for first push

2026-04-29 16:29:03 -04:00
parent 218de3b03b
commit 509259bcf2
19 changed files with 1911 additions and 2 deletions
--- a/packages/api/package.json
+++ b/packages/api/package.json
@@ -16,6 +16,7 @@
    "@shieldai/db": "0.1.0",
    "@shieldai/types": "0.1.0",
    "fastify": "^5.2.0",
-    "@shieldai/darkwatch": "0.1.0"
+    "@shieldai/darkwatch": "0.1.0",
+    "@shieldai/voiceprint": "0.1.0"
  }
 }
--- a/packages/api/src/routes/index.ts
+++ b/packages/api/src/routes/index.ts
@@ -13,3 +13,10 @@ export function darkwatchRoutes(fastify: FastifyInstance) {
    root.register(scans, { prefix: "/scan" });
  }, { prefix: "/api/v1/darkwatch" });
 }
+
+export function voiceprintRoutes(fastify: FastifyInstance) {
+  fastify.register(async (root) => {
+    const voiceprint = (await import("./voiceprint.routes")).voiceprintRoutes;
+    root.register(voiceprint);
+  }, { prefix: "/api/v1/voiceprint" });
+}
--- a/packages/api/src/routes/voiceprint.routes.ts
+++ b/packages/api/src/routes/voiceprint.routes.ts
@@ -0,0 +1,94 @@
+import { FastifyInstance } from "fastify";
+import { VoiceEnrollmentService } from "@shieldai/voiceprint";
+import { AnalysisService } from "@shieldai/voiceprint";
+import { BatchAnalysisService } from "@shieldai/voiceprint";
+
+export function voiceprintRoutes(fastify: FastifyInstance) {
+  const enrollmentService = new VoiceEnrollmentService();
+  const analysisService = new AnalysisService();
+  const batchService = new BatchAnalysisService();
+
+  fastify.post("/enroll", async (request, reply) => {
+    const userId = (request.user as { id: string })?.id;
+    if (!userId) return reply.code(401).send({ error: "User not authenticated" });
+
+    const body = request.body as { label: string; audio: string; sampleRate?: number };
+
+    const audioBuffer = Buffer.from(body.audio, "base64");
+    const enrollment = await enrollmentService.enroll(
+      { label: body.label, audioBuffer, sampleRate: body.sampleRate },
+      userId
+    );
+    return reply.code(201).send(enrollment);
+  });
+
+  fastify.get("/enrollments", async (request, reply) => {
+    const userId = (request.user as { id: string })?.id;
+    if (!userId) return reply.code(401).send({ error: "User not authenticated" });
+
+    const enrollments = await enrollmentService.listEnrollments(userId);
+    return reply.send(enrollments);
+  });
+
+  fastify.delete("/enrollments/:id", async (request, reply) => {
+    const userId = (request.user as { id: string })?.id;
+    if (!userId) return reply.code(401).send({ error: "User not authenticated" });
+
+    const enrollmentId = (request.params as { id: string }).id;
+    const result = await enrollmentService.removeEnrollment(userId, enrollmentId);
+    return reply.send({ removed: result });
+  });
+
+  fastify.post("/analyze", async (request, reply) => {
+    const userId = (request.user as { id: string })?.id;
+    if (!userId) return reply.code(401).send({ error: "User not authenticated" });
+
+    const body = request.body as { audio: string; sampleRate?: number; analysisType?: string };
+    const audioBuffer = Buffer.from(body.audio, "base64");
+
+    const result = await analysisService.analyze(
+      { audioBuffer, sampleRate: body.sampleRate, analysisType: body.analysisType },
+      userId
+    );
+    return reply.code(201).send(result);
+  });
+
+  fastify.get("/results/:id", async (request, reply) => {
+    const jobId = (request.params as { id: string }).id;
+    const result = await analysisService.getResult(jobId);
+
+    if (!result) return reply.code(404).send({ error: "Analysis result not found" });
+    return reply.send(result);
+  });
+
+  fastify.get("/results", async (request, reply) => {
+    const userId = (request.user as { id: string })?.id;
+    if (!userId) return reply.code(401).send({ error: "User not authenticated" });
+
+    const limit = parseInt((request.query as { limit?: string }).limit || "20", 10);
+    const results = await analysisService.getUserResults(userId, limit);
+    return reply.send(results);
+  });
+
+  fastify.post("/batch", async (request, reply) => {
+    const userId = (request.user as { id: string })?.id;
+    if (!userId) return reply.code(401).send({ error: "User not authenticated" });
+
+    const body = request.body as {
+      files: Array<{ name: string; audio: string; sampleRate?: number }>;
+      analysisType?: string;
+    };
+
+    const audioBuffers = body.files.map((f) => ({
+      name: f.name,
+      buffer: Buffer.from(f.audio, "base64"),
+      sampleRate: f.sampleRate,
+    }));
+
+    const result = await batchService.analyzeBatch(
+      { audioBuffers, analysisType: body.analysisType },
+      userId
+    );
+    return reply.code(201).send(result);
+  });
+}
--- a/packages/api/src/server.ts
+++ b/packages/api/src/server.ts
@@ -2,7 +2,7 @@ import Fastify from "fastify";
 import cors from "@fastify/cors";
 import helmet from "@fastify/helmet";
 import sensible from "@fastify/sensible";
-import { darkwatchRoutes } from "./routes";
+import { darkwatchRoutes, voiceprintRoutes } from "./routes";

 const app = Fastify({
  logger: {
@@ -16,6 +16,7 @@ async function bootstrap() {
  await app.register(sensible);

  await app.register(darkwatchRoutes);
+  await app.register(voiceprintRoutes);

  app.get("/health", async () => ({ status: "ok", timestamp: new Date().toISOString() }));

--- a/packages/core/src/audio/webrtc/audio-pipeline.ts
+++ b/packages/core/src/audio/webrtc/audio-pipeline.ts
@@ -0,0 +1,316 @@
+/**
+ * Audio Processing Pipeline for Real-Time Analysis
+ * Coordinates WebRTC stream capture with VoicePreprocess for continuous analysis
+ */
+
+import { WebRTCStreamCapture, createWebRTCCapture } from './stream-capture';
+
+// Type definitions for real-time processing
+export interface AudioChunk {
+  id: string;
+  timestamp: number;
+  data: Float32Array;
+  duration: number;
+}
+
+export interface VoiceprintResult {
+  chunkId: string;
+  timestamp: number;
+  features: AudioFeatures;
+  embedding: number[];
+  confidence: number;
+  status: 'complete' | 'error';
+}
+
+// Audio chunk configuration
+export interface AudioChunkConfig {
+  chunkDuration: number;
+  overlapDuration: number;
+  sampleRate: number;
+}
+
+// Preprocessor interfaces (copied from AudioPreprocessor for standalone usage)
+export interface PreprocessedAudio {
+  audio: Buffer;
+  sampleRate: number;
+  channels: number;
+  durationSec: number;
+}
+
+export interface AudioFeatures {
+  mfccs: number[][];
+  zeroCrossingRate: number;
+  spectralCentroid: number;
+  spectralRollOff: number;
+  durationSec: number;
+}
+
+interface PipelineConfig {
+  chunkDuration: number;     // 5000ms
+  overlapDuration: number;   // 1000ms
+  sampleRate: number;        // 16000 Hz
+  onAnalysisComplete?: (result: VoiceprintResult) => void;
+  onStreamError?: (error: Error) => void;
+  onError?: (error: Error) => void;
+}
+
+const DEFAULT_CONFIG: PipelineConfig = {
+  chunkDuration: 5000,
+  overlapDuration: 1000,
+  sampleRate: 16000
+};
+
+export class AudioPipeline {
+  private streamCapture: WebRTCStreamCapture | null = null;
+  private isRunning: boolean = false;
+  private chunkBuffer: AudioChunk[] = [];
+  private maxBufferLength: number = 10;
+  
+  private onChunkReady?: (chunk: AudioChunk) => void;
+  private onAnalysisComplete?: (result: VoiceprintResult) => void;
+  private onPipelineError?: (error: Error) => void;
+  
+  constructor(private config: PipelineConfig = DEFAULT_CONFIG) {}
+
+  /**
+   * Initialize pipeline components
+   */
+  async initialize(): Promise<void> {
+    // Initialize WebRTC stream capture
+    this.streamCapture = createWebRTCCapture({
+      chunkDuration: this.config.chunkDuration,
+      overlapDuration: this.config.overlapDuration,
+      sampleRate: this.config.sampleRate
+    });
+
+    // Connect WebRTC chunk processing
+    this.streamCapture.onChunkReady = (rawAudio, timestamp) => {
+      this.handleRawChunk(rawAudio, timestamp);
+    };
+
+    // Connect stream error handling
+    this.streamCapture.onStreamError = (error) => {
+      this.onPipelineError?.(error);
+    };
+
+    console.log('[Pipeline] Initialized');
+  }
+
+  /**
+   * Process raw audio chunk from WebRTC
+   */
+  private async handleRawChunk(rawAudio: Float32Array, timestamp: number): Promise<void> {
+    try {
+      // Create audio chunk
+      const chunk: AudioChunk = {
+        id: `chunk-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`,
+        timestamp,
+        data: rawAudio,
+        duration: this.config.chunkDuration / 1000
+      };
+
+      // Add to buffer
+      this.chunkBuffer.push(chunk);
+
+      // Maintain buffer size
+      if (this.chunkBuffer.length > this.maxBufferLength) {
+        const removed = this.chunkBuffer.shift();
+        if (removed) {
+          // Process removed chunk with overlap
+          await this.processChunk(removed);
+        }
+      }
+
+      // Process current chunk
+      await this.processChunk(chunk);
+
+    } catch (error) {
+      const err = error instanceof Error ? error : new Error(String(error));
+      console.error('[Pipeline] Error processing chunk:', err);
+      this.onPipelineError?.(err);
+    }
+  }
+
+  /**
+   * Convert Float32Array to 16-bit PCM Buffer
+   */
+  private float32ArrayToBuffer(floatData: Float32Array): Buffer {
+    const int16Array = new Int16Array(floatData.length);
+    for (let i = 0; i < floatData.length; i++) {
+      // Clamp and scale to 16-bit range
+      const clamped = Math.max(-1, Math.min(1, floatData[i]));
+      int16Array[i] = Math.round(clamped * 32767);
+    }
+    return Buffer.from(int16Array.buffer);
+  }
+
+  /**
+   * Process a single audio chunk (mock implementation)
+   */
+  private async processChunk(chunk: AudioChunk): Promise<VoiceprintResult> {
+    try {
+      // Generate mock features
+      // Convert Float32Array to Buffer properly
+      const int16Array = new Int16Array(chunk.data.length);
+      for (let i = 0; i < chunk.data.length; i++) {
+        const clamped = Math.max(-1, Math.min(1, chunk.data[i]));
+        int16Array[i] = Math.round(clamped * 32767);
+      }
+      const dataBuffer = Buffer.from(int16Array.buffer);
+      const features: AudioFeatures = await extractMockFeatures(dataBuffer, chunk.duration);
+
+      // Generate embedding (placeholder - would use actual embedding service)
+      const embedding = this.generatePlaceholderEmbedding(features);
+
+      // Create result
+      const result: VoiceprintResult = {
+        chunkId: chunk.id,
+        timestamp: chunk.timestamp,
+        features,
+        embedding,
+        confidence: 0.95, // Placeholder - would come from actual analysis
+        status: 'complete'
+      };
+
+      // Emit result
+      this.onAnalysisComplete?.(result);
+
+      return result;
+
+    } catch (error) {
+      const err = error instanceof Error ? error : new Error(String(error));
+      console.error('[Pipeline] Preprocessing error:', err);
+      this.onPipelineError?.(err);
+      throw err;
+    }
+  }
+
+  /**
+   * Generate placeholder embedding (would use actual embedding service)
+   */
+  private generatePlaceholderEmbedding(features: AudioFeatures): number[] {
+    // Placeholder embedding - would be replaced with actual embedding generation
+    const embedding: number[] = [];
+    
+    // Use spectral features as proxy for embedding
+    embedding.push(features.spectralCentroid);
+    embedding.push(features.spectralRollOff);
+    embedding.push(features.zeroCrossingRate);
+    
+    // MFCC summary (first few coefficients)
+    if (features.mfccs && features.mfccs.length > 0) {
+      for (let i = 0; i < Math.min(5, features.mfccs[0].length); i++) {
+        embedding.push(features.mfccs[0][i]);
+      }
+    }
+    
+    // Normalize and pad
+    while (embedding.length < 128) {
+      embedding.push(0);
+    }
+    
+    return embedding;
+  }
+
+  /**
+   * Start the real-time analysis pipeline
+   */
+  async start(): Promise<void> {
+    if (this.isRunning) {
+      console.log('[Pipeline] Already running');
+      return;
+    }
+
+    try {
+      await this.initialize();
+      if (this.streamCapture) {
+        await this.streamCapture.start();
+      }
+      this.isRunning = true;
+
+      console.log('[Pipeline] Real-time analysis started');
+
+    } catch (error) {
+      const err = error instanceof Error ? error : new Error(String(error));
+      console.error('[Pipeline] Failed to start:', err);
+      this.onPipelineError?.(err);
+      throw err;
+    }
+  }
+
+  /**
+   * Stop the pipeline
+   */
+  async stop(): Promise<void> {
+    if (!this.isRunning) {
+      return;
+    }
+
+    try {
+      // Drain remaining buffer
+      while (this.chunkBuffer.length > 0) {
+        const chunk = this.chunkBuffer.shift();
+        if (chunk) {
+          await this.processChunk(chunk);
+        }
+      }
+
+      // Stop WebRTC capture
+      if (this.streamCapture) {
+        this.streamCapture.stop();
+      }
+
+      this.isRunning = false;
+      console.log('[Pipeline] Stopped');
+
+    } catch (error) {
+      const err = error instanceof Error ? error : new Error(String(error));
+      console.error('[Pipeline] Error stopping:', err);
+      this.onPipelineError?.(err);
+      throw err;
+    }
+  }
+
+  /**
+   * Get pipeline status
+   */
+  getStatus(): {
+    isRunning: boolean;
+    bufferLength: number;
+    streamActive: boolean;
+  } {
+    return {
+      isRunning: this.isRunning,
+      bufferLength: this.chunkBuffer.length,
+      streamActive: this.streamCapture?.isRecording || false
+    };
+  }
+}
+
+/**
+ * Extract mock features from audio buffer
+ */
+async function extractMockFeatures(buffer: Buffer, duration: number): Promise<AudioFeatures> {
+    // Simple mock feature extraction for demonstration
+    // In production, this would use actual audio processing
+    const numMfccs = 13;
+    const mfccs: number[][] = [];
+    
+    // Generate mock MFCCs based on buffer hash
+    const bufferHash = buffer.reduce((acc, byte) => acc + byte, 0);
+    for (let i = 0; i < numMfccs; i++) {
+      const coefficients: number[] = [];
+      for (let j = 0; j < 20; j++) {
+        coefficients.push(Math.abs(Math.sin((i * j + bufferHash) * 0.1)) * 0.5 + 0.25);
+      }
+      mfccs.push(coefficients);
+    }
+    
+    return {
+      mfccs,
+      zeroCrossingRate: 0.02 + Math.random() * 0.03,
+      spectralCentroid: 1000 + Math.random() * 2000,
+      spectralRollOff: 3000 + Math.random() * 1000,
+      durationSec: duration
+    };
+  }
--- a/packages/core/src/audio/webrtc/stream-capture.ts
+++ b/packages/core/src/audio/webrtc/stream-capture.ts
@@ -0,0 +1,184 @@
+/**
+ * WebRTC Audio Stream Capture
+ * Captures audio from screen/audio sharing using WebRTC APIs
+ * Implements 5-second chunks with 1-second overlap for sliding window analysis
+ */
+
+interface WebRTCStreamConfig {
+  chunkDuration: number;  // 5000ms
+  overlapDuration: number; // 1000ms
+  sampleRate: number;     // 16000 Hz for voiceprint compatibility
+}
+
+const DEFAULT_CONFIG: WebRTCStreamConfig = {
+  chunkDuration: 5000,
+  overlapDuration: 1000,
+  sampleRate: 16000
+};
+
+export class WebRTCStreamCapture {
+  private stream: MediaStream | null = null;
+  private audioContext: AudioContext | null = null;
+  private analyser: AnalyserNode | null = null;
+  private source: MediaStreamAudioSourceNode | null = null;
+  private _isRecording: boolean = false;
+  
+  private buffer: Float32Array = new Float32Array(0);
+  public onChunkReady?: (chunk: Float32Array, timestamp: number) => void;
+  public onStreamError?: (error: Error) => void;
+  
+  constructor(private config: WebRTCStreamConfig = DEFAULT_CONFIG) {}
+
+  /**
+   * Check if currently recording
+   */
+  public isRecording: boolean = false;
+
+  /**
+   * Start capturing audio from screen/audio sharing
+   */
+  async start(): Promise<void> {
+    if (this.isRecording) {
+      console.log('[WebRTC] Already recording');
+      return;
+    }
+
+    try {
+      // Request screen/audio capture with audio
+      this.stream = await navigator.mediaDevices.getDisplayMedia({
+        video: true,      // Required for audio capture
+        audio: true
+      });
+
+      // Stop any existing tracks
+      this.stream.getTracks().forEach(track => track.stop());
+
+      // Create audio context and analyser
+      this.audioContext = new AudioContext({
+        sampleRate: this.config.sampleRate
+      });
+      this.analyser = this.audioContext.createAnalyser();
+      this.analyser.fftSize = 2048;
+
+      // Connect stream to audio graph
+      this.source = this.audioContext.createMediaStreamSource(this.stream);
+      this.source.connect(this.analyser);
+
+      this.isRecording = true;
+      console.log('[WebRTC] Stream capture started');
+
+      // Start processing loop
+      this.processAudio();
+
+      // Handle stream termination
+      this.stream.getVideoTracks()[0].onended = () => {
+        console.log('[WebRTC] User stopped sharing');
+        this.stop();
+      };
+
+    } catch (error) {
+      const err = error instanceof Error ? error : new Error(String(error));
+      console.error('[WebRTC] Failed to start stream capture:', err);
+      this.onStreamError?.(err);
+      throw err;
+    }
+  }
+
+  /**
+   * Process audio in real-time with sliding window
+   */
+  private processAudio(): void {
+    if (!this.audioContext || !this.analyser || !this.isRecording) return;
+
+    if (!this.analyser) return;
+    
+    const bufferLength = this.analyser.fftSize;
+    const buffer = new Float32Array(bufferLength);
+
+    const processFrame = () => {
+      if (!this.isRecording) return;
+
+      if (!this.analyser) return;
+      
+      this.analyser.getFloatTimeDomainData(buffer);
+
+      // Get current timestamp
+      const timestamp = this.audioContext?.currentTime ?? 0;
+
+      // Extract audio data for current frame
+      // Use first 512 samples for voice analysis (reduced for faster processing)
+      const audioData = buffer.slice(0, 512);
+
+      // Prepare chunk for analysis
+      if (audioData.length > 0) {
+        this.onChunkReady?.(audioData, timestamp);
+      }
+
+      // Schedule next frame with overlap
+      const frameDuration = this.config.chunkDuration - this.config.overlapDuration;
+      setTimeout(processFrame, frameDuration);
+    };
+
+    processFrame();
+  }
+
+  /**
+   * Stop audio capture
+   */
+  stop(): void {
+    this._isRecording = false;
+
+    if (this.stream) {
+      this.stream.getTracks().forEach(track => track.stop());
+      this.stream = null;
+    }
+
+    if (this.source) {
+      this.source.disconnect();
+      this.source = null;
+    }
+
+    if (this.analyser) {
+      this.analyser.disconnect();
+      this.analyser = null;
+    }
+
+    if (this.audioContext) {
+      this.audioContext.close();
+      this.audioContext = null;
+    }
+
+    console.log('[WebRTC] Stream capture stopped');
+  }
+
+  /**
+   * Get stream metadata
+   */
+  getMetadata(): {
+    isActive: boolean;
+    sampleRate: number;
+    channels: number;
+  } {
+    if (!this.stream) {
+      return { isActive: false, sampleRate: 0, channels: 0 };
+    }
+
+    const audioTrack = this.stream.getAudioTracks()[0];
+    if (!audioTrack) {
+      return { isActive: true, sampleRate: this.config.sampleRate, channels: 1 };
+    }
+
+    return {
+      isActive: true,
+      sampleRate: this.config.sampleRate,
+      channels: audioTrack.getSettings().channelCount || 1
+    };
+  }
+}
+
+/**
+ * Factory function for creating stream capture with auto-start
+ */
+export function createWebRTCCapture(config?: WebRTCStreamConfig): WebRTCStreamCapture {
+  return new WebRTCStreamCapture(config || DEFAULT_CONFIG);
+}
--- a/packages/core/tsconfig.json
+++ b/packages/core/tsconfig.json
@@ -0,0 +1,22 @@
+{
+  "compilerOptions": {
+    "target": "ES2020",
+    "module": "commonjs",
+    "lib": ["ES2020", "DOM"],
+    "outDir": "./dist",
+    "rootDir": "./src",
+    "strict": true,
+    "esModuleInterop": true,
+    "skipLibCheck": true,
+    "forceConsistentCasingInFileNames": true,
+    "declaration": true,
+    "resolveJsonModule": true,
+    "moduleResolution": "node",
+    "baseUrl": ".",
+    "paths": {
+      "@/*": ["src/*"]
+    }
+  },
+  "include": ["src/**/*"],
+  "exclude": ["node_modules", "dist"]
+}
--- a/packages/db/prisma/schema.prisma
+++ b/packages/db/prisma/schema.prisma
@@ -57,6 +57,25 @@ enum DataSource {
  HONEYPOT
 }

+enum AnalysisJobStatus {
+  PENDING
+  RUNNING
+  COMPLETED
+  FAILED
+}
+
+enum AnalysisType {
+  SYNTHETIC_DETECTION
+  VOICE_MATCH
+  BATCH
+}
+
+enum DetectionVerdict {
+  NATURAL
+  SYNTHETIC
+  UNCERTAIN
+}
+
 model User {
  id                  String    @id @default(uuid())
  email               String    @unique
@@ -138,3 +157,54 @@ model ScanJob {
  @@index([userId, status])
  @@index([createdAt])
 }
+
+model VoiceEnrollment {
+  id              String    @id @default(uuid())
+  userId          String
+  user            User      @relation(fields: [userId], references: [id], onDelete: Cascade)
+  label           String
+  embeddingVector Float[]
+  embeddingDim    Int       @default(192)
+  audioFilePath   String?
+  sampleRate      Int       @default(16000)
+  durationSec     Float?
+  createdAt       DateTime  @default(now())
+  updatedAt       DateTime  @updatedAt
+
+  @@index([userId])
+  @@index([embeddingDim])
+}
+
+model AnalysisJob {
+  id              String            @id @default(uuid())
+  userId          String
+  user            User              @relation(fields: [userId], references: [id], onDelete: Cascade)
+  analysisType    AnalysisType
+  audioFilePath   String
+  status          AnalysisJobStatus @default(PENDING)
+  result          AnalysisResult?
+  errorMessage    String?
+  completedAt     DateTime?
+  createdAt       DateTime          @default(now())
+
+  @@index([userId, status])
+  @@index([createdAt])
+}
+
+model AnalysisResult {
+  id                    String            @id @default(uuid())
+  analysisJobId         String            @unique
+  analysisJob           AnalysisJob       @relation(fields: [analysisJobId], references: [id], onDelete: Cascade)
+  syntheticScore        Float
+  verdict               DetectionVerdict
+  matchedEnrollmentId   String?
+  matchedSimilarity     Float?
+  confidence            Float
+  processingTimeMs      Int
+  modelVersion          String?
+  metadata              String?
+  createdAt             DateTime          @default(now())
+
+  @@index([analysisJobId])
+  @@index([verdict])
+}
--- a/packages/jobs/src/voiceprint.jobs.ts
+++ b/packages/jobs/src/voiceprint.jobs.ts
@@ -0,0 +1,54 @@
+import { Queue, Worker } from "bullmq";
+import { Redis } from "ioredis";
+import { AnalysisService } from "@shieldai/voiceprint";
+
+const redisUrl = process.env.REDIS_URL || "redis://localhost:6379";
+const connection = new Redis(redisUrl);
+
+const analysisQueue = new Queue("voiceprint-analysis", { connection });
+
+const analysisWorker = new Worker(
+  "voiceprint-analysis",
+  async (job) => {
+    const { userId, audioBuffer, sampleRate, analysisType } = job.data;
+    const analysisService = new AnalysisService();
+    const result = await analysisService.analyze(
+      {
+        audioBuffer: Buffer.from(audioBuffer, "base64"),
+        sampleRate,
+        analysisType,
+      },
+      userId
+    );
+    return { jobId: result.jobId, completedAt: new Date().toISOString() };
+  },
+  { connection, concurrency: 2 }
+);
+
+analysisWorker.on("completed", (job) => {
+  console.log(`[VoicePrint] Job ${job.id} completed: ${JSON.stringify(job.returnvalue)}`);
+});
+
+analysisWorker.on("failed", (job, err) => {
+  console.error(`[VoicePrint] Job ${job.id} failed: ${err.message}`);
+});
+
+export async function addAnalysisJob(
+  userId: string,
+  audioBuffer: Buffer,
+  sampleRate?: number,
+  analysisType?: string
+) {
+  return analysisQueue.add("analyze", {
+    userId,
+    audioBuffer: audioBuffer.toString("base64"),
+    sampleRate,
+    analysisType,
+  }, {
+    attempts: 3,
+    backoff: { type: "exponential", delay: 5000 },
+    jobId: `vp-${userId}-${Date.now()}`,
+  });
+}
+
+console.log("[VoicePrint] Analysis worker started");
--- a/packages/types/src/index.ts
+++ b/packages/types/src/index.ts
@@ -82,3 +82,62 @@ export interface AlertInput {
  channel: AlertChannel;
  dedupKey: string;
 }
+
+export const AnalysisJobStatus = {
+  PENDING: "PENDING",
+  RUNNING: "RUNNING",
+  COMPLETED: "COMPLETED",
+  FAILED: "FAILED",
+} as const;
+export type AnalysisJobStatus = (typeof AnalysisJobStatus)[keyof typeof AnalysisJobStatus];
+
+export const AnalysisType = {
+  SYNTHETIC_DETECTION: "SYNTHETIC_DETECTION",
+  VOICE_MATCH: "VOICE_MATCH",
+  BATCH: "BATCH",
+} as const;
+export type AnalysisType = (typeof AnalysisType)[keyof typeof AnalysisType];
+
+export const DetectionVerdict = {
+  NATURAL: "NATURAL",
+  SYNTHETIC: "SYNTHETIC",
+  UNCERTAIN: "UNCERTAIN",
+} as const;
+export type DetectionVerdict = (typeof DetectionVerdict)[keyof typeof DetectionVerdict];
+
+export interface VoiceEnrollmentInput {
+  label: string;
+  audioBuffer: Buffer;
+  sampleRate?: number;
+}
+
+export interface AnalyzeAudioInput {
+  audioBuffer: Buffer;
+  sampleRate?: number;
+  analysisType?: AnalysisType;
+}
+
+export interface BatchAnalyzeInput {
+  audioBuffers: Array<{ name: string; buffer: Buffer; sampleRate?: number }>;
+  analysisType?: AnalysisType;
+}
+
+export interface AnalysisResultOutput {
+  jobId: string;
+  syntheticScore: number;
+  verdict: DetectionVerdict;
+  confidence: number;
+  matchedEnrollmentId?: string;
+  matchedSimilarity?: number;
+  processingTimeMs: number;
+  modelVersion?: string;
+}
+
+export interface VoiceEnrollmentOutput {
+  id: string;
+  label: string;
+  embeddingDim: number;
+  sampleRate: number;
+  durationSec?: number;
+  createdAt: Date;
+}
--- a/services/voiceprint/package.json
+++ b/services/voiceprint/package.json
@@ -0,0 +1,19 @@
+{
+  "name": "@shieldai/voiceprint",
+  "version": "0.1.0",
+  "main": "./dist/index.js",
+  "types": "./dist/index.js",
+  "scripts": {
+    "build": "tsc",
+    "test": "vitest run",
+    "lint": "eslint src/"
+  },
+  "dependencies": {
+    "@shieldai/db": "0.1.0",
+    "@shieldai/types": "0.1.0",
+    "node-cache": "^5.1.2"
+  },
+  "exports": {
+    ".": "./src/index.ts"
+  }
+}
--- a/services/voiceprint/src/analysis/AnalysisService.ts
+++ b/services/voiceprint/src/analysis/AnalysisService.ts
@@ -0,0 +1,183 @@
+import prisma from "@shieldai/db";
+import { AudioPreprocessor, AudioFeatures } from "../preprocessor/AudioPreprocessor";
+import { EmbeddingService, EmbeddingOutput } from "../embedding/EmbeddingService";
+import { VoiceEnrollmentService } from "../enrollment/VoiceEnrollmentService";
+import {
+  AnalyzeAudioInput,
+  AnalysisJobStatus,
+  AnalysisType,
+  DetectionVerdict,
+  AnalysisResultOutput,
+} from "@shieldai/types";
+
+export class AnalysisService {
+  private preprocessor: AudioPreprocessor;
+  private embeddingService: EmbeddingService;
+  private enrollmentService: VoiceEnrollmentService;
+  private readonly syntheticThreshold = 0.7;
+  private readonly uncertainThreshold = 0.4;
+
+  constructor() {
+    this.preprocessor = new AudioPreprocessor();
+    this.embeddingService = new EmbeddingService();
+    this.enrollmentService = new VoiceEnrollmentService();
+  }
+
+  async analyze(input: AnalyzeAudioInput, userId: string): Promise<AnalysisResultOutput> {
+    const startTime = Date.now();
+
+    const job = await prisma.analysisJob.create({
+      data: {
+        userId,
+        analysisType: input.analysisType || AnalysisType.SYNTHETIC_DETECTION,
+        audioFilePath: `voiceprint/${userId}/${Date.now()}.wav`,
+        status: AnalysisJobStatus.RUNNING,
+      },
+    });
+
+    try {
+      const preprocessed = await this.preprocessor.preprocess(input.audioBuffer, input.sampleRate);
+      const features = await this.preprocessor.extractFeatures(preprocessed.audio);
+      const embedding = await this.embeddingService.extract(preprocessed.audio);
+
+      const syntheticScore = await this.classifySynthetic(features, embedding);
+      const verdict = this.determineVerdict(syntheticScore);
+      const confidence = this.computeConfidence(syntheticScore, verdict);
+
+      let matchedEnrollmentId: string | undefined;
+      let matchedSimilarity: number | undefined;
+
+      if (input.analysisType === AnalysisType.VOICE_MATCH) {
+        const match = await this.enrollmentService.matchVoice(input.audioBuffer, userId);
+        if (match) {
+          matchedEnrollmentId = match.enrollmentId;
+          matchedSimilarity = match.similarity;
+        }
+      }
+
+      const processingTimeMs = Date.now() - startTime;
+
+      const result = await prisma.analysisResult.create({
+        data: {
+          analysisJobId: job.id,
+          syntheticScore,
+          verdict,
+          confidence,
+          processingTimeMs,
+          matchedEnrollmentId,
+          matchedSimilarity,
+          modelVersion: this.embeddingService.getModelVersion(),
+        },
+      });
+
+      await prisma.analysisJob.update({
+        where: { id: job.id },
+        data: {
+          status: AnalysisJobStatus.COMPLETED,
+          completedAt: new Date(),
+        },
+      });
+
+      return {
+        jobId: job.id,
+        syntheticScore: result.syntheticScore,
+        verdict: result.verdict,
+        confidence: result.confidence,
+        matchedEnrollmentId: result.matchedEnrollmentId || undefined,
+        matchedSimilarity: result.matchedSimilarity || undefined,
+        processingTimeMs: result.processingTimeMs,
+        modelVersion: result.modelVersion || undefined,
+      };
+    } catch (err) {
+      const message = err instanceof Error ? err.message : "Analysis failed";
+      await prisma.analysisJob.update({
+        where: { id: job.id },
+        data: {
+          status: AnalysisJobStatus.FAILED,
+          errorMessage: message,
+          completedAt: new Date(),
+        },
+      });
+      throw err;
+    }
+  }
+
+  async getResult(jobId: string): Promise<AnalysisResultOutput | null> {
+    const job = await prisma.analysisJob.findUnique({
+      where: { id: jobId },
+      include: { result: true },
+    });
+
+    if (!job || !job.result) return null;
+
+    const r = job.result;
+    return {
+      jobId,
+      syntheticScore: r.syntheticScore,
+      verdict: r.verdict,
+      confidence: r.confidence,
+      matchedEnrollmentId: r.matchedEnrollmentId || undefined,
+      matchedSimilarity: r.matchedSimilarity || undefined,
+      processingTimeMs: r.processingTimeMs,
+      modelVersion: r.modelVersion || undefined,
+    };
+  }
+
+  async getUserResults(userId: string, limit: number = 20): Promise<AnalysisResultOutput[]> {
+    const jobs = await prisma.analysisJob.findMany({
+      where: { userId, status: AnalysisJobStatus.COMPLETED },
+      orderBy: { createdAt: "desc" },
+      take: limit,
+      include: { result: true },
+    });
+
+    return jobs
+      .filter((j) => j.result)
+      .map((j) => {
+        const r = j.result!;
+        return {
+          jobId: j.id,
+          syntheticScore: r.syntheticScore,
+          verdict: r.verdict,
+          confidence: r.confidence,
+          matchedEnrollmentId: r.matchedEnrollmentId || undefined,
+          matchedSimilarity: r.matchedSimilarity || undefined,
+          processingTimeMs: r.processingTimeMs,
+          modelVersion: r.modelVersion || undefined,
+        };
+      });
+  }
+
+  private async classifySynthetic(
+    features: AudioFeatures,
+    embedding: EmbeddingOutput
+  ): Promise<number> {
+    const modelScore = await this.embeddingService.classify(embedding.vector);
+
+    const zcrAnomaly = Math.abs(features.zeroCrossingRate - 0.05) / 0.05;
+    const spectralAnomaly = Math.abs(features.spectralCentroid - 500) / 500;
+
+    const artifactScore = Math.min(1, (zcrAnomaly + spectralAnomaly) / 4);
+
+    return 0.7 * modelScore + 0.3 * artifactScore;
+  }
+
+  private determineVerdict(score: number): DetectionVerdict {
+    if (score >= this.syntheticThreshold) return DetectionVerdict.SYNTHETIC;
+    if (score <= this.uncertainThreshold) return DetectionVerdict.NATURAL;
+    return DetectionVerdict.UNCERTAIN;
+  }
+
+  private computeConfidence(score: number, verdict: DetectionVerdict): number {
+    if (verdict === DetectionVerdict.SYNTHETIC) {
+      return Math.min(1, (score - this.syntheticThreshold) / (1 - this.syntheticThreshold));
+    }
+    if (verdict === DetectionVerdict.NATURAL) {
+      return Math.min(1, (this.uncertainThreshold - score) / this.uncertainThreshold);
+    }
+    return 1 - Math.min(
+      Math.abs(score - this.uncertainThreshold) / (this.syntheticThreshold - this.uncertainThreshold),
+      1
+    );
+  }
+}
--- a/services/voiceprint/src/analysis/BatchAnalysisService.ts
+++ b/services/voiceprint/src/analysis/BatchAnalysisService.ts
@@ -0,0 +1,125 @@
+import prisma from "@shieldai/db";
+import { AnalysisService } from "./AnalysisService";
+import {
+  BatchAnalyzeInput,
+  AnalysisJobStatus,
+  AnalysisType,
+  AnalysisResultOutput,
+} from "@shieldai/types";
+
+export class BatchAnalysisService {
+  private analysisService: AnalysisService;
+
+  constructor() {
+    this.analysisService = new AnalysisService();
+  }
+
+  async analyzeBatch(
+    input: BatchAnalyzeInput,
+    userId: string
+  ): Promise<BatchResult> {
+    const batchId = `batch_${Date.now()}_${Math.random().toString(36).slice(2, 8)}`;
+    const results: AnalysisResultOutput[] = [];
+    const errors: Array<{ name: string; error: string }> = [];
+
+    for (const audioInput of input.audioBuffers) {
+      try {
+        const result = await this.analysisService.analyze(
+          {
+            audioBuffer: audioInput.buffer,
+            sampleRate: audioInput.sampleRate,
+            analysisType: input.analysisType || AnalysisType.SYNTHETIC_DETECTION,
+          },
+          userId
+        );
+        results.push(result);
+      } catch (err) {
+        const message = err instanceof Error ? err.message : "Analysis failed";
+        errors.push({ name: audioInput.name, error: message });
+      }
+    }
+
+    const batchJob = await prisma.analysisJob.create({
+      data: {
+        userId,
+        analysisType: AnalysisType.BATCH,
+        audioFilePath: `voiceprint/${userId}/${batchId}`,
+        status: errors.length === input.audioBuffers.length
+          ? AnalysisJobStatus.FAILED
+          : AnalysisJobStatus.COMPLETED,
+        errorMessage:
+          errors.length > 0 ? `${errors.length} of ${input.audioBuffers.length} files failed` : undefined,
+        completedAt: new Date(),
+      },
+    });
+
+    return {
+      batchId,
+      jobId: batchJob.id,
+      totalFiles: input.audioBuffers.length,
+      successfulResults: results.length,
+      failedCount: errors.length,
+      results,
+      errors,
+    };
+  }
+
+  async getBatchResult(batchJobId: string): Promise<BatchResult | null> {
+    const job = await prisma.analysisJob.findUnique({
+      where: { id: batchJobId },
+      include: { result: true },
+    });
+
+    if (!job) return null;
+
+    const childJobs = await prisma.analysisJob.findMany({
+      where: {
+        userId: job.userId,
+        createdAt: { gte: job.createdAt, lt: new Date(job.createdAt.getTime() + 60000) },
+        id: { not: job.id },
+      },
+      include: { result: true },
+    });
+
+    const results: AnalysisResultOutput[] = [];
+    const errors: Array<{ name: string; error: string }> = [];
+
+    for (const childJob of childJobs) {
+      if (childJob.result) {
+        const r = childJob.result;
+        results.push({
+          jobId: childJob.id,
+          syntheticScore: r.syntheticScore,
+          verdict: r.verdict,
+          confidence: r.confidence,
+          matchedEnrollmentId: r.matchedEnrollmentId || undefined,
+          matchedSimilarity: r.matchedSimilarity || undefined,
+          processingTimeMs: r.processingTimeMs,
+          modelVersion: r.modelVersion || undefined,
+        });
+      } else if (childJob.errorMessage) {
+        errors.push({ name: childJob.audioFilePath, error: childJob.errorMessage });
+      }
+    }
+
+    return {
+      batchId: job.audioFilePath.split("/").pop() || job.id,
+      jobId: job.id,
+      totalFiles: childJobs.length,
+      successfulResults: results.length,
+      failedCount: errors.length,
+      results,
+      errors,
+    };
+  }
+}
+
+export interface BatchResult {
+  batchId: string;
+  jobId: string;
+  totalFiles: number;
+  successfulResults: number;
+  failedCount: number;
+  results: AnalysisResultOutput[];
+  errors: Array<{ name: string; error: string }>;
+}
--- a/services/voiceprint/src/embedding/EmbeddingService.ts
+++ b/services/voiceprint/src/embedding/EmbeddingService.ts
@@ -0,0 +1,196 @@
+import { spawn } from "child_process";
+
+const EMBEDDING_DIM = 192;
+const MODEL_VERSION = "ecapa-tdnn-0.1.0-mock";
+
+export class EmbeddingService {
+  private mlServiceUrl: string;
+
+  constructor() {
+    this.mlServiceUrl = process.env.VOICEPRINT_ML_URL || "http://localhost:8001";
+  }
+
+  async extract(audioBuffer: Buffer): Promise<EmbeddingOutput> {
+    const mlAvailable = await this.checkMLService();
+
+    if (mlAvailable) {
+      return this.extractViaML(audioBuffer);
+    }
+
+    return this.extractMock(audioBuffer);
+  }
+
+  async classify(embedding: number[]): Promise<number> {
+    const mlAvailable = await this.checkMLService();
+
+    if (mlAvailable) {
+      return this.classifyViaML(embedding);
+    }
+
+    return this.classifyMock(embedding);
+  }
+
+  getModelVersion(): string {
+    return MODEL_VERSION;
+  }
+
+  private async extractViaML(audioBuffer: Buffer): Promise<EmbeddingOutput> {
+    return new Promise((resolve, reject) => {
+      const jsonInput = audioBuffer.toString("base64");
+      const proc = spawn("python3", [
+        "-c",
+        `
+import urllib.request, json, sys
+req = urllib.request.Request(
+    "${this.mlServiceUrl}/embedding",
+    data=json.dumps({"audio": "${jsonInput.substring(0, 5000)}"}).encode(),
+    headers={"Content-Type": "application/json"}
+)
+try:
+    with urllib.request.urlopen(req, timeout=60) as resp:
+        data = json.loads(resp.read())
+        sys.stdout.write(json.dumps({"ok": True, "vector": data.get("embedding", []), "dim": data.get("dimension", ${EMBEDDING_DIM})}))
+except Exception as e:
+    sys.stdout.write(json.dumps({"ok": False, "error": str(e)}))
+`,
+      ]);
+
+      let output = "";
+      proc.stdout.on("data", (chunk) => { output += chunk.toString(); });
+      proc.on("close", (code) => {
+        try {
+          const result = JSON.parse(output);
+          if (result.ok && result.vector.length === EMBEDDING_DIM) {
+            resolve({ vector: result.vector, dimension: EMBEDDING_DIM });
+          } else {
+            resolve(this.generateMockFromBuffer(audioBuffer));
+          }
+        } catch {
+          resolve(this.generateMockFromBuffer(audioBuffer));
+        }
+      });
+    });
+  }
+
+  private async classifyViaML(embedding: number[]): Promise<number> {
+    return new Promise((resolve) => {
+      const proc = spawn("python3", [
+        "-c",
+        `
+import urllib.request, json, sys
+req = urllib.request.Request(
+    "${this.mlServiceUrl}/classify",
+    data=json.dumps({"embedding": ${JSON.stringify(embedding)}}).encode(),
+    headers={"Content-Type": "application/json"}
+)
+try:
+    with urllib.request.urlopen(req, timeout=30) as resp:
+        data = json.loads(resp.read())
+        sys.stdout.write(json.dumps({"score": data.get("synthetic_score", 0.5)}))
+except:
+    sys.stdout.write(json.dumps({"score": 0.5}))
+`,
+      ]);
+
+      let output = "";
+      proc.stdout.on("data", (chunk) => { output += chunk.toString(); });
+      proc.on("close", () => {
+        try {
+          const result = JSON.parse(output);
+          resolve(result.score || 0.5);
+        } catch {
+          resolve(0.5);
+        }
+      });
+    });
+  }
+
+  private async extractMock(audioBuffer: Buffer): Promise<EmbeddingOutput> {
+    return this.generateMockFromBuffer(audioBuffer);
+  }
+
+  private async classifyMock(embedding: number[]): Promise<number> {
+    const mean = embedding.reduce((s, v) => s + v, 0) / embedding.length;
+    const variance = embedding.reduce((s, v) => s + (v - mean) ** 2, 0) / embedding.length;
+    const stdDev = Math.sqrt(variance);
+
+    const syntheticIndicators = [
+      stdDev < 0.1 ? 0.8 : 0.2,
+      Math.abs(mean) > 0.5 ? 0.7 : 0.3,
+      this.hasArtifacts(embedding) ? 0.9 : 0.1,
+    ];
+
+    return syntheticIndicators.reduce((s, v) => s + v, 0) / syntheticIndicators.length;
+  }
+
+  private generateMockFromBuffer(audioBuffer: Buffer): EmbeddingOutput {
+    const seed = this.computeSeed(audioBuffer);
+    const rng = this.createRNG(seed);
+    const vector: number[] = [];
+
+    for (let i = 0; i < EMBEDDING_DIM; i++) {
+      const u1 = rng();
+      const u2 = rng();
+      const gauss = Math.sqrt(-2 * Math.log(u1)) * Math.cos(2 * Math.PI * u2);
+      vector.push(parseFloat(gauss.toFixed(6)));
+    }
+
+    const norm = Math.sqrt(vector.reduce((s, v) => s + v * v, 0));
+    const normalized = vector.map((v) => parseFloat((v / norm).toFixed(6)));
+
+    return { vector: normalized, dimension: EMBEDDING_DIM };
+  }
+
+  private hasArtifacts(embedding: number[]): boolean {
+    const window = 16;
+    let artifactCount = 0;
+
+    for (let i = 0; i < embedding.length - window; i += window) {
+      const slice = embedding.slice(i, i + window);
+      const localMean = slice.reduce((s, v) => s + v, 0) / slice.length;
+      const localVar = slice.reduce((s, v) => s + (v - localMean) ** 2, 0) / slice.length;
+
+      if (localVar < 0.001) artifactCount++;
+    }
+
+    return artifactCount > embedding.length / window / 3;
+  }
+
+  private async checkMLService(): Promise<boolean> {
+    return new Promise((resolve) => {
+      const proc = spawn("python3", [
+        "-c",
+        `
+import urllib.request, sys
+try:
+    urllib.request.urlopen("${this.mlServiceUrl}/health", timeout=2)
+    sys.exit(0)
+except:
+    sys.exit(1)
+`,
+      ]);
+      proc.on("close", (code) => resolve(code === 0));
+    });
+  }
+
+  private computeSeed(buffer: Buffer): number {
+    let hash = 0;
+    const sampleSize = Math.min(buffer.length, 1024);
+    for (let i = 0; i < sampleSize; i += 4) {
+      hash = ((hash << 5) - hash + buffer.readInt32LE(i)) | 0;
+    }
+    return Math.abs(hash);
+  }
+
+  private createRNG(seed: number): () => number {
+    return () => {
+      seed = (seed * 1664525 + 1013904223) & 0xffffffff;
+      return (seed >>> 0) / 0xffffffff;
+    };
+  }
+}
+
+export interface EmbeddingOutput {
+  vector: number[];
+  dimension: number;
+}
--- a/services/voiceprint/src/enrollment/VoiceEnrollmentService.ts
+++ b/services/voiceprint/src/enrollment/VoiceEnrollmentService.ts
@@ -0,0 +1,151 @@
+import prisma from "@shieldai/db";
+import { EmbeddingService } from "../embedding/EmbeddingService";
+import { FAISSIndex } from "../indexer/FAISSIndex";
+import { AudioPreprocessor } from "../preprocessor/AudioPreprocessor";
+import { VoiceEnrollmentInput, VoiceEnrollmentOutput } from "@shieldai/types";
+
+export class VoiceEnrollmentService {
+  private embeddingService: EmbeddingService;
+  private faissIndex: FAISSIndex;
+  private preprocessor: AudioPreprocessor;
+
+  constructor() {
+    this.embeddingService = new EmbeddingService();
+    this.faissIndex = new FAISSIndex();
+    this.preprocessor = new AudioPreprocessor();
+  }
+
+  async enroll(input: VoiceEnrollmentInput, userId: string): Promise<VoiceEnrollmentOutput> {
+    const preprocessed = await this.preprocessor.preprocess(input.audioBuffer, input.sampleRate);
+
+    const embedding = await this.embeddingService.extract(preprocessed.audio);
+
+    const enrollment = await prisma.voiceEnrollment.create({
+      data: {
+        userId,
+        label: input.label,
+        embeddingVector: embedding.vector,
+        embeddingDim: embedding.dimension,
+        sampleRate: preprocessed.sampleRate,
+        durationSec: preprocessed.durationSec,
+      },
+    });
+
+    await this.faissIndex.add(enrollment.id, embedding.vector);
+
+    return {
+      id: enrollment.id,
+      label: enrollment.label,
+      embeddingDim: enrollment.embeddingDim,
+      sampleRate: enrollment.sampleRate,
+      durationSec: enrollment.durationSec,
+      createdAt: enrollment.createdAt,
+    };
+  }
+
+  async listEnrollments(userId: string): Promise<VoiceEnrollmentOutput[]> {
+    const enrollments = await prisma.voiceEnrollment.findMany({
+      where: { userId },
+      orderBy: { createdAt: "desc" },
+      select: {
+        id: true,
+        label: true,
+        embeddingDim: true,
+        sampleRate: true,
+        durationSec: true,
+        createdAt: true,
+      },
+    });
+
+    return enrollments.map((e) => ({
+      id: e.id,
+      label: e.label,
+      embeddingDim: e.embeddingDim,
+      sampleRate: e.sampleRate,
+      durationSec: e.durationSec,
+      createdAt: e.createdAt,
+    }));
+  }
+
+  async removeEnrollment(userId: string, enrollmentId: string): Promise<boolean> {
+    const enrollment = await prisma.voiceEnrollment.findFirst({
+      where: { id: enrollmentId, userId },
+    });
+
+    if (!enrollment) {
+      throw new Error(`Enrollment ${enrollmentId} not found for user ${userId}`);
+    }
+
+    await prisma.voiceEnrollment.delete({ where: { id: enrollmentId } });
+    await this.faissIndex.remove(enrollmentId);
+
+    return true;
+  }
+
+  async getEnrollment(enrollmentId: string): Promise<VoiceEnrollmentOutput | null> {
+    const enrollment = await prisma.voiceEnrollment.findUnique({
+      where: { id: enrollmentId },
+      select: {
+        id: true,
+        label: true,
+        embeddingDim: true,
+        sampleRate: true,
+        durationSec: true,
+        createdAt: true,
+      },
+    });
+
+    if (!enrollment) return null;
+
+    return {
+      id: enrollment.id,
+      label: enrollment.label,
+      embeddingDim: enrollment.embeddingDim,
+      sampleRate: enrollment.sampleRate,
+      durationSec: enrollment.durationSec,
+      createdAt: enrollment.createdAt,
+    };
+  }
+
+  async matchVoice(
+    audioBuffer: Buffer,
+    userId: string,
+    threshold: number = 0.75
+  ): Promise<MatchResult | null> {
+    const preprocessed = await this.preprocessor.preprocess(audioBuffer);
+    const embedding = await this.embeddingService.extract(preprocessed.audio);
+
+    const matches = await this.faissIndex.search(embedding.vector, 5);
+
+    const enrollmentIds = matches.map((m) => m.id);
+    const enrollments = await prisma.voiceEnrollment.findMany({
+      where: {
+        id: { in: enrollmentIds },
+        userId,
+      },
+    });
+
+    let bestMatch: MatchResult | null = null;
+
+    for (const match of matches) {
+      const enrollment = enrollments.find((e) => e.id === match.id);
+      if (enrollment && match.similarity >= threshold) {
+        if (!bestMatch || match.similarity > bestMatch.similarity) {
+          bestMatch = {
+            enrollmentId: enrollment.id,
+            label: enrollment.label,
+            similarity: match.similarity,
+          };
+        }
+      }
+    }
+
+    return bestMatch;
+  }
+}
+
+export interface MatchResult {
+  enrollmentId: string;
+  label: string;
+  similarity: number;
+}
--- a/services/voiceprint/src/index.ts
+++ b/services/voiceprint/src/index.ts
@@ -0,0 +1,6 @@
+export * from "./preprocessor/AudioPreprocessor";
+export * from "./enrollment/VoiceEnrollmentService";
+export * from "./analysis/AnalysisService";
+export * from "./analysis/BatchAnalysisService";
+export * from "./embedding/EmbeddingService";
+export * from "./indexer/FAISSIndex";
--- a/services/voiceprint/src/indexer/FAISSIndex.ts
+++ b/services/voiceprint/src/indexer/FAISSIndex.ts
@@ -0,0 +1,86 @@
+export class FAISSIndex {
+  private store: Map<string, number[]> = new Map();
+  private readonly dimension = 192;
+  private initialized = false;
+
+  async initialize(): Promise<void> {
+    if (this.initialized) return;
+    this.initialized = true;
+  }
+
+  async add(id: string, vector: number[]): Promise<void> {
+    this.normalizeInPlace(vector);
+    this.store.set(id, [...vector]);
+  }
+
+  async remove(id: string): Promise<void> {
+    this.store.delete(id);
+  }
+
+  async search(
+    queryVector: number[],
+    topK: number = 5
+  ): Promise<SearchResult[]> {
+    const normalized = [...queryVector];
+    this.normalizeInPlace(normalized);
+
+    const scores: Array<{ id: string; similarity: number }> = [];
+
+    for (const [id, vector] of this.store.entries()) {
+      const similarity = this.cosineSimilarity(normalized, vector);
+      scores.push({ id, similarity });
+    }
+
+    scores.sort((a, b) => b.similarity - a.similarity);
+    return scores.slice(0, topK).map((s, i) => ({ rank: i + 1, id: s.id, similarity: s.similarity }));
+  }
+
+  async count(): Promise<number> {
+    return this.store.size;
+  }
+
+  async clear(): Promise<void> {
+    this.store.clear();
+  }
+
+  async loadFromDatabase(): Promise<void> {
+    const prisma = (await import("@shieldai/db")).default;
+    const enrollments = await prisma.voiceEnrollment.findMany({
+      select: { id: true, embeddingVector: true },
+    });
+
+    for (const enrollment of enrollments) {
+      this.store.set(enrollment.id, enrollment.embeddingVector);
+    }
+  }
+
+  private cosineSimilarity(a: number[], b: number[]): number {
+    let dotProduct = 0;
+    let normA = 0;
+    let normB = 0;
+
+    for (let i = 0; i < a.length; i++) {
+      dotProduct += a[i] * b[i];
+      normA += a[i] * a[i];
+      normB += b[i] * b[i];
+    }
+
+    const denominator = Math.sqrt(normA) * Math.sqrt(normB);
+    return denominator > 0 ? dotProduct / denominator : 0;
+  }
+
+  private normalizeInPlace(vector: number[]): void {
+    const norm = Math.sqrt(vector.reduce((s, v) => s + v * v, 0));
+    if (norm > 0) {
+      for (let i = 0; i < vector.length; i++) {
+        vector[i] /= norm;
+      }
+    }
+  }
+}
+
+export interface SearchResult {
+  rank: number;
+  id: string;
+  similarity: number;
+}
--- a/services/voiceprint/src/preprocessor/AudioPreprocessor.ts
+++ b/services/voiceprint/src/preprocessor/AudioPreprocessor.ts
@@ -0,0 +1,327 @@
+import { spawn } from "child_process";
+import { randomBytes } from "crypto";
+import { tmpdir } from "os";
+import { join } from "path";
+
+export class AudioPreprocessor {
+  private readonly targetSampleRate = 16000;
+  private readonly channels = 1;
+
+  async preprocess(audioBuffer: Buffer, inputSampleRate?: number): Promise<PreprocessedAudio> {
+    const tempInput = this.writeTempFile(audioBuffer, ".wav");
+    const tempOutput = this.writeTempFile(Buffer.alloc(0), ".wav");
+
+    const outputSampleRate = inputSampleRate || this.detectSampleRate(audioBuffer);
+
+    try {
+      await this.runPythonPreprocess(tempInput, tempOutput, outputSampleRate);
+
+      const processed = await this.readFile(tempOutput);
+      return {
+        audio: processed,
+        sampleRate: this.targetSampleRate,
+        channels: this.channels,
+        durationSec: processed.length / (this.targetSampleRate * this.channels * 2),
+      };
+    } catch (err) {
+      const fallback = await this.jsFallback(audioBuffer, outputSampleRate);
+      return fallback;
+    }
+  }
+
+  async preprocessBatch(
+    inputs: Array<{ buffer: Buffer; sampleRate?: number }>
+  ): Promise<PreprocessedAudio[]> {
+    const promises = inputs.map(async (input) => {
+      return this.preprocess(input.buffer, input.sampleRate);
+    });
+    return Promise.all(promises);
+  }
+
+  applyVAD(audioBuffer: Buffer, sampleRate: number): Buffer {
+    const int16Array = this.bufferToInt16(audioBuffer);
+    const silenceThreshold = 500;
+    const windowSize = Math.floor(sampleRate * 0.02);
+
+    const segments: number[][] = [];
+    let currentSegment: number[] = [];
+
+    for (let i = 0; i < int16Array.length; i += windowSize) {
+      const window = int16Array.slice(i, i + windowSize);
+      const rms = Math.sqrt(
+        window.reduce((sum, val) => sum + val * val, 0) / window.length
+      );
+
+      if (rms > silenceThreshold) {
+        currentSegment.push(...window);
+      } else if (currentSegment.length > 0) {
+        segments.push(currentSegment);
+        currentSegment = [];
+      }
+    }
+
+    if (currentSegment.length > 0) {
+      segments.push(currentSegment);
+    }
+
+    const merged = segments.flat();
+    return this.int16ToBuffer(merged);
+  }
+
+  normalizeAudio(audioBuffer: Buffer): Buffer {
+    const int16Array = this.bufferToInt16(audioBuffer);
+    const maxAmplitude = Math.max(...int16Array.map((v) => Math.abs(v)));
+    const targetMax = 32767 * 0.95;
+
+    if (maxAmplitude === 0) return audioBuffer;
+
+    const scale = targetMax / maxAmplitude;
+    const normalized = int16Array.map((v) => Math.round(v * scale));
+    return this.int16ToBuffer(normalized);
+  }
+
+  async extractFeatures(audioBuffer: Buffer): Promise<AudioFeatures> {
+    const int16Array = this.bufferToInt16(audioBuffer);
+    const sampleRate = this.targetSampleRate;
+    const windowSize = Math.floor(sampleRate * 0.025);
+    const hopLength = Math.floor(sampleRate * 0.01);
+
+    const mfccs: number[][] = [];
+    const numCoeffs = 13;
+
+    for (let i = 0; i < int16Array.length - windowSize; i += hopLength) {
+      const frame = int16Array.slice(i, i + windowSize);
+      const coeffs = this.computeMFCC(frame, numCoeffs);
+      mfccs.push(coeffs);
+    }
+
+    const zeroCrossingRate = this.computeZCR(int16Array);
+    const spectralCentroid = this.computeSpectralCentroid(int16Array);
+    const spectralRollOff = this.computeSpectralRollOff(int16Array);
+
+    return {
+      mfccs,
+      zeroCrossingRate,
+      spectralCentroid,
+      spectralRollOff,
+      durationSec: int16Array.length / sampleRate,
+    };
+  }
+
+  private async runPythonPreprocess(
+    inputPath: string,
+    outputPath: string,
+    inputSampleRate: number
+  ): Promise<void> {
+    return new Promise((resolve, reject) => {
+      const mlServiceUrl = process.env.VOICEPRINT_ML_URL || "http://localhost:8001";
+      const proc = spawn("python3", [
+        "-c",
+        `
+import urllib.request, json, sys
+req = urllib.request.Request(
+    "${mlServiceUrl}/preprocess",
+    data=json.dumps({"input_path": "${inputPath}", "output_path": "${outputPath}", "input_sr": ${inputSampleRate}}).encode(),
+    headers={"Content-Type": "application/json"}
+)
+try:
+    with urllib.request.urlopen(req, timeout=30) as resp:
+        sys.exit(0) if resp.status == 200 else sys.exit(1)
+except:
+    sys.exit(1)
+`,
+      ]);
+
+      proc.on("close", (code) => {
+        code === 0 ? resolve() : reject(new Error(`Python preprocess exited with code ${code}`));
+      });
+    });
+  }
+
+  private async jsFallback(
+    audioBuffer: Buffer,
+    inputSampleRate: number
+  ): Promise<PreprocessedAudio> {
+    let processed = audioBuffer;
+
+    if (inputSampleRate !== this.targetSampleRate) {
+      processed = this.resample(audioBuffer, inputSampleRate, this.targetSampleRate);
+    }
+
+    processed = this.applyVAD(processed, this.targetSampleRate);
+    processed = this.normalizeAudio(processed);
+
+    return {
+      audio: processed,
+      sampleRate: this.targetSampleRate,
+      channels: this.channels,
+      durationSec: processed.length / (this.targetSampleRate * this.channels * 2),
+    };
+  }
+
+  private resample(buffer: Buffer, fromRate: number, toRate: number): Buffer {
+    const int16 = this.bufferToInt16(buffer);
+    const ratio = fromRate / toRate;
+    const newLength = Math.round(int16.length / ratio);
+    const resampled: number[] = [];
+
+    for (let i = 0; i < newLength; i++) {
+      const srcIdx = Math.floor(i * ratio);
+      const nextIdx = Math.min(srcIdx + 1, int16.length - 1);
+      const frac = (i * ratio) - srcIdx;
+      resampled.push(Math.round(int16[srcIdx] * (1 - frac) + int16[nextIdx] * frac));
+    }
+
+    return this.int16ToBuffer(resampled);
+  }
+
+  private detectSampleRate(buffer: Buffer): number {
+    if (buffer.length < 44) return 16000;
+
+    const fmtOffset = buffer.toString("ascii", 12, 16).trim() === "fmt " ? 16 : 40;
+    if (fmtOffset + 4 <= buffer.length) {
+      const sr = buffer.readUInt32LE(fmtOffset + 4);
+      if (sr >= 8000 && sr <= 48000) return sr;
+    }
+    return 16000;
+  }
+
+  private bufferToInt16(buffer: Buffer): number[] {
+    const arr: number[] = new Array(buffer.length / 2);
+    for (let i = 0; i < arr.length; i++) {
+      arr[i] = buffer.readInt16LE(i * 2);
+    }
+    return arr;
+  }
+
+  private int16ToBuffer(arr: number[]): Buffer {
+    const buf = Buffer.alloc(arr.length * 2);
+    for (let i = 0; i < arr.length; i++) {
+      buf.writeInt16LE(arr[i], i * 2);
+    }
+    return buf;
+  }
+
+  private computeMFCC(frame: number[], numCoeffs: number): number[] {
+    const n = frame.length;
+    const fftSize = Math.pow(2, Math.ceil(Math.log2(n)) + 1);
+    const spectrum: number[] = new Array(fftSize / 2 + 1);
+
+    for (let k = 0; k < spectrum.length; k++) {
+      let real = 0, imag = 0;
+      for (let n = 0; n < frame.length; n++) {
+        const angle = (2 * Math.PI * k * n) / fftSize;
+        real += frame[n] * Math.cos(angle);
+        imag -= frame[n] * Math.sin(angle);
+      }
+      spectrum[k] = Math.sqrt(real * real + imag * imag);
+    }
+
+    const numFilters = 20;
+    const filterbank = this.createFilterbank(spectrum.length, numFilters);
+    const logEnergies: number[] = [];
+
+    for (let m = 0; m < numFilters; m++) {
+      let energy = 0;
+      for (let k = 0; k < spectrum.length; k++) {
+        energy += filterbank[m][k] * spectrum[k] * spectrum[k];
+      }
+      logEnergies.push(Math.log(Math.max(energy, 1e-10)));
+    }
+
+    const mfccs: number[] = [];
+    for (let d = 0; d < numCoeffs; d++) {
+      let coeff = 0;
+      for (let m = 0; m < numFilters; m++) {
+        coeff += logEnergies[m] * Math.cos(((2 * m + 1) * (d + 1) * Math.PI) / (2 * numFilters));
+      }
+      mfccs.push(coeff);
+    }
+
+    return mfccs;
+  }
+
+  private createFilterbank(numBins: number, numFilters: number): number[][] {
+    const filterbank: number[][] = Array.from({ length: numFilters }, () =>
+      new Array(numBins).fill(0)
+    );
+    const low = Math.floor(20 * numBins / 8000);
+    const high = Math.floor(5500 * numBins / 8000);
+    const spacing = (high - low) / (numFilters + 1);
+
+    for (let m = 1; m <= numFilters; m++) {
+      const center = Math.floor(low + m * spacing);
+      const prev = Math.floor(low + (m - 1) * spacing);
+      const next = Math.floor(low + (m + 1) * spacing);
+
+      for (let k = prev; k < center; k++) {
+        if (k > 0) filterbank[m - 1][k] = (k - prev) / (center - prev);
+      }
+      for (let k = center; k < next; k++) {
+        if (next - center > 0) filterbank[m - 1][k] = (next - k) / (next - center);
+      }
+    }
+
+    return filterbank;
+  }
+
+  private computeZCR(int16: number[]): number {
+    let crossings = 0;
+    for (let i = 1; i < int16.length; i++) {
+      if ((int16[i] > 0 && int16[i - 1] <= 0) || (int16[i] <= 0 && int16[i - 1] > 0)) {
+        crossings++;
+      }
+    }
+    return crossings / int16.length;
+  }
+
+  private computeSpectralCentroid(int16: number[]): number {
+    const n = int16.length;
+    let num = 0, den = 0;
+    for (let i = 0; i < n; i++) {
+      num += i * int16[i] * int16[i];
+      den += int16[i] * int16[i];
+    }
+    return den > 0 ? num / den : 0;
+  }
+
+  private computeSpectralRollOff(int16: number[]): number {
+    const n = int16.length;
+    let totalEnergy = 0;
+    for (let i = 0; i < n; i++) {
+      totalEnergy += int16[i] * int16[i];
+    }
+    const threshold = totalEnergy * 0.85;
+    let cumulative = 0;
+    for (let i = 0; i < n; i++) {
+      cumulative += int16[i] * int16[i];
+      if (cumulative >= threshold) return i;
+    }
+    return n - 1;
+  }
+
+  private writeTempFile(content: Buffer, ext: string): string {
+    const file = join(tmpdir(), `vp_${randomBytes(8).toString("hex")}${ext}`);
+    require("fs").writeFileSync(file, content);
+    return file;
+  }
+
+  private async readFile(path: string): Promise<Buffer> {
+    return require("fs").readFileSync(path);
+  }
+}
+
+export interface PreprocessedAudio {
+  audio: Buffer;
+  sampleRate: number;
+  channels: number;
+  durationSec: number;
+}
+
+export interface AudioFeatures {
+  mfccs: number[][];
+  zeroCrossingRate: number;
+  spectralCentroid: number;
+  spectralRollOff: number;
+  durationSec: number;
+}
--- a/services/voiceprint/tsconfig.json
+++ b/services/voiceprint/tsconfig.json
@@ -0,0 +1,8 @@
+{
+  "extends": "../../tsconfig.json",
+  "compilerOptions": {
+    "outDir": "./dist",
+    "rootDir": "./src"
+  },
+  "include": ["src/**/*"]
+}