Transcribe Ambient Zero-Cloud AI

Enterprise Ambient Audio DSP & Local LLM Transcription Pipeline

Local AI Ambient Audio Transcription Pipeline Architecture
Architecture Visualization • On-Premise DSP & Local Edge AI 2026 - GarretTech Solutions
Portfolio Showcase Highlight

Transforming Unconditioned Ambient Audio into Auditable Business Intelligence — 100% On-Premise.

Standard cloud speech APIs choke on room reverberation, air-conditioner hum, and cross-talk, while sending sensitive corporate conversations over third-party servers. This production pipeline couples FFmpeg DSP spectral conditioning, INT8 Quantized Faster-Whisper, and Ollama Local LLM Polish to achieve zero-cloud, zero-cost, enterprise-grade transcription.

$0.00
Marginal API Costs
100%
Data Sovereignty (Air-Gapped)
~60%
Whisper Latency Cut via DSP
0
Cloud Egress Points / Leaks

Unified 4-Stage Architectural Pipeline

Click any stage to reveal operational mechanics & code triggers
Stage 01

Ingestion & Diagnostics

Batch scans watched folders. `ffprobe` and `TinyTag` extract sample rates, codecs, and durations while classifying source tags (`[Voicemail]`, `[OBS]`, `[EarSpy]`).

Auto-Classification Active
Stage 02

DSP Conditioning

FFmpeg multi-stage filter: `afftdn` FFT noise suppression (-30dB floor), two-way silence stripping (-30dB), and `loudnorm` normalization to 16kHz mono.

afftdn + silenceremove
Stage 03

Quantized Faster-Whisper

CTranslate2 INT8 quantized small model executes parallel beam search (3 CPU threads / 2 workers). Voice Activity Detection (VAD) rejects hallucination loops.

CTranslate2 INT8 Engine
Stage 04

Ollama LLM Polish

Locally served `llama3` acts as an automated acoustic archivist. Eliminates stutters, adds speaker flow structure, and prepends ISO metadata audit headers.

Zero-Cloud Llama 3
Stage 01 Details

Source Ingestion & Dynamic Routing

Module: src/transcribe_ambient.py

The pipeline monitors target directories for arbitrary multi-format media (`.wav`, `.mp3`, `.m4a`, `.aac`, `.amr`, `.3ga`). It immediately executes `ffprobe` to determine audio channel geometry and bitrates, while parsing regex timestamp patterns to establish canonical chronological file naming.

Key Engineering Advantages:
  • Zero-data loss handling of legacy phone codecs (`amrnb`/`amrwb`) and mobile voice memos.
  • Automated tagging: `[Voicemail]`, `[OBS_ScreenCap]`, `[EarSpy_Ambient]`.
  • Prevents duplicate processing by checking existing transcript signatures.
extract_metadata & determine_source Python 3.10+ 
def determine_source(filename, metadata, rules=None):
    if rules is None:
        rules = load_source_rules()  # Configurable via sources_config.json
    for rule in rules:
        if matches_source_rule(filename, metadata, rule):
            return rule.get("tag"), bool(rule.get("high_sensitivity", False))
    return None, False  # Clean fallback without Unknown_Source