RRepoGEO

REPOGEO REPORT · LITE

MoonshotAI/Kimi-Audio

Default branch master · commit 349251e1 · scanned 5/8/2026, 9:57:41 PM

GitHub: 4,599 stars · 352 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 MoonshotAI/Kimi-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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the chosen open-source license (e.g., Apache-2.0, MIT, GPL-3.0). If a custom license is intended, state it clearly in the README and link to the full text.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    audio-foundation-model, audio-understanding, audio-generation, audio-conversation, speech-recognition, text-to-speech, speech-to-text, multimodal-ai, deep-learning
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://arxiv.org/pdf/2504.18425

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 MoonshotAI/Kimi-Audio
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
openai/whisper
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. openai/whisper · recommended 2×
  2. meta-llama/llama-models · recommended 1×
  3. mistralai/Mistral-7B-v0.1 · recommended 1×
  4. google/gemma · recommended 1×
  5. suno-ai/bark · recommended 1×
  • CATEGORY QUERY
    What open-source models can understand, generate, and converse using audio inputs?
    you: not recommended
    AI recommended (in order):
    1. Whisper (openai/whisper)
    2. LLaMA 3 (meta-llama/llama-models)
    3. Mistral Large/7B (mistralai/Mistral-7B-v0.1)
    4. Gemma (google/gemma)
    5. Bark (suno-ai/bark)
    6. VALL-E X (microsoft/VALLE-X)
    7. SeamlessM4T (facebookresearch/seamless_communication)
    8. Coqui TTS (coqui-ai/TTS)
    9. Vosk (alphacep/vosk-api)
    10. Kaldi (kaldi-asr/kaldi)
    11. GPT-2/NeoX (EleutherAI/gpt-neox)
    12. OpenVoice (myshell-ai/OpenVoice)

    AI recommended 12 alternatives but never named MoonshotAI/Kimi-Audio. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a versatile audio foundation model to build conversational AI applications.
    you: not recommended
    AI recommended (in order):
    1. OpenAI Whisper (openai/whisper)
    2. Google Speech-to-Text API
    3. AssemblyAI
    4. AWS Transcribe
    5. Hugging Face Transformers (huggingface/transformers)
    6. Deepgram
    7. Microsoft Azure Cognitive Services

    AI recommended 7 alternatives but never named MoonshotAI/Kimi-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
    fail

    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 MoonshotAI/Kimi-Audio?
    pass
    AI did not name MoonshotAI/Kimi-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 MoonshotAI/Kimi-Audio in production, what risks or prerequisites should they evaluate first?
    pass
    AI named MoonshotAI/Kimi-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 MoonshotAI/Kimi-Audio solve, and who is the primary audience?
    pass
    AI named MoonshotAI/Kimi-Audio explicitly

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

Embed your GEO score

Drop this badge into the README of MoonshotAI/Kimi-Audio. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/MoonshotAI/Kimi-Audio.svg)](https://repogeo.com/en/r/MoonshotAI/Kimi-Audio)
HTML
<a href="https://repogeo.com/en/r/MoonshotAI/Kimi-Audio"><img src="https://repogeo.com/badge/MoonshotAI/Kimi-Audio.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

MoonshotAI/Kimi-Audio — 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