Vardis/Whisper-Large-v2-Greek

Fine-Tuned Whisper Large

This is a Large-sized Whisper model fine-tuned for Greek speech transcription. It has 1.5B parameters and achieves improved transcription performance over the medium model.

• WER: 12.06%
• CER: 6.20%

Training Results

Step	Training Loss	Validation Loss	WER	CER
250	0.1776	0.1904	13.52%	6.74%
500	0.1478	0.1698	12.55%	6.38%
750	0.1229	0.1608	12.33%	6.24%
1000	0.1057	0.1605	12.15%	6.26%
1250	0.0864	0.1630	12.65%	6.65%
1500	0.0677	0.1643	13.23%	7.35%
1750	0.0618	0.1681	12.86%	6.86%
2000	0.0533	0.1686	12.98%	7.00%

Model Details

• Model Type: Whisper (Large)
• Fine-tuned From: OpenAI Whisper Large
• Language(s): Greek
• Parameters: 1.5B

How to Use

from transformers import WhisperProcessor, WhisperForConditionalGeneration
from peft import PeftModel
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

# Load base model and Greek fine-tuned LoRA weights
base_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2").to(device)
model = PeftModel.from_pretrained(base_model, "Vardis/Whisper-Large-v2-Greek").to(device)
processor = WhisperProcessor.from_pretrained("Vardis/Whisper-Large-v2-Greek")

# Load your audio waveform (e.g., using librosa or torchaudio)
audio_input = ...  

# Generate transcription
inputs = processor(audio_input, return_tensors="pt").input_features.to(device)
predicted_ids = model.generate(inputs)
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)

print(transcription)

Context / Reference

This model was developed as part of the work described in:

Georgilas, V., Stafylakis, T. (2025). Automatic Speech Recognition for Greek Medical Dictation.
The paper focuses on Greek medical ASR research in general and is not primarily about the model itself, but provides context for its development. Users are welcome to use the model freely for research and practical applications.

BibTeX citation:

@misc{georgilas2025greekasr,
  title={Automatic Speech Recognition for Greek Medical Dictation},
  author={Vardis Georgilas and Themos Stafylakis},
  year={2025},
  note={Available at: https://www.arxiv.org/abs/2509.23550}
}