Launch the Toolboxĭepending on whether you downloaded any 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. Other datasets are supported in the toolbox, see here. Extract the contents as /LibriSpeech/train-clean-100 where is a directory of your choosing. (Optional) Download Datasetsįor playing with the toolbox alone, I only recommend downloading LibriSpeech/train-clean-100. (Optional) Test Configurationīefore you download any dataset, you can begin by testing your configuration with: If this doesn't work for you, you can manually download them here.

Pretrained models are now downloaded automatically.

Install the remaining requirements with pip install -r requirements.txt.

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. This is necessary for reading audio files. I recommend setting up a virtual environment using venv, but this is optional. Python 3.5 or greater should work, but you'll probably have to tweak the dependencies' versions. A GPU is recommended for training and for inference speed, but is not mandatory. You can use your trained encoder models from this repo with it. Go here for more info.Ģ0/08/19: I'm working on resemblyzer, an independent package for the voice encoder (inference only). If you're planning to work on a serious project, my strong advice: find another TTS repo.

If this is not your case: proceed with this repository, but you might end up being disappointed by the results.

You will get a better voice quality and less prosody errors.

If you just want to clone your voice (and not someone else's): I recommend our free plan on Resemble.AI.

Find new instructions in the section below.ġ4/02/21: This repo now runs on PyTorch instead of Tensorflow, thanks to the help of I'm now working full time and I will rarely maintain this repo anymore. Mostly, I've worked on making setup easier. Generalized End-To-End Loss for Speaker VerificationĢ8/12/21: I've done a major maintenance update. Tacotron: Towards End-to-End Speech Synthesis Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis In the second and third stages, this representation is used as reference to generate speech given arbitrary text. In the first stage, one creates a digital representation of a voice from a few seconds of audio.

SV2TTS is a deep learning framework in three stages. Multispeaker Text-To-Speech Synthesis (SV2TTS) with a vocoder that works in real-time. This repository is an implementation of Transfer Learning from Speaker Verification to

Voice deepfake app