---
language:
- nyn
license: apache-2.0
base_model: openai/whisper-base.en
tags:
- whisper-event
- generated_from_trainer
datasets:
- tericlabs
metrics:
- wer
model-index:
- name: Whisper base Luganda
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Sunbird
      type: tericlabs
    metrics:
    - name: Wer
      type: wer
      value: 40.80100125156446
---

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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/ai-research-lab/huggingface/runs/p2aegccn)
# Whisper base Luganda

This model is a fine-tuned version of [openai/whisper-base.en](https://huggingface.co./openai/whisper-base.en) on the Sunbird dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6134
- Wer: 40.8010
- Cer: 10.6921

## 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: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     | Cer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|:-------:|
| 0.3459        | 6.3694  | 1000 | 0.5885          | 45.1815 | 14.4972 |
| 0.0532        | 12.7389 | 2000 | 0.5441          | 38.6733 | 10.0723 |
| 0.0108        | 19.1083 | 3000 | 0.6118          | 39.9249 | 10.2789 |
| 0.0044        | 25.4777 | 4000 | 0.6134          | 40.8010 | 10.6921 |


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1