kuleshov/audio-super-res

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1260 stars · 211 forks observed · 2026-08-28

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

A Python/TensorFlow implementation of neural network models for audio super resolution, upsampling low-resolution audio to higher sampling rates. It implements the Temporal FiLM and ICLR 2017 workshop models from the Kuleshov et al. papers, with scripts for preparing the VCTK speech dataset and training.

Use cases

• upsample low-resolution audio to higher sample rates with neural networks
• enhance speech audio quality using deep learning
• reproduce audio super resolution research papers
• train a temporal FiLM model on the VCTK dataset
• increase audio sampling rate of old recordings

When to choose

• you need neural audio upsampling and can work with TensorFlow 2.x
• you want to experiment with or extend published audio super resolution research
• you have the VCTK dataset and want a ready-made training pipeline

When to avoid

• you need a production-ready, actively maintained audio enhancement tool
• you prefer PyTorch or modern audio ML stacks
• you want a simple CLI without preparing large training datasets

Facets

library · maturity maintenance

machine-learning audio-processing deep-learning machine-learning deep-learning python audio-super-resolution speech-enhancement tensorflow signal-processing research-code audio linux macos

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markdown · JSON · MCP: product_card(name="kuleshov/audio-super-res")