Whisper Medium Mnong
This model is a fine-tuned version of openai/whisper-medium on the MnongAudio-v2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0471
- Wer: 7.2593
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
2.3012 | 0.1421 | 200 | 2.0190 | 142.3332 |
1.4133 | 0.2843 | 400 | 1.3463 | 99.4906 |
0.9797 | 0.4264 | 600 | 0.9503 | 80.8456 |
0.7402 | 0.5686 | 800 | 0.6821 | 62.0479 |
0.4908 | 0.7107 | 1000 | 0.4992 | 47.8349 |
0.3865 | 0.8529 | 1200 | 0.4090 | 42.6133 |
0.3031 | 0.9950 | 1400 | 0.3108 | 34.7937 |
0.203 | 1.1372 | 1600 | 0.2632 | 39.2511 |
0.1846 | 1.2793 | 1800 | 0.2209 | 28.3749 |
0.1313 | 1.4215 | 2000 | 0.1776 | 18.2119 |
0.0984 | 1.5636 | 2200 | 0.1525 | 18.8487 |
0.1009 | 1.7058 | 2400 | 0.1276 | 14.8242 |
0.0803 | 1.8479 | 2600 | 0.1034 | 12.1498 |
0.061 | 1.9900 | 2800 | 0.0910 | 11.7422 |
0.0327 | 2.1322 | 3000 | 0.0808 | 12.3535 |
0.026 | 2.2743 | 3200 | 0.0716 | 9.2970 |
0.024 | 2.4165 | 3400 | 0.0612 | 10.2649 |
0.0282 | 2.5586 | 3600 | 0.0552 | 8.0234 |
0.016 | 2.7008 | 3800 | 0.0488 | 7.8961 |
0.0237 | 2.8429 | 4000 | 0.0471 | 7.2593 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for legendary2910/Mnong-ASR-v1-enhanced
Base model
openai/whisper-medium