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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-lr1e-4 |
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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-lr1e-4 |
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1228 |
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- Accuracy: 0.9695 |
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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: 0.0001 |
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- train_batch_size: 32 |
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- eval_batch_size: 64 |
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- seed: 0 |
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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: 0.1 |
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- num_epochs: 15.0 |
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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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| 2.5357 | 1.0 | 399 | 2.7821 | 0.6209 | |
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| 2.7107 | 2.0 | 798 | 2.7331 | 0.6209 | |
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| 2.671 | 3.0 | 1197 | 2.7330 | 0.6209 | |
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| 14.208 | 4.0 | 1596 | 14.2660 | 0.7139 | |
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| 21.0916 | 5.0 | 1995 | 21.0315 | 0.8104 | |
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| 24.4471 | 6.0 | 2394 | 24.2357 | 0.9073 | |
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| 25.366 | 7.0 | 2793 | 25.0893 | 0.9273 | |
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| 25.1369 | 8.0 | 3192 | 24.8976 | 0.9394 | |
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| 0.4678 | 9.0 | 3591 | 0.2528 | 0.9435 | |
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| 0.3576 | 10.0 | 3990 | 0.1873 | 0.9613 | |
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| 0.3622 | 11.0 | 4389 | 0.1583 | 0.9645 | |
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| 0.2796 | 12.0 | 4788 | 0.1419 | 0.9666 | |
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| 0.3157 | 13.0 | 5187 | 0.1327 | 0.9693 | |
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| 0.2997 | 14.0 | 5586 | 0.1263 | 0.9694 | |
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| 0.2667 | 15.0 | 5985 | 0.1228 | 0.9695 | |
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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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