| |
| """FastAPI reference-voice search server. Free-text query -> BM25 or embedding (vector) similarity -> |
| top-N voices with their single best-of-three audio. Serves the demo page + audio + a /search endpoint. |
| Run: CUDA_VISIBLE_DEVICES=<g> uvicorn server:app --host 0.0.0.0 --port 8778""" |
| import os, json, re, pickle |
| import numpy as np |
| from fastapi import FastAPI, Query |
| from fastapi.responses import HTMLResponse, FileResponse, JSONResponse |
| DS = os.environ.get("DS_DIR", "/run/user/1001/vcluster_annot/ds") |
| meta = json.load(open(f"{DS}/meta.json")) |
| emb = np.load(f"{DS}/emb.npy") |
| bm25 = pickle.load(open(f"{DS}/bm25.pkl", "rb")) |
| info = json.load(open(f"{DS}/index_info.json")) |
| def tok(s): return re.sub(r"[^a-z0-9 ]", " ", s.lower()).split() |
| _model = None |
| def model(): |
| global _model |
| if _model is None: |
| from sentence_transformers import SentenceTransformer |
| _model = SentenceTransformer(info["embed_model"], trust_remote_code=True, |
| device=os.environ.get("EMB_DEVICE", "cuda")) |
| return _model |
| app = FastAPI() |
|
|
| @app.get("/search") |
| def search(q: str = Query(...), mode: str = "embed", k: int = 5): |
| if mode == "bm25": |
| sc = np.asarray(bm25.get_scores(tok(q))) |
| else: |
| qv = model().encode([q], normalize_embeddings=True)[0] |
| sc = emb @ qv |
| idx = np.argsort(-sc)[:k] |
| return JSONResponse([{**meta[i], "score": float(sc[i]), "rank": r+1} for r, i in enumerate(idx)]) |
|
|
| @app.get("/audio/{cid}.mp3") |
| def audio(cid: str): |
| p = f"{DS}/audio/{cid}.mp3" |
| return FileResponse(p) if os.path.exists(p) else JSONResponse({"error": "not found"}, 404) |
|
|
| @app.get("/", response_class=HTMLResponse) |
| def home(): |
| return open(f"{DS}/index.html").read() |
|
|
