Long-to-Shorts Engine (Whisper + Gemini)
A repeatable SOP to mine the best moments from long-form videos, cut them cleanly with transcript timestamps, and keep a daily posting cadence across platforms—without hiring an editor.
Who Is This For?
What Problem Does It Solve?
Challenge
Manual clipping takes 2-6 hours per long video.
Cuts often land mid-word and feel unprofessional.
Posting consistency breaks when you get busy.
Solution
AI mines 3-6 clips automatically and produces ready-to-post shorts.
Word-level timestamps enable cleaner cut points with subtle pre/post-roll.
Auto-schedule one clip per day for consecutive days to maintain cadence.
What You'll Achieve with This Toolkit
Convert one long video into a week of shorts that look native to each platform, while protecting your original resolution and reducing editing time dramatically.
Cleaner Cuts that Feel Human
Word-accurate timestamps enable cuts that avoid mid-word glitches, improving perceived quality and watch time.
Daily Cadence Without Burnout
Consecutive-day scheduling turns a single production session into multiple days of growth.
How It Works
Step 1: Collect the Source Video
Start with a single long-form video (podcast, webinar, interview, talk). Ensure you have the final master file to avoid compression artifacts that reduce subtitle accuracy.
A long-form video ready for repurposing
Selected for its single-token workflow that can accept a source video and later reuse the same integration for processing and publishing, reducing operational complexity.
Upload-Post
Unified Social Media API to auto-publish videos, images, and posts across 10+ networks
Step 2: Extract Audio for Accurate Transcription
Extract a clean audio track from the video before transcription. This improves ASR stability and makes downstream clip timestamps more reliable.
Audio waveform extracted from video
Chosen for its deterministic media processing, enabling repeatable audio extraction that aligns perfectly with later cut operations.
Step 3: Transcribe with Word-Level Timestamps
Run Whisper transcription and keep timestamps granular enough to avoid mid-word cuts. Store the transcript with timing so clip boundaries can be derived from what people actually said.
Transcript with timestamps per segment/word
Selected for its proven ASR quality and support for word-level timestamps, which is the key to professional-feeling cuts.
OpenAI Whisper (whisper-1)
Speech-to-text API for word-timestamp subtitles and automation-ready transcripts
Step 4: Mine 3-6 High-Retention Moments
Use Gemini to analyze the transcript and propose 3-6 short segments (15-60 seconds) with hook-first structure. Generate per-clip titles/descriptions so publishing isn't blocked on copywriting.
AI-selected clip timestamps and titles
Chosen for its multimodal/video understanding capabilities and strong transcript reasoning, making clip selection more signal-driven than manual guessing.
Step 5: Cut, Crop, and Export Platform-Ready Shorts
Use FFmpeg to cut clips by exact timestamps, then crop/pad intelligently for 9:16 outputs while preserving source resolution when possible. Add subtle pre/post-roll to avoid abrupt starts.
Short-form clip export settings 9:16
Selected for GPU-accelerated FFmpeg processing plus a job/status model, which makes batch cutting reliable without maintaining your own video servers.
Step 6: Schedule One Clip Per Day
Schedule each short to publish on consecutive days (e.g., 3 clips = next 3 days, 6 clips = next 6 days). Keep a consistent posting time per timezone to train audience expectations.
Content calendar with consecutive-day scheduling
Chosen because it combines multi-platform posting and scheduling in one integration, preventing the "log into 3 apps" bottleneck.
Upload-Post
Unified Social Media API to auto-publish videos, images, and posts across 10+ networks
Similar Workflows
Looking for different tools? Explore these alternative workflows.
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Frequently Asked Questions
Most common video formats work; the SOP supports both vertical and horizontal inputs, then outputs platform-ready 9:16 shorts using crop/pad logic.
Typically 3-6 clips, depending on video length and the number of high-signal moments the transcript contains.
Clip selection quality depends on audio clarity and speaker structure; fast scene changes and noisy audio can reduce transcript accuracy and therefore highlight selection.
You can use any LLM that can consume transcripts and output timestamps + titles; the SOP stays the same as long as the model can rank moments and produce structured clip plans.
Yes—if your publishing API supports additional networks, you can extend the final step without changing the upstream transcription and clip mining logic.