This is the Linux app named Parallel WaveGAN whose latest release can be downloaded as Version0.6.1sourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named Parallel WaveGAN with OnWorks for free.
Follow these instructions in order to run this app:
- 1. Downloaded this application in your PC.
- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 3. Upload this application in such filemanager.
- 4. Start the OnWorks Linux online or Windows online emulator or MACOS online emulator from this website.
- 5. From the OnWorks Linux OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 6. Download the application, install it and run it.
SCREENSHOTS
Ad
Parallel WaveGAN
DESCRIPTION
Parallel WaveGAN is an unofficial PyTorch implementation of several state-of-the-art non-autoregressive neural vocoders, centered on Parallel WaveGAN but also including MelGAN, Multiband-MelGAN, HiFi-GAN, and StyleMelGAN. Its main goal is to provide a real-time neural vocoder that can turn mel spectrograms into high-quality speech audio efficiently. The repository is designed to work hand-in-hand with ESPnet-TTS and NVIDIA Tacotron2-style front ends, so you can build complete TTS or singing voice synthesis pipelines. It includes a large collection of “Kaldi-style” recipes for many datasets such as LJSpeech, LibriTTS, VCTK, JSUT, CMU Arctic, and multiple singing voice corpora in Japanese, Mandarin, Korean, and more. The project provides pre-trained models, Colab demos, and example configurations, allowing researchers to quickly evaluate vocoder quality or adapt models to new datasets.
Features
- PyTorch implementations of Parallel WaveGAN, MelGAN, Multiband-MelGAN, HiFi-GAN, and StyleMelGAN
- Real-time neural vocoder compatible with ESPnet-TTS and Tacotron2 front ends
- Extensive set of Kaldi-style recipes for speech and singing datasets in multiple languages
- Pretrained models and Colab demos for quick listening tests and prototyping
- Flexible training pipeline with support for multi-GPU and distributed setups
- Very low real-time factor for fast mel-to-waveform conversion suitable for deployment
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/parallel-wavegan.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
