Parallel WaveGAN with Pytorch: A Comprehensive Overview
Introduction to Parallel WaveGAN
Parallel WaveGAN is an unofficial implementation that leverages Pytorch to provide cutting-edge non-autoregressive models for audio synthesis. This project includes various sophisticated models such as Parallel WaveGAN, MelGAN, Multiband-MelGAN, HiFi-GAN, and StyleMelGAN, all of which can be utilized to develop high-quality vocoders. These vocoders are pivotal in converting text to speech with a high degree of naturalness and intelligibility. Furthermore, the repository is designed to be compatible with ESPnet-TTS and NVIDIA's Tacotron2-based implementations, making it a versatile tool for end-to-end text-to-speech and singing synthesis.
Features and Models
- Parallel WaveGAN: A non-autoregressive model that synthesizes raw audio waveforms efficiently.
- MelGAN and Multiband-MelGAN: These models are known for their high-speed audio generation capabilities, suitable for real-time applications.
- HiFi-GAN: Offers high-fidelity audio output while remaining computationally efficient.
- StyleMelGAN: It focuses on generating style-rich audio outputs, which can be particularly useful in creative audio tasks.
The models can be mixed and matched to tailor a solution fitting specific vocoding requirements.
Compatibility and Demonstrations
Parallel WaveGAN is compatible with ESPnet2 and ESPnet1 for executing real-time text-to-speech. Demonstrations are available in Google Colab, offering users an interactive platform to experience these technologies firsthand. This accessibility allows users to witness the capabilities of these models in synthesizing real-time audio from textual input.
Recent Updates
The project is continually evolving, with recent updates such as support for singing voice vocoding and the availability of new recipes and pre-trained models. Notable additions include the LibriTTS-R recipe, single-speaker Korean recipe, additional pretrained models for StyleMelGAN and HiFi-GAN, and various dataset recipes for language and singing synthesis.
Setup and Installation
The Parallel WaveGAN repository can be set up using two primary methods: installing via pip or setting up a virtual environment. For distributed training scenarios, users may need to install NVIDIA's Apex toolkit.
Supported Recipes
Parallel WaveGAN supports a range of recipes inspired by the Kaldi-style framework, which include:
- Speech Datasets: LJSpeech, JSUT, VCTK, LibriTTS, CMU Arctic, and more.
- Singing Voice: Oniku Kurumi Utagoe DB, Kiritan, Ofuton P Utagoe DB.
- Languages: English, Japanese, Mandarin, Korean.
These recipes provide a structured process to train models on various datasets to meet diverse linguistic and musical requirements.
Performance and Speed
Parallel WaveGAN is optimized for both CPU and GPU, offering significant speed advantages. On a TITAN V GPU, it generates audio at a rapid pace well beyond real-time requirements. Even with CPUs, it performs efficiently, and models like MelGAN and Multi-band MelGAN offer further speed enhancements.
Conclusion
Parallel WaveGAN with Pytorch is a powerful repository for anyone interested in state-of-the-art speech synthesis. It provides a wide array of models, compatibility with existing speech synthesis frameworks, and valuable demonstrations and recipes. Whether for research or production, Parallel WaveGAN is a flexible tool that pushes the boundaries of what is possible in the field of audio generation.