voxon 1.0.0

High-fidelity voice cloning TTS for Apple Silicon — powered by Qwen3-TTS and ChatterboxTurbo via MLX

voxon

High-fidelity, real-time voice cloning on Apple Silicon.

One command to install. One command to speak.

pip install voxon
voxon "Hello, this is Voxon."

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Overview

voxon turns a 15-second audio clip into a permanent voice identity that you can synthesise speech through at any time. It runs entirely on-device — no cloud, no API keys, no data leaving your machine.

The synthesis engine loads into memory once and stays there as a background daemon. Subsequent calls have sub-second dispatch overhead regardless of text length.

Backends supported:

• Qwen3-TTS 1.7B (default) — Alibaba's 1.7B parameter multilingual TTS model, quantised to 8-bit for Apple Silicon via MLX
• ChatterboxTurbo — ResembleAI's fast, high-quality voice cloning model (opt-in via VOXON_MODEL=chatterbox-turbo)

Requirements: macOS 13+, Apple Silicon (M1 or later), Python 3.11+

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Prerequisites

System: macOS 13+, Apple Silicon (M1 or later), Python 3.11+

HuggingFace account (free, one-time):

The TTS model weights are hosted on HuggingFace and require a free account and a one-click licence acceptance before the first download.

1. Create a free account at huggingface.co
2. Accept the model licence — one click, no payment:
   • Qwen3-TTS (default): huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base
3. Generate a token at huggingface.co/settings/tokens (read-only is sufficient)
4. Log in once from your terminal:

pip install huggingface_hub
huggingface-cli login

After this, model weights download automatically on first use and are cached locally — you never need to do this again.

If you skip this step, voxon will print a clear error with the exact URL to visit when you first try to synthesise.

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Installation

pip install voxon

The installer pulls all required dependencies automatically, including MLX, PyTorch (CPU), faster-whisper, and the audio processing libraries. No manual configuration is needed.

First run: On the first synthesis call, model weights (~1–3 GB depending on backend) are downloaded from HuggingFace. This happens once and is cached locally.

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Quick Start

Step 1 — Prepare a reference voice

Record or export approximately 15 seconds of clean speech as any audio file (WAV, MP3, FLAC, AIFF). Then run:

voxon prep my_recording.wav

This processes the audio through a five-stage pipeline:

1. Loads and trims to the specified window
2. Resamples to 24 kHz mono
3. Applies stationary noise reduction
4. Normalises peak amplitude
5. Auto-transcribes using Whisper large-v3-turbo

Critical: Open the generated .txt file in ~/.voxon/voices/ and verify every word matches the audio exactly — including hesitations and fillers. Transcript accuracy directly determines cloning fidelity.

# Use a specific time window
voxon prep recording.wav --start 30 --duration 15

# Skip noise reduction (for already-clean audio)
voxon prep recording.wav --no-denoise

# Transcribe manually (write the .txt yourself after)
voxon prep recording.wav --no-transcribe

Step 2 — Speak

voxon "Hello, this is Voxon speaking."

On the first invocation, voxon starts the synthesis daemon, waits for it to warm up (model load + Metal shader compilation), then synthesises and plays the audio. Subsequent calls are immediate.

# Use a specific voice
voxon --voice alan "Hello world"

# Save the audio to a file
voxon "Hello world" --save output.wav

# Both play and save
voxon "This sentence will be played and saved." --save sentence.wav

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Command Reference

voxon "<text>" — Synthesise

voxon [--voice NAME] [--save FILE] "<text>"

Flag	Description
--voice NAME	Use a specific prepared voice. Restarts the daemon if the voice changes.
--save FILE	Save synthesised audio to this WAV file in addition to playing.

voxon prep <file> — Prepare a voice

voxon prep <FILE> [--start SECONDS] [--duration SECONDS]
                   [--out-wav PATH] [--out-transcript PATH]
                   [--no-denoise] [--no-transcribe]

Flag	Default	Description
--start	0	Start offset in seconds into the source file.
--duration	15	Duration to extract in seconds.
--out-wav	~/.voxon/voices/<name>_clean.wav	Output WAV path.
--out-transcript	~/.voxon/voices/<name>.txt	Output transcript path.
--no-denoise	—	Skip noise reduction.
--no-transcribe	—	Skip Whisper transcription (write the .txt manually).

voxon voices — List voices

voxon voices

Lists all prepared voices in ~/.voxon/voices/, indicating which is the current default and which is loaded in the running daemon.

voxon status — Daemon status

voxon status

Prints the daemon's current state: online/offline, active voice, backend model, and whether voice embeddings are cached.

voxon stop — Stop the daemon

voxon stop

Sends SIGTERM to the background daemon and removes the PID file.

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Voice Files

All runtime state is stored under ~/.voxon/:

~/.voxon/
├── config              # Active voice selection and other persisted settings
├── daemon.pid          # PID of the running synthesis daemon
├── daemon.log          # Daemon stdout/stderr — check this on errors
└── voices/
    ├── myvoice_clean.wav   # Cleaned reference audio
    ├── voxon.txt         # Exact transcript
    ├── alan_clean.wav
    └── alan.txt

To add a pre-existing voice (if you already have a clean WAV and transcript), copy the files to ~/.voxon/voices/ manually and run voxon voices to confirm detection.

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Configuration

voxon respects the following environment variables:

Variable	Default	Description
VOXON_PORT	7860	TCP port the synthesis daemon listens on.
VOXON_MODEL	chatterbox-turbo	Default TTS backend (chatterbox-turbo or qwen3).

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HTTP API

The daemon exposes a local HTTP API that you can query directly:

# Full WAV download
curl "http://localhost:7860/synthesize?text=Hello+world" -o out.wav

# Chunked streaming (lowest latency for long text)
curl -sN "http://localhost:7860/stream_chunked?text=Hello+world" -o out.wav

# Daemon health
curl http://localhost:7860/health

Endpoints:

Method	Path	Description
GET	/health	Returns daemon status and readiness.
GET	/synthesize?text=	Full synthesis, returns complete WAV.
GET	/stream?text=	Streaming WAV (synthesises first, then streams).
GET	/stream_chunked?text=	True sentence-level streaming — first audio arrives after the first sentence.

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Performance

All measurements on a single-speaker English sentence (~80 chars).

Mac	Model load	Warmup	Synthesis (1 sentence)
M1 8 GB	~30 s	~5 s	~3–5 s
M2 16 GB	~20 s	~4 s	~2–4 s
M3 16 GB	~15 s	~3 s	~1–3 s
M4 16 GB	~10 s	~2 s	~1–2 s

Model load and warmup happen once per daemon start. Voice embedding cache pre-computation (also once at startup) eliminates the dominant per-synthesis overhead on subsequent calls.

RTF (real-time factor) below 1.0 means synthesis is faster than real-time.

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Tips for Best Quality

Reference audio:

• 15 seconds, single speaker, no background music, quiet environment
• Any consistent microphone works — quality matters less than consistency

Transcript:

• Must match word-for-word, including "um", "uh", false starts, and any audible sounds
• A single wrong word or missing word will degrade output quality noticeably

Input text:

• Sentences of 15–80 characters synthesise fastest; longer inputs are auto-split
• Punctuation matters — commas and periods control synthesis rhythm

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Troubleshooting

Daemon fails to start:

cat ~/.voxon/daemon.log

Voice not found:

voxon voices   # list all prepared voices

Port conflict:

VOXON_PORT=7861 voxon "Hello world"

Model download stalls: The first run downloads model weights from HuggingFace. Check your network connection. Progress is visible in ~/.voxon/daemon.log.

ChatterboxTurbo "reference clip too short": The clip must be strictly longer than 5 seconds. Re-run voxon prep with a longer --duration.

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License

MIT — see LICENSE.