Migrating from Nova-3 to Flux

Migrate from Nova-3 to Flux, Deepgram’s conversational speech recognition purpose-built for interactive voice agents.

Key Benefits of Flux

Model-integrated turn detection (StartOfTurn, EagerEndOfTurn, TurnResumed, EndOfTurn)
Ultra-low latency ~260ms end-of-turn detection (p50 at defaults)
EagerEndOfTurn events let you start LLM responses early
Turn-based transcripts for clean agent logic
Same Nova 3 transcription quality
Simplified development one API replaces complex STT+VAD+endpointing pipelines, and conversation-native events.
High configurability - Configurable end-of-turn detection sensitivity, eager response thresholds, and turn-taking dynamics for optimized conversational flow

Audio Requirements

Encoding: See Audio Format Requirements table below
Sample rates: See Audio Format Requirements table below
Channels: Mono only
Chunk size: 80ms strongly recommended for optimal model performance and latency.

Audio Format Requirements

Audio Type	Encoding	Container	`encoding` param	`sample_rate` param	Supported Sample Rates
Raw	`linear16`, `linear32`, `mulaw`, `alaw`, `opus`, `ogg-opus`	None	Required	Required	`8000`, `16000`, `24000`, `44100`, `48000`
Containerized	`linear16`	WAV	Omit	Omit	Auto-detected from container
Containerized	`opus`	Ogg	Omit	Omit	Auto-detected from container
Containerized	`opus`	WebM	Omit	Omit	Auto-detected from container

Migrating from Nova 3 to Flux

This guide will help you migrate from Nova 3 to Flux by highlighting key differences, setup changes, and implementation patterns.

Differences

Nova 3	Flux
Streams transcripts continuously	Emits structured turn events
Requires custom logic for barge-in and turn-taking	Has built-in turn state machine
Returns transcripts only	Returns conversation events and transcripts
Designed for general real-time transcription	Designed for conversational voice agents
Focuses on accuracy and speed	Focuses on accuracy and turn awareness

Endpoint Usage

Nova 3:

Uses the listen v1 endpoint with the nova-3 model option.

wss://api.deepgram.com/v1/listen?model=nova-3

Flux:

Uses the listen v2 endpoint with the flux-general-en model option.

wss://api.deepgram.com/v2/listen?model=flux-general-en

Response Message Structure

Nova 3

1 {
2   "type": "Results",
3   "channel": "transcript",
4   "alternatives": [...]
5 }

Flux

1 {
2   "type": "TurnInfo",
3   "request_id": "2ba892a1-6c0d-4d92-9b89-0000000000",
4   "event": "Update",
5   "turn_index": 0,
6   "audio_window_start": 0,
7   "audio_window_end": 0.47999996,
8   "transcript": "",
9   "words": [...],
10   "end_of_turn_confidence": 0.0009,
11   "sequence_id": 2
12 }

In addition to the transcript, flux responses include the:

event field for turn-state changes
turn_index to track turn lifecycle
audio_window_start and audio_window_end to track the audio window.
end_of_turn_confidence to track the confidence of the end of turn.
sequence_id to track the sequence id of the messages.

Implementation Pattern Changes

Nova 3 Approach

Requires custom logic for barge-in and turn-taking.

Send audio
Receive streaming partial transcripts
Decide when to interrupt your agent manually

Flux Approach

Listens for structured events and removes the need for custom VAD or barge-in logic.

StartOfTurn: Interrupt agent if it’s speaking
EagerEndOfTurn: Medium-confidence end → start LLM reply
TurnResumed: User kept talking → cancel reply
EndOfTurn: High-confidence end → send transcript to LLM

By default, Flux only emits Update, StartOfTurn, and EndOfTurn.

Simple Approach: Enabling End of Turn

For more information on using Flux with EndOfTurn only see the Flux Getting Started Guide

This is a simple approach using only EndOfTurn (lower latency, less complex, less LLM calls).

To enable end of turn use the eot_threshold parameter which allows for a confidence of (0.5–0.9) for EndOfTurn events.

Example

1 wss://api.deepgram.com/v2/listen?model=flux-general-en&sample_rate=16000&encoding=linear16&eot_threshold=0.8

Optimized Approach: Enabling EagerEndOfTurn + EndOfTurn

This is an optimized approach using both EagerEndOfTurn and EndOfTurn (lower latency, slightly more complex, more LLM calls)

To enable eager end of turn use the eager_eot_threshold parameter which allows for a Confidence of (0.3–0.9). You can also set the eot_threshold with a confidence of (0.5–0.9) to handle EndOfTurn events and use the eot_timeout_ms which defaults to 5000 ms to force a timeout after a specified time.

Example

1 wss://api.deepgram.com/v2/listen?model=flux-general-en&sample_rate=16000&encoding=linear16&eager_eot_threshold=0.6&eot_threshold=0.8&eot_timeout_ms=7000

Nova 3 Migration Checklist

Update WebSocket endpoint to /v2/listen
Set model=flux-general-en and encoding=linear16
Adjust client to parse TurnInfo messages
Implement turn event handling (start, eager end of turn, turn resumed, end)
Tune eager_eot_threshold and eot_threshold for your use case
Remove custom VAD/barge-in logic (Flux handles this natively!)

1	{
2	"type": "Results",
3	"channel": "transcript",
4	"alternatives": [...]
5	}

1	{
2	"type": "TurnInfo",
3	"request_id": "2ba892a1-6c0d-4d92-9b89-0000000000",
4	"event": "Update",
5	"turn_index": 0,
6	"audio_window_start": 0,
7	"audio_window_end": 0.47999996,
8	"transcript": "",
9	"words": [...],
10	"end_of_turn_confidence": 0.0009,
11	"sequence_id": 2
12	}