---
language:
- sw
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
- asr
- sst
- swahili
datasets:
- mozilla-foundation/common_voice_13_0
model-index:
- name: Whisper Tiny Sw - Skier8402
  results: []
library_name: transformers
metrics:
- wer
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper Tiny Sw - Skier8402

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 13 dataset using the swahili only. 

## Model description

More information needed.

## Intended uses & limitations

The model was trained without enough noise added as a form of data augmentation. Do not use this production. I recommend using a larger version of whisper with
more hyperparameter tuning especially the learning rate, momentum, weight decay and adjusting the batch size.   

## Training and evaluation data

I followed the tutorial [here](https://huggingface.co/learn/audio-course/chapter5/fine-tuning). Very minimum edits to the code were done following 
this tutorial.   

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500

### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1