Diarize Studio
A visual debugger and playground for Deepgram's speaker diarization API that maps every word to its speaker in real time. Paste your audio URL or upload a file, fire the API, and instantly see a color-coded transcript with speaker labels, word-level timestamps, and confidence scores — no curl commands required.
Difficulty
intermediate
Category
Developer Tooling
Market Demand
Medium
Revenue Score
6/10
Platform
Web App
Vibe Code Friendly
⚡ YesHackathon Score
6/10
Validated by Real Pain
— sourced from real search demand
Developers are actively searching for how to access and interpret the speaker field returned per word in Deepgram's v1 listen diarization API response, indicating friction with parsing and debugging the raw output.
What is it?
Developers integrating Deepgram's v1 listen endpoint with diarization enabled constantly hit the same wall: the raw JSON response is deeply nested, the speaker field lives inside each word object, and debugging who said what requires manual parsing. Diarize Studio wraps the Deepgram API in a clean browser UI that sends the request, parses the response, and renders an interactive transcript where each speaker gets a color lane and every word is clickable to reveal its raw JSON payload. Users bring their own Deepgram API key so there is zero infrastructure cost to the builder. A one-click export produces a clean JSON, SRT subtitle file, or CSV mapping speaker IDs to word-level timestamps — the three formats developers actually need downstream. The tool is aimed at podcast tool builders, call analytics developers, and transcription SaaS teams who are prototyping diarization features and need to validate output before writing production code.
Why now?
Deepgram released significant diarization improvements in late 2023 and their developer community is actively growing — the word-level speaker field in the v1/listen response is new enough that tooling around it is essentially nonexistent, creating a first-mover window of 3-6 months.
- ▸BYOK (bring your own Deepgram API key) playground that fires the v1/listen endpoint with diarize:true and renders color-coded speaker lanes in under 10 seconds
- ▸Word-level inspector: click any word in the transcript to see its raw JSON object including speaker field, start/end timestamps, punctuated_word, and confidence score
- ▸One-click export to three formats: cleaned JSON (speaker-grouped), SRT subtitle file with speaker labels, and CSV with columns word, speaker, start, end, confidence
- ▸Session history: last 20 analyzed files saved to Supabase so users can diff diarization results across Deepgram model versions side by side
Target Audience
Developers and indie hackers building on Deepgram's speech-to-text API — estimated 15,000+ active Deepgram API users — who need to visually debug speaker diarization output without writing custom parsing scripts.
Example Use Case
Maya is building a podcast analytics SaaS. She uploads a 10-minute interview, hits Analyze, and within 8 seconds sees Speaker 0 and Speaker 1 color-coded across 847 words with confidence scores — she exports the CSV and pastes it straight into her backend schema design.
User Stories
- ▸As a developer integrating Deepgram diarization, I want to visually see which speaker said each word without parsing raw JSON manually, so that I can validate my API integration is working correctly before writing production code.
- ▸As a podcast tool builder, I want to export speaker-labeled word timestamps as a CSV, so that I can directly import them into my database schema without writing a custom parser.
- ▸As a Deepgram user testing different model versions, I want to compare diarization output across two audio sessions side by side, so that I can choose the model that best separates my target speakers.
Done When
- ✓Diarization analysis: done when user pastes a valid Deepgram API key and audio URL, clicks Analyze, and sees color-coded speaker lanes with every word labeled within 15 seconds.
- ✓Word inspector: done when clicking any word in the transcript opens a popover showing the exact raw JSON object including speaker, start, end, punctuated_word, and confidence fields.
- ✓Export: done when clicking Export CSV downloads a valid .csv file with headers word, speaker, start, end, confidence populated from the last analysis.
- ✓Payment: done when Stripe processes a Pro subscription and the user's session history limit increases from 3 to unlimited without page refresh.
Is it worth building?
$12/month x 80 users = $960 MRR at month 3, plus $49 lifetime deals averaging 20/month early on.
Unit Economics
CAC: ~$8 via Deepgram Discord + Twitter organic. LTV: $144 (12 months at $12/month) or $49 one-time. Payback: under 1 month for lifetime, 1 month for subscription. Gross margin: ~98% (no per-request AI cost).
Business Model
Freemium + one-time lifetime deal
Monetization Path
Free tier: 3 audio files per day, no export. Pro at $12/month or $49 lifetime unlocks unlimited files, all export formats, and saved session history. Target 8% free-to-paid conversion.
Revenue Timeline
First dollar: day 5 (lifetime deal). $1k MRR: month 4. $3k MRR: month 9 if bundled with other Deepgram feature explorers.
Estimated Monthly Cost
Vercel Pro: $20, Supabase free tier: $0, Stripe: 2.9% of revenue, no AI API cost (user brings own Deepgram key). Total fixed cost: ~$20/month.
Profit Potential
Side-income viable at $1k MRR in month 3; full-time stretch goal requires bundling additional Deepgram feature explorers.
Scalability
Medium — add team workspaces, batch file processing, webhook support, and a shareable transcript link for async review.
Success Metrics
Week 1: 200 signups via Show HN. Month 2: 85% of pro subscribers use the tool at least 3x/week. Month 3: 80 paying users.
Launch & Validation Plan
Post in Deepgram's Discord #developers channel asking if anyone wants a visual diarization debugger. DM 15 developers who have publicly tweeted about Deepgram diarization issues. Collect 10 beta signups before writing a line of code.
Customer Acquisition Strategy
First customer: post a 60-second Loom demo in Deepgram Discord offering free Pro access for first 10 users. Then: Show HN post, tweet thread showing the before/after of raw JSON vs. visual lanes, target r/speechtech and r/MachineLearning.
What's the competition?
Competition Level
Low
Similar Products
Deepgram's own console playground (no word-level inspector, no export), AssemblyAI playground (locked to AssemblyAI only) — neither solves the Deepgram-specific diarization debugging workflow.
Competitive Advantage
Deepgram's own playground does not show word-level speaker fields visually; competitors like AssemblyAI playground are locked to their own API. Diarize Studio is the only BYOK visual debugger purpose-built for the Deepgram diarization response schema.
Regulatory Risks
Low — audio is processed client-to-Deepgram directly via user's own API key; builder never stores raw audio. GDPR exposure is minimal.
What's the roadmap?
Feature Roadmap
V1 (launch): BYOK playground, color speaker lanes, word inspector, CSV/JSON/SRT export. V2 (month 2-3): session history, side-by-side model comparison, shareable transcript links. V3 (month 4+): batch file upload, team workspaces, Deepgram webhook listener for real-time streams.
Milestone Plan
Phase 1 (Week 1-2): core analyze route + TranscriptLane UI + WordInspector + export — done when a full audio file produces a downloadable CSV. Phase 2 (Week 3-4): Supabase Auth + Stripe Pro + session history — done when a paying user can log back in and see their last 20 sessions. Phase 3 (Month 2): side-by-side comparison view + shareable links — done when a user can send a public URL to a transcript without requiring the recipient to log in.
How do you build it?
Tech Stack
Next.js 14, Deepgram Node SDK, Tailwind CSS, Zustand, Stripe, Supabase — build with Cursor
Suggested Frameworks
Deepgram JS SDK, @deepgram/sdk, react-json-view, file-saver
Time to Ship
2 weeks
Required Skills
Deepgram SDK integration, Next.js API routes, JSON parsing, Stripe billing, Tailwind UI.
Resources
Deepgram diarization docs (deepgram.com/docs), Deepgram JS SDK GitHub, Stripe docs, Supabase quickstart.
MVP Scope
app/page.tsx (landing + upload form), app/api/analyze/route.ts (Deepgram proxy), app/dashboard/page.tsx (transcript viewer), lib/parse-diarization.ts (word-to-speaker mapper), components/TranscriptLane.tsx (color speaker UI), components/WordInspector.tsx (JSON popover)
Core User Journey
Land on page -> paste Deepgram API key -> upload audio file or URL -> click Analyze -> view color-coded speaker lanes -> click a word to inspect JSON -> export CSV -> upgrade to Pro for history.
Architecture Pattern
User uploads file or pastes URL -> Next.js API route proxies to Deepgram v1/listen with diarize:true -> response parsed by lib/parse-diarization.ts -> structured data stored in Zustand -> React renders TranscriptLane components -> export triggers file-saver.
Data Model
User has many Sessions. Session has one AudioFile (URL reference only, no binary storage) and one ParsedTranscript (JSONB). ParsedTranscript has many Words (speaker, start, end, confidence, punctuated_word).
Integration Points
Deepgram JS SDK for API calls, Stripe Checkout for payments, Supabase Auth for sessions and history storage, file-saver for client-side export.
V1 Scope Boundaries
V1 excludes: team workspaces, batch processing of multiple files, webhook listener, mobile app, white-label, and support for non-Deepgram STT providers.
Success Definition
A paying stranger uploads an audio file, sees speaker-labeled word-level output, exports a CSV, and does not contact support.
Challenges
Distribution is the main challenge — this is a niche developer tool, so reaching Deepgram users requires presence in their specific communities: Deepgram Discord, Twitter/X developer threads, and Hacker News Show HN posts.
Avoid These Pitfalls
Do not proxy or store user audio server-side — send API calls directly from the browser using the user's own key to avoid storage liability and infrastructure costs. Do not over-build the UI before validating that developers want a visual tool vs. a CLI — ship the browser playground first and only add a CLI wrapper if users explicitly ask.
Security Requirements
Deepgram API keys never logged server-side and only used in-memory per request. Supabase RLS ensures users can only read their own sessions. Rate limit analyze endpoint to 20 requests per hour per IP on free tier. HTTPS enforced via Vercel.
Infrastructure Plan
Vercel for Next.js hosting with edge functions for analyze route, Supabase for auth and session storage, GitHub Actions for CI running lint and type-check on every PR, Sentry for error tracking on API routes.
Performance Targets
Analyze API route under 500ms excluding Deepgram response time. Transcript render for 1000-word response under 300ms. Page load under 2s on 4G. Supabase queries under 100ms with proper indexes on user_id.
Go-Live Checklist
- ☐Deepgram API key is never logged or persisted server-side — verified via code audit.
- ☐Stripe payment flow tested end-to-end in test mode with a real card number.
- ☐Sentry error tracking live and receiving test events from staging.
- ☐Supabase RLS policies verified: user cannot query another user's sessions via direct API call.
- ☐Custom domain with SSL live on Vercel.
- ☐Privacy policy published covering audio URL handling and no-storage policy.
- ☐5 beta users from Deepgram Discord have completed full analyze-to-export journey without support.
- ☐Rollback plan documented: Vercel instant rollback to previous deployment SHA.
- ☐Launch post drafted for Deepgram Discord, Hacker News Show HN, and Twitter thread.
First Run Experience
On first load: a pre-filled demo Deepgram API key (read-only test key with a 60-second sample interview pre-loaded) lets users click Analyze immediately and see the full color-coded speaker output within 10 seconds — no signup required to experience the core value.
How to build it, step by step
1. Define Supabase schema in lib/db.ts: users, sessions, transcripts. 2. Set up Supabase project and enable Row Level Security. 3. Build app/api/analyze/route.ts to accept audio URL + Deepgram key and return parsed diarization JSON. 4. Build lib/parse-diarization.ts to extract word-speaker mappings from Deepgram response. 5. Build TranscriptLane component with Tailwind color classes per speaker ID. 6. Build WordInspector popover showing raw JSON on click. 7. Add Supabase Auth with Google OAuth. 8. Add Stripe Checkout for Pro plan with webhook to flip user.is_pro in DB. 9. Add export functions using file-saver for JSON, SRT, and CSV. 10. Deploy to Vercel and walk full journey: upload -> analyze -> inspect -> export -> upgrade.
Generated
May 13, 2026
Model
Claude Haiku
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.