---
language:
- sw
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_15_0
metrics:
- wer
model-index:
- name: swahili
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 15.0
      type: mozilla-foundation/common_voice_15_0
      config: lg
      split: validation
      args: 'config: lu, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 36.497908126611165
---

<!-- 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. -->

# swahili

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 15.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3824
- Wer: 36.4979

## 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: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.6697        | 0.1129 | 500  | 0.7159          | 64.0293 |
| 0.4719        | 0.2258 | 1000 | 0.5437          | 50.6878 |
| 0.4218        | 0.3388 | 1500 | 0.4773          | 45.0904 |
| 0.3896        | 0.4517 | 2000 | 0.4405          | 41.5501 |
| 0.3721        | 0.5646 | 2500 | 0.4173          | 39.9865 |
| 0.3386        | 0.6775 | 3000 | 0.3996          | 37.9094 |
| 0.3414        | 0.7904 | 3500 | 0.3883          | 37.3082 |
| 0.3078        | 0.9033 | 4000 | 0.3824          | 36.4979 |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.2+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1