Instructions to use stebox/whisperkit-atc-coreml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- WhisperKit
How to use stebox/whisperkit-atc-coreml with WhisperKit:
# Install CLI with Homebrew on macOS device brew install whisperkit-cli # View all available inference options whisperkit-cli transcribe --help # Download and run inference using whisper base model whisperkit-cli transcribe --audio-path /path/to/audio.mp3 # Or use your preferred model variant whisperkit-cli transcribe --model "large-v3" --model-prefix "distil" --audio-path /path/to/audio.mp3 --verbose
- Notebooks
- Google Colab
- Kaggle
WhisperKit ATC Models (CoreML)
CoreML-converted Whisper models fine-tuned for Air Traffic Control (ATC) transcription.
Models
| Model | Size | Recommended For | WER on ATCO2 |
|---|---|---|---|
small.en-atco2-asr |
~500MB | iPhone 12/13 | ~13.5% |
Usage with WhisperKit
import WhisperKit
let config = WhisperKitConfig(
model: "small.en-atco2-asr",
modelRepo: "skycaption/whisperkit-atc-coreml"
)
let whisperKit = try await WhisperKit(config)
Performance
These models achieve 84% better Word Error Rate compared to standard Whisper on ATC audio:
- Standard Whisper: ~50% WER on ATC
- ATC Fine-tuned: ~13.5% WER on ATCO2 dataset
Credits
- Original fine-tuning: jlvdoorn/WhisperATC (TU Delft)
- CoreML conversion: argmaxinc/whisperkittools
- App: SkyCaption
License
MIT License
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