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: 0.8
---

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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: 0.8614
- Accuracy: 0.8

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2268        | 0.99  | 56   | 2.1858          | 0.48     |
| 1.7472        | 2.0   | 113  | 1.6259          | 0.58     |
| 1.3293        | 2.99  | 169  | 1.1815          | 0.72     |
| 1.0368        | 4.0   | 226  | 1.0176          | 0.69     |
| 0.8106        | 4.99  | 282  | 0.8129          | 0.76     |
| 0.5371        | 6.0   | 339  | 0.8296          | 0.72     |
| 0.6545        | 6.99  | 395  | 0.7186          | 0.77     |
| 0.4676        | 8.0   | 452  | 0.6627          | 0.76     |
| 0.2729        | 8.99  | 508  | 0.5993          | 0.84     |
| 0.2113        | 10.0  | 565  | 0.6360          | 0.8      |
| 0.1475        | 10.99 | 621  | 0.6244          | 0.78     |
| 0.0616        | 12.0  | 678  | 0.6762          | 0.83     |
| 0.0429        | 12.99 | 734  | 0.7241          | 0.82     |
| 0.0259        | 14.0  | 791  | 0.7547          | 0.82     |
| 0.0207        | 14.99 | 847  | 0.7636          | 0.82     |
| 0.0179        | 16.0  | 904  | 0.7817          | 0.82     |
| 0.0304        | 16.99 | 960  | 0.7976          | 0.81     |
| 0.0146        | 18.0  | 1017 | 0.8193          | 0.81     |
| 0.0135        | 18.99 | 1073 | 0.8402          | 0.8      |
| 0.0136        | 19.82 | 1120 | 0.8614          | 0.8      |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
