anke01/Whisper-medium-uyghur-cer12

Whisper Uyghur ASR

Fine-tuned OpenAI Whisper-medium model for Uyghur Automatic Speech Recognition.

Model

• Base Model: openai/whisper-medium
• Language: Uyghur (ug)
• Checkpoint: step-2000

Performance

Metric	Value
WER	22.53%
CER	12.56%

Dataset

Split	Samples	Duration
Train	11,287	17.44h
Validation	1,045	1.37h
Test	795	0.86h
Total	13,127	19.67h

Sources:

• Common Voice Uyghur (CC0-1.0): 5,197 samples
• Uyghur Whisper Finetune (CC-BY-4.0): 7,930 samples

Installation

pip install torch transformers datasets librosa soundfile

Usage

from transformers import WhisperForConditionalGeneration, WhisperProcessor
import librosa

# Load model
model = WhisperForConditionalGeneration.from_pretrained("last_model")
processor = WhisperProcessor.from_pretrained("last_model")

# Transcribe
audio, sr = librosa.load("audio.wav", sr=16000)
inputs = processor(audio, sampling_rate=16000, return_tensors="pt")
predicted_ids = model.generate(**inputs)
text = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
print(text)

Test Result

See last_model/test_audio.aac and last_model/test_audio.txt for sample inference.

Project Structure

├── last_model/           # Fine-tuned model
│   ├── config.json
│   ├── model.safetensors
│   ├── tokenizer.json
│   ├── infer.py
│   ├── test_audio.aac
│   └── test_audio.txt
├── merged_dataset_clean/ # Training dataset
├── finetune_whisper.py   # Training script
├── training_output.log   # Training log
└── requirements.txt

Training Configuration

Parameter	Value
Epochs	10
Batch Size	8
Gradient Accumulation	2
Learning Rate	1e-5
Warmup Steps	500
FP16	True

License

• Code: MIT License
• Model: MIT License
• Dataset: CC0-1.0 / CC-BY-4.0