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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: 0.5261
- Wer: 0.3351
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 |
|---|---|---|---|---|
| 3.5764 | 1.0 | 500 | 2.3358 | 1.0 |
| 0.9494 | 2.01 | 1000 | 0.6086 | 0.5448 |
| 0.4527 | 3.01 | 1500 | 0.4731 | 0.4685 |
| 0.307 | 4.02 | 2000 | 0.4432 | 0.4341 |
| 0.2366 | 5.02 | 2500 | 0.4343 | 0.4025 |
| 0.1934 | 6.02 | 3000 | 0.4284 | 0.4105 |
| 0.154 | 7.03 | 3500 | 0.4709 | 0.3936 |
| 0.14 | 8.03 | 4000 | 0.4296 | 0.3889 |
| 0.1189 | 9.04 | 4500 | 0.4864 | 0.3862 |
| 0.1057 | 10.04 | 5000 | 0.4903 | 0.3776 |
| 0.1034 | 11.04 | 5500 | 0.4889 | 0.3838 |
| 0.0899 | 12.05 | 6000 | 0.4680 | 0.3701 |
| 0.0864 | 13.05 | 6500 | 0.4981 | 0.3608 |
| 0.0714 | 14.06 | 7000 | 0.4608 | 0.3589 |
| 0.0673 | 15.06 | 7500 | 0.4970 | 0.3754 |
| 0.0606 | 16.06 | 8000 | 0.5344 | 0.3618 |
| 0.0603 | 17.07 | 8500 | 0.4980 | 0.3675 |
| 0.0588 | 18.07 | 9000 | 0.5339 | 0.3601 |
| 0.0453 | 19.08 | 9500 | 0.4973 | 0.3526 |
| 0.0433 | 20.08 | 10000 | 0.5359 | 0.3572 |
| 0.0421 | 21.08 | 10500 | 0.4885 | 0.3532 |
| 0.0359 | 22.09 | 11000 | 0.5184 | 0.3471 |
| 0.032 | 23.09 | 11500 | 0.5230 | 0.3483 |
| 0.0333 | 24.1 | 12000 | 0.5512 | 0.3474 |
| 0.0279 | 25.1 | 12500 | 0.5102 | 0.3437 |
| 0.0232 | 26.1 | 13000 | 0.5195 | 0.3384 |
| 0.0237 | 27.11 | 13500 | 0.5350 | 0.3355 |
| 0.0209 | 28.11 | 14000 | 0.5432 | 0.3368 |
| 0.023 | 29.12 | 14500 | 0.5261 | 0.3351 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.12.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
