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Whisper Large V3 Turbo · Core ML (Option A1)

Optimized Core ML export of openai/whisper-large-v3-turbo targeting Apple Silicon. This repository distributes the production bundle used to reach ~0.024 real-time factor (RTF) described in the Option A1 implementation plan.

Contents

  • whisper-large-v3-turbo-coreml-fp16/ – unpacked model bundle with encoder/decoder mlpackages, tokenizer assets, and metadata.
  • whisper-large-v3-turbo-coreml-fp16.tar.gz – identical bundle packaged as a tarball for one-shot download.
  • whisper-large-v3-turbo-coreml-fp16.sha256 – checksum for the tarball.

Inside the directory you will find:

  • Encoder.mlpackage
  • DecoderWithCache.mlpackage (primary, tensor-cache decoder)
  • DecoderFull.mlpackage (fallback)
  • DecoderStateful.mlpackage (experimental; see note below)
  • Tokenizer + mel filter assets and metadata JSON files
  • README.md and MODEL_CARD.md with usage and performance details

Usage

  1. Download the snapshot via huggingface-cli or snapshot_download:

    from huggingface_hub import snapshot_download
model_path = snapshot_download("DRTR-J/whisper-large-v3-turbo-coreml-fp16")

  2. Point your Core ML integration (Swift, Rust, Tauri, etc.) at DecoderWithCache.mlpackage to achieve the advertised RTF ≈ 0.024 on Apple Silicon.

  3. The experimental DecoderStateful.mlpackage is included for completeness but still fails ANE planning (Core ML error -14); keep it disabled in production.

For full background and performance tables, consult whisper-large-v3-turbo-coreml-fp16/MODEL_CARD.md.

License

  • Base weights: OpenAI Whisper license
  • Packaging and auxiliary assets: MIT