REPOGEO REPORT · LITE

thewh1teagle/kokoro-onnx

Default branch main · commit 2bfb160c · scanned 5/19/2026, 9:46:58 PM

GitHub: 2,544 stars · 271 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 thewh1teagle/kokoro-onnx, 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 the README's opening sentence to highlight key differentiators

Why:

CURRENT

TTS with onnx runtime based on Kokoro-TTS

COPY-PASTE FIX

Fast, lightweight, and multi-language text-to-speech (TTS) in Python, powered by ONNX Runtime and based on Kokoro-TTS, offering multiple voices for near real-time performance.

mediumreadme#2

Move the 'Features' section higher in the README

Why:

CURRENT

The 'Features' section is currently located after the initial description and setup instructions.

COPY-PASTE FIX

Relocate the entire '## Features' section to appear immediately after the initial project description (the sentence following the main title and badges), before the 'Setup' section.

lowcomparison#3

Add a 'Why kokoro-onnx?' section highlighting differentiators

Why:

COPY-PASTE FIX

Add a new section titled `## Why kokoro-onnx?` immediately after the `## Features` section, with the content: "While other TTS solutions exist, kokoro-onnx is specifically engineered for extreme lightweightness (models as small as ~80MB quantized) and fast, near real-time performance, leveraging ONNX Runtime for efficient deployment across various platforms. It offers multi-language and multi-voice support, making it ideal for resource-constrained environments or applications requiring high-speed inference."

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 thewh1teagle/kokoro-onnx

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

rhasspy/piper

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

rhasspy/piper · recommended 1×
VITS · recommended 1×
FastSpeech2 · recommended 1×
HiFi-GAN · recommended 1×
VocGAN · recommended 1×

CATEGORY QUERY
How to implement fast, lightweight text-to-speech in Python with ONNX runtime?
you: not recommended
AI recommended (in order):
1. Piper TTS (rhasspy/piper)
2. VITS
3. FastSpeech2
4. HiFi-GAN
5. VocGAN
6. ESPnet (espnet/espnet)
7. Coqui TTS (coqui-ai/TTS)
AI recommended 7 alternatives but never named thewh1teagle/kokoro-onnx. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a Python library for multi-language, multi-voice speech synthesis with good performance.
you: not recommended
AI recommended (in order):
1. Google Cloud Text-to-Speech
2. Microsoft Azure Text to Speech
3. Amazon Polly
4. Eleven Labs
5. Coqui TTS
6. Mozilla TTS
7. gTTS
AI recommended 7 alternatives but never named thewh1teagle/kokoro-onnx. 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 thewh1teagle/kokoro-onnx?
pass
AI named thewh1teagle/kokoro-onnx explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts thewh1teagle/kokoro-onnx in production, what risks or prerequisites should they evaluate first?
pass
AI named thewh1teagle/kokoro-onnx 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 thewh1teagle/kokoro-onnx solve, and who is the primary audience?
pass
AI named thewh1teagle/kokoro-onnx 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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thewh1teagle/kokoro-onnx — 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