Whisper Large-v3 Turbo

This first-party Soniqo page documents Whisper Large-v3 Turbo from the local speech-swift / speech-core implementation. Hugging Face bundles are linked below after the integration notes.

Internal Page First

Landing cards and docs menus now point here first; source model and bundle links remain available from this page.

At a Glance

Model	Whisper Large-v3 Turbo
Role	General multilingual speech-to-text
Backend	CoreML fp16 on CPU and Neural Engine
Output	Text transcript
Languages	Whisper multilingual set, about 100 languages
License	MIT weights from OpenAI
Status	Ready through speech transcribe --engine whisper and the WhisperASR Swift product
Source	openai/whisper-large-v3-turbo
Swift product	`WhisperASR`
CLI / runtime	`speech transcribe --engine whisper`

Use

The snippet below mirrors the current speech-swift API or command exposed by the repo.

# Transcribe with the native CoreML Whisper runtime.
.build/release/speech transcribe recording.wav --engine whisper
.build/release/speech transcribe recording.wav --engine whisper --language en

Model Links

Implementation Notes

LibriSpeech test-clean slice on an M5 Pro: 1.40% WER, mean RTF 0.089, 6.1 s model load, 384 MB peak RSS — versus 1.53% / 0.085 / 100.2 s / 507 MB for a direct WhisperKit run of the same model.
The bundle splits into four CoreML models — mel spectrogram, audio encoder, decoder context prefill, and a KV-cached text decoder — with the encoder and decoder on the Neural Engine.
Greedy no-timestamp decoding with automatic language detection or a --language hint; audio is processed in 30 s chunks with a repeated-word guard against greedy hallucination loops.
Word timestamps, temperature fallback, and VAD-guided long-form seeking are not implemented yet; --model accepts turbo, large-v3-turbo, or any Hugging Face repo ID.