Offline Urdu Text to Speech for iOS with Distilled Matcha

QnVoice distills a pinned Urdu Matcha acoustic model to three flow steps with a learned mel correction and runs HiFi-GAN offline. On an iPhone 15 Pro, median warm synthesis time fell from 1.50 s to 1.30 s across 72 runs per model. The companion project contains the ONNX files, benchmark records, and paired samples. Native-speaker listening tests and commercial release review remain pending.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-07
DOI
https://doi.org/10.5281/zenodo.24029948
Primary Topic
Speech Recognition and Synthesis
Type
preprint
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preprint

Offline Urdu Text to Speech for iOS with Distilled Matcha

QnVoice
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Zenodo (CERN European Organization for Nuclear Research)
Speech Recognition and Synthesis
preprint

Offline Urdu Text to Speech for iOS with Distilled Matcha

QnVoice
preprint en

Abstract

QnVoice distills a pinned Urdu Matcha acoustic model to three flow steps with a learned mel correction and runs HiFi-GAN offline. On an iPhone 15 Pro, median warm synthesis time fell from 1.50 s to 1.30 s across 72 runs per model. The companion project contains the ONNX files, benchmark records, and paired samples. Native-speaker listening tests and commercial release review remain pending.

Zenodo (CERN European Organization for Nuclear Research)
Speech Recognition and Synthesis
DOIOpen Access PDFOpenAlex
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