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  1. Home
  2. AI Models
  3. Speech & Audio
  4. DeepSpeech
open sourcespeech

DeepSpeech

Tiny offline speech-to-text — runs on Raspberry Pi, full data privacy

Developed by Mozilla

Try Model
~47MParams
YesAPI
stableStability
0.9.3Version
Mozilla Public License 2.0License
TensorFlowFramework
YesRuns Local

Playground

Implementation Example

Example Prompt

user input
deepspeech --model deepspeech-0.9.3-models.pbmm --scorer deepspeech-0.9.3-models.scorer --audio test_audio.wav

Model Output

model response
Returns plain-text transcription such as: 'experience proves this' — character-level decoded output suitable for further processing (capitalization, punctuation) by downstream tools.

Examples

Real-World Applications

  • Offline voice assistants
  • accessibility apps
  • IoT smart home
  • privacy-sensitive transcription
  • embedded captioning
  • voice-controlled robotics.

Docs

Model Intelligence & Architecture

What is DeepSpeech?

DeepSpeech is an open-source automatic speech recognition (ASR) engine originally developed by Mozilla based on a research paper from Baidu. First released in 2017 with the final official version (0.9.3) in late 2020, it produces character-level speech transcription using an end-to-end deep learning approach.

It's licensed under the Mozilla Public License 2.0, free for commercial use. While Mozilla deprecated active DeepSpeech development in 2021, the project lives on through community forks and the Coqui STT successor.

Why DeepSpeech Is Still Used in 2026

DeepSpeech remains popular for privacy-first, offline, embedded speech recognition on devices like Raspberry Pi, Jetson Nano, and mobile phones. Its tiny model size (~50 MB for the streaming variant) and ability to run entirely on-device — with zero data leaving the system — make it valuable for accessibility apps, smart home devices, and privacy-sensitive applications.

Key Features and Capabilities

DeepSpeech supports real-time streaming speech recognition, language model integration via KenLM, custom hot-word detection, and full offline operation. It produces character-level outputs and supports custom language models for domain-specific vocabulary.

Who Should Use DeepSpeech?

DeepSpeech is built for embedded developers, accessibility tool makers, privacy-focused app developers, smart home device manufacturers, and research teams needing tiny, offline ASR.

Top Use Cases

Real-world applications include offline voice assistants, accessibility apps for the deaf and hard of hearing, IoT and smart home voice control, privacy-sensitive transcription (legal, medical), embedded captioning for kiosks, and voice-controlled robotics.

Where Can You Run It?

DeepSpeech runs on Linux, macOS, Windows, Raspberry Pi 3/4, NVIDIA Jetson, Android, iOS, and via JavaScript in the browser. It's available via pip install deepspeech, with native libraries for C++, Java, .NET, and Python.

How to Use DeepSpeech (Quick Start)

Install: pip install deepspeech. Download the pre-trained model: curl -LO https://github.com/mozilla/DeepSpeech/releases/download/v0.9.3/deepspeech-0.9.3-models.pbmm. Transcribe: deepspeech --model deepspeech-0.9.3-models.pbmm --audio audio.wav.

When Should You Choose DeepSpeech?

Choose DeepSpeech when you need tiny, offline, embedded ASR on resource-constrained devices. For modern higher-accuracy alternatives, look at Whisper.cpp tiny.en, Vosk, Coqui STT, or wav2vec 2.0.

Pricing

DeepSpeech is completely free under Mozilla Public License 2.0.

Pros and Cons

Pros: ✔ MPL-2.0 license ✔ Tiny ~50 MB model ✔ Runs on Raspberry Pi ✔ Streaming real-time ✔ Cross-platform (incl. mobile) ✔ Fully offline

Cons: ✘ Mozilla stopped active development ✘ Lower accuracy than Whisper ✘ English-focused (community models for other langs) ✘ Older architecture

Final Verdict

DeepSpeech is still relevant for privacy-first embedded ASR in 2026, though Whisper.cpp and Coqui STT are recommended for new projects. Discover more speech AI at FreeAPIHub.com.

Evaluation

Advantages & Limitations

Advantages
  • ✓ MPL-2.0 license
  • ✓ Tiny ~50 MB model
  • ✓ Runs on Raspberry Pi
  • ✓ Streaming real-time
  • ✓ Cross-platform (mobile included)
  • ✓ Fully offline
Limitations
  • ✗ Mozilla stopped active development
  • ✗ Lower accuracy than Whisper
  • ✗ English-focused
  • ✗ Older architecture

Important Notice

Verify Before You Decide

Last verified · Apr 29, 2026

The details on this page — including pricing, features, and availability — are based on our last review and may not reflect the provider's current offering. Providers update their products frequently, sometimes without prior notice.

What may have changed

Pricing Plans
Features & Limits
Availability
Terms & Policies

Always visit the official provider website to confirm the latest pricing, terms, and feature availability before subscribing or integrating.

Check official site

External Resources

Try the Model Official Website Source Code

Technical Details

Architecture
RNN-based End-to-End ASR with CTC loss
Stability
stable
Framework
TensorFlow
License
Mozilla Public License 2.0
Release Date
2017-11-30
Signup Required
No
API Available
Yes
Runs Locally
Yes

Rate Limits

No limits self-hosted

Pricing

Completely free under MPL-2.0

Best For

Embedded developers needing tiny, offline, private speech recognition

Alternative To

Google Speech-to-Text, AWS Transcribe, Whisper (for embedded)

Compare With

deepspeech vs whisperdeepspeech vs voskdeepspeech vs coquifree offline speech recognitionraspberry pi speech recognition

Tags

#Offline AI#Embedded AI#Deepspeech#Mozilla#Open Source AI#speech-recognition

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