---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: wav2vec2-base-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: 0.9
---

<!-- 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-finetuned-gtzan

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co./facebook/wav2vec2-base) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4268
- Accuracy: 0.9

## Model description

I have made it for audio corse Unit 4 Hands on.
Check my walktrough https://outleys.site/en/development/AI/hugging-face-audio-course-unit-4-handson-guide/

## 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: 4
- 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: 15

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 2.1998        | 0.8850  | 100  | 2.0267          | 0.34     |
| 1.8078        | 1.7699  | 200  | 1.5776          | 0.51     |
| 1.4427        | 2.6549  | 300  | 1.3546          | 0.57     |
| 1.1903        | 3.5398  | 400  | 1.1145          | 0.63     |
| 0.8872        | 4.4248  | 500  | 0.9314          | 0.74     |
| 0.8191        | 5.3097  | 600  | 0.9010          | 0.73     |
| 0.6717        | 6.1947  | 700  | 0.8036          | 0.75     |
| 0.576         | 7.0796  | 800  | 0.9977          | 0.75     |
| 0.481         | 7.9646  | 900  | 0.7552          | 0.81     |
| 0.3211        | 8.8496  | 1000 | 0.6521          | 0.83     |
| 0.2719        | 9.7345  | 1100 | 0.5343          | 0.86     |
| 0.1922        | 10.6195 | 1200 | 0.6005          | 0.87     |
| 0.1799        | 11.5044 | 1300 | 0.6158          | 0.84     |
| 0.1159        | 12.3894 | 1400 | 0.5496          | 0.88     |
| 0.0883        | 13.2743 | 1500 | 0.5128          | 0.88     |
| 0.0536        | 14.1593 | 1600 | 0.4268          | 0.9      |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1