asr
Skill82 starsChanged 8 months agoMITView on GitHub
Transcribe audio files to text using local speech recognition. Triggers on: "转录", "transcribe", "语音转文字", "ASR", "识别音频", "把这段音频转成文字".
- Installs packages
8 other projects use a version of this.
Adopted by
Most recently taken up by
- momori777/Artemistheir own version, first seen Oct 22, 2026
- huang-x-h/z-skillstheir own version, first seen Oct 20, 2026
- WncFht/agent-basic-skilltheir own version, first seen Oct 13, 2026
- jjyaoao/HelloAgentstheir own version, first seen Oct 6, 2026
- choupiyang/ZAI_SKILLStheir own version, first seen Apr 20, 2026
- twomathematicians-code/AgentForgetheir own version, first seen Apr 16, 2026
Used with
Projects with it also use gift-evaluator, Podcast Generate and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
dav-niu474Skillgift-evaluator
The PRIMARY tool for Spring Festival gift analysis and social interaction generation. Use this skill when users upload photos of gifts (alcohol, tea, supplements, etc.) to inquire about their value, authenticity, or how to respond socially. Integrates visual perception, market valuation, and HTML card generation.
Also in 5 projects that use this
dav-niu474SkillPodcast Generate
Generate podcast episodes from user-provided content or by searching the web for specified topics. If user uploads a text file/article, creates a dual-host dialogue podcast (or single-host upon request). If no content is provided, searches the web for information about the user-specified topic and generates a podcast. Duration scales with content size (3-20 minutes, ~240 chars/min). Uses z-ai-web-dev-sdk for LLM script generation and TTS audio synthesis. Outputs both a podcast script (Markdown) and a complete audio file (WAV).
Also in 5 projects that use this
- +10 more from dav-niu474
Other versions8 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 8
- jjyaoao/HelloAgents★ 3.2ksame name
- momori777/Artemis★ 437same name
- WncFht/agent-basic-skill★ 30same name
- choupiyang/ZAI_SKILLS★ 13same name
- huang-x-h/z-skills★ 4same name
- dav-niu474/MyClaude-Code★ 1same name
- SuperInstance/flux-fleet-scanner★ 1same name
- twomathematicians-code/AgentForge★ 1same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository18 other files it gives its agents.
- cola-avatar-packSkill
- content-parserSkill
- creatorSkill
- explainerSkill
- google-calendarSkill
- icloud-calendarSkill
- image-genSkill
- listenhub-cliSkill
- listenhubSkill
- listenhub-voiceSkill
- musicSkill
- outlook-calendarSkill
- podcastSkill
- slidesSkill
- ttsSkill
- universal-emailSkill
- video-genSkill
- voice-cloneSkill
The file
Show its rules and text
We split this skill into the rules it gives an agent
18 rules. 0 are common, 10 are distinctive, and 8 are very specific to this project.
0
Common
Most of the 6 edited versions we read keep these.
10
Distinctive
This author's own choices. Worth a look.
8
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 30
Run coli asr --help for current CLI options and supported flags.
- Distinctive: line 34
No shell scripts. Use direct commands only.
- Very specific: line 35
Always read config following shared/config-pattern.md before any interaction
- Very specific: line 36
Follow shared/cli-patterns.md for interaction patterns
- Distinctive: line 37
Never ask more than one question at a time
- Distinctive: line 40
Use the AskUserQuestion tool for every multiple-choice step — do NOT print options as plain text.
- Distinctive: line 42
After all parameters are collected, summarize and ask the user to confirm before running any transcription.
- Distinctive: line 51
Before config setup, silently check the environment:
- Very specific: line 66
If coli is missing, stop here and do not proceed.
- Very specific: line 70
Follow shared/config-pattern.md Step 0 (Zero-Question Boot).
- Distinctive: line 90
Only run when the user explicitly asks to reconfigure.
- Distinctive: line 111
If the user hasn't provided a file path, ask:
- Distinctive: line 115
Verify the file exists before proceeding.
- Very specific: line 131
Run coli asr with JSON output (to get metadata):
- Very specific: line 144
If polish is true, take the raw text from the transcription result and rewrite it to fix punctuation, remove filler words, and improve …
- Distinctive: line 161
If polished, show the polished version with a note that it was AI-refined.
- Distinctive: line 166
After presenting the result, ask:
- Very specific: line 175
If yes, write {audio-filename}-transcript.md to the current working directory (where the user is running Claude Code).
“Common” is measured: kept by at least 6 in 10 of the 6 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
When to Use
- User wants to transcribe an audio file to text
- User provides an audio file path and asks for transcription
- User says "转录", "识别", "transcribe", "语音转文字"
When NOT to Use
- User wants to synthesize speech from text (use
/tts) - User wants to create a podcast or explainer (use
/podcastor/explainer)
Purpose
Transcribe audio files to text using coli asr, which runs fully offline via local
speech recognition models. No API key required. Supports Chinese, English, Japanese,
Korean, and Cantonese (sensevoice model) or English-only (whisper model).
Run coli asr --help for current CLI options and supported flags.
Hard Constraints
- No shell scripts. Use direct commands only.
- Always read config following
shared/config-pattern.mdbefore any interaction - Follow
shared/cli-patterns.mdfor interaction patterns - Never ask more than one question at a time
<HARD-GATE>
Use the AskUserQuestion tool for every multiple-choice step — do NOT print options as
plain text. Ask one question at a time. Wait for the user's answer before proceeding.
After all parameters are collected, summarize and ask the user to confirm before
running any transcription.</HARD-GATE>Interaction Flow
Step 0: Prerequisites Check
Before config setup, silently check the environment:
COLI_OK=$(which coli 2>/dev/null && echo yes || echo no)
FFMPEG_OK=$(which ffmpeg 2>/dev/null && echo yes || echo no)
MODELS_DIR="$HOME/.coli/models"
MODELS_OK=$([ -d "$MODELS_DIR" ] && ls "$MODELS_DIR" | grep -q sherpa && echo yes || echo no)| Issue | Action |
coli not found | Block. Tell user to run npm install -g @marswave/coli first |
ffmpeg not found | Warn (WAV files still work). Suggest brew install ffmpeg / sudo apt install ffmpeg |
| Models not downloaded | Inform user: first transcription will auto-download models (~60MB) to ~/.coli/models/ |
If coli is missing, stop here and do not proceed.
Step 0: Config Setup
Follow shared/config-pattern.md Step 0 (Zero-Question Boot).
If file doesn't exist — silently create with defaults and proceed:
mkdir -p ".listenhub/asr"
echo '{"model":"sensevoice","polish":true}' > ".listenhub/asr/config.json"
CONFIG_PATH=".listenhub/asr/config.json"
CONFIG=$(cat "$CONFIG_PATH")Do NOT ask any setup questions. Proceed directly to the Interaction Flow with sensible defaults (sensevoice model, polish enabled).
If file exists — read config silently and proceed:
CONFIG_PATH=".listenhub/asr/config.json"
[ ! -f "$CONFIG_PATH" ] && CONFIG_PATH="$HOME/.listenhub/asr/config.json"
CONFIG=$(cat "$CONFIG_PATH")Setup Flow (user-initiated reconfigure only)
Only run when the user explicitly asks to reconfigure. Display current settings:
当前配置 (asr):
模型:sensevoice / whisper-tiny.en
润色:开启 / 关闭Ask in order:
- model: "默认使用哪个语音识别模型?"
- "sensevoice(推荐)" — 支持中英日韩粤,可检测语言、情绪、音频事件
- "whisper-tiny.en" — 仅英文
- polish: "转录后由 AI 润色文本?(修正标点、去语气词、提升可读性)"
- "是(推荐)" →
polish: true - "否,保留原始转录" →
polish: false
- "是(推荐)" →
Save all answers at once after collecting them.
Step 1: Get Audio File
If the user hasn't provided a file path, ask:
"请提供要转录的音频文件路径。"
Verify the file exists before proceeding.
Step 2: Confirm
准备转录:
文件:{filename}
模型:{model}
润色:{是 / 否}
继续?Step 3: Transcribe
Run coli asr with JSON output (to get metadata):
coli asr -j --model {model} "{file}"On first run, coli will automatically download the required model. This may take a
moment — inform the user if models haven't been downloaded yet.
Parse the JSON result to extract text, lang, emotion, event, duration.
Step 4: Polish (if enabled)
If polish is true, take the raw text from the transcription result and rewrite
it to fix punctuation, remove filler words, and improve readability. Preserve the
original meaning and speaker intent. Do not summarize or paraphrase.
Step 5: Present Result
Display the transcript directly in the conversation:
转录完成
{transcript text}
─────────────────
语言:{lang} · 情绪:{emotion} · 时长:{duration}sIf polished, show the polished version with a note that it was AI-refined. Offer to show the raw original on request.
Step 6: Export as Markdown (optional)
After presenting the result, ask:
Question: "保存为 Markdown 文件到当前目录?"
Options:
- "是" — save to current directory
- "否" — doneIf yes, write {audio-filename}-transcript.md to the current working directory
(where the user is running Claude Code). The file should contain the transcript text
(polished version if polish was enabled), with a front-matter header:
---
source: {original audio filename}
date: {YYYY-MM-DD}
model: {model used}
duration: {duration}s
lang: {detected language}
---
{transcript text}Composability
- Invoked by: future skills that need to transcribe recorded audio
- Invokes: nothing
Examples
"帮我转录这个文件 meeting.m4a"
- Check prerequisites
- Read config
- Confirm: meeting.m4a, sensevoice, polish on
- Run
coli asr -j --model sensevoice "meeting.m4a" - Polish the raw text
- Display inline
"transcribe interview.wav, no polish"
- Check prerequisites
- Read config
- Override polish to false for this session
- Run
coli asr -j --model sensevoice "interview.wav" - Display raw transcript inline
Does it work?
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