vocallet

🎤 Vocallet AI Service

A production-grade Python FastAPI microservice that processes voice commands for the Vocallet accessible finance application. Built for Indonesian users including visually impaired individuals, UMKM small businesses, and personal finance management.

---

📐 Architecture Overview

┌─────────────────────────────────────────────────────────────────────┐
│                         CLIENT (React 19 / Node.js)                  │
│              POST /api/voice-command  (multipart audio)              │
└───────────────────────────┬─────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────────────────┐
│                     VOCALLET AI SERVICE (FastAPI)                     │
│  ┌─────────────┐   ┌──────────────────┐   ┌──────────────────────┐  │
│  │ AudioService│   │   STT Service    │   │  Intent Service      │  │
│  │  validate   │──▶│  (Whisper STT)   │──▶│ (XLM-RoBERTa XNLI)  │  │
│  │  save temp  │   │  transcribe()    │   │  classify_intent()   │  │
│  │  load 16kHz │   │                  │   │  zero-shot labels    │  │
│  └─────────────┘   └──────────────────┘   └──────────────────────┘  │
│                                                                       │
│  ┌──────────────────────────────────────────────────────────────┐   │
│  │                     ModelRegistry (Singleton)                 │   │
│  │  _stt_pipeline (Whisper)    │    _intent_pipeline (RoBERTa)  │   │
│  │  Loaded ONCE at startup via asyncio.gather()                  │   │
│  └──────────────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────────┘

---

🛠️ Prerequisites

Installing ffmpeg

# Ubuntu/Debian
sudo apt-get install ffmpeg

# macOS (Homebrew)
brew install ffmpeg

# Windows (Chocolatey)
choco install ffmpeg

# Windows (Scoop)
scoop install ffmpeg

---

🚀 Quick Start (5 Steps)

1. Clone and enter the project

cd vocallet-ai-service

2. Create virtual environment

python -m venv venv

# Activate (Linux/macOS)
source venv/bin/activate

# Activate (Windows)
venv\Scripts\activate

3. Install dependencies

# CPU-only (recommended for most users)
pip install torch==2.3.1 torchaudio==2.3.1 --index-url https://download.pytorch.org/whl/cpu
pip install -r requirements.txt

# GPU (CUDA 12.1) — skip if using CPU
pip install torch==2.3.1 torchaudio==2.3.1 --index-url https://download.pytorch.org/whl/cu121
pip install -r requirements.txt

4. Configure environment

cp .env.example .env
# Edit .env as needed (optional — defaults work out of the box)

5. Run the service

uvicorn app.main:app --host 0.0.0.0 --port 8001 --reload

The service will be available at:

• API Docs (Swagger): http://localhost:8001/docs
• API Docs (ReDoc): http://localhost:8001/redoc
• Test UI: http://localhost:8001/ui/index.html
• Health: http://localhost:8001/health

⚠️ First run note: On first startup, models (~1.3GB total) will download from Hugging Face. This can take 5–20 minutes depending on your internet connection. The service will not accept requests until downloads complete.

---

🌍 Environment Variables

---

📡 API Documentation

POST /api/voice-command

Process a voice audio file and return the transcript and intent.

Request:

Content-Type: multipart/form-data
Body: file=<audio_file>

curl -X POST http://localhost:8001/api/voice-command \
  -F "file=@recording.wav;type=audio/wav"

Successful Response (200):

{
  "status": "success",
  "transcript": "tolong catat jualan basreng 50 ribu",
  "transcript_language": "id",
  "intent": "catat penjualan umkm",
  "confidence_score": 0.9241,
  "all_intent_scores": {
    "catat penjualan umkm": 0.9241,
    "catat pengeluaran": 0.0412,
    "hitung zakat": 0.0214,
    "baca laporan": 0.0091,
    "tidak diketahui": 0.0042
  },
  "processing_time_ms": 1234.56,
  "audio_duration_seconds": 3.2,
  "model_info": {
    "stt_model": "openai/whisper-small",
    "intent_model": "joeddav/xlm-roberta-large-xnli",
    "device": "cpu"
  }
}

Error Response (4xx/5xx):

{
  "status": "error",
  "error_code": "AUDIO_TOO_LONG",
  "message": "Audio duration (75.0s) exceeds maximum allowed (60s).",
  "detail": "...",
  "timestamp": "2024-08-10T12:34:56.789Z"
}

Error Codes:

---

GET /health

Returns service health and model loading status.

curl http://localhost:8001/health

{
  "status": "healthy",
  "service": "Vocallet AI Service",
  "version": "1.0.0",
  "uptime_seconds": 3600.5,
  "models": {
    "stt": {
      "name": "openai/whisper-small",
      "loaded": true,
      "load_time_seconds": 12.3,
      "device": "cpu",
      "error": null
    },
    "intent": {
      "name": "joeddav/xlm-roberta-large-xnli",
      "loaded": true,
      "load_time_seconds": 25.7,
      "device": "cpu",
      "error": null
    }
  }
}

Status values: healthy (both models loaded) | degraded (one model failed) | unhealthy (both failed)

---

GET /health/models

Detailed model information including GPU memory usage (if applicable).

curl http://localhost:8001/health/models

---

GET /health/ping

Simple liveness probe for Docker/Kubernetes healthchecks.

curl http://localhost:8001/health/ping
# Response: {"pong": true}

---

🧠 Model Information

Whisper STT Models

Intent Classification Models

---

🎵 Audio Format Support

---

🔧 Troubleshooting

"Model download is slow"

Models are downloaded from Hugging Face Hub on first run:

• whisper-small → ~242MB
• xlm-roberta-large-xnli → ~1.1GB

After the first download, they are cached in ./models_cache/ and reused on subsequent restarts. To pre-warm the cache:

python -c "
from transformers import pipeline
pipeline('automatic-speech-recognition', 'openai/whisper-small', model_kwargs={'cache_dir': './models_cache'})
pipeline('zero-shot-classification', 'joeddav/xlm-roberta-large-xnli', model_kwargs={'cache_dir': './models_cache'})
"

---

"CUDA out of memory"

Switch to CPU or a smaller model:

# In .env:
DEVICE=cpu
STT_MODEL_NAME=openai/whisper-tiny

Or use float16 for less VRAM usage (GPU only):

DEVICE=cuda
TORCH_DTYPE=float16

---

"Audio processing failed" / librosa error

Ensure ffmpeg is installed and available in your PATH:

ffmpeg -version

If missing, install it:

# Ubuntu
sudo apt-get install ffmpeg

# macOS
brew install ffmpeg

---

"Import errors" / ModuleNotFoundError

Make sure you are inside the virtual environment:

source venv/bin/activate  # Linux/macOS
venv\Scripts\activate     # Windows

# Then reinstall:
pip install torch torchaudio --index-url https://download.pytorch.org/whl/cpu
pip install -r requirements.txt

---

"TranscriptionError: Whisper returned an empty transcript"

The audio may be:

• Completely silent
• Contains only noise without recognizable speech
• Too short (< 0.5 seconds)

Try recording a clear, audible voice command.

---

🐳 Docker Deployment

Build and run with Docker Compose

# Build the image
docker-compose build

# Start service
docker-compose up -d

# View logs
docker-compose logs -f vocallet-ai

# Stop service
docker-compose down

Environment variables for Docker

# Pass HF token securely via host environment
export HF_TOKEN=hf_your_token_here
docker-compose up -d

Check service health

docker-compose ps
curl http://localhost:8001/health/ping

---

🔗 Integration with Node.js / Express

Example: Forwarding audio from Express to the Python microservice.

const axios = require('axios');
const FormData = require('form-data');
const multer = require('multer');

const upload = multer({ storage: multer.memoryStorage() });

// Express route: POST /voice-command
router.post('/voice-command', upload.single('audio'), async (req, res) => {
  try {
    const form = new FormData();
    form.append('file', req.file.buffer, {
      filename: req.file.originalname,
      contentType: req.file.mimetype,
    });

    const response = await axios.post(
      'http://localhost:8001/api/voice-command',
      form,
      {
        headers: form.getHeaders(),
        timeout: 60000  // 60s timeout for model inference
      }
    );

    res.json(response.data);
  } catch (err) {
    const detail = err.response?.data?.detail || err.message;
    res.status(err.response?.status || 500).json({
      error: 'voice_processing_failed',
      detail
    });
  }
});

---

🧪 Running Tests

# Install dev dependencies
pip install -r requirements-dev.txt

# Run all tests
pytest tests/ -v

# Run with coverage report
pytest tests/ -v --cov=app --cov-report=html

# Open coverage report
open htmlcov/index.html  # macOS
xdg-open htmlcov/index.html  # Linux

---

✅ First Run Checklist

When you start the service for the first time, here is what happens:

1. FastAPI boots up — Uvicorn starts, lifespan context begins
2. detect_device() runs — Auto-detects CUDA → MPS → CPU
3. asyncio.gather() kicks off — Both models begin downloading/loading concurrently
4. Whisper downloads — ~242MB for whisper-small; goes to ./models_cache/
5. XLM-RoBERTa downloads — ~1.1GB for xlm-roberta-large-xnli; goes to ./models_cache/
6. Models load into memory — Weights deserialized into PyTorch; moved to target device
7. "Vocallet AI Service ready!" is logged — Now accepting requests
8. GET /health returns "status": "healthy" — Both models are live

⏱️ Total startup time on first run (CPU): 5–25 minutes (mostly download) ⏱️ Total startup time on subsequent runs (cache warm): 30–90 seconds (model load only)

---

🌐 Available Endpoints Summary