vincentherrmann/pytorch-wavenet
An implementation of WaveNet with fast generation observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3423
- days_rel: n/a
- days_push: 2176
- n_releases_24m: 0
Adoption not part of the score
1029 stars · 231 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A PyTorch implementation of the WaveNet neural audio generation architecture with fast generation support. It includes dataset creation from audio files, multithreaded data loading, and TensorBoard logging.
Use cases
- generate audio with a wavenet model in pytorch
- train a wavenet on my own wav files
- fast wavenet audio sample generation
- learn how wavenet works with a demo notebook
- implement autoregressive audio synthesis
When to choose
- you want a simple, readable WaveNet implementation in PyTorch
- you need fast generation via the fast Wavenet technique
- you want a Jupyter-based demo to learn from
When to avoid
- you need production-grade, actively maintained audio synthesis
- you require modern PyTorch versions or recent Python support
- you need text-to-speech with pretrained voices
Facets
library · maturity maintenance
deep-learning audio-processing machine-learning deep-learning machine-learning python wavenet pytorch audio-generation neural-audio research-code audio
1 source
- readme: https://github.com/vincentherrmann/pytorch-wavenet · fetched 2026-08-28 · 23c4319cdb02
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| vincentherrmann/pytorch-wavenet | main | 32 |
For agents
markdown · JSON · MCP: product_card(name="vincentherrmann/pytorch-wavenet")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem