RRepoGEO

REPOGEO REPORT · LITE

ictnlp/LLaMA-Omni

Default branch main · commit c63fd722 · scanned 5/17/2026, 8:32:47 AM

GitHub: 3,141 stars · 223 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 ictnlp/LLaMA-Omni, 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 the project's exact scope and primary use case in the README's first paragraph

    Why:

    CURRENT
    LLaMA-Omni is a speech-language model built upon Llama-3.1-8B-Instruct. It supports low-latency and high-quality speech interactions, simultaneously generating both text and speech responses based on speech instructions.
    COPY-PASTE FIX
    LLaMA-Omni is an **end-to-end speech interaction model** built upon Llama-3.1-8B-Instruct, specifically designed for **building low-latency, high-quality conversational AI assistants**. It focuses exclusively on speech capabilities, simultaneously generating both text and speech responses from speech instructions, aiming to achieve GPT-4o level performance in speech-to-speech interactions.
  • mediumreadme#2
    Add a 'Use Cases' section to the README

    Why:

    COPY-PASTE FIX
    ## 🎯 Use Cases

- Building advanced conversational AI assistants
- Real-time speech-to-speech interaction systems
- Interactive voice agents and chatbots
- Applications requiring low-latency, high-quality speech generation from speech input
  • lowtopics#3
    Refine repository topics to include conversational AI and real-time aspects

    Why:

    CURRENT
    large-language-models, multimodal-large-language-models, speech-interaction, speech-language-model, speech-to-speech, speech-to-text
    COPY-PASTE FIX
    large-language-models, multimodal-large-language-models, speech-interaction, speech-language-model, speech-to-speech, speech-to-text, conversational-ai, ai-assistants, end-to-end-speech, real-time-speech

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 ictnlp/LLaMA-Omni
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Cloud Text-to-Speech
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Text-to-Speech · recommended 2×
  2. Google Cloud Speech-to-Text · recommended 2×
  3. Amazon Lex · recommended 2×
  4. OpenAI Whisper · recommended 2×
  5. ElevenLabs · recommended 2×
  • CATEGORY QUERY
    How to build an AI assistant with natural, low-latency speech conversation capabilities?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Dialogflow CX
    2. Google Cloud Text-to-Speech
    3. Google Cloud Speech-to-Text
    4. Amazon Lex
    5. Amazon Polly
    6. Amazon Transcribe
    7. Microsoft Azure Bot Service
    8. Azure Speech Service
    9. OpenAI Whisper
    10. ElevenLabs
    11. LangChain
    12. LlamaIndex
    13. GPT-4
    14. Claude 3
    15. Llama 3
    16. Rasa
    17. Vosk
    18. DeepSpeech
    19. MaryTTS
    20. Mozilla TTS

    AI recommended 20 alternatives but never named ictnlp/LLaMA-Omni. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What models provide high-quality, end-to-end speech-to-speech generation for conversational AI?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Text-to-Speech
    2. Google Cloud Speech-to-Text
    3. Dialogflow
    4. Media CDN
    5. AWS Polly
    6. AWS Transcribe
    7. Amazon Lex
    8. Azure Cognitive Services
    9. Azure Bot Service
    10. Language Understanding (LUIS)
    11. ElevenLabs
    12. OpenAI Whisper
    13. OpenAI TTS
    14. GPT-3.5
    15. GPT-4
    16. Hugging Face Transformers
    17. Whisper
    18. Wav2Vec2
    19. VITS
    20. Bark
    21. Meta's Voicebox

    AI recommended 21 alternatives but never named ictnlp/LLaMA-Omni. 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 ictnlp/LLaMA-Omni?
    pass
    AI named ictnlp/LLaMA-Omni explicitly

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

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

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

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ictnlp/LLaMA-Omni — 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