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.