# basveeling/wavenet

Keras WaveNet implementation

Repository: https://github.com/basveeling/wavenet
Canonical: https://ross.abutalabs.com/products/wavenet
Homepage: https://soundcloud.com/basveeling/wavenet-sample
Language: Python
License Family: other
Last push: 2023-03-24T22:30:58+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3642, "days_push": 1258, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1052, forks 213 (observed 2026-08-28T04:03:23.382669+00:00)

## What it is
A Keras implementation of DeepMind's WaveNet, a generative neural network model for raw audio synthesis. It supports training on datasets like VCTK and generating audio samples via a command-line interface.

## Use cases
- generate raw audio with a wavenet model
- train wavenet on the VCTK speech dataset
- implement wavenet in keras
- synthesize speech audio samples
- experiment with dilated convolution audio models

## When to choose
- you want a simple, readable Keras/Python implementation of WaveNet to study or modify
- you need to train a small WaveNet on raw audio like VCTK

## When to avoid
- you need Python 3 support - this implementation only works on Python 2
- you need a maintained, production-ready TTS system - the project is unmaintained and its pretrained model was removed
- you prefer TensorFlow backend - it was not recommended at the time of writing

## Facets
- artifact type: library
- maturity: abandoned
- function: deep-learning, audio-processing, machine-learning
- domain: deep-learning, speech-processing, machine-learning
- platform: python
- tags: wavenet, keras, theano, text-to-speech, generative-model, audio-synthesis, python2, audio, linux, macos

## Member repositories
- basveeling/wavenet (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:23.382669+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:59:42.243780+00:00, confidence not recorded.
  - readme: https://github.com/basveeling/wavenet (fetched 2026-08-28T04:03:23.382669+00:00, sha d2ddff975317)
  - homepage: https://soundcloud.com/basveeling/wavenet-sample (fetched 2026-08-29T13:01:17.751976+00:00, sha 9309ad3697e7)
- Data as of 2026-08-30T08:39:29.467469+00:00.