---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
  results: []
---

<!-- 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 on best epoch:
- Loss: 0.7305          
- Accuracy: 0.9

## Model description

Distilhubert is distilled version of the [HuBERT](https://huggingface.co/docs/transformers/model_doc/hubert) and pretrained on data set with 16k frequency. <br/>
Architecture of this model is CTC or Connectionist Temporal Classification is a technique that is used with encoder-only transformer. <br/>


## Training and evaluation data

Training + Evaluation data set is GTZAN which is a popular dataset of 999 songs for music genre classification. <br/>
Each song is a 30-second clip from one of 10 genres of music, spanning disco to metal.<br/>
Train set is 899 songs and Evaluation set is 100 songs remainings.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 35
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1728        | 1.0   | 225  | 2.0896          | 0.42     |
| 1.4211        | 2.0   | 450  | 1.4951          | 0.55     |
| 1.2155        | 3.0   | 675  | 1.0669          | 0.72     |
| 1.0175        | 4.0   | 900  | 0.8862          | 0.69     |
| 0.3516        | 5.0   | 1125 | 0.6265          | 0.83     |
| 0.6135        | 6.0   | 1350 | 0.6485          | 0.78     |
| 0.0807        | 7.0   | 1575 | 0.6567          | 0.78     |
| 0.0303        | 8.0   | 1800 | 0.7615          | 0.83     |
| 0.2663        | 9.0   | 2025 | 0.6612          | 0.86     |
| 0.0026        | 10.0  | 2250 | 0.8354          | 0.85     |
| 0.0337        | 11.0  | 2475 | 0.6768          | 0.87     |
| 0.0013        | 12.0  | 2700 | 0.7718          | 0.87     |
| 0.001         | 13.0  | 2925 | 0.7570          | 0.88     |
| 0.0008        | 14.0  | 3150 | 0.8170          | 0.89     |
| 0.0006        | 15.0  | 3375 | 0.7920          | 0.89     |
| 0.0005        | 16.0  | 3600 | 0.9859          | 0.83     |
| 0.0004        | 17.0  | 3825 | 0.8190          | 0.9      |
| 0.0003        | 18.0  | 4050 | 0.7305          | 0.9      |
| 0.0003        | 19.0  | 4275 | 0.8025          | 0.88     |
| 0.0002        | 20.0  | 4500 | 0.8208          | 0.87     |
| 0.0003        | 21.0  | 4725 | 0.7358          | 0.88     |
| 0.0002        | 22.0  | 4950 | 0.8681          | 0.87     |
| 0.0002        | 23.0  | 5175 | 0.7831          | 0.9      |
| 0.0003        | 24.0  | 5400 | 0.8583          | 0.88     |
| 0.0002        | 25.0  | 5625 | 0.8138          | 0.88     |
| 0.0002        | 26.0  | 5850 | 0.7871          | 0.89     |
| 0.0002        | 27.0  | 6075 | 0.8893          | 0.88     |
| 0.0002        | 28.0  | 6300 | 0.8284          | 0.89     |
| 0.0001        | 29.0  | 6525 | 0.8388          | 0.89     |
| 0.0001        | 30.0  | 6750 | 0.8305          | 0.9      |
| 0.0001        | 31.0  | 6975 | 0.8377          | 0.88     |
| 0.0153        | 32.0  | 7200 | 0.8496          | 0.88     |
| 0.0001        | 33.0  | 7425 | 0.8381          | 0.88     |
| 0.0001        | 34.0  | 7650 | 0.8440          | 0.88     |
| 0.0001        | 35.0  | 7875 | 0.8458          | 0.88     |


### Framework versions

- Transformers 4.29.2
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3