---
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-v1.5
  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:
        accuracy: 0.8111877154497023
    - name: Precision
      type: precision
      value:
        precision: 0.8133174791914387
    - name: Recall
      type: recall
      value:
        recall: 0.7365398420674802
    - name: F1
      type: f1
      value:
        f1: 0.7730269353927294
---

<!-- 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-v1.5

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: 0.1083
- Accuracy: {'accuracy': 0.8111877154497023}
- Precision: {'precision': 0.8133174791914387}
- Recall: {'recall': 0.7365398420674802}
- F1: {'f1': 0.7730269353927294}

## 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: 3e-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: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy                         | Precision                         | Recall                          | F1                           |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------:|:---------------------------------:|:-------------------------------:|:----------------------------:|
| 0.1718        | 0.1253 | 100  | 0.1705          | {'accuracy': 0.564243183954873}  | {'precision': 0.6190476190476191} | {'recall': 0.00466618808327351} | {'f1': 0.009262557890986818} |
| 0.1683        | 0.2506 | 200  | 0.1653          | {'accuracy': 0.6118771544970228} | {'precision': 0.7677642980935875} | {'recall': 0.15900933237616655} | {'f1': 0.26345524829021705}  |
| 0.1595        | 0.3759 | 300  | 0.1494          | {'accuracy': 0.6847383265434033} | {'precision': 0.6486175115207373} | {'recall': 0.6062455132806892}  | {'f1': 0.6267161410018552}   |
| 0.1299        | 0.5013 | 400  | 0.1266          | {'accuracy': 0.7608900031338138} | {'precision': 0.7008928571428571} | {'recall': 0.7889447236180904}  | {'f1': 0.7423167848699763}   |
| 0.1174        | 0.6266 | 500  | 0.1140          | {'accuracy': 0.7977123158884363} | {'precision': 0.7800674409891345} | {'recall': 0.747307968413496}   | {'f1': 0.7633363886342804}   |
| 0.1117        | 0.7519 | 600  | 0.1155          | {'accuracy': 0.7919147602632404} | {'precision': 0.7362281270252754} | {'recall': 0.8155061019382628}  | {'f1': 0.773841961852861}    |
| 0.1072        | 0.8772 | 700  | 0.1074          | {'accuracy': 0.8096208085239737} | {'precision': 0.8282490597576264} | {'recall': 0.7114142139267767}  | {'f1': 0.765398725622707}    |
| 0.106         | 1.0025 | 800  | 0.1078          | {'accuracy': 0.8077405202130994} | {'precision': 0.8175152749490835} | {'recall': 0.7203876525484566}  | {'f1': 0.7658843732112193}   |
| 0.1001        | 1.1278 | 900  | 0.1079          | {'accuracy': 0.810404261986838}  | {'precision': 0.8174858984689767} | {'recall': 0.7282842785355348}  | {'f1': 0.7703113135914958}   |
| 0.092         | 1.2531 | 1000 | 0.1083          | {'accuracy': 0.8111877154497023} | {'precision': 0.8133174791914387} | {'recall': 0.7365398420674802}  | {'f1': 0.7730269353927294}   |


### Framework versions

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