kan-bayashi/ParallelWaveGAN

Health v2 · maintenance only

Adoption not part of the score

1645 stars · 353 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Unofficial PyTorch implementations of non-autoregressive neural vocoders including Parallel WaveGAN, MelGAN, Multi-band MelGAN, HiFi-GAN, and StyleMelGAN, with pretrained models and training recipes. It is designed for real-time speech synthesis and integrates with ESPnet-TTS pipelines.

Use cases

• generate waveforms from mel spectrograms with a neural vocoder
• train a HiFi-GAN or Parallel WaveGAN vocoder on my own dataset
• build a real-time text-to-speech pipeline with ESPnet
• convert mel spectrograms to audio in real time
• fine-tune a pretrained vocoder for a new speaker or language
• train a singing voice vocoder

When to choose

• you need PyTorch vocoder models compatible with ESPnet-TTS
• you want multiple state-of-the-art vocoder architectures in one codebase with pretrained checkpoints
• you need real-time non-autoregressive waveform generation on GPU

When to avoid

• you need an officially maintained implementation or long-term support
• you want a vocoder outside the GAN-based mel-spectrogram family
• you need CPU-only real-time inference at production scale

Facets

library · maturity maintenance

machine-learning deep-learning audio-processing speech-recognition tts speech-processing machine-learning deep-learning python cross-platform vocoder parallel-wavegan melgan hifi-gan stylemelgan pytorch text-to-speech neural-vocoder pretrained-models espnet audio gpu

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