marsyas/gtzan
Updated • 1.61k • 17
How to use kfahn/ast-finetuned-audioset-v1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="kfahn/ast-finetuned-audioset-v1") # Load model directly
from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
extractor = AutoFeatureExtractor.from_pretrained("kfahn/ast-finetuned-audioset-v1")
model = AutoModelForAudioClassification.from_pretrained("kfahn/ast-finetuned-audioset-v1")# Load model directly
from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
extractor = AutoFeatureExtractor.from_pretrained("kfahn/ast-finetuned-audioset-v1")
model = AutoModelForAudioClassification.from_pretrained("kfahn/ast-finetuned-audioset-v1")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 |
|---|---|---|---|---|
| 0.8293 | 1.0 | 225 | 0.5020 | 0.85 |
| 1.4048 | 2.0 | 450 | 0.5068 | 0.84 |
| 0.8456 | 3.0 | 675 | 0.9684 | 0.82 |
| 0.004 | 4.0 | 900 | 0.5937 | 0.86 |
| 0.0981 | 5.0 | 1125 | 0.5353 | 0.87 |
| 0.0001 | 6.0 | 1350 | 0.6000 | 0.89 |
| 0.4493 | 7.0 | 1575 | 0.6168 | 0.9 |
| 0.0001 | 8.0 | 1800 | 0.5155 | 0.91 |
| 0.0001 | 9.0 | 2025 | 0.5043 | 0.91 |
| 0.0 | 10.0 | 2250 | 0.4979 | 0.91 |
Base model
MIT/ast-finetuned-audioset-10-10-0.4593
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="kfahn/ast-finetuned-audioset-v1")