marsyas/gtzan
Updated • 1.61k • 17
How to use pranjalks/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="pranjalks/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan") # Load model directly
from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
extractor = AutoFeatureExtractor.from_pretrained("pranjalks/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan")
model = AutoModelForAudioClassification.from_pretrained("pranjalks/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan")This model is a fine-tuned version of MIT/ast-finetuned-audioset-10-10-0.4593 on the GTZAN dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.8119 | 1.0 | 112 | 0.5849 | 0.82 |
| 0.4518 | 2.0 | 225 | 0.6924 | 0.75 |
| 0.3943 | 3.0 | 337 | 0.4569 | 0.86 |
| 0.0634 | 4.0 | 450 | 0.6410 | 0.85 |
| 0.1771 | 5.0 | 562 | 0.4193 | 0.91 |
| 0.0158 | 6.0 | 675 | 0.5743 | 0.89 |
| 0.0002 | 7.0 | 787 | 0.6801 | 0.87 |
| 0.0006 | 8.0 | 900 | 0.6696 | 0.86 |
| 0.0974 | 9.0 | 1012 | 0.5556 | 0.86 |
| 0.0001 | 10.0 | 1125 | 0.4910 | 0.87 |
| 0.0 | 11.0 | 1237 | 0.5283 | 0.87 |
| 0.0001 | 12.0 | 1350 | 0.4772 | 0.9 |
| 0.0001 | 13.0 | 1462 | 0.5688 | 0.87 |
| 0.0001 | 14.0 | 1575 | 0.5120 | 0.88 |
| 0.0 | 15.0 | 1687 | 0.5163 | 0.88 |
| 0.0 | 16.0 | 1800 | 0.5101 | 0.89 |
| 0.0 | 17.0 | 1912 | 0.5154 | 0.89 |
| 0.0 | 18.0 | 2025 | 0.5141 | 0.89 |
| 0.0 | 19.0 | 2137 | 0.5180 | 0.89 |
| 0.0 | 19.91 | 2240 | 0.5174 | 0.89 |
Base model
MIT/ast-finetuned-audioset-10-10-0.4593