Real-Time Voice Cloning

This repository is an implementation of Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis (SV2TTS) with a vocoder that works in real-time. This was my master’s thesis.

SV2TTS is a deep learning framework in three stages. In the first stage, one creates a digital representation of a voice from a few seconds of audio. In the second and third stages, this representation is used as reference to generate speech given arbitrary text.

Video demonstration (click the picture):

Papers implemented

URL	Designation	Title	Implementation source
1806.04558	SV2TTS	Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis	This repo
1802.08435	WaveRNN (vocoder)	Efficient Neural Audio Synthesis	fatchord/WaveRNN
1703.10135	Tacotron (synthesizer)	Tacotron: Towards End-to-End Speech Synthesis	fatchord/WaveRNN
1710.10467	GE2E (encoder)	Generalized End-To-End Loss for Speaker Verification	This repo

Heads up

Like everything else in Deep Learning, this repo has quickly gotten old. Many SaaS apps (often paying) will give you a better audio quality than this repository will. If you wish for an open-source solution with a high voice quality:

Check out paperswithcode for other repositories and recent research in the field of speech synthesis.
Check out Chatterbox for a similar project up to date with the 2025 SOTA in voice cloning

Running the toolbox

Both Windows and Linux are supported.

Install ffmpeg. This is necessary for reading audio files. Check if it’s installed by running in a command line
```
ffmpeg
```
Install uv for python package management ```
On Windows:
powershell -ExecutionPolicy ByPass -c “irm https://astral.sh/uv/install.ps1 | iex”
On Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

Alternatively, on any platform if you have pip installed you can do

pip install -U uv

3. Run one of the following commands

Run the toolbox if you have an NVIDIA GPU

uv run –extra cuda demo_toolbox.py

Use this if you don’t

uv run –extra cpu demo_toolbox.py

Run in command line if you don’t want the GUI

uv run –extra cuda demo_cli.py uv run –extra cpu demo_cli.py

Uv will automatically create a .venv directory for you with an appropriate python environment. [Open an issue](https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues) if this fails for you

### (Optional) Download Pretrained Models

Pretrained models are now downloaded automatically. If this doesn't work for you, you can manually download them from [Hugging Face](https://huggingface.co/CorentinJ/SV2TTS/tree/main).

### (Optional) Download Datasets

For playing with the toolbox alone, I only recommend downloading [`LibriSpeech/train-clean-100`](https://www.openslr.org/resources/12/train-clean-100.tar.gz). Extract the contents as `<datasets_root>/LibriSpeech/train-clean-100` where `<datasets_root>` is a directory of your choosing. Other datasets are supported in the toolbox, see [here](https://github.com/CorentinJ/Real-Time-Voice-Cloning/wiki/Training#datasets). You're free not to download any dataset, but then you will need your own data as audio files or you will have to record it with the toolbox.