transformers tensorboard safetensors hubert audio-classification generated_from_trainer dataset:marsyas/gtzan base_model:ntu-spml/distilhubert license:apache-2.0 model-index endpoints_compatible region:us
---
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: 0.88
---

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# 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: 0.8080
- Accuracy: 0.88

## 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: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7263        | 1.0   | 113  | 1.6331          | 0.59     |
| 1.1167        | 2.0   | 226  | 1.2046          | 0.65     |
| 0.774         | 3.0   | 339  | 0.8365          | 0.8      |
| 0.6507        | 4.0   | 452  | 0.7015          | 0.81     |
| 0.5046        | 5.0   | 565  | 0.6722          | 0.81     |
| 0.2632        | 6.0   | 678  | 0.6743          | 0.82     |
| 0.203         | 7.0   | 791  | 0.7351          | 0.84     |
| 0.0902        | 8.0   | 904  | 0.5898          | 0.86     |
| 0.0215        | 9.0   | 1017 | 0.6213          | 0.87     |
| 0.0097        | 10.0  | 1130 | 0.6948          | 0.86     |
| 0.1171        | 11.0  | 1243 | 0.6228          | 0.87     |
| 0.0054        | 12.0  | 1356 | 0.7101          | 0.86     |
| 0.0035        | 13.0  | 1469 | 0.7626          | 0.87     |
| 0.0028        | 14.0  | 1582 | 0.7659          | 0.86     |
| 0.0027        | 15.0  | 1695 | 0.6993          | 0.87     |
| 0.0023        | 16.0  | 1808 | 0.7345          | 0.87     |
| 0.0023        | 17.0  | 1921 | 0.8363          | 0.86     |
| 0.0018        | 18.0  | 2034 | 0.7779          | 0.88     |
| 0.0018        | 19.0  | 2147 | 0.8275          | 0.87     |
| 0.0018        | 20.0  | 2260 | 0.8080          | 0.88     |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
