Building DeepSpeech Binaries

This section describes how to rebuild binaries. We have already several prebuilt binaries for all the supported platform, it is highly advised to use them except if you know what you are doing.

If you’d like to build the DeepSpeech binaries yourself, you’ll need the following pre-requisites downloaded and installed:

It is required to use our fork of TensorFlow since it includes fixes for common problems encountered when building the native client files.

If you’d like to build the language bindings or the decoder package, you’ll also need:

  • SWIG >= 3.0.12. Unfortunately, NodeJS / ElectronJS after 10.x support on SWIG is a bit behind, and while there are pending patches proposed to upstream, it is not yet merged. The proper prebuilt patched version (covering linux, windows and macOS) of SWIG should get installed under native_client/ as soon as you build any bindings that requires it.

  • node-pre-gyp (for Node.JS bindings only)


If you follow these instructions, you should compile your own binaries of DeepSpeech (built on TensorFlow using Bazel).

For more information on configuring TensorFlow, read the docs up to the end of “Configure the Build”.

Checkout source code

Clone DeepSpeech source code (TensorFlow will come as a submdule):

git clone
git submodule sync tensorflow/
git submodule update --init tensorflow/

Bazel: Download & Install

First, install Bazel 3.1.0 following the Bazel installation documentation.

TensorFlow: Configure with Bazel

After you have installed the correct version of Bazel, configure TensorFlow:

cd tensorflow

Compile DeepSpeech


Within your TensorFlow directory, there should be a symbolic link to the DeepSpeech native_client directory. If it is not present, create it with the follow command:

cd tensorflow
ln -s ../native_client

You can now use Bazel to build the main DeepSpeech library, Add --config=cuda if you want a CUDA build.

bazel build --workspace_status_command="bash native_client/" --config=monolithic -c opt --copt=-O3 --copt="-D_GLIBCXX_USE_CXX11_ABI=0" --copt=-fvisibility=hidden //

The generated binaries will be saved to bazel-bin/native_client/.

Compile generate_scorer_package

Following the same setup as for above, you can rebuild the generate_scorer_package binary by adding its target to the command line: //native_client:generate_scorer_package. Using the example from above you can build the library and that binary at the same time:

bazel build --workspace_status_command="bash native_client/" --config=monolithic -c opt --copt=-O3 --copt="-D_GLIBCXX_USE_CXX11_ABI=0" --copt=-fvisibility=hidden // //native_client:generate_scorer_package

The generated binaries will be saved to bazel-bin/native_client/.

Compile Language Bindings

Now, cd into the DeepSpeech/native_client directory and use the Makefile to build all the language bindings (C++ client, Python package, Nodejs package, etc.).

cd ../DeepSpeech/native_client
make deepspeech

Installing your own Binaries

After building, the library files and binary can optionally be installed to a system path for ease of development. This is also a required step for bindings generation.

PREFIX=/usr/local sudo make install

It is assumed that $PREFIX/lib is a valid library path, otherwise you may need to alter your environment.

Install Python bindings

Included are a set of generated Python bindings. After following the above build and installation instructions, these can be installed by executing the following commands (or equivalent on your system):

cd native_client/python
make bindings
pip install dist/deepspeech*

The API mirrors the C++ API and is demonstrated in Refer to deepspeech.h for documentation.

Install NodeJS / ElectronJS bindings

After following the above build and installation instructions, the Node.JS bindings can be built:

cd native_client/javascript
make build
make npm-pack

This will create the package deepspeech-VERSION.tgz in native_client/javascript.

Install the CTC decoder package

To build the ds_ctcdecoder package, you’ll need the general requirements listed above (in particular SWIG). The command below builds the bindings using eight (8) processes for compilation. Adjust the parameter accordingly for more or less parallelism.

cd native_client/ctcdecode
make bindings NUM_PROCESSES=8
pip install dist/*.whl


RPi3 ARMv7 and LePotato ARM64

We do support cross-compilation. Please refer to our mozilla/tensorflow fork, where we define the following --config flags:

  • --config=rpi3 and --config=rpi3_opt for Raspbian / ARMv7

  • --config=rpi3-armv8 and --config=rpi3-armv8_opt for ARMBian / ARM64

So your command line for RPi3 and ARMv7 should look like:

bazel build --workspace_status_command="bash native_client/" --config=monolithic --config=rpi3 --config=rpi3_opt -c opt --copt=-O3 --copt=-fvisibility=hidden //

And your command line for LePotato and ARM64 should look like:

bazel build --workspace_status_command="bash native_client/" --config=monolithic --config=rpi3-armv8 --config=rpi3-armv8_opt -c opt --copt=-O3 --copt=-fvisibility=hidden //

While we test only on RPi3 Raspbian Buster and LePotato ARMBian Buster, anything compatible with armv7-a cortex-a53 or armv8-a cortex-a53 should be fine.

The deepspeech binary can also be cross-built, with TARGET=rpi3 or TARGET=rpi3-armv8. This might require you to setup a system tree using the tool multistrap and the multitrap configuration files: native_client/multistrap_armbian64_buster.conf and native_client/multistrap_raspbian_buster.conf. The path of the system tree can be overridden from the default values defined in through the RASPBIAN make variable.

cd ../DeepSpeech/native_client
make TARGET=<system> deepspeech

Android devices support

We have support for Android relying on TensorFlow Lite, with Java and JNI bindinds. For more details on how to experiment with those, please refer to the section below.

Please refer to TensorFlow documentation on how to setup the environment to build for Android (SDK and NDK required).

Using the library from Android project

We provide uptodate and tested libdeepspeech usable as an AAR package, for Android versions starting with 7.0 to 11.0. The package is published on JCenter, and the JCenter repository should be available by default in any Android project. Please make sure your project is setup to pull from this repository. You can then include the library by just adding this line to your, adjusting VERSION to the version you need:

implementation ''


You can build the using (ARMv7):

bazel build --workspace_status_command="bash native_client/" --config=monolithic --config=android --config=android_arm --define=runtime=tflite --action_env ANDROID_NDK_API_LEVEL=21 --cxxopt=-std=c++14 --copt=-D_GLIBCXX_USE_C99 //

Or (ARM64):

bazel build --workspace_status_command="bash native_client/" --config=monolithic --config=android --config=android_arm64 --define=runtime=tflite --action_env ANDROID_NDK_API_LEVEL=21 --cxxopt=-std=c++14 --copt=-D_GLIBCXX_USE_C99 //

Building libdeepspeech.aar

In the unlikely event you have to rebuild the JNI bindings, source code is available under the libdeepspeech subdirectory. Building depends on shared object: please ensure to place into the libdeepspeech/libs/{arm64-v8a,armeabi-v7a,x86_64}/ matching subdirectories.

Building the bindings is managed by gradle and should be limited to issuing ./gradlew libdeepspeech:build, producing an AAR package in ./libdeepspeech/build/outputs/aar/.

Please note that you might have to copy the file to a local Maven repository and adapt file naming (when missing, the error message should states what filename it expects and where).

Building C++ deepspeech binary

Building the deepspeech binary will happen through ndk-build (ARMv7):

cd ../DeepSpeech/native_client
$ANDROID_NDK_HOME/ndk-build APP_PLATFORM=android-21 APP_BUILD_SCRIPT=$(pwd)/ NDK_PROJECT_PATH=$(pwd) APP_STL=c++_shared TFDIR=$(pwd)/../tensorflow/ TARGET_ARCH_ABI=armeabi-v7a

And (ARM64):

cd ../DeepSpeech/native_client
$ANDROID_NDK_HOME/ndk-build APP_PLATFORM=android-21 APP_BUILD_SCRIPT=$(pwd)/ NDK_PROJECT_PATH=$(pwd) APP_STL=c++_shared TFDIR=$(pwd)/../tensorflow/ TARGET_ARCH_ABI=arm64-v8a

Android demo APK

Provided is a very simple Android demo app that allows you to test the library. You can build it with make apk and install the resulting APK file. Please refer to Gradle documentation for more details.

The APK should be produced in /app/build/outputs/apk/. This demo app might require external storage permissions. You can then push models files to your device, set the path to the file in the UI and try to run on an audio file. When running, it should first play the audio file and then run the decoding. At the end of the decoding, you should be presented with the decoded text as well as time elapsed to decode in miliseconds.

This application is very limited on purpose, and is only here as a very basic demo of one usage of the application. For example, it’s only able to read PCM mono 16kHz 16-bits file and it might fail on some WAVE file that are not following exactly the specification.

Running deepspeech via adb

You should use adb push to send data to device, please refer to Android documentation on how to use that.

Please push DeepSpeech data to /sdcard/deepspeech/, including:

  • output_graph.tflite which is the TF Lite model

  • External scorer file (available from one of our releases), if you want to use the scorer; please be aware that too big scorer will make the device run out of memory

Then, push binaries from native_client.tar.xz to /data/local/tmp/ds:

  • deepspeech



You should then be able to run as usual, using a shell from adb shell:

user@device$ cd /data/local/tmp/ds/
user@device$ LD_LIBRARY_PATH=$(pwd)/ ./deepspeech [...]

Please note that Android linker does not support rpath so you have to set LD_LIBRARY_PATH. Properly wrapped / packaged bindings does embed the library at a place the linker knows where to search, so Android apps will be fine.

Delegation API

TensorFlow Lite supports Delegate API to offload some computation from the main CPU. Please refer to TensorFlow’s documentation for details.

To ease with experimentations, we have enabled some of those delegations on our Android builds: * GPU, to leverage OpenGL capabilities * NNAPI, the Android API to leverage GPU / DSP / NPU * Hexagon, the Qualcomm-specific DSP

This is highly experimental:

  • Requires passing environment variable DS_TFLITE_DELEGATE with values of gpu, nnapi or hexagon (only one at a time)

  • Might require exported model changes (some Op might not be supported)

  • We can’t guarantee it will work, nor it will be faster than default implementation

Feedback on improving this is welcome: how it could be exposed in the API, how much performance gains do you get in your applications, how you had to change the model to make it work with a delegate, etc.

See the support / contact details