This is a fork of CorentinJ's amazing Voice Cloning repo
It is extremely encouraged that you visit that link instead of this one. This repo is mostly for my own edification. The goal of this repo is to have a stable, installable, and more slender version of this app. As such expect this to be frequently broken, or otherwise unhelpful to the vast majority reading this. Should this change, this paragraph will change.
| URL | Designation | Title | Implementation source |
|---|---|---|---|
| 1806.04558 | SV2TTS | Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis | See Original 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 | See Original Repo |
3/13/23 - Fork
Below is the installation which no longer works. I am keeping it here until it can be updated with more recent instructions.
- Both Windows and Linux are supported. A GPU is recommended for training and for inference speed, but is not mandatory.
- Python 3.7 is recommended. Python 3.5 or greater should work, but you'll probably have to tweak the dependencies' versions. I recommend setting up a virtual environment using
venv, but this is optional. - Install ffmpeg. This is necessary for reading audio files.
- Install PyTorch. Pick the latest stable version, your operating system, your package manager (pip by default) and finally pick any of the proposed CUDA versions if you have a GPU, otherwise pick CPU. Run the given command.
- Install the remaining requirements with
pip install -r requirements.txt
Pretrained models are now downloaded automatically. If this doesn't work for you, you can manually download them here.
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.
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 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.