Voicebox The open-source AI voice studio. Clone any voice. Generate speech. Dictate into any app. Talk to agents in voices you own. The full voice I/O stack, running locally on your machine. voicebox.sh • Docs • Download • Features • API • Troubleshooting Click the image above to watch the demo video on voicebox.sh
Tools
Tools
No tool list yet. Remote servers get one after the first health check.
FAQ
The open-source AI voice studio. Clone, dictate, create.
Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.
Does Voicebox need an API key?
No required environment variable is listed on this record.
What transport does Voicebox use?
Voicebox runs locally over stdio.
When was Voicebox last checked?
Last checked 2026-11-10. Voicebox The open-source AI voice studio. Clone any voice. Generate speech. Dictate into any app. Talk to agents in voices you own. The full voice I/O stack, running locally on your machine. voicebox.sh • Docs • Download • Features • API • Troubleshooting Click the image above to watch the demo video on voicebox.sh
From the publisher
Documentation
Built with Capy
Voicebox is built and maintained with Capy. A Capy Captain thread directs the project day to day, and its crew threads test, rebase, and review each contributor pull request on Linux and on a Mac Studio before Jamie merges it. Voicebox stays free and open source under the MIT license, with no splash screens, banners, or interruptions in the app.
Voicebox is a local-first AI voice studio — a free and open-source alternative to ElevenLabs and WisprFlow in one app. Clone voices from a few seconds of audio, generate speech in 23 languages across 8 TTS engines, dictate into any text field with a global hotkey, and give any MCP-aware AI agent a voice of your choosing.
The two cloud incumbents sit on opposite halves of the voice I/O loop — ElevenLabs on output, WisprFlow on input. Voicebox does both, bridges them with a bundled local LLM for refinement and per-profile personas, and runs the whole thing on your machine.
Expressive speech — paralinguistic tags like [laugh], [sigh], [gasp] via Chatterbox Turbo; natural-language delivery control via Qwen CustomVoice; voices described in plain language via Qwen VoiceDesign
Unlimited length — auto-chunking with crossfade for scripts, articles, and chapters
Stories editor — multi-track timeline for conversations, podcasts, and narratives
Voice input — global dictation hotkey with push-to-talk and toggle modes, accessibility-verified auto-paste on macOS, in-app mic on every text field, Whisper-based STT
Agent voice output — one tool call (voicebox_speak) and any MCP-aware agent (Claude Code, Cursor, Cline) speaks to you in a voice you've cloned
Voice personalities — attach a free-form persona to any voice profile, then Compose, Rewrite, or Respond via a bundled local LLM — agents can invoke the same modes over MCP
API-first — REST API plus a built-in MCP server for integrating voice I/O into your own apps and agents
Native performance — built with Tauri (Rust), not Electron
9 curated preset voices with natural-language delivery control — no reference audio required
Qwen VoiceDesign
10
Builds a voice from a written description — no reference audio or preset needed
LuxTTS
English
Lightweight (~1GB VRAM), 48kHz output, 150x realtime on CPU
Chatterbox Multilingual
23
Broadest language coverage — Arabic, Danish, Finnish, Greek, Hebrew, Hindi, Malay, Norwegian, Polish, Swahili, Swedish, Turkish and more
Chatterbox Turbo
English
Fast 350M model with paralinguistic emotion/sound tags
TADA (1B / 3B)
10
HumeAI speech-language model — 700s+ coherent audio, text-acoustic dual alignment
Kokoro
8
50 curated preset voices, tiny 82M model, fast CPU inference
Emotions & Paralinguistic Tags
Only Chatterbox Turbo interprets paralinguistic tags like [laugh] and
[sigh]. Qwen3-TTS, LuxTTS, Chatterbox Multilingual, and HumeAI TADA read them
literally as text.
With Chatterbox Turbo selected, type / in the text input to open the tag
inserter and add expressive tags inline with speech:
Smart splitting respects abbreviations, CJK punctuation, and [tags]
Generation Versions
Every generation supports multiple versions with provenance tracking:
Original — clean TTS output, always preserved
Effects versions — apply different effects chains from any source version
Takes — regenerate with a new seed for variation
Source tracking — each version records its lineage
Favorites — star generations for quick access
Async Generation Queue
Generation is non-blocking. Submit and immediately start typing the next one.
Serial execution queue prevents GPU contention
Real-time SSE status streaming
Failed generations can be retried
Stale generations from crashes auto-recover on startup
Voice Profile Management
Create profiles from audio files or record directly in-app
Import/export profiles to share or back up
Multi-sample support for higher quality cloning
Per-profile default effects chains
Organize with descriptions and language tags
Stories Editor
Multi-voice timeline editor for conversations, podcasts, and narratives.
Multi-track composition with drag-and-drop
Inline audio trimming and splitting
Auto-playback with synchronized playhead
Version pinning per track clip
Global Dictation & Voice Input
The other half of the voice I/O loop. Hold a hotkey anywhere on your system, speak, release — on macOS the transcript pastes straight into the focused text field. Or hit the mic on any Voicebox text input and dictate directly into the app.
Configurable chord bindings — hold-to-speak and tap-to-toggle chords, each rebindable in the in-app chord picker. Holding push-to-talk and tapping Space mid-hold upgrades into a toggle session without a gap in audio
Target-aware paste (macOS) — accessibility-verified injection into the focused text field, with atomic clipboard save/restore so your clipboard isn't clobbered
First-run permissions UX — in-app gates walk you through the macOS Accessibility and Input Monitoring grants with deep-links to System Settings
In-app mic button on every Voicebox text field — generation form, profile descriptions, story titles, anywhere you'd type
LLM refinement — optional cleanup of ums, stutters, and false starts before paste
On-screen pill — floating overlay surfacing recording, transcribing, refining, and speaking states. Same pill agents use when they speak to you, so there's one mental model for both directions of the loop
Speech-to-Text
Voicebox runs OpenAI Whisper for transcription — the same model that backs dictation, the Captures tab, and the /transcribe API. Running on MLX (Apple Silicon) or PyTorch (CUDA / ROCm / DirectML / CPU) depending on your platform.
Size
Notes
Base / Small / Medium / Large
Standard Whisper quality ladder
Turbo
~8x faster than Whisper Large, minimal quality loss
More engines (Parakeet v3, Qwen3-ASR) are planned — see Roadmap.
Captures
Every dictation, in-app recording, and uploaded audio file lands in the Captures tab — original audio paired with transcript, always preserved.
Replay, re-transcribe, refine — rerun STT with any Whisper size, or re-run the raw transcript through the local LLM with different flags (filler cleanup, self-correction removal, technical-term preservation)
Edit inline — tweak the transcript and save on blur
Play as voice profile — turn any capture into speech with a cloned voice, one click
Promote to voice sample — use a capture's audio + transcript as a reference sample on any voice profile
Local capture storage — original audio and transcript stay in your Voicebox data directory, with a folder shortcut in Settings
Agent Voice Output
Every agent gets a voice. One tool call and any MCP-aware agent can speak to you in a voice you've cloned — task completions, questions, notifications. The same pill that surfaces during dictation surfaces during agent speech, so you always see what's coming out of your machine.
// In any MCP-aware agent:
await voicebox_speak({
text: "Deploy complete.",
profile: "Morgan",
});
Also exposed as POST /speak for anything that doesn't speak MCP — ACP, A2A, shell scripts, custom harnesses.
Bidirectional pill — recording, transcribing, refining, and speaking are all states of the same OS-level overlay, so dictation and agent speech share one surface
Per-agent voice binding — in Settings → MCP, pin Claude Code to Morgan and Cursor to Scarlett so you can tell which agent is talking without looking. Each client's last_seen_at timestamp confirms the install actually took
Always visible — no silent background TTS; every agent-initiated speak surfaces the pill with the voice profile name for the full duration
HTTP + stdio transports — install as a URL in Claude Code / Cursor / Windsurf / VS Code MCP, or point stdio-only clients at the bundled voicebox-mcp binary
Voice Personalities
Attach a free-form personality to any voice profile — who this voice is, how they speak, what they care about. Two actions appear on the generate box when a personality is set, powered by a bundled Qwen3 LLM running entirely locally.
Compose — a shuffle button that drops a fresh in-character line into the textarea; edit and speak, or click again for a different take
Speak in character — a toggle that routes your input text through the personality LLM to be rewritten in their voice before TTS
Agents can reach the same rewrite path over MCP by passing personality: true to voicebox_speak, turning the tool into a text-in → personality-LLM → TTS pipeline. The same LLM backs dictation's refinement step — one LLM in the app, one model cache, one GPU-memory footprint.
Local LLM options: Qwen3 0.6B / 1.7B / 4B, sharing the TTS runtime (MLX on Apple Silicon, PyTorch elsewhere).
Use cases: agent dev loops (dictate a question, hear the answer in a cloned voice), interactive characters for games and narrative tools, speech assistance for people who can't speak in their original voice.
Model Management
Per-model unload to free GPU memory without deleting downloads
Custom models directory via VOICEBOX_MODELS_DIR
Model folder migration with progress tracking
Download cancel/clear UI
GPU Support
Platform
Backend
Notes
macOS (Apple Silicon)
MLX (Metal)
4-5x faster via Neural Engine
Windows (NVIDIA)
PyTorch (CUDA)
Auto-downloads CUDA binary from within the app
Linux (NVIDIA)
PyTorch (CUDA)
Use a local/remote Python backend with CUDA PyTorch
Linux (AMD)
PyTorch (ROCm)
Auto-configures HSA_OVERRIDE_GFX_VERSION
Windows (any GPU)
DirectML
Universal Windows GPU support
Intel Arc
IPEX/XPU
Intel discrete GPU acceleration
Any
CPU
Works everywhere, just slower
API
Voicebox exposes a REST API for integrating voice I/O into your own apps and agents.
# Generate speech
curl -X POST http://127.0.0.1:17493/generate \
-H "Content-Type: application/json" \
-d '{"text": "Hello world", "profile_id": "abc123", "language": "en"}'
# Agent voice output — any app or script can speak in a cloned voice
curl -X POST http://127.0.0.1:17493/speak \
-H "Content-Type: application/json" \
-H "X-Voicebox-Client-Id: my-script" \
-d '{"text": "Deploy complete.", "profile": "Morgan"}'
# Transcribe an audio file
curl -X POST http://127.0.0.1:17493/transcribe \
-F "[email protected]" \
-F "model=whisper-turbo"
# List voice profiles
curl http://127.0.0.1:17493/profiles
POST /speak accepts profile as a name (case-insensitive) or id, and resolves via the same precedence as the MCP tool: explicit arg → per-client binding → capture_settings.default_playback_voice_id.
MCP server
Voicebox ships a built-in Model Context Protocol server so any MCP-aware agent (Claude Code, Cursor, Windsurf, Cline, VS Code MCP extensions) can speak, transcribe, and browse captures and profiles.
Four tools ship: voicebox_speak, voicebox_transcribe, voicebox_list_captures, voicebox_list_profiles. Per-client voice bindings are managed in Voicebox → Settings → MCP. See the full MCP guide for tool signatures, resolution precedence, the speaking-pill contract, and security notes.
// In any MCP-aware agent:
await voicebox_speak({
text: "Tests passing. Ready to merge.",
profile: "Morgan", // optional — falls back to the per-client binding
personality: true, // optional — rewrites text through the profile's personality LLM first
});
Use cases: agent dev loops (voice in, voice out), game dialogue, podcast production, accessibility tools, voice assistants, content automation.
Full API documentation available at http://127.0.0.1:17493/docs.
Configurable source → transform → sink chains with webhook + MCP sinks and a preset editor
Streaming transcription
WebSocket /transcribe/stream for partial transcripts as you speak
End-to-end speech LLMs
Moshi, GLM-4-Voice, Qwen2.5 Omni — real voice-to-voice, no text between
Voice Design
Create new voices from text descriptions
Long-form capture
Dual-stream recorder (mic + system audio) with summary LLM transform
Platform sinks
Apple Notes, Obsidian, and other opt-in integrations
Plugin architecture
Extend with custom models, transforms, and sinks
Mobile companion
Control Voicebox from your phone
For the full engineering status, open-issue triage, and prioritized work queue, see docs/PROJECT_STATUS.md — a living document that tracks what's shipped, what's in-flight, candidate TTS engines under evaluation, and why we've accepted or backlogged specific integrations.
Development
See CONTRIBUTING.md for detailed setup and contribution guidelines.
Quick Start
git clone https://github.com/jamiepine/voicebox.git
cd voicebox
just setup # creates Python venv, installs all deps
just dev # starts backend + desktop app
Install just: brew install just or cargo install just. Run just --list to see all commands.
The repo ships a pre-wired .mcp.json at the root — running Claude Code inside this checkout picks up the Voicebox MCP tools automatically once the dev app is running.
Building Locally
just build # Build CPU server binary + Tauri app
just build-local # (Windows) Build CPU + CUDA server binaries + Tauri app
Adding New Voice Models
The multi-engine architecture makes adding new TTS engines straightforward. A step-by-step guide covers the full process: dependency research, backend protocol implementation, frontend wiring, and PyInstaller bundling.
The guide is optimized for AI coding agents. An agent skill can pick up a model name and handle the entire integration autonomously — you just test the build locally.
