---
base_model: openai/whisper-small
datasets:
- mozilla-foundation/common_voice_11_0
language:
- yo
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: Whisper Small Yo - Bola Ologundudu
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Common Voice 11.0
      type: mozilla-foundation/common_voice_11_0
      config: yo
      split: None
      args: 'config: yo, split: test'
    metrics:
    - type: wer
      value: 70.61345018098686
      name: Wer
---

<!-- 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 Yoruba - Bola Ologundudu

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2225
- Wer: 70.6135

## Model description

>>> from transformers import pipeline
>>> import torch

>>> modelName="ajibs75/whisper-small-yoruba"
>>> device = 0 if torch.cuda.is_available() else "cpu"
>>> pipe = pipeline(task="automatic-speech-recognition",model=modelName,chunk_length_s=30,device=device,)
>>> pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language="yo", task="transcribe")

>>> audio = "sample.mp3"
>>> text = pipe(audio)
>>> transacribed_audio = text["text"] 
>>> print(transacribed_audio)


## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.066         | 7.6923  | 1000 | 0.8962          | 74.0141 |
| 0.004         | 15.3846 | 2000 | 1.1411          | 71.6613 |
| 0.0004        | 23.0769 | 3000 | 1.1959          | 70.6516 |
| 0.0003        | 30.7692 | 4000 | 1.2225          | 70.6135 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.1.0+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1