---
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper_small_Yoruba
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: google/fleurs yo_ng
      type: google/fleurs
      config: yo_ng
      split: test
    metrics:
    - name: Wer
      type: wer
      value: 67.88663748364095
---

<!-- 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

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs yo_ng dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6773
- Wer: 67.8866

## 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: 8
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_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: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.013         | 36.35  | 400  | 1.4068          | 72.9681 |
| 0.0008        | 72.7   | 800  | 1.5546          | 68.4507 |
| 0.0003        | 109.09 | 1200 | 1.6400          | 67.9137 |
| 0.0002        | 145.43 | 1600 | 1.6773          | 67.8866 |
| 0.0002        | 181.78 | 2000 | 1.6901          | 68.1123 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2