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mozilla/deepspeech

★ 26,750C++Audience · developerComplexity · 4/5QuietLicenseSetup · moderate

TLDR

Mozilla's offline speech-to-text engine that converts spoken audio to text on-device without cloud connectivity. Runs efficiently on low-power hardware like Raspberry Pi.

Mindmap

mindmap
  root((DeepSpeech))
    What it does
      Speech to text
      Offline processing
      On-device only
    Key features
      Low power hardware
      Real-time transcription
      Privacy focused
    Tech stack
      C++
      TensorFlow
      Python bindings
    Use cases
      Smart home devices
      Transcription pipelines
      Privacy applications
    Status
      Discontinued
      Historical reference
      Community maintained

Things people build with this

USE CASE 1

Build offline voice control for smart home devices that don't require internet connectivity.

USE CASE 2

Create privacy-preserving transcription pipelines that process audio locally without sending data to external servers.

USE CASE 3

Deploy real-time speech recognition on embedded systems like Raspberry Pi for resource-constrained environments.

USE CASE 4

Integrate speech-to-text into applications where latency or data sovereignty requirements make cloud APIs impractical.

Tech stack

C++TensorFlowPythonJavaScript

Getting it running

Difficulty · moderate Time to first run · 30min

Requires downloading pre-trained model files and TensorFlow runtime; compilation may be needed depending on platform.

Mozilla Public License 2.0, use freely for any purpose including commercial, but modifications must be shared under the same license.

In plain English

DeepSpeech was Mozilla's open-source speech-to-text engine, software that listens to audio and converts spoken words into written text, entirely on-device without sending anything to the cloud. It was designed to run offline, which made it attractive for privacy-sensitive applications or situations where internet access wasn't available. A key technical achievement was its ability to run on low-power hardware: it could transcribe speech in real time on a Raspberry Pi (a credit-card-sized computer costing around $35), as well as on more powerful GPU servers. This range made it useful for everything from embedded smart home devices to large-scale transcription pipelines. Note: this project has been discontinued by Mozilla and is no longer actively maintained. For developers looking for a similar capability today, Mozilla's work here influenced several successor projects, and alternatives like Whisper (from OpenAI) have largely taken over this space. The code and pre-trained models remain available for historical reference or for projects that need to build on the existing foundation, but you should not start a new project expecting ongoing updates or support.

Copy-paste prompts

Prompt 1
Show me how to set up DeepSpeech on a Raspberry Pi and transcribe audio from a microphone in real time.
Prompt 2
How do I use the pre-trained DeepSpeech models to build a voice command system that works completely offline?
Prompt 3
Walk me through the Python API for DeepSpeech and show an example of transcribing a WAV file.
Prompt 4
What are the hardware requirements and performance benchmarks for running DeepSpeech on different devices?
Prompt 5
How can I fine-tune a DeepSpeech model on custom audio data for better accuracy on domain-specific speech?
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