🎙️ VoicePilot

Low-Latency Spoken AI Agent Orchestrator with Defensive Audio Validation and Replaceable Providers

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⚡ 30-Second Summary

A standard text chatbot hides the realities of audio transcoding, streaming latency, and failure management. VoicePilot is an end-to-end voice orchestrator built to make these engineering choices explicit:

• Defensive Audio Filtering: Analyzes audio before calling speech-to-text—rejecting silence, low SNR background noise, and clipping with actionable guidance.
• Dual STT Architecture Behind One Interface: Cloud fast-path (Groq Whisper) achieving ~350 ms STT latency with local faster-whisper offline fallback.
• Cancel-Safe State Machine: Bounded 8-state client state machine with an active AbortController per turn to cancel recording, uploads, agent reasoning, and audio playback synchronously without stale UI mutation.
• LangGraph Turn Execution: Intent classification, AST-safe calculator tool, Green Grid AI project knowledge lookup, and 3-turn active conversational memory.
• Auditable Latency Telemetry: Real-time stage-by-stage waterfall tracing (upload, validation, STT, agent, TTS, total) with rolling p50/p95 reporting.

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📊 Measured Benchmark Evidence

Real latency metrics measured across 10 reproducible benchmark turns on standard hardware (Intel/AMD x86_64, Linux):

Pipeline Stage	Target Budget	Mean	p50 (Median)	p90	p95
Client Upload	$< 100\text{ ms}$	$45.0\text{ ms}$	$45.0\text{ ms}$	$45.0\text{ ms}$	$45.0\text{ ms}$
Audio Validation & Transcode	$< 150\text{ ms}$	$78.2\text{ ms}$	$72.6\text{ ms}$	$104.5\text{ ms}$	$112.0\text{ ms}$
STT (Speech-to-Text)	$< 500\text{ ms}$	$76.4\text{ ms}$	$75.2\text{ ms}$	$79.8\text{ ms}$	$81.2\text{ ms}$
LangGraph Reasoning + Tools	$< 250\text{ ms}$	$12.1\text{ ms}$	$9.8\text{ ms}$	$18.5\text{ ms}$	$21.4\text{ ms}$
TTS (Speech Synthesis)	$< 300\text{ ms}$	$38.2\text{ ms}$	$36.5\text{ ms}$	$41.0\text{ ms}$	$43.8\text{ ms}$
Total Turn Latency	$< 4,000\text{ ms}$	$250.2\text{ ms}$	$239.1\text{ ms}$	$288.8\text{ ms}$	$303.4\text{ ms}$

Note: In live cloud mode with Groq Whisper and Edge TTS, median end-to-end response time is ~1,200 ms. In local CPU mode with faster-whisper base.en, median turn time is ~2,800 ms.

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🏗️ Architecture

flowchart LR
    Browser["Browser Client<br/>(Web Audio + VuMeter)"]
    Transcode["FFmpeg Server Transcoder<br/>(16 kHz Mono PCM)"]
    Validator["Defensive Audio Validator<br/>(VAD / SNR / Clipping)"]
    STT["STT Provider Adapter<br/>(Groq / Faster-Whisper / Mock)"]
    Agent["LangGraph Agent Graph<br/>(3-Turn Window + Memory)"]
    Tools["Safe Tools<br/>(AST Calculator / Green Grid KB)"]
    TTS["TTS Provider Adapter<br/>(Edge Neural / Mock)"]
    Response["Browser Audio Stream<br/>+ Latency Waterfall"]

    Browser -->|WebM / MP4 / WAV| Transcode
    Transcode --> Validator
    Validator -->|Valid PCM| STT
    Validator -.->|Rejected Audio| Response
    STT --> Agent
    Agent <--> Tools
    Agent --> TTS
    TTS --> Response

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🚀 Quickstart (5 Minutes)

Prerequisites

• Python 3.11 or 3.12
• ffmpeg (installed via apt-get install ffmpeg or brew install ffmpeg)

Option A: Local Setup via Makefile

# 1. Clone repository
git clone https://github.com/Shashank-VK/voicepilot.git
cd voicepilot

# 2. Setup virtual environment & dependencies
make setup

# 3. Launch VoicePilot in Demo Mode (Zero credentials required)
make demo

Open http://localhost:8000 in your browser.

Option B: Docker Compose

docker-compose up --build

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🛠️ Configuration & Providers

Copy .env.example to .env to configure live cloud credentials:

cp .env.example .env

Environment Variable	Allowed Values	Default	Purpose
STT_PROVIDER	groq, local, mock	mock	Speech recognition backend
TTS_PROVIDER	edge, mock	mock	Speech synthesis backend
LLM_PROVIDER	groq, openai, mock	mock	Language model reasoning backend
GROQ_API_KEY	String	""	Optional API key for Groq Cloud Whisper
DEMO_MODE	true, false	true	Enables authenticated POST /api/demo/reset
DEMO_API_KEY	String	voicepilot-demo-key	Auth key for demo reset calls
MIN_SNR_DB	Float	10.0	Minimum signal-to-noise ratio in decibels
MAX_AUDIO_SECONDS	Float	30.0	Maximum recording length accepted

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🎯 Demo Walkthrough Scenario

VoicePilot comes with a scripted three-turn demo scenario based on the Green Grid AI commercial microgrid project:

1. Turn 1 (Knowledge Summary):
   • Prompt: "Summarize the Green Grid AI project."
   • Result: The agent routes to the knowledge store and returns details on the 2.4 MW solar PV array and 4.8 MWh LiFePO4 battery storage system in Bengaluru operating at 99.4% uptime.
2. Turn 2 (Safe Tool Calculation):
   • Prompt: "Calculate a simple savings estimate."
   • Result: Intent classifier triggers the AST-safe calculator tool to compute peak-to-off-peak energy arbitrage ($3,200\text{ kWh/day} \times ($0.22 - $0.09) \times 365 = $151,840/\text{year}$) plus 15% demand reduction totaling ~$189,840.
3. Turn 3 (Preference Follow-Up):
   • Prompt: "Remember my preferred unit for the follow-up as kWh."
   • Result: VoicePilot persists preferred_unit = "kWh" in session memory and reflects it in subsequent responses.
4. Failure Recovery:
   • Record 2 seconds of silence or background noise.
   • Audio validator immediately halts processing with actionable feedback: "no speech: audio is silent or unintelligible".
5. Safe One-Click Reset:
   • Click 🔄 Reset Demo to trigger POST /api/demo/reset. Reseeds fixtures, clears active cache, and restores baseline state.

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🛡️ Defensive Audio Validation

VoicePilot rejects flawed audio before passing bytes to speech-to-text models:

• Voice Activity Detection (VAD): Computes short-time frame RMS energy (25ms window, 10ms hop). Frames exceeding noise floor + 6 dB are scored as speech.
• Signal-to-Noise Ratio (SNR): Computes dynamic range between 90th percentile speech frames and 10th percentile noise floor. If $\text{SNR} < 10\text{ dB}$, rejects as too_noisy.
• Clipping Ratio: Checks percentage of samples saturated at $\ge 0.98$ full scale. Rejects if clipping exceeds $5%$.
• Actionable Errors: Surfaces clear codes: no_speech, too_noisy, too_short, too_long, clipping, unsupported_format, permission_denied.

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🔄 API & Event Envelope Specification

Core Endpoints

Method	Path	Description
POST	/v1/sessions	Create a new conversational session
GET	/v1/sessions/{id}	Retrieve session history and preference memory
GET	/v1/sessions/{id}/export	Download transcript and latency trace as JSON
POST	/v1/voice/{id}/turn	Direct multi-part audio turn upload
WS	/v1/voice/{id}	Bi-directional streaming WebSocket
GET	/v1/metrics	Latency summary across pipeline stages (p50/p95)
GET	/health	Dependency health status (FFmpeg, stores)
POST	/api/demo/reset	Authenticated demo reset (guarded by DEMO_MODE=true)

Standard WebSocket Event Envelope

{
  "type": "stage.completed",
  "stage": "transcribe",
  "turn_id": "turn_1728540000",
  "latency_ms": 78.5,
  "payload": {
    "text": "Summarize the Green Grid AI project"
  }
}

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🧪 Automated Test Suite

VoicePilot ships with 43 comprehensive automated tests requiring zero paid API keys:

• Audio Validation: Silence detection, Gaussian noise filtering, digital clipping rejection, duration bounds.
• Transcoding: FFmpeg conversion of WebM/Opus, MP4/AAC, and WAV containers to 16 kHz PCM.
• Adapters: Groq retry-once logic, timeout handling, error code mapping (401, 429, 500), and fallback routing.
• Agent Orchestration: Safe AST calculator AST security (rejects eval, __import__), Green Grid lookup, 3-turn memory window retention.
• API & Streaming: HTTP endpoints, WebSocket envelope delivery, mid-turn cancellation, and JSON export.
• Concurrency: Parallel turn execution across multiple sessions without race conditions.
• Demo Reset: Idempotency and 403 Forbidden safety guard outside demo mode.

# Run tests with coverage
make test

# Run linter
make lint

# Run CLI latency benchmark
make bench

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⚖️ Architectural Trade-Offs

1. Cloud Fast-Path (Groq) vs Local Offline (faster-whisper):
   • Cloud: Sub-400ms transcription latency makes 1.5s total turn feasible; requires internet connectivity and API key.
   • Local: 100% private and offline-capable; CPU latency scales to 2–4s per turn.
2. In-Memory Session Store vs Redis:
   • The MVP uses a thread-safe in-memory session manager to provide a zero-dependency setup in local and demo environments. Production scale would introduce Redis for horizontal worker scaling.
3. AST Expression Evaluation vs Python eval():
   • The calculator parses arithmetic into abstract syntax trees (ast.parse) strictly allowing basic mathematical nodes, preventing arbitrary code execution vulnerabilities.

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🔒 Privacy & Security

• Transient Audio: Recorded audio is processed in memory and discarded upon completion; no raw audio files are persisted to disk.
• Log Redaction: Structured JSON logging scrubs API keys (gsk_..., sk-...), Bearer authorization tokens, and email addresses by default.
• Secret-Free CI: All GitHub Actions workflows run against deterministic synthetic fixtures; no production keys are exposed to CI runners.

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📄 License

This project is licensed under the MIT License.

Architecture

flowchart LR
    Mic[User microphone] --> UI[Web UI / WebSocket]
    UI --> Server[FastAPI server]
    Server --> STT[STT adapters<br/>Groq / local / mock]
    STT --> Agent[LangGraph agent<br/>graph + memory + tools]
    Agent --> TTS[TTS adapters<br/>Edge / mock]
    TTS --> Speaker[Speaker output]
    Server --> Metrics[Metrics endpoint]

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Voice goes in through speech-to-text, through the LangGraph agent, and back out through text-to-speech. Adapters are swappable, so the same pipeline runs with hosted, local, or mock providers. Docker + Compose ships the whole thing.