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| 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% |
1from transformers import WhisperProcessor, WhisperForConditionalGeneration
2from peft import PeftModel
3import torch
4
5device = "cuda" if torch.cuda.is_available() else "cpu"
6
7# Load base model and Greek fine-tuned LoRA weights
8base_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2").to(device)
9model = PeftModel.from_pretrained(base_model, "Vardis/Whisper-Large-v2-Greek").to(device)
10processor = WhisperProcessor.from_pretrained("Vardis/Whisper-Large-v2-Greek")
11
12# Load your audio waveform (e.g., using librosa or torchaudio)
13audio_input = ...
14
15# Generate transcription
16inputs = processor(audio_input, return_tensors="pt").input_features.to(device)
17predicted_ids = model.generate(inputs)
18transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
19
20print(transcription)1@misc{georgilas2025greekasr,
2 title={Automatic Speech Recognition for Greek Medical Dictation},
3 author={Vardis Georgilas and Themos Stafylakis},
4 year={2025},
5 note={Available at: https://www.arxiv.org/abs/2509.23550}
6}