diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..8855f84dfa5a66f7020f5651db5de165eabb0fc6 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,70 @@ +# ============================================================================= +# VoiceAuth API Dockerfile +# ============================================================================= +# Multi-stage build for optimized production image + +# ----------------------------------------------------------------------------- +# Stage 1: Builder +# ----------------------------------------------------------------------------- +FROM python:3.11-slim AS builder + +# Install build dependencies +RUN apt-get update && apt-get install -y --no-install-recommends \ + build-essential \ + && rm -rf /var/lib/apt/lists/* + +# Create virtual environment +RUN python -m venv /opt/venv +ENV PATH="/opt/venv/bin:$PATH" + +# Install Python dependencies +COPY requirements.txt . +RUN pip install --no-cache-dir --upgrade pip && \ + pip install --no-cache-dir -r requirements.txt + +# ----------------------------------------------------------------------------- +# Stage 2: Production +# ----------------------------------------------------------------------------- +FROM python:3.11-slim AS production + +# Install runtime dependencies +RUN apt-get update && apt-get install -y --no-install-recommends \ + ffmpeg \ + libsndfile1 \ + curl \ + && rm -rf /var/lib/apt/lists/* + +# Create non-root user +RUN useradd --create-home --uid 1000 appuser + +# Copy virtual environment from builder +COPY --from=builder /opt/venv /opt/venv +ENV PATH="/opt/venv/bin:$PATH" + +# Set working directory +WORKDIR /app + +# Copy application code +COPY --chown=appuser:appuser . . + +# Create directories for models and logs +RUN mkdir -p /app/models /app/logs && \ + chown -R appuser:appuser /app + +# Switch to non-root user +USER appuser + +# Environment variables +ENV PYTHONUNBUFFERED=1 \ + PYTHONDONTWRITEBYTECODE=1 \ + PORT=7860 + +# Expose port +EXPOSE 7860 + +# Health check +HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \ + CMD curl -f http://localhost:7860/api/health || exit 1 + +# Run application +CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"] diff --git a/app/__init__.py b/app/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2f54750c5b879ca62a33a9931d0181eb902e2aad --- /dev/null +++ b/app/__init__.py @@ -0,0 +1,4 @@ +"""VoiceAuth - AI Voice Detection API.""" + +__version__ = "1.0.0" +__author__ = "VoiceAuth Team" diff --git a/app/__pycache__/__init__.cpython-312.pyc b/app/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..013187ad5ea6b4bf790313e8348f3eaffcd33f07 Binary files /dev/null and b/app/__pycache__/__init__.cpython-312.pyc differ diff --git a/app/__pycache__/config.cpython-312.pyc b/app/__pycache__/config.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..52c7236e297fab6aa445a2d5e24ec29a9c6eb29c Binary files /dev/null and b/app/__pycache__/config.cpython-312.pyc differ diff --git a/app/__pycache__/main.cpython-312.pyc b/app/__pycache__/main.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ade6ea4926e557315516bd3fe9dae41147f9ad0d Binary files /dev/null and b/app/__pycache__/main.cpython-312.pyc differ diff --git a/app/api/__init__.py b/app/api/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5fa0ee83874ecb0bab9ef44dd2d5479eaec47e4b --- /dev/null +++ b/app/api/__init__.py @@ -0,0 +1,9 @@ +"""API package.""" + +from app.api.routes import health +from app.api.routes import voice_detection + +__all__ = [ + "health", + "voice_detection", +] diff --git a/app/api/__pycache__/__init__.cpython-312.pyc b/app/api/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..176c8afafd16e3dc95d4f8d5b203c0475d911595 Binary files /dev/null and b/app/api/__pycache__/__init__.cpython-312.pyc differ diff --git a/app/api/__pycache__/dependencies.cpython-312.pyc b/app/api/__pycache__/dependencies.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b20a4461e967892690a5655a0bf5e25c8b1bad36 Binary files /dev/null and b/app/api/__pycache__/dependencies.cpython-312.pyc differ diff --git a/app/api/dependencies.py b/app/api/dependencies.py new file mode 100644 index 0000000000000000000000000000000000000000..48ca97ce61328b481ba5f3302a916a7c33d1e219 --- /dev/null +++ b/app/api/dependencies.py @@ -0,0 +1,143 @@ +""" +FastAPI dependency injection. + +Provides dependencies for route handlers. +""" + +from typing import Annotated + +from fastapi import Depends +from fastapi import Header +from fastapi import HTTPException +from fastapi import Request +from fastapi import status + +from app.config import Settings +from app.config import get_settings +from app.ml.model_loader import ModelLoader +from app.services.voice_detector import VoiceDetector +from app.utils.logger import get_logger + +logger = get_logger(__name__) + + +def get_model_loader(request: Request) -> ModelLoader: + """ + Get ModelLoader from app state. + + Args: + request: FastAPI request object + + Returns: + ModelLoader instance from app state + """ + if hasattr(request.app.state, "model_loader"): + return request.app.state.model_loader + return ModelLoader() + + +def get_voice_detector( + model_loader: Annotated[ModelLoader, Depends(get_model_loader)], +) -> VoiceDetector: + """ + Get VoiceDetector instance. + + Args: + model_loader: ModelLoader from dependency + + Returns: + Configured VoiceDetector instance + """ + return VoiceDetector(model_loader=model_loader) + + +async def validate_api_key( + x_api_key: Annotated[ + str | None, + Header( + alias="x-api-key", + description="API key for authentication", + ), + ] = None, + settings: Annotated[Settings, Depends(get_settings)] = None, # type: ignore +) -> str: + """ + Validate API key from request header. + + Args: + x_api_key: API key from x-api-key header + settings: Application settings + + Returns: + Validated API key + + Raises: + HTTPException: 401 if API key is missing or invalid + """ + if settings is None: + settings = get_settings() + + if not x_api_key: + logger.warning("Request without API key") + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="API key is required. Provide it in the x-api-key header.", + headers={"WWW-Authenticate": "ApiKey"}, + ) + + # Get valid API keys + valid_keys = settings.api_keys_list + + if not valid_keys: + logger.error("No API keys configured on server") + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Server configuration error", + ) + + # Constant-time comparison to prevent timing attacks + key_valid = False + for valid_key in valid_keys: + if _constant_time_compare(x_api_key, valid_key): + key_valid = True + break + + if not key_valid: + logger.warning( + "Invalid API key attempt", + key_prefix=x_api_key[:8] + "..." if len(x_api_key) > 8 else "***", + ) + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid API key", + headers={"WWW-Authenticate": "ApiKey"}, + ) + + return x_api_key + + +def _constant_time_compare(val1: str, val2: str) -> bool: + """ + Constant-time string comparison to prevent timing attacks. + + Args: + val1: First string + val2: Second string + + Returns: + True if strings are equal + """ + if len(val1) != len(val2): + return False + + result = 0 + for x, y in zip(val1, val2): + result |= ord(x) ^ ord(y) + + return result == 0 + + +# Type aliases for cleaner route signatures +ValidatedApiKey = Annotated[str, Depends(validate_api_key)] +VoiceDetectorDep = Annotated[VoiceDetector, Depends(get_voice_detector)] +SettingsDep = Annotated[Settings, Depends(get_settings)] diff --git a/app/api/middleware/__init__.py b/app/api/middleware/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..60ddaff3d1ae19d79a1d2bd5413ceea11183ef43 --- /dev/null +++ b/app/api/middleware/__init__.py @@ -0,0 +1,13 @@ +"""Middleware package.""" + +from app.api.middleware.auth import APIKeyMiddleware +from app.api.middleware.error_handler import setup_exception_handlers +from app.api.middleware.rate_limiter import get_limiter +from app.api.middleware.rate_limiter import limiter + +__all__ = [ + "APIKeyMiddleware", + "setup_exception_handlers", + "limiter", + "get_limiter", +] diff --git a/app/api/middleware/__pycache__/__init__.cpython-312.pyc b/app/api/middleware/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2555a164db5067761e11aff0ad1c3c40ed9ea62d Binary files /dev/null and b/app/api/middleware/__pycache__/__init__.cpython-312.pyc differ diff --git a/app/api/middleware/__pycache__/auth.cpython-312.pyc b/app/api/middleware/__pycache__/auth.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..aa0b3b01b9264a3f8431f82ed12bb69e1d3cec9d Binary files /dev/null and b/app/api/middleware/__pycache__/auth.cpython-312.pyc differ diff --git a/app/api/middleware/__pycache__/error_handler.cpython-312.pyc b/app/api/middleware/__pycache__/error_handler.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c197c3c3eb998ca4563d1c377024c25b7ca1b72b Binary files /dev/null and b/app/api/middleware/__pycache__/error_handler.cpython-312.pyc differ diff --git a/app/api/middleware/__pycache__/rate_limiter.cpython-312.pyc b/app/api/middleware/__pycache__/rate_limiter.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9d3e0eed346dedbe5a5181b28381dc3c6b9cfe50 Binary files /dev/null and b/app/api/middleware/__pycache__/rate_limiter.cpython-312.pyc differ diff --git a/app/api/middleware/auth.py b/app/api/middleware/auth.py new file mode 100644 index 0000000000000000000000000000000000000000..dcb701d0112cee6aa463a7130165d217c9438283 --- /dev/null +++ b/app/api/middleware/auth.py @@ -0,0 +1,107 @@ +""" +API Key authentication middleware. + +Provides middleware for API key validation. +""" + +from starlette.middleware.base import BaseHTTPMiddleware +from starlette.requests import Request +from starlette.responses import JSONResponse + +from app.config import get_settings +from app.utils.logger import get_logger + +logger = get_logger(__name__) + + +class APIKeyMiddleware(BaseHTTPMiddleware): + """ + Middleware for API key authentication. + + This middleware checks for valid API keys on protected endpoints. + Public endpoints (health, docs) are excluded from authentication. + """ + + # Endpoints that don't require authentication + PUBLIC_PATHS: set[str] = { + "/api/health", + "/api/ready", + "/api/languages", + "/api/", + "/docs", + "/redoc", + "/openapi.json", + "/", + } + + async def dispatch(self, request: Request, call_next): + """ + Process request and validate API key for protected endpoints. + + Args: + request: Incoming request + call_next: Next middleware/handler + + Returns: + Response from next handler or 401 error + """ + # Skip authentication for public paths + path = request.url.path.rstrip("/") or "/" + + # Check if path is public + is_public = path in self.PUBLIC_PATHS or any( + path.startswith(public.rstrip("/")) for public in self.PUBLIC_PATHS if public != "/" + ) + + if is_public: + return await call_next(request) + + # Get API key from header + api_key = request.headers.get("x-api-key") + + if not api_key: + logger.warning( + "Request without API key", + path=path, + method=request.method, + ) + return JSONResponse( + status_code=401, + content={ + "status": "error", + "message": "API key is required", + }, + headers={"WWW-Authenticate": "ApiKey"}, + ) + + # Validate API key + settings = get_settings() + valid_keys = settings.api_keys_list + + if not valid_keys: + logger.error("No API keys configured") + return JSONResponse( + status_code=500, + content={ + "status": "error", + "message": "Server configuration error", + }, + ) + + if api_key not in valid_keys: + logger.warning( + "Invalid API key", + path=path, + key_prefix=api_key[:8] + "..." if len(api_key) > 8 else "***", + ) + return JSONResponse( + status_code=401, + content={ + "status": "error", + "message": "Invalid API key", + }, + headers={"WWW-Authenticate": "ApiKey"}, + ) + + # Continue to next handler + return await call_next(request) diff --git a/app/api/middleware/error_handler.py b/app/api/middleware/error_handler.py new file mode 100644 index 0000000000000000000000000000000000000000..3d74d63a128c6074785db27fd288d1d548282c6f --- /dev/null +++ b/app/api/middleware/error_handler.py @@ -0,0 +1,130 @@ +""" +Global error handling for FastAPI application. + +Provides exception handlers for consistent error responses. +""" + +from fastapi import FastAPI +from fastapi import HTTPException +from fastapi import Request +from fastapi.exceptions import RequestValidationError +from starlette.responses import JSONResponse + +from app.utils.logger import get_logger + +logger = get_logger(__name__) + + +async def validation_exception_handler( + request: Request, + exc: RequestValidationError, +) -> JSONResponse: + """ + Handle Pydantic validation errors. + + Formats validation errors into a consistent response format. + + Args: + request: Incoming request + exc: Validation exception + + Returns: + JSON response with error details + """ + errors = exc.errors() + + # Format error messages + error_messages = [] + for error in errors: + loc = " -> ".join(str(x) for x in error.get("loc", [])) + msg = error.get("msg", "Validation error") + error_messages.append(f"{loc}: {msg}") + + logger.warning( + "Validation error", + path=request.url.path, + errors=error_messages, + ) + + return JSONResponse( + status_code=422, + content={ + "status": "error", + "message": "Validation error", + "details": { + "errors": error_messages, + }, + }, + ) + + +async def http_exception_handler( + request: Request, + exc: HTTPException, +) -> JSONResponse: + """ + Handle HTTP exceptions. + + Formats HTTP exceptions into a consistent response format. + + Args: + request: Incoming request + exc: HTTP exception + + Returns: + JSON response with error details + """ + return JSONResponse( + status_code=exc.status_code, + content={ + "status": "error", + "message": exc.detail, + }, + headers=exc.headers, + ) + + +async def general_exception_handler( + request: Request, + exc: Exception, +) -> JSONResponse: + """ + Handle unexpected exceptions. + + Logs the exception and returns a generic error response. + + Args: + request: Incoming request + exc: Unexpected exception + + Returns: + JSON response with generic error message + """ + logger.exception( + "Unhandled exception", + path=request.url.path, + method=request.method, + error=str(exc), + ) + + return JSONResponse( + status_code=500, + content={ + "status": "error", + "message": "Internal server error", + }, + ) + + +def setup_exception_handlers(app: FastAPI) -> None: + """ + Register all exception handlers with the FastAPI app. + + Args: + app: FastAPI application instance + """ + app.add_exception_handler(RequestValidationError, validation_exception_handler) + app.add_exception_handler(HTTPException, http_exception_handler) + app.add_exception_handler(Exception, general_exception_handler) + + logger.debug("Exception handlers registered") diff --git a/app/api/middleware/rate_limiter.py b/app/api/middleware/rate_limiter.py new file mode 100644 index 0000000000000000000000000000000000000000..937914423a7c53e1613cc8f168503218f3902616 --- /dev/null +++ b/app/api/middleware/rate_limiter.py @@ -0,0 +1,90 @@ +""" +Rate limiting middleware using SlowAPI. + +Provides request rate limiting per API key. +""" + +from slowapi import Limiter +from slowapi.util import get_remote_address +from starlette.requests import Request + +from app.config import get_settings +from app.utils.logger import get_logger + +logger = get_logger(__name__) + + +def get_api_key_or_ip(request: Request) -> str: + """ + Extract rate limit key from request. + + Uses API key if present, otherwise falls back to IP address. + + Args: + request: Incoming request + + Returns: + Rate limit key (API key or IP) + """ + api_key = request.headers.get("x-api-key") + + if api_key: + # Use API key for per-key rate limiting + return f"key:{api_key}" + + # Fall back to IP address + return f"ip:{get_remote_address(request)}" + + +def get_limiter() -> Limiter: + """ + Create and configure rate limiter. + + Returns: + Configured Limiter instance + """ + settings = get_settings() + + # Build default limit string + default_limit = f"{settings.RATE_LIMIT_REQUESTS}/minute" + + return Limiter( + key_func=get_api_key_or_ip, + default_limits=[default_limit], + # Note: Redis storage will be configured in main.py if available + ) + + +# Global limiter instance +limiter = get_limiter() + + +def rate_limit_exceeded_handler(request: Request, exc: Exception): + """ + Handle rate limit exceeded errors. + + Args: + request: Request that exceeded the limit + exc: Rate limit exception + + Returns: + JSON response with 429 status + """ + from starlette.responses import JSONResponse + + logger.warning( + "Rate limit exceeded", + path=request.url.path, + client=get_api_key_or_ip(request), + ) + + return JSONResponse( + status_code=429, + content={ + "status": "error", + "message": "Rate limit exceeded. Please try again later.", + }, + headers={ + "Retry-After": "60", + }, + ) diff --git a/app/api/routes/__init__.py b/app/api/routes/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..24ad3b1226130d38802c9a5e12d71a247b79a847 --- /dev/null +++ b/app/api/routes/__init__.py @@ -0,0 +1,9 @@ +"""Routes package.""" + +from app.api.routes import health +from app.api.routes import voice_detection + +__all__ = [ + "health", + "voice_detection", +] diff --git a/app/api/routes/__pycache__/__init__.cpython-312.pyc b/app/api/routes/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5f51ab86e0a9c98fcd54c6af8e6608919ac6debb Binary files /dev/null and b/app/api/routes/__pycache__/__init__.cpython-312.pyc differ diff --git a/app/api/routes/__pycache__/federated.cpython-312.pyc b/app/api/routes/__pycache__/federated.cpython-312.pyc new file mode 100644 index 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0000000000000000000000000000000000000000..254939eb0fc8e2bc6feff9909cadc8bd13648a85 --- /dev/null +++ b/app/api/routes/federated.py @@ -0,0 +1,100 @@ +""" +Federated Learning API endpoints. + +Provides endpoints for FL operations: +- Client registration +- Contribution submission +- Federation status +""" + +from fastapi import APIRouter +from fastapi import HTTPException +from fastapi import status +from pydantic import BaseModel +from pydantic import Field + +from app.api.dependencies import ValidatedApiKey +from app.services.federated_learning import fl_manager + +router = APIRouter() + + +class ClientRegistrationRequest(BaseModel): + """Request to register as FL client.""" + + client_id: str = Field(..., min_length=3, max_length=64) + organization: str | None = Field(None, max_length=128) + + +class ContributionRequest(BaseModel): + """Request to submit a training contribution.""" + + client_id: str = Field(..., min_length=3, max_length=64) + gradient_hash: str = Field(..., min_length=16, max_length=128) + samples_trained: int = Field(..., ge=1, le=100000) + local_accuracy: float = Field(..., ge=0.0, le=1.0) + + +@router.post( + "/federated/register", + summary="Register as Federated Client", + description="Register as a federated learning participant.", +) +async def register_client( + request: ClientRegistrationRequest, + api_key: ValidatedApiKey, +) -> dict: + """Register a new federated learning client.""" + client = fl_manager.register_client( + client_id=request.client_id, + organization=request.organization, + ) + + return { + "status": "registered", + "client_id": client.client_id, + "organization": client.organization, + "registered_at": client.registered_at, + } + + +@router.post( + "/federated/contribute", + summary="Submit Training Contribution", + description="Submit model gradients from local training.", +) +async def submit_contribution( + request: ContributionRequest, + api_key: ValidatedApiKey, +) -> dict: + """Submit a training contribution.""" + result = fl_manager.submit_contribution( + client_id=request.client_id, + gradient_hash=request.gradient_hash, + samples_trained=request.samples_trained, + local_accuracy=request.local_accuracy, + ) + + return result + + +@router.get( + "/federated/status", + summary="Federation Status", + description="Get current federated learning status.", +) +async def federation_status() -> dict: + """Get federation status.""" + return fl_manager.get_federation_status() + + +@router.post( + "/federated/aggregate", + summary="Trigger Aggregation", + description="Trigger federated model aggregation (admin only).", +) +async def trigger_aggregation( + api_key: ValidatedApiKey, +) -> dict: + """Trigger model aggregation.""" + return fl_manager.simulate_aggregation() diff --git a/app/api/routes/health.py b/app/api/routes/health.py new file mode 100644 index 0000000000000000000000000000000000000000..73e2d78d2b03d679e4bb92dcf2fe586fc03832ae --- /dev/null +++ b/app/api/routes/health.py @@ -0,0 +1,101 @@ +""" +Health check endpoints. + +Provides health, readiness, and information endpoints. +""" + +from fastapi import APIRouter + +from app.api.dependencies import VoiceDetectorDep +from app.models.enums import SupportedLanguage +from app.models.response import HealthResponse +from app.models.response import LanguagesResponse + +router = APIRouter() + + +@router.get( + "/health", + response_model=HealthResponse, + summary="Health Check", + description="Check the health status of the API and ML model.", +) +async def health_check( + voice_detector: VoiceDetectorDep, +) -> HealthResponse: + """ + Get health status of the API. + + Returns model loading status, device info, and supported languages. + """ + health = voice_detector.health_check() + + return HealthResponse( + status=health["status"], + version=health["version"], + model_loaded=health["model_loaded"], + model_name=health.get("model_name"), + device=health.get("device"), + supported_languages=health["supported_languages"], + ) + + +@router.get( + "/ready", + summary="Readiness Check", + description="Check if the API is ready to accept requests.", +) +async def readiness_check( + voice_detector: VoiceDetectorDep, +) -> dict: + """ + Check if API is ready to accept requests. + + Returns ready status based on model availability. + """ + health = voice_detector.health_check() + + if health["model_loaded"]: + return {"status": "ready", "message": "API is ready to accept requests"} + else: + return {"status": "not_ready", "message": "Model is still loading"} + + +@router.get( + "/languages", + response_model=LanguagesResponse, + summary="Supported Languages", + description="Get the list of supported languages for voice detection.", +) +async def supported_languages() -> LanguagesResponse: + """ + Get list of supported languages. + + Returns all languages supported by the voice detection API. + """ + languages = SupportedLanguage.values() + + return LanguagesResponse( + languages=languages, + count=len(languages), + ) + + +@router.get( + "/", + summary="API Info", + description="Get basic API information.", +) +async def api_info() -> dict: + """ + Get basic API information. + + Returns API name, version, and documentation links. + """ + return { + "name": "VoiceAuth API", + "description": "AI-Generated Voice Detection API", + "version": "1.0.0", + "documentation": "/docs", + "supported_languages": SupportedLanguage.values(), + } diff --git a/app/api/routes/voice_detection.py b/app/api/routes/voice_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..2b3d07ca11e5b42690e57a639df08d813192a64f --- /dev/null +++ b/app/api/routes/voice_detection.py @@ -0,0 +1,150 @@ +""" +Voice detection API endpoint. + +Main endpoint for detecting AI-generated vs human voice. +""" + +from fastapi import APIRouter +from fastapi import HTTPException +from fastapi import status + +from app.api.dependencies import ValidatedApiKey +from app.api.dependencies import VoiceDetectorDep +from app.models.request import VoiceDetectionRequest +from app.models.response import ErrorResponse +from app.models.response import VoiceDetectionResponse +from app.utils.exceptions import AudioDecodeError +from app.utils.exceptions import AudioDurationError +from app.utils.exceptions import AudioFormatError +from app.utils.exceptions import AudioProcessingError +from app.utils.exceptions import InferenceError +from app.utils.exceptions import ModelNotLoadedError +from app.utils.logger import get_logger + +logger = get_logger(__name__) + +router = APIRouter() + + +@router.post( + "/voice-detection", + response_model=VoiceDetectionResponse, + response_model_include={"status", "language", "classification", "confidenceScore", "explanation"}, + responses={ + 200: { + "description": "Successful voice detection", + "model": VoiceDetectionResponse, + }, + 400: { + "description": "Invalid audio data", + "model": ErrorResponse, + }, + 401: { + "description": "Invalid or missing API key", + "model": ErrorResponse, + }, + 422: { + "description": "Validation error", + "model": ErrorResponse, + }, + 429: { + "description": "Rate limit exceeded", + "model": ErrorResponse, + }, + 500: { + "description": "Internal server error", + "model": ErrorResponse, + }, + 503: { + "description": "Model not loaded", + "model": ErrorResponse, + }, + }, + summary="Detect AI-Generated Voice", + description=""" +Analyze a voice sample to determine if it's AI-generated or spoken by a human. + +**Supported Languages:** Tamil, English, Hindi, Malayalam, Telugu + +**Input Requirements:** +- Audio must be Base64-encoded MP3 +- Duration: 0.5s to 30s +- One audio sample per request + +**Response:** +- Classification: AI_GENERATED or HUMAN +- Confidence score: 0.0 to 1.0 +- Human-readable explanation + """, +) +async def detect_voice( + request: VoiceDetectionRequest, + voice_detector: VoiceDetectorDep, + api_key: ValidatedApiKey, +) -> VoiceDetectionResponse: + """ + Detect whether a voice sample is AI-generated or human. + + Args: + request: Voice detection request with audio data + voice_detector: VoiceDetector service dependency + api_key: Validated API key from header + + Returns: + VoiceDetectionResponse with classification result + """ + try: + result = await voice_detector.detect( + audio_base64=request.audioBase64, + language=request.language, + ) + return result + + except AudioDecodeError as e: + logger.warning("Audio decode error", error=str(e)) + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail=f"Failed to decode audio: {e.message}", + ) from e + + except AudioFormatError as e: + logger.warning("Audio format error", error=str(e)) + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail=f"Invalid audio format: {e.message}", + ) from e + + except AudioDurationError as e: + logger.warning("Audio duration error", error=str(e), details=e.details) + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail=e.message, + ) from e + + except AudioProcessingError as e: + logger.error("Audio processing error", error=str(e)) + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail=f"Audio processing failed: {e.message}", + ) from e + + except ModelNotLoadedError as e: + logger.error("Model not loaded", error=str(e)) + raise HTTPException( + status_code=status.HTTP_503_SERVICE_UNAVAILABLE, + detail="Voice detection model is not available. Please try again later.", + ) from e + + except InferenceError as e: + logger.error("Inference error", error=str(e)) + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Voice analysis failed. Please try again.", + ) from e + + except Exception as e: + logger.exception("Unexpected error in voice detection") + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Internal server error", + ) from e diff --git a/app/config.py b/app/config.py new file mode 100644 index 0000000000000000000000000000000000000000..43ff1c73cda3a2aa2efc33d26b2904cc975dbd50 --- /dev/null +++ b/app/config.py @@ -0,0 +1,124 @@ +""" +Application configuration using Pydantic Settings. + +Loads configuration from environment variables and .env file. +""" + +from functools import lru_cache +from typing import Literal + +import torch +from pydantic import Field +from pydantic import field_validator +from pydantic_settings import BaseSettings +from pydantic_settings import SettingsConfigDict + + +class Settings(BaseSettings): + """Application settings loaded from environment variables.""" + + model_config = SettingsConfigDict( + env_file=".env", + env_file_encoding="utf-8", + case_sensitive=False, + extra="ignore", + ) + + # ------------------------------------------------------------------------- + # Application Settings + # ------------------------------------------------------------------------- + APP_NAME: str = "VoiceAuth API" + APP_VERSION: str = "1.0.0" + DEBUG: bool = False + HOST: str = "0.0.0.0" + PORT: int = 8000 + + # ------------------------------------------------------------------------- + # Security Settings + # ------------------------------------------------------------------------- + API_KEYS: str = Field( + default="", + description="Comma-separated list of valid API keys", + ) + CORS_ORIGINS: str = Field( + default="http://localhost:3000,http://localhost:8000", + description="Comma-separated list of allowed CORS origins", + ) + RATE_LIMIT_REQUESTS: int = Field(default=100, ge=1) + RATE_LIMIT_PERIOD: int = Field(default=60, ge=1, description="Period in seconds") + + # ------------------------------------------------------------------------- + # ML Model Settings + # ------------------------------------------------------------------------- + MODEL_NAME: str = "facebook/wav2vec2-base" + MODEL_PATH: str = "" + DEVICE: str = "auto" + MAX_AUDIO_DURATION: float = Field(default=30.0, ge=1.0) + MIN_AUDIO_DURATION: float = Field(default=0.5, ge=0.1) + SAMPLE_RATE: int = Field(default=16000, ge=8000, le=48000) + + # ------------------------------------------------------------------------- + # Redis Settings + # ------------------------------------------------------------------------- + REDIS_URL: str = "redis://localhost:6379" + REDIS_DB: int = 0 + + # ------------------------------------------------------------------------- + # Logging Settings + # ------------------------------------------------------------------------- + LOG_LEVEL: Literal["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"] = "INFO" + LOG_FORMAT: Literal["json", "console"] = "json" + + # ------------------------------------------------------------------------- + # Computed Properties + # ------------------------------------------------------------------------- + @property + def api_keys_list(self) -> list[str]: + """Parse comma-separated API keys into a list.""" + if not self.API_KEYS: + return [] + return [key.strip() for key in self.API_KEYS.split(",") if key.strip()] + + @property + def cors_origins_list(self) -> list[str]: + """Parse comma-separated CORS origins into a list.""" + if not self.CORS_ORIGINS: + return [] + return [origin.strip() for origin in self.CORS_ORIGINS.split(",") if origin.strip()] + + @property + def torch_device(self) -> str: + """Determine the appropriate torch device.""" + if self.DEVICE == "auto": + return "cuda" if torch.cuda.is_available() else "cpu" + return self.DEVICE + + @property + def model_identifier(self) -> str: + """Get the model path or name to load.""" + return self.MODEL_PATH if self.MODEL_PATH else self.MODEL_NAME + + # ------------------------------------------------------------------------- + # Validators + # ------------------------------------------------------------------------- + @field_validator("DEVICE") + @classmethod + def validate_device(cls, v: str) -> str: + """Validate device configuration.""" + valid_devices = {"auto", "cpu", "cuda", "mps"} + # Allow cuda:N format + if v.startswith("cuda:"): + return v + if v not in valid_devices: + raise ValueError(f"Device must be one of {valid_devices} or 'cuda:N' format") + return v + + +@lru_cache +def get_settings() -> Settings: + """ + Get cached settings instance. + + Uses lru_cache to ensure settings are only loaded once. + """ + return Settings() diff --git a/app/main.py b/app/main.py new file mode 100644 index 0000000000000000000000000000000000000000..6b5f3ab0e340e1e0d2692b24122b06cd19e70660 --- /dev/null +++ b/app/main.py @@ -0,0 +1,182 @@ +""" +VoiceAuth API - Main Application Entry Point. + +FastAPI application for AI-generated voice detection. +""" + +from contextlib import asynccontextmanager + +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware +from slowapi import _rate_limit_exceeded_handler +from slowapi.errors import RateLimitExceeded + +from app.api.middleware.error_handler import setup_exception_handlers +from app.api.middleware.rate_limiter import limiter +from app.api.routes import health +from app.api.routes import voice_detection +from app.config import get_settings +from app.ml.model_loader import ModelLoader +from app.utils.logger import get_logger +from app.utils.logger import setup_logging + +# Initialize logging first +setup_logging() +logger = get_logger(__name__) + + +@asynccontextmanager +async def lifespan(app: FastAPI): + """ + Application lifespan manager. + + Handles startup and shutdown events: + - Startup: Load ML model, run warmup + - Shutdown: Unload model, cleanup + """ + # ========================================================================= + # STARTUP + # ========================================================================= + logger.info("Starting VoiceAuth API...") + + # Initialize model loader + model_loader = ModelLoader() + app.state.model_loader = model_loader + + # Load ML model + logger.info("Loading ML model...") + try: + await model_loader.load_model_async() + logger.info("ML model loaded successfully") + + # Run warmup inference + logger.info("Running model warmup...") + model_loader.warmup() + logger.info("Model warmup complete") + + except Exception as e: + logger.error("Failed to load ML model", error=str(e)) + # Continue without model for health checks + # Actual detection will fail with proper error + + logger.info("VoiceAuth API is ready!") + + yield + + # ========================================================================= + # SHUTDOWN + # ========================================================================= + logger.info("Shutting down VoiceAuth API...") + + # Unload model + if hasattr(app.state, "model_loader"): + app.state.model_loader.unload_model() + + logger.info("Shutdown complete") + + +def create_app() -> FastAPI: + """ + Create and configure the FastAPI application. + + Returns: + Configured FastAPI application + """ + settings = get_settings() + + # Create FastAPI app + app = FastAPI( + title=settings.APP_NAME, + version=settings.APP_VERSION, + description=""" +# VoiceAuth - AI Voice Detection API + +Detect whether a voice sample is **AI-generated** or **human-spoken** across 5 languages. + +## Supported Languages +- Tamil +- English +- Hindi +- Malayalam +- Telugu + +## Authentication +All detection requests require an API key in the `x-api-key` header. + +## Rate Limiting +Default: 100 requests per minute per API key. + """, + lifespan=lifespan, + docs_url="/docs", + redoc_url="/redoc", + openapi_url="/openapi.json", + ) + + # ========================================================================= + # MIDDLEWARE + # ========================================================================= + + # CORS + app.add_middleware( + CORSMiddleware, + allow_origins=settings.cors_origins_list or ["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], + ) + + # Rate limiting + app.state.limiter = limiter + app.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler) + + # ========================================================================= + # EXCEPTION HANDLERS + # ========================================================================= + setup_exception_handlers(app) + + # ========================================================================= + # ROUTES + # ========================================================================= + app.include_router( + health.router, + prefix="/api", + tags=["Health"], + ) + app.include_router( + voice_detection.router, + prefix="/api", + tags=["Voice Detection"], + ) + + # Federated Learning routes (Phase 2) + from app.api.routes import federated + app.include_router( + federated.router, + prefix="/api", + tags=["Federated Learning"], + ) + + return app + + +# Create application instance +app = create_app() + + +def main() -> None: + """Run the application using uvicorn.""" + import uvicorn + + settings = get_settings() + + uvicorn.run( + "app.main:app", + host=settings.HOST, + port=settings.PORT, + reload=settings.DEBUG, + log_level=settings.LOG_LEVEL.lower(), + ) + + +if __name__ == "__main__": + main() diff --git a/app/ml/__init__.py b/app/ml/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4c57f2fe1337dd24bfd532ddc7a453633e18d9c7 --- /dev/null +++ b/app/ml/__init__.py @@ -0,0 +1,11 @@ +"""Machine Learning pipeline package.""" + +from app.ml.inference import InferenceEngine +from app.ml.model_loader import ModelLoader +from app.ml.preprocessing import AudioPreprocessor + +__all__ = [ + "ModelLoader", + "InferenceEngine", + "AudioPreprocessor", +] diff --git a/app/ml/__pycache__/__init__.cpython-312.pyc b/app/ml/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..80f5d2cd8c6221859612b819d313d943f7dfabbf Binary files /dev/null and b/app/ml/__pycache__/__init__.cpython-312.pyc differ diff --git a/app/ml/__pycache__/inference.cpython-312.pyc b/app/ml/__pycache__/inference.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8535952952cf54ac5693978e4dc8e932b1f958f1 Binary files /dev/null and b/app/ml/__pycache__/inference.cpython-312.pyc differ diff --git a/app/ml/__pycache__/model_loader.cpython-312.pyc b/app/ml/__pycache__/model_loader.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6309b66c273218ef843c65590b1252aa2bedaa95 Binary files /dev/null and b/app/ml/__pycache__/model_loader.cpython-312.pyc differ diff --git a/app/ml/__pycache__/preprocessing.cpython-312.pyc b/app/ml/__pycache__/preprocessing.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..71731237ece320d103a2a07ec88b57f601f6da40 Binary files /dev/null and b/app/ml/__pycache__/preprocessing.cpython-312.pyc differ diff --git a/app/ml/inference.py b/app/ml/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..436e35e056e956e737d410198de713814016a448 --- /dev/null +++ b/app/ml/inference.py @@ -0,0 +1,235 @@ +""" +Inference engine for voice classification. + +Handles model inference and result processing. +""" + +import time +from typing import TYPE_CHECKING + +import torch +import torch.nn.functional as F + +from app.models.enums import Classification +from app.utils.constants import ID_TO_LABEL +from app.utils.exceptions import InferenceError +from app.utils.logger import get_logger + +if TYPE_CHECKING: + from transformers import Wav2Vec2ForSequenceClassification + from transformers import Wav2Vec2Processor + +logger = get_logger(__name__) + + +class InferenceEngine: + """ + Inference engine for Wav2Vec2 voice classification. + + Handles running model inference and converting outputs + to classification results. + """ + + def __init__( + self, + model: "Wav2Vec2ForSequenceClassification", + processor: "Wav2Vec2Processor", + device: str = "cpu", + ) -> None: + """ + Initialize InferenceEngine. + + Args: + model: Loaded Wav2Vec2ForSequenceClassification model + processor: Wav2Vec2Processor for preprocessing + device: Device to run inference on + """ + self.model = model + self.processor = processor + self.device = device + + def predict( + self, + input_tensors: dict[str, torch.Tensor], + ) -> tuple[Classification, float]: + """ + Run inference and return classification result. + + Args: + input_tensors: Preprocessed input tensors with input_values + + Returns: + Tuple of (Classification, confidence_score) + + Raises: + InferenceError: If inference fails + """ + try: + start_time = time.perf_counter() + + # Ensure model is in eval mode + self.model.eval() + + # Run inference without gradient computation + with torch.no_grad(): + outputs = self.model(**input_tensors) + + # Get logits + logits = outputs.logits + + # Apply softmax to get probabilities + probabilities = F.softmax(logits, dim=-1) + + # Get predicted class and confidence + confidence, predicted_class = torch.max(probabilities, dim=-1) + + # Convert to Python types + predicted_class_id = predicted_class.item() + confidence_score = confidence.item() + + # Get label from model's config or fallback to our mapping + if hasattr(self.model.config, 'id2label') and self.model.config.id2label: + model_label = self.model.config.id2label.get(predicted_class_id, "HUMAN") + # Convert pretrained model labels to standard format + model_label_lower = str(model_label).lower() + + ai_keywords = ["fake", "spoof", "synthetic", "ai", "deepfake", "generated"] + is_ai = any(keyword in model_label_lower for keyword in ai_keywords) + + if is_ai: + label = "AI_GENERATED" + else: + label = "HUMAN" + else: + label = ID_TO_LABEL.get(predicted_class_id, "HUMAN") + + classification = Classification(label) + + inference_time_ms = (time.perf_counter() - start_time) * 1000 + + logger.info( + "Inference complete", + classification=classification.value, + confidence=round(confidence_score, 4), + inference_time_ms=round(inference_time_ms, 2), + ) + + return classification, confidence_score + + except Exception as e: + logger.error("Inference failed", error=str(e)) + raise InferenceError( + f"Model inference failed: {e}", + details={"error": str(e)}, + ) from e + + def predict_with_probabilities( + self, + input_tensors: dict[str, torch.Tensor], + ) -> dict: + """ + Run inference and return full probability distribution. + + Args: + input_tensors: Preprocessed input tensors + + Returns: + Dictionary with classification, confidence, and all probabilities + """ + try: + self.model.eval() + + with torch.no_grad(): + outputs = self.model(**input_tensors) + + logits = outputs.logits + probabilities = F.softmax(logits, dim=-1) + + # Get all probabilities + probs = probabilities.squeeze().cpu().numpy() + + # Get predicted class + confidence, predicted_class = torch.max(probabilities, dim=-1) + predicted_class_id = predicted_class.item() + label = ID_TO_LABEL.get(predicted_class_id, "HUMAN") + + return { + "classification": Classification(label), + "confidence": float(confidence.item()), + "probabilities": { + "HUMAN": float(probs[0]) if len(probs) > 0 else 0.0, + "AI_GENERATED": float(probs[1]) if len(probs) > 1 else 0.0, + }, + } + + except Exception as e: + logger.error("Inference with probabilities failed", error=str(e)) + raise InferenceError( + f"Model inference failed: {e}", + details={"error": str(e)}, + ) from e + + def batch_predict( + self, + input_tensors: dict[str, torch.Tensor], + ) -> list[tuple[Classification, float]]: + """ + Run batch inference. + + Args: + input_tensors: Batched preprocessed input tensors + + Returns: + List of (Classification, confidence) tuples + """ + try: + self.model.eval() + + with torch.no_grad(): + outputs = self.model(**input_tensors) + + logits = outputs.logits + probabilities = F.softmax(logits, dim=-1) + + results = [] + for i in range(probabilities.shape[0]): + confidence, predicted_class = torch.max(probabilities[i], dim=-1) + predicted_class_id = predicted_class.item() + label = ID_TO_LABEL.get(predicted_class_id, "HUMAN") + results.append((Classification(label), float(confidence.item()))) + + return results + + except Exception as e: + logger.error("Batch inference failed", error=str(e)) + raise InferenceError( + f"Batch inference failed: {e}", + details={"error": str(e)}, + ) from e + + def get_hidden_states( + self, + input_tensors: dict[str, torch.Tensor], + ) -> torch.Tensor: + """ + Extract hidden states for explainability. + + Args: + input_tensors: Preprocessed input tensors + + Returns: + Hidden state tensor from last layer + """ + self.model.eval() + + with torch.no_grad(): + outputs = self.model( + **input_tensors, + output_hidden_states=True, + ) + + # Return last hidden state + if hasattr(outputs, "hidden_states") and outputs.hidden_states: + return outputs.hidden_states[-1] + + return torch.tensor([]) diff --git a/app/ml/model_loader.py b/app/ml/model_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..7d7a61b09920995281cee5c805502a3dc8fdebe5 --- /dev/null +++ b/app/ml/model_loader.py @@ -0,0 +1,246 @@ +""" +Model loader for Wav2Vec2 voice classification model. + +Handles loading, caching, and management of the ML model. +""" + +import gc +import threading +from typing import TYPE_CHECKING + +import torch +from transformers import Wav2Vec2ForSequenceClassification +from transformers import Wav2Vec2Processor + +from app.config import get_settings +from app.utils.constants import ID_TO_LABEL +from app.utils.constants import LABEL_TO_ID +from app.utils.exceptions import ModelNotLoadedError +from app.utils.logger import get_logger + +if TYPE_CHECKING: + from transformers import PreTrainedModel + +logger = get_logger(__name__) + + +class ModelLoader: + """ + Singleton model loader for Wav2Vec2 classification model. + + Handles lazy loading, caching, and memory management of the ML model. + Thread-safe for production use. + """ + + _instance: "ModelLoader | None" = None + _lock: threading.Lock = threading.Lock() + + def __new__(cls) -> "ModelLoader": + """Ensure only one instance exists (Singleton pattern).""" + if cls._instance is None: + with cls._lock: + if cls._instance is None: + cls._instance = super().__new__(cls) + cls._instance._initialized = False + return cls._instance + + def __init__(self) -> None: + """Initialize ModelLoader if not already initialized.""" + if getattr(self, "_initialized", False): + return + + self.settings = get_settings() + self.model: Wav2Vec2ForSequenceClassification | None = None + self.processor: Wav2Vec2Processor | None = None + self.device: str = self.settings.torch_device + self._model_lock = threading.Lock() + self._initialized = True + + logger.info( + "ModelLoader initialized", + device=self.device, + model_identifier=self.settings.model_identifier, + ) + + @property + def is_loaded(self) -> bool: + """Check if model is loaded and ready for inference.""" + return self.model is not None and self.processor is not None + + def load_model(self) -> None: + """ + Load the Wav2Vec2 model and processor. + + Thread-safe loading with proper error handling. + + Raises: + Exception: If model loading fails + """ + with self._model_lock: + if self.is_loaded: + logger.debug("Model already loaded, skipping") + return + + model_identifier = self.settings.model_identifier + + logger.info("Loading Wav2Vec2 model", model=model_identifier, device=self.device) + + try: + # Load processor - try model first, fallback to base wav2vec2 + try: + self.processor = Wav2Vec2Processor.from_pretrained( + model_identifier, + trust_remote_code=False, + ) + except Exception: + # Fine-tuned models often don't have processor, use base + logger.info("Using base wav2vec2 processor") + self.processor = Wav2Vec2Processor.from_pretrained( + "facebook/wav2vec2-base", + trust_remote_code=False, + ) + + # Load model with classification head + # For pretrained deepfake models, use their existing configuration + self.model = Wav2Vec2ForSequenceClassification.from_pretrained( + model_identifier, + trust_remote_code=False, + ignore_mismatched_sizes=True, # Allow different classifier sizes + ) + + # Move model to device + self.model = self.model.to(self.device) + + # Set to evaluation mode + self.model.eval() + + # Log memory usage + if self.device.startswith("cuda"): + memory_allocated = torch.cuda.memory_allocated() / (1024**3) + logger.info( + "Model loaded successfully", + device=self.device, + gpu_memory_gb=round(memory_allocated, 2), + ) + else: + logger.info("Model loaded successfully", device=self.device) + + except Exception as e: + self.model = None + self.processor = None + logger.error("Failed to load model", error=str(e)) + raise + + async def load_model_async(self) -> None: + """ + Async wrapper for model loading. + + Useful for FastAPI lifespan context. + """ + # Run in thread pool to avoid blocking + import asyncio + + loop = asyncio.get_event_loop() + await loop.run_in_executor(None, self.load_model) + + def get_model(self) -> tuple[Wav2Vec2ForSequenceClassification, Wav2Vec2Processor]: + """ + Get the loaded model and processor. + + Returns: + Tuple of (model, processor) + + Raises: + ModelNotLoadedError: If model is not loaded + """ + if not self.is_loaded: + raise ModelNotLoadedError( + "Model not loaded. Call load_model() first.", + details={"model_identifier": self.settings.model_identifier}, + ) + + return self.model, self.processor # type: ignore + + def unload_model(self) -> None: + """ + Unload model and free memory. + + Useful for memory management in constrained environments. + """ + with self._model_lock: + if self.model is not None: + del self.model + self.model = None + + if self.processor is not None: + del self.processor + self.processor = None + + # Force garbage collection + gc.collect() + + # Clear CUDA cache if using GPU + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + logger.info("Model unloaded, memory freed") + + def warmup(self) -> None: + """ + Run a warmup inference to initialize CUDA kernels. + + This reduces latency on the first real inference. + """ + if not self.is_loaded: + logger.warning("Cannot warmup - model not loaded") + return + + logger.info("Running model warmup...") + + try: + # Create dummy input + dummy_audio = torch.randn(1, 16000) # 1 second of audio + + model, processor = self.get_model() + + # Preprocess dummy audio + inputs = processor( + dummy_audio.squeeze().numpy(), + sampling_rate=16000, + return_tensors="pt", + padding=True, + ) + + inputs = {k: v.to(self.device) for k, v in inputs.items()} + + # Run warmup inference + with torch.no_grad(): + _ = model(**inputs) + + logger.info("Model warmup complete") + + except Exception as e: + logger.warning("Warmup failed (non-critical)", error=str(e)) + + def health_check(self) -> dict: + """ + Get model health status. + + Returns: + Dictionary with health information + """ + status = { + "model_loaded": self.is_loaded, + "device": self.device, + "model_identifier": self.settings.model_identifier, + } + + if self.device.startswith("cuda") and torch.cuda.is_available(): + status["gpu_memory_allocated_gb"] = round( + torch.cuda.memory_allocated() / (1024**3), 2 + ) + status["gpu_memory_reserved_gb"] = round( + torch.cuda.memory_reserved() / (1024**3), 2 + ) + + return status diff --git a/app/ml/preprocessing.py b/app/ml/preprocessing.py new file mode 100644 index 0000000000000000000000000000000000000000..5ffa789cf8cf4e8cb380a79d098ffde12f06f766 --- /dev/null +++ b/app/ml/preprocessing.py @@ -0,0 +1,155 @@ +""" +Audio preprocessing for Wav2Vec2 model. + +Handles conversion from audio arrays to model input tensors. +""" + +import numpy as np +import torch +from transformers import Wav2Vec2Processor + +from app.utils.constants import TARGET_SAMPLE_RATE +from app.utils.logger import get_logger + +logger = get_logger(__name__) + + +class AudioPreprocessor: + """ + Preprocessor for preparing audio data for Wav2Vec2 model. + + Converts numpy audio arrays into the tensor format expected + by the Wav2Vec2ForSequenceClassification model. + """ + + def __init__( + self, + processor: Wav2Vec2Processor, + device: str = "cpu", + ) -> None: + """ + Initialize AudioPreprocessor. + + Args: + processor: Wav2Vec2Processor instance + device: Target device for tensors (cpu/cuda) + """ + self.processor = processor + self.device = device + self.sample_rate = TARGET_SAMPLE_RATE + + def validate_input(self, audio_array: np.ndarray) -> bool: + """ + Validate audio array for processing. + + Args: + audio_array: Input audio array + + Returns: + True if valid + + Raises: + ValueError: If validation fails + """ + if not isinstance(audio_array, np.ndarray): + raise ValueError(f"Expected numpy array, got {type(audio_array)}") + + if audio_array.ndim != 1: + raise ValueError(f"Expected 1D array, got {audio_array.ndim}D") + + if len(audio_array) == 0: + raise ValueError("Audio array is empty") + + if np.isnan(audio_array).any(): + raise ValueError("Audio array contains NaN values") + + if np.isinf(audio_array).any(): + raise ValueError("Audio array contains infinite values") + + return True + + def preprocess( + self, + audio_array: np.ndarray, + return_attention_mask: bool = True, + ) -> dict[str, torch.Tensor]: + """ + Preprocess audio array for model inference. + + Args: + audio_array: 1D numpy array of audio samples (16kHz, normalized) + return_attention_mask: Whether to return attention mask + + Returns: + Dictionary with input_values and optionally attention_mask + """ + # Validate input + self.validate_input(audio_array) + + # Ensure float32 + audio_array = audio_array.astype(np.float32) + + # Process through Wav2Vec2Processor + inputs = self.processor( + audio_array, + sampling_rate=self.sample_rate, + return_tensors="pt", + padding=True, + return_attention_mask=return_attention_mask, + ) + + # Move to target device + inputs = {key: value.to(self.device) for key, value in inputs.items()} + + logger.debug( + "Audio preprocessed for model", + input_length=inputs["input_values"].shape[-1], + device=self.device, + ) + + return inputs + + def preprocess_batch( + self, + audio_arrays: list[np.ndarray], + return_attention_mask: bool = True, + ) -> dict[str, torch.Tensor]: + """ + Preprocess a batch of audio arrays. + + Args: + audio_arrays: List of 1D numpy arrays + return_attention_mask: Whether to return attention mask + + Returns: + Dictionary with batched input_values and optionally attention_mask + """ + # Validate all inputs + for i, audio in enumerate(audio_arrays): + try: + self.validate_input(audio) + except ValueError as e: + raise ValueError(f"Invalid audio at index {i}: {e}") from e + + # Ensure float32 + audio_arrays = [audio.astype(np.float32) for audio in audio_arrays] + + # Process batch through Wav2Vec2Processor + inputs = self.processor( + audio_arrays, + sampling_rate=self.sample_rate, + return_tensors="pt", + padding=True, + return_attention_mask=return_attention_mask, + ) + + # Move to target device + inputs = {key: value.to(self.device) for key, value in inputs.items()} + + logger.debug( + "Batch preprocessed for model", + batch_size=len(audio_arrays), + device=self.device, + ) + + return inputs diff --git a/app/models/__init__.py b/app/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f21c718d6e929015d81e72d31b0a982c87ed49ed --- /dev/null +++ b/app/models/__init__.py @@ -0,0 +1,19 @@ +"""Pydantic models package.""" + +from app.models.enums import AudioFormat +from app.models.enums import Classification +from app.models.enums import SupportedLanguage +from app.models.request import VoiceDetectionRequest +from app.models.response import ErrorResponse +from app.models.response import HealthResponse +from app.models.response import VoiceDetectionResponse + +__all__ = [ + "SupportedLanguage", + "Classification", + "AudioFormat", + "VoiceDetectionRequest", + "VoiceDetectionResponse", + "ErrorResponse", + "HealthResponse", +] diff --git a/app/models/__pycache__/__init__.cpython-312.pyc b/app/models/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..49d378087e2dfa862b80329e089dd3ed0362a278 Binary files /dev/null and b/app/models/__pycache__/__init__.cpython-312.pyc differ diff 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0000000000000000000000000000000000000000..bc9c72168f5dfe8b6550daa9139a58c3c8730228 --- /dev/null +++ b/app/models/enums.py @@ -0,0 +1,57 @@ +""" +Enumeration types for VoiceAuth API. + +Defines supported languages, classification results, and audio formats. +""" + +from enum import Enum + + +class SupportedLanguage(str, Enum): + """ + Supported languages for voice detection. + + The API supports these 5 Indian languages for AI voice detection. + """ + + TAMIL = "Tamil" + ENGLISH = "English" + HINDI = "Hindi" + MALAYALAM = "Malayalam" + TELUGU = "Telugu" + + @classmethod + def values(cls) -> list[str]: + """Get all language values as a list.""" + return [lang.value for lang in cls] + + +class Classification(str, Enum): + """ + Voice classification result. + + Indicates whether the detected voice is AI-generated or human. + """ + + AI_GENERATED = "AI_GENERATED" + HUMAN = "HUMAN" + + @property + def is_synthetic(self) -> bool: + """Check if classification indicates synthetic voice.""" + return self == Classification.AI_GENERATED + + +class AudioFormat(str, Enum): + """ + Supported audio input formats. + + Currently only MP3 is supported as per competition requirements. + """ + + MP3 = "mp3" + + @classmethod + def values(cls) -> list[str]: + """Get all format values as a list.""" + return [fmt.value for fmt in cls] diff --git a/app/models/request.py b/app/models/request.py new file mode 100644 index 0000000000000000000000000000000000000000..5671ee2b27034b948122ccda5810b3fa76e87af7 --- /dev/null +++ b/app/models/request.py @@ -0,0 +1,99 @@ +""" +Request models for VoiceAuth API. + +Defines Pydantic models for API request validation. +""" + +import base64 +import re +from typing import Annotated + +from pydantic import BaseModel +from pydantic import ConfigDict +from pydantic import Field +from pydantic import field_validator + +from app.models.enums import AudioFormat +from app.models.enums import SupportedLanguage + + +class VoiceDetectionRequest(BaseModel): + """ + Request model for voice detection endpoint. + + Accepts Base64-encoded MP3 audio in one of 5 supported languages. + """ + + model_config = ConfigDict( + json_schema_extra={ + "example": { + "language": "Tamil", + "audioFormat": "mp3", + "audioBase64": "SUQzBAAAAAAAI1RTU0UAAAAPAAADTGF2ZjU2LjM2LjEwMAAAAAAA...", + } + } + ) + + language: Annotated[ + SupportedLanguage, + Field( + description="Language of the audio content. Must be one of: Tamil, English, Hindi, Malayalam, Telugu" + ), + ] + + audioFormat: Annotated[ + AudioFormat, + Field( + default=AudioFormat.MP3, + description="Format of the audio file. Currently only 'mp3' is supported", + ), + ] = AudioFormat.MP3 + + audioBase64: Annotated[ + str, + Field( + min_length=100, + description="Base64-encoded MP3 audio data. Minimum 100 characters for valid audio", + ), + ] + + @field_validator("audioBase64") + @classmethod + def validate_base64(cls, v: str) -> str: + """ + Validate that the string is valid Base64. + + Args: + v: The base64 string to validate + + Returns: + The validated base64 string + + Raises: + ValueError: If the string is not valid base64 + """ + # Remove any whitespace + v = v.strip() + + # Check for valid base64 characters + base64_pattern = re.compile(r"^[A-Za-z0-9+/]*={0,2}$") + if not base64_pattern.match(v): + raise ValueError("Invalid Base64 encoding: contains invalid characters") + + # Try to decode to verify it's valid base64 + try: + # Add padding if needed + padding = 4 - len(v) % 4 + if padding != 4: + v += "=" * padding + + decoded = base64.b64decode(v) + if len(decoded) < 100: + raise ValueError("Decoded audio data is too small to be a valid MP3 file") + + except Exception as e: + if "Invalid Base64" in str(e) or "too small" in str(e): + raise + raise ValueError(f"Invalid Base64 encoding: {e}") from e + + return v.rstrip("=") + "=" * (4 - len(v.rstrip("=")) % 4) if len(v.rstrip("=")) % 4 else v diff --git a/app/models/response.py b/app/models/response.py new file mode 100644 index 0000000000000000000000000000000000000000..16be2aaf9d183b3bbe62c0784abd877d14589413 --- /dev/null +++ b/app/models/response.py @@ -0,0 +1,264 @@ +""" +Response models for VoiceAuth API. + +Defines Pydantic models for API responses. + +PHASE 1 ENHANCED: Includes Risk Score, Quality Score, Temporal Analysis. +""" + +from typing import Annotated +from typing import Any +from typing import Literal +from typing import Optional + +from pydantic import BaseModel +from pydantic import ConfigDict +from pydantic import Field + +from app.models.enums import Classification + + +class VoiceDetectionResponse(BaseModel): + """ + Successful voice detection response. + + Contains classification result, confidence score, explanation, + and comprehensive analysis data. + + PHASE 1 FEATURES: + - deepfakeRiskScore: Business-friendly risk rating + - audioQuality: Input quality assessment + - temporalAnalysis: Breathing, pauses, rhythm analysis + - audioForensics: Spectral and energy analysis + - performanceMetrics: Processing time breakdown + """ + + model_config = ConfigDict( + json_schema_extra={ + "example": { + "status": "success", + "language": "Tamil", + "classification": "AI_GENERATED", + "confidenceScore": 0.91, + "explanation": "Strong evidence of AI-generated speech: absence of natural breathing sounds and mechanically consistent pause patterns detected", + "deepfakeRiskScore": { + "score": 87, + "level": "HIGH", + "recommendation": "Manual review required before approval", + }, + "audioQuality": { + "score": 85, + "rating": "GOOD", + "reliability": "High confidence in detection results", + }, + "temporalAnalysis": { + "breathingDetected": False, + "breathingNaturalness": 0.0, + "pauseMechanicalScore": 0.78, + "rhythmConsistency": 0.85, + "anomalyScore": 0.72, + "verdict": "HIGH_ANOMALY", + }, + "audioForensics": { + "spectralCentroid": 1523.45, + "pitchStability": 0.89, + "jitter": 0.0021, + "energyConsistency": 0.92, + "silenceRatio": 0.08, + "aiLikelihood": 0.76, + }, + "performanceMetrics": { + "audioProcessingMs": 45.23, + "forensicsAnalysisMs": 12.87, + "temporalAnalysisMs": 8.45, + "modelInferenceMs": 127.45, + "totalProcessingMs": 193.00, + }, + } + } + ) + + status: Annotated[ + Literal["success"], + Field(description="Response status, always 'success' for successful detections"), + ] = "success" + + language: Annotated[ + str, + Field(description="Language of the analyzed audio"), + ] + + classification: Annotated[ + Classification, + Field(description="Classification result: AI_GENERATED or HUMAN"), + ] + + confidenceScore: Annotated[ + float, + Field( + ge=0.0, + le=1.0, + description="Calibrated confidence score between 0.0 and 1.0", + ), + ] + + explanation: Annotated[ + str, + Field( + max_length=250, + description="Human-readable explanation based on comprehensive analysis", + ), + ] + + # NEW: Deepfake Risk Score + deepfakeRiskScore: Annotated[ + Optional[dict[str, Any]], + Field( + default=None, + description="Business-friendly risk score (0-100) with level and recommendation", + ), + ] = None + + # NEW: Audio Quality Score + audioQuality: Annotated[ + Optional[dict[str, Any]], + Field( + default=None, + description="Input audio quality assessment affecting detection reliability", + ), + ] = None + + # NEW: Temporal Analysis + temporalAnalysis: Annotated[ + Optional[dict[str, Any]], + Field( + default=None, + description="Temporal anomaly analysis (breathing, pauses, rhythm)", + ), + ] = None + + # Audio Forensics + audioForensics: Annotated[ + Optional[dict[str, float]], + Field( + default=None, + description="Detailed audio forensics analysis metrics", + ), + ] = None + + # Performance Metrics + performanceMetrics: Annotated[ + Optional[dict[str, float]], + Field( + default=None, + description="Performance timing breakdown", + ), + ] = None + + +class ErrorResponse(BaseModel): + """ + Error response model. + + Returned when the API encounters an error. + """ + + model_config = ConfigDict( + json_schema_extra={ + "example": { + "status": "error", + "message": "Invalid API key or malformed request", + } + } + ) + + status: Annotated[ + Literal["error"], + Field(description="Response status, always 'error' for error responses"), + ] = "error" + + message: Annotated[ + str, + Field(description="Human-readable error message"), + ] + + details: Annotated[ + Optional[dict[str, Any]], + Field(default=None, description="Additional error details if available"), + ] = None + + +class HealthResponse(BaseModel): + """ + Health check response model. + + Returned by health check endpoints. + """ + + model_config = ConfigDict( + json_schema_extra={ + "example": { + "status": "healthy", + "version": "1.0.0", + "model_loaded": True, + "model_name": "facebook/wav2vec2-base", + "device": "cuda", + "supported_languages": ["Tamil", "English", "Hindi", "Malayalam", "Telugu"], + "features": [ + "audio_forensics", + "temporal_anomaly_detection", + "deepfake_risk_score", + "audio_quality_score", + ], + } + } + ) + + status: Annotated[ + str, + Field(description="Health status: 'healthy' or 'unhealthy'"), + ] + + version: Annotated[ + str, + Field(description="API version"), + ] + + model_loaded: Annotated[ + bool, + Field(description="Whether the ML model is loaded and ready"), + ] + + model_name: Annotated[ + Optional[str], + Field(default=None, description="Name of the loaded model"), + ] = None + + device: Annotated[ + Optional[str], + Field(default=None, description="Device used for inference (cpu/cuda)"), + ] = None + + supported_languages: Annotated[ + list[str], + Field(description="List of supported languages"), + ] + + features: Annotated[ + Optional[list[str]], + Field(default=None, description="List of enabled features"), + ] = None + + +class LanguagesResponse(BaseModel): + """Response model for supported languages endpoint.""" + + languages: Annotated[ + list[str], + Field(description="List of supported language names"), + ] + + count: Annotated[ + int, + Field(description="Number of supported languages"), + ] diff --git a/app/services/__init__.py b/app/services/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0b30e2c7b34a69d91d7890181771a6f3d0068ec7 --- /dev/null +++ b/app/services/__init__.py @@ -0,0 +1,19 @@ +"""Services package.""" + +from app.services.audio_forensics import AudioForensicsAnalyzer +from app.services.audio_processor import AudioProcessor +from app.services.explainability import ExplainabilityService +from app.services.score_calculators import AudioQualityScorer +from app.services.score_calculators import RiskScoreCalculator +from app.services.temporal_detector import TemporalAnomalyDetector +from app.services.voice_detector import VoiceDetector + +__all__ = [ + "AudioProcessor", + "AudioForensicsAnalyzer", + "ExplainabilityService", + "TemporalAnomalyDetector", + "RiskScoreCalculator", + "AudioQualityScorer", + "VoiceDetector", +] diff --git a/app/services/__pycache__/__init__.cpython-312.pyc b/app/services/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4a4fec60a15e85d68cbc851972c0536cb3d253fb Binary files /dev/null and b/app/services/__pycache__/__init__.cpython-312.pyc differ diff --git a/app/services/__pycache__/audio_forensics.cpython-312.pyc b/app/services/__pycache__/audio_forensics.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..413c8f566870546cf150450611c93eada43114fa Binary files /dev/null and b/app/services/__pycache__/audio_forensics.cpython-312.pyc differ diff --git a/app/services/__pycache__/audio_processor.cpython-312.pyc b/app/services/__pycache__/audio_processor.cpython-312.pyc new file mode 100644 index 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0000000000000000000000000000000000000000..d2fd3f7ab6c6ba87df7b79e4f47d23874c7b1eb0 --- /dev/null +++ b/app/services/audio_forensics.py @@ -0,0 +1,318 @@ +""" +Audio Forensics Analyzer for deepfake detection. + +Extracts low-level audio features that help distinguish +AI-generated speech from human speech. +""" + +import numpy as np +from scipy import signal +from scipy.fft import fft + +from app.utils.logger import get_logger + +logger = get_logger(__name__) + + +class AudioForensicsAnalyzer: + """ + Forensic analysis of audio to detect AI-generated patterns. + + analyzes spectral characteristics, pitch stability, and silence patterns + to identify artifacts typical of neural vocoders. + """ + + def __init__(self, sample_rate: int = 16000): + """Initialize analyzer with sample rate.""" + self.sample_rate = sample_rate + + def analyze(self, audio_array: np.ndarray) -> dict: + """ + Perform comprehensive forensic analysis on audio. + + Args: + audio_array: Normalized audio samples (16kHz, mono) + + Returns: + Dictionary with forensic metrics and AI likelihood indicators + """ + logger.debug("Starting forensic audio analysis") + + # Compute all forensic features + spectral = self._analyze_spectral(audio_array) + temporal = self._analyze_temporal(audio_array) + pitch = self._analyze_pitch_stability(audio_array) + energy = self._analyze_energy_patterns(audio_array) + + # Combine into forensic report + forensics = { + "spectral": spectral, + "temporal": temporal, + "pitch": pitch, + "energy": energy, + "ai_indicators": self._compute_ai_indicators(spectral, temporal, pitch, energy), + } + + logger.debug("Forensic analysis complete", indicators=forensics["ai_indicators"]) + return forensics + + def _analyze_spectral(self, audio: np.ndarray) -> dict: + """Analyze spectral characteristics.""" + # Compute FFT + n = len(audio) + fft_vals = np.abs(fft(audio))[:n // 2] + freqs = np.fft.fftfreq(n, 1 / self.sample_rate)[:n // 2] + + # Spectral centroid (center of mass of spectrum) + spectral_centroid = np.sum(freqs * fft_vals) / (np.sum(fft_vals) + 1e-10) + + # Spectral flatness (measure of noise-like vs tonal) + # AI often has higher flatness in certain bands + geometric_mean = np.exp(np.mean(np.log(fft_vals + 1e-10))) + arithmetic_mean = np.mean(fft_vals) + 1e-10 + spectral_flatness = geometric_mean / arithmetic_mean + + # Spectral rolloff (frequency below which 85% of energy is contained) + cumsum = np.cumsum(fft_vals) + rolloff_idx = np.searchsorted(cumsum, 0.85 * cumsum[-1]) + spectral_rolloff = freqs[min(rolloff_idx, len(freqs) - 1)] + + # Spectral bandwidth + spectral_bandwidth = np.sqrt( + np.sum(((freqs - spectral_centroid) ** 2) * fft_vals) / (np.sum(fft_vals) + 1e-10) + ) + + return { + "centroid_hz": round(float(spectral_centroid), 2), + "flatness": round(float(spectral_flatness), 4), + "rolloff_hz": round(float(spectral_rolloff), 2), + "bandwidth_hz": round(float(spectral_bandwidth), 2), + } + + def _analyze_temporal(self, audio: np.ndarray) -> dict: + """Analyze temporal characteristics.""" + # Zero crossing rate (how often signal crosses zero) + zero_crossings = np.sum(np.abs(np.diff(np.sign(audio)))) / 2 + zcr = zero_crossings / len(audio) + + # RMS energy + rms = np.sqrt(np.mean(audio ** 2)) + + # Compute short-time energy variance (humans have more variation) + frame_size = int(0.025 * self.sample_rate) # 25ms frames + hop_size = int(0.010 * self.sample_rate) # 10ms hop + + energies = [] + for i in range(0, len(audio) - frame_size, hop_size): + frame = audio[i:i + frame_size] + energies.append(np.sum(frame ** 2)) + + energy_variance = np.var(energies) if energies else 0 + + # Silence ratio (AI often has different silence patterns) + silence_threshold = 0.01 * np.max(np.abs(audio)) + silence_samples = np.sum(np.abs(audio) < silence_threshold) + silence_ratio = silence_samples / len(audio) + + return { + "zero_crossing_rate": round(float(zcr), 6), + "rms_energy": round(float(rms), 6), + "energy_variance": round(float(energy_variance), 8), + "silence_ratio": round(float(silence_ratio), 4), + } + + def _analyze_pitch_stability(self, audio: np.ndarray) -> dict: + """ + Analyze pitch stability. + + AI-generated speech often has unnaturally stable pitch. + Humans have natural pitch variations (jitter). + """ + # Use autocorrelation for pitch estimation + frame_size = int(0.030 * self.sample_rate) # 30ms frames + hop_size = int(0.010 * self.sample_rate) # 10ms hop + + pitches = [] + for i in range(0, len(audio) - frame_size, hop_size): + frame = audio[i:i + frame_size] + + # Autocorrelation + corr = np.correlate(frame, frame, mode='full') + corr = corr[len(corr) // 2:] + + # Find first peak after initial decay + d = np.diff(corr) + start = np.where(d > 0)[0] + + if len(start) > 0: + start = start[0] + peak = np.argmax(corr[start:]) + start + if peak > 0 and corr[peak] > 0.3 * corr[0]: + pitch = self.sample_rate / peak + if 50 < pitch < 500: # Human voice range + pitches.append(pitch) + + if len(pitches) < 2: + return { + "mean_pitch_hz": 0, + "pitch_std": 0, + "pitch_stability": 1.0, # Unknown = assume stable + "jitter": 0, + } + + pitches = np.array(pitches) + mean_pitch = np.mean(pitches) + pitch_std = np.std(pitches) + + # Pitch stability (inverse of variation) - high = AI-like + pitch_stability = 1.0 / (1.0 + pitch_std / (mean_pitch + 1e-10)) + + # Jitter (frame-to-frame pitch variation) - low = AI-like + jitter = np.mean(np.abs(np.diff(pitches))) / (mean_pitch + 1e-10) + + return { + "mean_pitch_hz": round(float(mean_pitch), 2), + "pitch_std": round(float(pitch_std), 4), + "pitch_stability": round(float(pitch_stability), 4), + "jitter": round(float(jitter), 6), + } + + def _analyze_energy_patterns(self, audio: np.ndarray) -> dict: + """Analyze energy envelope patterns.""" + # Compute envelope using Hilbert transform + analytic_signal = signal.hilbert(audio) + envelope = np.abs(analytic_signal) + + # Envelope smoothness (AI is often smoother) + envelope_diff = np.abs(np.diff(envelope)) + envelope_roughness = np.mean(envelope_diff) + + # Attack/decay characteristics + # Find amplitude peaks + peaks, _ = signal.find_peaks(envelope, height=0.1 * np.max(envelope)) + + if len(peaks) > 1: + # Measure consistency of peaks (AI is more consistent) + peak_heights = envelope[peaks] + peak_consistency = 1.0 - (np.std(peak_heights) / (np.mean(peak_heights) + 1e-10)) + else: + peak_consistency = 0.5 + + return { + "envelope_roughness": round(float(envelope_roughness), 6), + "peak_consistency": round(float(peak_consistency), 4), + "dynamic_range": round(float(np.max(envelope) - np.min(envelope)), 4), + } + + def _compute_ai_indicators( + self, + spectral: dict, + temporal: dict, + pitch: dict, + energy: dict, + ) -> dict: + """ + Compute features indicating AI generation (Tuned for modern TTS). + + Modern AI (ElevenLabs etc) adds simulated breaths and jitter, so we must + be more sensitive to 'slightly too perfect' signals. + """ + indicators = {} + + # 1. Pitch Consistency + # AI pitch tracks are smoother than human vocal cords even with simulated emotion + pitch_stability = pitch.get("pitch_stability", 0.5) + # RELAXED: Needs very high stability (>0.75) to be suspicious + # This prevents high-quality human voice from flagging + indicators["pitch_regularity"] = min(1.0, pitch_stability / 0.75) + + # 2. Jitter (Micro-fluctuations) + # Real voices have chaotic micro-tremors. AI simulates them but often perfectly. + jitter = pitch.get("jitter", 0.02) + # RELAXED: Only extremely low jitter (<0.025) is suspicious + indicators["low_jitter"] = max(0.0, 1.0 - (jitter / 0.025)) + + # 3. Energy/Envelope Smoothness + # Neural vocoders produce smoother envelopes than air pressure from lungs + roughness = energy.get("envelope_roughness", 0.01) + # RELAXED: < 0.03 is suspicious + indicators["smooth_envelope"] = max(0.0, 1.0 - (roughness / 0.03)) + + # 4. Silence/Noise Floor + # Check if silence is "too digital" (low variance in zero crossing) + zcr = temporal.get("zero_crossing_rate", 0.1) + # RELAXED: Only mathematical silence (<0.01) is suspicious + indicators["unnatural_silence"] = 1.0 if zcr < 0.01 else 0.0 + + # 5. Energy Consistency (Peaks) + peak_consistency = energy.get("peak_consistency", 0.5) + indicators["energy_consistency"] = peak_consistency if peak_consistency > 0.8 else 0.0 + + # --- Aggressive Scoring for Robustness --- + # We assume if ANY strong indicator is present, chance of AI is high. + + scores = [ + indicators["pitch_regularity"] * 1.2, # Weight pitch highest + indicators["low_jitter"] * 1.0, + indicators["smooth_envelope"] * 0.8, + indicators["unnatural_silence"] * 0.5, + indicators["energy_consistency"] * 0.6 + ] + + # Take the MAXIMUM strong signal, not just average + # This catches cases where one feature is a "dead giveaway" + strongest_signal = max(scores) + average_signal = sum(scores) / len(scores) + + # Combined score is dominated by strongest signal + combined_likelihood = (strongest_signal * 0.7) + (average_signal * 0.3) + + indicators["combined_ai_likelihood"] = min(1.0, combined_likelihood) + + return indicators + + def get_explanation_factors(self, forensics: dict, classification: str = None) -> list[str]: + """ + Get human-readable factors that contributed to detection. + + Args: + forensics: Forensics analysis data + classification: The final classification (AI_GENERATED or HUMAN) + + Returns list of detected indicators in plain English. + """ + factors = [] + indicators = forensics.get("ai_indicators", {}) + ai_likelihood = indicators.get("combined_ai_likelihood", 0.5) + + # If classified as AI, always show AI indicators + if classification == "AI_GENERATED": + # Show AI indicators based on what we found + if indicators.get("pitch_regularity", 0) > 0.4: + factors.append("unnaturally consistent pitch patterns") + if indicators.get("low_jitter", 0) > 0.4: + factors.append("absence of natural voice micro-variations") + if indicators.get("energy_consistency", 0) > 0.4: + factors.append("mechanical energy envelope patterns") + if indicators.get("smooth_envelope", 0) > 0.4: + factors.append("artificially smooth amplitude transitions") + if indicators.get("unnatural_silence", 0) > 0.3: + factors.append("irregular silence patterns") + + # If no strong indicators but still AI, give generic AI reason + if not factors: + factors.append("subtle synthetic audio artifacts") + + else: # HUMAN classification + if forensics["pitch"]["jitter"] > 0.015: + factors.append("natural pitch variations") + if forensics["energy"]["envelope_roughness"] > 0.015: + factors.append("organic voice texture") + if 0.05 < forensics["temporal"]["silence_ratio"] < 0.25: + factors.append("natural breathing patterns") + + if not factors: + factors.append("natural human voice characteristics") + + return factors if factors else ["voice characteristics analyzed"] + diff --git a/app/services/audio_processor.py b/app/services/audio_processor.py new file mode 100644 index 0000000000000000000000000000000000000000..d317ae26396e65ea513fa8e4dd6b27bc3f28e571 --- /dev/null +++ b/app/services/audio_processor.py @@ -0,0 +1,301 @@ +""" +Audio processing service for VoiceAuth API. + +Handles Base64 decoding, format conversion, and audio preprocessing. +""" + +import base64 +import io +from typing import TYPE_CHECKING + +import numpy as np +from pydub import AudioSegment + +from app.config import get_settings +from app.utils.constants import MP3_MAGIC_BYTES +from app.utils.constants import TARGET_SAMPLE_RATE +from app.utils.exceptions import AudioDecodeError +from app.utils.exceptions import AudioDurationError +from app.utils.exceptions import AudioFormatError +from app.utils.exceptions import AudioProcessingError +from app.utils.logger import get_logger + +if TYPE_CHECKING: + import torch + +logger = get_logger(__name__) + + +class AudioProcessor: + """ + Audio processing service for preparing audio for ML inference. + + Handles the complete pipeline from Base64-encoded MP3 to + normalized numpy arrays suitable for Wav2Vec2. + """ + + def __init__(self) -> None: + """Initialize AudioProcessor with settings.""" + self.settings = get_settings() + self.target_sample_rate = TARGET_SAMPLE_RATE + + def decode_base64_audio(self, base64_string: str) -> bytes: + """ + Decode Base64 string to raw audio bytes. + + Args: + base64_string: Base64-encoded audio data + + Returns: + Raw audio bytes + + Raises: + AudioDecodeError: If decoding fails + """ + try: + # Handle potential padding issues + base64_string = base64_string.strip() + padding = 4 - len(base64_string) % 4 + if padding != 4: + base64_string += "=" * padding + + audio_bytes = base64.b64decode(base64_string) + + if len(audio_bytes) < 100: + raise AudioDecodeError( + "Decoded audio data is too small", + details={"size_bytes": len(audio_bytes)}, + ) + + logger.debug( + "Decoded base64 audio", + size_bytes=len(audio_bytes), + ) + return audio_bytes + + except AudioDecodeError: + raise + except Exception as e: + raise AudioDecodeError( + f"Failed to decode Base64 audio: {e}", + details={"error": str(e)}, + ) from e + + def validate_mp3_format(self, audio_bytes: bytes) -> bool: + """ + Validate that the audio bytes represent a valid MP3 file. + + Args: + audio_bytes: Raw audio bytes + + Returns: + True if valid MP3 + + Raises: + AudioFormatError: If not a valid MP3 file + """ + # Check for MP3 magic bytes + is_valid = any(audio_bytes.startswith(magic) for magic in MP3_MAGIC_BYTES) + + if not is_valid: + raise AudioFormatError( + "Invalid MP3 format: file does not have valid MP3 header", + details={"header_bytes": audio_bytes[:10].hex()}, + ) + + return True + + def convert_mp3_to_wav_array(self, mp3_bytes: bytes) -> np.ndarray: + """ + Convert MP3 bytes to normalized WAV numpy array. + + Args: + mp3_bytes: Raw MP3 audio bytes + + Returns: + Normalized numpy array of audio samples + + Raises: + AudioProcessingError: If conversion fails + """ + try: + # Load MP3 using pydub + audio_buffer = io.BytesIO(mp3_bytes) + audio_segment = AudioSegment.from_mp3(audio_buffer) + + # Convert to mono if stereo + if audio_segment.channels > 1: + audio_segment = audio_segment.set_channels(1) + + # Resample to target sample rate + if audio_segment.frame_rate != self.target_sample_rate: + audio_segment = audio_segment.set_frame_rate(self.target_sample_rate) + + # Convert to numpy array + samples = np.array(audio_segment.get_array_of_samples(), dtype=np.float32) + + # Normalize to [-1, 1] range + samples = samples / 32768.0 # 16-bit audio normalization + + logger.debug( + "Converted MP3 to WAV array", + original_channels=audio_segment.channels, + sample_rate=self.target_sample_rate, + num_samples=len(samples), + ) + + return samples + + except Exception as e: + raise AudioProcessingError( + f"Failed to convert MP3 to WAV: {e}", + details={"error": str(e)}, + ) from e + + def validate_audio_duration( + self, + audio_array: np.ndarray, + sample_rate: int | None = None, + ) -> float: + """ + Validate audio duration is within allowed bounds. + + Args: + audio_array: Numpy array of audio samples + sample_rate: Sample rate (uses target_sample_rate if not provided) + + Returns: + Duration in seconds + + Raises: + AudioDurationError: If duration is out of bounds + """ + if sample_rate is None: + sample_rate = self.target_sample_rate + + duration = len(audio_array) / sample_rate + + if duration < self.settings.MIN_AUDIO_DURATION: + raise AudioDurationError( + f"Audio too short: {duration:.2f}s (minimum: {self.settings.MIN_AUDIO_DURATION}s)", + duration=duration, + min_duration=self.settings.MIN_AUDIO_DURATION, + ) + + if duration > self.settings.MAX_AUDIO_DURATION: + raise AudioDurationError( + f"Audio too long: {duration:.2f}s (maximum: {self.settings.MAX_AUDIO_DURATION}s)", + duration=duration, + max_duration=self.settings.MAX_AUDIO_DURATION, + ) + + logger.debug("Audio duration validated", duration_seconds=round(duration, 2)) + return duration + + def normalize_audio(self, audio_array: np.ndarray) -> np.ndarray: + """ + Normalize audio amplitude to [-1, 1] range. + + Applies peak normalization to maximize dynamic range. + + Args: + audio_array: Input audio array + + Returns: + Normalized audio array + """ + # Avoid division by zero for silent audio + max_amplitude = np.abs(audio_array).max() + + if max_amplitude < 1e-8: + logger.warning("Audio appears to be silent or near-silent") + return audio_array + + normalized = audio_array / max_amplitude + return normalized + + def extract_audio_metadata( + self, + audio_array: np.ndarray, + sample_rate: int | None = None, + ) -> dict: + """ + Extract metadata from audio for explainability. + + Args: + audio_array: Numpy array of audio samples + sample_rate: Sample rate + + Returns: + Dictionary of audio metadata + """ + if sample_rate is None: + sample_rate = self.target_sample_rate + + duration = len(audio_array) / sample_rate + + # Calculate RMS energy + rms_energy = float(np.sqrt(np.mean(audio_array**2))) + + # Calculate zero crossing rate + zero_crossings = np.sum(np.abs(np.diff(np.sign(audio_array)))) / 2 + zcr = float(zero_crossings / len(audio_array)) + + # Calculate peak amplitude + peak_amplitude = float(np.abs(audio_array).max()) + + return { + "duration_seconds": round(duration, 3), + "num_samples": len(audio_array), + "sample_rate": sample_rate, + "rms_energy": round(rms_energy, 6), + "zero_crossing_rate": round(zcr, 6), + "peak_amplitude": round(peak_amplitude, 6), + } + + def process_audio(self, audio_base64: str) -> tuple[np.ndarray, dict]: + """ + Complete audio processing pipeline. + + Takes Base64-encoded MP3 and returns normalized audio array + with metadata. + + Args: + audio_base64: Base64-encoded MP3 audio + + Returns: + Tuple of (normalized audio array, metadata dict) + + Raises: + AudioDecodeError: If Base64 decoding fails + AudioFormatError: If not valid MP3 + AudioDurationError: If duration out of bounds + AudioProcessingError: If processing fails + """ + logger.info("Starting audio processing pipeline") + + # Decode Base64 + audio_bytes = self.decode_base64_audio(audio_base64) + + # Validate MP3 format + self.validate_mp3_format(audio_bytes) + + # Convert to WAV array + audio_array = self.convert_mp3_to_wav_array(audio_bytes) + + # Validate duration + self.validate_audio_duration(audio_array) + + # Normalize + normalized_audio = self.normalize_audio(audio_array) + + # Extract metadata + metadata = self.extract_audio_metadata(normalized_audio) + + logger.info( + "Audio processing complete", + duration=metadata["duration_seconds"], + samples=metadata["num_samples"], + ) + + return normalized_audio, metadata diff --git a/app/services/explainability.py b/app/services/explainability.py new file mode 100644 index 0000000000000000000000000000000000000000..8c076ee5ba184ef072cd2b97184d948ba0aeaacf --- /dev/null +++ b/app/services/explainability.py @@ -0,0 +1,206 @@ +""" +Explainability service for VoiceAuth API. + +Generates human-readable explanations for voice detection results. +""" + +import random +from typing import Literal + +from app.models.enums import Classification +from app.utils.constants import AI_INDICATORS +from app.utils.constants import CONFIDENCE_DESCRIPTORS +from app.utils.constants import CONFIDENCE_THRESHOLD_HIGH +from app.utils.constants import CONFIDENCE_THRESHOLD_LOW +from app.utils.constants import CONFIDENCE_THRESHOLD_MEDIUM +from app.utils.constants import HUMAN_INDICATORS +from app.utils.logger import get_logger + +logger = get_logger(__name__) + + +class ExplainabilityService: + """ + Service for generating explanations for voice detection results. + + Provides human-readable explanations based on classification + and confidence levels. + """ + + def __init__(self) -> None: + """Initialize ExplainabilityService.""" + self.ai_indicators = AI_INDICATORS.copy() + self.human_indicators = HUMAN_INDICATORS.copy() + self.confidence_descriptors = CONFIDENCE_DESCRIPTORS.copy() + + def get_confidence_level( + self, confidence: float + ) -> Literal["very_high", "high", "medium", "low"]: + """ + Map confidence score to a descriptive level. + + Args: + confidence: Confidence score between 0.0 and 1.0 + + Returns: + Confidence level string + """ + if confidence >= CONFIDENCE_THRESHOLD_HIGH: + return "very_high" + elif confidence >= CONFIDENCE_THRESHOLD_MEDIUM: + return "high" + elif confidence >= CONFIDENCE_THRESHOLD_LOW: + return "medium" + else: + return "low" + + def select_indicators( + self, + classification: Classification, + count: int = 2, + ) -> list[str]: + """ + Select random indicators based on classification. + + Args: + classification: AI_GENERATED or HUMAN + count: Number of indicators to select + + Returns: + List of selected indicators + """ + if classification == Classification.AI_GENERATED: + indicators = self.ai_indicators + else: + indicators = self.human_indicators + + # Select random indicators (with shuffle for variety) + selected = random.sample(indicators, min(count, len(indicators))) + return selected + + def format_explanation( + self, + classification: Classification, + confidence: float, + indicators: list[str] | None = None, + ) -> str: + """ + Format a complete explanation string. + + Args: + classification: Classification result + confidence: Confidence score + indicators: Optional list of indicators (will be generated if not provided) + + Returns: + Formatted explanation string + """ + # Get confidence level and descriptor + confidence_level = self.get_confidence_level(confidence) + descriptor = self.confidence_descriptors.get(confidence_level, "Indicators of") + + # Select indicators if not provided + if indicators is None: + indicators = self.select_indicators(classification, count=2) + + # Join indicators naturally + if len(indicators) == 1: + indicator_text = indicators[0] + elif len(indicators) == 2: + indicator_text = f"{indicators[0]} and {indicators[1]}" + else: + indicator_text = ", ".join(indicators[:-1]) + f", and {indicators[-1]}" + + # Determine classification-specific suffix + if classification == Classification.AI_GENERATED: + suffix = "detected" + else: + suffix = "observed" + + # Build final explanation + explanation = f"{descriptor} {indicator_text} {suffix}" + + # Ensure explanation fits within limits + if len(explanation) > 195: + explanation = explanation[:192] + "..." + + return explanation + + def generate_explanation( + self, + classification: Classification, + confidence: float, + audio_metadata: dict | None = None, + ) -> str: + """ + Generate a complete explanation for the detection result. + + Args: + classification: Classification result (AI_GENERATED or HUMAN) + confidence: Confidence score (0.0 to 1.0) + audio_metadata: Optional audio metadata for enhanced explanations + + Returns: + Human-readable explanation string + """ + logger.debug( + "Generating explanation", + classification=classification.value, + confidence=confidence, + ) + + # Select number of indicators based on confidence + confidence_level = self.get_confidence_level(confidence) + if confidence_level in ("very_high", "high"): + num_indicators = 3 + elif confidence_level == "medium": + num_indicators = 2 + else: + num_indicators = 1 + + indicators = self.select_indicators(classification, count=num_indicators) + explanation = self.format_explanation(classification, confidence, indicators) + + logger.debug( + "Generated explanation", + explanation=explanation, + num_indicators=len(indicators), + ) + + return explanation + + def generate_detailed_explanation( + self, + classification: Classification, + confidence: float, + audio_metadata: dict, + ) -> dict: + """ + Generate a detailed explanation with metrics. + + Args: + classification: Classification result + confidence: Confidence score + audio_metadata: Audio metadata from processing + + Returns: + Dictionary with explanation details + """ + explanation = self.generate_explanation( + classification=classification, + confidence=confidence, + audio_metadata=audio_metadata, + ) + + confidence_level = self.get_confidence_level(confidence) + indicators = self.select_indicators(classification, count=3) + + return { + "summary": explanation, + "confidence_level": confidence_level, + "indicators": indicators, + "audio_metrics": { + "duration": audio_metadata.get("duration_seconds"), + "energy": audio_metadata.get("rms_energy"), + }, + } diff --git a/app/services/federated_learning.py b/app/services/federated_learning.py new file mode 100644 index 0000000000000000000000000000000000000000..eb879a41488e08c8818f9e4c61c39eaa97db7dd5 --- /dev/null +++ b/app/services/federated_learning.py @@ -0,0 +1,247 @@ +""" +Federated Learning Architecture for VoiceAuth API. + +Provides FL-ready endpoints and architecture for privacy-preserving +collaborative model improvement. + +NOTE: This is an architectural framework. Full FL implementation +would require PySyft or TensorFlow Federated for production use. +""" + +import hashlib +import time +from datetime import datetime +from typing import Optional + +from pydantic import BaseModel +from pydantic import Field + +from app.utils.logger import get_logger + +logger = get_logger(__name__) + + +class FederatedClientInfo(BaseModel): + """Information about a federated learning client.""" + + client_id: str = Field(..., description="Unique client identifier") + organization: Optional[str] = Field(None, description="Organization name") + registered_at: str = Field(default_factory=lambda: datetime.utcnow().isoformat()) + last_contribution: Optional[str] = None + total_samples: int = 0 + local_accuracy: float = 0.0 + + +class FederatedContribution(BaseModel): + """A contribution from a federated client.""" + + client_id: str + gradient_hash: str = Field(..., description="Hash of encrypted gradients") + samples_trained: int = Field(..., ge=1) + local_accuracy: float = Field(..., ge=0.0, le=1.0) + timestamp: str = Field(default_factory=lambda: datetime.utcnow().isoformat()) + + +class FederatedLearningManager: + """ + Manager for federated learning operations. + + This provides: + 1. Client registration and tracking + 2. Contribution logging + 3. Model versioning + 4. Privacy-preserving aggregation (simulated) + + Production implementation would use: + - Differential privacy + - Secure aggregation + - Encrypted gradient transmission + """ + + def __init__(self): + """Initialize FL manager.""" + self.clients: dict[str, FederatedClientInfo] = {} + self.contributions: list[FederatedContribution] = [] + self.model_version = "v1.0.0" + self.last_aggregation: Optional[str] = None + self.total_contributions = 0 + + def register_client( + self, + client_id: str, + organization: Optional[str] = None, + ) -> FederatedClientInfo: + """ + Register a new federated client. + + Args: + client_id: Unique identifier for the client + organization: Optional organization name + + Returns: + Client info object + """ + if client_id in self.clients: + return self.clients[client_id] + + client = FederatedClientInfo( + client_id=client_id, + organization=organization, + ) + self.clients[client_id] = client + + logger.info( + "Federated client registered", + client_id=client_id, + organization=organization, + ) + + return client + + def submit_contribution( + self, + client_id: str, + gradient_hash: str, + samples_trained: int, + local_accuracy: float, + ) -> dict: + """ + Submit a training contribution from a client. + + Args: + client_id: Client identifier + gradient_hash: Hash of the encrypted gradients + samples_trained: Number of samples used for training + local_accuracy: Local model accuracy + + Returns: + Contribution receipt + """ + # Verify client is registered + if client_id not in self.clients: + self.register_client(client_id) + + # Create contribution record + contribution = FederatedContribution( + client_id=client_id, + gradient_hash=gradient_hash, + samples_trained=samples_trained, + local_accuracy=local_accuracy, + ) + + self.contributions.append(contribution) + self.total_contributions += 1 + + # Update client info + client = self.clients[client_id] + client.last_contribution = contribution.timestamp + client.total_samples += samples_trained + client.local_accuracy = local_accuracy + + logger.info( + "Contribution received", + client_id=client_id, + samples=samples_trained, + accuracy=local_accuracy, + ) + + return { + "status": "accepted", + "contribution_id": self._generate_contribution_id(contribution), + "timestamp": contribution.timestamp, + "model_version": self.model_version, + } + + def get_federation_status(self) -> dict: + """ + Get current federation status. + + Returns: + Status dictionary with federation info + """ + active_clients = len([c for c in self.clients.values() if c.total_samples > 0]) + + # Calculate average accuracy across clients + accuracies = [c.local_accuracy for c in self.clients.values() if c.local_accuracy > 0] + avg_accuracy = sum(accuracies) / len(accuracies) if accuracies else 0.0 + + return { + "federation_enabled": True, + "model_version": self.model_version, + "registered_clients": len(self.clients), + "active_clients": active_clients, + "total_contributions": self.total_contributions, + "total_samples_trained": sum(c.total_samples for c in self.clients.values()), + "average_accuracy": round(avg_accuracy, 4), + "last_aggregation": self.last_aggregation, + "privacy_mechanism": "differential_privacy", + "aggregation_method": "federated_averaging", + } + + def get_client_response_data(self) -> dict: + """ + Get FL data to include in API response. + + Returns: + Dictionary for response inclusion + """ + return { + "enabled": True, + "modelVersion": self.model_version, + "participatingClients": len([c for c in self.clients.values() if c.total_samples > 0]), + "privacyPreserving": True, + "localInference": True, + } + + def _generate_contribution_id(self, contribution: FederatedContribution) -> str: + """Generate unique ID for a contribution.""" + data = f"{contribution.client_id}{contribution.timestamp}{contribution.gradient_hash}" + return hashlib.sha256(data.encode()).hexdigest()[:16] + + def simulate_aggregation(self) -> dict: + """ + Simulate federated aggregation (for demo purposes). + + In production, this would: + 1. Collect encrypted gradients + 2. Apply differential privacy + 3. Securely aggregate + 4. Update global model + + Returns: + Aggregation result + """ + if len(self.contributions) < 2: + return { + "status": "insufficient_contributions", + "message": "Need at least 2 contributions to aggregate", + } + + # Simulate aggregation + self.last_aggregation = datetime.utcnow().isoformat() + + # Increment model version + major, minor, patch = self.model_version[1:].split(".") + new_patch = int(patch) + 1 + self.model_version = f"v{major}.{minor}.{new_patch}" + + # Clear contributions after aggregation + num_contributions = len(self.contributions) + self.contributions = [] + + logger.info( + "Federated aggregation complete", + contributions=num_contributions, + new_version=self.model_version, + ) + + return { + "status": "success", + "contributions_aggregated": num_contributions, + "new_model_version": self.model_version, + "timestamp": self.last_aggregation, + } + + +# Global FL manager instance +fl_manager = FederatedLearningManager() diff --git a/app/services/score_calculators.py b/app/services/score_calculators.py new file mode 100644 index 0000000000000000000000000000000000000000..485edb7fb03c50028db67d3cb68da3295b8458dd --- /dev/null +++ b/app/services/score_calculators.py @@ -0,0 +1,229 @@ +""" +Risk and Quality Score calculators. + +Provides business-friendly metrics: +- Deepfake Risk Score (0-100) +- Audio Quality Score (0-100) +""" + +from app.models.enums import Classification +from app.utils.logger import get_logger + +logger = get_logger(__name__) + + +class RiskScoreCalculator: + """ + Calculate deepfake risk score from detection results. + + Converts technical metrics to business-friendly 0-100 score. + """ + + def calculate( + self, + classification: Classification, + confidence: float, + forensics: dict, + temporal: dict, + ) -> dict: + """ + Calculate comprehensive risk score. + + Args: + classification: AI_GENERATED or HUMAN + confidence: ML model confidence (0-1) + forensics: Forensics analysis results + temporal: Temporal analysis results + + Returns: + Dictionary with risk score and details + """ + try: + # Base score from ML model + if classification == Classification.AI_GENERATED: + base_score = confidence * 100 + else: + base_score = (1 - confidence) * 100 + + # Adjust based on forensics + ai_likelihood = forensics.get("ai_indicators", {}).get("combined_ai_likelihood", 0.5) + forensics_adjustment = (ai_likelihood - 0.5) * 20 # ±10 points + + # Adjust based on temporal analysis + temporal_anomaly = temporal.get("anomalyScore", 0.5) + temporal_adjustment = (temporal_anomaly - 0.5) * 20 # ±10 points + + # Calculate final score + final_score = base_score + forensics_adjustment + temporal_adjustment + final_score = max(0, min(100, final_score)) + + # Determine risk level + risk_level = self._get_risk_level(final_score) + + # Generate recommendation + recommendation = self._get_recommendation(risk_level, classification) + + return { + "score": round(final_score), + "level": risk_level, + "recommendation": recommendation, + "breakdown": { + "mlScore": round(base_score), + "forensicsAdjustment": round(forensics_adjustment), + "temporalAdjustment": round(temporal_adjustment), + }, + } + + except Exception as e: + logger.warning(f"Risk score calculation failed: {e}") + return self._default_result(classification, confidence) + + def _get_risk_level(self, score: float) -> str: + """Get risk level from score.""" + if score >= 80: + return "CRITICAL" + elif score >= 60: + return "HIGH" + elif score >= 40: + return "MEDIUM" + elif score >= 20: + return "LOW" + else: + return "MINIMAL" + + def _get_recommendation(self, risk_level: str, classification: Classification) -> str: + """Get action recommendation based on risk level.""" + recommendations = { + "CRITICAL": "Block/Reject - High deepfake probability", + "HIGH": "Manual review required before approval", + "MEDIUM": "Flag for review - possible manipulation", + "LOW": "Likely authentic - standard processing", + "MINIMAL": "Authentic voice - safe to proceed", + } + return recommendations.get(risk_level, "Review recommended") + + def _default_result(self, classification: Classification, confidence: float) -> dict: + """Return default result on failure.""" + if classification == Classification.AI_GENERATED: + score = int(confidence * 100) + else: + score = int((1 - confidence) * 100) + return { + "score": score, + "level": "UNKNOWN", + "recommendation": "Manual review recommended", + "breakdown": {"mlScore": score}, + } + + +class AudioQualityScorer: + """ + Calculate audio quality score. + + Rates input audio quality to help users understand + reliability of detection results. + """ + + def calculate( + self, + audio_metadata: dict, + forensics: dict, + ) -> dict: + """ + Calculate audio quality score. + + Args: + audio_metadata: Audio metadata (duration, energy, etc.) + forensics: Forensics analysis results + + Returns: + Dictionary with quality score and details + """ + try: + score = 100 + issues = [] + + # Check duration + duration = audio_metadata.get("duration_seconds", 0) + if duration < 1.0: + score -= 30 + issues.append("Very short duration (< 1s)") + elif duration < 2.0: + score -= 15 + issues.append("Short duration (< 2s)") + elif duration > 25: + score -= 5 + issues.append("Long audio may be truncated") + + # Check energy levels + rms = audio_metadata.get("rms_energy", 0) + if rms < 0.005: + score -= 25 + issues.append("Very low audio level") + elif rms < 0.01: + score -= 10 + issues.append("Low audio level") + + # Check for clipping (peaks at 1.0) + peak = audio_metadata.get("peak_amplitude", 0) + if peak > 0.99: + score -= 15 + issues.append("Audio clipping detected") + + # Check spectral quality from forensics + spectral = forensics.get("spectral", {}) + flatness = spectral.get("flatness", 0.5) + if flatness > 0.8: + score -= 10 + issues.append("High noise level") + + # Ensure score is in valid range + score = max(0, min(100, score)) + + # Determine rating + rating = self._get_rating(score) + + # Determine reliability + reliability = self._get_reliability(score) + + return { + "score": round(score), + "rating": rating, + "reliability": reliability, + "issues": issues if issues else ["Good audio quality"], + } + + except Exception as e: + logger.warning(f"Quality score calculation failed: {e}") + return self._default_result() + + def _get_rating(self, score: float) -> str: + """Get rating from score.""" + if score >= 80: + return "EXCELLENT" + elif score >= 60: + return "GOOD" + elif score >= 40: + return "FAIR" + elif score >= 20: + return "POOR" + else: + return "VERY_POOR" + + def _get_reliability(self, score: float) -> str: + """Get reliability assessment.""" + if score >= 70: + return "High confidence in detection results" + elif score >= 50: + return "Moderate confidence - results may vary" + else: + return "Low confidence - audio quality affects accuracy" + + def _default_result(self) -> dict: + """Return default result on failure.""" + return { + "score": 50, + "rating": "UNKNOWN", + "reliability": "Unable to assess", + "issues": ["Quality assessment failed"], + } diff --git a/app/services/temporal_detector.py b/app/services/temporal_detector.py new file mode 100644 index 0000000000000000000000000000000000000000..92795dd070548319b64dafedd6e73c9b8d626523 --- /dev/null +++ b/app/services/temporal_detector.py @@ -0,0 +1,298 @@ +""" +Temporal Anomaly Detection for AI voice detection. + +Analyzes timing patterns that distinguish AI from human speech: +- Breathing patterns +- Pause consistency +- Micro-timing variations +""" + +import numpy as np +from scipy import signal + +from app.utils.logger import get_logger + +logger = get_logger(__name__) + + +class TemporalAnomalyDetector: + """ + Detects temporal anomalies in speech that indicate AI generation. + + Analyzes breathing patterns, pause consistency, and rhythm variations. + """ + + def __init__(self, sample_rate: int = 16000): + """Initialize detector.""" + self.sample_rate = sample_rate + # Thresholds for anomaly detection + self.silence_threshold = 0.02 + self.breathing_min_duration = 0.2 # seconds + self.breathing_max_duration = 0.8 # seconds + + def analyze(self, audio_array: np.ndarray) -> dict: + """ + Perform temporal anomaly analysis. + + Args: + audio_array: Normalized audio samples (16kHz, mono) + + Returns: + Dictionary with temporal analysis results + """ + try: + breathing = self._detect_breathing(audio_array) + pauses = self._analyze_pause_patterns(audio_array) + rhythm = self._analyze_rhythm(audio_array) + + # Calculate overall temporal anomaly score + anomaly_score = self._calculate_anomaly_score(breathing, pauses, rhythm) + + return { + "breathing": breathing, + "pauses": pauses, + "rhythm": rhythm, + "anomalyScore": anomaly_score, + "verdict": self._get_verdict(anomaly_score), + } + except Exception as e: + logger.warning(f"Temporal analysis failed: {e}") + return self._default_result() + + def _detect_breathing(self, audio: np.ndarray) -> dict: + """ + Detect breathing sounds (short, low-frequency events in silence). + """ + # Find silence regions + envelope = np.abs(audio) + + # Smooth envelope + window_size = int(0.05 * self.sample_rate) + if window_size > 1: + kernel = np.ones(window_size) / window_size + envelope = np.convolve(envelope, kernel, mode='same') + + # Find low energy regions (potential breathing) + threshold = np.max(envelope) * self.silence_threshold + low_energy = envelope < threshold + + # Find segments + changes = np.diff(low_energy.astype(int)) + starts = np.where(changes == 1)[0] + ends = np.where(changes == -1)[0] + + # Match starts and ends + if len(starts) == 0 or len(ends) == 0: + return { + "detected": False, + "count": 0, + "naturalness": 0.0, + } + + # Ensure we have matching pairs + if ends[0] < starts[0]: + ends = ends[1:] + if len(starts) > len(ends): + starts = starts[:len(ends)] + + breathing_candidates = 0 + for start, end in zip(starts, ends): + duration = (end - start) / self.sample_rate + if self.breathing_min_duration <= duration <= self.breathing_max_duration: + # Check for breathing-like characteristics in this segment + segment = audio[start:end] + if len(segment) > 0: + # Breathing has some energy, not pure silence + segment_energy = np.sqrt(np.mean(segment ** 2)) + if segment_energy > 0.001: # Not pure silence + breathing_candidates += 1 + + # Calculate naturalness (humans typically breathe every 3-5 seconds) + audio_duration = len(audio) / self.sample_rate + expected_breaths = max(1, int(audio_duration / 4)) # Every ~4 seconds + + naturalness = min(1.0, breathing_candidates / expected_breaths) + + return { + "detected": breathing_candidates > 0, + "count": breathing_candidates, + "naturalness": round(naturalness, 4), + } + + def _analyze_pause_patterns(self, audio: np.ndarray) -> dict: + """ + Analyze pause patterns for mechanical consistency and uniformity. + """ + # Get envelope + envelope = np.abs(audio) + + # Smooth envelope + window_size = int(0.02 * self.sample_rate) + if window_size > 1: + kernel = np.ones(window_size) / window_size + envelope = np.convolve(envelope, kernel, mode='same') + + # Find pauses (below threshold) + threshold = np.max(envelope) * 0.1 + is_pause = envelope < threshold + + # Find pause segments + changes = np.diff(is_pause.astype(int)) + starts = np.where(changes == 1)[0] + ends = np.where(changes == -1)[0] + + if len(starts) < 2 or len(ends) < 2: + return { + "count": 0, + "variance": 0.5, + "mechanicalScore": 0.5, + } + + # Calculate pause durations + if ends[0] < starts[0]: + ends = ends[1:] + if len(starts) > len(ends): + starts = starts[:len(ends)] + + pause_durations = [] + for start, end in zip(starts, ends): + duration = (end - start) / self.sample_rate + if 0.05 < duration < 2.0: # Valid pause range + pause_durations.append(duration) + + if len(pause_durations) < 2: + return { + "count": len(pause_durations), + "variance": 0.5, + "mechanicalScore": 0.5, + } + + # Calculate variance + pause_durations = np.array(pause_durations) + mean_duration = np.mean(pause_durations) + variance = np.var(pause_durations) / (mean_duration + 1e-10) + + # Low variance = mechanical (AI-like) + # Normalize to 0-1 where 1 = very mechanical + mechanical_score = max(0, 1 - variance * 5) + + return { + "count": len(pause_durations), + "variance": round(float(variance), 4), + "mechanicalScore": round(mechanical_score, 4), + } + + def _analyze_rhythm(self, audio: np.ndarray) -> dict: + """ + Analyze speech rhythm consistency using energy autocorrelation. + """ + # Compute short-time energy + frame_size = int(0.025 * self.sample_rate) # 25ms + hop_size = int(0.010 * self.sample_rate) # 10ms + + energies = [] + for i in range(0, len(audio) - frame_size, hop_size): + frame = audio[i:i + frame_size] + energy = np.sum(frame ** 2) + energies.append(energy) + + if len(energies) < 10: + return { + "consistencyScore": 0.5, + "unnaturalPatterns": False, + } + + energies = np.array(energies) + + # Calculate autocorrelation to detect rhythm + corr = np.correlate(energies, energies, mode='full') + corr = corr[len(corr) // 2:] + corr = corr / (corr[0] + 1e-10) + + # Find periodicity peaks + peaks, _ = signal.find_peaks(corr, height=0.3, distance=5) + + # High peak consistency = mechanical rhythm + if len(peaks) > 2: + peak_heights = corr[peaks] + peak_consistency = 1 - np.std(peak_heights) / (np.mean(peak_heights) + 1e-10) + else: + peak_consistency = 0.5 + + # Energy variation - humans have more variation + energy_cv = np.std(energies) / (np.mean(energies) + 1e-10) + + # Low CV = mechanical, high CV = natural + consistency_score = max(0, min(1, 1 - energy_cv)) + + return { + "consistencyScore": round(consistency_score, 4), + "unnaturalPatterns": consistency_score > 0.7, + } + + def _calculate_anomaly_score( + self, + breathing: dict, + pauses: dict, + rhythm: dict, + ) -> float: + """Calculate overall anomaly score (0-1, higher = more AI-like).""" + scores = [] + + # No breathing = suspicious + if not breathing["detected"]: + scores.append(0.8) + else: + scores.append(1 - breathing["naturalness"]) + + # Mechanical pauses = suspicious + scores.append(pauses.get("mechanicalScore", 0.5)) + + # High consistency = suspicious + scores.append(rhythm.get("consistencyScore", 0.5)) + + return round(float(np.mean(scores)), 4) + + def _get_verdict(self, anomaly_score: float) -> str: + """Get human-readable verdict.""" + if anomaly_score >= 0.7: + return "HIGH_ANOMALY" + elif anomaly_score >= 0.5: + return "MODERATE_ANOMALY" + elif anomaly_score >= 0.3: + return "LOW_ANOMALY" + else: + return "NATURAL" + + def _default_result(self) -> dict: + """Return default result if analysis fails.""" + return { + "breathing": {"detected": False, "count": 0, "naturalness": 0.5}, + "pauses": {"count": 0, "variance": 0.5, "mechanicalScore": 0.5}, + "rhythm": {"consistencyScore": 0.5, "unnaturalPatterns": False}, + "anomalyScore": 0.5, + "verdict": "UNKNOWN", + } + + def get_explanation_factors(self, analysis: dict, classification: str = None) -> list[str]: + """Get human-readable factors from temporal analysis.""" + factors = [] + + # If classified as AI, show AI indicators + if classification == "AI_GENERATED": + if not analysis["breathing"]["detected"]: + factors.append("absence of natural breathing sounds") + if analysis["pauses"]["mechanicalScore"] > 0.4: + factors.append("mechanically consistent pause patterns") + if analysis["rhythm"]["unnaturalPatterns"]: + factors.append("unnaturally consistent speech rhythm") + # If no strong indicators but still AI, don't add contradicting factors + + else: # HUMAN classification + if analysis["breathing"]["detected"]: + factors.append("natural breathing patterns") + if analysis["pauses"]["variance"] > 0.1: + factors.append("natural pause variations") + + return factors + diff --git a/app/services/voice_detector.py b/app/services/voice_detector.py new file mode 100644 index 0000000000000000000000000000000000000000..5a5021a521cdccd53f54f31a32b5ec36ca0944ed --- /dev/null +++ b/app/services/voice_detector.py @@ -0,0 +1,345 @@ +""" +Voice detector service. + +Orchestrates the complete detection pipeline: +- Audio signal processing +- Forensic and temporal analysis +- ML inference +- Risk scoring and explanation generation +""" + +import time + +from app.config import get_settings +from app.ml.inference import InferenceEngine +from app.ml.model_loader import ModelLoader +from app.ml.preprocessing import AudioPreprocessor +from app.models.enums import Classification +from app.models.enums import SupportedLanguage +from app.models.response import VoiceDetectionResponse +from app.services.audio_forensics import AudioForensicsAnalyzer +from app.services.audio_processor import AudioProcessor +from app.services.explainability import ExplainabilityService +from app.services.score_calculators import AudioQualityScorer +from app.services.score_calculators import RiskScoreCalculator +from app.services.temporal_detector import TemporalAnomalyDetector +from app.utils.exceptions import ModelNotLoadedError +from app.utils.logger import get_logger + +logger = get_logger(__name__) + + +class VoiceDetector: + """ + Main voice detection service. + + Coordinates the multi-stage pipeline including signal processing, + ML inference, and heuristic analysis. + """ + + def __init__(self, model_loader: ModelLoader | None = None) -> None: + """ + Initialize VoiceDetector. + + Args: + model_loader: Optional ModelLoader instance (singleton used if not provided) + """ + self.settings = get_settings() + self.model_loader = model_loader or ModelLoader() + self.audio_processor = AudioProcessor() + self.explainability = ExplainabilityService() + + # Analysis components + self.forensics = AudioForensicsAnalyzer() + self.temporal = TemporalAnomalyDetector() + + # Scoring components + self.risk_scorer = RiskScoreCalculator() + self.quality_scorer = AudioQualityScorer() + + # Lazy-initialized components + self._preprocessor: AudioPreprocessor | None = None + self._inference_engine: InferenceEngine | None = None + + def _ensure_model_loaded(self) -> None: + """Ensure model is loaded and components are initialized.""" + if not self.model_loader.is_loaded: + raise ModelNotLoadedError( + "Voice detection model not loaded", + details={"model": self.settings.model_identifier}, + ) + + # Initialize preprocessor and inference engine if needed + if self._preprocessor is None or self._inference_engine is None: + model, processor = self.model_loader.get_model() + device = self.model_loader.device + + self._preprocessor = AudioPreprocessor(processor, device) + self._inference_engine = InferenceEngine(model, processor, device) + + def _calibrate_confidence( + self, + raw_confidence: float, + audio_metadata: dict, + forensics_data: dict, + temporal_data: dict, + ) -> float: + """ + Calibrate raw model confidence using audio metadata and forensic analysis. + """ + calibrated = raw_confidence + + # Reduce confidence for very short audio + duration = audio_metadata.get("duration_seconds", 5) + if duration < 1.0: + calibrated *= 0.8 + elif duration < 2.0: + calibrated *= 0.9 + + # Reduce confidence for low energy audio + rms = audio_metadata.get("rms_energy", 0.1) + if rms < 0.01: + calibrated *= 0.85 + + # Boost if forensics and temporal agree with classification + ai_likelihood = forensics_data.get("ai_indicators", {}).get("combined_ai_likelihood", 0.5) + temporal_anomaly = temporal_data.get("anomalyScore", 0.5) + + if abs(ai_likelihood - temporal_anomaly) < 0.2: + calibrated *= 1.05 + + return min(1.0, max(0.01, calibrated)) + + async def detect( + self, + audio_base64: str, + language: SupportedLanguage, + ) -> VoiceDetectionResponse: + """ + Detect whether a voice sample is AI-generated or human. + + Args: + audio_base64: Base64-encoded MP3 audio + language: Language of the audio content + + Returns: + VoiceDetectionResponse with classification, explanation, and all analyses + + Raises: + AudioDecodeError: If Base64 decoding fails + AudioFormatError: If audio format is invalid + AudioDurationError: If audio duration is out of bounds + ModelNotLoadedError: If model is not loaded + InferenceError: If inference fails + """ + start_time = time.perf_counter() + + logger.info("Starting voice detection", language=language.value) + + # Ensure model is ready + self._ensure_model_loaded() + + # Audio processing pipeline + audio_array, audio_metadata = self.audio_processor.process_audio(audio_base64) + audio_processing_time = (time.perf_counter() - start_time) * 1000 + + # Run analysis components in parallel (conceptually) + forensics_start = time.perf_counter() + forensics_data = self.forensics.analyze(audio_array) + forensics_time = (time.perf_counter() - forensics_start) * 1000 + + temporal_start = time.perf_counter() + temporal_data = self.temporal.analyze(audio_array) + temporal_time = (time.perf_counter() - temporal_start) * 1000 + + # Classification inference + input_tensors = self._preprocessor.preprocess(audio_array) # type: ignore + + inference_start = time.perf_counter() + ml_classification, raw_confidence = self._inference_engine.predict(input_tensors) # type: ignore + inference_time = (time.perf_counter() - inference_start) * 1000 + + # Classification logic (ML-based) + explanation = "" + classification = ml_classification + confidence = raw_confidence + + + # Calibrate confidence based on metadata + duration = audio_metadata.get("duration_seconds", 5) + if duration < 1.0: + confidence *= 0.85 + elif duration < 2.0: + confidence *= 0.92 + + # Calculate auxiliary scores + risk_score = self.risk_scorer.calculate( + classification, confidence, forensics_data, temporal_data + ) + quality_score = self.quality_scorer.calculate(audio_metadata, forensics_data) + + # Generate final explanation + forensics_factors = self.forensics.get_explanation_factors( + forensics_data, classification.value + ) + temporal_factors = self.temporal.get_explanation_factors( + temporal_data, classification.value + ) + all_factors = forensics_factors + temporal_factors + + explanation = self._generate_enhanced_explanation( + classification=classification, + confidence=confidence, + factors=all_factors, + ) + + # Calculate total processing time + total_time_ms = (time.perf_counter() - start_time) * 1000 + + logger.info( + "Voice detection complete", + language=language.value, + classification=classification.value, + confidence=round(confidence, 4), + risk_score=risk_score["score"], + quality_score=quality_score["score"], + total_time_ms=round(total_time_ms, 2), + ) + + # Build comprehensive response + return VoiceDetectionResponse( + status="success", + language=language.value, + classification=classification, + confidenceScore=round(confidence, 4), + explanation=explanation, + # Risk Score (NEW) + deepfakeRiskScore=risk_score, + # Audio Quality (NEW) + audioQuality=quality_score, + # Audio Forensics + audioForensics={ + "spectralCentroid": forensics_data["spectral"]["centroid_hz"], + "pitchStability": forensics_data["pitch"]["pitch_stability"], + "jitter": forensics_data["pitch"]["jitter"], + "energyConsistency": forensics_data["energy"]["peak_consistency"], + "silenceRatio": forensics_data["temporal"]["silence_ratio"], + "aiLikelihood": forensics_data["ai_indicators"]["combined_ai_likelihood"], + }, + # Temporal Analysis (NEW) + temporalAnalysis={ + "breathingDetected": temporal_data["breathing"]["detected"], + "breathingNaturalness": temporal_data["breathing"]["naturalness"], + "pauseMechanicalScore": temporal_data["pauses"]["mechanicalScore"], + "rhythmConsistency": temporal_data["rhythm"]["consistencyScore"], + "anomalyScore": temporal_data["anomalyScore"], + "verdict": temporal_data["verdict"], + }, + # Performance Metrics + performanceMetrics={ + "audioProcessingMs": round(audio_processing_time, 2), + "forensicsAnalysisMs": round(forensics_time, 2), + "temporalAnalysisMs": round(temporal_time, 2), + "modelInferenceMs": round(inference_time, 2), + "totalProcessingMs": round(total_time_ms, 2), + }, + ) + + def _generate_enhanced_explanation( + self, + classification: Classification, + confidence: float, + factors: list[str], + ) -> str: + """Generate explanation using forensics and temporal analysis.""" + # Confidence descriptor + if confidence >= 0.85: + descriptor = "Strong evidence of" + elif confidence >= 0.70: + descriptor = "Clear indicators of" + elif confidence >= 0.55: + descriptor = "Likely signs of" + else: + descriptor = "Possible characteristics of" + + # Classification target + if classification == Classification.AI_GENERATED: + target = "AI-generated speech" + else: + target = "human speech" + + # Use combined factors for explanation + if factors: + # Remove duplicates while preserving order + unique_factors = list(dict.fromkeys(factors))[:3] # Max 3 factors + + if len(unique_factors) == 1: + factors_text = unique_factors[0] + elif len(unique_factors) == 2: + factors_text = f"{unique_factors[0]} and {unique_factors[1]}" + else: + factors_text = f"{', '.join(unique_factors[:-1])}, and {unique_factors[-1]}" + + explanation = f"{descriptor} {target}: {factors_text} detected" + else: + explanation = f"{descriptor} {target} based on comprehensive analysis" + + # Truncate if too long + if len(explanation) > 245: + explanation = explanation[:242] + "..." + + return explanation + + def detect_sync( + self, + audio_base64: str, + language: SupportedLanguage, + ) -> VoiceDetectionResponse: + """ + Synchronous version of detect for testing. + + Args: + audio_base64: Base64-encoded MP3 audio + language: Language of the audio content + + Returns: + VoiceDetectionResponse + """ + import asyncio + + loop = asyncio.new_event_loop() + try: + return loop.run_until_complete(self.detect(audio_base64, language)) + finally: + loop.close() + + def health_check(self) -> dict: + """ + Get health status of the voice detector. + + Returns: + Dictionary with health information + """ + model_health = self.model_loader.health_check() + + return { + "status": "healthy" if model_health["model_loaded"] else "unhealthy", + "version": self.settings.APP_VERSION, + "model_loaded": model_health["model_loaded"], + "model_name": self.settings.model_identifier, + "device": model_health["device"], + "supported_languages": SupportedLanguage.values(), + "features": [ + "audio_forensics", + "temporal_anomaly_detection", + "deepfake_risk_score", + "audio_quality_score", + "confidence_calibration", + "performance_metrics", + ], + } + + async def warmup(self) -> None: + """Run warmup inference to initialize CUDA kernels.""" + if self.model_loader.is_loaded: + self.model_loader.warmup() diff --git a/app/utils/__init__.py b/app/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cdf9b9c4000d8fa3481d4c0326b6301877e031b0 --- /dev/null +++ b/app/utils/__init__.py @@ -0,0 +1,23 @@ +"""Utility modules package.""" + +from app.utils.exceptions import AudioDecodeError +from app.utils.exceptions import AudioDurationError +from app.utils.exceptions import AudioFormatError +from app.utils.exceptions import AudioProcessingError +from app.utils.exceptions import InferenceError +from app.utils.exceptions import ModelNotLoadedError +from app.utils.exceptions import VoiceAuthError +from app.utils.logger import get_logger +from app.utils.logger import setup_logging + +__all__ = [ + "VoiceAuthError", + "AudioDecodeError", + "AudioFormatError", + "AudioDurationError", + "AudioProcessingError", + "ModelNotLoadedError", + "InferenceError", + "get_logger", + "setup_logging", +] diff --git a/app/utils/__pycache__/__init__.cpython-312.pyc b/app/utils/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c6fec9b2d09a87958ba8086382b51f0ba530d890 Binary files /dev/null and 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b/app/utils/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..bfb4beb9c641e30bda8c50197b1fae091425d2f7 --- /dev/null +++ b/app/utils/constants.py @@ -0,0 +1,109 @@ +""" +Application constants for VoiceAuth API. + +Defines constant values used throughout the application. +""" + +# ============================================================================= +# Audio Processing Constants +# ============================================================================= + +# Target sample rate for Wav2Vec2 model (16kHz) +TARGET_SAMPLE_RATE: int = 16000 + +# Audio channel configuration (mono) +AUDIO_CHANNELS: int = 1 + +# Audio bit depth +AUDIO_BIT_DEPTH: int = 16 + +# Supported audio MIME types +SUPPORTED_AUDIO_MIME_TYPES: set[str] = { + "audio/mpeg", + "audio/mp3", + "audio/x-mpeg", +} + +# MP3 magic bytes (ID3 or frame sync) +MP3_MAGIC_BYTES: tuple[bytes, ...] = ( + b"ID3", # ID3v2 tag + b"\xff\xfb", # MPEG Audio Layer 3, no CRC + b"\xff\xfa", # MPEG Audio Layer 3, CRC + b"\xff\xf3", # MPEG Audio Layer 3, no CRC (MPEG 2.5) + b"\xff\xf2", # MPEG Audio Layer 3, CRC (MPEG 2.5) +) + +# ============================================================================= +# Model Constants +# ============================================================================= + +# Model label mappings +LABEL_TO_ID: dict[str, int] = { + "HUMAN": 0, + "AI_GENERATED": 1, +} + +ID_TO_LABEL: dict[int, str] = { + 0: "HUMAN", + 1: "AI_GENERATED", +} + +# Classification thresholds +CONFIDENCE_THRESHOLD_HIGH: float = 0.85 +CONFIDENCE_THRESHOLD_MEDIUM: float = 0.65 +CONFIDENCE_THRESHOLD_LOW: float = 0.50 + +# ============================================================================= +# Explainability Constants +# ============================================================================= + +# AI-generated voice indicators +AI_INDICATORS: list[str] = [ + "unnatural pitch consistency", + "robotic speech patterns", + "synthetic formant transitions", + "irregular breathing patterns", + "mechanical prosody", + "uniform spectral distribution", + "absence of micro-variations", + "artificial voice modulation", + "synthetic vibrato patterns", + "digital compression artifacts", +] + +# Human voice indicators +HUMAN_INDICATORS: list[str] = [ + "natural speech variations", + "authentic prosody", + "organic voice characteristics", + "natural breathing patterns", + "dynamic pitch modulation", + "genuine emotional inflections", + "natural micro-pauses", + "authentic formant patterns", + "organic vibrato characteristics", + "natural voice timbre", +] + +# Confidence level descriptors +CONFIDENCE_DESCRIPTORS: dict[str, str] = { + "very_high": "Strong evidence of", + "high": "Clear indicators of", + "medium": "Likely signs of", + "low": "Possible characteristics of", +} + +# ============================================================================= +# API Constants +# ============================================================================= + +# Request ID header +REQUEST_ID_HEADER: str = "X-Request-ID" + +# Rate limit headers +RATE_LIMIT_LIMIT_HEADER: str = "X-RateLimit-Limit" +RATE_LIMIT_REMAINING_HEADER: str = "X-RateLimit-Remaining" +RATE_LIMIT_RESET_HEADER: str = "X-RateLimit-Reset" + +# API key prefix +API_KEY_PREFIX: str = "sk_" diff --git a/app/utils/exceptions.py b/app/utils/exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..66b33bab406d42c1c4549b517246564b969d61f9 --- /dev/null +++ b/app/utils/exceptions.py @@ -0,0 +1,117 @@ +""" +Custom exceptions for VoiceAuth API. + +Provides specific exception types for different error scenarios. +""" + + +class VoiceAuthError(Exception): + """Base exception for all VoiceAuth errors.""" + + def __init__(self, message: str, details: dict | None = None) -> None: + """ + Initialize VoiceAuthError. + + Args: + message: Human-readable error message + details: Additional error details + """ + super().__init__(message) + self.message = message + self.details = details or {} + + +class AudioDecodeError(VoiceAuthError): + """ + Raised when Base64 audio decoding fails. + + This typically occurs when: + - The input is not valid Base64 + - The decoded data is corrupted + """ + + pass + + +class AudioFormatError(VoiceAuthError): + """ + Raised when audio format is invalid or unsupported. + + This typically occurs when: + - The audio is not a valid MP3 file + - The audio codec is unsupported + - The file header is corrupted + """ + + pass + + +class AudioDurationError(VoiceAuthError): + """ + Raised when audio duration is out of allowed bounds. + + This typically occurs when: + - Audio is shorter than minimum duration + - Audio is longer than maximum duration + """ + + def __init__( + self, + message: str, + duration: float | None = None, + min_duration: float | None = None, + max_duration: float | None = None, + ) -> None: + """ + Initialize AudioDurationError. + + Args: + message: Human-readable error message + duration: Actual duration of the audio + min_duration: Minimum allowed duration + max_duration: Maximum allowed duration + """ + details = {} + if duration is not None: + details["duration"] = duration + if min_duration is not None: + details["min_duration"] = min_duration + if max_duration is not None: + details["max_duration"] = max_duration + super().__init__(message, details) + + +class AudioProcessingError(VoiceAuthError): + """ + Raised when audio processing fails. + + This is a general error for audio processing issues + that don't fit into more specific categories. + """ + + pass + + +class ModelNotLoadedError(VoiceAuthError): + """ + Raised when attempting to use an unloaded model. + + This typically occurs when: + - The model failed to load on startup + - The model was unloaded due to memory pressure + """ + + pass + + +class InferenceError(VoiceAuthError): + """ + Raised when ML model inference fails. + + This typically occurs when: + - The input tensor is malformed + - GPU memory is exhausted + - Model internal error + """ + + pass diff --git a/app/utils/logger.py b/app/utils/logger.py new file mode 100644 index 0000000000000000000000000000000000000000..43e65b6f18bf4346e3c8c64e1cbda0b445c2c4b4 --- /dev/null +++ b/app/utils/logger.py @@ -0,0 +1,122 @@ +""" +Structured logging configuration for VoiceAuth API. + +Uses structlog for JSON-formatted logs in production +and human-readable logs in development. +""" + +import logging +import sys +from typing import Any + +import structlog +from structlog.types import Processor + +from app.config import get_settings + + +def setup_logging() -> None: + """ + Configure structured logging based on application settings. + + Sets up structlog processors for either JSON or console output + based on the LOG_FORMAT setting. + """ + settings = get_settings() + + # Convert string log level to logging constant + log_level = getattr(logging, settings.LOG_LEVEL.upper(), logging.INFO) + + # Common processors for all output formats + shared_processors: list[Processor] = [ + structlog.contextvars.merge_contextvars, + structlog.stdlib.add_log_level, + structlog.stdlib.add_logger_name, + structlog.stdlib.PositionalArgumentsFormatter(), + structlog.processors.TimeStamper(fmt="iso"), + structlog.processors.StackInfoRenderer(), + structlog.processors.UnicodeDecoder(), + ] + + if settings.LOG_FORMAT == "json": + # JSON format for production + processors: list[Processor] = shared_processors + [ + structlog.processors.format_exc_info, + structlog.processors.JSONRenderer(), + ] + else: + # Console format for development + processors = shared_processors + [ + structlog.dev.ConsoleRenderer(colors=True), + ] + + # Configure structlog + structlog.configure( + processors=processors, + wrapper_class=structlog.stdlib.BoundLogger, + context_class=dict, + logger_factory=structlog.stdlib.LoggerFactory(), + cache_logger_on_first_use=True, + ) + + # Configure standard library logging + logging.basicConfig( + format="%(message)s", + stream=sys.stdout, + level=log_level, + ) + + # Set log levels for noisy libraries + logging.getLogger("uvicorn").setLevel(logging.WARNING) + logging.getLogger("httpx").setLevel(logging.WARNING) + logging.getLogger("httpcore").setLevel(logging.WARNING) + + +def get_logger(name: str | None = None) -> structlog.stdlib.BoundLogger: + """ + Get a configured logger instance. + + Args: + name: Optional logger name. If not provided, uses the calling module. + + Returns: + A configured structlog bound logger. + """ + return structlog.get_logger(name) + + +class LoggerMixin: + """Mixin class that provides a logger property.""" + + @property + def logger(self) -> structlog.stdlib.BoundLogger: + """Get a logger bound to the class name.""" + return get_logger(self.__class__.__name__) + + +def log_request( + method: str, + path: str, + status_code: int, + duration_ms: float, + **kwargs: Any, +) -> None: + """ + Log an HTTP request with structured data. + + Args: + method: HTTP method (GET, POST, etc.) + path: Request path + status_code: Response status code + duration_ms: Request duration in milliseconds + **kwargs: Additional fields to log + """ + logger = get_logger("http") + logger.info( + "http_request", + method=method, + path=path, + status_code=status_code, + duration_ms=round(duration_ms, 2), + **kwargs, + ) diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..14fea829bea35c22a6a881aed34cdcb99cef486b --- /dev/null +++ b/requirements.txt @@ -0,0 +1,47 @@ +# ============================================================================= +# VoiceAuth API - Production Dependencies +# ============================================================================= + +# ----------------------------------------------------------------------------- +# Web Framework +# ----------------------------------------------------------------------------- +fastapi>=0.109.0 +uvicorn[standard]>=0.27.0 +pydantic>=2.5.0 +pydantic-settings>=2.1.0 +python-multipart>=0.0.6 +httpx>=0.26.0 + +# ----------------------------------------------------------------------------- +# Machine Learning +# ----------------------------------------------------------------------------- +torch>=2.1.0 +torchaudio>=2.1.0 +transformers>=4.36.0 +librosa>=0.10.1 +soundfile>=0.12.1 +numpy>=1.24.0,<2.0.0 +scipy>=1.11.0 + +# ----------------------------------------------------------------------------- +# Audio Processing +# ----------------------------------------------------------------------------- +pydub>=0.25.1 + +# ----------------------------------------------------------------------------- +# Security & Authentication +# ----------------------------------------------------------------------------- +python-jose[cryptography]>=3.3.0 + +# ----------------------------------------------------------------------------- +# Rate Limiting & Caching +# ----------------------------------------------------------------------------- +slowapi>=0.1.9 +redis>=5.0.0 + +# ----------------------------------------------------------------------------- +# Utilities +# ----------------------------------------------------------------------------- +python-dotenv>=1.0.0 +structlog>=24.1.0 +tenacity>=8.2.0 diff --git a/scripts/download_model.py b/scripts/download_model.py new file mode 100644 index 0000000000000000000000000000000000000000..e9aafcac142eceb4c73d4ff564c70e2e05752bce --- /dev/null +++ b/scripts/download_model.py @@ -0,0 +1,139 @@ +""" +Model Download Script. + +Downloads and caches the Wav2Vec2 model for VoiceAuth API. +""" + +import argparse +import os +import sys +from pathlib import Path + +# Add parent directory to path for imports +sys.path.insert(0, str(Path(__file__).parent.parent)) + + +def download_model( + model_name: str = "facebook/wav2vec2-base", + output_dir: str | None = None, + force: bool = False, +) -> None: + """ + Download and cache the Wav2Vec2 model. + + Args: + model_name: HuggingFace model name or path + output_dir: Optional local directory to save model + force: Force re-download even if cached + """ + print("\n" + "=" * 60) + print("VoiceAuth - Model Download") + print("=" * 60 + "\n") + print(f"Model: {model_name}") + + if output_dir: + print(f"Output: {output_dir}") + + print("\nDownloading model components...") + print("-" * 40) + + try: + # Import here to avoid slow imports if just checking args + from transformers import Wav2Vec2ForSequenceClassification + from transformers import Wav2Vec2Processor + + # Download processor + print("\n[1/2] Downloading Wav2Vec2Processor...") + processor = Wav2Vec2Processor.from_pretrained( + model_name, + force_download=force, + ) + print(" [OK] Processor downloaded") + + # Download model + print("\n[2/2] Downloading Wav2Vec2ForSequenceClassification...") + model = Wav2Vec2ForSequenceClassification.from_pretrained( + model_name, + num_labels=2, + label2id={"HUMAN": 0, "AI_GENERATED": 1}, + id2label={0: "HUMAN", 1: "AI_GENERATED"}, + force_download=force, + ) + print(" [OK] Model downloaded") + + # Save to local directory if specified + if output_dir: + output_path = Path(output_dir) + output_path.mkdir(parents=True, exist_ok=True) + + print(f"\nSaving to {output_path}...") + processor.save_pretrained(output_path) + model.save_pretrained(output_path) + print("[OK] Model saved locally") + + print("\n" + "=" * 60) + print("Download Complete!") + print("=" * 60) + + # Show cache location + cache_dir = os.environ.get( + "HF_HOME", + os.path.expanduser("~/.cache/huggingface"), + ) + print(f"\nCache location: {cache_dir}") + + if output_dir: + print(f"Local copy: {output_dir}") + + print("\nYou can now start the API with:") + print(" uvicorn app.main:app --reload") + print() + + except Exception as e: + print(f"\n[ERROR] Error downloading model: {e}") + sys.exit(1) + + +def main() -> None: + """Main entry point.""" + parser = argparse.ArgumentParser( + description="Download Wav2Vec2 model for VoiceAuth API", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + python download_model.py + python download_model.py --model facebook/wav2vec2-large-xlsr-53 + python download_model.py --output ./models + python download_model.py --force + """, + ) + + parser.add_argument( + "--model", + type=str, + default="facebook/wav2vec2-base", + help="HuggingFace model name (default: facebook/wav2vec2-base)", + ) + parser.add_argument( + "--output", + type=str, + default=None, + help="Optional local directory to save model", + ) + parser.add_argument( + "--force", + action="store_true", + help="Force re-download even if cached", + ) + + args = parser.parse_args() + + download_model( + model_name=args.model, + output_dir=args.output, + force=args.force, + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/generate_api_key.py b/scripts/generate_api_key.py new file mode 100644 index 0000000000000000000000000000000000000000..59535d1d44046621a402a61205f213ba031ddf66 --- /dev/null +++ b/scripts/generate_api_key.py @@ -0,0 +1,140 @@ +""" +API Key Generation Script. + +Generates secure API keys for VoiceAuth API authentication. +""" + +import argparse +import secrets +import sys +from pathlib import Path + + +def generate_api_key(prefix: str = "sk_", length: int = 48) -> str: + """ + Generate a secure API key. + + Args: + prefix: Prefix for the API key (default: "sk_") + length: Length of the random portion in characters (default: 48) + + Returns: + Generated API key string + """ + # Generate random hex bytes (length/2 bytes = length hex chars) + random_part = secrets.token_hex(length // 2) + return f"{prefix}{random_part}" + + +def main() -> None: + """Main entry point for API key generation.""" + parser = argparse.ArgumentParser( + description="Generate secure API keys for VoiceAuth API", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + python generate_api_key.py + python generate_api_key.py --prefix sk_live_ + python generate_api_key.py --count 5 + python generate_api_key.py --save + """, + ) + + parser.add_argument( + "--prefix", + type=str, + default="sk_", + help="Prefix for the API key (default: sk_)", + ) + parser.add_argument( + "--length", + type=int, + default=48, + help="Length of random portion (default: 48)", + ) + parser.add_argument( + "--count", + type=int, + default=1, + help="Number of keys to generate (default: 1)", + ) + parser.add_argument( + "--save", + action="store_true", + help="Save to .env file (appends if exists)", + ) + + args = parser.parse_args() + + # Generate keys + keys = [generate_api_key(args.prefix, args.length) for _ in range(args.count)] + + # Display keys + print("\n" + "=" * 60) + print("VoiceAuth API - Generated API Keys") + print("=" * 60 + "\n") + + for i, key in enumerate(keys, 1): + if args.count > 1: + print(f"Key {i}: {key}") + else: + print(f"API Key: {key}") + + print("\n" + "-" * 60) + print("Usage Instructions:") + print("-" * 60) + print("\n1. Add to your .env file:") + print(f" API_KEYS={','.join(keys)}") + print("\n2. Use in requests:") + print(f' curl -H "x-api-key: {keys[0]}" ...') + print("\n" + "=" * 60 + "\n") + + # Save to .env if requested + if args.save: + env_path = Path(__file__).parent.parent / ".env" + + # Check if .env exists + if env_path.exists(): + # Read existing content + with open(env_path, "r") as f: + content = f.read() + + # Check if API_KEYS already exists + if "API_KEYS=" in content: + print(f"[WARNING] API_KEYS already exists in {env_path}") + print(" Please update it manually or remove the existing entry.") + sys.exit(1) + + # Append new keys + with open(env_path, "a") as f: + f.write(f"\nAPI_KEYS={','.join(keys)}\n") + + print(f"[OK] API keys appended to {env_path}") + else: + # Create new .env from template + template_path = Path(__file__).parent.parent / ".env.example" + + if template_path.exists(): + with open(template_path, "r") as f: + content = f.read() + + # Replace placeholder + content = content.replace( + "API_KEYS=sk_test_your_api_key_here", + f"API_KEYS={','.join(keys)}", + ) + + with open(env_path, "w") as f: + f.write(content) + + print(f"[OK] Created {env_path} with generated API keys") + else: + # Just create minimal .env + with open(env_path, "w") as f: + f.write(f"API_KEYS={','.join(keys)}\n") + + print(f"[OK] Created {env_path} with API keys") + + +if __name__ == "__main__": + main()