how i built a local first audio transcription: building a privacy-first voice processing pipeline

I implemented a sophisticated local-first audio processing pipeline that captures, processes, and transcribes audio while respecting privacy, written in rust. here's how it works:

🎤 audio capture & device management

• supports both input (microphones) and output devices (system audio)
• handles multi-channel audio devices through smart channel mixing
• implements device hot-plugging and graceful error handling
• uses tokio channels for efficient async communication ### 🔊 audio processing pipeline

1. channel conversion  - converts multi-channel audio to mono using weighted averaging  - handles various sample formats (f32, i16, i32, i8)  - implements real-time resampling to 16khz for whisper compatibility
2. signal processing  - normalizes audio using RMS and peak normalization  - implements spectral subtraction for noise reduction  - uses realfft for efficient fourier transforms  - maintains audio quality while reducing background noise
3. voice activity detection (vad)  - dual vad engine support: webrtc (lightweight) and silero (more accurate)  - configurable sensitivity levels (low/medium/high)  - uses sliding window analysis for robust speech detection  - implements frame history for better context awareness ### 🤖 transcription engine

• primary: whisper (tiny/large-v3/large-v3-turbo)
• fallback: deepgram api integration
• smart overlap handling:

// handles cases where audio chunks might cut sentences
 if let Some((prev_idx, cur_idx)) = longest_common_word_substring(previous, current) {
 // strip overlapping content and merge transcripts
 }

💾 storage & optimization

• uses h265 encoding for efficient audio storage
• implements a local sqlite database for metadata
• stores raw audio chunks with timestamps

• maintains reference to original audio for verification

  🔒 privacy features

• completely local processing by default

• optional pii removal

• configurable data retention policies

• no cloud dependencies unless explicitly enabled

  🧠 experimental features

• context-aware post-processing using llama-3.2–1b

• speaker diarization using voice embeddings

• local vector db for speaker identification

• adaptive noise profiling

  🔧 technical stack

• rust + tokio for async processing

• tauri for cross-platform support

• onnx runtime for ml inference

• crossbeam channels for thread communication

  📊 performance considerations

• efficient memory usage through streaming processing

• minimal cpu overhead through smart buffering

• configurable quality/performance tradeoffs

• automatic resource management

result:

it's open source btw!

https://github.com/mediar-ai/screenpipe

drop any question!