Audio Classification
Transformers
PyTorch
TensorBoard
audio-spectrogram-transformer
Generated from Trainer
Eval Results (legacy)
Instructions to use ramsri818/ast-finetuned-gtzan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ramsri818/ast-finetuned-gtzan with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="ramsri818/ast-finetuned-gtzan")# pip install -U transformers accelerate # Load model directly from transformers import AutoFeatureExtractor, AutoModelForAudioClassification extractor = AutoFeatureExtractor.from_pretrained("ramsri818/ast-finetuned-gtzan") model = AutoModelForAudioClassification.from_pretrained("ramsri818/ast-finetuned-gtzan", device_map="auto") - Notebooks
- Google Colab
- Kaggle
update model card README.md
Browse files
README.md
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- generated_from_trainer
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datasets:
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- marsyas/gtzan
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model-index:
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- name: ast-finetuned-gtzan
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results:
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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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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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- eval_runtime: 12.0837
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- eval_samples_per_second: 8.276
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- eval_steps_per_second: 2.069
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- epoch: 4.0
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- step: 900
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## Model description
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 20
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### Framework versions
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- Transformers 4.32.0.dev0
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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-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: 0.93
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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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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: 0.4436
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- Accuracy: 0.93
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## Model description
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 20
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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| 0.0001 | 1.0 | 225 | 0.5546 | 0.89 |
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| 1.204 | 2.0 | 450 | 0.9484 | 0.81 |
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| 0.4719 | 3.0 | 675 | 0.7417 | 0.85 |
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| 0.0132 | 4.0 | 900 | 0.7101 | 0.9 |
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| 0.0527 | 5.0 | 1125 | 0.8170 | 0.86 |
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| 0.0 | 6.0 | 1350 | 0.6406 | 0.93 |
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| 0.3099 | 7.0 | 1575 | 0.8426 | 0.84 |
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| 0.0 | 8.0 | 1800 | 0.9173 | 0.89 |
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| 0.0 | 9.0 | 2025 | 0.7142 | 0.9 |
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| 0.0602 | 10.0 | 2250 | 0.4718 | 0.92 |
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| 0.0003 | 11.0 | 2475 | 0.9860 | 0.9 |
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| 0.0001 | 12.0 | 2700 | 0.5918 | 0.91 |
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| 0.0 | 13.0 | 2925 | 0.4886 | 0.92 |
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| 0.0 | 14.0 | 3150 | 0.4562 | 0.93 |
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| 0.0 | 15.0 | 3375 | 0.4360 | 0.94 |
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| 0.0 | 16.0 | 3600 | 0.4433 | 0.94 |
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| 0.0 | 17.0 | 3825 | 0.4454 | 0.94 |
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| 0.0 | 18.0 | 4050 | 0.4454 | 0.94 |
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| 0.0 | 19.0 | 4275 | 0.4434 | 0.93 |
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| 0.0 | 20.0 | 4500 | 0.4436 | 0.93 |
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### Framework versions
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- Transformers 4.32.0.dev0
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