---
language:
- ln
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Base Lingala - BrainTheos
  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: 25.050916496945007
---

<!-- 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 Lingala - BrainTheos

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7265
- Wer: 25.0509

## 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
- training_steps: 4000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0081        | 21.0  | 1000 | 0.6218          | 29.8710 |
| 0.0016        | 42.01 | 2000 | 0.6865          | 25.1188 |
| 0.0009        | 63.01 | 3000 | 0.7152          | 24.9151 |
| 0.0007        | 85.0  | 4000 | 0.7265          | 25.0509 |


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.12.1.dev0
- Tokenizers 0.13.3