RRepoGEO

REPOGEO REPORT · LITE

dimastatz/whisper-flow

Default branch main · commit 1f60bbf2 · scanned 6/15/2026, 5:17:46 AM

GitHub: 768 stars · 111 forks

AI VISIBILITY SCORE
40 /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
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 dimastatz/whisper-flow, 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
    Clarify project's core identity immediately in README

    Why:

    CURRENT
    The 'About The Project' section starts with a general explanation of OpenAI Whisper.
    COPY-PASTE FIX
    Add the following sentence as the very first line under '## About The Project': 'Whisper-Flow is a Python framework designed for real-time, continuous transcription of audio streams using OpenAI\'s Whisper model, enabling immediate text output from live or streaming audio chunks.'
  • mediumtopics#2
    Add keywords for real-time streaming and framework

    Why:

    CURRENT
    pypi-package, python, speech-to-text, transcription, whisper
    COPY-PASTE FIX
    pypi-package, python, speech-to-text, transcription, whisper, real-time, streaming, audio-streaming, framework
  • mediumreadme#3
    Add a 'Why Whisper-Flow?' or 'Comparison' section

    Why:

    COPY-PASTE FIX
    Add a new section, perhaps titled 'Why Whisper-Flow? (vs. Cloud APIs or Batch Processing)' that explains its value proposition as a local, real-time framework for Whisper, contrasting it with managed cloud services or traditional batch transcription tools. For example: 'Unlike managed cloud services (e.g., Google Cloud Speech-to-Text, AWS Transcribe) or general-purpose batch transcription tools, Whisper-Flow provides a local, extensible framework for integrating real-time, continuous Whisper transcription directly into your Python applications, giving you full control over the processing pipeline.'

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 dimastatz/whisper-flow
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
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Speech-to-Text · recommended 1×
  2. AWS Transcribe · recommended 1×
  3. Azure Cognitive Services Speech · recommended 1×
  4. AssemblyAI · recommended 1×
  5. Deepgram · recommended 1×
  • CATEGORY QUERY
    How to get immediate text from live audio streams instead of waiting for full files?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Speech-to-Text
    2. AWS Transcribe
    3. Azure Cognitive Services Speech
    4. AssemblyAI
    5. Deepgram
    6. OpenAI Whisper

    AI recommended 6 alternatives but never named dimastatz/whisper-flow. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Python library for continuous speech-to-text processing of audio chunks?
    you: not recommended
    AI recommended (in order):
    1. AssemblyAI Python SDK (AssemblyAI/assemblyai-python-sdk)
    2. Google Cloud Speech-to-Text API (googleapis/python-speech)
    3. AWS Transcribe (boto/boto3)
    4. OpenAI Whisper (openai/whisper)
    5. Vosk (alphacep/vosk-api)
    6. Mozilla DeepSpeech (mozilla/DeepSpeech)

    AI recommended 6 alternatives but never named dimastatz/whisper-flow. 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 dimastatz/whisper-flow?
    pass
    AI named dimastatz/whisper-flow explicitly

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

  • If a team adopts dimastatz/whisper-flow in production, what risks or prerequisites should they evaluate first?
    pass
    AI named dimastatz/whisper-flow 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 dimastatz/whisper-flow solve, and who is the primary audience?
    pass
    AI named dimastatz/whisper-flow explicitly

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
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