Whisper Large-v3 Fine-tuned for Crimean Tatar (Qırımtatarca)
This model is a fine-tuned version of openai/whisper-large-v3 for Automatic Speech Recognition (ASR) in Crimean Tatar language.
Model Description
- Base model: openai/whisper-large-v3 (1.54B parameters)
- Language: Crimean Tatar (crh) - both Cyrillic and Latin scripts
- Fine-tuning dataset: servinosmanov/tts-crh-sevil-fixed
- Best WER: 13.67% (with repetition penalty)
Training Details
- Epochs: ~17 (early stopping to prevent overfitting)
- Best checkpoint: epoch 12.7 with WER 16.0%
- Learning rate: 1e-5
- Batch size: 4 (with gradient accumulation steps: 4)
- Optimizer: AdamW
Usage
from transformers import WhisperForConditionalGeneration, WhisperProcessor
import torch
import librosa
# Load model
model_name = "servinosmanov/whisper-large-v3-crh"
processor = WhisperProcessor.from_pretrained(model_name)
model = WhisperForConditionalGeneration.from_pretrained(model_name)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
model.eval()
# Load and process audio
audio, sr = librosa.load("your_audio.wav", sr=16000)
input_features = processor(
audio,
sampling_rate=16000,
return_tensors="pt"
).input_features.to(device)
# Generate transcription (with repetition penalty recommended)
with torch.no_grad():
predicted_ids = model.generate(
input_features,
max_length=225,
num_beams=5,
repetition_penalty=1.2,
no_repeat_ngram_size=3,
)
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
print(transcription)
Important Notes
- Repetition Penalty: It's recommended to use
repetition_penalty=1.2andno_repeat_ngram_size=3during inference to prevent repetition loops - Script Support: Works with both Cyrillic (Меним адым...) and Latin (Menim adım...) scripts
Comparison with whisper-medium
| Model | WER |
|---|---|
| whisper-medium (fine-tuned) | 26.47% |
| whisper-large-v3 (this model) | 13.67% |
| Improvement | 48.4% relative |
Limitations
- Trained primarily on female voice (Sevil speaker)
- May have reduced accuracy on very long utterances
- Best results with clear audio at 16kHz sampling rate
License
Apache 2.0 (same as base Whisper model)
Citation
If you use this model, please cite:
@misc{whisper-large-v3-crh,
author = {Servin Osmanov},
title = {Whisper Large-v3 Fine-tuned for Crimean Tatar},
year = {2025},
publisher = {HuggingFace},
url = {https://huggingface.co/servinosmanov/whisper-large-v3-crh}
}