transformers tensorboard safetensors whisper audio-classification generated_from_trainer dataset:marsyas/gtzan base_model:openai/whisper-large-v3 license:apache-2.0 model-index endpoints_compatible region:us
---
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: whisper-large-v3-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: 0.94
---

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# whisper-large-v3-finetuned-gtzan

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2657
- Accuracy: 0.94

## 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: 4e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1646        | 0.5   | 28   | 1.8012          | 0.55     |
| 1.0152        | 1.0   | 56   | 0.8618          | 0.79     |
| 1.1129        | 1.49  | 84   | 0.7426          | 0.8      |
| 0.8163        | 1.99  | 112  | 0.8078          | 0.75     |
| 0.4374        | 2.49  | 140  | 0.6259          | 0.81     |
| 0.4607        | 2.99  | 168  | 0.5424          | 0.83     |
| 0.4225        | 3.48  | 196  | 0.3723          | 0.89     |
| 0.1769        | 3.98  | 224  | 0.3517          | 0.9      |
| 0.0927        | 4.48  | 252  | 0.3385          | 0.89     |
| 0.0159        | 4.98  | 280  | 0.3985          | 0.88     |
| 0.0119        | 5.48  | 308  | 0.4626          | 0.9      |
| 0.029         | 5.97  | 336  | 0.4292          | 0.91     |
| 0.0064        | 6.47  | 364  | 0.2710          | 0.93     |
| 0.0057        | 6.97  | 392  | 0.2665          | 0.93     |
| 0.0048        | 7.47  | 420  | 0.2784          | 0.93     |
| 0.0049        | 7.96  | 448  | 0.2550          | 0.94     |
| 0.0049        | 8.46  | 476  | 0.3011          | 0.94     |
| 0.0044        | 8.96  | 504  | 0.2759          | 0.94     |
| 0.0045        | 9.46  | 532  | 0.2661          | 0.94     |
| 0.0048        | 9.96  | 560  | 0.2657          | 0.94     |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
