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--- |
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license: bsd-3-clause |
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base_model: MIT/ast-finetuned-audioset-10-10-0.4593 |
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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: ast-finetuned-audioset-10-10-0.4593-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: 0.9 |
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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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# ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan |
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This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the GTZAN dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4718 |
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- Accuracy: 0.9 |
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## Model description |
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This model was generated as part of the [HF Audio course](https://huggingface.co/learn/audio-course/), I enjoyed it and currently this architecture achieves an amazing accuracy of 0.9 on music-genre classification task. |
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The Audio Spectrogram Transformer is equivalent to [ViT](https://huggingface.co/docs/transformers/model_doc/vit), but applied on audio. Audio is first turned into an image (as a spectrogram), after which a Vision Transformer is applied. The model gets state-of-the-art results on several audio classification benchmarks. |
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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: 4 |
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- eval_batch_size: 4 |
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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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- global_step: 2250 |
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- training_loss: 0.23970948094350752 |
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- train_runtime: 1982.7909 |
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- train_samples_per_second: 4.534 |
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- train_steps_per_second: 1.135 |
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- total_flos: 6.094112254328832e+17 |
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- train_loss: 0.23970948094350752 |
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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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| 0.9734 | 1.0 | 225 | 0.6194 | 0.82 | |
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| 0.7734 | 2.0 | 450 | 0.4650 | 0.86 | |
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| 0.7703 | 3.0 | 675 | 0.8101 | 0.78 | |
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| 0.0052 | 4.0 | 900 | 0.5021 | 0.89 | |
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| 0.2316 | 5.0 | 1125 | 0.4968 | 0.9 | |
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| 0.0001 | 6.0 | 1350 | 0.5484 | 0.87 | |
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| 0.5337 | 7.0 | 1575 | 0.4673 | 0.89 | |
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| 0.0 | 8.0 | 1800 | 0.4868 | 0.89 | |
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| 0.0 | 9.0 | 2025 | 0.4709 | 0.9 | |
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| 0.0 | 10.0 | 2250 | 0.4718 | 0.9 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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