RRepoGEO

REPOGEO REPORT · LITE

juanmc2005/diart

Default branch main · commit 392d53a1 · scanned 5/10/2026, 10:42:13 PM

GitHub: 1,974 stars · 163 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 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 introduction to clarify core value and relationship to pyannote.audio

    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 for **real-time speaker diarization** in streaming audio. It simplifies building AI-powered audio applications by providing a streamlined API for online diarization, leveraging state-of-the-art models (e.g., from `pyannote.audio`) to recognize different speakers as a conversation progresses.
  • mediumcomparison#2
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., '## 🆚 Diart vs. Alternatives', explaining its niche: 'Unlike cloud-based APIs, Diart offers a local, customizable, and real-time framework for speaker diarization, giving developers full control over their audio pipelines without vendor lock-in. While it can integrate with foundational libraries like `pyannote.audio`, Diart focuses on providing a simplified, end-to-end solution for online streaming applications.'
  • lowtopics#3
    Reorder topics to emphasize core features

    Why:

    CURRENT
    deep-learning, real-time, speaker-diarization, speaker-embedding, streaming-audio, transcription, voice-activity-detection
    COPY-PASTE FIX
    speaker-diarization, real-time, streaming-audio, speaker-embedding, voice-activity-detection, deep-learning, 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 juanmc2005/diart
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA Riva
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA Riva · recommended 1×
  2. Google Cloud Speech-to-Text API · recommended 1×
  3. Amazon Transcribe · recommended 1×
  4. Microsoft Azure Cognitive Services - Speech Service · recommended 1×
  5. pyannote.audio · recommended 1×
  • CATEGORY QUERY
    How can I implement real-time speaker identification and separation in streaming audio?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Riva
    2. Google Cloud Speech-to-Text API
    3. Amazon Transcribe
    4. Microsoft Azure Cognitive Services - Speech Service
    5. pyannote.audio
    6. Kaldi
    7. SpeechBrain
    8. DeepMind's VoiceFilter-Lite
    9. Mozilla DeepSpeech
    10. TensorFlow
    11. PyTorch

    AI recommended 11 alternatives but never named juanmc2005/diart. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python deep learning libraries are best for real-time voice activity detection and transcription?
    you: not recommended
    AI recommended (in order):
    1. Whisper (openai/whisper)
    2. faster-whisper (guillaumekln/faster-whisper)
    3. PyTorch (pytorch/pytorch)
    4. TensorFlow (tensorflow/tensorflow)
    5. Silero VAD (snakers4/silero-vad)
    6. Hugging Face Transformers (huggingface/transformers)
    7. Wav2Vec2
    8. HuBERT
    9. SpeechRecognition (Uberi/speech_recognition)
    10. Google Speech Recognition API
    11. CMU Sphinx
    12. DeepSpeech (mozilla/DeepSpeech)
    13. Kaldi (kaldi-asr/kaldi)

    AI recommended 13 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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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite
juanmc2005/diart — RepoGEO report