---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- Leonel-Maia/fongbe-splitted
metrics:
- wer
model-index:
- name: whisper-small-splitted
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Leonel-Maia/fongbe-splitted
      type: Leonel-Maia/fongbe-splitted
    metrics:
    - name: Wer
      type: wer
      value: 0.11257288805358778
---

<!-- 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. -->

# whisper-small-splitted

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Leonel-Maia/fongbe-splitted dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1084
- Wer: 0.1126

## 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-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- 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_steps: 500
- num_epochs: 60.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 0.1324        | 2.1094  | 500  | 0.1909          | 0.3307 |
| 0.0383        | 4.2187  | 1000 | 0.1153          | 0.1131 |
| 0.0184        | 6.3281  | 1500 | 0.1084          | 0.1126 |
| 0.0098        | 8.4374  | 2000 | 0.1122          | 0.1014 |
| 0.0076        | 10.5468 | 2500 | 0.1101          | 0.0966 |
| 0.0075        | 12.6562 | 3000 | 0.1156          | 0.0992 |


### Framework versions

- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1