This folder contains a native client for running queries on an exported DeepSpeech model, bindings for Python and Node.JS for using an exported DeepSpeech model programatically, and a CTC beam search decoder implementation that scores beams using a language model, needed for training a DeepSpeech model. We provide pre-built binaries for Linux and macOS.
To download the pre-built binaries, use util/taskcluster.py:
python util/taskcluster.py --target /path/to/destination/folder
If you need some binaries different than current master, like v0.2.0-alpha.6, you can use --branch:
python3 util/taskcluster.py --branch "v0.2.0-alpha.6"This will download and extract native_client.tar.xz which includes the deepspeech binary and associated libraries as well as the custom decoder OP. taskcluster.py will download binaries for the architecture of the host by default, but you can override that behavior with the --arch parameter. See the help info with python util/taskcluster.py -h for more details.
If you want the CUDA capable version of the binaries, use --arch gpu. Note that for now we don't publish CUDA-capable macOS binaries.
Running inference might require some runtime dependencies to be already installed on your system. Those should be the same, whatever the bindings you are using:
- libsox2
- libstdc++6
- libgomp1
- libpthread
Please refer to your system's documentation on how to install those dependencies.
For the Python bindings, you can use pip:
pip install deepspeech
Check the main README for more details about setup and virtual environment use.
For Node.JS bindings, use npm install deepspeech to install it. Please note that as of now, we only support Node.JS versions 4, 5 and 6. Once SWIG has support we can build for newer versions.
Check the main README for more details.
If you'd like to build the binaries yourself, you'll need the following pre-requisites downloaded/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
- node-pre-gyp (for Node.JS bindings only)
Create a symbolic link in your TensorFlow checkout to the DeepSpeech native_client directory. If your DeepSpeech and TensorFlow checkouts are side by side in the same directory, do:
cd tensorflow
ln -s ../DeepSpeech/native_client ./
Before building the DeepSpeech client libraries, you will need to prepare your environment to configure and build TensorFlow. Preferably, checkout the version of tensorflow which is currently supported by DeepSpeech (see requirements.txt), and use the bazel version recommended by TensorFlow for that version. Then, follow the instructions on the TensorFlow site for your platform, up to the end of 'Configure the installation'.
After that, you can build the Tensorflow and DeepSpeech libraries using the following command.
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
If your build target requires extra flags, add them, like, for example --config=cuda if you do a CUDA build. Note that the generated binaries will show up under bazel-bin/native_client/ (e.g., including generate_trie in case the //native_client:generate_trie option was present).
Finally, you can change to the native_client directory and use the Makefile. By default, the Makefile will assume there is a TensorFlow checkout in a directory above the DeepSpeech checkout. If that is not the case, set the environment variable TFDIR to point to the right directory.
cd ../DeepSpeech/native_client
make deepspeech
We do support cross-compilation ; please refer to our mozilla/tensorflow fork, where we define the following --config flags:
--config=rpi3and--config=rpi3_optfor Raspbian / ARMv7--config=rpi3-armv8and--config=rpi3-armv8_optfor ARMBian / ARM64
So your command line for RPi3 / 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 / 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 / LePotato ARMBian stretch, anything compatible with armv7-a cortex-a53 / 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 RASPBIAN make variable.
cd ../DeepSpeech/native_client
make TARGET=<system> deepspeech
We have preliminary support for Android relying on TensorFlow Lite, with Java / 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
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.
The client can be run via the Makefile. The client will accept audio of any format your installation of SoX supports.
ARGS="--model /path/to/output_graph.pbmm --alphabet /path/to/alphabet.txt --audio /path/to/audio/file.wav" make run
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.
After following the above build and installation instructions, the Node.JS bindings can be built:
cd native_client/javascript
make package
make npm-pack
This will create the package deepspeech-VERSION.tgz in native_client/javascript.
To build the ds_ctcdecoder package, you'll need the general requirements listed above (in particular SWIG). The command below builds the bindings using 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