metadata
license: gpl-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-ug
results: []
datasets:
- mozilla-foundation/common_voice_15_0
pipeline_tag: automatic-speech-recognition
language:
- ug
whisper-small-ug
This model is a fine-tuned version of openai/whisper-small on the None dataset. The model is trained on transcripts written in Uyghur Latin Script via utilising Uzbek Tokeniser , as Uyghur Tokeniser is not included in Whisper. Therefore, the output of the model is in Uyghur Latin Script. To convert the output to the Uyghur Arabic Script, you can use the Uyghur script converter: https://github.com/neouyghur/ScriptConverter4Uyghur
or you can use online script converter: https://www.yulghun.com/imla/convert.html
It achieves the following results on the evaluation set:
- Loss: 0.3563
- Wer: 26.8793
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: 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 |
|---|---|---|---|---|
| 0.2677 | 1.43 | 1000 | 0.4063 | 34.1157 |
| 0.1035 | 2.85 | 2000 | 0.3375 | 29.2183 |
| 0.0226 | 4.28 | 3000 | 0.3472 | 27.5155 |
| 0.0073 | 5.71 | 4000 | 0.3563 | 26.8793 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0