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xiph
LPCNet
C2019–2023
xiph / LPCNet

LPCNet

Efficient neural speech synthesis

3Distinct people
3Mentions
0Explicit recommenders
1.2KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Efficient neural speech synthesis

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

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Also in these discussions

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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year3 dated mentions

Observed mentions: 2019-12-11 09:05:37 UTC to 2023-01-06 07:16:05 UTC.

012019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20192020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20212022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC101
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC101
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC000
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC101

What Hacker News says

3 sources
My app [0] currently uses a mildly customized version of FastSpeech 2 [1] with LPCNet [2] vocoder, which I consider "good quality" @ 16kHz. Faster than realtime on mobile CPU (at least, on anything upwards of a mid-range 2017 device - I can stream practically instantly on my iPhone 11). Using a different vocoder with mobile GPU could probably get even faster (which I don't want to do, for various reasons), and desktop CPU is usually even faster. There are various other flavours that can deliver faster synthesis (NixTTS comes to mind), but IMO they sacrifice quality even further. "Good quality" is subjective, obviously. To me, it's perfectly audible, but there's definitely a noticeable difference in quality compared to the heavier diffusion-based models. It's much less crisp and loses some of the more subtle inflections, plosives, etc. Fo
Read the rest
r my purposes (language learning), it's fine for the time being but eventually it would be nice to move to a higher-end model. [0] https://polyvox.app [1] https://arxiv.org/abs/2006.04558 [2] https://github.com/xiph/LPCNet/
In VALL-E: Neural codec language models are zero-shot text to speech synthesizers
Lowest bitrate speech codecs have rates like 450bps, 700bps (Codec2) and 1600bps (LPCNet) https://github.com/mozilla/LPCNet https://github.com/drowe67/codec2 Conceivably one could reach 39 bps with near-realtime speech, tone, inflection and tempo recognition. Put the result into a zstd compressed SSML stream and perhaps even reach 16 bps. https://stackoverflow.com/questions/46108940/is-there-a-way-...
In Disaster.radio: a disaster-resilient communications network powered by the sun

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