What is the difference between Whisper and Deepgram for transcription?
OpenAI Whisper and Deepgram are both production-grade ASR systems but optimised for different use cases. Whisper is an open-source model that can be self-hosted (data stays on your infrastructure) or called via the OpenAI API. It has near-human accuracy on English (4.4% WER on standard benchmarks), supports 100+ languages, and is the best choice for batch transcription where latency is not a constraint. Deepgram is a managed API service optimised for real-time streaming transcription delivering partial transcripts with <300ms latency, making it the correct choice for live captioning, real-time agent assist, and voice interfaces where users see transcription as they speak. For batch transcription of meeting recordings or call logs: Whisper. For real-time streaming: Deepgram. ClickMasters uses both depending on the latency requirement.
What is speaker diarisation and why does it matter?
Speaker diarisation is the process of determining "who spoke when" in a multi-speaker audio recording segmenting the transcript by speaker identity. Without diarisation, a meeting transcript is a single stream of text with no attribution: "The deadline is Friday. What about the API integration? We need to finish that first." With diarisation: "Speaker 1 (CEO): The deadline is Friday. Speaker 2 (CTO): What about the API integration? Speaker 1 (CEO): We need to finish that first." Diarisation is implemented with pyannote-audio (a speaker segmentation model) applied before transcription the audio is segmented by speaker, each segment is transcribed, and the transcript is reconstructed with speaker labels. For meeting intelligence, call analytics, and interview transcription, diarisation is essential without it, the transcript has limited business value.
Can speech recognition work for heavily accented or technical speech?
Yes, with proper configuration and fine-tuning. Whisper's multilingual model has broad accent coverage because it was trained on diverse web audio it handles most global English accents (Indian, Australian, British, South African, etc.) better than many commercial ASR systems. For highly specialised vocabulary (medical terminology, legal Latin phrases, proprietary product names, financial instrument tickers), custom vocabulary can be injected into the Whisper prompt (the model is primed with expected terms significantly reducing hallucination of unfamiliar words). For persistent specialised vocabulary use cases, Whisper can be fine-tuned on a domain-specific dataset typically 10-100 hours of labelled audio from the target domain reducing WER by 30-50% on in-domain audio. ClickMasters recommends starting with custom vocabulary injection (fast and cheap) before committing to fine-tuning.
Is it possible to deploy speech recognition on-premises for sensitive data?
Yes. OpenAI Whisper is fully open-source and can be deployed on your own infrastructure either on-premises GPU servers or within your private AWS/GCP/Azure VPC. Audio never leaves your environment. Deployment options: Whisper served via a FastAPI endpoint on an AWS EC2 G5 instance (GPU-accelerated processes a 60-minute meeting in ~2 minutes), or faster-whisper (a CTranslate2-optimised Whisper implementation 4x faster than the original with the same accuracy) for high-throughput batch transcription. For real-time streaming in a private environment, NVIDIA Riva (enterprise-grade on-premises ASR) or a self-hosted Whisper with streaming chunking can replace Deepgram. ClickMasters deploys self-hosted ASR for healthcare, legal, and financial services clients where audio content cannot be sent to external APIs.
What is Speech Recognition and what does it include?
Speech Recognition is the process of building software systems that deliver specific business capabilities through purpose-built software. A complete speech recognition engagement includes: discovery and scoping (defining the business requirements, technical constraints, and success metrics before any code is written), architecture design (defining the system structure, technology choices, and integration points), iterative development (2-week sprint cycles with working software demonstrated at each review), quality assurance (automated testing in CI, manual acceptance testing in staging, and performance testing under load), and deployment and handover (production deployment, documentation, and a 30-day post-launch support period). ClickMasters delivers speech recognition as a fixed-price engagement with the scope agreed before work begins.
How long does Speech Recognition take?
Speech Recognition timelines by scope: a minimum viable product or proof of concept (4-8 weeks), a standard commercial product with core features (8-16 weeks), a complex system with multiple integrations and compliance requirements (16-32 weeks), and an enterprise platform with multiple user types and advanced functionality (6-12 months). These timelines assume a dedicated ClickMasters engineering team, a fixed scope agreed at the start, and external dependencies (API credentials, design assets, third-party approvals) resolved before the sprint in which they are needed. Timeline slippage almost always traces back to one of three causes: scope additions during the build, unresolved external dependencies, or an architecture decision that needs to be revisited mid-project. ClickMasters addresses all three in the scoping workshop.
How much does Speech Recognition cost?
Speech Recognition pricing by engagement type: a discovery and scoping workshop ($2,500-$5,000, 3-5 days, producing a written scope document and fixed-price proposal), an MVP or initial product build ($15,000-$50,000, 8-16 weeks, depending on scope and integration complexity), a full commercial product ($40,000-$120,000, 3-6 months), and an enterprise system ($80,000-$250,000+, 6-12 months). All ClickMasters speech recognition engagements are fixed-price with milestone-based payments tied to deliverables -- the client pays when the deliverable is accepted, not on a monthly retainer regardless of progress. Prices are in USD; GBP, EUR, CAD, and AUD equivalents available on request.
What technology stack does ClickMasters use for Speech Recognition?
ClickMasters selects the technology stack based on the project's specific requirements rather than using a fixed stack for all speech recognition engagements. For web applications: Next.js (React) with TypeScript for frontend, Node.js or Python (FastAPI) for backend, PostgreSQL or MongoDB for database, AWS or Vercel for deployment. For mobile: React Native with Expo for cross-platform, or Swift/Kotlin for native iOS/Android where native performance is required. For AI: OpenAI or Anthropic APIs for LLM integration, Python with FastAPI for ML pipelines, Pinecone or Weaviate for vector databases. For data: dbt for transformation, Airflow or Dagster for orchestration, Snowflake or BigQuery for warehousing. The technology recommendation is made in the discovery session based on the performance requirements, team's future maintainability, and the client's existing technology environment.
What makes ClickMasters different from other Speech Recognition companies?
ClickMasters differentiates from other speech recognition companies through: fixed-price contracts (the price is agreed before work begins and does not change unless the scope changes -- unlike time-and-materials agencies where cost is open-ended), sprint-based delivery (working software demonstrated every 2 weeks, not a big reveal at the end of the project), timezone overlap with US/UK/AU clients (ClickMasters engineers are available during client business hours for standups, reviews, and escalations), US/UK/EU compliance knowledge (CCPA, UK GDPR, HIPAA, SOC 2, PCI DSS -- not generic offshore compliance awareness but specific implementation expertise), and outcome-first scoping (the business outcome the software will produce is defined, quantified, and agreed before the technical specification is written). ClickMasters is based in Pakistan and serves clients in the USA, UK, Canada, Australia, and Western Europe.
How does ClickMasters ensure quality in Speech Recognition?
Quality assurance for speech recognition at ClickMasters: automated testing (unit tests covering critical business logic, integration tests for API endpoints, end-to-end tests for critical user journeys using Playwright or Cypress -- all running in GitHub Actions CI on every PR merge), code review (every PR reviewed by a senior ClickMasters engineer before merge -- the gate that catches architectural issues before they become technical debt), acceptance testing (ClickMasters QA tests every story against its acceptance criteria in the staging environment before the sprint review -- the client only reviews complete, tested features), performance testing (load testing at 2x and 5x expected peak load before launch using k6 -- the validation that the system handles the expected user volume), and Definition of Done (a checklist that every story must pass before it is counted as complete -- including tests, acceptance criteria verification, analytics events, and accessibility).
Does ClickMasters work with clients outside Pakistan?
ClickMasters delivers speech recognition for clients in the USA, UK, Canada, Australia, Germany, UAE, and other markets. All client communication is in English, sprint ceremonies are scheduled at the client's business hours, contracts are in USD (or GBP/EUR/AUD on request), and all deliverables meet the compliance requirements of the client's jurisdiction. ClickMasters is incorporated in Pakistan and operates as a software development services company serving international clients exclusively.
What happens after the speech recognition project is delivered?
After delivery, ClickMasters provides: a 30-day post-launch support period included in the fixed price (bug fixes for issues that emerge in production, questions about the codebase, and assistance with any launch issues), source code handover (all code committed to the client's GitHub/GitLab organisation with full commit history), documentation (README, architecture diagram, environment setup guide, and API documentation), and the option to continue on a monthly retainer for ongoing development, maintenance, and feature additions. ClickMasters does not impose vendor lock-in -- the client owns 100% of the code and can continue development with any team after handover.
