marsyas/gtzan
Updated • 1.61k • 17
How to use fruzti/wav2vec2-base-100k-voxpopuli-finetuned-gtzan with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="fruzti/wav2vec2-base-100k-voxpopuli-finetuned-gtzan") # Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("fruzti/wav2vec2-base-100k-voxpopuli-finetuned-gtzan")
model = AutoModelForAudioClassification.from_pretrained("fruzti/wav2vec2-base-100k-voxpopuli-finetuned-gtzan")# Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("fruzti/wav2vec2-base-100k-voxpopuli-finetuned-gtzan")
model = AutoModelForAudioClassification.from_pretrained("fruzti/wav2vec2-base-100k-voxpopuli-finetuned-gtzan")This model is a fine-tuned version of facebook/wav2vec2-base-100k-voxpopuli 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 |
|---|---|---|---|---|
| 0.0777 | 1.0 | 8 | 0.9974 | 0.86 |
| 0.0276 | 2.0 | 16 | 1.1353 | 0.83 |
| 0.326 | 3.0 | 24 | 1.2362 | 0.81 |
| 0.123 | 4.0 | 32 | 1.1119 | 0.84 |
| 0.0225 | 5.0 | 40 | 1.1009 | 0.85 |
| 0.0776 | 6.0 | 48 | 1.0709 | 0.85 |
| 0.025 | 7.0 | 56 | 1.1126 | 0.84 |
| 0.0163 | 8.0 | 64 | 1.0823 | 0.84 |
| 0.0193 | 9.0 | 72 | 1.0818 | 0.84 |
| 0.0209 | 10.0 | 80 | 1.0838 | 0.84 |
Base model
facebook/wav2vec2-base-100k-voxpopuli
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="fruzti/wav2vec2-base-100k-voxpopuli-finetuned-gtzan")