FRE-4533: Merge apps/{api,web,mobile} and shared-db into ShieldAI repo
Merge FrenoCorp apps into ShieldAI packages/: - packages/api: merged routes (notifications), middleware (auth, rate-limit, error, logging), config, services (darkwatch, spamshield, voiceprint), tests - packages/web: new SolidJS web app stub - packages/mobile: new SolidJS mobile app stub - packages/shared-db: new Prisma DB package (separate from existing packages/db) - pnpm-workspace.yaml: restored (apps/* removed, already covered by packages/*) Next: reconcile packages/shared-db with packages/db, and fix server.ts correlationRoutes import
This commit is contained in:
594
packages/api/src/services/voiceprint/voiceprint.service.ts
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594
packages/api/src/services/voiceprint/voiceprint.service.ts
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@@ -0,0 +1,594 @@
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import { prisma, VoiceEnrollment, VoiceAnalysis } from '@shieldsai/shared-db';
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import {
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voicePrintEnv,
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AnalysisJobStatus,
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DetectionType,
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ConfidenceLevel,
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audioPreprocessingConfig,
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voicePrintFeatureFlags,
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} from './voiceprint.config';
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import { checkFlag } from './voiceprint.feature-flags';
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// Audio preprocessing service
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export class AudioPreprocessor {
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/**
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* Normalize audio to 16kHz mono with VAD and noise reduction.
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* Returns preprocessing metadata and the processed audio buffer.
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*/
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async preprocess(
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audioBuffer: Buffer,
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options?: {
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sourceSampleRate?: number;
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channels?: number;
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}
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): Promise<{
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buffer: Buffer;
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metadata: {
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sampleRate: number;
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channels: number;
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duration: number;
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format: string;
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};
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}> {
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const duration = this.estimateDuration(audioBuffer, options?.sourceSampleRate ?? 44100);
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if (duration < voicePrintEnv.ENROLLMENT_MIN_DURATION_SEC) {
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throw new Error(
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`Audio too short: ${duration.toFixed(1)}s < ${voicePrintEnv.ENROLLMENT_MIN_DURATION_SEC}s minimum`
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);
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}
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if (duration > voicePrintEnv.ENROLLMENT_MAX_DURATION_SEC) {
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throw new Error(
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`Audio too long: ${duration.toFixed(1)}s > ${voicePrintEnv.ENROLLMENT_MAX_DURATION_SEC}s maximum`
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);
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}
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// TODO: Integrate with Python librosa/torchaudio for actual preprocessing
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// For MVP, return original buffer with target metadata
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return {
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buffer: audioBuffer,
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metadata: {
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sampleRate: audioPreprocessingConfig.sampleRate,
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channels: audioPreprocessingConfig.channels,
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duration,
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format: 'wav',
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},
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};
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}
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/**
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* Apply Voice Activity Detection to remove silence segments.
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*/
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async applyVAD(buffer: Buffer): Promise<Buffer> {
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// TODO: Integrate with Python webrtcvad or silero-vad
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// For MVP, return original buffer
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return buffer;
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}
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/**
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* Estimate audio duration from buffer size and sample rate.
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*/
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private estimateDuration(
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buffer: Buffer,
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sampleRate: number
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): number {
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const bytesPerSample = 2;
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const channels = 1;
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const samples = buffer.length / (bytesPerSample * channels);
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return samples / sampleRate;
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}
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}
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// Voice enrollment service
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export class VoiceEnrollmentService {
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/**
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* Enroll a new voice profile from audio data.
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*/
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async enroll(
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userId: string,
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name: string,
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audioBuffer: Buffer
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): Promise<VoiceEnrollment> {
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const preprocessor = new AudioPreprocessor();
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const processed = await preprocessor.preprocess(audioBuffer);
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const embeddingService = new EmbeddingService();
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const embedding = await embeddingService.extract(processed.buffer);
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const voiceHash = this.computeEmbeddingHash(embedding);
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const enrollment = await prisma.voiceEnrollment.create({
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data: {
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userId,
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name,
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voiceHash,
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audioMetadata: {
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...processed.metadata,
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embeddingDimensions: embedding.length,
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enrollmentTimestamp: new Date().toISOString(),
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},
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},
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});
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// Index in FAISS for similarity search
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const faissIndex = new FAISSIndex();
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await faissIndex.add(enrollment.id, embedding);
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return enrollment;
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}
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/**
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* List all enrollments for a user.
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*/
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async listEnrollments(
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userId: string,
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options?: {
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isActive?: boolean;
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limit?: number;
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offset?: number;
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}
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): Promise<VoiceEnrollment[]> {
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return prisma.voiceEnrollment.findMany({
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where: {
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userId,
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...(options?.isActive !== undefined && { isActive: options.isActive }),
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},
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orderBy: { createdAt: 'desc' },
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take: options?.limit ?? 50,
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skip: options?.offset ?? 0,
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});
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}
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/**
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* Get a single enrollment by ID.
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*/
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async getEnrollment(
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enrollmentId: string,
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userId: string
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): Promise<VoiceEnrollment | null> {
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return prisma.voiceEnrollment.findFirst({
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where: {
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id: enrollmentId,
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userId,
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},
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});
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}
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/**
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* Remove (deactivate) an enrollment.
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*/
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async removeEnrollment(
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enrollmentId: string,
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userId: string
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): Promise<VoiceEnrollment> {
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const enrollment = await this.getEnrollment(enrollmentId, userId);
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if (!enrollment) {
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throw new Error('Enrollment not found');
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}
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const faissIndex = new FAISSIndex();
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await faissIndex.remove(enrollmentId);
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return prisma.voiceEnrollment.update({
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where: { id: enrollmentId },
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data: { isActive: false },
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});
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}
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/**
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* Search for similar enrollments using FAISS.
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*/
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async findSimilar(
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embedding: number[],
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topK: number = 5
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): Promise<Array<{ enrollment: VoiceEnrollment; similarity: number }>> {
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const faissIndex = new FAISSIndex();
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const results = await faissIndex.search(embedding, topK);
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const enrollmentIds = results.map((r) => r.id);
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const enrollments = await prisma.voiceEnrollment.findMany({
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where: { id: { in: enrollmentIds } },
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});
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return results.map((r, i) => ({
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enrollment: enrollments[i],
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similarity: r.similarity,
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}));
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}
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private computeEmbeddingHash(embedding: number[]): string {
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let hash = 0;
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for (let i = 0; i < embedding.length; i++) {
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hash = ((hash << 5) - hash) + embedding[i];
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hash |= 0;
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}
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return `vp_${Math.abs(hash).toString(16)}_${embedding.length}`;
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}
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}
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// Audio analysis service
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export class AnalysisService {
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/**
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* Analyze a single audio file for synthetic voice detection.
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*/
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async analyze(
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userId: string,
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audioBuffer: Buffer,
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options?: {
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enrollmentId?: string;
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audioUrl?: string;
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}
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): Promise<VoiceAnalysis> {
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const preprocessor = new AudioPreprocessor();
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const processed = await preprocessor.preprocess(audioBuffer);
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const audioHash = this.computeAudioHash(audioBuffer);
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const embeddingService = new EmbeddingService();
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const analysisResult = await embeddingService.analyze(processed.buffer);
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const isSynthetic = analysisResult.confidence >= voicePrintEnv.SYNTHETIC_THRESHOLD;
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const voiceAnalysis = await prisma.voiceAnalysis.create({
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data: {
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userId,
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enrollmentId: options?.enrollmentId,
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audioHash,
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isSynthetic,
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confidence: analysisResult.confidence,
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analysisResult: {
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...analysisResult,
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processedMetadata: processed.metadata,
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analysisTimestamp: new Date().toISOString(),
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modelVersion: 'ecapa-tdnn-v1-mock',
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},
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audioUrl: options?.audioUrl ?? '',
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},
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});
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return voiceAnalysis;
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}
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/**
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* Get analysis result by ID.
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*/
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async getResult(
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analysisId: string,
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userId: string
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): Promise<VoiceAnalysis | null> {
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return prisma.voiceAnalysis.findFirst({
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where: {
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id: analysisId,
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userId,
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},
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});
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}
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/**
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* Get analysis history for a user.
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*/
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async getHistory(
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userId: string,
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options?: {
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limit?: number;
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offset?: number;
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isSynthetic?: boolean;
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}
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): Promise<VoiceAnalysis[]> {
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return prisma.voiceAnalysis.findMany({
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where: {
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userId,
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...(options?.isSynthetic !== undefined && { isSynthetic: options.isSynthetic }),
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},
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orderBy: { createdAt: 'desc' },
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take: options?.limit ?? 50,
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skip: options?.offset ?? 0,
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});
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}
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private computeAudioHash(buffer: Buffer): string {
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let hash = 0;
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const sampleSize = Math.min(buffer.length, 1024);
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for (let i = 0; i < sampleSize; i += 8) {
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hash = ((hash << 5) - hash) + buffer.readUInt8(i);
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hash |= 0;
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}
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return `audio_${Math.abs(hash).toString(16)}`;
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}
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}
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// Batch analysis service
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export class BatchAnalysisService {
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/**
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* Analyze multiple audio files in a batch.
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*/
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async analyzeBatch(
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userId: string,
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files: Array<{
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name: string;
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buffer: Buffer;
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audioUrl?: string;
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}>,
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options?: {
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enrollmentId?: string;
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}
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): Promise<{
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jobId: string;
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results: VoiceAnalysis[];
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summary: {
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total: number;
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synthetic: number;
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natural: number;
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failed: number;
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};
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}> {
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if (files.length > voicePrintEnv.BATCH_MAX_FILES) {
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throw new Error(
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`Batch too large: ${files.length} > ${voicePrintEnv.BATCH_MAX_FILES} max`
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);
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}
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const analysisService = new AnalysisService();
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const results: VoiceAnalysis[] = [];
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let synthetic = 0;
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let natural = 0;
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let failed = 0;
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for (const file of files) {
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try {
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const result = await analysisService.analyze(userId, file.buffer, {
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enrollmentId: options?.enrollmentId,
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audioUrl: file.audioUrl,
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});
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results.push(result);
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if (result.isSynthetic) {
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synthetic++;
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} else {
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natural++;
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}
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} catch (error) {
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console.error(`Batch analysis failed for ${file.name}:`, error);
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failed++;
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}
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}
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const jobId = `batch_${Date.now()}_${Math.random().toString(36).slice(2, 8)}`;
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return {
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jobId,
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results,
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summary: {
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total: files.length,
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synthetic,
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natural,
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failed,
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},
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};
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}
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}
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// Embedding service — ECAPA-TDNN inference wrapper
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export class EmbeddingService {
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private initialized = false;
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/**
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* Initialize the ECAPA-TDNN model.
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*/
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async initialize(): Promise<void> {
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if (this.initialized) return;
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// TODO: Connect to Python ML service for real inference
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// const response = await fetch(`${voicePrintEnv.ML_SERVICE_URL}/initialize`, {
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// method: 'POST',
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// body: JSON.stringify({ modelPath: voicePrintEnv.ECAPA_TDNN_MODEL_PATH }),
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// });
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this.initialized = true;
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console.log('Embedding service initialized (mock model)');
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}
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/**
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* Extract voice embedding from audio.
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*/
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async extract(audioBuffer: Buffer): Promise<number[]> {
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await this.initialize();
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// TODO: Call Python ML service
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// const response = await fetch(`${voicePrintEnv.ML_SERVICE_URL}/embed`, {
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// method: 'POST',
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// body: audioBuffer,
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// });
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// const data = await response.json();
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// return data.embedding;
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// Mock: generate deterministic embedding based on buffer content
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const dims = voicePrintEnv.EMBEDDING_DIMENSIONS;
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const embedding: number[] = new Array(dims);
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let hash = 0;
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for (let i = 0; i < Math.min(audioBuffer.length, 256); i++) {
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hash = ((hash << 5) - hash) + audioBuffer[i];
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hash |= 0;
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}
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for (let i = 0; i < dims; i++) {
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hash = ((hash << 5) - hash) + i;
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hash |= 0;
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embedding[i] = (Math.abs(hash) % 1000) / 1000.0;
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}
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|
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// L2 normalize
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const norm = Math.sqrt(embedding.reduce((s, v) => s + v * v, 0));
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return embedding.map((v) => v / norm);
|
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}
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/**
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* Run full analysis: embedding + synthetic detection.
|
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*/
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async analyze(audioBuffer: Buffer): Promise<{
|
||||
confidence: number;
|
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detectionType: DetectionType;
|
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features: Record<string, number>;
|
||||
embedding: number[];
|
||||
}> {
|
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const embedding = await this.extract(audioBuffer);
|
||||
|
||||
// TODO: Run synthetic voice detection model
|
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// For MVP, use heuristic based on embedding statistics
|
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const confidence = this.estimateSyntheticConfidence(audioBuffer, embedding);
|
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const detectionType =
|
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confidence >= voicePrintEnv.SYNTHETIC_THRESHOLD
|
||||
? DetectionType.SYNTHETIC_VOICE
|
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: DetectionType.NATURAL;
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const features = this.extractAnalysisFeatures(audioBuffer, embedding);
|
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|
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return {
|
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confidence,
|
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detectionType,
|
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features,
|
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embedding,
|
||||
};
|
||||
}
|
||||
|
||||
private estimateSyntheticConfidence(
|
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buffer: Buffer,
|
||||
embedding: number[]
|
||||
): number {
|
||||
// Heuristic features for synthetic detection
|
||||
const meanAmplitude =
|
||||
buffer.reduce((s, v) => s + v, 0) / buffer.length / 255;
|
||||
const embeddingStdDev =
|
||||
Math.sqrt(
|
||||
embedding.reduce((s, v) => s + (v - embedding.reduce((a, b) => a + b) / embedding.length) ** 2, 0) /
|
||||
embedding.length
|
||||
) || 0;
|
||||
|
||||
// Combine features into confidence score
|
||||
const amplitudeScore = Math.abs(meanAmplitude - 0.5) * 2;
|
||||
const embeddingScore = 1.0 - Math.min(1.0, embeddingStdDev * 2);
|
||||
|
||||
return Math.min(
|
||||
1.0,
|
||||
amplitudeScore * 0.3 + embeddingScore * 0.4 + Math.random() * 0.3
|
||||
);
|
||||
}
|
||||
|
||||
private extractAnalysisFeatures(
|
||||
buffer: Buffer,
|
||||
embedding: number[]
|
||||
): Record<string, number> {
|
||||
const meanAmplitude =
|
||||
buffer.reduce((s, v) => s + v, 0) / buffer.length / 255;
|
||||
const zeroCrossings = buffer.reduce((count, v, i, arr) => {
|
||||
return i > 0 && ((v - 128) * (arr[i - 1] - 128) < 0) ? count + 1 : count;
|
||||
}, 0);
|
||||
|
||||
return {
|
||||
mean_amplitude: meanAmplitude,
|
||||
zero_crossing_rate: zeroCrossings / buffer.length,
|
||||
embedding_energy: embedding.reduce((s, v) => s + v * v, 0),
|
||||
embedding_entropy: this.calculateEntropy(embedding),
|
||||
};
|
||||
}
|
||||
|
||||
private calculateEntropy(values: number[]): number {
|
||||
const bins = 20;
|
||||
const histogram = new Array(bins).fill(0);
|
||||
const min = Math.min(...values);
|
||||
const max = Math.max(...values);
|
||||
const range = max - min || 1;
|
||||
|
||||
for (const v of values) {
|
||||
const bin = Math.min(bins - 1, Math.floor(((v - min) / range) * bins));
|
||||
histogram[bin]++;
|
||||
}
|
||||
|
||||
let entropy = 0;
|
||||
const total = values.length;
|
||||
for (const count of histogram) {
|
||||
if (count > 0) {
|
||||
const p = count / total;
|
||||
entropy -= p * Math.log2(p);
|
||||
}
|
||||
}
|
||||
return entropy;
|
||||
}
|
||||
}
|
||||
|
||||
// FAISS index wrapper for voice fingerprint matching
|
||||
export class FAISSIndex {
|
||||
private indexPath: string;
|
||||
private initialized = false;
|
||||
|
||||
constructor(path?: string) {
|
||||
this.indexPath = path ?? voicePrintEnv.FAISS_INDEX_PATH;
|
||||
}
|
||||
|
||||
/**
|
||||
* Initialize or load the FAISS index.
|
||||
*/
|
||||
async initialize(): Promise<void> {
|
||||
if (this.initialized) return;
|
||||
|
||||
// TODO: Load FAISS index from disk
|
||||
// const faiss = require('faiss-node');
|
||||
// this.index = faiss.readIndex(this.indexPath);
|
||||
|
||||
this.initialized = true;
|
||||
console.log(`FAISS index initialized at ${this.indexPath}`);
|
||||
}
|
||||
|
||||
/**
|
||||
* Add an enrollment embedding to the index.
|
||||
*/
|
||||
async add(enrollmentId: string, embedding: number[]): Promise<void> {
|
||||
await this.initialize();
|
||||
|
||||
// TODO: Add to FAISS index
|
||||
// this.index.add([embedding]);
|
||||
// Store mapping: enrollmentId -> index position
|
||||
console.log(`Added enrollment ${enrollmentId} to FAISS index`);
|
||||
}
|
||||
|
||||
/**
|
||||
* Remove an enrollment from the index.
|
||||
*/
|
||||
async remove(enrollmentId: string): Promise<void> {
|
||||
await this.initialize();
|
||||
|
||||
// TODO: Remove from FAISS index
|
||||
console.log(`Removed enrollment ${enrollmentId} from FAISS index`);
|
||||
}
|
||||
|
||||
/**
|
||||
* Search for similar voice embeddings.
|
||||
*/
|
||||
async search(
|
||||
embedding: number[],
|
||||
topK: number = 5
|
||||
): Promise<Array<{ id: string; similarity: number }>> {
|
||||
await this.initialize();
|
||||
|
||||
// TODO: Query FAISS index
|
||||
// const [distances, indices] = this.index.search([embedding], topK);
|
||||
// Map indices back to enrollment IDs
|
||||
|
||||
// Mock: return empty results
|
||||
return [];
|
||||
}
|
||||
|
||||
/**
|
||||
* Save the index to disk.
|
||||
*/
|
||||
async save(): Promise<void> {
|
||||
await this.initialize();
|
||||
// TODO: Write FAISS index to disk
|
||||
console.log(`FAISS index saved to ${this.indexPath}`);
|
||||
}
|
||||
}
|
||||
|
||||
// Export singleton instances
|
||||
export const audioPreprocessor = new AudioPreprocessor();
|
||||
export const voiceEnrollmentService = new VoiceEnrollmentService();
|
||||
export const analysisService = new AnalysisService();
|
||||
export const batchAnalysisService = new BatchAnalysisService();
|
||||
export const embeddingService = new EmbeddingService();
|
||||
Reference in New Issue
Block a user