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---
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 |