wft-test-model
This model is a fine-tuned version of openai/whisper-tiny on the hf-internal-testing/librispeech_asr_dummy dataset. It achieves the following results on the evaluation set:
- Loss: 0.1248
- Wer: 4.7244
- Cer: 92.6847
- Decode Time: 0.5481
- Wer Time: 0.0069
- Cer Time: 0.0040
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: 0.0005
- train_batch_size: 4
- 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: 50
- training_steps: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Decode Time | Wer Time | Cer Time |
|---|---|---|---|---|---|---|---|---|
| 2.4107 | 0.1 | 10 | 1.9892 | 303.5433 | 117.1875 | 0.5449 | 0.0307 | 0.0039 |
| 1.2109 | 1.01 | 20 | 1.1659 | 155.1181 | 91.2642 | 0.5278 | 0.0062 | 0.0036 |
| 0.8855 | 1.11 | 30 | 0.8104 | 30.7087 | 56.8182 | 0.4832 | 0.0069 | 0.0041 |
| 0.4367 | 2.02 | 40 | 0.6315 | 25.1969 | 74.5739 | 0.5295 | 0.0058 | 0.0034 |
| 0.4398 | 2.12 | 50 | 0.4566 | 17.3228 | 91.9744 | 0.6078 | 0.0055 | 0.0030 |
| 0.2291 | 3.03 | 60 | 0.3006 | 9.0551 | 100.7102 | 0.5659 | 0.0058 | 0.0031 |
| 0.2281 | 3.13 | 70 | 0.2144 | 7.4803 | 90.4830 | 0.5507 | 0.0046 | 0.0030 |
| 0.111 | 4.04 | 80 | 0.1736 | 5.9055 | 89.3466 | 0.6595 | 0.0063 | 0.0032 |
| 0.0695 | 4.14 | 90 | 0.1345 | 4.7244 | 87.9261 | 0.6369 | 0.0402 | 0.0182 |
| 0.0761 | 5.05 | 100 | 0.1248 | 4.7244 | 92.6847 | 0.5481 | 0.0069 | 0.0040 |
Framework versions
- PEFT 0.13.2
- Transformers 4.45.2
- Pytorch 2.5.0
- Datasets 3.0.2
- Tokenizers 0.20.1