---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- balbus-classifier
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: miosipof/whisper-small-ft-balbus-sep28k-v1.6
  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: 0.8100908806016922
    - name: Precision
      type: precision
      value: 0.8183656957928802
    - name: Recall
      type: recall
      value: 0.7261306532663316
    - name: F1
      type: f1
      value: 0.7694941042221377
---

<!-- 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-v1.6

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: 0.1091
- Accuracy: 0.8101
- Precision: 0.8184
- Recall: 0.7261
- F1: 0.7695
- Roc-auc: 0.8006

## 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: 2e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.5
- training_steps: 1200
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Roc-auc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.1683        | 0.2506 | 200  | 0.1682          | 0.5730   | 0.7364    | 0.0341 | 0.0652 | 0.5123  |
| 0.1494        | 0.5013 | 400  | 0.1446          | 0.7084   | 0.6603    | 0.6838 | 0.6718 | 0.7056  |
| 0.1212        | 0.7519 | 600  | 0.1236          | 0.7629   | 0.6917    | 0.8245 | 0.7523 | 0.7699  |
| 0.1088        | 1.0025 | 800  | 0.1107          | 0.8062   | 0.8337    | 0.6945 | 0.7578 | 0.7936  |
| 0.0955        | 1.2531 | 1000 | 0.1106          | 0.8081   | 0.8036    | 0.7416 | 0.7713 | 0.8006  |
| 0.0997        | 1.5038 | 1200 | 0.1091          | 0.8101   | 0.8184    | 0.7261 | 0.7695 | 0.8006  |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.2.0
- Datasets 3.2.0
- Tokenizers 0.20.3