---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- balbus-classifier
metrics:
- accuracy
model-index:
- name: whisper-medium-ft-balbus
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: Balbus dataset
      type: balbus-classifier
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.465
---

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# whisper-medium-ft-balbus

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Balbus dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7451
- Accuracy: 0.465

## 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: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 450  | 0.6979          | 0.535    |
| 4.2819        | 2.0   | 900  | 1.1246          | 0.465    |
| 0.8774        | 3.0   | 1350 | 0.7451          | 0.465    |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.19.1