# haoheliu/versatile_audio_super_resolution

Versatile audio super resolution (any -> 48kHz) with AudioSR.

Repository: https://github.com/haoheliu/versatile_audio_super_resolution
Canonical: https://ross.abutalabs.com/products/versatile_audio_super_resolution
Language: Python
License: MIT
License Family: permissive
Last push: 2025-08-27T06:39:47+00:00

## Health v2 (maintenance only)
Score: 45/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 39, release rhythm 35, longevity 78
- inputs: {"age_days": 1092, "days_push": 371, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1951, forks 201 (observed 2026-08-28T04:05:58.218717+00:00)

## What it is
AudioSR is a Python library and CLI tool that performs versatile audio super-resolution, upsampling any audio (music, speech, sound effects) to 48kHz using diffusion-based models. It offers basic and speech-specific checkpoints, a Gradio demo, and batch processing of audio files.

## Use cases
- upsample low-quality audio to 48khz
- enhance resolution of music recordings
- improve speech audio fidelity
- batch super-resolve a list of audio files
- restore old or compressed audio samples
- super-resolve sound effects for games or film

## When to choose
- you need to increase the sampling rate and fidelity of arbitrary audio types
- you want a ready-to-use CLI or pip-installable model rather than building your own
- you have GPU resources and want state-of-the-art diffusion-based audio enhancement

## When to avoid
- you need real-time or low-latency enhancement
- you lack a GPU and cannot tolerate slow inference
- you only need simple resampling without fidelity enhancement (use ffmpeg instead)

## Facets
- artifact type: library
- maturity: active
- function: audio-processing, machine-learning, cli
- domain: machine-learning, media
- platform: python, cli, cross-platform
- tags: audio-super-resolution, diffusion-models, speech-enhancement, music-enhancement, 48khz, gradio-demo, audio, gpu

## Member repositories
- haoheliu/versatile_audio_super_resolution (main) score 45

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:58.218717+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 2026-08-30T03:06:49.419233+00:00, confidence not recorded.
  - readme: https://github.com/haoheliu/versatile_audio_super_resolution (fetched 2026-08-28T04:05:58.218717+00:00, sha 4250d97497fd)
- Data as of 2026-08-30T08:39:29.467469+00:00.