Results

Held-out validation (2,024 samples, ATCO2 + ATCoSIM), normalized WER/CER (lowercase, punctuation- and article-stripped):

≈39% relative WER and ≈61% relative CER reduction vs the small fine-tune — mostly from eliminating hallucinated insertions. Inference is ~2× the small model's latency (still faster than real-time on Apple Silicon / a modern GPU).

Training

Usage

python
Copy code
import librosa, torch
from transformers import WhisperProcessor, WhisperForConditionalGeneration

repo = "SingularityUS/ATC-whisper-turbo-v1"
processor = WhisperProcessor.from_pretrained(repo, language="en", task="transcribe")
model = WhisperForConditionalGeneration.from_pretrained(repo).eval()

audio, _ = librosa.load("atc_clip.wav", sr=16000)
features = processor(audio, sampling_rate=16000, return_tensors="pt").input_features
with torch.no_grad():
    ids = model.generate(features)
print(processor.batch_decode(ids, skip_special_tokens=True)[0])

Intended use & limitations

For English ATC VHF radio transcription. The validation set is predominantly clean studio-quality audio (ATCoSIM), so real-world noisy-VHF error rates will be higher. Research / non-safety-critical use only.

License & data

Base model is MIT-licensed (OpenAI Whisper). Training data (ATCO2, ATCoSIM) is subject to its respective licenses; this fine-tune is intended for research use — confirm the applicable terms before any commercial use.