_dataset = dataset['test']
input_speech = test_dataset[42]['audio']
pipe = pipeline(model='chuuhtetnaing/whisper-small-myanmar')
output = pipe(input_speech, generate_kwargs={"language": "myanmar", "task": "transcribe"})
print(output['text']) # ကျွန်မ ပြည်ပ မှာ ပညာသင် တော့ စာမေးပွဲ ကို တပတ်တခါ စစ်တယ်
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.2566 | 1.0 | 36 | 0.8893 | 215.0045 |
| 0.8862 | 2.0 | 72 | 0.6243 | 388.6465 |
| 0.3546 | 3.0 | 108 | 0.2046 | 316.8744 |
| 0.1839 | 4.0 | 144 | 0.1695 | 81.3001 |
| 0.1198 | 5.0 | 180 | 0.1385 | 63.8914 |
| 0.0969 | 6.0 | 216 | 0.1583 | 66.0285 |
| 0.084 | 7.0 | 252 | 0.1539 | 70.6589 |
| 0.0628 | 8.0 | 288 | 0.1603 | 61.3090 |
| 0.0565 | 9.0 | 324 | 0.1424 | 60.3295 |
| 0.0355 | 10.0 | 360 | 0.1457 | 58.1478 |
| 0.0299 | 11.0 | 396 | 0.1547 | 57.7916 |
| 0.0183 | 12.0 | 432 | 0.1543 | 54.3633 |
| 0.0131 | 13.0 | 468 | 0.1532 | 54.1407 |
| 0.011 | 14.0 | 504 | 0.1604 | 53.8736 |
| 0.0083 | 15.0 | 540 | 0.1630 | 54.0516 |
| 0.0042 | 16.0 | 576 | 0.1711 | 52.1371 |
| 0.0034 | 17.0 | 612 | 0.1670 | 52.5824 |
| 0.0022 | 18.0 | 648 | 0.1649 | 52.5378 |
| 0.0013 | 19.0 | 684 | 0.1802 | 52.1817 |
| 0.0014 | 20.0 | 720 | 0.1820 | 53.1612 |
| 0.002 | 21.0 | 756 | 0.1792 | 52.7159 |
| 0.0016 | 22.0 | 792 | 0.1796 | 50.7124 |
| 0.0004 | 23.0 | 828 | 0.1803 | 50.4007 |
| 0.0003 | 24.0 | 864 | 0.1804 | 49.4657 |
| 0.0001 | 25.0 | 900 | 0.1819 | 49.2431 |
| 0.0 | 26.0 | 936 | 0.1857 | 49.0205 |
| 0.0 | 27.0 | 972 | 0.1879 | 49.1541 |
| 0.0 | 28.0 | 1008 | 0.1893 | 49.1095 |
| 0.0 | 29.0 | 1044 | 0.1901 | 49.1095 |
| 0.0 | 30.0 | 1080 | 0.1904 | 49.0650 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.15.1