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Building DeepSpeech Binaries

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:

Dependencies

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".

TensorFlow: Clone & Checkout

Clone our fork of TensorFlow and checkout the correct version:

git clone https://github.com/mozilla/tensorflow.git
git checkout origin/r1.13

Bazel: Download & Install

First, find the version of Bazel you need for this TensorFlow release. Next, download and install the correct version of Bazel.

TensorFlow: Configure with Bazel

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

cd tensorflow
./configure

Compile DeepSpeech

Compile libdeepspeech.so & generate_trie

Within your TensorFlow checkout, create a symbolic link to the DeepSpeech native_client directory. Assuming DeepSpeech and TensorFlow checkouts are in the same directory, do:

cd tensorflow
ln -s ../DeepSpeech/native_client ./

You can now use Bazel to build the main DeepSpeech library, libdeepspeech.so, as well as the generate_trie binary. Add --config=cuda if you want a CUDA build.

bazel build --config=monolithic -c opt --copt=-O3 --copt="-D_GLIBCXX_USE_CXX11_ABI=0" --copt=-fvisibility=hidden //native_client:libdeepspeech.so //native_client:generate_trie

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.). Set the environment variable TFDIR to point to your TensorFlow checkout.

TFDIR=~/tensorflow
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 client.py. Refer to deepspeech.h for documentation.

Install Node.JS 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

Cross-building

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 --config=monolithic --config=rpi3 --config=rpi3_opt -c opt --copt=-O3 --copt=-fvisibility=hidden //native_client:libdeepspeech.so //native_client:generate_trie

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

bazel build --config=monolithic --config=rpi3-armv8 --config=rpi3-armv8_opt -c opt --copt=-O3 --copt=-fvisibility=hidden //native_client:libdeepspeech.so //native_client:generate_trie

While we test only on RPi3 Raspbian Stretch and LePotato ARMBian stretch, 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_stretch.conf and native_client/multistrap_raspbian_stretch.conf. The path of the system tree can be overridden from the default values defined in definitions.mk through the RASPBIAN make variable.

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

Android devices

We have preliminary support for Android relying on TensorFlow Lite, with Java and JNI bindinds. For more details on how to experiment with those, please refer to native_client/java/README.md.

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

You can build the libdeepspeech.so using (ARMv7):

bazel build --config=monolithic --config=android --config=android_arm --define=runtime=tflite --action_env ANDROID_NDK_API_LEVEL=21 --cxxopt=-std=c++11 --copt=-D_GLIBCXX_USE_C99 //native_client:libdeepspeech.so

Or (ARM64):

bazel build --config=monolithic --config=android --config=android_arm64 --define=runtime=tflite --action_env ANDROID_NDK_API_LEVEL=21 --cxxopt=-std=c++11 --copt=-D_GLIBCXX_USE_C99 //native_client:libdeepspeech.so

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)/Android.mk 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)/Android.mk NDK_PROJECT_PATH=$(pwd) APP_STL=c++_shared TFDIR=$(pwd)/../../tensorflowx/ TARGET_ARCH_ABI=arm64-v8a