WhisperLocal Plugin

Local Speech-to-Text via whisper.cpp, Metal-accelerated on Apple Silicon. Implemented in pure Rust using the whisper-rs crate — no Python involved.

Setup

1. Download a GGML model

mkdir -p models
curl -L -o models/ggml-large-v3.bin \
  https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-large-v3.bin

Other available sizes (smaller = faster, less accurate):

Model	Size	Notes
`ggml-tiny.bin`	~75 MB	Very fast, lower accuracy
`ggml-base.bin`	~142 MB	Good balance for testing
`ggml-small.bin`	~466 MB	Good accuracy
`ggml-medium.bin`	~1.5 GB	High accuracy
`ggml-large-v3.bin`	~3.1 GB	Best accuracy, recommended
`ggml-large-v3-turbo.bin`	~1.6 GB	large-v3 speed-optimised

All models: https://huggingface.co/ggerganov/whisper.cpp

2. Configure `config.yml`

plugins:
  whisper_local:
    model: "models/ggml-large-v3.bin"
    language: "it"           # BCP-47 code, or "auto" for detection
    load_at_startup: false   # false (default) = lazy load on first use
    idle_timeout_secs: 1200  # unload after 20 min idle; 0 = never unload

Option	Default	Effect
`model`	— (required)	Path to the GGML `.bin` file
`language`	`auto`	BCP-47 code or `auto`. Applied live — runtime changes take effect on the next transcription without a reload
`load_at_startup`	`false`	When the model first loads: `false` = lazily on the first transcription, `true` = eagerly in `start()` (warm, no first-call latency)
`idle_timeout_secs`	`1200`	When the model unloads: after this many seconds of inactivity. `0` = never unload (stays resident once loaded)

The two timing options are orthogonal and cover the whole spectrum:

`load_at_startup`	`idle_timeout_secs`	Behaviour
`false`	`1200`	Default — load on first use, free ~2 GB after 20 min idle
`true`	`0`	Always resident — eager load, never unload (legacy behaviour)
`true`	`1200`	Warm at startup, but freed if unused
`false`	`0`	Load on first use, then stay resident

3. Build

The first cargo build compiles whisper.cpp (a few minutes). Subsequent builds are cached.

How it works

Telegram voice message (OGG/Opus)
  │
  ▼ ffmpeg → 16 kHz mono WAV
  ▼ hound  → Vec<f32> PCM samples
  ▼ whisper.cpp (Metal GPU) → text
  │
  ▼ forwarded to LLM as a normal text message

Audio conversion uses the system ffmpeg binary (must be installed: brew install ffmpeg). Inference runs on Apple Silicon GPU via Metal. Falls back to CPU if Metal is unavailable.

Memory management (lazy load + idle unload)

The GGML weights are ~2 GB, so the plugin keeps them in memory only while they are actually useful. The model lives in a shared, droppable cell (LazyModel):

start() validates the model path and registers a lightweight transcriber, but does not load the weights unless load_at_startup: true. The registered handle holds no strong reference to the weights, so they can be freed at any time.
First transcription triggers ensure_loaded(), which loads the weights once (concurrent first-callers wait on a single load) and records a last-used timestamp.
A background eviction task ticks every 60 s and unloads the model once it has been idle for idle_timeout_secs. Set idle_timeout_secs: 0 to disable eviction.
Unloading is refcount-safe: an in-flight transcription holds its own handle to the weights, so memory is reclaimed only after it finishes. The actual free runs on a blocking thread (whisper.cpp GPU cleanup).

Trade-off: after an unload, the next transcription pays the reload cost (a few seconds). The OS page cache usually keeps the .bin warm, so the reload is mostly memory copy + Metal allocation rather than disk I/O. Use load_at_startup: true / idle_timeout_secs: 0 if you prefer zero first-call latency over reclaiming the RAM.

Integration with TranscribeManager

WhisperLocalPlugin does not expose itself as Arc<dyn Transcribe> directly. At start() it registers a lightweight WhisperLocalTranscriber handle into skald.transcribe_manager; at stop() it deregisters it. Callers never reference the plugin type — they ask the manager:

if let Some(t) = skald.transcribe_manager.get().await {
    let text = t.transcribe(audio, "ogg").await?;
}

See ../plugins.md for the TranscribeManager API and the Transcribe trait.

When to Update This File

The audio conversion pipeline changes
Default recommended models change
Registration/deregistration logic in start()/stop() changes
The lazy-load / idle-eviction lifecycle or its config options change

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