# Real-Time-Voice-Cloning

**Repository Path**: jkd5170/Real-Time-Voice-Cloning

## Basic Information

- **Project Name**: Real-Time-Voice-Cloning
- **Description**: Clone a voice in 5 seconds to generate arbitrary speech in real-time
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-09-07
- **Last Updated**: 2024-05-30

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Real-Time Voice Cloning
This repository is an implementation of [Transfer Learning from Speaker Verification to
Multispeaker Text-To-Speech Synthesis](https://arxiv.org/pdf/1806.04558.pdf) (SV2TTS) with a vocoder that works in real-time. Feel free to check [my thesis](https://matheo.uliege.be/handle/2268.2/6801) 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](https://i.imgur.com/8lFUlgz.png)](https://www.youtube.com/watch?v=-O_hYhToKoA)



### Papers implemented  
| URL | Designation | Title | Implementation source |
| --- | ----------- | ----- | --------------------- |
|[**1806.04558**](https://arxiv.org/pdf/1806.04558.pdf) | **SV2TTS** | **Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis** | This repo |
|[1802.08435](https://arxiv.org/pdf/1802.08435.pdf) | WaveRNN (vocoder) | Efficient Neural Audio Synthesis | [fatchord/WaveRNN](https://github.com/fatchord/WaveRNN) |
|[1712.05884](https://arxiv.org/pdf/1712.05884.pdf) | Tacotron 2 (synthesizer) | Natural TTS Synthesis by Conditioning Wavenet on Mel Spectrogram Predictions | [Rayhane-mamah/Tacotron-2](https://github.com/Rayhane-mamah/Tacotron-2)
|[1710.10467](https://arxiv.org/pdf/1710.10467.pdf) | GE2E (encoder)| Generalized End-To-End Loss for Speaker Verification | This repo |

## News
**20/08/19:** I'm working on [resemblyzer](https://github.com/resemble-ai/Resemblyzer), an independent package for the voice encoder. You can use your trained encoder models from this repo with it.

**06/07/19:** Need to run within a docker container on a remote server? See [here](https://sean.lane.sh/posts/2019/07/Running-the-Real-Time-Voice-Cloning-project-in-Docker/).

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


## Quick start
### Requirements
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](https://pytorch.org/get-started/locally/) (>=1.0.1).

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](https://github.com/CorentinJ/Real-Time-Voice-Cloning/wiki/Pretrained-models).

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

`python demo_cli.py`

If all tests pass, you're good to go.

### Datasets
For playing with the toolbox alone, I only recommend downloading [`LibriSpeech/train-clean-100`](http://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.

### Toolbox
You can then try the toolbox:

`python demo_toolbox.py -d <datasets_root>`  
or  
`python demo_toolbox.py`  

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](https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues/11#issuecomment-504733590).

## Wiki
- **How it all works** (WIP - [stub](https://github.com/CorentinJ/Real-Time-Voice-Cloning/wiki/How-it-all-works), you might be better off reading my thesis until it's done)
- [**Training models yourself**](https://github.com/CorentinJ/Real-Time-Voice-Cloning/wiki/Training)
- **Training with other data/languages** (WIP - see [here](https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues/30#issuecomment-507864097) for now)
- [**TODO and planned features**](https://github.com/CorentinJ/Real-Time-Voice-Cloning/wiki/TODO-&-planned-features) 

## Contribution
Feel free to open issues or PRs for any problem you may encounter, typos that you see or aspects that are confusing. Contributions are welcome, open an issue or email me if you have something you want to work on. I will ignore emails regarding technical assistance, please make an issue instead.

I'm working full-time as of June 2019, so I can't maintain this repo as much as I wish I could - but I try to reply to every issue.