LOCAL AI INFRASTRUCTURE · SPEECH

Local ASR Server

A local speech-recognition building block for exploring voice workflows that can stay on infrastructure you control. The goal is to test where local ASR is useful, what constraints appear, and how it can plug into private AI applications.

DECIDE

Why keep speech recognition closer to where audio is captured?

The project exists to explore whether private voice workflows can reduce dependence on hosted transcription APIs while preserving a useful application boundary.

Privacy boundary

Keep speech closer to where it is captured instead of making a hosted transcription API mandatory.

Control boundary

Make the speech layer something a private AI workflow can integrate and reason about explicitly.

BUILD

Treat ASR as reusable infrastructure, not an isolated demo.

The intended role is a local speech-recognition primitive that can plug into other private AI applications rather than remaining a one-off transcription screen.

Local speech layer

Own the recognition step inside infrastructure you control.

Application boundary

Keep the speech capability usable by higher-level private AI workflows.

Constraint discovery

Use the implementation to expose runtime and hardware trade-offs that a product must handle.

TEST

Put the speech primitive inside a real private workflow.

Private meeting intelligence is one concrete use case for testing whether local ASR remains useful once it becomes part of a broader product pipeline.

RELATED USE CASE

Meeting intelligence

Local ASR can act as the speech layer for workflows that need transcripts before adding speaker context, analysis or project memory.

Explore ClosedRoom

MEASURE

Measure the constraints before making performance claims.

The questions already defined for this project are runtime behavior, latency, hardware constraints and integration into local AI workflows. Numeric benchmark results are not published on this page yet.

EVIDENCE BOUNDARY

This page defines the measurement target, not a benchmark result.

The project is being used to understand where local ASR is viable and what hardware/runtime constraints appear. Until representative measurements are published here, the page should not imply a latency or quality advantage over hosted alternatives.

Supported today

Local ASR is positioned as an infrastructure primitive
Private/offline-first workflows are the target integration context
Latency, runtime behavior and hardware constraints are explicit evaluation targets

Not claimed here

Published latency benchmark on representative hardware
Published transcription-quality comparison against hosted alternatives

DECIDE AGAIN

Keep the local path as a hypothesis to validate, not a universal answer.

The current direction is to use local ASR where privacy and infrastructure control matter, while withholding stronger performance conclusions until the target-device evidence exists.

CURRENT DECISION

Local-first for the boundary; evidence-first for performance.

Local ASR is valuable as a controllable speech primitive for private workflows. The next decision depends on measured runtime, latency and hardware behavior rather than assuming local execution is automatically the best option.

Local execution remains the default experiment boundary
Private meeting workflows provide a concrete test bed
Performance conclusions wait for representative measurements