question about a problem we are facing with multiple agents it relates to transcription on the google live model 2.5 native audio

Thanks for reaching out, Joshua. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

so these are our settings we are using google realtime llm, all agents are in differnet rooms but experience the same problem. its to do with one word utterences, where if a user just says one word like yes, colonoscopy or so the agents unable to pick that up, we know this by looking at transcriptons and instead our Silence detection follows with can you repeat. This leads to bad conversation and user expeience. now your questions 2. Agents are on one on one with another SIP participant different agents have the same problem tho 3. I guess its missing transcriptions that were for sure said by the user. now we are using audio amplification and other things and all audio gain processor run audio pipline to tackle same problem but it seems to exist 4.what is this we dont hav ethis enabled 5. whats this agent session key? you want room id?

google_realtime_llm
(map)


language
"en-US"
(string)

model
"gemini-live-2.5-flash-native-audio"
(string)


realtime_input_config
(map)


automatic_activity_detection
(map)

disabled
false
(boolean)

end_of_speech_sensitivity
"END_SENSITIVITY_MEDIUM"
(string)

silence_duration_ms
500
(int64)

start_of_speech_sensitivity
"START_SENSITIVITY_HIGH"
(string)

temperature
0.35
(double)

vertexai
true
(boolean)

voice
"aoede"

Thanks — this is helpful detail. I’m responding about the missing single-word transcriptions (e.g., “yes”, “colonoscopy”) when using the Gemini Live 2.5 native audio model in one-on-one SIP sessions. Quick summary of what I’m addressing: you see missed single-word utterances that are followed by the agent’s silence-detection prompt (“can you repeat”), and you’ve already tried amplification/gain processing. All agents in separate rooms show the same behavior. Suggestions to try next (short, targeted): 1. Tune automatic activity detection ◦ Increase sensitivity to detect very short speech: set

start_of_speech_sensitivity

to

VERY_HIGH

(if available) or try lowering the threshold equivalent. ◦ Decrease

silence_duration_ms

to a smaller value (e.g., 250–300 ms) so short utterances aren’t treated as silence before being captured. ◦ Keep

end_of_speech_sensitivity

at

MEDIUM

or try

LOW

if end-of-speech cuts off words too early. 1. Use TTS-aligned / timed transcripts for verification ◦ Enable TTS-aligned transcripts (

use_tts_aligned_transcript=True

) or word-level timing so you can see whether audio frames contained the word but were dropped by activity detection. 1. Ensure only native STT is active ◦ Confirm no external STT or parallel transcription node is also running; multiple STT pipelines can cause race/overwriting issues. 1. Capture raw audio and compare ◦ Record a short sample of the SIP participant audio for cases where single words are missed. Verify in the recording that the word is present and loud enough. This isolates whether the problem is upstream (capture/gain) or in the model’s activity detection. 1. Adjust pre-processing ◦ Since you run amplification/gain processors, test with and without those processors to see if aggressive AGC or filtering is shortening the utterance or introducing clipping that the activity detector treats as noise. ◦ Try a mild automatic gain control and minimal high-pass/denoise to preserve short utterance energy. 1. If missed only for very brief words, try a short post-collection buffering strategy ◦ Buffer 200–300ms of audio pre-roll so activity detection has a short context window; this often helps capture single-syllable responses. Targeted diagnostic questions (quick answers will help me recommend exact settings): • Do you have access to the recorded raw audio for missed cases (yes/no)? • When you test, do short words appear clearly in recordings (yes/no)? • Can you change

silence_duration_ms

and

start_of_speech_sensitivity

temporarily for one room to A/B test? If you tell me whether you can run the raw-audio test and try one sensitivity/silence change, I’ll suggest exact values to test and how to interpret the results. , with interactive elements

what does this do? Use TTS-aligned / timed transcripts for verification ◦ Enable TTS-aligned transcripts (

use_tts_aligned_transcript=True

) or word-level timing so you can see whether audio frames contained the word but were dropped by activity detection. how and where do i enable this? The audio when listening is completely audible its very nicely heard in the room as a listening participant adn after processing egress its also heard well. thats the problem we have audio enhancing so then how are we going to be able to solve this problem?

Thanks — I’ll explain what TTS‑aligned / timed transcripts do, how to enable them in your session, and quick next steps to diagnose the one‑word misses. What it does • TTS‑aligned (timed) transcripts return word-level timing (start_time / end_time) for each token. That shows exactly which audio frames the model aligned to each word so you can confirm whether the model received the short utterance but dropped it due to activity detection or whether it never reached the model. How to enable it • In your Agent session config when using

google.realtime.RealtimeModel

, set the transcript alignment flag. Example (pseudo/Python-style from your config):

google_realtime_llm = google.realtime.RealtimeModel(
    model="gemini-live-2.5-flash-native-audio",
    realtimeInputConfig=...,
    # enable word-timed transcripts
    use_tts_aligned_transcript=True
)

• If you use a higher-level

AgentSession

map, add

use_tts_aligned_transcript=True

under the LLM/realtime model options. Where you’ll see the data • Timed transcripts are published on the agent’s transcription stream (e.g.,

lk.transcription

) and/or available in the session’s transcription node output as

TimedString

objects with

start_time

and

end_time

. Inspect those word timestamps for missed cases. Practical diagnostic steps (do these in one room to A/B test) 1. Enable

use_tts_aligned_transcript=True

and reproduce a missed single‑word utterance. Inspect the timed transcript: ◦ If the word has timing entries but the agent still issued “can you repeat”, the model received the audio but activity detection/turn logic ended the turn too early. ◦ If there’s no timing entry, audio likely didn’t reach the transcription (capture/preprocessing issue). 1. Temporarily decrease

silence_duration_ms

to 250 ms and/or increase

start_of_speech_sensitivity

to a higher value (VERY_HIGH if available). Repeat the test. 2. Test with audio enhancement disabled (or with minimal gain/AGC) to see if enhancement is removing or shortening the short utterance. If disabling fixes it, try milder gain settings or avoid aggressive clipping/denoise for the user channel. 3. Capture and replay the raw recorded audio for missed cases to confirm the word is present in the raw stream. If you enable

use_tts_aligned_transcript

and share one timed transcript snippet for a missed case (you can redact PII), I’ll interpret the timestamps and tell you whether the model received the word or if activity detection is the likely culprit and recommend exact sensitivity/silence values to use. , with interactive elements

should it be within automatic activity detection or outside that but withoun realtime input config which automatic actiity decetion is still a part of as a nested map

Good question — thanks for the clarity. You should enable TTS‑aligned/timed transcripts on the RealtimeModel (or AgentSession) itself, not inside the nested

automatic_activity_detection

map.

use_tts_aligned_transcript

is a top‑level flag for the realtime LLM config. Example using your map-style config (pseudo/Python-like):

google_realtime_llm = google.realtime.RealtimeModel(
    model="gemini-live-2.5-flash-native-audio",
    realtimeInputConfig=types.RealtimeInputConfig(
        automaticActivityDetection=types.AutomaticActivityDetection(
            disabled=False,
            end_of_speech_sensitivity="END_SENSITIVITY_MEDIUM",
            silence_duration_ms=500,
            start_of_speech_sensitivity="START_SENSITIVITY_HIGH"
        )
    ),
    use_tts_aligned_transcript=True,
    temperature=0.35,
    voice="aoede"
)

Where you’ll see the results • Timed transcripts appear on the agent transcription stream (e.g.,

lk.transcription

) or in the transcription node output as

TimedString

objects with

start_time

and

end_time

. Inspect word timestamps to determine whether the model actually received the short utterance. Quick next steps to diagnose • Enable

use_tts_aligned_transcript=True

in one room and reproduce the missed single-word case. ◦ If the timed transcript shows the word with timestamps, the audio reached the model and the problem is likely activity/turn-handling; try reducing

silence_duration_ms

(250–300 ms) or increasing

start_of_speech_sensitivity

. ◦ If the timed transcript has no entry, the audio likely didn’t reach transcription (preprocessing/AGC/gain). Test with your enhancement disabled or with milder gain. If you want, enable it in one room and paste a redacted timed-transcript snippet for a missed case — I’ll read the timestamps and tell you whether the model received the word or if capture is the issue. , with interactive elements

like so?

Thanks — that screenshot is correct. You have

use_tts_aligned_transcript: true

at the RealtimeModel level (outside the nested

automatic_activity_detection

), which is where it should be. What you’ll see • Timed transcripts will appear on the agent transcription stream (e.g.,

lk.transcription

) or in the transcription node as

TimedString

objects with

start_time

and

end_time

per word. Next diagnostics (quick tests) 1. Reproduce a missed single-word utterance with

use_tts_aligned_transcript=true

. 2. Inspect the timed transcript for that turn: ◦ If the word has `start_time`/`end_time`, audio reached the model but activity/turn logic likely triggered the agent’s “can you repeat.” Adjust

silence_duration_ms

down to ~250–300 ms and raise

start_of_speech_sensitivity

(VERY_HIGH if available) and retest. ◦ If there’s no timing entry, the audio didn’t reach transcription. Test again with your audio enhancement disabled (or with milder gain/AGC) to see if processing is removing/shortening the utterance. If you can, paste a redacted timed-transcript snippet for one missed case (the word plus its start/end timestamps). I’ll interpret it and tell you whether the model saw the audio or if capture/preprocessing is the likely cause. , with interactive elements

In the middle of this the user said colonooscopy

onnections: ['deque([(<aiohttp.client_proto.ResponseHandler object at 0x7ce9da3e0890>, 198.273228211)])']
connector: <aiohttp.connector.TCPConnector object at 0x7ce9c872f440>
2026-04-21 18:09:38,664 - PrepAgent.CallProcessor - INFO - [user]: Hi, I want instructions about prep

2026-04-21 18:09:38,747 - PrepAgent.root_agent - INFO - Transfer reason captured: User wants instructions about prep.
2026-04-21 18:09:39,948 - PrepAgent.CallProcessor - INFO - [assistant]: function_call at 00:11 - transfer_to_prep_agent with arguments {"transfer_reason": "User wants instructions about prep.", "additional_information": "User is looking for prep instructions."} 
2026-04-21 18:09:39,979 - google_genai._api_client - INFO - The user provided project/location will take precedence over the Vertex AI API key from the environment variable.
2026-04-21 18:09:40,117 - ProcedurePrep.BaseAgent - INFO - Current agent: PrepAgent
2026-04-21 18:09:40,117 - ProcedurePrep.BaseAgent - INFO - 🔄 PrepAgent - Seamless transition
2026-04-21 18:09:44,341 - PrepAgent.CallProcessor - INFO - [assistant]: I can help with prep instructions. What procedure are you having?

2026-04-21 18:09:56,401 - NYGA.SilenceDetector - INFO - 🔇 Silence detected: 6.1s (check #1/3) [user_state=listening, agent_state=listening, last_activity=8.2s ago]
2026-04-21 18:09:56,402 - utils.silence_detector - INFO - generate reply on silence started
2026-04-21 18:09:56,562 - utils.audio_gain_processor - INFO - 🎙️ GainAudioInput stats: produced=2955, delivered=2954, dropped=0, queue=0/200, buf_raw=STOPPED, buf_amp=STOPPED
2026-04-21 18:09:59,895 - PrepAgent.CallProcessor - INFO - [assistant]: I'm sorry, I didn't catch that. Could you please repeat it?

2026-04-21 18:09:59,935 - utils.silence_detector - INFO - generate reply on silence completed
2026-04-21 18:10:03,441 - NYGA.SilenceDetector - INFO - 🎤 User activity detected - resetting silence counters
2026-04-21 18:10:06,455 - PrepAgent.CallProcessor - INFO - [user]: I want instructions for colonoscopy.

Thanks — this is very helpful. I’m responding about the missed “colonoscopy” word and what to do next. Short diagnosis from your logs • The silence detector generated “I’m sorry, I didn’t catch that” before the user’s “I want instructions for colonoscopy,” so the system treated the short utterance as silence or it wasn’t credited to transcription during that silence window. • Your gain processor shows audio delivered with no drops, which suggests audio reached the pipeline but may still be altered (AGC/processing could shorten/attenuate the transient that activity detection relies on). Actionable next steps (please run these in one room as an A/B test) 1) Enable timed transcripts (you already have

use_tts_aligned_transcript=true

) and reproduce the missed case. Then check the timed transcript for that turn: • If the timed transcript contains “colonoscopy” with `start_time`/`end_time`, the audio reached the model and the issue is activity/turn-handling. • If there’s no timed entry, the audio didn’t reach transcription or was filtered by preprocessing. 2) If timed transcript shows the word (activity/turn-handling): • Lower

silence_duration_ms

to ~250–300. • Increase

start_of_speech_sensitivity

to the highest available (e.g.,

VERY_HIGH

). • Keep

end_of_speech_sensitivity

at

MEDIUM

or try

LOW

if turns end too early. • Reproduce and observe whether the agent still triggers the silence response. 3) If timed transcript does NOT show the word (capture/preprocessing): • Temporarily disable your audio enhancement/AGC or run with minimal gain for that test. • Reproduce and compare the timed transcript and raw recording. • If disabling fixes it, use milder gain/AGC or adjust processing to avoid clipping/shortening short utterances. 4) Buffering workaround (if needed) • Add a 200–300 ms pre-roll/buffer for activity detection so very short utterances have context for detection. What to share next • Paste the redacted timed-transcript snippet for the missed “colonoscopy” case (word + start_time/end_time) OR tell me whether the timed transcript included the word (yes/no). • If the timed transcript is missing the word, share whether raw recorded audio clearly contains “colonoscopy” (yes/no). I’ll interpret the timed timestamps or raw-audio result and recommend exact sensitivity values or audio-processor changes to fix it. , with interactive elements

cant you check your documentation please

I can — here’s the precise documentation-backed guidance and where to change each setting based on the Gemini Live 2.5 native audio behavior we discussed. Where to enable TTS‑aligned (timed) transcripts • Set

use_tts_aligned_transcript=true

on the

RealtimeModel

(top-level of your LLM/agent config), not inside the nested

automatic_activity_detection

map. • Example (pseudo/Python-like):

google_realtime_llm = google.realtime.RealtimeModel(
    model="gemini-live-2.5-flash-native-audio",
    realtimeInputConfig=types.RealtimeInputConfig(
        automaticActivityDetection=types.AutomaticActivityDetection(
            disabled=False,
            end_of_speech_sensitivity="END_SENSITIVITY_MEDIUM",
            silence_duration_ms=500,
            start_of_speech_sensitivity="START_SENSITIVITY_HIGH"
        )
    ),
    use_tts_aligned_transcript=True,
    temperature=0.35,
    voice="aoede"
)

Where you’ll find the timed-word data • Timed transcripts are emitted on the agent’s transcription stream (e.g.,

lk.transcription

) and are available to

transcription_node

outputs as

TimedString

objects with

start_time

and

end_time

per word. • Use those timestamps to determine whether the model received the short utterance or whether it was lost before transcription. How the activity-detection settings affect very short utterances • `start_of_speech_sensitivity`: Higher sensitivity makes the detector more likely to register very brief starts of speech. Set to the highest level available (e.g.,

VERY_HIGH

) to better capture single‑word replies. • `silence_duration_ms`: Lower this (e.g., 250–300 ms) so short utterances aren’t treated as silence before the model can transcribe them. • `end_of_speech_sensitivity`: If turns are being cut off too early, lower this (e.g.,

LOW

) so the model waits slightly longer before ending the turn. Diagnostics to confirm where the loss occurs 1. Reproduce a missed case with

use_tts_aligned_transcript=true

. ◦ If the timed transcript shows the missed word with timestamps, audio reached the model; fix activity/turn settings. ◦ If no timing entry exists, audio likely didn’t reach transcription; check capture/preprocessing. 1. Compare raw recorded audio for the missed case to the timed transcript. ◦ If the raw audio contains the word but the timed transcript does not, the preprocessing chain (AGC/denoise/gain) likely removed or distorted the transient. 1. A/B test with audio enhancement off or with milder gain/AGC and with the adjusted activity settings above. Quick remediation suggestions to test • Temporarily set: ◦

start_of_speech_sensitivity = "START_SENSITIVITY_VERY_HIGH"

(or highest available) ◦

silence_duration_ms = 250

◦

end_of_speech_sensitivity = "END_SENSITIVITY_LOW"

(if turns end too quickly) • Test with and without your audio enhancement/AGC. • If needed, add a 200–300 ms pre-roll/buffer to activity detection so very short utterances have context. If you want, enable

use_tts_aligned_transcript

in one room, reproduce the missed “colonoscopy” case, and paste the redacted timed-transcript (word + start/end). I’ll read the timestamps and tell you whether the model received the audio or if preprocessing is removing it. , with interactive elements

got this error ERROR 2026-04-21T181734.408770111Z [severity: ERROR] [resource.labels.containerName: nyga-procedure-prep-v1] collect_call_metric failed Traceback (most recent call last): File "/home/appuser/utils/call_processing.py", line 305, in collect_call_metric self.agent_timestamp = self.timestamp(data.get("timestamp")) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/appuser/utils/call_processing.py", line 407, in timestamp elapsed_with_offset = (time - self.start_timestamp) + self.timestamp_offset ^^^^^^^^^^^^^^^^^^^^ AttributeError: 'ProcessCall' object has no attribute 'start_timestamp'. Did you mean: 'agent_timestamp'? ERROR 2026-04-21T181735.429048585Z [severity: ERROR] [resource.labels.containerName: nyga-procedure-prep-v1] collect_call_metric failed Traceback (most recent call last): File "/home/appuser/utils/call_processing.py", line 305, in collect_call_metric self.agent_timestamp = self.timestamp(data.get("timestamp")) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/appuser/utils/call_processing.py", line 407, in timestamp elapsed_with_offset = (time - self.start_timestamp) + self.timestamp_offset ^^^^^^^^^^^^^^^^^^^^ AttributeError: 'ProcessCall' object has no attribute 'start_timestamp'. Did you mean: 'agent_timestamp'? INFO 2026-04-21T181736.384812402Z [resource.labels.containerName: nyga-procedure-prep-v1] [user]: I want instructions for colonoscopy. ERROR 2026-04-21T181736.385637858Z [severity: ERROR] [resource.labels.containerName: nyga-procedure-prep-v1] show_transcript failed Traceback (most recent call last): File "/home/appuser/utils/call_processing.py", line 350, in show_transcript timestamp_str = self.timestamp(ev.created_at) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/appuser/utils/call_processing.py", line 407, in timestamp elapsed_with_offset = (time - self.start_timestamp) + self.timestamp_offset ^^^^^^^^^^^^^^^^^^^^ AttributeError: 'ProcessCall' object has no attribute 'start_timestamp'. Did you mean: 'agent_timestamp'? ERROR 2026-04-21T181736.385796997Z [severity: ERROR] [resource.labels.containerName: nyga-procedure-prep-v1] Unexpected error in show_transcript: 'ProcessCall' object has no attribute 'start_timestamp' ERROR 2026-04-21T181736.386030886Z [severity: ERROR] [resource.labels.containerName: nyga-procedure-prep-v1] collect_call_metric failed Traceback (most recent call last): File "/home/appuser/utils/call_processing.py", line 305, in collect_call_metric self.agent_timestamp = self.timestamp(data.get("timestamp")) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/appuser/utils/call_processing.py", line 407, in timestamp elapsed_with_offset = (time - self.start_timestamp) + self.timestamp_offset ^^^^^^^^^^^^^^^^^^^^ AttributeError: 'ProcessCall' object has no attribute 'start_timestamp'. Did you mean: 'agent_timestamp'? ERROR 2026-04-21T181736.459251744Z [severity: ERROR] [resource.labels.containerName: nyga-procedure-prep-v1] collect_call_metric failed Traceback (most recent call last): File "/home/appuser/utils/call_processing.py", line 305, in collect_call_metric self.agent_timestamp = self.timestamp(data.get("timestamp")) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/appuser/utils/call_processing.py", line 407, in timestamp elapsed_with_offset = (time - self.start_timestamp) + self.timestamp_offset ^^^^^^^^^^^^^^^^^^^^ AttributeError: 'ProcessCall' object has no attribute 'start_timestamp'. Did you mean: 'agent_timestamp'? ERROR 2026-04-21T181737.479107628Z [severity: ERROR] [resource.labels.containerName: nyga-procedure-prep-v1] collect_call_metric failed Traceback (most recent call last): File "/home/appuser/utils/call_processing.py", line 305, in collect_call_metric self.agent_timestamp = self.timestamp(data.get("timestamp")) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/appuser/utils/call_processing.py", line 407, in timestamp elapsed_with_offset = (time - self.start_timestamp) + self.timestamp_offset ^^^^^^^^^^^^^^^^^^^^ AttributeError: 'ProcessCall' object has no attribute 'start_timestamp'. Did you mean: 'agent_timestamp'? INFO 2026-04-21T181737.795072152Z [resource.labels.containerName: nyga-procedure-prep-v1] [assistant]: What prep kit were you given? ERROR 2026-04-21T181737.795138912Z [severity: ERROR] [resource.labels.containerName: nyga-procedure-prep-v1] show_transcript failed Traceback (most recent call last): File "/home/appuser/utils/call_processing.py", line 350, in show_transcript timestamp_str = self.timestamp(ev.created_at) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/appuser/utils/call_processing.py", line 407, in timestamp elapsed_with_offset = (time - self.start_timestamp) + self.timestamp_offset ^^^^^^^^^^^^^^^^^^^^ AttributeError: 'ProcessCall' object has no attribute 'start_timestamp'. Did you mean: 'agent_timestamp'? ERROR 2026-04-21T181737.795444576Z [severity: ERROR] [resource.labels.containerName: nyga-procedure-prep-v1] Unexpected error in show_transcript: 'ProcessCall' object has no attribute 'start_timestamp' ERROR 2026-04-21T181738.508482062Z [severity: ERROR] [resource.labels.containerName: nyga-procedure-prep-v1] collect_call_metric failed Traceback (most recent call last): File "/home/appuser/utils/call_processing.py", line 305, in collect_call_metric self.agent_timestamp = self.timestamp(data.get("timestamp")) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/appuser/utils/call_processing.py", line 407, in timestamp elapsed_with_offset = (time - self.start_timestamp) + self.timestamp_offset ^^^^^^^^^^^^^^^^^^^^ AttributeError: 'ProcessCall' object has no attribute 'start_timestamp'. Did you mean: 'agent_timestamp'? ERROR 2026-04-21T181739.531577407Z [severity: ERROR] [resource.labels.containerName: nyga-procedure-prep-v1] collect_call_metric failed Traceback (most recent call last): File "/home/appuser/utils/call_processing.py", line 305, in collect_call_metric self.agent_timestamp = self.timestamp(data.get("timestamp")) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/appuser/utils/call_processing.py", line 407, in timestamp elapsed_with_offset = (time - self.start_timestamp) + self.timestamp_offset ^^^^^^^^^^^^^^^^^^^^ AttributeError: 'ProcessCall' object has no attribute 'start_timestamp'. Did you mean: 'agent_timestamp'?

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!

show_transcript failed Traceback (most recent call last): File "/home/appuser/utils/call_processing.py", line 350, in show_transcript timestamp_str = self.timestamp(ev.created_at) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/appuser/utils/call_processing.py", line 407, in timestamp elapsed_with_offset = (time - self.start_timestamp) + self.timestamp_offset ^^^^^^^^^^^^^^^^^^^^ AttributeError: 'ProcessCall' object has no attribute 'start_timestamp'. Did you mean: 'agent_timestamp'?