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--- |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- marsyas/gtzan |
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metrics: |
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- accuracy |
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model-index: |
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- name: wav2vec2-base-finetuned-gtzan |
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results: |
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- task: |
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name: Audio Classification |
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type: audio-classification |
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dataset: |
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name: GTZAN |
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type: marsyas/gtzan |
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config: all |
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split: train |
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args: all |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.87 |
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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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# wav2vec2-base-finetuned-gtzan |
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the GTZAN dataset. |
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It achieves the following results on the evaluation set: |
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- Accuracy: 0.87 |
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- Loss: 0.4960 |
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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: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | Validation Loss | |
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|:-------------:|:-----:|:----:|:--------:|:---------------:| |
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| 1.9026 | 1.0 | 113 | 0.47 | 1.8157 | |
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| 1.4077 | 2.0 | 226 | 0.65 | 1.3151 | |
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| 1.1509 | 3.0 | 339 | 0.71 | 1.0788 | |
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| 0.8387 | 4.0 | 452 | 0.76 | 0.9460 | |
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| 0.5495 | 5.0 | 565 | 0.72 | 0.8380 | |
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| 0.5633 | 6.0 | 678 | 0.85 | 0.5783 | |
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| 0.4959 | 7.0 | 791 | 0.84 | 0.5539 | |
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| 0.1397 | 8.0 | 904 | 0.86 | 0.4837 | |
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| 0.1556 | 9.0 | 1017 | 0.87 | 0.5125 | |
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| 0.0785 | 10.0 | 1130 | 0.87 | 0.4960 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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