Streaming speech-to-text APIs with Nova-3 for live captioning and Flux for voice agents
Deepgram documents speech-to-text at developers.deepgram.com with WebSocket streaming on `/v1/listen` for general real-time transcription (Nova-3 model, diarization, and search features per API reference) and `/v2/listen` for conversational Flux models with integrated end-of-turn detection (StartOfTurn, EndOfTurn, EagerEndOfTurn events). Official SDKs expose `deepgram.listen.v1.connect` and `deepgram.listen.v2.connect` for binary audio streams. Docs contrast Flux—optimized for voice agents with lower turn-detection latency—against Nova-3 for meetings, IVR, and agent-assist workloads, and describe latency measurement guides targeting sub-300 ms streaming for Nova-3. Self-hosted deployments can run Flux on dedicated Engine nodes with `/v2/listen` enabled per self-hosted configuration guides.
Use cases
- Live meeting captioning and call analytics with Nova-3 on `/v1/listen`
- Voice-agent stacks that need built-in end-of-turn instead of separate VAD pipelines
- Compare Flux vs Nova-3 latency before choosing an agent architecture
- Measure streaming STT latency in production with documented benchmarking tools
- Deploy Flux self-hosted when cloud API routing must stay on-prem
Key features
- WebSocket `/v1/listen` streaming with Nova-3 and optional diarization per Listen v1 reference
- WebSocket `/v2/listen` Flux turn-based events for conversational agents
- Official SDK `listen.v1.connect` and `listen.v2.connect` helpers for binary audio
- Latency documentation and tooling (for example stt_stream_file) for EOT measurement
- Self-hosted Flux on dedicated Engine nodes with separate `/v2/listen` routing per deployment docs
Who Is It For?
- Voice-agent engineers building real-time conversational products
- Developers adding streaming STT to video or telephony pipelines
- Platform teams evaluating self-hosted vs Deepgram-managed STT
Frequently Asked Questions
- When should I use `/v2/listen` instead of `/v1/listen`?
- Deepgram docs position Flux on `/v2/listen` for conversational voice agents with turn detection; Nova-3 on `/v1/listen` for general streaming transcription.
- Does Flux replace Nova-3 everywhere?
- No—the comparison guide lists different endpoints and feature sets; pick per workload (agents vs meetings/IVR).
- How is audio sent on the WebSocket?
- API references require binary audio frames on the streaming connection with encoding parameters documented per Listen v1/v2 schemas.
Related
Related
3 Indexed items
AssemblyAI
AssemblyAI documents Voice AI APIs at assemblyai.com/docs where developers transcribe and analyze audio via REST at `https://api.assemblyai.com` and real-time WebSockets at `wss://streaming.assemblyai.com` (EU pre-recorded host `api.eu.assemblyai.com` per cloud residency docs). Pre-recorded transcription requires an explicit `speech_models` array on every `POST /v2/transcript` request—docs recommend `universal-3-pro` with `universal-2` fallback for 99-language coverage. The platform also publishes a Voice Agent API for speech-to-speech agents, Speech Understanding features (diarization, sentiment, summarization), Guardrails, and an LLM Gateway to run frontier models on transcripts.
Chroma
Chroma documents an open-source embedding database at docs.trychroma.com for storing and querying vectors, metadata, and full-text fields in Python and JavaScript clients. Official guides cover ephemeral in-memory collections, persistent local storage, self-hosted server deployments, and Chroma Cloud at trychroma.com with authentication tokens. The docs describe collection CRUD, `add`/`query`/`get`/`update`/`delete` APIs, embedding functions (default and third-party), hybrid search, and multitenancy patterns for RAG and agent memory workloads per the documentation index.
Groq Cloud API
GroqCloud exposes hosted language, speech, and compound workloads through Groq’s HTTP APIs. Documentation highlights compatibility with OpenAI client libraries when you point `base_url` at Groq’s OpenAI-compatible endpoint and supply a Groq API key, alongside first-party Groq SDKs for Python and JavaScript. Pricing pages publish per-model token rates (USD) for on-demand inference.