---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- balbus-classifier
metrics:
- accuracy
model-index:
- name: miosipof/whisper-small-ft-balbus-sep28k-v1
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: Apple dataset
      type: balbus-classifier
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7953596287703016
---

<!-- 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. -->

# miosipof/whisper-small-ft-balbus-sep28k-v1

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Apple dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5668
- Accuracy: 0.7954

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 404  | 0.4751          | 0.7748   |
| 0.494         | 2.0   | 808  | 0.4533          | 0.7901   |
| 0.3256        | 3.0   | 1212 | 0.5668          | 0.7954   |


### Framework versions

- Transformers 4.48.1
- Pytorch 2.2.0
- Datasets 3.2.0
- Tokenizers 0.21.0