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wav2vec2-base-repro-timit
This model is a fine-tuned version of patrickvonplaten/wav2vec2-base-repro-960h-libri-85k-steps on the TIMIT_ASR - NA dataset. It achieves the following results on the evaluation set:
- Loss: 0.8562
- Wer: 0.5484
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: 0.0001
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 5.9793 | 0.69 | 100 | 5.4532 | 1.0 |
| 2.9066 | 1.38 | 200 | 2.9070 | 1.0 |
| 2.2562 | 2.07 | 300 | 2.0323 | 1.0 |
| 1.5273 | 2.76 | 400 | 1.1510 | 0.8001 |
| 1.1085 | 3.45 | 500 | 0.9521 | 0.7053 |
| 0.813 | 4.14 | 600 | 0.8617 | 0.6702 |
| 0.8434 | 4.83 | 700 | 0.8068 | 0.6393 |
| 0.9631 | 5.52 | 800 | 0.7863 | 0.6248 |
| 0.707 | 6.21 | 900 | 0.7476 | 0.5973 |
| 0.5568 | 6.9 | 1000 | 0.7350 | 0.5911 |
| 0.6171 | 7.59 | 1100 | 0.7171 | 0.5841 |
| 0.7011 | 8.28 | 1200 | 0.7318 | 0.5798 |
| 0.5546 | 8.97 | 1300 | 0.7447 | 0.5767 |
| 0.4278 | 9.66 | 1400 | 0.7481 | 0.5650 |
| 0.3576 | 10.34 | 1500 | 0.7443 | 0.5713 |
| 0.5506 | 11.03 | 1600 | 0.7574 | 0.5664 |
| 0.4127 | 11.72 | 1700 | 0.8043 | 0.5631 |
| 0.3251 | 12.41 | 1800 | 0.7738 | 0.5550 |
| 0.3119 | 13.1 | 1900 | 0.7829 | 0.5516 |
| 0.4371 | 13.79 | 2000 | 0.8025 | 0.5556 |
| 0.3772 | 14.48 | 2100 | 0.8451 | 0.5559 |
| 0.2942 | 15.17 | 2200 | 0.8300 | 0.5556 |
| 0.2503 | 15.86 | 2300 | 0.8417 | 0.5541 |
| 0.3671 | 16.55 | 2400 | 0.8568 | 0.5528 |
| 0.3867 | 17.24 | 2500 | 0.8521 | 0.5510 |
| 0.2614 | 17.93 | 2600 | 0.8479 | 0.5523 |
| 0.2441 | 18.62 | 2700 | 0.8558 | 0.5494 |
| 0.3059 | 19.31 | 2800 | 0.8553 | 0.5474 |
| 0.3734 | 20.0 | 2900 | 0.8562 | 0.5484 |
Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.14.1.dev0
- Tokenizers 0.10.3
