w2v2-ks-jpqd-finetuned-student
This model is a fine-tuned version of anton-l/wav2vec2-base-ft-keyword-spotting on the superb dataset. It achieves the following results on the evaluation set:
- Loss: 0.0641
- Accuracy: 0.9815
The model is quantized and structurally pruned (sparisty=80 in transformer block linear layers)
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: 0.0002
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4606 | 1.0 | 399 | 0.1543 | 0.9723 |
| 14.8746 | 2.0 | 798 | 14.9490 | 0.9681 |
| 24.7043 | 3.0 | 1197 | 24.6662 | 0.9706 |
| 30.626 | 4.0 | 1596 | 30.4279 | 0.9732 |
| 33.4796 | 5.0 | 1995 | 33.3182 | 0.9750 |
| 34.4405 | 6.0 | 2394 | 34.2327 | 0.9744 |
| 34.1743 | 7.0 | 2793 | 34.0161 | 0.9741 |
| 33.47 | 8.0 | 3192 | 33.2669 | 0.9748 |
| 0.2278 | 9.0 | 3591 | 0.1125 | 0.9757 |
| 0.2259 | 10.0 | 3990 | 0.0848 | 0.9778 |
| 0.1629 | 11.0 | 4389 | 0.0734 | 0.9788 |
| 0.1658 | 12.0 | 4788 | 0.0736 | 0.9803 |
| 0.2264 | 13.0 | 5187 | 0.0658 | 0.9803 |
| 0.1564 | 14.0 | 5586 | 0.0677 | 0.9819 |
| 0.1716 | 15.0 | 5985 | 0.0641 | 0.9815 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
- Downloads last month
- 2