---
language:
- sw
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Swahili
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_11_0 sw
      type: mozilla-foundation/common_voice_11_0
      config: sw
      split: test
      args: sw
    metrics:
    - name: Wer
      type: wer
      value: 23.724554196406032
---

<!-- 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 Swahili

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co./openai/whisper-small) on the mozilla-foundation/common_voice_11_0 sw dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6442
- Wer: 23.7246

## 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: 32
- 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: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2694        | 1.07  | 1000 | 0.5438          | 26.8354 |
| 0.2306        | 3.02  | 2000 | 0.5081          | 23.9231 |
| 0.0467        | 4.09  | 3000 | 0.5648          | 24.4085 |
| 0.0239        | 6.03  | 4000 | 0.5994          | 23.8634 |
| 0.0123        | 7.1   | 5000 | 0.6442          | 23.7246 |


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.1.dev0
- Tokenizers 0.13.3