CleanScript — Strip Filler Words From Any Transcript in 10 Seconds
Every podcaster and interviewer has stared at a transcript full of 'like, you know, I mean, basically' wondering why transcription tools ship raw garbage. CleanScript takes any .vtt, .txt, or .srt file and returns a polished, filler-free transcript in under 10 seconds. No Descript subscription, no manual find-and-replace hell.
Difficulty
beginner
Category
NLP & Text AI
Market Demand
High
Revenue Score
7/10
Platform
Web App
Vibe Code Friendly
⚡ YesHackathon Score
🏆 8/10
Validated by Real Pain
— sourced from real community discussions
Podcasters and video creators consistently complain that transcription tools like Otter and Whisper produce filler-word-laden output that requires 20-40 minutes of manual cleaning per episode before it is publishable.
What is it?
Otter.ai, Whisper, and every other transcription tool dump raw speech-to-text output — filler words, false starts, repeated phrases and all. Podcasters and interviewers then spend 20-40 minutes manually cleaning each transcript before it becomes show notes, a blog post, or a caption file. CleanScript is a one-page web uploader: drop your .vtt, .txt, or .srt file, click clean, and download a polished transcript with all configurable filler words stripped and false starts collapsed. V1 is a regex + NLP hybrid (no LLM needed for basic cleaning), keeping costs near zero. The Otter API integration lets Otter users clean directly from their account without downloading. Buildable in 3 days — this is the definition of a weekend ship.
Why now?
Whisper and Otter adoption has exploded in the past 12 months, flooding podcasters with raw messy transcripts — the volume of filler-word complaints in podcast communities has never been higher, and a weekend-shippable NLP micro-tool is the perfect answer.
- ▸File upload: accepts .vtt, .srt, and .txt transcript files and returns cleaned output instantly.
- ▸Configurable filler list: user can add or remove words from the default strip list before cleaning.
- ▸Diff preview: shows exactly what was removed highlighted in red before the user downloads.
- ▸Otter.ai direct integration: connect Otter account and clean transcripts without downloading.
Target Audience
Independent podcasters, journalists, and YouTube creators — roughly 500k active English-language podcasters who transcribe regularly.
Example Use Case
Marcus, an indie podcast host publishing 3 episodes per week, uploads his Whisper-generated .vtt files and downloads filler-free transcripts for show notes in 10 seconds instead of 30 minutes — saving 90 minutes every week.
User Stories
- ▸As a podcast host, I want to upload my Whisper transcript and download a filler-free version in under 30 seconds, so that I can publish show notes without manual editing.
- ▸As a journalist, I want to configure my own filler word list before cleaning, so that I preserve intentional casual language for quotes.
- ▸As a YouTube creator, I want to see a diff of every word removed before downloading, so that I can verify nothing important was stripped.
Done When
- ✓Upload: done when user drags a .vtt file onto the uploader and sees a before/after word count within 5 seconds.
- ✓Diff preview: done when removed filler words appear highlighted in red in a side-by-side view before download.
- ✓Custom filler list: done when user adds a custom word, clicks clean, and that word is stripped from the output.
- ✓Paywall: done when free user hits their 5th transcript and sees a Stripe checkout prompt before the download button appears.
Is it worth building?
$15/month x 100 users = $1,500 MRR at month 2. $40/month x 100 users = $4,000 MRR at month 4. Math assumes 5% conversion from free tier of 2,000 monthly visitors via podcast communities.
Unit Economics
CAC: $8 via Reddit organic (free tool virality). LTV: $360 (24 months at $15/month). Payback: under 1 month. Gross margin: 95%.
Business Model
SaaS subscription
Monetization Path
Free: 5 transcripts/month. $15/month: 50 transcripts. $40/month: unlimited. Upgrade triggered by hitting free cap.
Revenue Timeline
First dollar: week 2 via free-to-paid flip. $1k MRR: month 2. $5k MRR: month 6.
Estimated Monthly Cost
Vercel: $20, Supabase: $25, Stripe fees: ~$15. Total: ~$60/month at launch (no LLM API needed for V1).
Profit Potential
Lifestyle business viable at $3k-$8k MRR with minimal infra cost.
Scalability
Medium — add speaker-aware cleaning, custom filler word lists, and bulk upload for agencies.
Success Metrics
Week 1: 200 free-tier users. Week 3: 20 paying customers. Month 2: 80% month-1 retention.
Launch & Validation Plan
Post a free web tool in r/podcasting and 3 podcast Discord servers, collect 50 emails before adding Stripe, then flip to paid.
Customer Acquisition Strategy
First customer: post a free version in r/podcasting and podcast Discord communities, collect 200 users organically, then add Stripe paywall on the 6th transcript. Ongoing: SEO targeting 'clean transcript filler words', YouTube tutorial, ProductHunt.
What's the competition?
Competition Level
Low
Similar Products
Descript (full video editor, overkill for transcript cleaning, $24/month), Otter.ai (transcribes but does not clean output), Simon Says (expensive enterprise tool) — none offer dead-simple filler-word stripping as a standalone micro-tool.
Competitive Advantage
10x faster than Descript for this specific task, $30/month cheaper, and no subscription lock-in for occasional users.
Regulatory Risks
Low regulatory risk. GDPR: transcripts processed in memory only, not stored unless user opts in.
What's the roadmap?
Feature Roadmap
V1 (launch): file upload, filler stripping, diff preview, Stripe paywall. V2 (month 2-3): Otter API direct integration, custom filler lists, bulk upload. V3 (month 4+): speaker-aware cleaning, agency team plans, API access.
Milestone Plan
Phase 1 (Week 1): cleaner engine, file upload, diff preview ship. Phase 2 (Week 2): Stripe paywall, Supabase usage tracking, demo seed data. Phase 3 (Month 2): Otter integration, 50 paying users, SEO landing page live.
How do you build it?
Tech Stack
Next.js, compromise.js (NLP), Otter API (optional), Supabase, Stripe — build with Lovable for full UI, Cursor for NLP logic, v0 for upload component.
Suggested Frameworks
compromise.js, natural (npm), Otter.ai API
Time to Ship
3 days
Required Skills
Next.js file upload handling, regex and NLP with compromise.js, Stripe billing.
Resources
compromise.js docs, Otter API docs, Next.js file upload tutorial, Stripe Checkout quickstart.
MVP Scope
app/page.tsx (landing + upload hero), app/api/clean/route.ts (cleaning engine), app/api/otter/route.ts (Otter API proxy), lib/cleaner.ts (regex + compromise NLP), components/DiffPreview.tsx (before/after diff), components/FillerConfig.tsx (custom word list UI), seed.ts (demo transcript), .env.example (required env vars).
Core User Journey
Upload transcript -> configure filler list -> see diff preview -> download cleaned file -> hit cap -> upgrade to paid.
Architecture Pattern
User uploads file -> Next.js API route -> compromise.js strips fillers -> diff computed -> cleaned file returned as download -> usage logged to Supabase -> cap check triggers Stripe upgrade prompt.
Data Model
User has many CleanJobs. CleanJob stores file name, filler count removed, word count before and after, timestamp. User has one UsagePlan with monthly transcript count.
Integration Points
Stripe for payments, Supabase for usage tracking and auth, Otter API for direct account integration, Resend for upgrade nudge emails.
V1 Scope Boundaries
V1 excludes: video file input, speaker diarization, team accounts, API access, mobile app.
Success Definition
A podcaster the founder has never met finds CleanScript via a Reddit search, upgrades to paid after hitting the free cap, and returns the following week.
Challenges
Distribution is the hardest problem — podcasters share tools in niche Discord servers and subreddits, not on ProductHunt. Getting first 10 paying customers requires being active in r/podcasting and podcast Facebook groups for weeks before launch. Budget 4x more time for community distribution than for building.
Avoid These Pitfalls
Do not add LLM-based cleaning in V1 — regex plus compromise.js is fast, free, and accurate enough to charge for. Do not store transcript content server-side without explicit user consent. Do not build Otter integration before 50 paying users confirm they want it.
Security Requirements
Supabase Auth with magic link, transcript files processed in memory and never persisted without consent, rate limiting 30 uploads/min per IP, GDPR deletion endpoint required.
Infrastructure Plan
Vercel for Next.js, Supabase for auth and usage DB, no file storage needed (process in memory), Sentry for errors — total ~$60/month.
Performance Targets
200 DAU at launch, cleaning API under 500ms for files up to 50k words, page load under 1.5s, no caching needed at V1 scale.
Go-Live Checklist
- ☐Security audit complete.
- ☐Stripe checkout tested end-to-end.
- ☐Sentry live and catching errors.
- ☐Demo transcript pre-loaded on first run.
- ☐Custom domain and SSL configured.
- ☐Privacy policy and terms published.
- ☐10 beta podcasters signed off.
- ☐Rollback plan documented.
- ☐r/podcasting and ProductHunt posts drafted.
First Run Experience
On first run: a sample 800-word podcast transcript with 47 filler words is pre-loaded. User can immediately click Clean and see the diff preview with all fillers highlighted in red. No account or API key required to try the demo.
How to build it, step by step
1. Define Supabase schema for User, CleanJob, UsagePlan in a schema.sql file. 2. Run npx create-next-app with TypeScript and Tailwind. 3. Install compromise, natural, and file-saver npm packages. 4. Build file upload endpoint in app/api/clean/route.ts that parses .vtt, .srt, and .txt. 5. Implement filler-word stripping logic in lib/cleaner.ts using regex and compromise.js sentence detection. 6. Build DiffPreview component showing removed words highlighted in red. 7. Add usage tracking in Supabase and cap check at 5 transcripts for free users. 8. Wire Stripe Checkout for $15/month and $40/month plans triggered by cap hit. 9. Add seed demo transcript so first-run shows a populated before/after diff instantly. 10. Deploy to Vercel and walk the full journey from upload to paid upgrade without any manual setup.
Generated
May 9, 2026
Model
claude-sonnet-4-6
Disclaimer: Ideas on this site are AI-generated and may contain inaccuracies. Revenue estimates, market demand figures, and financial projections are illustrative assumptions only — not financial advice. Do your own research before making any business or investment decisions. Technology availability, pricing, and market conditions change rapidly; always verify details independently.