# Text-to-Audio/AudioLCM

PyTorch Implementation of AudioLCM (ACM-MM'24): a efficient and high-quality text-to-audio generation with latent consistency model.

Repository: https://github.com/Text-to-Audio/AudioLCM
Canonical: https://ross.abutalabs.com/products/audiolcm
Language: Python
License Family: other
Last push: 2025-07-01T14:26:54+00:00

## Health v2 (maintenance only)
Score: 37/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 29, release rhythm 35, longevity 58
- inputs: {"age_days": 823, "days_push": 428, "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 1165, forks 158 (observed 2026-08-28T04:03:50.139304+00:00)

## What it is
AudioLCM is a PyTorch implementation of an ACM-MM'24 paper for efficient, high-quality text-to-audio generation using latent consistency models. It includes pretrained models and inference APIs for generating audio from text prompts.

## Use cases
- generate audio from a text description
- create sound effects from prompts
- batch generate audio samples for multiple prompts
- research text-to-audio generation models
- produce ambient soundscapes from text

## When to choose
- you need fast, high-fidelity audio generation from text prompts
- you want to experiment with latent consistency models for audio
- you need pretrained models for sound effect generation

## When to avoid
- you need a production-ready service with an API and license guarantees
- you need speech synthesis rather than general audio/sound effects
- you cannot run GPU inference locally

## Facets
- artifact type: library
- maturity: active
- function: audio-processing, machine-learning, deep-learning, tts
- domain: machine-learning, artificial-intelligence
- platform: python, cross-platform
- tags: text-to-audio, latent-consistency-model, diffusion, pytorch, generative-audio, research, audio, gpu

## Member repositories
- Text-to-Audio/AudioLCM (main) score 37

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:50.139304+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 2026-08-30T06:29:19.998134+00:00, confidence not recorded.
  - readme: https://github.com/Text-to-Audio/AudioLCM (fetched 2026-08-28T04:03:50.139304+00:00, sha 4ad25eed2ef5)
- Data as of 2026-08-30T08:39:29.467469+00:00.