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---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.85
---
<!-- 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. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5569
- Accuracy: 0.85
## 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: 8e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 192
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.85 | 4 | 2.2836 | 0.14 |
| 2.2984 | 1.92 | 9 | 2.2574 | 0.18 |
| 2.2856 | 2.99 | 14 | 2.2060 | 0.32 |
| 2.2478 | 3.84 | 18 | 2.1331 | 0.37 |
| 2.1775 | 4.91 | 23 | 1.9859 | 0.47 |
| 2.0557 | 5.97 | 28 | 1.8086 | 0.52 |
| 1.8764 | 6.83 | 32 | 1.6783 | 0.53 |
| 1.7133 | 7.89 | 37 | 1.5235 | 0.54 |
| 1.5661 | 8.96 | 42 | 1.4048 | 0.58 |
| 1.4544 | 9.81 | 46 | 1.3279 | 0.6 |
| 1.3365 | 10.88 | 51 | 1.2591 | 0.67 |
| 1.2228 | 11.95 | 56 | 1.1587 | 0.7 |
| 1.1298 | 12.8 | 60 | 1.1476 | 0.68 |
| 1.0601 | 13.87 | 65 | 1.0066 | 0.77 |
| 0.9886 | 14.93 | 70 | 0.9855 | 0.76 |
| 0.923 | 16.0 | 75 | 0.9767 | 0.73 |
| 0.923 | 16.85 | 79 | 0.8896 | 0.79 |
| 0.8539 | 17.92 | 84 | 0.8421 | 0.78 |
| 0.788 | 18.99 | 89 | 0.8270 | 0.8 |
| 0.7253 | 19.84 | 93 | 0.7764 | 0.82 |
| 0.6523 | 20.91 | 98 | 0.6998 | 0.85 |
| 0.5853 | 21.97 | 103 | 0.6891 | 0.87 |
| 0.5372 | 22.83 | 107 | 0.7106 | 0.8 |
| 0.4815 | 23.89 | 112 | 0.6542 | 0.82 |
| 0.4461 | 24.96 | 117 | 0.6136 | 0.87 |
| 0.3841 | 25.81 | 121 | 0.6338 | 0.81 |
| 0.3505 | 26.88 | 126 | 0.6082 | 0.87 |
| 0.3143 | 27.95 | 131 | 0.5776 | 0.88 |
| 0.2913 | 28.8 | 135 | 0.5833 | 0.86 |
| 0.2519 | 29.87 | 140 | 0.5543 | 0.89 |
| 0.2234 | 30.93 | 145 | 0.5606 | 0.84 |
| 0.1994 | 32.0 | 150 | 0.5726 | 0.86 |
| 0.1994 | 32.85 | 154 | 0.5391 | 0.86 |
| 0.1789 | 33.92 | 159 | 0.5908 | 0.83 |
| 0.1615 | 34.99 | 164 | 0.5498 | 0.85 |
| 0.1444 | 35.84 | 168 | 0.5389 | 0.85 |
| 0.1303 | 36.91 | 173 | 0.5829 | 0.84 |
| 0.1192 | 37.97 | 178 | 0.5278 | 0.87 |
| 0.1074 | 38.83 | 182 | 0.6011 | 0.83 |
| 0.1001 | 39.89 | 187 | 0.5260 | 0.87 |
| 0.0935 | 40.96 | 192 | 0.5778 | 0.84 |
| 0.0885 | 41.81 | 196 | 0.5563 | 0.86 |
| 0.0827 | 42.88 | 201 | 0.5556 | 0.86 |
| 0.0785 | 43.95 | 206 | 0.5807 | 0.84 |
| 0.0767 | 44.8 | 210 | 0.5649 | 0.85 |
| 0.0722 | 45.87 | 215 | 0.5551 | 0.85 |
| 0.0718 | 46.93 | 220 | 0.5432 | 0.86 |
| 0.0701 | 48.0 | 225 | 0.5720 | 0.85 |
| 0.0701 | 48.85 | 229 | 0.5695 | 0.85 |
| 0.068 | 49.92 | 234 | 0.5642 | 0.85 |
| 0.0673 | 50.99 | 239 | 0.5571 | 0.85 |
| 0.0672 | 51.2 | 240 | 0.5569 | 0.85 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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