---
library_name: transformers
language:
- sw
widget:
  - example_title: speech sample 1
    src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
  - example_title: speech sample 2
    src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper Small SW-eolang
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 17
      type: mozilla-foundation/common_voice_17_0
      config: sw
      split: test
      args: sw
    metrics:
    - name: Wer
      type: wer
      value: 27.951115548558043
---

<!-- 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 SW-eolang

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5136
- Wer Ortho: 36.8520
- Wer: 27.9511

## 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: 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: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer Ortho | Wer     |
|:-------------:|:------:|:----:|:---------------:|:---------:|:-------:|
| 0.4894        | 0.1721 | 500  | 0.7495          | 47.1590   | 39.6183 |
| 0.4068        | 0.3441 | 1000 | 0.6356          | 44.4535   | 36.3763 |
| 0.4137        | 0.5162 | 1500 | 0.5934          | 41.9094   | 33.4866 |
| 0.3759        | 0.6882 | 2000 | 0.5590          | 41.4031   | 33.1765 |
| 0.38          | 0.8603 | 2500 | 0.5293          | 37.2958   | 28.8699 |
| 0.2027        | 1.0323 | 3000 | 0.5235          | 37.4755   | 29.0340 |
| 0.2089        | 1.2044 | 3500 | 0.5149          | 35.8239   | 27.4845 |
| 0.2282        | 1.3765 | 4000 | 0.5136          | 36.8520   | 27.9511 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.1.0
- Datasets 2.21.0
- Tokenizers 0.19.1