中文
FreeAudio & Video5 nodes
Score 69

Video Transcription Workflow

Drop a video or podcast — faster-whisper transcribes with timestamps, LLMs add speaker labels and chapter markers, output SRT + DOCX + plain text.

What does this workflow do?

音频流预处理(降噪/增益归一化/VAD 语音活性检测)→ Faster-Whisper Large 高精度转写 → 说话人聚类与分离 → 时间戳对齐 → AI 语篇分割(按话题切换自动分段)→ 内容分类(观点/背景/行动/讨论)→ 金句与关键数据提取 → 可编辑 Word 输出

System flow

音频流预处理(降噪/增益归一化/VAD 语音活性检测)→ Faster-Whisper Large 高精度转写 → 说话人聚类与分离 → 时间戳对齐 → AI 语篇分割(按话题切换自动分段)→ 内容分类(观点/背景/行动/讨论)→ 金句与关键数据提取 → 可编辑 Word 输出

Pipeline breakdown

Pillow audio Faster-Whisper audio LLM Reasoning json LLM Reasoning json Python-DOCX json
  1. Pillow pillow — Image pre-processing — deskew, denoise, crop and normalise before analysis audio → audio
  2. Faster-Whisper faster-whisper — Local speech-to-text transcription with timestamps and speaker turns audio → json
  3. LLM Reasoning llm — Large-language-model step that extracts, classifies, validates or writes structured output json → json
  4. LLM Reasoning llm — Large-language-model step that extracts, classifies, validates or writes structured output json → json
  5. Python-DOCX python-docx — Renders the final Word document with headings, tables and styling json → docx

Workflow Reputation Score

Based on executions, success rate, update frequency, component quality, and user rating.

69Overall
★★★★ 4.2
Executions
25
Success rate
96
Update frequency
65
Component quality
88
User rating
84

Based on executions, success rate, update frequency, component quality, and user rating.

Components used

Pillowpillow

Image pre-processing — deskew, denoise, crop and normalise before analysis

python-pillow/Pillow ↗
Faster-Whisperfaster-whisper

Local speech-to-text transcription with timestamps and speaker turns

SYSTRAN/faster-whisper ↗
LLM Reasoningllm

Large-language-model step that extracts, classifies, validates or writes structured output

FlowSync proprietary
Python-DOCXpython-docx

Renders the final Word document with headings, tables and styling

python-openxml/python-docx ↗

How is this different from n8n or Zapier?

Unlike generic workflow builders, FlowSync workflows ship pre-configured with AI skills baked into every node — OCR, LLM reasoning, transcription, super-resolution — not just webhook triggers. Each node is white-box auditable: you see input, output, and config. Deployment is instant — no self-hosting, no API-key per node.

Use cases

  • 视频内容多平台分发
  • 播客逐字稿生成
  • 采访录音整理
  • 在线课程字幕

Author & Source

This workflow is maintained by the FlowSync team. All components are open-source or commercially licensed. Source code for each component is linked in the Components section above.

Pricing: Free tier available · FREE tier · Official

Field notes

Descript $24/月,我一个月只出 4 期视频。这个免费版效果一样,金句提取功能 Descript 还要加钱。

— UP主 小张, B站知识区

采访录音自动转文字+标时间戳,写稿效率提升太多了。

— 记者 陈, 调查记者

Data handling

上传的音视频仅在转录过程中临时使用,完成后即刻清除。文字稿仅存储于您的账户。语音数据不会被用于任何模型训练或第三方共享。

FlowSync Workflow Marketplace · 69 reputation score · 25% execution · 96% success · 5-node white-box pipeline · maintained by FlowSync Official

Ready to run?

Drop a video or podcast — faster-whisper transcribes with timestamps, LLMs add speaker labels and chapter markers, output SRT + DOCX + plain text.