RRepoGEO

REPOGEO REPORT · LITE

gpt-omni/mini-omni

Default branch main · commit 26c31d5b · scanned 6/20/2026, 12:23:07 PM

GitHub: 3,562 stars · 309 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
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 gpt-omni/mini-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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening statement to clearly define the project

    Why:

    CURRENT
    # Mini-Omni
    
    <p align="center"><strong style="font-size: 18px;">
    Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming
    </strong>
    </p>
    
    <p align="center">
    🤗 <a href="https://huggingface.co/gpt-omni/mini-omni">Hugging Face</a>   | 📖 <a href="https://github.com/gpt-omni/mini-omni">Github</a> 
    |     📑 <a href="https://arxiv.org/abs/2408.16725">Technical report</a> |
    🤗 <a href="https://huggingface.co/datasets/gpt-omni/VoiceAssistant-400K">Datasets</a>
    </p>
    
    Mini-Omni is an open-source multimodal large language model that can **hear, talk while thinking**. Featuring real-time end-to-end speech input and **streaming audio output** conversational capabilities.
    COPY-PASTE FIX
    # Mini-Omni: An Open-Source Multimodal LLM for Real-time Conversational AI
    
    <p align="center"><strong style="font-size: 18px;">
    Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming
    </strong>
    </p>
    
    <p align="center">
    🤗 <a href="https://huggingface.co/gpt-omni/mini-omni">Hugging Face</a>   | 📖 <a href="https://github.com/gpt-omni/mini-omni">Github</a> 
    |     📑 <a href="https://arxiv.org/abs/2408.16725">Technical report</a> |
    🤗 <a href="https://huggingface.co/datasets/gpt-omni/VoiceAssistant-400K">Datasets</a>
    </p>
    
    Mini-Omni is a cutting-edge open-source multimodal large language model (LLM) designed for real-time conversational AI. It uniquely enables models to **hear, talk while thinking**, featuring end-to-end speech input and streaming audio output capabilities without needing separate ASR or TTS models.
  • mediumreadme#2
    Add a 'Why Mini-Omni?' or 'Comparison' section to highlight differentiators

    Why:

    COPY-PASTE FIX
    ## Why Mini-Omni?
    
    Mini-Omni stands out as an open-source, end-to-end multimodal large language model that integrates hearing, thinking, and talking into a single system. Unlike traditional approaches that rely on separate ASR and TTS components (like Vosk, Whisper, or Coqui TTS), Mini-Omni offers seamless, real-time speech-to-speech conversational capabilities. Compared to large proprietary models (e.g., GPT-4o, Gemini), Mini-Omni provides an accessible, resource-efficient, and fully transparent open-source alternative for developers and researchers building next-generation conversational AI.

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 gpt-omni/mini-omni
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
alphacep/vosk-api
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. alphacep/vosk-api · recommended 1×
  2. coqui-ai/TTS · recommended 1×
  3. openai/whisper · recommended 1×
  4. suno-ai/bark · recommended 1×
  5. guillaumekln/faster-whisper · recommended 1×
  • CATEGORY QUERY
    What open-source models offer real-time end-to-end speech input and streaming audio output?
    you: not recommended
    AI recommended (in order):
    1. Vosk (alphacep/vosk-api)
    2. Coqui TTS (coqui-ai/TTS)
    3. Whisper (openai/whisper)
    4. Bark (suno-ai/bark)
    5. faster-whisper (guillaumekln/faster-whisper)
    6. Picovoice Rhino (Picovoice/rhino)
    7. Picovoice Porcupine (Picovoice/porcupine)
    8. Kaldi (kaldi-asr/kaldi)
    9. MaryTTS (marytts/marytts)
    10. DeepSpeech (mozilla/DeepSpeech)
    11. Tacotron 2
    12. WaveNet

    AI recommended 12 alternatives but never named gpt-omni/mini-omni. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a multimodal LLM that can generate text and audio simultaneously for conversations.
    you: not recommended
    AI recommended (in order):
    1. Google Gemini
    2. OpenAI GPT-4o
    3. Meta Llama 3
    4. ElevenLabs
    5. Azure AI Speech
    6. Azure OpenAI Service
    7. AWS Polly
    8. Amazon Bedrock
    9. Amazon SageMaker

    AI recommended 9 alternatives but never named gpt-omni/mini-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
    warn

    Suggestion:

  • 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 gpt-omni/mini-omni?
    pass
    AI named gpt-omni/mini-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 gpt-omni/mini-omni in production, what risks or prerequisites should they evaluate first?
    pass
    AI named gpt-omni/mini-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 gpt-omni/mini-omni solve, and who is the primary audience?
    pass
    AI named gpt-omni/mini-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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gpt-omni/mini-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