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A Soniqo project.

Open-source desktop app for content creators. Clone a voice from a short reference clip, write a script line by line, and synthesize the whole thing in that voice — with inline emotion markers for tone.

A blind A/B/C — a real voice, the same voice cloned locally by Speech Studio on a MacBook, and the same voice cloned by ElevenLabs in the cloud. Can you tell which is which?

Watch on YouTube → (30 sec)

Status: v0 — audio-only MVP. Runs on macOS 15+ (Apple Silicon) and Windows / Linux (x86_64): macOS clones via MLX, Windows/Linux via speech-core's on-device LiteRT backend. Video playback against the timeline and an audio-over-video export step are on the roadmap.

1. Drop a short reference clip of a speaker → register a cloned voice. Repeat for as many speakers as you need.
2. Write a script line per clip and pick which speaker says it. Wrap the line in an emotion marker — (whispering) Just stay quiet for a moment, please. — and the synth will follow that direction.
3. Hit Synthesize to render every line in the assigned cloned voice. The synth pipeline auto-grades each take with on-device ASR and retries with a different seed if the line came out wrong.
4. Play the script to hear the whole scene back-to-back. Export a single WAV mix (export wiring is in progress).

The clone is local. The synth is local. No audio leaves your machine.

• Tauri 2 shell (Rust + the OS-native WebView) so the shipped app is a small native binary, not a Chromium fork.
• React + Vite frontend for the timeline, voice library, and script editor.
• A warm sidecar process holds the speech engine resident so per-line synthesis is fast after the first warm-up. Tauri spawns it once and talks NDJSON over stdin/stdout. On macOS this is the Swift sidecar (swift-sidecar/, MLX); on Windows/Linux the C++ sidecar (core-sidecar/, LiteRT).
• CosyVoice 3 is the default engine on macOS via speech-swift (MLX). Windows/Linux default to VoxCPM2 via speech-core (LiteRT). On macOS you can switch engines from the toolbar: VoxCPM2, Qwen3-TTS, Chatterbox (multilingual cloning across 23 languages), OmniVoice (600+ language cloning), Indic-Mio (Hindi/Indic emotion tags), and Fish Audio S2 Pro (experimental clone + bracket markers). Those MLX engines are macOS-only; Windows/Linux runs VoxCPM2.

Switch engine from the toolbar dropdown (macOS only — Windows/Linux always use VoxCPM2, so the dropdown doesn't appear).

The MLX engines (CosyVoice 3, Qwen3-TTS, Chatterbox, OmniVoice, Indic-Mio, Fish Audio S2 Pro) are macOS-only; Windows/Linux run VoxCPM2 through speech-core's LiteRT backend.

¹ Chatterbox has no free-text style input — emotion markers map to an expressiveness/intensity level (more vs. less expressive), not a specific emotion.

² OmniVoice supports broad voice-design attributes such as accent, age, gender, pitch, and whisper. Studio only passes valid instruct vocabulary items: whisper maps directly, while emotion markers map to pitch hints (high pitch, low pitch, etc.). Strong emotions are approximations, not true emotional acting.

³ Indic-Mio is exposed as an experimental Hindi/Indic emotion-marker engine. It uses suffix tags such as <happy> / <angry> and clones from reference audio through WavLM → MioCodec global speaker embeddings. It does not need a reference transcript.

⁴ Fish Audio S2 Pro uses bracket markers such as [excited], [angry], and [whisper]. It needs an accurate reference transcript for cloning, and the public weights are research/non-commercial unless separately licensed.

Wrap a line in a parenthetical tag to steer the prosody:

(dramatic) I never thought we'd make it this far.
(warm) I knew you would make it, no matter what.
(whispering) Just stay quiet for a moment, please.
(intense) Then we end this together. Tonight.

Supported tags include soft, warm, whispering, intense, excited, happy, calm, serious, surprised, sad, angry, dramatic, laughs.

How a marker is applied depends on the engine: VoxCPM2 and CosyVoice 3 turn it into a short natural-language style instruction (custom tags like (slow and dreamy) pass through verbatim); OmniVoice maps only known markers to its fixed instruct vocabulary (whisper or pitch hints) and drops anything unmappable; Chatterbox maps markers to an expressiveness/intensity level (it has no per-emotion control, only more vs. less expressive); Indic-Mio appends supported suffix tags like <happy>; Fish Audio S2 Pro appends bracket tags like [excited]; Qwen3-TTS ignores markers (they're stripped from the text).

Current open-source/on-device candidates for Hindi voice cloning with stronger emotion control:

• VoxCPM2 remains the best product-fit baseline for Speech Studio: Apache-2.0, on-device, supports Hindi, and supports controllable voice cloning through natural-language style guidance. It still needs a Hindi emotion bake-off against the models below before we claim strong acting range.
• Fish Audio S2 Pro looks strongest for expressive control: 80+ languages, short-reference voice cloning, and inline free-form emotion/prosody tags such as [angry], [sad], [whisper], and [shouting]. It is now available as an experimental macOS engine, but its public model license is research/non-commercial unless separately licensed, so it remains outside the default path.
• Svara-TTS v1 is the most relevant Indic-specific open model: Apache-2.0, 19 Indian languages, tags such as <happy>, <sad>, <anger>, and <fear>, plus zero-shot adaptation paths. Exact speaker similarity is less proven than VoxCPM2/Fish, but it is a good candidate for Hindi emotion fine-tuning.
• Chatterbox Multilingual Hindi is the permissive small fallback: MIT, a dedicated Hindi checkpoint, and zero-shot voice cloning. Its control is mainly exaggeration/intensity rather than explicit angry/sad/crying-style acting.
• OmniVoice is best kept for broad language coverage. Its instruction space is fixed to voice-design attributes, so Studio treats whisper as real control and maps other markers only to pitch.

Useful references: VoxCPM2, Fish Audio S2 Pro, Svara-TTS v1, Chatterbox Hindi, OmniVoice.

Grab the latest build from the releases page:

Every platform downloads its speech model on first run and caches it, so the installers stay small:

• macOS — .dmg (~46 MB); drag into /Applications. First run pulls the CosyVoice 3 MLX weights into ~/Library/Caches/qwen3-speech/; selecting VoxCPM2 later pulls its ~2.75 GB MLX weights.
• Windows — .msi or the NSIS -setup.exe. First run pulls the ~8.8 GB VoxCPM2-LiteRT bundle into %LOCALAPPDATA%\speech-core.
• Linux — .deb or .AppImage. First run pulls the same bundle into ~/.cache/speech-core.

The Windows/Linux LiteRT bundle is fp16 and needs ~10 GiB of free RAM to load — an 8 GB machine may fall short.

The macOS build is signed and notarized (from v0.0.5 on) — it opens like any other app, no Gatekeeper hoops. The Windows installers are still unsigned: SmartScreen needs More info → Run anyway.

If the in-app download keeps failing on a flaky or slow network (Download stalled for …: no progress / Failed to download …), fetch the model yourself and place it where the app looks. Two pieces: the model weights and a small set of tokenizer files.

Option A — hf CLI (recommended: it resumes interrupted downloads):

pip install -U huggingface_hub

hf download aufklarer/VoxCPM2-MLX-int8 \
  --local-dir ~/Library/Caches/qwen3-speech/models/aufklarer/VoxCPM2-MLX-int8

hf download openbmb/VoxCPM2 \
  config.json tokenizer.json tokenizer_config.json \
  tokenization_voxcpm2.py special_tokens_map.json \
  --local-dir ~/Library/Caches/qwen3-speech-voxcpm2-tokenizer/models/openbmb/VoxCPM2

Option B — browser: download the files from aufklarer/VoxCPM2-MLX-int8 and openbmb/VoxCPM2 into the same two directories. Minimum set: the model directory needs config.json plus every *.safetensors file; the tokenizer directory needs the five files listed in the command above.

The app detects the files on the next launch and skips the download entirely. If you overrode SONIQO_VOXCPM2_MODEL_ID, substitute that repo id in the model path. Source builds launched from a terminal can also extend the in-app stall patience with HF_DOWNLOAD_STALL_TIMEOUT=<seconds>.

Common: Rust 1.95+ via rustup (. "$HOME/.cargo/env" if cargo isn't on PATH), Node 20+ and pnpm 11+.

• macOS: 15+ on Apple Silicon (M1/M2/M3/M4), Xcode 26+ (Swift 6.0 toolchain).
• Windows / Linux (x86_64): a C++17 toolchain + CMake 3.16+, and a built speech-core checkout with the LiteRT backend (-DSPEECH_CORE_WITH_LITERT=ON -DLITERT_DIR=...) plus the VoxCPM2-LiteRT model bundle.

pnpm install                          # installs the frontend + Tauri CLI
cd swift-sidecar && swift build       # builds the Swift sidecar
cd .. && pnpm tauri dev               # launches the app, hot-reloads the UI

The selected MLX engine downloads on first synth (into ~/Library/Caches/qwen3-speech/). For VoxCPM2 specifically, see Manual VoxCPM2 model download if your network keeps dropping it.

pnpm install
# Build the C++ sidecar against your speech-core checkout (defaults to ../speech-core):
cmake -B core-sidecar/build core-sidecar -DSPEECH_CORE_DIR=../speech-core
cmake --build core-sidecar/build --config Release
# Point it at the VoxCPM2-LiteRT bundle, then launch:
export SONIQO_VOXCPM2_BUNDLE_DIR=/path/to/speech-core/scripts/models-voxcpm2
pnpm tauri dev

Measured on an Apple Silicon Mac (M-series, unified memory). The resident column is the real process footprint (Activity Monitor's "Memory" — vmmap physical footprint), which is the figure to check against your RAM. MLX active/peak is MLX's own accounting (peak is over a multi-line session). Note: plain ps rss under-reports by ~3× on Apple Silicon — Metal unified-memory buffers don't count as RSS, so use the resident figures below.

The selectable VoxCPM2 MLX engine:

The macOS engines load separately when selected — only one is resident at a time (switching unloads the previous): Chatterbox ~4 GB resident (1.3 GB on disk), CosyVoice 3 lighter than VoxCPM2, Qwen3-TTS (1.7B bf16) heavier. OmniVoice is downloaded and loaded separately when selected.

The MLX buffer cache is capped at 1 GB (SONIQO_MLX_CACHE_MB to override) — without that cap, peak grows to tens of GB on long sessions as varying-shape buffers accumulate. Override the VoxCPM2 model with SONIQO_VOXCPM2_MODEL_ID=aufklarer/VoxCPM2-MLX-bf16 if you want the higher-fidelity weights.

Hit Load demo in the top bar. It bootstraps a Scene 04 storyboard with two cloned voices (Anna and Marek) and four lines of dialogue — one with each emotion marker — then synthesizes everything through the currently selected engine (CosyVoice 3 by default on macOS).

cd swift-sidecar && swift build -c release
cd .. && pnpm tauri build             # produces .app + .dmg under src-tauri/target/release/bundle/

• speech-swift — Apple Silicon speech engines (VoxCPM2, CosyVoice3, Qwen3-TTS, Chatterbox, OmniVoice, Indic-Mio, Fish Audio S2 Pro, Parakeet, Silero VAD).
• speech-core — C++ engines (VoxCPM2 cloning on Windows/Linux, plus STT, VAD, denoise).

See AGENTS.md for project conventions. Short version: branch → PR → merge, no force-pushes, no AI co-author trailers, never commit unless explicitly asked.

Apache License 2.0 — same as speech-swift and speech-core.