ctrlv-speechrecognition-model
This model is a fine-tuned version of facebook/wav2vec2-base on the TIMIT dataset. It achieves the following results on the evaluation set:
- Loss: 0.4730
- Wer: 0.3031
Test WER in TIMIT dataset
- Wer: 0.189
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: 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: 60
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.53 | 3.45 | 500 | 1.4021 | 0.9307 |
| 0.6077 | 6.9 | 1000 | 0.4255 | 0.4353 |
| 0.2331 | 10.34 | 1500 | 0.3887 | 0.3650 |
| 0.1436 | 13.79 | 2000 | 0.3579 | 0.3393 |
| 0.1021 | 17.24 | 2500 | 0.4447 | 0.3440 |
| 0.0797 | 20.69 | 3000 | 0.4041 | 0.3291 |
| 0.0657 | 24.14 | 3500 | 0.4262 | 0.3368 |
| 0.0525 | 27.59 | 4000 | 0.4937 | 0.3429 |
| 0.0454 | 31.03 | 4500 | 0.4449 | 0.3244 |
| 0.0373 | 34.48 | 5000 | 0.4363 | 0.3288 |
| 0.0321 | 37.93 | 5500 | 0.4519 | 0.3204 |
| 0.0288 | 41.38 | 6000 | 0.4440 | 0.3145 |
| 0.0259 | 44.83 | 6500 | 0.4691 | 0.3182 |
| 0.0203 | 48.28 | 7000 | 0.5062 | 0.3162 |
| 0.0171 | 51.72 | 7500 | 0.4762 | 0.3129 |
| 0.0166 | 55.17 | 8000 | 0.4772 | 0.3090 |
| 0.0147 | 58.62 | 8500 | 0.4730 | 0.3031 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3