whisper-large-v3-myanmar
This model is a fine-tuned version of openai/whisper-large-v3 on the chuuhtetnaing/myanmar-speech-dataset-openslr-80 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1752
- Wer: 54.8976
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-large-v3-myanmar')
output = pipe(input_speech, generate_kwargs={"language": "myanmar", "task": "transcribe"})
print(output['text']) # ကျမ ပြည်ပ မှာ ပညာသင် တော့ စာမေးပွဲ ကို တပတ်တခါ စစ်တယ်
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 20
- eval_batch_size: 20
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 60
- 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 |
|---|---|---|---|---|
| 0.9771 | 1.0 | 42 | 0.7598 | 100.0 |
| 0.3477 | 2.0 | 84 | 0.2140 | 89.8931 |
| 0.2244 | 3.0 | 126 | 0.1816 | 79.0294 |
| 0.1287 | 4.0 | 168 | 0.1510 | 71.9947 |
| 0.1029 | 5.0 | 210 | 0.1575 | 77.8718 |
| 0.0797 | 6.0 | 252 | 0.1315 | 70.5254 |
| 0.0511 | 7.0 | 294 | 0.1143 | 70.5699 |
| 0.03 | 8.0 | 336 | 0.1154 | 68.1656 |
| 0.0211 | 9.0 | 378 | 0.1289 | 69.1897 |
| 0.0151 | 10.0 | 420 | 0.1318 | 66.7854 |
| 0.0113 | 11.0 | 462 | 0.1478 | 69.1451 |
| 0.0079 | 12.0 | 504 | 0.1484 | 66.2066 |
| 0.0053 | 13.0 | 546 | 0.1389 | 65.0935 |
| 0.0031 | 14.0 | 588 | 0.1479 | 64.3811 |
| 0.0014 | 15.0 | 630 | 0.1611 | 64.8264 |
| 0.001 | 16.0 | 672 | 0.1627 | 63.3571 |
| 0.0012 | 17.0 | 714 | 0.1546 | 65.0045 |
| 0.0006 | 18.0 | 756 | 0.1566 | 64.5147 |
| 0.0006 | 20.0 | 760 | 0.1581 | 64.6928 |
| 0.0002 | 21.0 | 798 | 0.1621 | 63.9804 |
| 0.0003 | 22.0 | 836 | 0.1664 | 60.8638 |
| 0.0002 | 23.0 | 874 | 0.1663 | 58.5040 |
| 0.0 | 24.0 | 912 | 0.1699 | 55.8326 |
| 0.0 | 25.0 | 950 | 0.1715 | 55.0312 |
| 0.0 | 26.0 | 988 | 0.1730 | 54.9866 |
| 0.0 | 27.0 | 1026 | 0.1740 | 54.8976 |
| 0.0 | 28.0 | 1064 | 0.1747 | 54.8976 |
| 0.0 | 29.0 | 1102 | 0.1751 | 54.8976 |
| 0.0 | 30.0 | 1140 | 0.1752 | 54.8976 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.15.1