marsyas/gtzan
Updated • 1.61k • 17
How to use AshokKakunuri/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan-Ashok with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="AshokKakunuri/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan-Ashok") # Load model directly
from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
extractor = AutoFeatureExtractor.from_pretrained("AshokKakunuri/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan-Ashok")
model = AutoModelForAudioClassification.from_pretrained("AshokKakunuri/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan-Ashok")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 |
|---|---|---|---|---|
| 1.0215 | 1.0 | 112 | 0.6979 | 0.82 |
| 0.5726 | 2.0 | 225 | 0.4903 | 0.84 |
| 0.402 | 3.0 | 337 | 0.5950 | 0.82 |
| 0.0031 | 4.0 | 450 | 0.7435 | 0.84 |
| 0.0015 | 5.0 | 562 | 0.6883 | 0.84 |
| 0.001 | 6.0 | 675 | 0.5155 | 0.88 |
| 0.0002 | 7.0 | 787 | 0.4624 | 0.9 |
| 0.0002 | 8.0 | 900 | 0.3535 | 0.9 |
| 0.1006 | 9.0 | 1012 | 0.3671 | 0.9 |
| 0.0001 | 9.96 | 1120 | 0.3592 | 0.91 |
Base model
MIT/ast-finetuned-audioset-10-10-0.4593