Real-Time Voice Cloning

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. Feel free to check my thesis if you’re curious or if you’re looking for info I haven’t documented yet (don’t hesitate to make an issue for that too). Mostly I would recommend giving a quick look to the figures beyond the introduction.

SV2TTS is a three-stage deep learning framework that allows to create a numerical representation of a voice from a few seconds of audio, and to use it to condition a text-to-speech model trained to generalize to new voices.

Video demonstration (click the picture):

Toolbox demo

Papers implemented

URLDesignationTitleImplementation source
1806.04558SV2TTSTransfer Learning from Speaker Verification to Multispeaker Text-To-Speech SynthesisThis repo
1802.08435WaveRNN (vocoder)Efficient Neural Audio Synthesisfatchord/WaveRNN
1712.05884Tacotron 2 (synthesizer)Natural TTS Synthesis by Conditioning Wavenet on Mel Spectrogram PredictionsRayhane-mamah/Tacotron-2
1710.10467GE2E (encoder)Generalized End-To-End Loss for Speaker VerificationThis repo


06/07/19: Need to run on a remote server within a docker container? See here.

25/06/19: Experimental support for low-memory GPUs (~2gb) added for the synthesizer. Pass --low_mem to or to enable it. It adds a big overhead, so it’s not recommended if you have enough VRAM.

Quick start


You will need the following whether you plan to use the toolbox only or to retrain the models.

Python 3.7. Python 3.6 might work too, but I wouldn’t go lower because I make extensive use of pathlib.

Run pip install -r requirements.txt to install the necessary packages. Additionally you will need PyTorch.

A GPU is mandatory, but you don’t necessarily need a high tier GPU if you only want to use the toolbox.

Pretrained models

Download the latest here.


Before you download any dataset, you can begin by testing your configuration with:


If all tests pass, you’re good to go.


For playing with the toolbox alone, I only recommend downloading LibriSpeech/train-clean-100. 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. 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.


You can then try the toolbox:

python -d <datasets_root>

depending on whether you downloaded any datasets. If you are running an X-server or if you have the error Aborted (core dumped), see this issue.


Source: Github