omnivoice

name

omnivoice

description

Local TTS, voice cloning, voice design, and video dubbing via the OmniVoice Studio MCP server (open-source ElevenLabs alternative; nothing leaves the machine, runs on MPS/CUDA/CPU). Use when: (1) generating speech from text in any of 646 languages, (2) cloning a voice from a 3-second reference clip, (3) designing a voice by gender/age/accent/pitch/style, (4) dubbing a video into another language, (5) listing voice profiles or personality presets, (6) producing narration where privacy, cost, or absent API keys matter, (7) non-English narration where Edge TTS/kokoro fall short, (8) batch audio for blog posts or content pipelines. Triggers: 'omnivoice', 'voice clone', 'clone this voice', 'tts', 'narrate', 'generate speech', 'voice synthesis', 'dub video', 'voice design', 'local tts', 'multilingual voice', 'narrate this post', 'elevenlabs alternative'.

OmniVoice

Overview

Generate audio locally via the OmniVoice Studio MCP server. Tools: generate_speech, list_voices, list_personalities, list_languages, check_health. Resources: voice://{id}, history://recent.

Prerequisites — Backend Must Be Running

The MCP tools all hit $OMNIVOICE_API_URL (default http://localhost:3900). If the backend is down, every tool returns a connection error. Install + boot:

git clone https://github.com/debpalash/OmniVoice-Studio.git "$OMNIVOICE_HOME"
cd "$OMNIVOICE_HOME"
uv sync
VIRTUAL_ENV="$(pwd)/.venv" uv pip install 'mcp[cli]'

Then:

scripts/check-health.sh        # exit 0 if up
scripts/start-backend.sh       # boot in background (MPS/CUDA auto-detected)

First synthesis call lazy-downloads the k2-fsa/OmniVoice model (~2.4 GB) from HuggingFace — cached on subsequent boots.

Task Index — Pick the Right Tool

Task	Tool	Notes
Verify backend is up	check_health	Returns `{"status":"ok","device":"mps
Text → audio with a saved voice	generate_speech(text, profile_id)	Returns base64 WAV. profile_id="demo0001" is the bundled demo voice
Text → audio without a clone (voice design)	generate_speech(text, instruct="…")	Omit profile_id; pass an instruct like "warm middle-aged female narrator, calm pace"
Multilingual narration	generate_speech(text, language="es")	Any ISO 639 code or "Auto"
List existing voices	list_voices	Returns id, name, type, personality
List personality presets	list_personalities	Returns narrator / casual / news-anchor / etc. with their instruct strings
List supported languages	list_languages	646 total; returns 20 popular + the full count

For non-trivial decisions (which engine to use, when to pick OmniVoice over kokoro / Edge TTS / ElevenLabs), see references/engines-comparison.md.

For MCP wiring details, backend lifecycle, troubleshooting, and a clean teardown, see references/mcp-setup.md.

Common Workflows

1. One-shot narration with the demo voice

# As called through the MCP client (your agent will do this for you):
result = generate_speech(
    text="Hello — this is OmniVoice generating speech locally.",
    profile_id="demo0001",
    language="English",
    steps=16,                   # 8 = fast/draft · 16 = balanced · 32 = quality
)
# result is JSON with audio_id, generation_time_s, audio_duration_s, format, wav_base64

Benchmark: 4.2 s of audio in ~24 s server-side on Apple Silicon MPS at 16 diffusion steps.

2. Save the WAV to disk and play

Tool returns base64 PCM WAV (16-bit, mono, 24 kHz). Decode + write:

import base64, json
payload = json.loads(result_text)            # parse JSON the tool returns
open("out.wav","wb").write(base64.b64decode(payload["wav_base64"]))

On macOS: afplay out.wav. Convert to MP3 with ffmpeg -i out.wav -codec:a libmp3lame -b:a 128k out.mp3.

3. Voice clone — end-to-end recipe

Cloning needs a 3-10 second reference clip the model will use as a speaker embedding. The MCP server does NOT expose profile creation — it only reads existing profiles. Two paths to create one:

Path A — bundled helper (macOS, recommended for fresh clones):

scripts/record-reference.sh ~/Downloads/my-ref.wav 12 1
# args: output_path raw_duration_sec mic_index
# Default mic_index=1 (MacBook built-in); list devices via:
#   ffmpeg -f avfoundation -list_devices true -i ""

The script gives audible countdown + start/stop cues via macOS say + /System/Library/Sounds/Ping.aiff so the user knows when to speak (terminal stdout is buffered — text "speak now" prompts arrive too late). It records a longer raw window, then trims to ~10 seconds of speech via silenceremove + atrim, plays back for verification, and prints the next-step curl command.

Path B — manual:

# 1. Record (mono, 24 kHz native — matches model's internal rate)
ffmpeg -f avfoundation -i ":1" -t 12 -ac 1 -ar 24000 raw.wav

# 2. Trim leading silence + take first 10 sec of speech
ffmpeg -i raw.wav \
  -af "silenceremove=start_periods=1:start_silence=0.05:start_threshold=-40dB,atrim=end=10" \
  -ac 1 -ar 24000 ref.wav

# 3. Verify
ffmpeg -i ref.wav -af volumedetect -f null - 2>&1 | grep volume   # max should be > -20 dB
afplay ref.wav

POST to /profiles (multipart/form-data — required fields: name, ref_audio):

curl -X POST http://127.0.0.1:3900/profiles \
  -F "name=carlos-clone" \
  -F "ref_audio=@ref.wav" \
  -F "ref_text=The exact text spoken in the clip" \
  -F "language=English" \
  | python3 -m json.tool
# returns { "id": "abc12345", "name": "carlos-clone" }

Once created, pass profile_id to generate_speech (via MCP) or directly via POST /generate. Profiles persist in SQLite + reference-audio files at ~/Library/Application Support/OmniVoice/voices/<id>.<ext> (the backend preserves the uploaded extension — .wav if you uploaded a WAV, .mp3 if MP3, etc.). State persists across backend restarts.

Reference clip tips that materially affect quality:

Factor	Why it matters
Single speaker	Mixed speakers blur the embedding
Clean speech, no music/noise	Model embeds the noise too
Natural prosody (avoid pangrams)	Diffusion samples replicate prosody, not just timbre
3-10 sec is the sweet spot	< 3 s lacks information; > 10 s adds compute without quality gain
Match ref_text to what's spoken	Improves alignment, especially on noisy refs
language correct	Wrong language → cross-lingual transfer artifacts
Loudness peak ≥ -15 dB	Quiet refs work but normalize poorly

4. Voice design (no reference clip)

Skip profile_id; provide an instruct string describing the desired voice:

generate_speech(
    text="Welcome to the future of agentic systems.",
    instruct="warm middle-aged female narrator, calm authoritative pace, documentary style",
)

Get pre-made instructs via list_personalities and copy the one matching the brief (narrator, casual, news-anchor, etc.).

5. Video dubbing (web UI only)

The MCP server does not expose the dubbing endpoint. The full transcribe → translate → re-voice → mux pipeline lives behind the desktop UI (bun run desktop in $OMNIVOICE_HOME) and the /dub/* REST routes. When the user asks to dub a video, point them to the UI; surface this skill only for the synthesis primitives above.

When NOT to use OmniVoice

• Fast English-only narration on weak hardware → kokoro-tts is ~10× smaller and 2× realtime on CPU (see references/engines-comparison.md)
• Lowest-friction one-off TTS → Edge TTS needs no install or backend
• Highest possible quality regardless of cost → ElevenLabs still wins on English narration polish; OmniVoice ties or wins on multilingual + cloning
• Real-time streaming dictation → use the OmniVoice desktop widget (⌘+⇧+Space), not the MCP server

Resources

• references/engines-comparison.md — Decision tree across OmniVoice / kokoro / Voicebox / Edge TTS / ElevenLabs / cloud APIs
• references/mcp-setup.md — MCP wiring, backend lifecycle, env vars, troubleshooting
• scripts/check-health.sh — curl /health, exit 0/1
• scripts/start-backend.sh — Start uvicorn on 127.0.0.1:3900 with health probe
• scripts/stop-backend.sh — Clean shutdown via kill -TERM on the bound PID
• scripts/record-reference.sh — macOS-only: record + trim + verify a reference clip for cloning, with audible cues (say + system beeps) that bypass terminal output buffering

Backend Swagger / OpenAPI: http://127.0.0.1:3900/docs (when backend is up).

Upstream: github.com/debpalash/OmniVoice-Studio — FSL-1.1-ALv2 (free for personal/internal/non-commercial; auto-converts to Apache-2.0 two years after each release).