RRepoGEO

REPOGEO REPORT · LITE

vilassn/whisper_android

Default branch master · commit e9a293e5 · scanned 6/10/2026, 8:43:27 AM

GitHub: 668 stars · 112 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
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 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 vilassn/whisper_android, 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 README's opening to highlight unique Android/TFLite/Whisper combination

    Why:

    CURRENT
    # Offline Speech Recognition with Whisper & TFLite
This repository offers two Android apps leveraging the OpenAI Whisper speech-to-text model. One app uses the TensorFlow Lite Java API for easy Java integration, while the other employs the TensorFlow Lite Native API for enhanced performance. It also includes a Python script for model generation and pre-built APKs for straightforward deployment.
    COPY-PASTE FIX
    **# Offline OpenAI Whisper Speech-to-Text for Android with TensorFlow Lite**
This repository provides a complete solution for integrating high-accuracy, on-device OpenAI Whisper speech-to-text capabilities into Android applications, leveraging TensorFlow Lite for optimized performance. It includes two Android apps (Java and Native TFLite APIs), a Python script for model conversion, and pre-built APKs, making it ideal for developers building offline mobile transcription and translation features.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Set the 'Homepage' field in the repository's 'About' section to a URL for a live demo, documentation, or a dedicated project page.
  • mediumtopics#3
    Remove irrelevant TTS topics

    Why:

    CURRENT
    android, asr, automatic-speech-recognition, embedded, mobile, offline, openai, speech-recognition, tensorflow, tensorflowlite, text-to-speech, texttospeech, tflite, transcribe, transcription, translation, tts, whisper
    COPY-PASTE FIX
    android, asr, automatic-speech-recognition, embedded, mobile, offline, openai, speech-recognition, tensorflow, tensorflowlite, tflite, transcribe, transcription, translation, whisper

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 vilassn/whisper_android
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Apple Speech Framework
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Apple Speech Framework · recommended 2×
  2. Google Cloud Speech-to-Text · recommended 1×
  3. Mozilla DeepSpeech · recommended 1×
  4. Vosk · recommended 1×
  5. Picovoice Rhino Speech-to-Text · recommended 1×
  • CATEGORY QUERY
    How to implement accurate offline speech-to-text functionality in a mobile application?
    you: not recommended
    AI recommended (in order):
    1. Apple Speech Framework
    2. Google Cloud Speech-to-Text
    3. Mozilla DeepSpeech
    4. Vosk
    5. Picovoice Rhino Speech-to-Text
    6. CMU Sphinx

    AI recommended 6 alternatives but never named vilassn/whisper_android. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best options for on-device voice transcription and translation in an app?
    you: not recommended
    AI recommended (in order):
    1. Apple Speech Framework
    2. Google ML Kit

    AI recommended 2 alternatives but never named vilassn/whisper_android. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 vilassn/whisper_android?
    pass
    AI did not name vilassn/whisper_android — 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 vilassn/whisper_android in production, what risks or prerequisites should they evaluate first?
    pass
    AI named vilassn/whisper_android 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 vilassn/whisper_android solve, and who is the primary audience?
    pass
    AI did not name vilassn/whisper_android — 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?

Embed your GEO score

Drop this badge into the README of vilassn/whisper_android. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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vilassn/whisper_android — 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