.gitignore

@@ -1,11 +0,0 @@
-venv/
-logs/
-__pycache__/
-*.pyc
-*.pid
-*.log
-ahserver/
-appPublic/
-longtasks/
-sqlor/
-wwwroot/

README.md

@@ -0,0 +1,2 @@
+# voiceprint
+

app/voiceprint_app.py

@@ -1,12 +0,0 @@
-def extract_embedding(audio_path):
-    signal = MODEL.load_audio(audio_path)
-    if isinstance(signal, tuple):
-        signal = signal[0]
-    signal = signal.unsqueeze(0).to(DEVICE)
-    with torch.no_grad():
-        emb = MODEL.encode_batch(signal)
-    return emb.squeeze().cpu().numpy().tolist()
-
-def verify_speakers(audio_path, ref_path):
-    score, pred = MODEL.verify_files(audio_path, ref_path)
-    return float(score), bool(pred)

conf/config.json

@@ -1,33 +0,0 @@
-{
-    "device": "cuda:1",
-    "redis_url": "redis://127.0.0.1:6379",
-    "worker_cnt": 2,
-    "logger": {
-        "name": "voiceprint",
-        "levelname": "info",
-        "logfile": "$[workdir]$/logs/voiceprint.log"
-    },
-    "website": {
-        "paths": [
-            ["$[workdir]$/wwwroot", ""]
-        ],
-        "client_max_size": 10000,
-        "host": "0.0.0.0",
-        "port": 9087,
-        "coding": "utf-8",
-        "indexes": ["index.html"],
-        "startswiths": [
-            {"leading": "/api/voiceprint-submit", "registerfunction": "voiceprint_submit"},
-            {"leading": "/api/voiceprint-status", "registerfunction": "voiceprint_status"},
-            {"leading": "/api/voiceprint/extract", "registerfunction": "voiceprint_extract"},
-            {"leading": "/api/voiceprint/verify", "registerfunction": "voiceprint_verify"}
-        ],
-        "processors": [
-            [".tmpl", "tmpl"],
-            [".ui", "bui"],
-            [".dspy", "dspy"]
-        ],
-        "session_max_time": 3000,
-        "session_issue_time": 2500
-    }
-}

download_model.py

@@ -1,24 +0,0 @@
-#!/usr/bin/env python3
-"""Download ECAPA-TDNN model via ModelScope to /share/models/ecapa-tdnn/"""
-import os, sys, time
-
-MODEL_ID = 'iic/speech_ecapa-tdnn_sv_en_voxceleb_16k'
-TARGET = '/share/models/ecapa-tdnn'
-
-os.makedirs(TARGET, exist_ok=True)
-
-# Try ModelScope first
-for attempt in range(3):
-    try:
-        from modelscope import snapshot_download
-        print(f"Attempt {attempt+1}: ModelScope download...")
-        snapshot_download(MODEL_ID, local_dir=TARGET)
-        print("SUCCESS via ModelScope")
-        sys.exit(0)
-    except Exception as e:
-        print(f"ModelScope failed: {e}")
-        time.sleep(10)
-
-# Fallback: try HF mirror
-print("Trying HF mirror...")
-os.system(f'export HF_ENDPOINT=https://hf-mirror.com && python3 -c "from huggingface_hub import snapshot_download; snapshot_download(\'speechbrain/spkrec-ecapa-voxceleb\', local_dir=\'{TARGET}\')"')

start.sh

@@ -1,8 +0,0 @@
-#!/bin/bash
-cd "$(dirname "$0")"
-mkdir -p logs
-setsid python3 app/voiceprint_app.py -p 9087 >> logs/voiceprint.log 2>&1 &
-echo $! > voiceprint.pid
-echo "Voiceprint PID=$(cat voiceprint.pid)"
-sleep 3
-curl -s http://localhost:9087/api/health && echo "" || echo "(checking...)"

stop.sh

@@ -1,9 +0,0 @@
-#!/bin/bash
-cd "$(dirname "$0")"
-if [ -f voiceprint.pid ]; then
-    kill $(cat voiceprint.pid) 2>/dev/null
-    rm -f voiceprint.pid
-    echo "Voiceprint stopped"
-else
-    pkill -f voiceprint_app.py 2>/dev/null && echo "Voiceprint stopped (via pkill)"
-fi

vp/__init__.py

@@ -1,7 +0,0 @@
-from ahserver.serverenv import ServerEnv
-from vp.engine import VoiceprintEngine
-
-
-def load_voiceprint():
-    env = ServerEnv()
-    env.voiceprint = VoiceprintEngine()

vp/engine.py

@@ -1,63 +0,0 @@
-"""ECAPA-TDNN Voiceprint Engine — loads model, processes extract/verify tasks."""
-import torch
-from longtasks.longtasks import LongTasks
-from appPublic.worker import awaitify
-from appPublic.jsonConfig import getConfig
-from appPublic.log import debug
-from speechbrain.inference.speaker import SpeakerRecognition
-import numpy as np
-
-
-class VoiceprintEngine(LongTasks):
-    def __init__(self):
-        self.config = getConfig()
-        super().__init__(self.config.redis_url, 'voiceprint', worker_cnt=self.config.worker_cnt)
-        self.load_model()
-
-    def load_model(self):
-        device = self.config.device
-        debug(f'loading ECAPA-TDNN on {device}...')
-        self.verifier = SpeakerRecognition.from_hparams(
-            source="speechbrain/spkrec-ecapa-voxceleb",
-            savedir="/share/models/ecapa-tdnn",
-            run_opts={"device": device}
-        )
-        debug('ECAPA-TDNN loaded')
-
-    async def process_task(self, payload, workerid=None):
-        task_type = payload.get('task_type', 'extract')
-        audio_file = payload.get('audio_file', '')
-        if not audio_file:
-            return {'status': 'FAILED', 'result': 'missing audio_file'}
-
-        if task_type == 'extract':
-            f = awaitify(self._extract)
-            return await f(audio_file)
-        elif task_type == 'verify':
-            ref_file = payload.get('reference_file', '')
-            if not ref_file:
-                return {'status': 'FAILED', 'result': 'missing reference_file'}
-            f = awaitify(self._verify)
-            return await f(audio_file, ref_file)
-        return {'status': 'FAILED', 'result': f'unknown task_type: {task_type}'}
-
-    def _extract(self, audio_path):
-        signal = self.verifier.load_audio(audio_path, 16000)
-        t = torch.tensor(signal).unsqueeze(0).to(self.config.device)
-        emb = self.verifier.encode_batch(t)
-        vec = emb.squeeze().cpu().numpy().tolist()
-        return {
-            'status': 'SUCCEEDED',
-            'embedding': vec,
-            'embedding_dim': len(vec),
-            'usage': {'audio_duration': round(len(signal) / 16000, 2)},
-        }
-
-    def _verify(self, audio_path, ref_path):
-        score, pred = self.verifier.verify_files(audio_path, ref_path)
-        return {
-            'status': 'SUCCEEDED',
-            'similarity': round(float(score), 4),
-            'is_same_speaker': bool(pred),
-            'usage': {},
-        }