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Video/Audio Recording System Implementation Plan

Overview

This document outlines the complete implementation plan for adding video/audio recording capabilities to the AI-powered interview coach system. The implementation will be 100% local and open-source, integrating seamlessly with the existing architecture.

Current System Analysis

Existing Architecture

  • Backend: FastAPI with SQLAlchemy ORM, PostgreSQL database, Redis caching
  • Frontend: React/TypeScript with Material-UI components
  • Interview Flow: Session creation → Question display → Answer submission → AI evaluation
  • File Storage: Local storage pattern in uploads/ directory with UUID-prefixed filenames
  • Database: Alembic migrations, soft deletes, proper indexing and relationships

Key Integration Points

  • Answer Model: Currently stores answer_text, time_taken, submitted_at
  • File Upload Pattern: Existing file_upload.py utility for resume uploads
  • API Structure: RESTful endpoints under /interviews namespace
  • Frontend Components: InterviewSessionPage.tsx handles question display and answer submission

Implementation Plan

STEP 1: Database Schema Extension

1.1 Add Recording Fields to Answer Model

File: Ai_powered_interview_coach/backend/app/models/answer.py

Add new columns to support recording data:

# Recording fields
audio_url = Column(String(500), nullable=True)
video_url = Column(String(500), nullable=True) 
recording_duration = Column(Float, nullable=True)  # Duration in seconds
recording_format = Column(String(20), nullable=True)  # webm, mp4, etc.
transcription = Column(Text, nullable=True)
voice_analysis = Column(JSON, nullable=True)  # Speaking pace, filler words, etc.

1.2 Create Database Migration

File: Ai_powered_interview_coach/backend/alembic/versions/008_add_recording_fields.py

Generate Alembic migration to add recording fields to existing answers table:

  • Add columns with NULL defaults for backward compatibility
  • Ensure existing functionality continues working
  • Add indexes for recording_url fields for efficient queries

STEP 2: Backend Recording Service

2.1 Media Processing Service

File: Ai_powered_interview_coach/backend/app/services/media_service.py

Create comprehensive media processing service:

class MediaService:
    def __init__(self):
        self.whisper_model = None  # Lazy load
        
    async def process_recording(self, audio_file, video_file=None, user_id: int, question_id: int):
        # 1. Validate files (format, size, duration)
        # 2. Save to permanent storage
        # 3. Extract audio from video if needed
        # 4. Run Whisper transcription
        # 5. Perform voice analysis with librosa
        # 6. Return structured results

Key Features:

  • Singleton Whisper Model: Load once, reuse for all transcriptions
  • CPU/GPU Detection: Automatically use GPU if available, fallback to CPU
  • File Validation: Check format (webm), size (100MB max), duration limits
  • Error Handling: Graceful degradation if transcription fails
  • Memory Management: Clean temporary files, prevent memory leaks

2.2 Storage Management

File: Ai_powered_interview_coach/backend/app/utils/media_storage.py

Extend existing file storage pattern:

# Directory structure
storage/
├── media/
│   ├── audio/          # Audio recordings
│   ├── video/          # Video recordings  
│   └── temp/           # Temporary processing files

Security Features:

  • User-specific subdirectories
  • Filename format: user_{user_id}_question_{question_id}_{timestamp}.webm
  • Path traversal prevention
  • File access authorization

STEP 3: API Endpoints

3.1 Recording Upload Endpoint

File: Ai_powered_interview_coach/backend/app/routes/media.py

@router.post("/upload-recording")
async def upload_recording(
    audio_file: UploadFile,
    video_file: Optional[UploadFile] = None,
    question_id: int = Form(...),
    session_id: int = Form(...),
    current_user: User = Depends(get_current_user),
    db: Session = Depends(get_db)
):
    # 1. Validate session ownership
    # 2. Process recording files
    # 3. Update answer record with recording data
    # 4. Return transcription and analysis results

3.2 Media Serving Endpoint

File: Ai_powered_interview_coach/backend/app/main.py

Mount static file serving for recordings:

app.mount("/media", StaticFiles(directory="storage/media"), name="media")

Authorization: Middleware to ensure users can only access their own recordings.

3.3 Health Check Endpoint

File: Ai_powered_interview_coach/backend/app/routes/media.py

@router.get("/health")
async def media_health_check():
    return {
        "whisper_loaded": whisper_model is not None,
        "storage_accessible": check_storage_directories(),
        "processing_ready": True
    }

STEP 4: Frontend Recording Component

4.1 Recording Hook

File: Ai_powered_interview_coach/frontend/src/hooks/useMediaRecorder.ts

Custom React hook for recording functionality:

export const useMediaRecorder = () => {
    const [isRecording, setIsRecording] = useState(false);
    const [recordingTime, setRecordingTime] = useState(0);
    const [mediaRecorder, setMediaRecorder] = useState<MediaRecorder | null>(null);
    
    const startRecording = async (includeVideo: boolean = false) => {
        // Request permissions
        // Initialize MediaRecorder
        // Start recording timer
    };
    
    const stopRecording = async (): Promise<Blob[]> => {
        // Stop recording
        // Return audio/video blobs
    };
};

4.2 Recording UI Component

File: Ai_powered_interview_coach/frontend/src/components/interview/RecordingControls.tsx

Recording interface component:

interface RecordingControlsProps {
    onRecordingComplete: (audioBlob: Blob, videoBlob?: Blob) => void;
    disabled?: boolean;
}

export const RecordingControls: React.FC<RecordingControlsProps> = ({
    onRecordingComplete,
    disabled = false
}) => {
    // Recording button with visual feedback
    // Timer display
    // Permission status
    // Error handling UI
};

4.3 Integration with Interview Page

File: Ai_powered_interview_coach/frontend/src/pages/interview/InterviewSessionPage.tsx

Integrate recording into existing interview flow:

  • Add recording controls below answer textarea
  • Upload recording when answer is submitted
  • Display transcription results
  • Show voice analysis feedback

STEP 5: Dependencies and Requirements

5.1 Backend Dependencies

File: Ai_powered_interview_coach/backend/requirements.txt

Add new dependencies:

# Audio/Video Processing
faster-whisper==0.10.0
librosa==0.10.1
soundfile==0.12.1
ffmpeg-python==0.2.0

# File format support
python-magic==0.4.27

5.2 System Requirements

  • FFmpeg: Required for audio/video processing
  • CUDA (optional): For GPU-accelerated Whisper transcription
  • Storage: Adequate disk space for recording files

STEP 6: Voice Analysis Features

6.1 Analysis Metrics

Using librosa for voice analysis:

  • Speaking Pace: Words per minute calculation
  • Pause Analysis: Silence detection and timing
  • Filler Words: Detection of "um", "uh", "like", etc.
  • Volume Consistency: Audio level analysis
  • Confidence Indicators: Voice stability metrics

6.2 Analysis Results Schema

voice_analysis = {
    "speaking_pace_wpm": 150,
    "total_speaking_time": 45.2,
    "pause_count": 8,
    "average_pause_duration": 1.2,
    "filler_word_count": 3,
    "filler_words": ["um", "uh"],
    "volume_consistency": 0.85,
    "confidence_score": 0.78
}

STEP 7: Error Handling and Resilience

7.1 Graceful Degradation

  • Transcription Failure: Continue with text-only answer
  • Recording Upload Failure: Retry mechanism with exponential backoff
  • Processing Timeout: Background processing for long recordings
  • Storage Issues: Fallback to temporary storage with cleanup

7.2 User Experience

  • Permission Handling: Clear messaging for microphone/camera access
  • Progress Indicators: Upload progress and processing status
  • Fallback Options: Always allow text-only answers
  • Error Recovery: Retry options and clear error messages

STEP 8: Performance Optimization

8.1 Processing Efficiency

  • Lazy Loading: Load Whisper model only when needed
  • Background Tasks: Use Celery for long-running transcription
  • File Compression: Optimize recording file sizes
  • Caching: Cache transcription results

8.2 Storage Management

  • File Cleanup: Automatic cleanup of old recordings
  • Compression: Compress older recordings to save space
  • Monitoring: Track storage usage and performance

STEP 9: Security Considerations

9.1 File Security

  • Upload Validation: Strict file type and size validation
  • Path Security: Prevent directory traversal attacks
  • Access Control: User-specific file access only
  • Sanitization: Clean filenames and metadata

9.2 Privacy Protection

  • Local Processing: All transcription and analysis done locally
  • Data Retention: Clear policies for recording storage
  • User Control: Options to delete recordings
  • Encryption: Consider encrypting stored recordings

STEP 10: Testing Strategy

10.1 Unit Tests

  • Media service functionality
  • File upload and validation
  • Transcription accuracy
  • Voice analysis metrics

10.2 Integration Tests

  • End-to-end recording workflow
  • API endpoint functionality
  • Frontend recording components
  • Error handling scenarios

10.3 Performance Tests

  • Large file handling
  • Concurrent recording processing
  • Storage capacity limits
  • Memory usage monitoring

STEP 11: Deployment Considerations

11.1 System Setup

  • FFmpeg installation instructions
  • Whisper model download and setup
  • Storage directory configuration
  • Permission requirements

11.2 Configuration

  • Environment variables for storage paths
  • Recording quality settings
  • Processing timeout configurations
  • Cleanup job scheduling

STEP 12: Documentation and User Guide

12.1 Technical Documentation

  • API endpoint documentation
  • Database schema changes
  • Configuration options
  • Troubleshooting guide

12.2 User Documentation

  • Recording feature overview
  • Browser compatibility requirements
  • Permission setup instructions
  • Best practices for recording

Implementation Timeline

  1. Phase 1 (Database & Backend Core): Steps 1-3 (Database, Media Service, API)
  2. Phase 2 (Frontend Integration): Steps 4-5 (Recording UI, Dependencies)
  3. Phase 3 (Analysis & Polish): Steps 6-8 (Voice Analysis, Error Handling, Performance)
  4. Phase 4 (Security & Testing): Steps 9-10 (Security, Testing)
  5. Phase 5 (Deployment & Docs): Steps 11-12 (Deployment, Documentation)

Success Criteria

  • ✅ Users can record audio/video during interviews
  • ✅ Recordings are transcribed locally using Whisper
  • ✅ Voice analysis provides meaningful feedback
  • ✅ System remains 100% local and open-source
  • ✅ Existing functionality is not broken
  • ✅ Performance is acceptable for typical use cases
  • ✅ Error handling provides good user experience

Risk Mitigation

  • Compatibility Issues: Extensive browser testing
  • Performance Problems: Background processing and optimization
  • Storage Limitations: Monitoring and cleanup strategies
  • User Adoption: Clear documentation and gradual rollout
  • Technical Complexity: Modular implementation with fallbacks

This implementation plan ensures a robust, secure, and user-friendly recording system that integrates seamlessly with the existing interview coach architecture while maintaining the requirement for 100% local, open-source operation.