marsyas/gtzan
Updated • 1.89k • 17
How to use ahnhs2k/distilhubert-finetuned-gtzan with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="ahnhs2k/distilhubert-finetuned-gtzan") # Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("ahnhs2k/distilhubert-finetuned-gtzan")
model = AutoModelForAudioClassification.from_pretrained("ahnhs2k/distilhubert-finetuned-gtzan")This model is a fine-tuned version of ntu-spml/distilhubert 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 |
|---|---|---|---|---|
| 1.9886 | 1.0 | 113 | 1.8000 | 0.58 |
| 1.412 | 2.0 | 226 | 1.2203 | 0.72 |
| 1.0097 | 3.0 | 339 | 0.9380 | 0.78 |
| 0.8725 | 4.0 | 452 | 0.8014 | 0.77 |
| 0.7465 | 5.0 | 565 | 0.6994 | 0.83 |
| 0.5016 | 6.0 | 678 | 0.6867 | 0.84 |
| 0.3178 | 7.0 | 791 | 0.6199 | 0.85 |
| 0.268 | 8.0 | 904 | 0.6097 | 0.86 |
| 0.1877 | 9.0 | 1017 | 0.6241 | 0.86 |
| 0.1548 | 10.0 | 1130 | 0.5926 | 0.88 |
Base model
ntu-spml/distilhubert