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README
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. 1. Install ffmpeg. This is necessary for reading audio files. Check if it's installed by running in a command line ffmpeg 2. 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 environm
Top issues
#30Support for other languages106 comments · 2019-07-02#1072Fixing neonsecret fork with RUS support (newbie questions)23 comments · 2022-05-27#934File structure for training (encoder, synthesizer (vocoder))24 comments · 2021-12-02#664Can't train on two GPU's21 comments · 2021-02-16#41how to improve voice quality?15 comments · 2019-07-10#1166TypeError: load() takes 1 positional argument but 2 were given11 comments · 2023-02-20#1164Error10 comments · 2023-02-20#1221Numpy error (numpy version 1.24.3)8 comments · 2023-05-31