Meet OmniVoice Studio: A Local, Open-Source Alternative to ElevenLabs

ElevenLabs charges between $5 and $330 per month for voice AI services. Every audio file you process goes through their cloud servers. For those looking for an open source alternative of ElevenLabs, OmniVoice Studio is good fit as an open-source desktop application that runs the same categories of tasks locally. It is a very interesting individual project that handles voice cloning, video dubbing, real-time dictation, vocal isolation, and speaker diarization — without sending data to an external server.

What OmniVoice Studio Does

The application bundles six distinct capabilities. Understanding each one helps clarify what the system is doing under the hood.

Voice cloning works from a 3-second audio clip. The system uses zero-shot learning, meaning it clones a voice it has never been trained on before. It does this by conditioning a diffusion-based TTS model on the short reference audio. The underlying model, OmniVoice from k2-fsa, supports 600+ languages.

Voice design lets you build a new voice from parameters: gender, age, accent, pitch, speed, emotion, and dialect — without cloning any existing voice.

Video dubbing takes a YouTube URL or a local video file. It runs transcription using WhisperX, translates the transcript, synthesizes new audio using the TTS engine, and exports an MP4. The entire pipeline runs locally.

The dictation widget is a system-wide floating overlay. On macOS it activates via ⌘+⇧+Space from any application. It streams transcription via WebSocket and auto-pastes the result into whatever app is in focus.

The Batch Queue lets you drop up to 50 videos and walk away, with per-job progress bars tracking each one through the full pipeline.

The MCP Server exposes OmniVoice Studio’s capabilities to any MCP client — including Claude, Cursor, or your own tooling.

The Architecture

The project uses a React frontend talking to a FastAPI backend. The backend exposes 97 API endpoints, uses Server-Sent Events (SSE) for streaming updates, and stores data in SQLite.

Four core ML libraries handle the heavy work:

WhisperX handles automatic speech recognition (ASR) with word-level alignment. It supports 99 languages for transcription.

Demucs (Meta) handles source separation. It splits speech from background music and preserves both stems independently.

Pyannote handles speaker diarization — identifying which speaker said which words in a multi-speaker audio file. It is used together with WhisperX.

AudioSeal (Meta) embeds an invisible neural watermark into generated audio. This watermark survives compression and serves as AI provenance metadata.

The desktop wrapper is built with Tauri, a Rust-based framework for cross-platform native apps. The codebase is 56% Python, 23.6% JavaScript, 11% CSS, 3.4% Shell, 3.3% Rust, and 2.6% TypeScript.

For GPU support, the backend auto-detects CUDA (NVIDIA), MPS (Apple Silicon Metal), and ROCm (AMD). With 8 GB VRAM or less, TTS automatically offloads to CPU during transcription. No configuration is required.

Six TTS Engines, One Backend Registry

OmniVoice Studio ships a pluggable multi-engine TTS backend. You can switch engines in Settings → TTS Engine or by setting the OMNIVOICE_TTS_BACKEND environment variable.

The six built-in engines are OmniVoice (default, 600+ languages), CosyVoice 3 (9 languages plus 18 dialects, Apache-2.0), MLX-Audio (Apple Silicon-only, includes Kokoro and Qwen3-TTS among others), VoxCPM2 (30 languages, Apache-2.0), MOSS-TTS-Nano (20 languages, runs realtime on CPU), and KittenTTS (English-only, CPU-only, MIT).

Adding a custom engine takes roughly 50 lines of Python. You subclass TTSBackend in backend/services/tts_backend.py and register it in the _REGISTRY dictionary at the bottom of that file.

Language Coverage

ElevenLabs supports 32 languages. OmniVoice Studio supports 646 languages for TTS and 99 languages for transcription via WhisperX. Translation coverage depends on the target language pair.

Getting Started

Prerequisites are ffmpeg, Bun, and uv. Clone the repo, then run:

Copy CodeCopiedUse a different Browser

uv sync bun install bun dev

The frontend loads at http://localhost:5173 and the API runs on port 8000. Model weights download automatically on first generation.

Marktechpost’s Visual Explainer

OmniVoice Studio — How to Use It 01 / 08

What Is OmniVoice Studio?

OmniVoice Studio is an open-source desktop application for voice cloning, video dubbing, real-time dictation, and speaker diarization. Everything runs locally on your machine. No API keys, no cloud account, no subscription required.

646 languages supported for TTS via the default OmniVoice engine

99 languages for transcription via WhisperX

Available on macOS, Windows, and Linux

GPU is optional — full pipeline runs on CPU

Free for personal, educational, and research use (FSL-1.1-ALv2)

OmniVoice Studio — How to Use It 02 / 08

System Requirements

A GPU is optional. Without one, TTS runs approximately 3× slower on CPU. With ≤8 GB VRAM, TTS automatically offloads to CPU during transcription — no config needed.

ComponentMinimumRecommended

OSWin 10 / macOS 12+ / Ubuntu 20.04+Any modern 64-bit OS

RAM8 GB16 GB+

VRAM4 GB (auto-offloads)8 GB+ (RTX 3060+)

Disk10 GB free20 GB+ SSD

Python3.10+3.11–3.12

GPUOptionalCUDA / MPS / ROCm

OmniVoice Studio — How to Use It 03 / 08

Installation

The project recommends running from source. Install three prerequisites first: ffmpeg, Bun (JS runtime), and uv (Python package manager).

git clone https://github.com/debpalash/OmniVoice-Studio.git cd OmniVoice-Studio uv sync bun install bun dev

Frontend loads at http://localhost:5173 | API runs on port 8000.

Model weights download automatically on first generation.

Pre-built installers available: macOS DMG, Windows MSI, Linux AppImage and .deb — see the Releases page on GitHub.

OmniVoice Studio — How to Use It 04 / 08

Voice Cloning

Voice cloning uses zero-shot learning — it clones a voice from a clip as short as 3 seconds, without prior training on that voice. The default OmniVoice engine conditions a diffusion-based TTS model on the reference audio.

Go to the Voice Clone tab in the UI

Upload or record a 3-second audio clip of the target voice

Enter your text and select a target language (646 available)

Click Generate — output is saved to your project library

Voice Gallery: Search YouTube, browse categories, and download reference clips directly inside the app to build your voice library.

OmniVoice Studio — How to Use It 05 / 08

Video Dubbing

The full dubbing pipeline runs locally: transcribe → translate → synthesize → mux. Demucs isolates vocals so the original background audio is preserved in the final export.

Go to the Dub tab — paste a YouTube URL or upload a local file

WhisperX transcribes speech with word-level alignment

Select a target language; translation runs automatically

TTS engine re-voices the transcript; Demucs preserves background audio

Export the final MP4 with dubbed audio mixed in

Batch Queue: Drop up to 50 videos and walk away. Each job has its own progress bar tracking through the full pipeline.

OmniVoice Studio — How to Use It 06 / 08

Dictation & Speaker Diarization

Dictation works system-wide from any application. Diarization identifies individual speakers in a multi-speaker audio file using Pyannote + WhisperX.

Press ⌘+⇧+Space (macOS) to open the floating dictation widget

Speech streams via WebSocket and auto-pastes into the active input field

Upload a multi-speaker file to the Diarization tab

Pyannote identifies who said what; each speaker gets an auto-extracted voice profile

Assign a TTS voice per speaker for per-speaker dubbing

Hugging Face token required for Pyannote diarization. See docs/setup/huggingface-token.md in the repo.

OmniVoice Studio — How to Use It 07 / 08

TTS Engines

Six TTS engines are built in. Switch via Settings → TTS Engine or the env var:

OMNIVOICE_TTS_BACKEND=cosyvoice

EngineLanguagesClonePlatform

OmniVoice (default)600+✓CUDA / MPS / CPU

CosyVoice 39 + 18 dialects✓CUDA / MPS / CPU

MLX-AudioMultiVariesApple Silicon only

VoxCPM230✓CUDA / MPS / CPU

MOSS-TTS-Nano20✓CUDA / CPU

KittenTTSEnglish✗CPU only

Custom engine: Subclass TTSBackend in backend/services/tts_backend.py and add it to _REGISTRY. ~50 lines of Python.

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MCP Server & Resources

OmniVoice Studio ships a built-in MCP Server, exposing voice and dubbing capabilities to any MCP-compatible client — Claude, Cursor, or your own tooling — without opening the desktop UI.

MCP Server starts alongside the FastAPI backend on bun dev

Point your MCP client at the local server to access all endpoints

AudioSeal (Meta) embeds an invisible neural watermark in all generated audio for AI provenance

GitHub: github.com/debpalash/OmniVoice-Studio

Install docs: docs/install/ (macos / windows / linux / docker)

Troubleshooting: docs/install/troubleshooting.md

Discord: discord.gg/bzQavDfVV9

Key Takeaways

OmniVoice Studio runs voice cloning, dubbing, diarization, and dictation fully locally — no API keys or cloud account needed.

It supports 646 languages for TTS and 99 for transcription via WhisperX; ElevenLabs supports 32 languages.

The backend is FastAPI + SQLite + WhisperX + Demucs + Pyannote + AudioSeal, wrapped in a Tauri desktop app.

An MCP Server is built in, making OmniVoice usable from Claude, Cursor, or any MCP client.

Adding a custom TTS engine requires subclassing TTSBackend in approximately 50 lines of Python.

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