Audio Classification
Transformers
TensorBoard
Safetensors
hubert
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("rcodina/exemple3")
model = AutoModelForAudioClassification.from_pretrained("rcodina/exemple3")Quick Links
exemple3
This model is a fine-tuned version of ntu-spml/distilhubert on the GTZAN dataset. It achieves the following results on the evaluation set:
- Loss: 2.2569
- Accuracy: 0.3
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.3832 | 1.0 | 15 | 2.2895 | 0.1 |
| 2.2049 | 2.0 | 30 | 2.2569 | 0.3 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for rcodina/exemple3
Base model
ntu-spml/distilhubertDataset used to train rcodina/exemple3
Evaluation results
- Accuracy on GTZANself-reported0.300
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="rcodina/exemple3")