Add speech-to-text to a Pipecat voice agent

TL;DR: In a natural voice agent conversation, anything over 500ms end-to-end feels stilted. Transcription latency is the first line item in that budget, and it sets the ceiling for everything downstream. This guide walks through wiring Solaria-1 into a Pipecat pipeline, tuning VAD thresholds, and handling failure modes, so the STT layer is a decision you can revisit without a rewrite. Because each component is independently swappable, choosing your STT provider is an architectural constraint you control, not one the framework imposes.

Add speech-to-text to a LiveKit voice agent

TL;DR: Voice agent latency comes from every layer, but the LLM accounts for the largest share of your total budget. Self-hosting open-source STT models burns GPU budget on cold-start delays and accuracy that degrades on accented speech. Integrating our Solaria-1 streaming API with LiveKit gives you partial transcripts in under 103ms and final transcripts at approximately 300ms, with true code-switching across 100+ languages. This guide delivers production-ready Python and Node.js code to connect LiveKit's audio egress to Gladia, configure silence detection, and drive natural turn-taking using live partials.

Add speech-to-text to a Recall.ai meeting bot

TL;DR: Recall.ai handles the platform-level complexity of joining Zoom, Meet, and Teams calls while we handle transcription, diarization, and enrichment. This guide walks through the full integration: spawning a bot, routing recorded audio to our async API, enabling pyannoteAI Precision-2 diarization, and mapping speaker labels to participant names using timestamp overlap. Choose Solaria-3 for post-meeting accuracy on English and European business audio, Solaria-1 for real-time captions or broad language coverage. On Growth and Enterprise plans, your audio is never used to train our models.

Add speech-to-text to a VAPI voice agent

TL;DR: Most voice agent failures start in the speech-to-text (STT) layer, not the large language model (LLM). A single misheard entity cascades into tool-calling failures, CRM errors, and broken routing logic. Integrating Solaria-1 as a custom transcriber in Vapi delivers partial transcripts in under 103ms and final transcripts at approximately 270ms, with native code-switching across 100+ languages. The swap requires a two-step connection: a POST request to our live sessions endpoint returns a per-session WebSocket URL, which you then pass into your Vapi custom-transcriber configuration.

Get Zoom transcripts via API: real-time and async

TL;DR: Getting Zoom transcripts via API means choosing between two architectures: a pull-based REST workflow that fetches VTT files after Cloud Recording completes, or a real-time media bot that streams raw PCM audio over WebSockets during the meeting. The decision hinges on one constraint: whether your product needs transcript data during the call or after it ends. Zoom's native engine has limited configuration options and degrades on accented or non-English speech, so teams building CRM automation, coaching tools, or multilingual meeting assistants typically route audio to a dedicated STT layer for word-level timestamps, speaker attribution, and structured outputs.

Transcribe phone calls and push them to HubSpot with Gladia and Zapier

TL;DR: Every wrong name or missed entity in a call transcript silently corrupts your CRM data downstream, and self-hosted pipelines compound this with GPU maintenance overhead and poor accuracy on accented speech. This guide shows you how to route call recordings from Twilio or Aircall through Zapier to our async API, run LLM extraction on the diarized transcript, and push structured deal properties and engagement logs to HubSpot without maintaining custom infrastructure. Multiple customers have the Gladia API layer running in under 24 hours. The remaining pipeline configuration time depends on your Zapier, LLM, and HubSpot setup complexity.

Cutting transcription cost per audio hour for meeting assistants

TL;DR: Transcription cost and accuracy are critical drivers of unit economics for meeting assistant builders. Headline API rates are deceptive because hidden fees for diarization, translation, and billing increments routinely double the effective cost per hour. Self-hosting open-source models introduces GPU underutilization and DevOps overhead that can exceed managed API costs by approximately 3x at early-stage volume. Teams that switch to all-inclusive pricing (diarization, translation, and sentiment bundled at as low as $0.20/hr on our Growth plan) recover meaningful margin without waiting for enterprise-tier volume to justify the conversation.

Scaling real-time STT for high-concurrency voice agents

TL;DR: Scaling real-time voice agents to hundreds of concurrent calls requires moving from stateless CPU-based autoscaling to stateful WebSocket connection management. The failure mode is predictable: architectures that handle test calls at low concurrency struggle when traffic spikes, producing latency spikes and dropped audio. The fix involves scaling on active connection counts, implementing ping/pong heartbeat monitoring to reclaim hanging sessions, and enforcing hard connection limits to protect downstream LLM and TTS layers. Additional capacity comes online without pre-provisioning, so the STT layer is not a fixed ceiling as session counts grow.

When transcript quality causes churn: an NPS-driven QA playbook

TL;DR: When meeting summaries contain errors, users often churn quietly rather than filing support tickets. Transcript quality is the silent ceiling of every conversational AI product: a single substitution error fed into an LLM can corrupt an entire action item list, CRM entry, or coaching scorecard. This playbook shows product leaders how to build an automated, telemetry-driven QA loop connecting transcript health to churn risk, using confidence scoring, strategic sampling, diarization audits, and model selection matched to your audio profile.