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wav2vec2-base-timit-eng
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5195
- Wer: 0.3418
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: 8
- 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: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.5159 | 1.0 | 500 | 1.7153 | 1.0291 |
| 0.8502 | 2.01 | 1000 | 0.5204 | 0.5146 |
| 0.431 | 3.01 | 1500 | 0.4491 | 0.4537 |
| 0.3073 | 4.02 | 2000 | 0.3883 | 0.4190 |
| 0.2338 | 5.02 | 2500 | 0.4453 | 0.4230 |
| 0.1956 | 6.02 | 3000 | 0.4599 | 0.3981 |
| 0.1594 | 7.03 | 3500 | 0.4240 | 0.3916 |
| 0.1423 | 8.03 | 4000 | 0.4756 | 0.3975 |
| 0.1252 | 9.04 | 4500 | 0.4427 | 0.3827 |
| 0.1064 | 10.04 | 5000 | 0.4489 | 0.3809 |
| 0.101 | 11.04 | 5500 | 0.4531 | 0.3961 |
| 0.0877 | 12.05 | 6000 | 0.4881 | 0.3883 |
| 0.0817 | 13.05 | 6500 | 0.5023 | 0.3774 |
| 0.0703 | 14.06 | 7000 | 0.5078 | 0.3679 |
| 0.0663 | 15.06 | 7500 | 0.5279 | 0.3620 |
| 0.0584 | 16.06 | 8000 | 0.5112 | 0.3653 |
| 0.0579 | 17.07 | 8500 | 0.4959 | 0.3633 |
| 0.0572 | 18.07 | 9000 | 0.4676 | 0.3626 |
| 0.0502 | 19.08 | 9500 | 0.5216 | 0.3503 |
| 0.0432 | 20.08 | 10000 | 0.4946 | 0.3480 |
| 0.0417 | 21.08 | 10500 | 0.4949 | 0.3532 |
| 0.0335 | 22.09 | 11000 | 0.5485 | 0.3557 |
| 0.032 | 23.09 | 11500 | 0.5087 | 0.3464 |
| 0.0334 | 24.1 | 12000 | 0.5313 | 0.3498 |
| 0.0263 | 25.1 | 12500 | 0.5148 | 0.3457 |
| 0.0242 | 26.1 | 13000 | 0.5232 | 0.3442 |
| 0.0235 | 27.11 | 13500 | 0.5122 | 0.3418 |
| 0.0221 | 28.11 | 14000 | 0.5074 | 0.3407 |
| 0.0215 | 29.12 | 14500 | 0.5195 | 0.3418 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu116
- Datasets 1.18.3
- Tokenizers 0.13.2
