---
library_name: transformers
language:
- lin
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Small Lingala
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: ln_cd
      split: validation
      args: ln_cd
    metrics:
    - name: Wer
      type: wer
      value: 19.83622350674374
---

<!-- 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 Lingala

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: 0.5608
- Wer: 19.8362

## 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
- optimizer: Use OptimizerNames.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
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.0939        | 4.7619  | 1000 | 0.4101          | 20.1182 |
| 0.0034        | 9.5238  | 2000 | 0.4968          | 19.7772 |
| 0.0013        | 14.2857 | 3000 | 0.5139          | 19.3226 |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0