---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- KasuleTrevor/lingala_20hr
metrics:
- wer
model-index:
- name: whisper-ling-asr
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: KasuleTrevor/lingala_20hr
      type: KasuleTrevor/lingala_20hr
    metrics:
    - name: Wer
      type: wer
      value: 0.2979101583539804
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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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/abdouaziz/wolof-asr/runs/4gbjp7dq)
# whisper-ling-asr

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the KasuleTrevor/lingala_20hr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4835
- Wer: 0.2979

## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 12000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 0.3703        | 2.9851  | 1000 | 0.5100          | 0.2735 |
| 0.1487        | 5.9701  | 2000 | 0.4835          | 0.2979 |
| 0.0556        | 8.9552  | 3000 | 0.5050          | 0.2544 |
| 0.0171        | 11.9403 | 4000 | 0.5401          | 0.2370 |
| 0.0051        | 14.9254 | 5000 | 0.5673          | 0.2177 |


### Framework versions

- Transformers 4.43.0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1