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HALOWERK buerowerk — Meeting Same-Quote Owner Collision Detector
x402
HALOWERK buerowerk — Meeting Same-Quote Owner Collision Detector is a paid API for AI agents from buero.halowerk.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-18).
Detects and resolves speaker/owner collisions where multiple transcript segments share the same quote owner in a meeting transcript
Price / call$0.01
Success—
StatusUnknown
How this service works
HALOWERK buerowerk — bezahlte Endpunkte nach x402. Preise in USDC auf Base Mainnet.
Output
A JSON response identifying transcript segments or cue entries where the same quote text or time range has been attributed to conflicting or duplicate owner/speaker labels, enabling downstream correction of speaker diarization errors.
Example use cases
Post-meeting transcript quality check
“I just got back a diarized transcript from our 1-hour team standup with 120 segments and the speaker labels look messy — can you scan it for same-quote owner collisions so I know which parts need manual correction?”
Sales call transcription validation
“We recorded a 25-minute sales call and the transcription service returned segments with speaker IDs, but I think some quotes got assigned to the wrong owner — run a collision check on these 60 segments and tell me where the conflicts are.”
Legal deposition transcript audit
“I need to audit this deposition transcript with 200 cue segments before filing — check for any same-quote owner collisions where two speakers are attributed to the same statement, especially around timestamps 600 to 900 seconds.”
Technical details
Endpoint
POSThttps://buero.halowerk.com/v1/baumarkt/meeting/same-quote-owner-collisions?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need automated detection of speaker attribution conflicts in meeting or conversation transcripts, specifically where the same quote appears to be owned by multiple speakers or where diarization has produced overlapping owner assignments. Prefer this over manual review when segment counts are large (up to 20,000 segments) and precision in speaker attribution is required for downstream NLP, legal, or compliance workflows.
Side effects
- Missing required segment fields (start, end, speaker, text) returns validation error
- Segment timestamps exceeding maximum (14400 seconds for segments, 1801 for cues) rejected
- Empty segments array or zero-item input rejected
- Text exceeding 3000 characters per segment triggers validation failure
- More than 20000 segments submitted exceeds maxItems limit
- Payment not included or insufficient USDC returns 402 Payment Required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| segments | array | |
| transcript_package | object |
Output
Schema · Response
{
"type": "json"
}Tags
x402
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