RRepoGEO

REPOGEO REPORT · LITE

pyannote/pyannote-audio

Default branch main · commit 78c0d16a · scanned 6/18/2026, 5:22:21 AM

GitHub: 10,133 stars · 1,071 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
52 /100
Needs work
Category recall
1 / 2
Avg rank #6.0 when recommended
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 pyannote/pyannote-audio, 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
  • highabout#1
    Clarify 'about' description to emphasize PyTorch library nature

    Why:

    CURRENT
    Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
    COPY-PASTE FIX
    An open-source PyTorch library providing state-of-the-art neural building blocks and end-to-end pipelines for speaker diarization, including speech activity, speaker change, overlapped speech detection, and speaker embedding.
  • highreadme#2
    Refine README's opening paragraph for clearer positioning

    Why:

    CURRENT
    `pyannote.audio` is an open-source toolkit written in Python for speaker diarization. Based on PyTorch machine learning framework, it comes with state-of-the-art pretrained models and pipelines, that can be further finetuned to your own data for even better performance.
    COPY-PASTE FIX
    `pyannote.audio` is a state-of-the-art, end-to-end open-source PyTorch toolkit for speaker diarization. It provides neural building blocks and pretrained pipelines for tasks like speech activity detection, speaker change detection, overlapped speech detection, and speaker embedding, designed for researchers and developers.
  • mediumtopics#3
    Add topics reinforcing its identity as a deep learning Python library

    Why:

    CURRENT
    overlapped-speech-detection, pretrained-models, pytorch, speaker-change-detection, speaker-diarization, speaker-embedding, speaker-recognition, speaker-verification, speech-activity-detection, speech-processing, voice-activity-detection
    COPY-PASTE FIX
    deep-learning-library, machine-learning-toolkit, overlapped-speech-detection, pretrained-models, python-library, pytorch, speaker-change-detection, speaker-diarization, speaker-embedding, speaker-recognition, speaker-verification, speech-activity-detection, speech-processing, voice-activity-detection

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
1 / 2
50% of queries surface pyannote/pyannote-audio
Avg rank
#6.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
NVIDIA/NeMo
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA/NeMo · recommended 1×
  2. SpeechBrain/SpeechBrain · recommended 1×
  3. Google Cloud Speech-to-Text API · recommended 1×
  4. Microsoft Azure Cognitive Services Speech Service · recommended 1×
  5. Amazon Transcribe · recommended 1×
  • CATEGORY QUERY
    How can I accurately identify and separate different speakers in an audio recording?
    you: #6
    AI recommended (in order):
    1. NVIDIA NeMo (NVIDIA/NeMo)
    2. SpeechBrain (SpeechBrain/SpeechBrain)
    3. Google Cloud Speech-to-Text API
    4. Microsoft Azure Cognitive Services Speech Service
    5. Amazon Transcribe
    6. pyannote.audio (pyannote/pyannote-audio) ← you
    7. Kaldi (kaldi-asr/kaldi)
    Show full AI answer
  • CATEGORY QUERY
    What are good PyTorch libraries for detecting multiple speakers and their speech segments?
    you: not recommended
    AI recommended (in order):
    1. Nemo (NVIDIA NeMo)
    2. PyTorch-Kaldi
    3. SpeechBrain
    4. pyannote.audio
    5. TorchAudio (part of PyTorch)
    6. Asteroid

    AI recommended 6 alternatives but never named pyannote/pyannote-audio. 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 pyannote/pyannote-audio?
    pass
    AI did not name pyannote/pyannote-audio — 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 pyannote/pyannote-audio in production, what risks or prerequisites should they evaluate first?
    pass
    AI named pyannote/pyannote-audio 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 pyannote/pyannote-audio solve, and who is the primary audience?
    pass
    AI named pyannote/pyannote-audio 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
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