sussurro

Offline neural machine translation and voice for English and the Romance languages. Speak or type in one language, read or hear it in another — across English, Spanish, French and Italian, fully on-device: no server, no network at runtime.

The translation engine is built from scratch on ggml; speech recognition uses whisper.cpp, and speech synthesis uses Piper voices via sherpa-onnx (ONNX Runtime).

The name is Italian for whisper.

Status — v0.7

A multilingual, offline translator with a native desktop app: choose a source and a target language, then type or speak, and read or hear the result — every stage is also usable from the command line.

• Translate — English, Spanish, French, Italian, in every direction. Encoder–decoder Transformers (OPUS-MT / Marian) reimplemented on ggml, with greedy and beam-search decoding, an incremental KV cache, sentence splitting, and q8_0 / q4_0 / f16 weights.
• Listen — multilingual speech-to-text via whisper.cpp.
• Speak — text-to-speech via sherpa-onnx running a Piper voice per language, played through miniaudio.
• Desktop app — a Tauri UI (Liquid Glass) wrapping all of the above.

Everything runs locally (Metal + Accelerate on Apple Silicon).

Tested on: Apple Silicon (M5 Max), macOS Tahoe 26.4. Other Apple Silicon Macs and macOS versions should work but are untested. Intel Macs, Windows and Linux aren't verified yet — contributions welcome.

Languages

Two kinds of model cover all twelve directions among en / es / fr / it, with no pivoting:

• a single multilingual model for Romance ↔ Romance, where the target language is chosen by a sentence-initial token (>>fra<<, >>spa<<, >>ita<<);
• bilingual models for English ↔ Romance (no token needed).

From → To	Model	-l token
en → fr / es / it	tc-en-fr / tc-en-es / tc-en-it	—
fr → en	tc-fr-en	—
it → en	tc-it-en	—
es → en	es-en	—
it/es → fr	tc-itc-itc	fra
fr/es → it	tc-itc-itc	ita
fr/it → es	tc-itc-itc	spa

es → en is the one pair with no tc-big model, so it uses the classic opus-mt-es-en instead — slightly older, but a high-resource pair, so quality holds.

The desktop app picks the right model and token automatically from the chosen languages; from the CLI you select them yourself.

Components

• sussurro_core — translation library (model loading, SentencePiece tokenizer, encoder, decoder).
• sussurro — CLI: translate text (-l <lang> selects the target on multilingual models).
• sussurro-quantize — quantize a model to q8_0 / q4_0.
• sussurro-interpret — speech → text (whisper); add -m to also translate, or omit it to transcribe only.
• sussurro-speak — text → speech (WAV, and --play to play it).
• scripts/loop.sh — a simple voice-to-voice demo (Italian audio in → English spoken out).
• app/ — the Tauri desktop application.

Dependencies

• ggml, whisper.cpp, miniaudio — git submodules under third_party/.
• SentencePiece — fetched and built automatically by CMake (FetchContent, v0.2.0).
• sherpa-onnx — prebuilt C API library, downloaded manually; only needed for sussurro-speak.
• Tauri v2 (Rust + Node), cpal, hound — for the desktop app in app/.

Build (engine)

git clone --recurse-submodules https://github.com/whispem/sussurro.cpp.git
cd sussurro.cpp
cmake -B build && cmake --build build -j

The first build also fetches/builds SentencePiece and compiles whisper.cpp — a few minutes, once. If you cloned without --recurse-submodules: git submodule update --init --recursive.

sussurro-speak is built only once sherpa-onnx is present (see Text-to-speech below).

Models & voices

Translation models

pip install -r requirements.txt

# Romance <-> Romance (target chosen at run time by the -l token)
python scripts/convert.py --model Helsinki-NLP/opus-mt-tc-big-itc-itc --outfile models/tc-itc-itc.gguf

# English <-> Romance (bilingual)
python scripts/convert.py --model Helsinki-NLP/opus-mt-tc-big-en-fr --outfile models/tc-en-fr.gguf
python scripts/convert.py --model Helsinki-NLP/opus-mt-tc-big-en-es --outfile models/tc-en-es.gguf
python scripts/convert.py --model Helsinki-NLP/opus-mt-tc-big-en-it --outfile models/tc-en-it.gguf
python scripts/convert.py --model Helsinki-NLP/opus-mt-tc-big-fr-en --outfile models/tc-fr-en.gguf
python scripts/convert.py --model Helsinki-NLP/opus-mt-tc-big-it-en --outfile models/tc-it-en.gguf

# Spanish -> English (classic OPUS-MT)
python scripts/convert.py --model Helsinki-NLP/opus-mt-es-en --outfile models/es-en.gguf

Each .gguf is self-contained (weights, hyper-parameters, and SentencePiece tokenizers), f16 by default (add --dtype f32 for full precision). To shrink them, quantize from an f32 export:

python scripts/convert.py --model Helsinki-NLP/opus-mt-tc-big-en-fr --outfile models/tc-en-fr.f32.gguf --dtype f32
./build/sussurro-quantize models/tc-en-fr.f32.gguf models/tc-en-fr.q8_0.gguf q8_0

Speech-to-text model (whisper)

bash third_party/whisper.cpp/models/download-ggml-model.sh small

whisper is already multilingual; the source language is selected at run time (-l).

Text-to-speech: sherpa-onnx + a voice per language

Download the prebuilt sherpa-onnx C API library (macOS arm64 shown; pick your platform's -shared asset from the releases):

cd third_party
curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/v1.13.3/sherpa-onnx-v1.13.3-osx-arm64-shared.tar.bz2
tar xf sherpa-onnx-v1.13.3-osx-arm64-shared.tar.bz2
mv sherpa-onnx-v1.13.3-osx-arm64-shared sherpa-onnx
rm sherpa-onnx-v1.13.3-osx-arm64-shared.tar.bz2
xattr -dr com.apple.quarantine sherpa-onnx   # macOS only
cd ..

Then one Piper voice per output language:

cd models
for v in fr_FR-tom-medium en_US-ryan-medium es_ES-davefx-medium it_IT-paola-medium; do
  curl -SL -O "https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/vits-piper-$v.tar.bz2"
  tar xf "vits-piper-$v.tar.bz2" && rm "vits-piper-$v.tar.bz2"
done
cd ..

Re-run cmake -B build && cmake --build build -j so sussurro-speak gets built.

Command-line usage

Translate text (multilingual model needs a target token; bilingual models do not):

./build/sussurro -m models/tc-itc-itc.gguf -p "Ciao, come stai?" -l fra   # -> French
./build/sussurro -m models/tc-en-it.gguf   -p "Hello, how are you?"       # -> Italian

Transcribe speech (16 kHz mono WAV), optionally translating in the same pass:

./build/sussurro-interpret -w third_party/whisper.cpp/models/ggml-small.bin -a clip.wav -l es
./build/sussurro-interpret -w third_party/whisper.cpp/models/ggml-small.bin -a clip.wav -l it -m models/tc-it-en.gguf

Synthesize speech (and play it):

./build/sussurro-speak -k models/vits-piper-es_ES-davefx-medium -t "Hola, ¿cómo estás?" --play

Voice-to-voice demo — Italian audio in, English spoken out:

./scripts/loop.sh clip.wav

Desktop app (Tauri)

app/ is a desktop front-end built with Tauri v2 (Rust backend + a vanilla web UI) in the Liquid Glass interface: pick a source and target language, then speak or type, read the result, and hear it in that language's voice. The swap button reverses the two languages.

Prerequisites: Rust 1.77+ and Node.js 20+ (plus Xcode Command Line Tools on macOS).

cd app
npm install
npm run tauri dev

The app calls the compiled engine binaries directly, so before running you need: the binaries built (cmake --build build -j at the repo root), the models and voices in place (above), and the REPO constant in app/src-tauri/src/lib.rs set to this repo's absolute path. Microphone capture is native (via cpal); macOS asks for permission on first use (declared in app/src-tauri/Info.plist).

Note — early build. The app shells out to the local engine binaries using an absolute path, so it runs on the machine where the repo lives; it is not yet a self-contained, shareable bundle. Bundling the engine, models and voices into the app and code-signing it are possible future work.

Roadmap

• Windows and Linux support (developed on Apple Silicon; PRs very welcome).
• A self-contained, code-signed desktop bundle.
• More languages and pairs (Portuguese is one token away on the Romance model).
• A more expressive voice (e.g. Qwen3-TTS).
• Keyboard navigation in the app's language pickers.

License & credits

sussurro's own source: released under the MIT License.

Built on the work of others, each under its own license:

• Helsinki-NLP OPUS-MT models — CC-BY 4.0.
• ggml / whisper.cpp (ggml-org) — MIT.
• SentencePiece (Google) — Apache-2.0.
• sherpa-onnx (k2-fsa) — Apache-2.0.
• miniaudio (David Reid) — public domain / MIT-0.
• Piper voices (OHF-Voice / rhasspy) — see each voice's model card.
• Tauri (Tauri Programme / CommonsConservancy), cpal, hound — MIT / Apache-2.0.