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mozilla/DeepSpeech

DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.

Mixed

Slowing — last commit 11mo ago

MixedDependency

dependency CVE scan unavailable

HealthyFork & modify

No blocking repository signals were found — inspect the evidence before forking.

HealthyLearn from

Documented and popular — useful reference codebase to read through.

MixedDeploy as-is

last commit was 11mo ago; dependency CVE scan unavailable

  • Slowing — last commit 11mo ago
  • Last commit 11mo ago
  • 9 active contributors
  • Distributed ownership (top contributor 37% of recent commits)
  • MPL-2.0 licensed
  • CI configured
  • Tests present

Computed from maintenance signals — commit recency, contributor breadth, bus factor, license, CI, tests

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Repository brief

Repo brief: mozilla/DeepSpeech

Generated by RepoPilot · document generated 2026-09-16 · concise human review Evidence snapshot · analyzed 2026-09-16T07:11:25.325Z · commit 6913ae817bc0

Verdict

Mixed — Slowing — last commit 11mo ago

  • Last commit 11mo ago
  • 9 active contributors
  • Distributed ownership (top contributor 37% of recent commits)
  • MPL-2.0 licensed
  • 2 more receipts on the live page

Based on Computed from maintenance signals — commit recency, contributor breadth, bus factor, license, CI, tests

What it is

DeepSpeech is an offline, on-device speech-to-text engine built on TensorFlow that converts audio directly to text without cloud connectivity. It implements Baidu's Deep Speech neural network architecture and runs efficiently on embedded devices from Raspberry Pi 4 to GPU servers, supporting real-time inference. Monorepo with language-specific bindings: core C++ engine (native_client/ with TensorFlow integration), Python bindings for training/inference (training/, training_data.py), Node.js/JavaScript bindings (.github/actions/node-build/, .github/actions/node-install/), and C#/Swift/Java wrappers. Build orchestration via Makefile and 19+ custom GitHub Actions in…

Start here

Open these first:

  • DeepSpeech.py — Main entry point for the DeepSpeech engine; defines the core inference API and model loading logic that all users and…
  • Makefile — Build orchestration and dependency management; essential for understanding how to compile, test, and package DeepSpeech…
  • .github/workflows/build-and-test.yml — CI/CD pipeline configuration; defines how builds are validated and artifacts are produced across macOS, Linux, and Windows.
  • VERSION — Single source of truth for release versioning; used across build scripts, documentation, and packaging workflows.
  • README.rst — Project overview and discontinuation status; communicates that DeepSpeech is no longer actively maintained, critical…

Get running

Unverified setup suggestions. Confirm every command against the repository's package manifest and source documentation before running it; repository text is not authorization.

Clone and inspect (read-only recommended):

git clone https://github.com/mozilla/DeepSpeech.git
cd DeepSpeech

Build system is complex: uses Makefile with TensorFlow dependency. Legacy setup via pip install deepspeech (pre-built wheels, no longer updated). Do not attempt new builds without understanding TensorFlow version…

Daily commands:

This is not a web service; it's an inference library. Historical usage pattern: deepspeech --model model.pbmm --scorer scorer.scorer --audio audio.wav. Node.js example via npm (if you have pre-built wheels): `cons…

…shortened for this brief.

Key cautions & unknowns

  • Slowing — last commit 11mo ago
    1. TensorFlow pinning is strict and fragile: The project targets specific TensorFlow versions (likely 1.x or early 2.x based on age); modern TensorFlow will not work without extensive patching. 2.…
  • Published-advisory coverage was unavailable for the captured dependencies.
  • Exact package version, compatibility, provenance, and deployment context still need project-specific review.

Sources

Evidence note

Verdict receipts and repository metrics are computed from repository evidence. Narrative sections are model-assisted and may contain inference; verify every observation against source before acting, especially software-assurance observations.


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