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Audio & Voice AI

Real Time Voice Cloning

Real Time Voice Cloning is a Python implementation of the SV2TTS deep-learning framework. It derives a digital representation of a voice from a few seconds of audio, then uses that representation to synthesize speech from arbitrary text with a real-time vocoder. The repository includes a graphical toolbox and command-line entry points.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Real Time Voice Cloning — OpenViST project profile in Audio & Voice AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want a Windows or Linux Python toolbox or CLI for voice-conditioned speech generation, and can work with a local deep-learning setup.

Who it is for

  • People who want to generate speech from text conditioned on a short voice sample.
  • Developers and researchers exploring speaker representations, speaker verification, or multispeaker text-to-speech.

Documented uses

  • Create a digital voice representation from a short audio sample.
  • Generate speech from arbitrary text using that representation.
  • Explore a Python implementation combining speaker encoding, text-to-speech synthesis, and vocoding.
Before you choose

Requirements & limits

The README documents Windows and Linux setup, with separate NVIDIA GPU and CPU commands. It does not require a dataset to try the toolbox if you provide audio files or record audio in it.

  • The toolbox requires FFmpeg and uv in the documented setup.
  • The README offers separate execution commands for NVIDIA GPU and CPU environments.
  • The project author cautions that the repository has aged and that many paid SaaS apps may offer better audio quality.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the toolbox on Windows or Linux

Prepare the documented environment and launch the graphical toolbox.

Before you start

  • A Windows or Linux system
  • FFmpeg installed for reading audio files
  • uv installed for Python package management
  • An NVIDIA GPU or CPU, to select the corresponding documented command

Steps

  1. Install FFmpeg and check that it is available by running ffmpeg in a command line.

  2. Install uv using the Windows or Linux instructions in the README, or install it with pip.

  3. Run the command below for an NVIDIA GPU, or the command below for CPU use.

    Documented command
    uv run --extra cuda demo_toolbox.py
    Documented command
    uv run --extra cpu demo_toolbox.py

Expected outcome

The documented command launches the graphical toolbox, with uv creating a `.venv` environment.

Guide limitations

  • The README says pretrained models download automatically; it provides a Hugging Face manual-download option if that fails.
  • The README cautions that the repository has aged and that many paid SaaS apps may provide better audio quality.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
CorentinJ/Real-Time-Voice-Cloning · Official homepage title · retained in its source language
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