---
datasets: []
language:
- en
library_name: coremltools
tags:
- coreml
- whisper
- speech-recognition
license: apache-2.0
task:
- type: automatic-speech-recognition
---

# Core ML Whisper Large V3 Turbo (FP16)

Core ML export of [`openai/whisper-large-v3-turbo`](https://huggingface.co/openai/whisper-large-v3-turbo) tuned for Apple Silicon. The bundle reflects the *Option A1* configuration and reaches ~0.024 real-time factor (RTF) when `DecoderWithCache.mlpackage` is used.

## Files

- `whisper-large-v3-turbo-coreml-fp16/Encoder.mlpackage`
- `whisper-large-v3-turbo-coreml-fp16/DecoderWithCache.mlpackage` (primary, tensor KV cache)
- `whisper-large-v3-turbo-coreml-fp16/DecoderFull.mlpackage` (fallback)
- `whisper-large-v3-turbo-coreml-fp16/DecoderStateful.mlpackage` (experimental MLState build)
- Tokenizer + mel assets (`tokenizer.json`, `vocab.json`, `merges.txt`, `whisper_mel_filters.json`, etc.)
- `whisper-large-v3-turbo-coreml-fp16.tar.gz` and `whisper-large-v3-turbo-coreml-fp16.sha256`

## Download & Verify

```bash
hf download DRTR-J/whisper-large-v3-turbo-coreml-fp16 whisper-large-v3-turbo-coreml-fp16.tar.gz
hf download DRTR-J/whisper-large-v3-turbo-coreml-fp16 whisper-large-v3-turbo-coreml-fp16.sha256
shasum -a 256 -c whisper-large-v3-turbo-coreml-fp16.sha256
tar -xzf whisper-large-v3-turbo-coreml-fp16.tar.gz
```

## Usage

1. Load `Encoder.mlpackage` and `DecoderWithCache.mlpackage` with Core ML (`MLModel(contentsOf:)`, `MLModelConfiguration(computeUnits: .cpuAndNeuralEngine)`).
2. Feed the tokenizer assets into Hugging Face `AutoProcessor`/`tokenizers` to maintain parity with the PyTorch reference.
3. Optionally fall back to:
   - `DecoderFull.mlpackage` for non-cached decoding.
   - `DecoderStateful.mlpackage` for experimentation (ANE planner still returns error -14 on macOS 15 betas).