REPOGEO REPORT · LITE

janhq/ichigo

Default branch main · commit 21c5ad7c · scanned 5/17/2026, 3:16:49 PM

GitHub: 2,484 stars · 149 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

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 janhq/ichigo, 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 H1 and opening paragraph to clarify core purpose

Why:

CURRENT

# :strawberry: Ichigo: A simple speech package for developers

Welcome to **Ichigo**, a streamlined speech package designed to empower developers with cutting-edge speech models and tools...

COPY-PASTE FIX

# :strawberry: Ichigo: Local Real-time Voice AI for Developers

Welcome to **Ichigo**, a streamlined Python package and FastAPI service for **local, real-time voice AI**, offering cutting-edge Automatic Speech Recognition (ASR), Text-to-Speech (TTS), and Speech Language Model (SLM) capabilities. Designed for developers, Ichigo simplifies the integration of powerful speech models, letting you focus on deploying and improving your systems without the complexities of audio processing.

hightopics#2

Add relevant topics to improve categorization and searchability

Why:

CURRENT

(none)

COPY-PASTE FIX

voice-ai, speech-to-text, text-to-speech, speech-recognition, speech-language-model, real-time, local-inference, python, fastapi, asr, tts, llm

highlicense#3

Add a LICENSE file and clarify licensing in the README

Why:

CURRENT

(no LICENSE file detected — the repo has no recognizable license)

COPY-PASTE FIX

Create a `LICENSE` file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0, depending on project intent). Additionally, add a section to the README under 'About' or 'Installation' titled 'License' stating: 'Ichigo is released under the [License Name] License. See the [LICENSE file](LICENSE) for details.'

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 janhq/ichigo

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Vosk

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Vosk · recommended 2×
Mozilla DeepSpeech · recommended 2×
Whisper · recommended 1×
Llama 3 · recommended 1×
Mixtral 8x7B · recommended 1×

CATEGORY QUERY
Need a local speech-to-text and speech language model for a real-time voice assistant.
you: not recommended
AI recommended (in order):
1. Whisper
2. Llama 3
3. Mixtral 8x7B
4. Gemma
5. Ollama
6. Vosk
7. Mozilla DeepSpeech
8. Picovoice Rhino
9. Picovoice Porcupine
10. Coqui STT
AI recommended 10 alternatives but never named janhq/ichigo. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What frameworks enable developers to build on-device voice AI applications with Python?
you: not recommended
AI recommended (in order):
1. Picovoice Porcupine & Rhino
2. Mozilla DeepSpeech
3. Vosk
4. OpenVINO
5. TensorFlow Lite
6. PyTorch Mobile
AI recommended 6 alternatives but never named janhq/ichigo. 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 janhq/ichigo?
pass
AI named janhq/ichigo explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts janhq/ichigo in production, what risks or prerequisites should they evaluate first?
pass
AI named janhq/ichigo 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 janhq/ichigo solve, and who is the primary audience?
pass
AI did not name janhq/ichigo — 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?

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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/janhq/ichigo"><img src="https://repogeo.com/badge/janhq/ichigo.svg" alt="RepoGEO" /></a>

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janhq/ichigo — 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