AI-Powered Voicemail Detection in VICIdial

VICIdial voicemail detection automates identification of human vs. machine responses, enabling smarter routing, reporting, and agent workflows. This guide covers selecting an AI model, integrating with VICIdial AGI, tuning performance, and monitoring outcomes.

Prerequisites

• VICIdial 2.15+ installation with AGI enabled
• Python 3.8+ environment on the dialer server
• Hugging Face or similar AI model repository access
• Dependencies: PyTorch, torchaudio (or TensorFlow equivalents)
• Basic knowledge of AGI scripting and Go/Python programming

1  Selecting an AI Model

Popular open-source options:

Recommendation: Start with AudioClassifierCNN on Hugging Face for out-of-the-box performance.

2  Integration Architecture

• AGI Bridge: Use a Python AGI script to stream the first 3 seconds of audio from Asterisk to the AI model.
• Model Server: Host model inference locally or on a GPU-enabled container for low latency.
• Decision Logic: AGI script returns HUMAN or VOICEMAIL; Asterisk dialplan routes accordingly.
• Webhook/Event: Log detection results to an external service or database for reporting.

3  Implementation Steps

1. **Install dependencies:** `pip install torch torchaudio py-agi-framework`.
2. **Download model:** Use `transformers` to load AudioClassifierCNN checkpoint.
3. **Write AGI script:** Capture 3 seconds via AGI `STREAM FILE`, run inference, and return detection code.
4. **Configure dialplan:** In `extensions.conf`, invoke AGI script before answering or early in call logic.
5. **Handle routing:** Based on AGI result, route to Live Agent or Voicemail context.

4  Tuning & Performance

• **Audio Preprocessing:** Normalize volume and trim silence to improve accuracy.
• **Batch Inference:** Queue multiple calls per script to leverage GPU throughput.
• **Threshold Adjustment:** Calibrate model confidence threshold to balance false positives vs. false negatives.
• **Caching Results:** Cache repeated numbers’ results to avoid reprocessing callbacks.

5  Monitoring & Analytics

• Log each detection event with timestamp, call ID, and confidence score.
• Use Grafana dashboards to display human vs. voicemail rates over time.
• Set alerts for sudden drops in detection accuracy or inference latency > 500 ms.
• Periodically sample recordings for manual validation and model retraining.

Best Practices

1. Retrain or fine-tune your model every quarter with new voicemail samples.
2. Version your AGI scripts and model checkpoints in Git for reproducibility.
3. Isolate inference servers in their own security group to limit external exposure.
4. Use rate limiting to prevent model API abuse under peak loads.
5. Document your inference pipeline and maintain runbooks for troubleshooting.

Next Steps

• Explore multilingual models to detect voicemail in non-English calls.
• Integrate voice biometrics to verify caller identity post-human detection.
• Implement real-time transcription for quality monitoring and analytics.
• Deploy edge inference with TensorRT or ONNX for ultra-low latency in high-volume centers.

For code samples and detailed dialplan snippets, visit the VICIdial AI GitHub repository or consult the Manager Manual.

Commercial Support: For professional assistance and custom integration, email us at vicigeek@gmail.com.