---
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: whisper-base-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: 0.62
---

<!-- 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. -->

# whisper-base-finetuned-gtzan

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8944
- Accuracy: 0.62

## 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: 4
- 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.3577        | 1.0   | 200  | 1.9551          | 0.35     |
| 2.0492        | 2.0   | 400  | 2.0333          | 0.27     |
| 2.0331        | 3.0   | 600  | 1.9196          | 0.3      |
| 1.3732        | 4.0   | 800  | 1.6705          | 0.34     |
| 1.7021        | 5.0   | 1000 | 1.7006          | 0.335    |
| 1.907         | 6.0   | 1200 | 1.7489          | 0.36     |
| 1.611         | 7.0   | 1400 | 1.5347          | 0.45     |
| 1.1989        | 8.0   | 1600 | 1.4835          | 0.465    |
| 2.0049        | 9.0   | 1800 | 1.3681          | 0.525    |
| 0.9562        | 10.0  | 2000 | 1.4732          | 0.49     |
| 0.4145        | 11.0  | 2200 | 1.2645          | 0.555    |
| 1.5859        | 12.0  | 2400 | 1.3992          | 0.51     |
| 1.5115        | 13.0  | 2600 | 1.2638          | 0.545    |
| 0.9777        | 14.0  | 2800 | 1.4003          | 0.57     |
| 0.831         | 15.0  | 3000 | 1.3377          | 0.575    |
| 1.3201        | 16.0  | 3200 | 1.5033          | 0.575    |
| 1.1711        | 17.0  | 3400 | 1.5239          | 0.555    |
| 0.4201        | 18.0  | 3600 | 1.6902          | 0.555    |
| 0.346         | 19.0  | 3800 | 1.9733          | 0.525    |
| 0.5619        | 20.0  | 4000 | 2.1321          | 0.555    |
| 0.645         | 21.0  | 4200 | 2.1219          | 0.625    |
| 0.2672        | 22.0  | 4400 | 2.2037          | 0.555    |
| 0.2826        | 23.0  | 4600 | 2.7297          | 0.565    |
| 0.4265        | 24.0  | 4800 | 3.3848          | 0.5      |
| 0.0319        | 25.0  | 5000 | 3.5627          | 0.59     |
| 0.0024        | 26.0  | 5200 | 3.7420          | 0.6      |
| 0.0332        | 27.0  | 5400 | 3.7159          | 0.63     |
| 0.0009        | 28.0  | 5600 | 3.8011          | 0.635    |
| 0.0001        | 29.0  | 5800 | 3.8852          | 0.615    |
| 0.0001        | 30.0  | 6000 | 3.8944          | 0.62     |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3