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--- |
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license: apache-2.0 |
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tags: |
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- audio-classification |
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- generated_from_trainer |
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datasets: |
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- superb |
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metrics: |
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- accuracy |
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model-index: |
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- name: w2v2-ks-jpqd-quant-all-finetuned-student |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# w2v2-ks-jpqd-quant-all-finetuned-student |
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This model is a fine-tuned version of [anton-l/wav2vec2-base-ft-keyword-spotting](https://huggingface.co/anton-l/wav2vec2-base-ft-keyword-spotting) on the superb dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0933 |
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- Accuracy: 0.9769 |
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This model is quantized. The input is also quantized. |
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Structured Sparsity in transformer block linear layers is 64%. |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 7e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.5 |
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- num_epochs: 12.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.4481 | 1.0 | 399 | 0.2105 | 0.9469 | |
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| 5.6584 | 2.0 | 798 | 5.5480 | 0.9428 | |
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| 8.7915 | 3.0 | 1197 | 8.6634 | 0.9601 | |
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| 10.4775 | 4.0 | 1596 | 10.2819 | 0.9553 | |
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| 10.9142 | 5.0 | 1995 | 10.7770 | 0.9657 | |
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| 10.9478 | 6.0 | 2394 | 10.7637 | 0.9660 | |
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| 0.2765 | 7.0 | 2793 | 0.1335 | 0.9678 | |
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| 0.2532 | 8.0 | 3192 | 0.1075 | 0.9732 | |
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| 0.2837 | 9.0 | 3591 | 0.1109 | 0.9700 | |
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| 0.2 | 10.0 | 3990 | 0.1006 | 0.9765 | |
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| 0.1742 | 11.0 | 4389 | 0.0930 | 0.9776 | |
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| 0.1718 | 12.0 | 4788 | 0.0933 | 0.9769 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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