# xiph/LPCNet

Efficient neural speech synthesis

Repository: https://github.com/xiph/LPCNet
Canonical: https://ross.abutalabs.com/products/lpcnet
Language: C
License: BSD-3-Clause
License Family: permissive
Last push: 2024-09-21T03:59:42+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2869, "days_push": 711, "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 1221, forks 306 (observed 2026-08-28T04:04:02.098581+00:00)

## What it is
LPCNet is a low-complexity C implementation of the WaveRNN-based LPCNet neural vocoder for efficient speech synthesis and compression. It applies linear prediction to WaveRNN to achieve real-time neural speech synthesis on modest hardware, and has been used for low-bitrate speech coding and packet loss concealment in Opus.

## Use cases
- synthesize speech from acoustic features on CPU
- build a low-bitrate neural speech codec
- implement packet loss concealment for VoIP
- research low-complexity neural vocoders
- run neural speech synthesis in real time without a GPU

## When to choose
- you need a proven, low-CPU neural vocoder in C
- you are researching LPC-based neural speech synthesis or coding
- you want to study the techniques later folded into Opus (PLC, deep redundancy)

## When to avoid
- you want actively maintained software - the README recommends switching to FARGAN
- you need the best quality-to-complexity ratio, since FARGAN outperforms it at 1/5 the cost
- you want a high-level Python training framework out of the box

## Facets
- artifact type: library
- maturity: abandoned
- function: speech-recognition, audio-processing, machine-learning, deep-learning
- domain: speech-processing, machine-learning
- platform: c, windows, cross-platform
- tags: speech-synthesis, vocoder, wavernn, lpc, neural-vocoder, low-complexity, packet-loss-concealment, speech-coding, audio, linux, macos

## Member repositories
- xiph/LPCNet (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:02.098581+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 2026-08-30T06:15:54.459162+00:00, confidence not recorded.
  - readme: https://github.com/xiph/LPCNet (fetched 2026-08-28T04:04:02.098581+00:00, sha 3072038b8e20)
- Data as of 2026-08-30T08:39:29.467469+00:00.