---
base_model: m3hrdadfi/wav2vec2-base-100k-gtzan-music-genres
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: wav2vec2-base-100k-gtzan-music-genres-finetuned-gtzan
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: GTZAN
      type: marsyas/gtzan
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.98
---

<!-- 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. -->

# wav2vec2-base-100k-gtzan-music-genres-finetuned-gtzan

This model is a fine-tuned version of [m3hrdadfi/wav2vec2-base-100k-gtzan-music-genres](https://huggingface.co/m3hrdadfi/wav2vec2-base-100k-gtzan-music-genres) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6843
- Accuracy: 0.98

## 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: 8
- 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 2.1932        | 0.9976 | 53   | 2.1037          | 0.82     |
| 1.9212        | 1.9953 | 106  | 1.8040          | 0.8267   |
| 1.6379        | 2.9929 | 159  | 1.5650          | 0.8667   |
| 1.4604        | 3.9906 | 212  | 1.3201          | 0.9267   |
| 1.2249        | 4.9882 | 265  | 1.1253          | 0.94     |
| 1.075         | 5.9859 | 318  | 0.9814          | 0.96     |
| 0.911         | 6.9835 | 371  | 0.8447          | 0.9667   |
| 0.852         | 8.0    | 425  | 0.7628          | 0.9667   |
| 0.7625        | 8.9976 | 478  | 0.7117          | 0.9733   |
| 0.7099        | 9.9765 | 530  | 0.6843          | 0.98     |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1