transformers 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: 0.835
---

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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: 0.9299
- Accuracy: 0.835

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1474        | 1.0   | 100  | 2.1098          | 0.47     |
| 1.5063        | 2.0   | 200  | 1.5695          | 0.575    |
| 1.2171        | 3.0   | 300  | 1.1629          | 0.685    |
| 0.9388        | 4.0   | 400  | 0.9617          | 0.7      |
| 0.6208        | 5.0   | 500  | 0.9273          | 0.685    |
| 0.6771        | 6.0   | 600  | 0.7753          | 0.785    |
| 0.5799        | 7.0   | 700  | 0.8492          | 0.695    |
| 0.1527        | 8.0   | 800  | 0.6581          | 0.805    |
| 0.0586        | 9.0   | 900  | 0.6788          | 0.82     |
| 0.0355        | 10.0  | 1000 | 0.7627          | 0.81     |
| 0.0186        | 11.0  | 1100 | 0.7585          | 0.82     |
| 0.0102        | 12.0  | 1200 | 0.8328          | 0.825    |
| 0.0074        | 13.0  | 1300 | 0.8543          | 0.835    |
| 0.0063        | 14.0  | 1400 | 0.8574          | 0.83     |
| 0.0271        | 15.0  | 1500 | 0.8889          | 0.835    |
| 0.0043        | 16.0  | 1600 | 0.9197          | 0.83     |
| 0.0045        | 17.0  | 1700 | 0.9130          | 0.835    |
| 0.0036        | 18.0  | 1800 | 0.9242          | 0.835    |
| 0.0042        | 19.0  | 1900 | 0.9279          | 0.835    |
| 0.0034        | 20.0  | 2000 | 0.9299          | 0.835    |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.14.7
- Tokenizers 0.15.0
