audio-transcribe-summarize

通过 SenseAudio ASR 接口把音频或视频文件转写为文本，支持模型选择、说话人分离、情感识别、时间戳与翻译，大文件用 ffmpeg 自动切分。随后按模板生成要点、详细摘要、行动项、引语与完整转写等结构化笔记。

q1lin570

热度 51.77

最近更新 2026-05-11 17:33:17

分类：效率依赖：代码执行 / 读写文件 / 第三方 API / 内部调用大模型应用场景：摘要总结 / 会议纪要 / 翻译 / 笔记整理

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add audio-transcribe-summarize

Metadata

name: audio-transcribe-summarize
description: Transcribe audio/video files to text and generate structured summaries using SenseAudio ASR API. Use when the user asks to transcribe, summarize, or take notes from audio files, video files, recordings, meetings, lectures, podcasts, or interviews.

Audio/Video Transcription & Summarization

Transcribe audio/video files using the SenseASR API (api.senseaudio.cn), then summarize the content into structured notes.

{baseDir} refers to this skill's directory.

Prerequisites

Environment variable SENSEAUDIO_API_KEY configured (get your key at https://senseaudio.cn/platform/api-key)
Python 3.8+ with requests installed
For large files (>10MB): ffmpeg installed for splitting（macOS: brew install ffmpeg，Windows: ffmpeg.org 下载并加入 PATH，Linux: apt install ffmpeg）

Quick Start

Run the transcription script:

python {baseDir}/scripts/transcribe.py <audio_file> [--model sense-asr-pro] [--language zh] [--speakers] [--sentiment] [--translate en]

The script outputs a transcript .txt file alongside the source file
Read the transcript and generate a summary (see Summary Format below)

Workflow

Step 1: Assess the Audio File

Check file size and format:

Supported formats: wav, mp3, ogg, flac, aac, m4a, mp4
Max file size per request: 10MB
If file > 10MB, the script auto-splits using ffmpeg

Step 2: Choose the Right Model

Model	Use When
`sense-asr-lite`	Quick batch transcription, simple audio, cost-sensitive
`sense-asr`	General transcription, need speaker separation or timestamps
`sense-asr-pro`	High accuracy needed: meetings, interviews, complex audio
`sense-asr-deepthink`	Noisy audio, dialects, heavy jargon, speech-to-clean-text

Default to sense-asr-pro for best quality.

Step 3: Transcribe

Run the transcription script. Key options:

# Basic transcription
python {baseDir}/scripts/transcribe.py recording.mp3

# Meeting with multiple speakers + emotion
python {baseDir}/scripts/transcribe.py meeting.wav \
  --model sense-asr-pro \
  --speakers --max-speakers 4 \
  --sentiment \
  --timestamps segment

# Transcribe and translate to English
python {baseDir}/scripts/transcribe.py lecture.mp3 \
  --model sense-asr \
  --translate en

Step 4: Summarize

After transcription, read the transcript file and produce a summary using the format below.

Summary Format

Generate summaries in this structure:

# [Title - inferred from content]

**Source**: filename.mp3
**Duration**: X min Y sec
**Date**: YYYY-MM-DD
**Speakers**: [if speaker diarization was used]

## Key Points
- Point 1
- Point 2
- ...

## Detailed Summary
[2-4 paragraph summary of the content organized by topic/chronology]

## Action Items
- [ ] Action item 1 (assigned to Speaker X, if applicable)
- [ ] Action item 2

## Notable Quotes
> "Direct quote from transcript" — Speaker X, [timestamp if available]

## Full Transcript
<details>
<summary>Click to expand full transcript</summary>

[Full transcript text here, with speaker labels and timestamps if available]

</details>

Adapt the template based on content type:

Meeting: emphasize action items, decisions, speaker contributions
Lecture/Talk: emphasize key concepts, learning points, structure
Interview: emphasize Q&A pairs, key responses
Podcast: emphasize topics discussed, interesting insights

API Reference

For full SenseASR API parameters and response formats, see api-reference.md.