How I Trained My Own On-Premises Speech-to-Speech AI System (Fully Offline)

#voip #sip #ai #gpu

In this post, I’ll share how I trained and deployed a complete speech-to-speech AI pipeline that runs entirely offline, without any dependency on third-party cloud APIs.

Using open-source models and GPU acceleration, I built a multilingual voice communication engine that can listen, understand, translate, and speak in real time.

What I Built

The system captures live audio, detects speech, transcribes it, processes it through a local language model, translates it into another language, and finally speaks it back — all in milliseconds.

Here’s a summary of the steps:

Capture incoming voice audio in raw PCM format
Detect voice activity and trim silence
Transcribe speech using Whisper
Generate intelligent responses using a fine-tuned LLaMA model
Translate back to the original language using NLLB
Convert the translated text to speech using MMS TTS
Send the audio response back to the user in real time

Tech Stack

Language: CSharp, Python
Audio Format: 16kHz, mono, 16-bit PCM
Speech Recognition: Whisper (OpenAI)
VAD: Silero VAD
LLM: LLaMA 3.2B / 8B (fine-tuned)
Translation: Meta NLLB-200 3.3B
Text-to-Speech: MMS TTS (Facebook)
Hardware: NVIDIA H200 GPU (Blackwell), also tested with H100

Pipeline Overview

Audio Input
Voice Activity Detection (Silero Model)
Speech Recognition (Whisper)
Language Model (LLaMA)
Translation (NLLB)
Text-to-Speech (MMS TTS)
Audio Output

Hardware Recommendations

To achieve real-time performance, I recommend:

Primary GPU: NVIDIA H200 (Blackwell, 141GB HBM3e)
Alternative: NVIDIA H100 (80GB HBM3)

What’s Next

I’m currently extending this system to include:

Language-based routing for SIP calls
Real-time speaker sentiment detection
WebRTC Integration for Website-Based AI Voice Agents

To test and develop an AI-powered support agent, I connected the pipeline with our SIP Server by using SIP SDK to handle real-time RTP audio, enabling full duplex voice conversations between callers and the AI agent over standard VoIP calls.