RRepoGEO

REPOGEO REPORT · LITE

juanmc2005/diart

Default branch main · commit 392d53a1 · scanned 6/21/2026, 12:16:46 AM

GitHub: 1,987 stars · 164 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
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 juanmc2005/diart, 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 emphasize real-time, online capabilities for live audio streams

    Why:

    CURRENT
    Diart is a python framework to build AI-powered real-time audio applications. Its key feature is the ability to recognize different speakers in real time with state-of-the-art performance, a task commonly known as "speaker diarization".
    COPY-PASTE FIX
    Diart is a Python framework designed for building AI-powered real-time audio applications, specializing in identifying multiple speakers in live audio streams. It provides state-of-the-art speaker diarization, voice activity detection, and transcription capabilities for online processing.
  • mediumreadme#2
    Add a dedicated 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## 🆚 Diart vs. Other Libraries

Diart stands out by focusing on **real-time, online processing** for live audio streams. While powerful libraries like `pyannote.audio`, `SpeechBrain`, and `Nemo` offer robust offline speaker diarization and speech processing, Diart is engineered from the ground up for low-latency, incremental analysis, making it ideal for interactive and streaming applications. It provides a simpler API for deploying state-of-the-art models in production for tasks like speaker diarization, voice activity detection, and transcription in real-time scenarios.
  • mediumreadme#3
    Explicitly highlight Voice Activity Detection (VAD) and Transcription capabilities in the README introduction

    Why:

    CURRENT
    Its key feature is the ability to recognize different speakers in real time with state-of-the-art performance, a task commonly known as "speaker diarization".
    COPY-PASTE FIX
    Its key features include state-of-the-art real-time speaker diarization, voice activity detection, and transcription, enabling robust AI-powered audio applications.

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 juanmc2005/diart
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pyannote.audio
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Pyannote.audio · recommended 1×
  2. SpeechBrain · recommended 1×
  3. Nemo · recommended 1×
  4. OpenVINO · recommended 1×
  5. Vosk · recommended 1×
  • CATEGORY QUERY
    How to identify multiple speakers in a live audio stream using a Python library?
    you: not recommended
    AI recommended (in order):
    1. Pyannote.audio
    2. SpeechBrain
    3. Nemo
    4. OpenVINO
    5. Vosk

    AI recommended 5 alternatives but never named juanmc2005/diart. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python framework helps build real-time AI applications for voice activity and transcription?
    you: not recommended
    AI recommended (in order):
    1. Vosk API
    2. OpenAI Whisper
    3. faster-whisper
    4. whisper-timestamped
    5. Picovoice Porcupine
    6. Picovoice Rhino
    7. Picovoice Leopard
    8. Google Cloud Speech-to-Text API
    9. AssemblyAI API
    10. Mozilla DeepSpeech

    AI recommended 10 alternatives but never named juanmc2005/diart. 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 juanmc2005/diart?
    pass
    AI named juanmc2005/diart explicitly

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

  • If a team adopts juanmc2005/diart in production, what risks or prerequisites should they evaluate first?
    pass
    AI named juanmc2005/diart 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 juanmc2005/diart solve, and who is the primary audience?
    pass
    AI named juanmc2005/diart explicitly

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

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juanmc2005/diart — 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