ParallelWaveGAN
Unofficial Parallel WaveGAN (+ MelGAN & Multi-band MelGAN & HiFi-GAN & StyleMelGAN) with Pytorch
GraphCanon updated 3w · GitHub synced 3w · 28 views this month
Decision brief
ParallelWaveGAN synthesizes speech from mel-spectrograms using PyTorch-based GANs with support for distributed multi-GPU training.
Good fit when
- When needing to generate natural speech from mel-spectrogram inputs, especially in environments requiring scalable GPU computing.
- For applications where high-quality speech synthesis aligning closely with input spectrogram patterns is critical.
Avoid when
- If you're working without access to a PyTorch-compatible GPU setup or need real-time processing capabilities not supported here.
- In scenarios preferring tools that don't require specific Python and library versions for compatibility.
Observed Jul 12, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Dormant (828d since push)
- As of 3w
- Provenance
- Not a fork · Personal account
- As of 3w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/kan-bayashi/ParallelWaveGANSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A repository for generating realistic speech from mel-spectrograms using various PyTorch-based generative adversarial networks.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Jul 29, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Tags
README
Requirements
This repository is tested on Ubuntu 20.04 with a GPU Titan V.
- Python 3.8+
- Cuda 11.0+
- CuDNN 8+
- NCCL 2+ (for distributed multi-gpu training)
- libsndfile (you can install via
sudo apt install libsndfile-devin ubuntu) - jq (you can install via
sudo apt install jqin ubuntu) - sox (you can install via
sudo apt install soxin ubuntu)
Different cuda version should be working but not explicitly tested.
All of the codes are tested on Pytorch 1.8.1, 1.9, 1.10.2, 1.11.0, 1.12.1, 1.13.1, 2.0.1 and 2.1.0.
If you want to use distributed training, please install
command to install apex.
$ make apex
Note that we specify cuda version used to compile pytorch wheel.
If you want to use different cuda version, please check `tools/Makefile` to change the pytorch wheel to be installed.
---
# If not, please install via pip
$ pip install parallel_wavegan
---
# Please install this repository in ESPnet conda (or virtualenv) environment
$ . ./path.sh && pip install -U parallel_wavegan
For agents
This page has a .md twin and JSON over the API.