Audio Classification
Transformers
PyTorch
TensorBoard
hubert
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("sandychoii/audio-classification")
model = AutoModelForAudioClassification.from_pretrained("sandychoii/audio-classification")Quick Links
audio-classification
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: 0.2132
- Accuracy: 0.94
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: 8
- eval_batch_size: 8
- 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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3207 | 1.0 | 25 | 0.2132 | 0.94 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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Model tree for sandychoii/audio-classification
Base model
ntu-spml/distilhubertDataset used to train sandychoii/audio-classification
Evaluation results
- Accuracy on GTZANself-reported0.940
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="sandychoii/audio-classification")