DeepSpeech native client, language bindings and custom decoder
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
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
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.
If you're looking to train a model, you now have a
libctc_decoder_with_kenlm.so file that you can pass to the
--decoder_library_path parameter of
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:
Please refer to your system's documentation on how to install those dependencies.
Installing the language bindings
For the Python bindings, you can use
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:
We recommend using our fork of TensorFlow since it includes fixes for common problems encountered when building the native client files, you can get it here.
If you'd like to build the language bindings, you'll also need:
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 bazel version 0.10.0. 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 commands. Please note that the flags for
libctc_decoder_with_kenlm.so differs a little bit.
bazel build -c opt --copt=-O3 --copt="-D_GLIBCXX_USE_CXX11_ABI=0" //native_client:libctc_decoder_with_kenlm.so 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.
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
Building with AOT model
First, please note that this is still experimental. AOT model relies on TensorFlow's AOT tfcompile tooling. It takes a protocol buffer file graph as input, and produces a .so library that one can call from C++ code. To experiment, you will need to build TensorFlow from github.com/mozilla/tensorflow r1.6 branch. Follow TensorFlow's documentation for the configuration of your system. When building, you will have to add some extra parameter and targets.
--define=DS_NATIVE_MODEL=1: to toggle AOT support.
--define=DS_MODEL_TIMESTEPS=x: to define how many timesteps you want to handle. Relying on prebuilt model implies we need to use a fixed value for how much audio value we want to use. Timesteps defines that value, and an audio file bigger than this will just be dealt with over several samples. This means there's a compromise between quality and minimum audio segment you want to handle.
--define=DS_MODEL_FRAMESIZE=y: to define your model framesize, this is the second component of your model's input layer shape. Can be extracted using TensorFlow's
--define=DS_MODEL_FILE=/path/to/graph.pb: the model you want to use
//native_client:libdeepspeech_model.so: to produce
In the end, the previous example becomes (no change for
bazel build --config=monolithic -c opt --copt=-O3 --copt=-fvisibility=hidden --define=DS_NATIVE_MODEL=1 --define=DS_MODEL_TIMESTEPS=64 --define=DS_MODEL_FRAMESIZE=494 --define=DS_MODEL_FILE=/tmp/model.ldc93s1.pb //native_client:libdeepspeech_model.so //native_client:libdeepspeech.so //native_client:generate_trie
Later, when building either
deepspeech binaries or bindings, you will have to add some extra variables to your
make command-line (assuming
TFDIR points to your TensorFlow's git clone):
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 make bindings sudo pip install dist/deepspeech*
After following the above build and installation instructions, the Node.JS bindings can be built:
This will create the package