can you tell me which STT models from openAI support aligned transcripts?

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i would like to know which STT models can I use for Adaptive interruption handling ?

Thanks — I’m responding to your question about which STT models you can use for adaptive interruption handling. Short answer: • Use AssemblyAI, Deepgram (Flux), or Speechmatics — these STT providers include endpointing/turn-detection features that enable adaptive interruption handling when integrated with LiveKit. • You can also use any STT model together with LiveKit’s built-in turn detector (semantic turn detection) to get adaptive interruption behavior. • For best responsiveness, pair STT-based endpointing with a VAD plugin (for example, Silero). What each option offers: • AssemblyAI: punctuation + silence-based endpointing. Works with

turn_detection="stt"

and you can tune silence and VAD thresholds for adaptive behavior. • Deepgram (Flux): acoustic + semantic endpointing with tunable thresholds like

eager_eot_threshold

and `eot_threshold`; enable via STTv2 plugin and

turn_detection="stt"

. • Speechmatics: ML-based SMART_TURN endpointing (

turn_detection_mode="SMART_TURN"

) and can integrate with external turn detection for more control. • LiveKit Turn Detector: a multilingual, semantic turn detector that adapts VAD timeouts using transcript semantics — useful if you want model-agnostic, conversational-aware interruption handling. Next steps I can help with: • If you tell me which provider or runtime you’re planning to use (AssemblyAI, Deepgram, Speechmatics, OpenAI/GPT, or LiveKit-native), I can give configuration snippets and recommended parameter values for

min_turn_silence

,

max_turn_silence

,

vad_threshold

, or EoT thresholds. • If you want, I can also explain how to combine a VAD plugin (Silero) with STT turn detection for the most responsive barge-in behavior. Which provider or setup are you targeting? Sources: AssemblyAI STT | LiveKit Documentation | Deepgram STT | LiveKit Documentation | Speechmatics STT plugin guide | LiveKit Documentation | Using a transformer to improve end of turn detection | LiveKit | Turns overview | LiveKit Documentation, with interactive elements

openai

Thanks — I’m responding to your follow-up asking specifically about OpenAI. Short answer: • OpenAI STT models do not provide built-in aligned (word-level) transcripts in the sources I checked. • OpenAI does not offer a native, model-based adaptive interruption/turn-detection feature in the LiveKit integrations I reviewed. What you can do with OpenAI instead: • Use LiveKit’s turn detector (semantic turn detection) together with OpenAI STT to implement adaptive interruption handling. The LiveKit turn detector adapts VAD timeouts using transcript semantics and works across STT providers. • Pair OpenAI STT with a VAD plugin (for example, Silero) for low-latency voice activity signals and responsive barge-in. VAD handles immediate audio activity while the turn detector uses transcripts for semantic endpointing. • If you need word-level alignment, choose an STT provider that offers aligned transcripts (AssemblyAI, Deepgram, or Speechmatics) and either: ◦ Use that STT for alignment while still sending text to OpenAI for NLU/assistant logic, or ◦ Post-process OpenAI timestamps (if you receive partial timestamps) by aligning tokens via forced-alignment tools — but aligned transcripts are not confirmed as an OpenAI feature. If you’d like, I can: • Show an example AgentSession configuration combining OpenAI STT + LiveKit turn detector + Silero VAD. • Suggest exact parameters to tune (e.g.,

min_turn_silence

,

max_turn_silence

,

vad_threshold

) for responsive interruption handling with OpenAI. • Outline a design for hybrid approach (use Deepgram/AssemblyAI for alignment, then forward transcripts to OpenAI for understanding). Which of those would help you most right now? , with interactive elements

Show an example AgentSession configuration combining OpenAI STT + LiveKit turn detector + Silero VAD.

Thanks — I’m responding to your request for an example AgentSession configuration that combines OpenAI STT, LiveKit’s semantic turn detector, and the Silero VAD plugin for adaptive interruption handling. Below is a concise example in Python plus brief explanations and tuning suggestions. 1) Example AgentSession (Python)

from livekit.agents import AgentSession
from livekit.plugins import openai, silero
from livekit.plugins.turn_detector.multilingual import MultilingualModel

session = AgentSession(
    # Use OpenAI STT plugin (example model name; pick the OpenAI model you have access to)
    stt=openai.STT(model="gpt-4o-transcribe"),

    # Silero VAD plugin for low-latency voice activity detection
    vad=silero.VAD.load(),

    # LiveKit multilingual semantic turn detector for adaptive endpointing
    turn_detection=MultilingualModel(),

    # Optional: tune these turn detector / VAD parameters if available on your runtime
    # min_turn_silence=0.2,
    # max_turn_silence=1.0,
    # vad_threshold=0.5,
)

2) What each piece does • `stt=openai.STT(...)`: Transcribes incoming audio. OpenAI handles the speech→text; turn semantics are handled separately. • `vad=silero.VAD.load()`: Emits fast voice activity events so the system can react immediately to barge-in or sudden speech. • `turn_detection=MultilingualModel()`: Uses transcript semantics to adapt VAD timeouts and decide end-of-turn, reducing false interruptions and allowing natural turn-taking. 3) Tuning recommendations • Reduce

min_turn_silence

for more aggressive interruption (shorter silence before treating as end-of-turn). • Increase

max_turn_silence

to avoid premature cutoffs on longer pauses. • Set

vad_threshold

to balance sensitivity vs. noise (lower = more sensitive). • Test combinations: VAD handles immediate barge-in; the turn detector confirms semantic completion to avoid chopping sentences. 4) Deployment notes • Ensure the OpenAI STT plugin and turn-detector/VAD plugins are installed for your agent runtime. • If you need word-level aligned timestamps (word alignment), OpenAI STT does not provide aligned transcripts in the sources checked — use an STT provider that supports alignment (AssemblyAI, Deepgram, Speechmatics) or a forced-alignment post-processing step. • If you want, I can produce: ◦ A full runnable agent.py example with download/run instructions. ◦ Concrete parameter values to start testing (I’ll tailor them to your expected environment: quiet room vs noisy background, latency requirements, single speaker vs multi-party). Which would you like next — a runnable example, or suggested parameter values for your environment? Sources: OpenAI STT plugin guide | LiveKit Documentation | Silero VAD plugin | LiveKit Documentation | LiveKit turn detector plugin | LiveKit Documentation | Turns overview | LiveKit Documentation | Build a multilingual voice agent that automatically switches languages | Li..., with interactive elements

here is how i instantiate stt: stt=inference.STT( model="deepgram/flux-general", language="en" ), and here is the error: File "/app/.venv/lib/python3.13/site-packages/livekit/agents/voice/agent_activity.py", line 3182, in _resolve_interruption_detection and self.stt.capabilities.aligned_transcript ^^^^^^^^^^^^^^^^^^^^^ AttributeError: 'tuple' object has no attribute 'capabilities'

Thanks — I’m responding to the AttributeError you hit when instantiating STT (

'tuple' object has no attribute 'capabilities'

). I’ll explain what’s happening, why it occurs, and give concrete fixes and checks you can run. What the error means • The interruption-detection code expects

self.stt

to be an STT instance object that has a

capabilities

attribute. • Your call to

inference.STT(model="deepgram/flux-general", language="en")

appears to be returning a tuple instead of an STT instance. When the agent tries to access

self.stt.capabilities

, Python raises the AttributeError because tuples have no

capabilities

. Most likely causes • You imported/used the wrong constructor/API:

inference.STT(...)

in your environment may be returning a (model, options) tuple instead of a plugin instance. • You may be using a string descriptor vs plugin class inconsistently (both are supported in different contexts). • A version mismatch in your livekit-agents package could cause different return types. Quick checks (run these to narrow it down) 1. Inspect the returned value immediately after instantiation:

from livekit.agents import inference

stt = inference.STT(model="deepgram/flux-general", language="en")
print(type(stt))
print(stt)

Expected: type should be a plugin STT class (not

tuple

). If you see

tuple

, that confirms the root cause. 1. If you used a string descriptor anywhere, try the string form instead: • Use

stt="deepgram/flux-general:en"

in AgentSession (LiveKit will resolve it to the proper object internally). Recommended fixes Option A — Use the plugin class directly (recommended for explicit control):

from livekit.agents import AgentSession
from livekit.plugins import deepgram, silero
from livekit.plugins.turn_detector.multilingual import MultilingualModel

session = AgentSession(
    stt=deepgram.STT(model="flux-general", language="en"),
    vad=silero.VAD.load(),
    turn_detection=MultilingualModel(),
)

Option B — Use the string descriptor (works if you want the inference resolver to handle instantiation):

from livekit.agents import AgentSession
from livekit.plugins import silero
from livekit.plugins.turn_detector.multilingual import MultilingualModel

session = AgentSession(
    stt="deepgram/flux-general:en",
    vad=silero.VAD.load(),
    turn_detection=MultilingualModel(),
)

Option C — If you must use

inference.STT(...)

, confirm you imported the correct `inference`:

from livekit.agents import inference
# or check if you intended: from livekit.plugins import deepgram

If

inference.STT

is returning a tuple in your package version, switch to Option A or B. Additional debugging steps • Print the stack/type where error occurs to confirm the object class before the attribute access. • Verify your installed package versions and upgrade if outdated: ◦ Ensure you have the latest livekit-agents and plugin packages. • If you see tuple contents, examine them — sometimes it’s

(descriptor, options)

and you can adapt your code to pass only the expected piece, but prefer the plugin or string-descriptor approach above. Notes about aligned transcripts / capabilities (relevant because the code checks

capabilities.aligned_transcript

) • The agent code checks

stt.capabilities.aligned_transcript

to decide logic paths. If

stt

isn’t an object, the check fails. • If you need aligned transcripts, Deepgram can provide alignment features; confirm the plugin instance reports

capabilities.aligned_transcript

= True after you instantiate correctly. If you want, I can: • Inspect the output of

print(type(stt))

and the printed value you get and tell you the exact line to change. • Provide a small runnable file (agent.py) using the plugin class that you can drop into your environment to verify everything works. Which check would you like to run first — the

print(type(stt))

debug or I should give a runnable example file using the plugin class? Sources: OpenAI STT plugin guide | LiveKit Documentation | OpenAI TTS plugin guide | LiveKit Documentation | Text and transcriptions | LiveKit Documentation | examples/voice_agents | livekit/livekit_composite | DeepWiki | LiveKit turn detector plugin | LiveKit Documentation, with interactive elements

i got email saying "Your project xxxx has recently reached 38 concurrent barge in requests per minute, which is nearing the limit of 45. To avoid service disruption for your users, please review your options in the settings page for your project." - where is this set, configured and how can it be changed?

I don't have the answer you're looking for. You could also try asking your question: • in one of the other Slack channels or • to https://deepwiki.com/livekit/livekit_composite which is trained on all LiveKit source code If you find the answer, please post it here to help others!