NVIDIA/waveglow

Health v2 · maintenance only

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Adoption not part of the score

2339 stars · 535 forks observed · 2026-08-28

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

WaveGlow is a PyTorch implementation of a flow-based generative network that synthesizes high-quality speech audio from mel-spectrograms, combining ideas from Glow and WaveNet without auto-regression. It includes pretrained models, inference scripts, and training code for single- or multi-GPU setups.

Use cases

• generate speech audio from mel-spectrograms
• vocoder for text-to-speech pipeline
• train a flow-based audio synthesis model on LJSpeech
• fast non-autoregressive waveform generation on GPU
• convert mel-spectrogram files to wav audio
• research on generative models for speech

When to choose

• you need a GPU-accelerated vocoder to turn mel-spectrograms into audio
• you want non-autoregressive synthesis with quality comparable to WaveNet
• you want to train or fine-tune a flow-based vocoder on your own dataset

When to avoid

• you need actively maintained code with recent framework compatibility
• you lack an NVIDIA GPU, since inference and training are GPU-oriented
• you need the latest state-of-the-art vocoder quality or CPU-friendly inference

Facets

library · maturity maintenance

tts audio-processing deep-learning machine-learning speech-processing deep-learning machine-learning python cross-platform vocoder flow-based-generative-model mel-spectrogram pytorch speech-synthesis waveglow audio gpu linux

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markdown · JSON · MCP: product_card(name="NVIDIA/waveglow")