Production Speech-to-Text That Actually Ships
Whisper is the best open speech-to-text model in production today. Wiring it into a real product — with streaming, diarization, multi-language, and the right hosting — is what separates a working transcript from a demo.
Crafted by UnfoldCRO
The Problem
Speech-to-Text Looks Simple Until You Ship It
30-Second Chunks Break the UX
Whisper API processes 30-second audio segments. Long calls, podcasts, and meetings need careful chunking, overlap handling, and reconciliation — or words get dropped at boundaries.
No Speaker Diarization Out of the Box
Whisper transcribes — it does not identify speakers. Meeting summaries, call analytics, and podcast transcripts need a separate diarization pipeline merged with the transcript.
Latency Kills Voice Agents
Voice agents need sub-second time-to-first-word. The Whisper API is too slow for real-time interaction. Self-hosted faster-whisper or Realtime API is the only path.
Cost Spikes on Long Audio
At $0.006 per minute, a high-volume product can rack up a four-figure bill quickly. Self-hosting open-source Whisper variants is dramatically cheaper at scale — but only if engineered right.
A Whisper Pipeline Built for Real Audio, Not Demos
We design the chunking, streaming, diarization, and post-processing layers around Whisper so you get production-grade transcripts. We pick API or self-hosted based on volume and latency, and we add the post-processing — punctuation, capitalization, formatting — that makes transcripts readable.
Whisper API for low-volume, faster-whisper or whisper.cpp for high-volume self-hosted
Smart chunking with overlap reconciliation so no word gets dropped
Speaker diarization (pyannote, NVIDIA NeMo) merged with the transcript timeline
Multi-language detection and translation with quality benchmarks per language
Post-processing: punctuation, capitalization, profanity filtering, custom vocabulary
Building a Voice Feature?
Whether it is meeting transcription, voice agents, podcast tools, or accessibility captions — we can scope a pilot in two weeks.
What You Get
Your Whisper Pipeline
Audio Ingestion
Upload, streaming, and live-audio ingestion with pre-processing (resampling, denoising, normalization) before Whisper sees it.
Chunking & Streaming
Smart 30-second chunking with overlap, or true streaming via Realtime API or self-hosted streaming variants.
Speaker Diarization
Speakers identified, labeled, and merged into the transcript so meeting and call recordings are usable.
Language Detection & Translation
Automatic language detection, optional translation to English (or other targets), and per-language quality benchmarks.
Post-Processing & Formatting
Punctuation, capitalization, custom vocabulary (product names, jargon), and output in plain text, JSON, SRT, or VTT.
Cost & Quality Telemetry
Per-call cost tracking, word-error-rate monitoring on a golden audio set, and alerts when quality regresses.
How It Works
From Audio to Production Transcripts
Audio Profile Audit
We sample your real audio — call quality, languages, speaker count, background noise — and benchmark Whisper variants against it. The right model depends on the actual data, not the brochure.
Architecture Decision
API vs self-hosted, streaming vs batch, with-or-without diarization. We model cost-per-hour at projected volumes and pick the architecture that holds up.
Pipeline Implementation
Ingestion, chunking, transcription, diarization, post-processing, and storage built as a reliable pipeline with retries and idempotency.
Quality Tuning
Custom vocabulary, prompt biasing, post-processing rules, and language-specific tuning to push word-error-rate down on your actual content.
Latency Optimization
If voice-agent latency matters, we move to streaming, GPU-hosted faster-whisper, or Realtime API and tune until time-to-first-word is under a second.
Operate & Improve
Continuous quality monitoring, cost dashboards, and a roadmap of fine-tunes (custom domain models) when the volume justifies it.
Typical results
Results That Speak
0+
Projects Delivered
0+
Industries Served
0%
Cost Saving via Self-Hosting
0+
Supported Languages
0%
Word Error Rate (English)
0s
Time-to-First-Word (Streaming)
What Our Clients Say
Testimonials
Rajkumar Venkatachalam
E-Commerce Expert | Conversion & Retention Strategist | Co-Founder of Neidhal.Com, Neidhal.Com
Abhijith Shetty
Founder, Gubbachhi | MICAn | Digit Insurance, McCann, Dentsu, Lowe Lintas, Leo Burnett, Tech Mahindra, Gubbachhi
Surbhi Sarda
SEO Strategist | Guiding Brands for Local & AI Search Ready
Nikita Sharma
Founder | Guide Businesses in Brand Perception & Digital Experience, ICraftAds
Ajay Binani
AI Automation Systems Learner | Author & Speaker on Minimalism, Get You At
Samriddhi Nagdev
Founder - Artcetra Design Studio | Brand Identity Designer, Artcetra Design Studio
The Difference
Why UnfoldCRO?
API + Open Source Fluency
We work with Whisper API, faster-whisper, whisper.cpp, and Distil-Whisper. We pick the right one for your cost, latency, and quality bar — not the easiest one to integrate.
Diarization Done Right
Speaker identification merged with transcripts using pyannote or NVIDIA NeMo. Output is conversation-ready, not a wall of unattributed text.
Latency Engineering
Sub-second time-to-first-word for voice agents, with GPU-hosted streaming, optimized model selection, and audio pre-processing.
Cost-Per-Hour Discipline
Self-hosting Whisper at scale can cut cost-per-hour by 80%. We model the breakeven and build the pipeline that gets you there.
Frequently Asked Questions
Ready to Get Started?
Book a discovery call. We will benchmark Whisper on your actual audio, model the cost at scale, and propose the architecture that fits.