marsyas/gtzan
Updated • 1.62k • 17
How to use derek-thomas/Hubert_emotion-finetuned-gtzan-efficient with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="derek-thomas/Hubert_emotion-finetuned-gtzan-efficient") # Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("derek-thomas/Hubert_emotion-finetuned-gtzan-efficient")
model = AutoModelForAudioClassification.from_pretrained("derek-thomas/Hubert_emotion-finetuned-gtzan-efficient")This model is a fine-tuned version of Rajaram1996/Hubert_emotion 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 |
|---|---|---|---|---|
| 2.2127 | 1.0 | 113 | 2.2191 | 0.25 |
| 1.9102 | 2.0 | 226 | 2.0018 | 0.37 |
| 1.7139 | 3.0 | 339 | 1.7588 | 0.4 |
| 1.5825 | 4.0 | 452 | 1.5608 | 0.41 |
| 1.1426 | 5.0 | 565 | 1.4300 | 0.5 |
| 1.8976 | 6.0 | 678 | 1.1726 | 0.56 |
| 0.9303 | 7.0 | 791 | 1.1559 | 0.56 |
| 0.8845 | 8.0 | 904 | 1.1501 | 0.65 |
| 0.2069 | 9.0 | 1017 | 1.2055 | 0.58 |
| 1.9863 | 10.0 | 1130 | 1.0804 | 0.62 |
| 2.0317 | 11.0 | 1243 | 1.2341 | 0.65 |
Base model
Rajaram1996/Hubert_emotion