FunASR/docs/community_growth_20k.md

FunASR Community Growth Plan: 20,000+ GitHub Stars

Baseline on 2026-05-24: modelscope/FunASR has about 16.2k GitHub stars. The next target is 20k+ stars, which means earning roughly 3.8k additional stars through better onboarding, stronger proof, and repeatable launches.

This plan focuses on useful adoption work rather than vanity marketing: if more users can install, evaluate, deploy, and share FunASR successfully, stars follow naturally.

North-star metrics

• GitHub stars: 20,000+
• Monthly PyPI downloads: sustained growth after each release
• README quick-start success: first transcription in under 5 minutes
• Deployment success: API server, WebSocket, Docker, and vLLM examples verified on fresh machines
• Community throughput: faster issue triage, more external PRs, more user showcases

Audience segments

Audience	What they need	Repository surface that should serve them
ASR developers	Fast local transcript, model choice, Python API	README quick start, tutorial, model zoo
Production teams	Private deployment, streaming, API compatibility, Docker	runtime docs, OpenAI API example, vLLM guide
Agent builders	Speech input to Claude/Cursor/LangChain/Dify/AutoGen	MCP server and OpenAI API examples
Researchers	Benchmarks, reproducibility, citations, model details	benchmark report, docs, model cards
Chinese users	Chinese docs, ModelScope path, deployment in China	README_zh, docs/zh, ModelScope links
International users	English docs, Hugging Face path, comparison with Whisper	README, HF models, benchmark story

Workstream 1: Convert first-time visitors

• Keep the README first screen focused on speed, supported tasks, one-call usage, and links to docs.
• Keep the benchmark table close to the quick start, with exact benchmark scope and full report link.
• Maintain a short model-selection table: SenseVoice, Paraformer, Fun-ASR-Nano, Qwen3-ASR, GLM-ASR, streaming, punctuation, VAD, speaker, emotion.
• Add visible links to deployment, agent integration, contribution, and discussions.
• Avoid burying install and first inference behind long research history.

Workstream 2: Make deployment shareable

• Verify funasr-server --device cuda on a clean GPU machine and publish the exact command/output.
• Add curl examples for /v1/audio/transcriptions and WebSocket streaming.
• Keep Docker image tags explicit and document CPU/GPU differences.
• Add a small deployment decision table: Python API vs OpenAI API vs WebSocket vs Docker vs vLLM vs Triton.
• Turn common deployment failures into FAQ entries.

Workstream 3: Launch content that earns stars

Use one release note or article per clear story:

• "FunASR is 170x realtime and CPU viable for long audio."
• "Self-hosted OpenAI-compatible speech transcription with one command."
• "Streaming ASR with VAD, punctuation, and speaker diarization."
• "MCP speech input for Claude/Cursor and agent workflows."
• "vLLM acceleration for Fun-ASR-Nano."
• "Chinese dialect, accent, meeting, and industrial ASR examples."

For each launch, prepare:

• GitHub release notes with GIF/screenshot or terminal output
• README update and docs link
• Hugging Face and ModelScope model-card update
• Homepage update on modelscope.github.io/FunASR
• Short posts for developer communities
• A pinned GitHub discussion for feedback

Workstream 4: Community operations

• Use issue templates to collect reproducible environment, audio, and deployment details.
• Triage issues weekly with labels: bug, question, documentation, deployment, enhancement, good first issue.
• Convert repeated support answers into docs within 48 hours.
• Keep 5-10 good first issue tasks ready for contributors.
• Thank external contributors in release notes.

Workstream 5: External proof

• Publish reproducible benchmark scripts and raw configuration for the 184-file benchmark.
• Encourage users to share production stories, dashboards, or public demos.
• Add third-party integrations and community projects to README when they are maintained and runnable.
• Keep citations and paper links visible for researchers.

30-day execution checklist

Week 1: Repository conversion

• Verify README quick start on clean CPU and GPU environments.
• Verify all README links and docs links.
• Add or refresh CONTRIBUTING, PR template, and issue templates.
• Add a short FAQ for install/deployment failures.
• Confirm GitHub repo description and topics mention ASR, speech-recognition, streaming, diarization, OpenAI-compatible API, MCP, and vLLM.

Week 2: Deployment proof

• Publish API server curl examples.
• Publish WebSocket streaming examples.
• Validate Docker CPU and GPU images.
• Validate vLLM guide on one GPU server.
• Record concise terminal outputs for release notes.

Week 3: Launch package

• Create a GitHub release focused on one sharp value proposition.
• Update PyPI release description if needed.
• Update Hugging Face organization/model cards.
• Update ModelScope model cards and demos.
• Update homepage hero and docs entry points.

Week 4: Distribution and feedback

• Share the release in relevant developer communities.
• Pin a GitHub discussion for the release.
• Triage all new issues within 48 hours.
• Convert top 3 support questions into docs.
• Track stars, PyPI downloads, issue volume, and docs traffic.

Release-note template

## Why this release matters

FunASR now lets you ...

## Try it in 60 seconds

```bash
pip install -U funasr
funasr-server --device cuda
```

## Highlights

- ...
- ...
- ...

## Verified environments

- GPU:
- CPU:
- Docker:

## Links

- Quick start:
- Deployment guide:
- Benchmark:
- Discussion:

中文摘要

目标不是单纯“求 Star”，而是让更多用户能更快完成安装、转写、部署和分享。FunASR 距离 20k stars 还需要约 3.8k 增量，最有效的抓手是：首屏转化、可复现 benchmark、OpenAI 兼容 API/流式/vLLM/Docker 部署闭环、HF/ModelScope/官网同步更新，以及高效的 issue/PR 社区运营。