wav2vec2-base-finetuned-stop-classification-1
This model is a fine-tuned version of facebook/wav2vec2-base on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.2121
- Accuracy: 0.9285
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: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
0.6921 | 0.99 | 18 | 0.6781 | 0.6185 |
0.612 | 1.97 | 36 | 0.5797 | 0.7207 |
0.5046 | 2.96 | 54 | 0.3820 | 0.8569 |
0.3956 | 4.0 | 73 | 0.2827 | 0.9012 |
0.3428 | 4.99 | 91 | 0.2915 | 0.8903 |
0.3222 | 5.97 | 109 | 0.2333 | 0.9162 |
0.3033 | 6.96 | 127 | 0.2403 | 0.9162 |
0.2743 | 8.0 | 146 | 0.2129 | 0.9237 |
0.2494 | 8.99 | 164 | 0.2121 | 0.9285 |
0.2543 | 9.86 | 180 | 0.2199 | 0.9251 |
Framework versions
- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.7.1
- Tokenizers 0.13.2
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