---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- balbus-classifier
metrics:
- accuracy
model-index:
- name: miosipof/whisper-medium-ft-balbus-sep28k-v1.4
  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: 0.8006894390473206
---

<!-- 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-medium-ft-balbus-sep28k-v1.4

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Apple dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1096
- Accuracy: 0.8007

## 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-06
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.5
- training_steps: 500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1827        | 0.0313 | 50   | 0.1708          | 0.5653   |
| 0.1681        | 0.0627 | 100  | 0.1632          | 0.6341   |
| 0.1603        | 0.0940 | 150  | 0.1498          | 0.6924   |
| 0.1443        | 0.1254 | 200  | 0.1371          | 0.7404   |
| 0.139         | 0.1567 | 250  | 0.1258          | 0.7640   |
| 0.1194        | 0.1880 | 300  | 0.1158          | 0.7880   |
| 0.105         | 0.2194 | 350  | 0.1173          | 0.7914   |
| 0.1114        | 0.2507 | 400  | 0.1143          | 0.7965   |
| 0.1214        | 0.2820 | 450  | 0.1109          | 0.8001   |
| 0.1178        | 0.3134 | 500  | 0.1096          | 0.8007   |


### Framework versions

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