---
language:
- ig
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- google/fleurs
- deepdml/igbo-dict-expansion-16khz
- deepdml/igbo-dict-16khz
metrics:
- wer
model-index:
- name: Whisper Base ig
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: google/fleurs
      type: google/fleurs
      config: ig_ng
      split: test
      args: ig_ng
    metrics:
    - name: Wer
      type: wer
      value: 54.948739128322245
---
<!-- 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 Base ig

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the google/fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0933
- Wer: 54.9487
- Cer: 21.3532

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.04
- training_steps: 5000

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     | Cer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|
| 0.2087        | 0.2    | 1000 | 0.8427          | 54.4143 | 20.1160 |
| 0.0734        | 1.0814 | 2000 | 0.9702          | 55.5707 | 21.6200 |
| 0.0609        | 1.2814 | 3000 | 1.0272          | 54.0256 | 20.4927 |
| 0.0336        | 2.1628 | 4000 | 1.0804          | 54.4337 | 20.4677 |
| 0.0341        | 3.0442 | 5000 | 1.0933          | 54.9487 | 21.3532 |


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1

## Citation

Please cite the model using the following BibTeX entry:

```bibtex
@misc{deepdml/whisper-base-ig-mix-norm,
      title={Fine-tuned Whisper base ASR model for speech recognition in Lingala},
      author={Jimenez, David},
      howpublished={\url{https://huggingface.co/deepdml/whisper-base-ig-mix-norm}},
      year={2025}
    }
```