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--- |
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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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pipeline_tag: audio-classification |
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tags: |
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- music |
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- audio |
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--- |
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# Description |
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This model is a specialized version of the <b>distilhubert</b> model fine-tuned on the <b>gtzan</b> dataset for the task of Music Genre Classification. |
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## Development |
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- Kaggle Notebook: [Audio Data: Music Genre Classification](https://www.kaggle.com/code/lusfernandotorres/audio-data-music-genre-classification) |
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## Training Parameters |
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```python |
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evaluation_strategy = 'epoch', |
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save_strategy = 'epoch', |
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load_best_model_at_end = True, |
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metric_for_best_model = 'accuracy', |
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learning_rate = 5e-5, |
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seed = 42, |
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per_device_train_batch_size = 8, |
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per_device_eval_batch_size = 8, |
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gradient_accumulation_steps = 1, |
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num_train_epochs = 15, |
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warmup_ratio = 0.1, |
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fp16 = True, |
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save_total_limit = 2, |
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report_to = 'none' |
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``` |
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## Training and Validation Results |
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```python |
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Epoch Training Loss Validation Loss Accuracy |
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1 No log 2.050576 0.395000 |
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2 No log 1.387915 0.565000 |
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3 No log 1.141497 0.665000 |
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4 No log 1.052763 0.675000 |
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5 1.354600 0.846402 0.745000 |
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6 1.354600 0.858698 0.750000 |
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7 1.354600 0.864531 0.730000 |
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8 1.354600 0.765039 0.775000 |
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9 1.354600 0.790847 0.785000 |
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10 0.250100 0.873926 0.785000 |
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11 0.250100 0.928275 0.770000 |
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12 0.250100 0.851429 0.780000 |
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13 0.250100 0.922214 0.770000 |
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14 0.250100 0.916481 0.780000 |
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15 0.028000 0.946075 0.770000 |
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TrainOutput(global_step=1500, training_loss=0.5442592652638754, |
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metrics={'train_runtime': 12274.2966, 'train_samples_per_second': 0.976, |
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'train_steps_per_second': 0.122, 'total_flos': 8.177513845536e+17, 'train_loss': 0.5442592652638754, 'epoch': 15.0}) |
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``` |
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## Reference |
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This model is based on the original <b>HuBERT</b> architecture, as detailed in: |
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Hsu et al. (2021). HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units. [arXiv:2106.07447](https://arxiv.org/pdf/2106.07447.pdf) |
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