Initial release: DictIA v0.8.14-alpha (fork de Speakr, AGPL-3.0)
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InnovA AI
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src/services/speaker_identification.py
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228
src/services/speaker_identification.py
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"""
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Shared speaker identification service.
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Provides LLM-based speaker identification from transcript context,
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used by both the web UI (recordings.py) and REST API (api_v1.py).
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"""
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import os
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import re
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import json
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from flask import current_app
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def identify_speakers_from_transcript(transcription_data, user_id):
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"""
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Identify speakers in a transcription using an LLM.
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Args:
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transcription_data: List of transcript segments (already parsed JSON).
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user_id: Current user's ID (for token tracking).
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Returns:
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dict mapping original speaker labels to identified names.
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Values are empty string "" for unidentified speakers.
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Raises:
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ValueError: If LLM API key is not configured.
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Exception: On LLM call failure.
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"""
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from src.services.llm import call_llm_completion
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from src.utils import safe_json_loads
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from src.models import SystemSetting
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# Extract unique speakers in order of appearance
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seen_speakers = set()
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unique_speakers = []
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for segment in transcription_data:
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speaker = segment.get('speaker')
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if speaker and speaker not in seen_speakers:
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seen_speakers.add(speaker)
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unique_speakers.append(speaker)
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if not unique_speakers:
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return {}
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# Normalize all labels to SPEAKER_XX format for the LLM
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speaker_to_label = {}
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for idx, speaker in enumerate(unique_speakers):
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speaker_to_label[speaker] = f'SPEAKER_{str(idx).zfill(2)}'
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# Create temporary transcript with normalized labels
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formatted_lines = []
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for segment in transcription_data:
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original_speaker = segment.get('speaker')
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label = speaker_to_label.get(original_speaker, 'Unknown Speaker')
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sentence = segment.get('sentence', '')
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formatted_lines.append(f"[{label}]: {sentence}")
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formatted_transcription = "\n".join(formatted_lines)
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speaker_labels = list(speaker_to_label.values())
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current_app.logger.info(f"[Auto-Identify] Formatted transcript (first 500 chars): {formatted_transcription[:500]}")
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current_app.logger.info(f"[Auto-Identify] Speaker labels: {speaker_labels}")
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# Apply configurable transcript length limit
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transcript_limit = SystemSetting.get_setting('transcript_length_limit', 30000)
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if transcript_limit == -1:
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transcript_text = formatted_transcription
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else:
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transcript_text = formatted_transcription[:transcript_limit]
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prompt = f"""Analyse cette transcription de conversation et identifie les noms des locuteurs à partir du contexte et du contenu de leurs dialogues.
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Les locuteurs à identifier sont : {', '.join(speaker_labels)}
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Indices à chercher :
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- Noms mentionnés par d'autres locuteurs quand ils s'adressent à quelqu'un
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- Présentations ou références à son propre nom
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- Indices contextuels sur les rôles, relations ou postes
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- Toute mention directe de noms dans le dialogue
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Transcription complète :
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{transcript_text}
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À partir de cette conversation, identifie les noms les plus probables pour chaque locuteur. Porte une attention particulière à la façon dont les locuteurs s'adressent les uns aux autres.
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Réponds avec un seul objet JSON où les clés sont les étiquettes de locuteurs (ex. "SPEAKER_01") et les valeurs sont les noms complets identifiés. Si un nom ne peut pas être déterminé, utilise une chaîne vide "".
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Exemple :
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{{
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"SPEAKER_01": "Marie Lavoie",
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"SPEAKER_03": "Jean Tremblay",
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"SPEAKER_05": ""
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}}
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Réponse JSON :
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"""
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current_app.logger.info("[Auto-Identify] Calling LLM")
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use_schema = os.environ.get('AUTO_IDENTIFY_RESPONSE_SCHEMA', '').strip() in ('1', 'true', 'yes')
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system_msg = (
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"You are an expert in analyzing conversation transcripts to identify speakers "
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"based on contextual clues in the dialogue. Analyze the conversation carefully "
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"to find names mentioned when speakers address each other or introduce themselves. "
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"Your response must be a single, valid JSON object containing only the requested "
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"speaker identifications."
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)
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response_content = None
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if use_schema:
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# Build JSON schema response format with constrained keys
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schema_properties = {label: {"type": "string"} for label in speaker_labels}
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schema_response_format = {
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"type": "json_schema",
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"json_schema": {
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"name": "speaker_identification",
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"strict": True,
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"schema": {
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"type": "object",
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"properties": schema_properties,
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"required": speaker_labels,
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"additionalProperties": False
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}
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}
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}
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schema_prompt = prompt + f"\n\nIMPORTANT: Your JSON response must contain exactly these keys: {', '.join(speaker_labels)}"
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try:
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current_app.logger.info("[Auto-Identify] Trying json_schema response format")
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completion = call_llm_completion(
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messages=[
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{"role": "system", "content": system_msg},
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{"role": "user", "content": schema_prompt}
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],
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temperature=0.2,
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response_format=schema_response_format,
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user_id=user_id,
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operation_type='speaker_identification'
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)
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response_content = completion.choices[0].message.content
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current_app.logger.info(f"[Auto-Identify] LLM Raw Response (schema mode): {response_content}")
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except Exception as schema_err:
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current_app.logger.warning(f"[Auto-Identify] json_schema mode failed, falling back to json_object: {schema_err}")
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response_content = None
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if response_content is None:
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completion = call_llm_completion(
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messages=[
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{"role": "system", "content": system_msg},
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{"role": "user", "content": prompt}
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],
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temperature=0.2,
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user_id=user_id,
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operation_type='speaker_identification'
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)
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response_content = completion.choices[0].message.content
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current_app.logger.info(f"[Auto-Identify] LLM Raw Response: {response_content}")
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identified_map = safe_json_loads(response_content, {})
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current_app.logger.info(f"[Auto-Identify] Parsed identified_map: {identified_map}")
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# --- Sanitize identified_map ---
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identified_map = _sanitize_identified_map(identified_map, speaker_labels)
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current_app.logger.info(f"[Auto-Identify] Sanitized identified_map: {identified_map}")
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# Map back to original speaker labels
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final_speaker_map = {}
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for original_speaker, temp_label in speaker_to_label.items():
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if temp_label in identified_map:
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final_speaker_map[original_speaker] = identified_map[temp_label]
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current_app.logger.info(f"[Auto-Identify] Final speaker_map: {final_speaker_map}")
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return final_speaker_map
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def _sanitize_identified_map(identified_map, speaker_labels):
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"""
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Clean up LLM output: handle inverted maps, strip commentary,
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clear placeholders, etc.
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"""
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speaker_label_re = re.compile(r'^SPEAKER_\d{2}$')
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# Detect inverted map ({name: "SPEAKER_XX"}) and flip it
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if identified_map and all(
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speaker_label_re.match(str(v)) for v in identified_map.values() if v
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) and not any(speaker_label_re.match(str(k)) for k in identified_map.keys()):
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current_app.logger.warning("[Auto-Identify] Detected inverted map, flipping keys/values")
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identified_map = {v: k for k, v in identified_map.items() if v}
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sanitized = {}
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for speaker_label, identified_name in identified_map.items():
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# Skip entries whose key isn't a valid SPEAKER_XX label
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if not speaker_label_re.match(str(speaker_label)):
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continue
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if not identified_name or not isinstance(identified_name, str):
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sanitized[speaker_label] = ""
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continue
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name = identified_name.strip()
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# Clear generic placeholders
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if name.lower() in ["unknown", "n/a", "not available", "unclear", "unidentified", ""]:
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sanitized[speaker_label] = ""
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continue
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# Clear label-to-label entries (e.g. "SPEAKER_01": "SPEAKER_02")
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if speaker_label_re.match(name):
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sanitized[speaker_label] = ""
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continue
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# Strip parenthetical content: "John (the host)" -> "John"
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name = re.sub(r'\s*\([^)]*\)', '', name).strip()
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# Take first name segment before comma, semicolon, or slash
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name = re.split(r'[,;/]', name)[0].strip()
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# Collapse whitespace
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name = re.sub(r'\s+', ' ', name)
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# Final check: if result still matches SPEAKER_XX, clear it
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if speaker_label_re.match(name) or not name:
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sanitized[speaker_label] = ""
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continue
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sanitized[speaker_label] = name
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return sanitized
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