Build Voice Agents for ALL Languages (LiveKit + Gladia Complete Guide) walks you through setting up a multilingual voice agent using LiveKit and Gladia’s Solaria transcriber, with friendly, step-by-step guidance. You’ll get clear instructions for obtaining API keys, configuring the stack, and running the system locally before deploying it to the cloud.
The tutorial explains how to enable seamless language switching across Spanish, English, German, Polish, Hebrew, and Dutch, and covers terminal configuration, code changes, key security, and testing the agent. It’s ideal if you’re building voice AI for international clients or just exploring multilingual voice capabilities.
Overview of project goals and scope
This project guides you to build a multilingual voice agent that combines LiveKit for real-time WebRTC audio and Gladia Solaria for transcription. Your objective is to create an agent that can participate in live audio rooms, capture microphone input or incoming participant audio, stream that audio to a transcription service, and feed the transcriptions into agent logic (LLMs or scripted responses) to produce replies or actions in the same session. The goal is a low-latency, robust, and extensible pipeline that works locally for prototyping and can be migrated to cloud deployments.
Define the objective of a multilingual voice agent using LiveKit and Gladia Solaria
You want an agent that hears, understands, and responds across languages. LiveKit handles joining rooms, publishing and subscribing to audio tracks, and routing media between participants. Gladia Solaria provides high-quality multilingual speech-to-text, with streaming capabilities so you can transcribe audio in near real time. Together, these components let your agent detect language, transcribe audio, call your application logic or an LLM, and optionally synthesize or publish audio replies to the room.
Target languages and supported language features (Spanish, English, German, Polish, Hebrew, Dutch, etc.)
Target languages include Spanish, English, German, Polish, Hebrew, Dutch, and others you want to add. Support should include accurate transcription, language detection, per-request language hints, and handling of right-to-left languages such as Hebrew. You should plan for codecs, punctuation and casing output, diarization or speaker labeling if needed, and domain-specific vocabulary for names or technical terms in each language.
Primary use cases: international customer support, multilingual assistants, demos and prototypes
Primary use cases are international customer support where callers speak various languages, multilingual virtual assistants that help global users, demos and prototypes to validate multilingual flows, and in-product support tools. You can also use this stack for language learning apps, cross-language conferencing features, and accessible interfaces for multilingual teams.
High-level architecture and data flow overview
At a high level, audio originates from participants or your agent’s TTS, flows through LiveKit as media tracks, and gets forwarded or captured by your application (media relay or server-side client). Your app streams audio chunks to Gladia Solaria for transcription. Transcripts return as streaming events or batches to your app, which then feeds text to agent logic or LLMs. The agent decides a response and optionally triggers TTS, which you publish back to LiveKit as an audio track. Authentication, key management, and orchestration sit around this flow to secure and scale it.
Success criteria and expected outcomes for local and cloud deployments
Success criteria include stable low-latency transcription (<1–2s for streaming), reliable reconnection across nats, correct language detection target languages, and maintainable code adding languages or models. local deployments, success means you can run end-to-end locally with your microphone speaker, test switching, debug easily. cloud scalable room handling, proper key management, turn servers connectivity, monitoring transcription quotas latency.< />>
Prerequisites and environment checklist
Accounts and access: LiveKit account or self-hosted LiveKit server, Gladia account and API access
You need either a LiveKit managed account or credentials to a self-hosted LiveKit server and a Gladia account with Solaria API access and a usable API key. Ensure the accounts are provisioned with sufficient quotas and that you can generate API keys scoped for development and production use.
Local environment: supported OS, Python version, Node.js if needed, package managers
Your local environment can be macOS, Linux, or Windows Subsystem for Linux. Use a recent Python 3.10+ runtime for server-side integration and Node.js 16+ if you have a front-end or JavaScript client. Ensure package managers like pip and npm/yarn are installed. You may also work entirely in Node or Python depending on your preferred SDKs.
Optional tools: Docker, Kubernetes, ngrok, Postman or HTTP client
Docker helps run self-hosted LiveKit and related services. Kubernetes is useful for cloud orchestration if you deploy at scale. ngrok or localtunnel helps expose local endpoints for remote testing. Postman or any HTTP client helps test API requests to Gladia and LiveKit REST endpoints.
Hardware considerations for local testing: microphone, speakers, network
For reliable testing, use a decent microphone and speakers or headset to avoid echo. Test on a wired or stable Wi-Fi network to minimize jitter and packet loss when validating streaming performance. If you plan to synthesize audio, ensure your machine can play audio streams reliably.
Permissions and firewall requirements for WebRTC and media ports
Open outbound UDP and TCP ports as required by your STUN/TURN and LiveKit configuration. If self-hosting LiveKit, ensure the server’s ports for signaling and media are reachable. Configure firewall rules to allow TURN relay traffic and check that enterprise networks allow WebRTC traffic or provide a TURN relay.
LiveKit setup and configuration
Choosing between managed LiveKit service and self-hosted LiveKit server
Choose managed LiveKit when you want less operational overhead and predictable updates; choose self-hosted if you need custom network control, on-premises deployment, or tighter data residency. Managed is faster to get started; self-hosting gives control over scaling and integration with your VPC and TURN infrastructure.
Installing LiveKit server or connecting to managed endpoint
If self-hosting, use Docker images or distribution packages to install the LiveKit server and configure its environment variables. If using managed LiveKit, obtain your API keys and the signaling endpoint and configure your clients to connect to that endpoint. In both cases, verify the signaling URL and that the server accepts JWT-authenticated connections.
Configuring keys, JWT authentication and room policies
Configure key pairs and JWT signing keys to create join tokens with appropriate grants (room join, publish, subscribe). Design room policies that control who can publish, record, or create rooms. For agents, create scoped tokens that limit privileges to the minimum needed for their role.
ICE/STUN/TURN configuration for reliable connectivity across NAT
Configure public STUN servers and one or more TURN servers for reliable NAT traversal. Test across NAT types and mobile networks. For production, ensure TURN is authenticated and accessible with sufficient bandwidth, as TURN will relay media when direct P2P is not possible.
Room design patterns for agents: one-to-one, one-to-many, and relay rooms
Design rooms for your use-cases: one-to-one for direct agent-to-user interactions, one-to-many for demos or broadcasts, and relay rooms where a server-side agent subscribes to multiple participant tracks and relays responses. For scalability, consider separate rooms per conversation or a room-per-client pattern with an agent joining as needed.
Gladia Solaria transcriber setup
Registering for Gladia and understanding Solaria transcription capabilities
Sign up for Gladia, register an application, and obtain an API key for Solaria. Understand supported languages, streaming vs batch endpoints, punctuation and formatting options, and features like diarization, timestamps, and confidence scores. Confirm capabilities for the languages you plan to support.
Selecting transcription models and options for multilingual support
Choose models optimized for multilingual accuracy or language-specific models for higher fidelity. For low-latency streaming, pick streaming-capable models and configure options for output formatting and telemetry. When available, prefer models that support mixed-language recognition if you expect code-switching.
Real-time streaming vs batch transcription tradeoffs
Streaming transcription gives low latency and partial results but can be more complex to implement and might cost more per minute. Batch transcription is simpler and good for recorded sessions, but it adds delay. For interactive agents, streaming is usually required to maintain a natural conversational pace.
Handling language detection and per-request language hints
Use Gladia’s language detection if available, or send explicit language hints when you know the expected language. Per-request hints reduce detection errors and speed up transcription accuracy. If language detection is used, set confidence thresholds and fallback languages.
Monitoring quotas, rate limits and usage patterns
Track your usage and set up alerts for quota exhaustion. Streaming can consume significant bandwidth and token quotas; monitor per-minute usage, concurrent streams, and rate limits. Plan for graceful degradation or queued processing when quotas are hit.
Authentication and API key management
Generating and scoping API keys for LiveKit and Gladia
Generate distinct API keys for LiveKit and Gladia. Scope keys by environment (dev, staging, prod) and by role when possible (agent, admin). For LiveKit, use signing keys to mint short-lived JWT tokens with limited grants. For Gladia, create keys that can be rotated and that have usage limits set.
Secure storage patterns: environment variables, secret managers, vaults
Store keys in environment variables for local dev but use secret managers (AWS Secrets Manager, GCP Secret Manager, HashiCorp Vault) for cloud deployments. Ensure keys aren’t checked into version control. Use runtime injection for containers and managed rotations.
Key rotation and revocation practices
Rotate keys periodically and have procedures for immediate revocation if a key is compromised. Use short-lived tokens where possible and automate rotation during deployments. Maintain an incident runbook for re-issuing credentials and invalidating cached tokens.
Least-privilege setup for production agents
Grant agents only the privileges they need: publish/subscribe to specific rooms, transcribe audio, but not administrative room creation unless necessary. Minimize blast radius by using separate keys for different microservices.
Local development strategies to avoid leaking secrets
For local development, keep a .env file excluded from version control and use a sample .env.example committed to the repo. Use local mock servers or reduced-privilege test keys. Educate team members about secret hygiene.
Terminal and local configuration examples
Recommended .env file structure and example variables for both services
A recommended .env includes variables like LIVEKIT_API_KEY, LIVEKIT_API_SECRET, LIVEKIT_URL, GLADIA_API_KEY, and ENVIRONMENT. Example lines: LIVEKIT_URL=https://your-livekit.example.com LIVEKIT_API_KEY=lk_dev_xxx LIVEKIT_API_SECRET=lk_secret_xxx GLADIA_API_KEY=gladia_sk_xxx
Sample terminal commands to start LiveKit client and local transcriber integration
You can start your server with commands like npm run start or python app.py depending on the stack. Example: export $(cat .env) && npm run dev or source .env && python -m myapp.server. Use verbose flags for initial troubleshooting: npm run dev — –verbose or python app.py –debug.
Using ngrok or localtunnel to expose local ports for remote testing
Expose your local webhook or signaling endpoint for remote devices with ngrok: ngrok http 3000 and then use the generated public URL to test mobile or remote participants. Remember to secure these tunnels and rotate them frequently.
Debugging startup issues using verbose logging and test endpoints
Enable verbose logging for LiveKit clients and your Gladia integration to capture connection events, ICE candidate exchanges, and transcription stream openings. Test endpoints with curl or Postman to ensure authentication works: send a small audio chunk and confirm you receive transcription events.
Automating local setup with scripts or a Makefile
Automate environment setup with scripts or a Makefile: make install to install dependencies, make env to create .env from .env.example, make start to run the dev server. Automation reduces onboarding friction and ensures consistent local environments.
Codebase walkthrough and required code changes
Repository structure and important modules: audio capture, WebRTC, transcriber client, agent logic
Organize your repo into modules: client (web or native UI), server (session management, LiveKit token generation), audio (capture and playback utilities), transcriber (Gladia client and streaming handlers), and agent (LLM orchestration, intent handling, TTS). Clear separation of concerns makes maintenance and testing easier.
Implementing LiveKit client integration and media track management
Implement LiveKit clients to join rooms, publish local audio tracks, and subscribe to remote tracks. Manage media tracks so you can selectively forward or capture participant streams for transcription. Handle reconnection logic and reattach tracks on session restore.
Integrating Gladia Solaria API for streaming transcription calls
From your server or media relay, open a streaming connection to Gladia Solaria with proper authentication. Stream PCM/Opus audio chunks with the expected sample rate and encoding. Handle partial transcript events and finalization so your agent can act on interim as well as finalized text.
Coordinating transcription results with agent logic and LLM calls
Pipe incoming transcripts to your agent logic and, where needed, to an LLM. Use interim results for real-time UI hints but wait for final segments for critical decisions. Implement debouncing or aggregation for short utterances so you reduce unnecessary LLM calls.
Recommended abstractions and interfaces for maintainability and extension
Abstract the transcriber behind an interface (start_stream, send_chunk, end_stream, on_transcript) so you can swap Gladia for another provider in future. Similarly, wrap LiveKit operations in a room manager class. This reduces coupling and helps scale features like additional languages or TTS engines.
Real-time audio streaming and media handling
How WebRTC integrates with LiveKit: tracks, publishers, and subscribers
WebRTC streams are represented as tracks in LiveKit. You publish audio tracks to the room, and other participants subscribe as needed. LiveKit manages mixing, forwarding, and scalability. Use appropriate audio constraints to ensure consistent sample rates and mono channel for transcription.
Choosing audio codecs and settings for low latency and good quality
Use Opus for low latency and robust handling of network conditions. Choose sample rates supported by your transcription model (often 16 kHz or 48 kHz) and ensure your pipeline resamples correctly before sending to Solaria. Keep audio mono if the transcriber expects single-channel input.
Chunking audio for streaming transcription and buffering strategies
Chunk audio into small frames (e.g., 20–100 ms frames aggregated into 500–1000 ms packets) compatible with both WebRTC and the transcription streaming API. Buffer enough audio to smooth jitter but not so much that latency increases. Implement a circular buffer with backpressure controls to drop or compress less-important audio when overloaded.
Handling packet loss, jitter, and adaptive bitrate
Implement jitter buffers, and let WebRTC handle adaptive bitrate negotiation. Monitor packet loss and consider reconnect or quality reduction strategies when loss is high. Turn on retransmission features if supported and use TURN as fallback when direct paths fail.
Syncing audio playback and TTS responses to avoid overlap
Coordinate playback so TTS responses don’t overlap with incoming speech. Mute the agent’s transcriber or pause processing while your synthesized audio plays, or use voice activity detection to wait until the user finishes speaking. If you must mix, tag agent-origin audio so you can ignore it during transcription.
Multilingual transcription strategies and language switching
Automatic language detection vs explicit language hints per request
Automatic detection is convenient but can misclassify short utterances or noisy audio. You should use detection for unknown or mixed audiences, and explicit language hints when you can constrain expected languages (e.g., a user selects Spanish). A hybrid approach — hinting with fallback to detection — often performs best.
Dynamically switching transcription language mid-session
Support dynamic switching by letting your app send language hints or by restarting the transcription stream with a new language parameter when detection indicates a switch. Ensure your state machine handles interim partials and that you don’t lose context during restarts.
Handling mixed-language utterances and code-switching
For code-switching, use models that support multilingual recognition and enable word-level confidence scores. Consider segmenting utterances and allowing multiple hypotheses, then apply post-processing to select the most coherent result. You can also run language detection on smaller segments and transcribe each with the best language hint.
Improving accuracy with domain-specific vocabularies and custom lexicons
Add domain-specific terms, names, or acronyms to custom vocabularies or lexicons if Solaria supports them. Provide hint lists per request for expected entities. This improves accuracy for specialized contexts like product names or technical jargon.
Fallback strategies when detection fails and confidence thresholds
Set confidence thresholds for auto-detected language and transcription quality. When below threshold, either prompt the user to choose their language, retry with alternate models, or flag the segment for human review. Graceful fallback preserves user experience and reduces erroneous actions.
Conclusion
Recap of steps to build a multilingual voice agent with LiveKit and Gladia
You’ve outlined the end-to-end flow: set up LiveKit for real-time media, configure Gladia Solaria for streaming transcription, secure keys and infrastructure, wire transcriptions into agent logic, and iterate on encoding, buffering, and language strategies. Local testing with tools like ngrok lets you prototype quickly before moving to cloud deployments.
Recommended roadmap from prototype to production deployment
Start with a local prototype: single-room, one-to-one interactions, a couple of target languages, and streaming transcription. Validate detection and turnaround times. Next, harden with TURN servers, key rotation, monitoring, and automated deployments. Finally, scale rooms and concurrency, add observability, and implement failover for transcription and media relays.
Key tradeoffs to consider when supporting many languages
Tradeoffs include cost and latency for streaming many concurrent languages, model selection between general multilingual vs language-specific models, and complexity of handling code-switching. More languages increase testing and maintenance overhead, so prioritize languages by user impact.
Next steps and how to gather feedback from real users
Deploy to a small group of real users or internal testers, instrument interactions for errors and misrecognitions, and collect qualitative feedback. Use transcripts and confidence metrics to spot frequent failure modes and iterate on vocabulary, model choices, or UI language hints.
Where to get help, report issues, and contribute improvements
If you encounter issues, collect logs, reproduction steps, and examples of mis-transcribed audio. Use your vendor’s support channels and your community or internal teams for debugging. Contribute improvements by documenting edge cases you fixed and modularizing your integration so others can reuse connectors or patterns.
This guide gives you a practical structure to build, iterate, and scale a multilingual voice agent using LiveKit and Gladia Solaria. You can now prototype locally, validate language workflows like Spanish, English, German, Polish, Hebrew, and Dutch, and plan a safe migration to production with monitoring, secure keys, and robust network configuration.
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