CPU-Only Real-Time Speech-to-Text API Deployed for Private Voice Workflows

How GenAI Protos packaged Moonshine STT into an always-on, low-footprint English transcription service with OpenAI-compatible access.

AI Answer Summary

GenAI Protos deployed Moonshine STT as an always-on CPU speech-to-text service for English real-time transcription workflows. The service runs without GPU dependency, exposes OpenAI-compatible audio transcription routes, and supports low-footprint voice capture for edge, accessibility, and private voice application prototypes.

01

Executive Summary

GenAI Protos deployed Moonshine STT as a lightweight speech-to-text service for English voice workflows that do not need to occupy GPU capacity. The service is designed for always-on operation with a small CPU memory footprint.

The implementation exposes OpenAI-compatible audio transcription endpoints through the private gateway and includes additional operational routes for health, model metadata, and raw transcription output.

The result is a practical edge-friendly STT pattern for voice commands, live transcription, accessibility tools, call logging, and rapid voice AI prototyping.

02

At a Glance

Use case
Always-on CPU speech-to-text service for English voice workflows.
Deployment style
FastAPI service wrapping Moonshine ONNX, running without GPU dependency.
Primary interface
OpenAI-compatible audio transcription endpoint through the GenAI Protos gateway.
Resource profile
Approximately 300 MB RAM for tiny and 600 MB RAM for base; 0 GPU.
Operational profile
Always-on service designed for continuous low-impact operation.
Technical outcome
Deployed as part of the GenAI Protos DGX Spark model catalog and private gateway pattern.
03

The Challenge

Not every voice AI workflow should consume GPU capacity. Voice commands, accessibility overlays, edge devices, local dictation, and always-on capture often need simple, fast transcription with predictable resource use.

The challenge was to package a CPU-only speech model as a private API service that could stay available continuously while remaining easy for applications to call.

  • Deliver speech-to-text without depending on GPU availability.
  • Keep the service small enough for always-on operation.
  • Expose transcription through a familiar API shape.
  • Support raw text and JSON-style responses for different application needs.
  • Document performance, resource footprint, and model options for developers.
04

What GenAI Protos Built

GenAI Protos packaged Moonshine STT into a private CPU transcription service with gateway access and lightweight operations.

  • FastAPI service wrapping Moonshine ONNX models.
  • OpenAI-compatible transcription route for application compatibility.
  • Tiny and base model options for different footprint and quality needs.
  • Always-on CPU deployment with no GPU requirement.
  • Operational endpoints for health, model listing, feature metadata, and raw text output.
05

Solution Architecture

The architecture is intentionally small: CPU model serving, gateway access, and simple transcript output.

CPU-Only Real-Time Speech-to-Text API Deployed for Private Voice Workflows Architecture Diagram
Voice input
Audio from commands, meetings, lectures, accessibility tools, or local voice apps.
Gateway layer
Provides governed access and routes audio transcription requests.
STT service
FastAPI service runs Moonshine ONNX inference on CPU.
Model options
FastAPI service runs Moonshine ONNX inference on CPU.
Model options
Tiny and base models support tradeoffs between memory footprint and transcription performance.
Output layer
Returns JSON, verbose JSON, text, or raw transcription depending on the route.
06

Prompt-to-Output Workflow

The voice-to-text workflow shows how lightweight English audio transcription runs through a private gateway and CPU-only Moonshine service for low-footprint voice applications.

1
Audio Submission

A user or application sends an audio file to the private transcription endpoint.

2
Gateway Routing

The gateway authenticates the request and forwards it to the CPU STT service.

3
CPU Inference

Moonshine runs inference through the selected tiny or base model.

4
Transcript Return

The service returns transcript text with optional performance metadata.

5
Voice Workflow Use

The transcript can power commands, captions, logging, accessibility, or downstream AI workflows.

07

Implementation Highlights

This section captures the CPU inference design, always-on operating model, API compatibility, model options, and operational visibility provided by the Moonshine STT service.

CPU inference
Moonshine was deployed through ONNX runtime without GPU dependency.
Always-on operation
The service was designed for continuous availability with low idle memory usage.
API compatibility
Standard audio transcription endpoints support easy integration.
Model choice
Tiny and base models provide deployment flexibility for footprint and speed.
Operational visibility
Health and model metadata routes make the service easier to inspect and operate.
08

Measured Technical Details

The technical details below record the available models, language scope, RAM footprint, GPU usage, performance profile, configured limits, cost fields, and catalog context.

Models
Moonshine tiny and Moonshine base.
Language scope
English-only transcription according to the model reference.
RAM footprint
Approximately 300 MB for tiny and 600 MB for base.
GPU usage
0 GPU required.
Performance
Base model documented at around 9x real-time speedup in the reference.
Rate limit
200 requests per minute and 60,000 tokens per minute configured.
Cost fields
Input cost configured per second for tiny and base model entries.
Catalog context
Part of a DGX Spark catalog containing 30 model names across 19 services.
09

Why This Matters

The value of this service is not only speech-to-text. The stronger pattern is an always-on, CPU-only transcription layer that can support voice capture without competing with GPU-heavy AI workloads.

Always-On Voice CaptureApplications can keep speech input available for commands, accessibility, logging, and lightweight transcription workflows.
GPU Capacity ProtectionCPU inference keeps accelerator memory free for larger LLM, image, video, or multilingual speech workloads.
Edge-Friendly IntegrationThe low memory footprint makes the pattern suitable for local, private, low-bandwidth, or offline-first environments.
Developer SimplicityOpenAI-compatible transcription access gives application teams a familiar route for adding voice-to-text features.
10

Results

The implementation provides a private always-on STT service that is practical for voice applications where GPU use would be unnecessary or expensive.

Outcome What changed
CPU-only transcription Speech-to-text runs without occupying GPU capacity.
Always-on readiness The service can remain available for voice input with a low system footprint.
Developer compatibility OpenAI-compatible transcription access simplifies integration.
Edge-friendly pattern The service is suitable for local, low-bandwidth, or offline-first voice workflows.
Operational clarity Model options, response formats, performance headers, and health routes are documented.
11

Reusable Pattern

This pattern is useful when a private voice system needs constant availability without tying up accelerator resources.

  • Use CPU STT for always-on or low-throughput voice capture.
  • Reserve GPU models for heavier multilingual or long-form transcription workloads.
  • Expose the service through a standard transcription API.
  • Document response formats and performance metadata for downstream systems.
  • Pair with local TTS for compact voice I/O systems.

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