NATSpeech: A Non-Autoregressive Text-to-Speech Framework
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add advertising hereThis repo contains official PyTorch implementation of:
- PortaSpeech: Portable and High-Quality Generative Text-to-Speech (NeurIPS 2021)
Demo page | HuggingFace🤗 Demo - DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism (DiffSpeech) (AAAI 2022)
Demo page | Project page | HuggingFace🤗 Demo
Key Features
We implement the following features in this framework:
- Data processing for non-autoregressive Text-to-Speech
using Montreal Forced Aligner. - Convenient and scalable framework for training and inference.
- Simple but efficient random-access dataset implementation.
Install Dependencies
## We tested on Linux/Ubuntu 18.04. ## Install Python 3.6+ first (Anaconda recommended). export PYTHONPATH=. # build a virtual env (recommended). python -m venv venv source venv/bin/activate # install requirements. pip install -U pip pip install Cython numpy==1.19.1 pip install torch==1.9.0 # torch>=1.9.0 recommended pip install -r requirements.txt sudo apt install -y sox libsox-fmt-mp3 bash mfa_usr/install_mfa.sh # install forced alignment tool
Documents
Citation
If you find this useful for your research, please cite the following papers:
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add advertising here- PortaSpeech
@article{ren2021portaspeech, title={PortaSpeech: Portable and High-Quality Generative Text-to-Speech}, author={Ren, Yi and Liu, Jinglin and Zhao, Zhou}, journal={Advances in Neural Information Processing Systems}, volume={34}, year={2021} }
- DiffSpeech
@article{liu2021diffsinger, title={Diffsinger: Singing voice synthesis via shallow diffusion mechanism}, author={Liu, Jinglin and Li, Chengxi and Ren, Yi and Chen, Feiyang and Liu, Peng and Zhao, Zhou}, journal={arXiv preprint arXiv:2105.02446}, volume={2}, year={2021} }