feat: service inventory, ERP reports, sqlite-vec integration, visualize tool
- Add SERVICE_INVENTORY_V1.0.0.md (25 source-verified tools, 3.7GB) - Add ERP_SELECTION_REPORT.md (Odoo CE vs ERPNext comparison) - Add SFTPGO_ODOO_REPLACEMENT.md (SFTPGo migration plan) - Add SERVICE_GO_GITEA_BUILD.md (Go compiler + Gitea build report) - Add release visualize command (face trace heatmap + identity filter) - Add sqlite-vec integration (160MB SQLite with vec0 vector tables) - Add export_identities.py, export_sqlite.py, render_face_heatmap.py - Add Go, Gitea, Rust/Cargo, Swift, yt-dlp, SQLite, sqlite-vec to service CLI - Fix package to include identities and identity_bindings in data.sql - Update release list to show all deployed video stats - Add V1.0.0 YAML frontmatter to all docs (DOCS_STANDARD compliant)
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Accusys
164
scripts/speaker_assign.py
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164
scripts/speaker_assign.py
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#!/opt/homebrew/bin/python3.11
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"""
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Speaker Assignment: cluster voice vectors from Qdrant, assign speaker IDs to DB chunks.
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"""
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import json, sys, time
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import psycopg2
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import numpy as np
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from urllib.request import Request, urlopen
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from sklearn.cluster import AgglomerativeClustering
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from sklearn.metrics.pairwise import cosine_similarity
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UUID = sys.argv[1] if len(sys.argv) > 1 else "23b1c872379d4ec06479e5ed39eef4c5"
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QDRANT = "http://localhost:6333"
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DB = "dbname=momentry user=accusys"
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COLLECTION = "momentry_dev_voice"
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print(f"=== Speaker Assignment for {UUID} ===")
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# Step 1: Read voice vectors from Qdrant
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print("Reading voice vectors from Qdrant...")
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vectors = []
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chunk_ids = []
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# We need to scroll through all points
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offset = None
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while True:
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data = {"limit": 100, "with_payload": True, "with_vector": True}
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if offset is not None:
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data["offset"] = offset
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req = Request(f"{QDRANT}/collections/{COLLECTION}/points/scroll",
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data=json.dumps(data).encode(),
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headers={"Content-Type": "application/json"}, method="POST")
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resp = json.loads(urlopen(req).read())
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result = resp["result"]
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points = result.get("points", [])
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if not points:
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break
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for pt in points:
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payload = pt.get("payload", {})
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cid = payload.get("chunk_id", "")
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# Only get vectors for THIS UUID's chunks
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# Filter by checking DB later, or rely on Qdrant payload
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vectors.append(pt["vector"])
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chunk_ids.append(cid)
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offset = result.get("next_page_offset")
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if offset is None:
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break
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print(f" Read {len(vectors)} vectors...")
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print(f"Total vectors: {len(vectors)}")
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# Step 2: Filter to only our UUID's chunks (from DB)
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conn = psycopg2.connect(DB)
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cur = conn.cursor()
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cur.execute("SELECT chunk_id FROM dev.chunk WHERE file_uuid = %s AND chunk_type = 'sentence' ORDER BY id", (UUID,))
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db_chunk_ids = set(row[0] for row in cur.fetchall())
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print(f"DB chunk_ids: {len(db_chunk_ids)}")
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# Filter vectors to match DB chunks
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filtered_vectors = []
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filtered_chunk_ids = []
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for v, cid in zip(vectors, chunk_ids):
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if cid in db_chunk_ids:
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filtered_vectors.append(v)
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filtered_chunk_ids.append(cid)
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vectors = filtered_vectors
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chunk_ids = filtered_chunk_ids
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print(f"Matched vectors: {len(vectors)}")
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# Sort by chunk_id (which is numeric string)
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indices = sorted(range(len(chunk_ids)), key=lambda i: int(chunk_ids[i]) if chunk_ids[i].isdigit() else 0)
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vectors = [vectors[i] for i in indices]
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chunk_ids = [chunk_ids[i] for i in indices]
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# Step 3: Read speaker_change from asr.json
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asr_path = f"/Users/accusys/momentry/output_dev/{UUID}.asr.json"
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with open(asr_path) as f:
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asr_data = json.load(f)
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segments = asr_data.get("segments", [])
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speaker_changes = {}
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for seg in segments:
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speaker_changes[seg["chunk_id"]] = seg.get("speaker_change", False)
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# Step 4: Cluster embeddings
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print("Clustering...")
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X = np.array(vectors)
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# Compute cosine distance matrix
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# Cosine distance = 1 - cosine_similarity
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cos_sim = cosine_similarity(X)
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cos_dist = 1 - cos_sim
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# Use AgglomerativeClustering with cosine distance
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# Determine optimal n_clusters by looking at speaker_change boundaries
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# First pass: use speaker_change as hard boundaries to get initial clusters
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# Then refine
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# Simpler: use a distance threshold
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n = len(vectors)
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labels = np.full(n, -1, dtype=int)
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current_speaker = 0
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# Start with first chunk as speaker 0
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labels[0] = current_speaker
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centroids = [np.array(vectors[0])] # per-cluster centroid
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for i in range(1, n):
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has_change = speaker_changes.get(chunk_ids[i], False)
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vec = np.array(vectors[i])
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if has_change:
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# Speaker change: check if this is a NEW speaker or returning to a previous one
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# Compare with centroid of current speaker vs others
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similarities = [float(np.dot(vec, c) / (np.linalg.norm(vec) * np.linalg.norm(c) + 1e-10)) for c in centroids]
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best_sim = max(similarities) if similarities else 0
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best_cluster = similarities.index(best_sim) if similarities else 0
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if best_sim > 0.65 and best_cluster != current_speaker:
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# Returning to a previous speaker
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labels[i] = best_cluster
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elif best_sim < 0.55:
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# New speaker
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current_speaker = len(centroids)
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labels[i] = current_speaker
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centroids.append(vec)
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else:
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# Stay with current speaker (false change detection)
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labels[i] = current_speaker
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centroids[current_speaker] = (centroids[current_speaker] + vec) / 2
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else:
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# No speaker change: same speaker as previous
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labels[i] = current_speaker
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centroids[current_speaker] = (centroids[current_speaker] + vec) / 2
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n_speakers = len(set(labels))
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print(f"Identified {n_speakers} unique speakers")
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# Step 5: Update DB chunks with speaker assignment
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print("Updating DB chunks...")
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# Map: chunk_id -> speaker_id
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speaker_map = {}
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for cid, label in zip(chunk_ids, labels):
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speaker_map[cid] = f"SPEAKER_{label}"
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updated = 0
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for cid, spk_id in speaker_map.items():
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cur.execute("""
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UPDATE dev.chunk SET metadata = COALESCE(metadata, '{}'::jsonb) || %s::jsonb
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WHERE file_uuid = %s AND chunk_id = %s AND chunk_type = 'sentence'
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""", (json.dumps({"speaker_id": spk_id}), UUID, cid))
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updated += 1
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conn.commit()
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print(f"Updated {updated} chunks with speaker IDs")
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# Step 6: Save speaker map
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speaker_map_path = f"/Users/accusys/momentry/output_dev/{UUID}.speaker_map.json"
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with open(speaker_map_path, "w") as f:
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json.dump({"speakers": n_speakers, "assignments": speaker_map}, f, indent=2)
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print(f"Speaker map saved: {speaker_map_path}")
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cur.close()
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conn.close()
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print("=== Done ===")
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