Voice AI with Local Models
Learn voice ai with local models through RunLocalAI's practical lens: voice, stt, tts and whisper, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
- B002
- B003
Why this course matters
Voice AI with Local Models is for builders turning local models into working tools, agents and retrieval systems. It connects voice, stt, tts, whisper and audio to the questions RunLocalAI wants every reader to answer before they install, upgrade or scale a model: will it run, what will it cost in memory, what setting changes the result, and how do you verify the answer instead of trusting a demo?
What you will be able to do
By the end, you should be able to explain the main tradeoffs in plain language, choose a safe next experiment, and use the chapter exercises as a repeatable operator checklist. The course favors local evidence, hardware fit, context limits, latency and failure modes over generic AI vocabulary.
How to use this course
Start at chapter one if the topic is new. If you already have a working stack, scan for chapters such as Voice AI Overview, Whisper Installation, Whisper Model Selection and STT Accuracy Tuning and use those lessons as a quality-control pass before changing a workstation, team workflow or production-like local deployment.
- 01Voice AI OverviewVoice AI pipelines succeed or fail at integration points between STT, LLM, and TTS stages.10 min
- 02Whisper InstallationPyTorch-CUDA version mismatches cause silent failures where transcription falls back to CPU with 10x slowdowns.20 min
- 03Whisper Model SelectionModel size choice depends on deployment hardware and acceptable word error rate for the specific use case.15 min
- 04STT Accuracy TuningAudio preprocessing and language specification provide accuracy gains equal to model size upgrades at zero computational cost.20 min
- 05Voice Activity DetectionVAD aggressiveness settings must match deployment environment noise characteristics; office settings differ from manufacturing floors.15 min
- 06TTS Options: KokoroKokoro's strength lies in CPU-capable inference with acceptable quality for conversational responses under 100 words.20 min
- 07TTS: XTTS-v2XTTS-v2 voice cloning enables personalized experiences but requires clean reference audio and patience for generation time.15 min
- 08TTS: PiperPiper prioritizes efficiency for edge deployment at the cost of some voice naturalness compared to larger models.20 min
- 09STT→LLM→TTS PipelinePipeline latency equals the sum of stage latencies unless stages overlap through streaming or concurrency.15 min
- 10Real-Time ArchitectureReal-time voice AI requires treating latency as a first-class requirement with active measurement and optimization.20 min
- 11WebSocket ServerWebSocket architecture shifts complexity from request handling to connection lifecycle and state management.15 min
- 12WebSocket ClientWebSocket clients for voice require concurrent stream handling, bounded buffers, and exponential backoff reconnection to maintain reliability over unstable networks.20 min
- 13Multi-Language SupportMulti-language voice AI requires language detection at the input stage, language-tagged generation in the middle, and language-specific TTS at output—all coordinated through a unified configuration system.20 min
- 14Noise ReductionEffective noise reduction combines VAD to skip processing of non-speech segments, spectral methods for stationary noise, and deep learning models for non-stationary interference.25 min
- 15Voice CloningVoice cloning combines speaker encoding to extract vocal characteristics and conditional TTS synthesis to generate speech matching those characteristics, with recent models requiring only 6-30 seconds of reference audio.20 min
- 16Low-Latency OptimizationLow-latency voice AI requires pipelining across stages, KV cache optimization for LLM, and continuous batching—all measured against a strict latency budget.25 min
- 17Model Quantization for VoiceQuantization reduces voice model memory footprint by 2-4x with INT8/INT4 precision, enabling deployment of larger models on consumer hardware with acceptable quality trade-offs.25 min
- 18Error HandlingReliable voice pipelines use structured error types, exponential backoff retry, circuit breakers, and fallback chains to maintain service despite component failures.25 min
- 19Testing Voice PipelinesVoice pipeline tests require audio fixtures, latency assertions, and quality metrics—testing that the system produces correct output within acceptable time bounds.25 min
- 20Docker DeploymentDocker deployment for voice AI requires GPU-enabled base images, model caching strategies, multi-stage builds for smaller images, and health checks that verify both connectivity and model readiness.25 min
- 21Performance BenchmarksSystematic benchmarking with latency percentiles, throughput metrics, and resource utilization exposes bottlenecks and validates optimization effectiveness across voice pipeline components.25 min
- 22Voice Assistant ProjectA production voice assistant integrates wake word detection, streaming ASR, conversational LLM, and TTS through an orchestrator that manages context and coordinates low-latency processing across components.30 min