efeslab/LiteASR

[EMNLP Main '25] LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation

35
/ 100
Emerging

This project helps operations engineers and developers deploy powerful automatic speech recognition (ASR) systems, like OpenAI's Whisper, more efficiently. It takes an existing Whisper model and compresses its core processing component, leading to faster transcription of audio files with minimal loss in accuracy. This is ideal for those managing ASR infrastructure or building speech-enabled applications.

148 stars. No commits in the last 6 months.

Use this if you need to run high-quality speech-to-text transcription faster or on devices with limited computational power, without significantly compromising accuracy.

Not ideal if you are a casual user simply looking to transcribe a few audio files without needing to optimize system performance or manage model deployment.

speech-to-text audio-transcription ML-deployment computational-efficiency AI-infrastructure
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 7 / 25

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Stars

148

Forks

5

Language

Python

License

Apache-2.0

Last pushed

May 18, 2025

Commits (30d)

0

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