Asr logo

Asr

Organization
marswaveai
asr

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".

Overview

Publishermarswaveai
Repositoryskills
Skill nameasr
Stars
82
Forks
3
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by marswaveai on GitHub. Read the source before you install it.

Installation

Install the Asr AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/marswaveai/skills/tree/main/asr
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/marswaveai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/asr .claude/skills/asr
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Asr in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Asr on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Asr is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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 /podcast or /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.md before any interaction
  • Follow shared/cli-patterns.md for interaction patterns
  • Never ask more than one question at a time

Interaction Flow

Step 0: Prerequisites Check

Before config setup, silently check the environment:

bash
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)
IssueAction
coli not foundBlock. Tell user to run npm install -g @marswave/coli first
ffmpeg not foundWarn (WAV files still work). Suggest brew install ffmpeg / sudo apt install ffmpeg
Models not downloadedInform 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:

bash
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:

bash
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:

  1. 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
  2. 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

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):

bash
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}s

If 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" — done

If 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:

markdown
---
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"

  1. Check prerequisites
  2. Read config
  3. Confirm: meeting.m4a, sensevoice, polish on
  4. Run coli asr -j --model sensevoice "meeting.m4a"
  5. Polish the raw text
  6. Display inline

"transcribe interview.wav, no polish"

  1. Check prerequisites
  2. Read config
  3. Override polish to false for this session
  4. Run coli asr -j --model sensevoice "interview.wav"
  5. Display raw transcript inline

Frequently asked questions

What does the Asr AI skill do?

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".

Why use Asr on TypingMind?

Because you install it once and use it with any model. Asr is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Asr in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/marswaveai/skills/tree/main/asr. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Asr?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Asr?

As many as you like. As long as a model supports skills, you can use Asr with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Asr AI skill free?

Yes. It is published on GitHub by marswaveai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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