---
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-tiny-ft-balbus-sep28k-v1.1
  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: 0.7718583516139141
---

<!-- 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-tiny-ft-balbus-sep28k-v1.1

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: 0.4870
- Accuracy: 0.7719

## 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: 1e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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: 0.5
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6991        | 0.1253 | 100  | 0.6929          | 0.4616   |
| 0.686         | 0.2506 | 200  | 0.6816          | 0.5577   |
| 0.6776        | 0.3759 | 300  | 0.6726          | 0.5631   |
| 0.6591        | 0.5013 | 400  | 0.6472          | 0.6244   |
| 0.6317        | 0.6266 | 500  | 0.6115          | 0.6802   |
| 0.5836        | 0.7519 | 600  | 0.5672          | 0.7104   |
| 0.5415        | 0.8772 | 700  | 0.5192          | 0.7499   |
| 0.4856        | 1.0025 | 800  | 0.4999          | 0.7667   |
| 0.4886        | 1.1278 | 900  | 0.4894          | 0.7715   |
| 0.4727        | 1.2531 | 1000 | 0.4870          | 0.7719   |


### Framework versions

- Transformers 4.48.0
- Pytorch 2.2.0
- Datasets 3.2.0
- Tokenizers 0.21.0