---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: whisper-tiny-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: 0.87
pipeline_tag: audio-classification
---

<!-- 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-tiny-finetuned-gtzan

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7650
- Accuracy: 0.87

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 2.0205        | 0.3274 | 37   | 1.6041          | 0.41     |
| 1.3349        | 0.6549 | 74   | 0.9462          | 0.67     |
| 1.1646        | 0.9823 | 111  | 0.9334          | 0.72     |
| 0.8737        | 1.3097 | 148  | 0.8974          | 0.64     |
| 0.8703        | 1.6372 | 185  | 0.7014          | 0.78     |
| 0.811         | 1.9646 | 222  | 0.8678          | 0.7      |
| 0.6429        | 2.2920 | 259  | 0.9130          | 0.66     |
| 0.6366        | 2.6195 | 296  | 0.7061          | 0.78     |
| 0.5858        | 2.9469 | 333  | 0.5549          | 0.82     |
| 0.3959        | 3.2743 | 370  | 0.5577          | 0.82     |
| 0.3343        | 3.6018 | 407  | 0.6203          | 0.83     |
| 0.3358        | 3.9292 | 444  | 0.8755          | 0.76     |
| 0.2574        | 4.2566 | 481  | 0.7690          | 0.79     |
| 0.1799        | 4.5841 | 518  | 0.7350          | 0.85     |
| 0.212         | 4.9115 | 555  | 0.6767          | 0.84     |
| 0.1553        | 5.2389 | 592  | 0.7819          | 0.84     |
| 0.1065        | 5.5664 | 629  | 0.9823          | 0.83     |
| 0.1151        | 5.8938 | 666  | 0.7709          | 0.84     |
| 0.0107        | 6.2212 | 703  | 0.7156          | 0.88     |
| 0.0564        | 6.5487 | 740  | 0.7283          | 0.88     |
| 0.0501        | 6.8761 | 777  | 0.7763          | 0.87     |
| 0.0846        | 7.2035 | 814  | 0.8221          | 0.83     |
| 0.0372        | 7.5310 | 851  | 0.7526          | 0.87     |
| 0.0015        | 7.8584 | 888  | 0.7705          | 0.87     |
| 0.0209        | 8.1858 | 925  | 0.7020          | 0.86     |
| 0.0114        | 8.5133 | 962  | 0.8043          | 0.86     |
| 0.0011        | 8.8407 | 999  | 0.7608          | 0.88     |
| 0.0018        | 9.1681 | 1036 | 0.7623          | 0.88     |
| 0.0009        | 9.4956 | 1073 | 0.7708          | 0.87     |
| 0.0219        | 9.8230 | 1110 | 0.7650          | 0.87     |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1