Voice AI Overview — Voice AI with Local Models (Chapter 1)

Voice AI systems transform spoken language into actionable responses delivered as synthesized speech. The core architecture consists of three sequential stages: automatic speech recognition (ASR) converts audio waveforms into text, a language model processes that text and generates responses, and text-to-speech (TTS) converts responses back into audio.

Each stage introduces latency and potential errors. ASR accuracy depends on audio quality, background noise, and speaker dialect. LLM generation depends on prompt engineering and model size. TTS quality depends on voice cloning fidelity and prosody naturalness. Understanding these tradeoffs guides system design decisions.

The local-only approach eliminates network transmission delays and privacy concerns. Round-trip time for voice interactions depends solely on inference speeds rather than API response times. This matters for real-time applications like customer service agents or interactive robots.

Modern open-source models enable capable performance across all three stages. Whisper provides accurate multilingual transcription. Large language models like Llama variants handle reasoning and response generation. TTS engines like Kokoro, XTTS-v2, and Piper offer voice options across quality and speed spectrums.

Pipeline architecture choices affect memory footprint and throughput. Running all models simultaneously requires substantial VRAM, but batched processing can reduce peak memory usage at the cost of latency. Threading models across CPU cores supplements GPU capacity when VRAM is constrained.

Local verification checkpoint

Run the smallest example from this chapter in a local workspace and record the package version, runtime, data path, and observed output. If the result depends on model size, vector count, CPU/GPU backend, or available memory, note that constraint beside the exercise so the lesson remains reproducible.