REPOGEO REPORT · LITE

FunAudioLLM/Fun-ASR

Default branch main · commit f766a7e0 · scanned 5/30/2026, 11:51:38 PM

GitHub: 1,193 stars · 116 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 FunAudioLLM/Fun-ASR, 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

Reposition README intro to highlight open-source toolkit nature

Why:

CURRENT

Fun-ASR is an end-to-end speech recognition large model launched by Tongyi Lab. It is trained on tens of millions of hours of real speech data, possessing powerful contextual understanding capabilities and industry adaptability. It supports low-latency real-time transcription and covers 31 languages.

COPY-PASTE FIX

Fun-ASR is an **open-source toolkit** for industrial-grade, end-to-end speech recognition, launched by Tongyi Lab. It is trained on tens of millions of hours of real speech data, possessing powerful contextual understanding capabilities and industry adaptability. It supports low-latency real-time transcription and covers 31 languages.

mediumreadme#2

Add a 'Comparison' section to the README

Why:

COPY-PASTE FIX

Add a new section, e.g., "## Why Fun-ASR? (Open-Source vs. Commercial APIs)" or "## Comparison with Cloud ASR Services", detailing Fun-ASR's advantages (e.g., self-hosting, data privacy, customization, cost-effectiveness for high volume) compared to services like Google Cloud Speech-to-Text, AWS Transcribe, Azure Cognitive Services Speech, AssemblyAI, and Deepgram.

lowtopics#3

Add 'open-source' and 'self-hosted' to repository topics

Why:

CURRENT

31-languages, asr, audio-language-model, chinese-dialects, fun-asr, llm-asr, multilingual-asr, pytorch, real-time-asr, speaker-diarization, speech-recognition, speech-to-text, transcription, whisper-alternative

COPY-PASTE FIX

31-languages, asr, audio-language-model, chinese-dialects, fun-asr, llm-asr, multilingual-asr, open-source, pytorch, real-time-asr, self-hosted, speaker-diarization, speech-recognition, speech-to-text, transcription, whisper-alternative

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 FunAudioLLM/Fun-ASR

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

AssemblyAI

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

AssemblyAI · recommended 2×
Deepgram · recommended 2×
Google Cloud Speech-to-Text API · recommended 1×
AWS Transcribe · recommended 1×
Azure Cognitive Services Speech · recommended 1×

CATEGORY QUERY
I need a robust speech-to-text solution supporting many languages with speaker diarization.
you: not recommended
AI recommended (in order):
1. Google Cloud Speech-to-Text API
2. AWS Transcribe
3. Azure Cognitive Services Speech
4. AssemblyAI
5. Deepgram
6. OpenAI Whisper (openai/whisper)
AI recommended 6 alternatives but never named FunAudioLLM/Fun-ASR. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are good alternatives for accurate, real-time voice transcription across multiple dialects and accents?
you: not recommended
AI recommended (in order):
1. Google Cloud Speech-to-Text
2. AWS Amazon Transcribe
3. Microsoft Azure Speech-to-Text
4. Deepgram
5. AssemblyAI
6. OpenAI Whisper
7. Rev.ai
AI recommended 7 alternatives but never named FunAudioLLM/Fun-ASR. 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 FunAudioLLM/Fun-ASR?
pass
AI named FunAudioLLM/Fun-ASR explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts FunAudioLLM/Fun-ASR in production, what risks or prerequisites should they evaluate first?
pass
AI named FunAudioLLM/Fun-ASR 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 FunAudioLLM/Fun-ASR solve, and who is the primary audience?
pass
AI named FunAudioLLM/Fun-ASR explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/FunAudioLLM/Fun-ASR.svg)](https://repogeo.com/en/r/FunAudioLLM/Fun-ASR)

HTML

<a href="https://repogeo.com/en/r/FunAudioLLM/Fun-ASR"><img src="https://repogeo.com/badge/FunAudioLLM/Fun-ASR.svg" alt="RepoGEO" /></a>

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite