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This model is a fine-tuned version of facebook/wav2vec2-base on the GTZAN dataset. It achieves the following results on the evaluation set: Loss: 0.5249 Accuracy: 0.88
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Source description
This model is a fine-tuned version of facebook/wav2vec2-base on the GTZAN dataset. It achieves the following results on the evaluation set:
More information needed
Sources
1 sourceVerified Sep 19
Model artifacts
1 artifactSource excerpts
2 excerptsMore information needed
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 4.3372 | 1.0 | 57 | 2.1037 | 0.37 |
| 3.5091 | 2.0 | 114 | 1.7248 | 0.55 |
| 2.9802 | 3.0 | 171 | 1.4436 | 0.57 |
| 2.3109 | 4.0 | 228 | 1.3017 | 0.66 |
| 1.8822 | 5.0 | 285 | 1.1528 | 0.68 |
--- library_name: transformers license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.88 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-finetuned-gtzan This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.5249 - Accuracy: 0.88 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 0.1 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.3372 | 1.0 | 57 | 2.1037 | 0.37 | | 3.5091 | 2.0 | 114 | 1.7248 | 0.55 | | 2.9802 | 3.0 | 171 | 1.4436 | 0.57 | | 2.3109 | 4.0 | 228 | 1.3017 | 0.66 | | 1.8822 | 5.0 | 285 | 1.1528 | 0.68 | | 1.9275 | 6.0 | 342 | 0.9594 | 0.72 | | 1.6896 | 7.0 | 399 | 0.7778 | 0.85 | | 1.2257 | 8.0 | 456 | 0.7596 | 0.83 | | 0.9672 | 9.0 | 513 | 0.8690 | 0.79 | | 0.8144 | 10.0 | 570 | 0.5945 | 0.88 | | 0.7628 | 11.0 | 627 | 0.7198 | 0.81 | | 0.5727 | 12.0 | 684 | 0.5887 | 0.87 | | 0.4335 | 13.0 | 741 | 0.5249 | 0.88 | ### Framework versions - Transformers 5.0.0.dev0 - Pytorch 2.9.0+cu126 - Datasets 2.18.0 - Tokenizers 0.22.1
Source context: 9 downloads · 0 likes · Pipeline audio-classification · Library transformers · Repo titmambyves6/wav2vec2-base-finetuned-gtzan
| 1.9275 | 6.0 | 342 | 0.9594 | 0.72 |
| 1.6896 | 7.0 | 399 | 0.7778 | 0.85 |
| 1.2257 | 8.0 | 456 | 0.7596 | 0.83 |
| 0.9672 | 9.0 | 513 | 0.8690 | 0.79 |
| 0.8144 | 10.0 | 570 | 0.5945 | 0.88 |
| 0.7628 | 11.0 | 627 | 0.7198 | 0.81 |
| 0.5727 | 12.0 | 684 | 0.5887 | 0.87 |
| 0.4335 | 13.0 | 741 | 0.5249 | 0.88 |
