RRepoGEO

REPOGEO REPORT · LITE

davabase/whisper_real_time

Default branch master · commit bfc75c0d · scanned 6/19/2026, 1:12:49 PM

GitHub: 2,939 stars · 479 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
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
3 / 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 davabase/whisper_real_time, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    real-time, speech-to-text, transcription, whisper, openai, python, audio, live-transcription
  • highlicense#2
    Create a LICENSE file to formalize public domain status

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a file named 'UNLICENSE' in the root of the repository with the standard UNLICENSE text to formally dedicate the code to the public domain.
  • mediumreadme#3
    Reposition README opening to emphasize utility over "demo"

    Why:

    CURRENT
    This is a demo of real time speech to text with OpenAI's Whisper model. It works by constantly recording audio in a thread and concatenating the raw bytes over multiple recordings.
    COPY-PASTE FIX
    Implement real-time speech-to-text transcription using OpenAI's Whisper model. This repository provides a robust solution for continuously recording audio and processing it for live transcription.

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 davabase/whisper_real_time
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Cloud Speech-to-Text API
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Speech-to-Text API · recommended 2×
  2. AssemblyAI · recommended 2×
  3. AWS Transcribe · recommended 2×
  4. OpenAI Whisper · recommended 1×
  5. faster-whisper · recommended 1×
  • CATEGORY QUERY
    What are good options for real-time speech-to-text transcription in Python?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Whisper
    2. faster-whisper
    3. ctranslate2
    4. Google Cloud Speech-to-Text API
    5. AssemblyAI
    6. DeepSpeech
    7. Vosk
    8. AWS Transcribe

    AI recommended 8 alternatives but never named davabase/whisper_real_time. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I implement continuous audio transcription for live applications efficiently?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Speech-to-Text API
    2. AWS Transcribe
    3. Azure Cognitive Services Speech
    4. Deepgram
    5. AssemblyAI
    6. OpenAI Whisper (openai/whisper)
    7. Mozilla DeepSpeech (mozilla/DeepSpeech)

    AI recommended 7 alternatives but never named davabase/whisper_real_time. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 davabase/whisper_real_time?
    pass
    AI named davabase/whisper_real_time explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts davabase/whisper_real_time in production, what risks or prerequisites should they evaluate first?
    pass
    AI named davabase/whisper_real_time 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 davabase/whisper_real_time solve, and who is the primary audience?
    pass
    AI named davabase/whisper_real_time 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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davabase/whisper_real_time — 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