API Comparison Table

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Diarization error rate (DER) explained

TL;DR: Your transcription model might achieve a 5% Word Error Rate, but your meeting summaries can still be completely unreliable if Diarization Error Rate (DER) spikes. DER is the metric that determines whether your system correctly identifies who spoke each word, measured as the sum of three error types: Missed Speech, False Alarm, and Speaker Confusion. For production multi-speaker pipelines, a DER below 15% is the threshold for reliable speaker-labeled analytics; below 10% is the target for clean audio with controlled conditions, such as high-quality meeting assistant output. Accurate speaker attribution directly determines the reliability of downstream LLM summaries and CRM data. Our async pipeline, powered by pyannoteAI's Precision-2 model, delivers up to 3x lower DER than alternatives on conversational speech.

Speech-To-Text

Latency benchmarks for streaming speech-to-text (TTLB and P99)

TL;DR: A voice agent with a 150ms average STT latency sounds fast in a slide deck, but if its P99 spikes to 1.2 seconds, one in every hundred conversational turns breaks. This piece maps the full end-to-end streaming latency budget (network Round Trip Time (RTT), audio buffering, model inference, Voice Activity Detection (VAD) endpointing), explains why P99 and TTLB are the metrics that matter most for production user experience, and shows how to build a reproducible test harness. We cover how our Solaria-1 model delivers first partials under 103ms and final transcripts around 300ms, backed by an open, reproducible benchmark methodology.

Speech-To-Text

Multi-tenant, white-label speech-to-text for platforms

TL;DR: Building a compliant multi-tenant STT layer requires strict data isolation at the key level, granular cost attribution per tenant, and contractual infrastructure guarantees that flow through to your own SLAs. Self-hosting open-source models introduces DevOps overhead, scaling unpredictability, and the absence of built-in tenant isolation features that a managed API provides by default. Managed infrastructure with per-client keys, certified data handling, and all-inclusive pricing removes most of that build cost ($0.20–$0.61/hr with diarization, translation, and entity recognition included, compared with $240K–$480K/yr in dedicated engineering to self-host) but the isolation and attribution architecture still has to be designed correctly regardless of which vendor provides it.

Ebook: Ultimate guide to using LLMs with speech recognition

Published on Jan 7, 2025

Large Language Models (LLMs) have enabled businesses to build advanced AI-driven features, but navigating the many available models and optimization techniques isn't always easy.

If you’re looking to combine speech recognition (STT) and LLMs for cutting-edge voice apps, look no further! Our ultimate guide is finally here, and it’s filled with valuable strategies and hands-on insights from our work with hundreds of audio-first companies and extensive interviews with experts in AI note-taking, sales enablement and customer support.

What you'll learn: