marsyas/gtzan
Updated • 1.61k • 17
How to use molinari135/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="molinari135/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan") # Load model directly
from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
extractor = AutoFeatureExtractor.from_pretrained("molinari135/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan")
model = AutoModelForAudioClassification.from_pretrained("molinari135/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 |
|---|---|---|---|---|
| 0.7406 | 1.0 | 56 | 1.0012 | 0.66 |
| 0.3306 | 1.99 | 112 | 0.4705 | 0.83 |
| 0.2461 | 2.99 | 168 | 0.5012 | 0.83 |
| 0.0756 | 4.0 | 225 | 0.5697 | 0.84 |
| 0.1149 | 5.0 | 281 | 0.5627 | 0.87 |
| 0.0012 | 5.99 | 337 | 0.6342 | 0.84 |
| 0.0007 | 6.99 | 393 | 0.4624 | 0.89 |
| 0.0005 | 8.0 | 450 | 0.6121 | 0.87 |
| 0.0275 | 9.0 | 506 | 0.5096 | 0.87 |
| 0.0003 | 9.96 | 560 | 0.5243 | 0.87 |
Base model
MIT/ast-finetuned-audioset-10-10-0.4593