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wav2vec2-base-timit-demo-google-colab
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: 0.5707
- Wer: 0.3388
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
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.5072 | 1.0 | 500 | 1.8786 | 0.9741 |
| 0.8836 | 2.01 | 1000 | 0.5147 | 0.5317 |
| 0.4576 | 3.01 | 1500 | 0.4774 | 0.4591 |
| 0.3056 | 4.02 | 2000 | 0.4393 | 0.4343 |
| 0.2349 | 5.02 | 2500 | 0.4404 | 0.4022 |
| 0.1946 | 6.02 | 3000 | 0.4564 | 0.3991 |
| 0.1624 | 7.03 | 3500 | 0.4428 | 0.3947 |
| 0.1421 | 8.03 | 4000 | 0.4312 | 0.3878 |
| 0.131 | 9.04 | 4500 | 0.4345 | 0.3853 |
| 0.1115 | 10.04 | 5000 | 0.4318 | 0.3753 |
| 0.1024 | 11.04 | 5500 | 0.5053 | 0.3798 |
| 0.0895 | 12.05 | 6000 | 0.5044 | 0.3782 |
| 0.0856 | 13.05 | 6500 | 0.4893 | 0.3665 |
| 0.0755 | 14.06 | 7000 | 0.4868 | 0.3662 |
| 0.0724 | 15.06 | 7500 | 0.5084 | 0.3681 |
| 0.0635 | 16.06 | 8000 | 0.5367 | 0.3530 |
| 0.0603 | 17.07 | 8500 | 0.5255 | 0.3604 |
| 0.0609 | 18.07 | 9000 | 0.5407 | 0.3678 |
| 0.0486 | 19.08 | 9500 | 0.5312 | 0.3630 |
| 0.047 | 20.08 | 10000 | 0.5498 | 0.3518 |
| 0.0437 | 21.08 | 10500 | 0.5326 | 0.3571 |
| 0.0379 | 22.09 | 11000 | 0.5644 | 0.3608 |
| 0.035 | 23.09 | 11500 | 0.5956 | 0.3539 |
| 0.0333 | 24.1 | 12000 | 0.5967 | 0.3517 |
| 0.0289 | 25.1 | 12500 | 0.5274 | 0.3399 |
| 0.0268 | 26.1 | 13000 | 0.5609 | 0.3406 |
| 0.0256 | 27.11 | 13500 | 0.5451 | 0.3448 |
| 0.0249 | 28.11 | 14000 | 0.5804 | 0.3413 |
| 0.0236 | 29.12 | 14500 | 0.5707 | 0.3388 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.12.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
