w2v2-ks-jpqd-quant-all-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: 0.0933
- Accuracy: 0.9769
This model is quantized. The input is also quantized. Structured Sparsity in transformer block linear layers is 64%.
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: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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: 0.5
- num_epochs: 12.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4481 | 1.0 | 399 | 0.2105 | 0.9469 |
| 5.6584 | 2.0 | 798 | 5.5480 | 0.9428 |
| 8.7915 | 3.0 | 1197 | 8.6634 | 0.9601 |
| 10.4775 | 4.0 | 1596 | 10.2819 | 0.9553 |
| 10.9142 | 5.0 | 1995 | 10.7770 | 0.9657 |
| 10.9478 | 6.0 | 2394 | 10.7637 | 0.9660 |
| 0.2765 | 7.0 | 2793 | 0.1335 | 0.9678 |
| 0.2532 | 8.0 | 3192 | 0.1075 | 0.9732 |
| 0.2837 | 9.0 | 3591 | 0.1109 | 0.9700 |
| 0.2 | 10.0 | 3990 | 0.1006 | 0.9765 |
| 0.1742 | 11.0 | 4389 | 0.0930 | 0.9776 |
| 0.1718 | 12.0 | 4788 | 0.0933 | 0.9769 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2