mpazaryna
commented
2 weeks ago DENO FEYNMAN VOICE IMPLEMENTATION Backend Architecture Design
- TECHNOLOGY STACK
Core:
- Deno Runtime
- Oak (middleware framework)
- Deno Deploy (optional for deployment)
- WebSocket for real-time communication
- Deno FFmpeg for audio processing
- Deno KV for storage
Frontend:
- Fresh framework
- Preact
- Islands Architecture
- WebRTC for voice capture
- KEY COMPONENTS
A. Server Architecture
// app.ts
import { Application } from "https://deno.land/x/oak/mod.ts";
import { router } from "./routes.ts";
const app = new Application();
// Basic CORS setup
app.use(async (ctx, next) => {
ctx.response.headers.set("Access-Control-Allow-Origin", "*");
await next();
});
app.use(router.routes());
app.use(router.allowedMethods());
await app.listen({ port: 8000 });B. Voice Processing Service
// services/voice.ts
export class VoiceProcessor {
async processAudio(audioBuffer: ArrayBuffer) {
// Handle incoming audio stream
const processed = await this.convertToWav(audioBuffer);
return processed;
}
private async convertToWav(buffer: ArrayBuffer) {
// Audio format conversion logic
return buffer;
}
}C. Transcription Service
// services/transcription.ts
export class TranscriptionService {
async transcribe(audioData: ArrayBuffer): Promise<string> {
// Integrate with preferred speech-to-text service
// Could use Whisper API or similar
return "transcribed text";
}
}D. Analysis Service
// services/analysis.ts
interface AnalysisResult {
clarity: number;
simplicity: number;
completeness: number;
suggestions: string[];
}
export class AnalysisService {
async analyzeExplanation(text: string): Promise<AnalysisResult> {
// Integrate with AI service for analysis
return {
clarity: 0,
simplicity: 0,
completeness: 0,
suggestions: [],
};
}
}- DATA STORAGE
Using Deno KV:
// db/store.ts
interface Session {
id: string;
timestamp: number;
transcription: string;
analysis: AnalysisResult;
}
export class SessionStore {
private kv: Deno.Kv;
constructor() {
this.kv = await Deno.openKv();
}
async saveSession(session: Session): Promise<void> {
await this.kv.set(["sessions", session.id], session);
}
async getSession(id: string): Promise<Session | null> {
const session = await this.kv.get(["sessions", id]);
return session.value as Session;
}
}- API ROUTES
// routes.ts
import { Router } from "https://deno.land/x/oak/mod.ts";
const router = new Router();
router
.post("/sessions", async (ctx) => {
// Handle new session creation
})
.post("/sessions/:id/audio", async (ctx) => {
// Handle audio upload and processing
})
.get("/sessions/:id", async (ctx) => {
// Retrieve session data
})
.get("/sessions/:id/analysis", async (ctx) => {
// Get session analysis
});
export { router };- REAL-TIME COMPONENTS
WebSocket Handler:
// ws/handler.ts
export class WebSocketHandler {
private sessions: Map<string, WebSocket>;
constructor() {
this.sessions = new Map();
}
handleConnection(ws: WebSocket, sessionId: string) {
this.sessions.set(sessionId, ws);
ws.onmessage = async (event) => {
// Handle real-time audio streaming
};
}
}- DEPLOYMENT CONSIDERATIONS
- Deno Deploy for easy deployment
- Edge functions for optimal latency
- Consider regional data requirements
- WebRTC STUN/TURN server needs
- SECURITY CONSIDERATIONS
// middleware/security.ts
export async function securityMiddleware(
ctx: Context,
next: () => Promise<void>,
) {
ctx.response.headers.set("X-Content-Type-Options", "nosniff");
ctx.response.headers.set("X-Frame-Options", "DENY");
ctx.response.headers.set("X-XSS-Protection", "1; mode=block");
await next();
}- DEVELOPMENT WORKFLOW
# Run locally
deno run --allow-net --allow-read app.ts
# Run tests
deno test
# Deploy
deno deploy app.tsBenefits of Using Deno:
- Built-in TypeScript support
- Secure by default
- Modern ES modules
- Built-in testing
- Standard formatting
- Easy deployment
- Web standard APIs
- Great performance
Would you like me to elaborate on any part of this architecture or focus on implementing a specific component first?
FEYNMAN VOICE LEARNING SYSTEM Design Document v1.0
A. Natural Learning Flow
B. Learning Objectives
Learning Components:
User Roles:
Phase 1: Preparation
Phase 2: Verbal Explanation
Phase 3: Analysis
Phase 4: Review
A. New Topic Learning
B. Understanding Deepening
C. Teaching Practice
Quantitative:
Qualitative:
Core Features:
Integration Points:
Feature: Voice Learning Session
Scenario: Starting a New Learning Session Given the user has opened the application When they select "New Session" Then the system should initialize recording capability And display the session timer And enable the voice input
Scenario: Recording an Explanation Given the user is in an active session When they press the record button Then the system should begin voice recording And show real-time transcription And indicate recording status
Scenario: Completing an Explanation Given the user is recording When they press stop Then the system should end the recording And process the transcription And initiate the analysis phase
Scenario: Receiving Analysis Given the explanation is complete When the analysis is finished Then the system should display: | Clarity score | | Simplicity measure | | Completeness rating | | Improvement areas |
Scenario: Saving Session Results Given the analysis is complete When the user selects "Save Session" Then the system should store: | Recording | | Transcription | | Analysis results | | Session metadata |
Scenario: Reviewing Past Sessions Given the user has saved sessions When they access "Session History" Then they should see: | Session date | | Topic | | Performance metrics | | Improvement trend |
Development Phases:
MVP Implementation
Feature Enhancement
System Integration
Would you like to focus on any particular aspect of this design document or shall we proceed with refining specific components?