REPOGEO REPORT · LITE

k2-fsa/sherpa-ncnn

Default branch master · commit c61e50d6 · scanned 5/20/2026, 9:37:07 PM

GitHub: 1,683 stars · 213 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

33 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

2 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface k2-fsa/sherpa-ncnn, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition the README's introduction to highlight unique value

Why:

CURRENT

## Introduction
This repository supports running the following functions **locally**…

COPY-PASTE FIX

## Introduction
**Sherpa-ncnn delivers real-time, entirely offline speech recognition and voice activity detection (VAD) optimized for edge devices.** Leveraging next-gen Kaldi with ncnn, it provides high-performance, cross-platform inference for iOS, Android, Linux, macOS, Windows, and embedded systems, enabling robust on-device AI without an internet connection.

mediumtopics#2

Add more specific platform and use-case topics

Why:

CURRENT

asr, c, cpp, csharp, go, kotlin, python, speech-recognition, vad, voice-activity-detection

COPY-PASTE FIX

asr, c, cpp, csharp, go, kotlin, python, speech-recognition, vad, voice-activity-detection, android-development, ios-development, embedded-systems, edge-ai, offline-speech-recognition, real-time-asr, cross-platform

mediumreadme#3

Add a 'Comparison with Alternatives' section to the README

Why:

COPY-PASTE FIX

## Comparison with Alternatives

(Add a section here comparing Sherpa-ncnn's unique advantages, such as its ncnn optimization, specific platform support, or offline capabilities, against common alternatives like Vosk, Picovoice, or Mozilla DeepSpeech.)

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface k2-fsa/sherpa-ncnn

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Vosk

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Vosk · recommended 2×
Picovoice Rhino Speech-to-Intent · recommended 1×
Picovoice Porcupine Wake Word · recommended 1×
Picovoice Cheetah Speech-to-Text · recommended 1×
Mozilla DeepSpeech · recommended 1×

CATEGORY QUERY
Need a library for real-time, offline speech recognition across multiple desktop and mobile platforms.
you: not recommended
AI recommended (in order):
1. Vosk
2. Picovoice Rhino Speech-to-Intent
3. Picovoice Porcupine Wake Word
4. Picovoice Cheetah Speech-to-Text
5. Mozilla DeepSpeech
6. Coqui STT
7. PocketSphinx
AI recommended 7 alternatives but never named k2-fsa/sherpa-ncnn. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are good open-source options for local voice activity detection with C# or Python?
you: not recommended
AI recommended (in order):
1. WebRTC VAD
2. webrtcvad
3. Vosk
4. Silero VAD
5. PyTorch Hub
6. PyTorch
7. ONNX Runtime
8. pyannote.audio
9. SpeechRecognition
10. NAudio
AI recommended 10 alternatives but never named k2-fsa/sherpa-ncnn. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of k2-fsa/sherpa-ncnn?
pass
AI did not name k2-fsa/sherpa-ncnn — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts k2-fsa/sherpa-ncnn in production, what risks or prerequisites should they evaluate first?
pass
AI named k2-fsa/sherpa-ncnn explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo k2-fsa/sherpa-ncnn solve, and who is the primary audience?
pass
AI named k2-fsa/sherpa-ncnn explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/k2-fsa/sherpa-ncnn.svg)](https://repogeo.com/en/r/k2-fsa/sherpa-ncnn)

HTML

<a href="https://repogeo.com/en/r/k2-fsa/sherpa-ncnn"><img src="https://repogeo.com/badge/k2-fsa/sherpa-ncnn.svg" alt="RepoGEO" /></a>

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k2-fsa/sherpa-ncnn — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite