---
language:
- multilingual
license: apache-2.0
base_model: openai/whisper-medium
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- abiyo27/BibleTTS_Ewe-Bible
metrics:
- wer
model-index:
- name: Whisper_Small_Ewe
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: BibleTTS
      type: abiyo27/BibleTTS_Ewe-Bible
      config: default
      split: None
      args: 'config: ewe, split: train'
    metrics:
    - name: Wer
      type: wer
      value: 10.094952523738131
---

<!-- 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_Ewe

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the BibleTTS dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1021
- Wer: 10.0950

## 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: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 14000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer     |
|:-------------:|:------:|:-----:|:---------------:|:-------:|
| 0.2196        | 0.1802 | 4000  | 0.1780          | 19.3903 |
| 0.1587        | 0.3605 | 8000  | 0.1375          | 13.4933 |
| 0.1162        | 0.5407 | 12000 | 0.1021          | 10.0950 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1