Tried the new amd feature, seems like the initial transcript also goes into the main stt pipeline, chaotic...

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python

Thanks — helpful to know you’re on Python. Below are two practical approaches you can use: pause main STT while AMD runs, or dedupe overlapping transcripts. Pick one that fits your architecture. 1) Pause main STT during AMD (recommended) • Run AMD first, with audio/STT input disabled for the main pipeline. • Only start the main session/STT after AMD finishes and you’ve decided to continue. Example pattern (pseudo-Python, adapt to your SDK calls):

# 1. Initialize detector and STT client but DO NOT start main STT stream
detector = AMDDetector(...)
main_stt = STTClient(...)

# 2. Run AMD and wait for result
amd_result = detector.execute(audio_source)  # blocks until AMD completes

# 3. Inspect AMD result (human, voicemail, IVR, unavailable)
if amd_result.is_human:
    # 4. Start main STT/audio only after AMD finishes
    main_stt.start_stream(audio_source)
else:
    # handle voicemail/IVR path without starting main STT
    handle_non_human(amd_result)

2) Dedupe overlapping transcripts (if you must run both concurrently) • Track AMD transcript timestamps and drop or ignore main-STT transcripts that overlap that time range. • Use a short grace window around AMD timestamps to account for latency. Example dedupe logic:

amd_intervals = []  # list of (start_ms, end_ms) from AMD

# when AMD emits transcript segments:
amd_intervals.append((seg.start_ms, seg.end_ms))

# when main STT emits a transcript segment:
def is_overlapping(seg):
    for s,e in amd_intervals:
        if not (seg.end_ms < s or seg.start_ms > e):
            return True
    return False

if not is_overlapping(main_seg):
    process_main_transcript(main_seg)
else:
    # drop or mark as duplicate
    discard(main_seg)

Which approach would you like a full example for in your exact SDK (LiveKit Python client or another STT lib)? If you share the functions you call to start STT/AMD, I’ll tailor the code to your setup. , with interactive elements

detection_options

DetectionOptions

Override the default timing thresholds and classification prompt. Pass a dict with any of the following keys:

human_speech_threshold

(default

2.5

),

human_silence_threshold

(default

0.5

),

machine_silence_threshold

(default

1.5

),

no_speech_threshold

(default

10.0

),

timeout

(default

20.0

), or

prompt

. All thresholds are in seconds. Values not provided fall back to library defaults. explain more in detail for each params, so little detail on doc

Thanks — I’ll explain each

DetectionOptions

parameter in detail and give guidance on how changing them affects AMD behavior. I’m responding to your request for more detail about

human_speech_threshold

,

human_silence_threshold

,

machine_silence_threshold

,

no_speech_threshold

,

timeout

, and

prompt

. •

human_speech_threshold

(seconds) ◦ What it is: minimum continuous duration of detected human-like speech required before the detector will consider the call reached a human. ◦ Effect: Higher values require a longer uninterrupted human speech segment to classify as human (reduces false positives from short utterances); lower values make the detector quicker to mark “human” but can increase false positives from brief sounds. ◦ When to raise/lower: raise for noisy environments or if human answers typically speak longer; lower for short greetings or languages with brief responses. Default: 2.5s. •

human_silence_threshold

(seconds) ◦ What it is: allowable silence after human speech before concluding the human segment has ended (used to separate speech segments). ◦ Effect: A longer silence threshold keeps a human classification active across short pauses (prevents splitting one utterance into multiple segments); a shorter value treats pauses as end-of-speech sooner. ◦ When to adjust: increase if humans pause frequently or speak with gaps; decrease if you want faster transition detection after a pause. Default: 0.5s. •

machine_silence_threshold

(seconds) ◦ What it is: silence duration after machine/voicemail-like audio before the detector decides the machine segment ended. ◦ Effect: Longer threshold tolerates pauses inside machine/voicemail messages (prevents premature end); shorter threshold ends machine detection faster. ◦ When to change: raise for longer voicemail messages or IVR prompts with pauses; lower to speed up detection if machine audio is concise. Default: 1.5s. •

no_speech_threshold

(seconds) ◦ What it is: time of continuous near-silence (no speech) that the detector interprets as “no one answered” or the line is silent. ◦ Effect: Shorter values classify silence as no-answer sooner; longer values wait longer before deciding nothing was spoken. ◦ When to adjust: lower if you want fast failover for unanswered calls; raise if carrier delays or ringback tones may produce silence initially. Default: 10.0s. •

timeout

(seconds) ◦ What it is: the maximum total time AMD will run before giving up and returning a best-effort classification. ◦ Effect: Shorter time bounds overall AMD latency but risks inconclusive/incorrect results; longer time increases confidence at the cost of latency. ◦ When to change: shorten for low-latency flows that can tolerate some misclassifications; lengthen when accuracy is paramount. Default: 20.0s. •

prompt

(string) ◦ What it is: custom LLM prompt used to classify the AMD transcript into categories (human, voicemail, IVR, unavailable, etc.). ◦ Effect: Tailors classification rules, examples, or language handling — a more specific prompt can reduce misclassification for your call types or languages. ◦ When to customize: when default prompt misclassifies language, dialects, or particular IVR/voicemail styles; include examples or rules for edge cases. ◦ Guidance: keep prompt concise, include positive and negative examples, and mention expected labels and confidence thresholds if supported. Best-practice tips • Start with defaults, then tune one parameter at a time while testing with representative call samples. • If you see false human detections from short noises, increase

human_speech_threshold

. • If voicemails get split or mis-timed, increase

machine_silence_threshold

. • Use

timeout

to balance accuracy vs. latency for your outbound workflow. • Use

prompt

to improve classification on unusual languages/IVRs — include labeled examples. If you’d like, share a few sample call recordings or describe the typical answer behavior (short greetings, long voicemails, IVR prompts, noisy lines), and I’ll recommend concrete threshold values for Python usage. , with interactive elements