---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-large
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: whisper-swahili-large-v0.1
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: sw
      split: test
      args: sw
    metrics:
    - name: Wer
      type: wer
      value: 26.32352799853929
---

<!-- 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-swahili-large-v0.1

This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4162
- Wer: 26.3235

## 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: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 1.1388        | 0.0681 | 250  | 0.6673          | 65.1304 |
| 0.4408        | 0.1362 | 500  | 0.5600          | 33.9259 |
| 0.3612        | 0.2042 | 750  | 0.4609          | 30.1521 |
| 0.3057        | 0.2723 | 1000 | 0.4162          | 26.3235 |


### Framework versions

- Transformers 4.52.0.dev0
- Pytorch 2.6.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1