---
base_model: openai/whisper-small
datasets:
- fleurs
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: whisper-small-wolof
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: fleurs
      type: fleurs
      config: wo_sn
      split: test
      args: wo_sn
    metrics:
    - type: wer
      value: 0.9217902350813744
      name: Wer
---

<!-- 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-wolof

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co./openai/whisper-small) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8726
- Wer: 0.9218

## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 4.5232        | 0.9790 | 35   | 3.5807          | 1.3809 |
| 3.7127        | 1.9860 | 71   | 2.4567          | 1.1817 |
| 2.2111        | 2.9371 | 105  | 1.8726          | 0.9218 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1