tomlepaine/fast-wavenet

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1772 stars · 303 forks observed · 2026-08-28

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

A Python/TensorFlow library implementing an efficient O(L) generation algorithm for Wavenet-style autoregressive models using dynamic programming, avoiding redundant convolution operations of naive O(2^L) generation. It also applies to any causal dilated convolutional network, such as streaming classification or regression.

Use cases

• generate audio samples from a trained wavenet model faster
• speed up autoregressive wavenet inference
• run causal dilated convolutions efficiently on streaming data
• implement fast generation for dilated cnn models
• compare naive vs efficient wavenet generation timing
• research efficient inference for autoregressive deep learning models

When to choose

• you need fast generation from a Wavenet or causal dilated convolution model
• you are doing research on efficient autoregressive inference and want a reference implementation
• you work in TensorFlow and want a simple example of the O(L) generation algorithm

When to avoid

• you need a maintained production library - the project has not been updated since 2017
• you train Wavenet models from scratch on large corpora - this focuses on generation, not training
• you use PyTorch or other frameworks - the implementation is TensorFlow-based

Facets

library · maturity abandoned

deep-learning machine-learning audio-processing benchmarking deep-learning machine-learning speech-processing python wavenet dilated-convolutions autoregressive-generation tensorflow dynamic-programming audio-generation audio

Member repositories

For agents

markdown · JSON · MCP: product_card(name="tomlepaine/fast-wavenet")