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library_name: transformers
license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: hubert-base-ls960-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7391304347826086
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hubert-base-ls960-finetuned-gtzan
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1234
- Accuracy: 0.7391
## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4277 | 1.0 | 25 | 1.5627 | 0.4783 |
| 1.4946 | 2.0 | 50 | 1.4727 | 0.5217 |
| 1.051 | 3.0 | 75 | 1.3207 | 0.6087 |
| 1.0897 | 4.0 | 100 | 1.3614 | 0.6522 |
| 1.1461 | 5.0 | 125 | 1.3143 | 0.5652 |
| 0.6919 | 6.0 | 150 | 1.1131 | 0.6087 |
| 0.7273 | 7.0 | 175 | 1.4138 | 0.6522 |
| 0.5955 | 8.0 | 200 | 1.2106 | 0.6957 |
| 0.4823 | 9.0 | 225 | 1.1681 | 0.6087 |
| 0.5178 | 10.0 | 250 | 1.1616 | 0.6522 |
| 0.4635 | 11.0 | 275 | 0.9685 | 0.7826 |
| 0.4622 | 12.0 | 300 | 0.9625 | 0.7826 |
| 0.3048 | 13.0 | 325 | 1.0364 | 0.7391 |
| 0.1576 | 14.0 | 350 | 1.0571 | 0.7391 |
| 0.1876 | 15.0 | 375 | 1.1234 | 0.7391 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
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