# ADR-0003 — Transcription engine: whisper-rs (whisper.cpp) with pluggable backends

- **Status:** Accepted
- **Date:** 2026-06-30
- **Context source:** Design doc §"Transcription Engine", §"Transcription Modes"

## Context

WA needs accurate, offline, multilingual speech-to-text with both a low-latency streaming mode
and a higher-accuracy batch mode, runnable across CPU and several GPU vendors, and (separately)
on NPUs.

## Decision

Use **`whisper-rs`** (Rust bindings to **whisper.cpp**) as the primary engine, behind a
`transcription::Transcriber` trait so the concrete engine is swappable. whisper.cpp gives:
- CPU (AVX2/AVX-512) baseline that runs anywhere,
- **Vulkan** for cross-vendor GPU (AMD/Intel/NVIDIA),
- **CUDA** for NVIDIA fast path,
- selectable model sizes (tiny→large) for the speed/accuracy and "low-overhead" presets,
- multilingual models for FR-LANG-1.

The **NPU** path is *not* served by whisper.cpp (it has no NPU backend); it is a second
`Transcriber` implementation using ONNX Runtime — see ADR-0004. Both implementations satisfy the
same trait and emit the same `TranscriptSegment` stream.

## Consequences

- **Positive:** one mature dependency covers CPU + all desktop GPUs; model-size switching maps
  directly to the performance presets; trait boundary lets the NPU/ONNX engine slot in without
  touching callers (audio, storage, UI).
- **Negative:** two engines (whisper.cpp + ONNX) to maintain for full hardware coverage;
  whisper.cpp diarization is weak, so diarization is a separate component (ADR-0005); building
  acceleration features on Windows CI needs the right toolchain (CUDA/Vulkan SDKs) — gated by
  Cargo features so CPU-only always builds.
- **Streaming mode:** feed fixed audio windows with overlap; emit interim segments, then finalize.
- **Batch mode:** optional re-run with a larger model after stop for maximum accuracy.

## Revisit if
A single engine gains solid NPU + GPU + CPU coverage (would let us drop the ONNX engine), or a
materially better local ASR model (e.g. Parakeet) outperforms Whisper for our languages.