Keysat
46 lines
2.1 KiB
Python
46 lines
2.1 KiB
Python
"""Speaker-name identification (§4.5 enhancement).
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In a 1-on-1 interview the host introduces the guest by name at the top. Reading the transcript head
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with the LLM, we attach a real NAME to each diarized speaker → voiceprints.person_label. This gives
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the independence graph a SECOND, orthogonal overlap signal: the same NAMED guest across two shows is
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a shared_guest edge even when the voiceprints don't cluster (different mic/codec/room). It complements
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voiceprint cosine matching and is robust to fingerprint drift — exactly the case the operator flagged.
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"""
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from __future__ import annotations
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import json
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import logging
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log = logging.getLogger(__name__)
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_SYS = (
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'You identify the speakers in a podcast/interview transcript. Each line is "LABEL: text". '
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"Using the introduction and context, determine each LABEL's real full name and role. In an "
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"interview the host normally introduces themselves and the guest within the first minute. Only "
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"assert a name you can actually support from the text — if you cannot tell, use null. "
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'Return ONLY JSON: {"speakers": {"<LABEL>": {"name": "Full Name" or null, '
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'"role": "host"|"guest"|"panelist"|"unknown", "confidence": "low"|"med"|"high"}}}.'
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)
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def identify_speakers(backend, transcript_head: str, *, source_name: str, host_hint: str | None = None) -> dict:
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"""Returns {label: {name, role, confidence}}. `backend` is any extract.backends backend."""
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ctx = f"Show: {source_name}."
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if host_hint:
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ctx += f" The show's usual host is {host_hint}."
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ctx += "\n\nTRANSCRIPT (beginning):\n" + transcript_head
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messages = [{"role": "system", "content": _SYS}, {"role": "user", "content": ctx}]
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raw = backend.complete_json(messages, max_tokens=600)
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try:
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obj = json.loads(raw)
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except Exception:
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i, j = raw.find("{"), raw.rfind("}")
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if i < 0 or j < 0:
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return {}
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try:
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obj = json.loads(raw[i:j + 1])
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except Exception:
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return {}
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spk = obj.get("speakers", {}) if isinstance(obj, dict) else {}
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return spk if isinstance(spk, dict) else {}
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