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](https://arxiv.org/pdf/1806.04558.pdf) (SV2TTS) with a vocoder that works in real-time. This was my [master's thesis](https://matheo.uliege.be/handle/2268.2/6801). 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): [](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) | | [1703.10135](https://arxiv.org/pdf/1703.10135.pdf) | Tacotron (synthesizer) | Tacotron: Towards End-to-End Speech Synthesis | [fatchord/WaveRNN](https://github.com/fatchord/WaveRNN) | | [1710.10467](https://arxiv.org/pdf/1710.10467.pdf) | 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](https://paperswithcode.com/task/speech-synthesis/) for other repositories and recent research in the field of speech synthesis. - Check out [Chatterbox](https://github.com/resemble-ai/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](https://ffmpeg.org/download.html#get-packages). 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 environment. [Open an issue](https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues) if this fails for you ### (Optional) Download Pretrained Models Pretrained models are now downloaded automatically. If this doesn't work for you, you can manually download them from [Hugging Face](https://huggingface.co/CorentinJ/SV2TTS/tree/main). ### (Optional) Download Datasets For playing with the toolbox alone, I only recommend downloading [`LibriSpeech/train-clean-100`](https://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.