audio-transcribe-summarize

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

1. Run the transcription script:

bash

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

2. The script outputs a transcript .txt file alongside the source file
3. 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:

bash

# 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:

markdown

# [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.