---
library_name: transformers
language:
- ug
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Small Fine-tuned with Uyghur Common Voice
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 15
      type: mozilla-foundation/common_voice_15_0
    metrics:
    - name: Wer
      type: wer
      value: 34.99609273248242
---

<!-- 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 Fine-tuned with Uyghur Common Voice

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Uyghur Common Voice dataset.

As a proof-of-concept, only 3264 recordings (\~5.5 hrs of audio) were used for training, and 937 recordings (\~1.5 hrs of audio) were used for validation. 
You may find the full dataset for Uyghur and other languages here: https://commonvoice.mozilla.org/en/datasets.

This model achieves the following results on the evaluation set:
- Loss: 0.5105
- Wer Ortho: 41.6377
- Wer: 34.9961

## 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: 0.0001
- 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: 50
- training_steps: 500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer Ortho | Wer     |
|:-------------:|:------:|:----:|:---------------:|:---------:|:-------:|
| 0.0574        | 2.4510 | 500  | 0.5105          | 41.6377   | 34.9961 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1