REPOGEO REPORT · LITE

open-mmlab/Amphion

Default branch main · commit 26f68831 · scanned 5/26/2026, 6:57:12 PM

GitHub: 9,817 stars · 814 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 open-mmlab/Amphion, 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 opening to clarify domain-specific reproducible research

Why:

CURRENT

**Amphion (/æmˈfaɪən/) is a toolkit for Audio, Music, and Speech Generation.** Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audio, music, and speech generation research and development.

COPY-PASTE FIX

**Amphion (/æmˈfaɪən/) is a comprehensive toolkit for Audio, Music, and Speech Generation.** It serves as a dedicated platform for reproducible research *specifically within generative audio tasks*, designed to help junior researchers and engineers get started in this field.

highreadme#2

Emphasize 'junior researcher' focus and unique visualizations earlier

Why:

CURRENT

Amphion offers a unique feature: **visualizations** of classic models or architectures. We believe that these visualizations are beneficial for junior researchers and engineers who wish to gain a better understanding of the model.

COPY-PASTE FIX

To further aid junior researchers, Amphion offers unique **visualizations** of classic models and architectures, fostering a deeper understanding.

mediumtopics#3

Add specific topics for better categorization

Why:

CURRENT

audio-generation, audio-synthesis, audioldm, audit, emilia, fastspeech2, maskgct, music-generation, naturalspeech2, singing-voice-conversion, speech-synthesis, text-to-audio, text-to-speech, vall-e, vits, vocoder, voice-conversion

COPY-PASTE FIX

audio-generation, audio-synthesis, audioldm, audit, emilia, fastspeech2, maskgct, music-generation, naturalspeech2, singing-voice-conversion, speech-synthesis, text-to-audio, text-to-speech, vall-e, vits, vocoder, voice-conversion, reproducible-research, generative-audio-platform, audio-ml-toolkit

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 open-mmlab/Amphion

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Hugging Face Transformers

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Transformers · recommended 1×
Hugging Face Diffusers · recommended 1×
ESPnet · recommended 1×
Mozilla Common Voice · recommended 1×
DeepSpeech · recommended 1×

CATEGORY QUERY
What open-source toolkit helps junior researchers get started with audio and speech generation?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. Hugging Face Diffusers
3. ESPnet
4. Mozilla Common Voice
5. DeepSpeech
6. Tacotron 2
7. WaveGlow
8. torchaudio
9. OpenVINO
AI recommended 9 alternatives but never named open-mmlab/Amphion. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a robust platform for reproducible research in music and voice synthesis.
you: not recommended
AI recommended (in order):
1. Weights & Biases
2. MLflow
3. Neptune.ai
4. DVC
5. Comet ML
6. Guild AI
AI recommended 6 alternatives but never named open-mmlab/Amphion. 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 open-mmlab/Amphion?
pass
AI named open-mmlab/Amphion explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts open-mmlab/Amphion in production, what risks or prerequisites should they evaluate first?
pass
AI named open-mmlab/Amphion 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 open-mmlab/Amphion solve, and who is the primary audience?
pass
AI named open-mmlab/Amphion 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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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/open-mmlab/Amphion"><img src="https://repogeo.com/badge/open-mmlab/Amphion.svg" alt="RepoGEO" /></a>

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Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite