RRepoGEO

REPOGEO REPORT · LITE

athena-team/athena

Default branch master · commit 0be22c15 · scanned 6/14/2026, 3:42:57 AM

GitHub: 969 stars · 197 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 athena-team/athena, 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
    Strengthen README's opening to clarify project domain

    Why:

    CURRENT
    # Athena

*Athena* is an open-source implementation of end-to-end speech processing engine.
    COPY-PASTE FIX
    # Athena: End-to-End Speech Processing Engine (ASR, TTS, VAD, KWS)

*Athena* is a comprehensive, open-source, TensorFlow-based engine for end-to-end speech processing, including Automatic Speech Recognition (ASR), Text-to-Speech (TTS), Voice Activity Detection (VAD), and Keyword Spotting (KWS).
  • highabout#2
    Refine repository description for clarity and keywords

    Why:

    CURRENT
    an open-source implementation of sequence-to-sequence based speech processing engine
    COPY-PASTE FIX
    An open-source, TensorFlow-based, end-to-end speech processing engine for ASR, TTS, VAD, and KWS, supporting sequence-to-sequence models and deployment.
  • mediumreadme#3
    Add a 'Why Athena?' or 'Comparison' section to README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, e.g., `## Why Choose Athena?` or `## Comparison with Other Speech Engines`, detailing its strengths (e.g., TensorFlow 2.x focus, specific model implementations, deployment features, Kaldi-free approach) and how it stands out from competitors.

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 athena-team/athena
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
mozilla/DeepSpeech
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. mozilla/DeepSpeech · recommended 2×
  2. espnet/espnet · recommended 2×
  3. NVIDIA/NeMo · recommended 2×
  4. coqui-ai/TTS · recommended 1×
  5. openai/whisper · recommended 1×
  • CATEGORY QUERY
    What open-source library provides end-to-end speech recognition and synthesis capabilities?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeech (mozilla/DeepSpeech)
    2. Coqui TTS (coqui-ai/TTS)
    3. ESPnet (espnet/espnet)
    4. OpenAI Whisper (openai/whisper)
    5. NVIDIA NeMo (NVIDIA/NeMo)
    6. Kaldi (kaldi-asr/kaldi)
    7. Merlin (CSTR-Edinburgh/merlin)
    8. Mycroft Mimic 3 (MycroftAI/mimic3)
    9. Vosk (alphacep/vosk-api)

    AI recommended 9 alternatives but never named athena-team/athena. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a TensorFlow-based engine for speech processing, including ASR and TTS deployment.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA NeMo (NVIDIA/NeMo)
    2. TensorFlow ASR (TensorFlowASR/TensorFlowASR)
    3. TensorFlowTTS (TensorFlowTTS/TensorFlowTTS)
    4. Mozilla DeepSpeech (mozilla/DeepSpeech)
    5. ESPnet (espnet/espnet)
    6. Hugging Face Transformers (huggingface/transformers)

    AI recommended 6 alternatives but never named athena-team/athena. 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 athena-team/athena?
    pass
    AI named athena-team/athena explicitly

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

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

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

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athena-team/athena — 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