Using a Pre-trained Model

Inference using a DeepSpeech pre-trained model can be done with a client/language binding package. We have four clients/language bindings in this repository, listed below, and also a few community-maintained clients/language bindings in other repositories, listed further down in this README.

Running deepspeech might, see below, require some runtime dependencies to be already installed on your system:

  • sox - The Python and Node.JS clients use SoX to resample files to 16kHz.

  • libgomp1 - libsox (statically linked into the clients) depends on OpenMP. Some people have had to install this manually.

  • libstdc++ - Standard C++ Library implementation. Some people have had to install this manually.

  • libpthread - On Linux, some people have had to install libpthread manually. On Ubuntu, libpthread is part of the libpthread-stubs0-dev package.

  • Redistribuable Visual C++ 2015 Update 3 (64-bits) - On Windows, it might be required to ensure this is installed. Please download from Microsoft.

Please refer to your system’s documentation on how to install these dependencies.

CUDA dependency

The GPU capable builds (Python, NodeJS, C++, etc) depend on the same CUDA runtime as upstream TensorFlow. Currently with TensorFlow 1.15 it depends on CUDA 10.0 and CuDNN v7.6. See the TensorFlow documentation.

Getting the pre-trained model

If you want to use the pre-trained English model for performing speech-to-text, you can download it (along with other important inference material) from the DeepSpeech releases page. Alternatively, you can run the following command to download the model files in your current directory:

wget https://github.com/mozilla/DeepSpeech/releases/download/v0.7.4/deepspeech-0.7.4-models.pbmm
wget https://github.com/mozilla/DeepSpeech/releases/download/v0.7.4/deepspeech-0.7.4-models.scorer

Model compatibility

DeepSpeech models are versioned to keep you from trying to use an incompatible graph with a newer client after a breaking change was made to the code. If you get an error saying your model file version is too old for the client, you should either upgrade to a newer model release, re-export your model from the checkpoint using a newer version of the code, or downgrade your client if you need to use the old model and can’t re-export it.

Using the Python package

Pre-built binaries which can be used for performing inference with a trained model can be installed with pip3. You can then use the deepspeech binary to do speech-to-text on an audio file:

For the Python bindings, it is highly recommended that you perform the installation within a Python 3.5 or later virtual environment. You can find more information about those in this documentation.

We will continue under the assumption that you already have your system properly setup to create new virtual environments.

Create a DeepSpeech virtual environment

In creating a virtual environment you will create a directory containing a python3 binary and everything needed to run deepspeech. You can use whatever directory you want. For the purpose of the documentation, we will rely on $HOME/tmp/deepspeech-venv. You can create it using this command:

$ virtualenv -p python3 $HOME/tmp/deepspeech-venv/

Once this command completes successfully, the environment will be ready to be activated.

Activating the environment

Each time you need to work with DeepSpeech, you have to activate this virtual environment. This is done with this simple command:

$ source $HOME/tmp/deepspeech-venv/bin/activate

Installing DeepSpeech Python bindings

Once your environment has been set-up and loaded, you can use pip3 to manage packages locally. On a fresh setup of the virtualenv, you will have to install the DeepSpeech wheel. You can check if deepspeech is already installed with pip3 list.

To perform the installation, just use pip3 as such:

$ pip3 install deepspeech

If deepspeech is already installed, you can update it as such:

$ pip3 install --upgrade deepspeech

Alternatively, if you have a supported NVIDIA GPU on Linux, you can install the GPU specific package as follows:

$ pip3 install deepspeech-gpu

See the release notes to find which GPUs are supported. Please ensure you have the required CUDA dependency.

You can update deepspeech-gpu as follows:

$ pip3 install --upgrade deepspeech-gpu

In both cases, pip3 should take care of installing all the required dependencies. After installation has finished, you should be able to call deepspeech from the command-line.

Note: the following command assumes you downloaded the pre-trained model.

deepspeech --model deepspeech-0.7.4-models.pbmm --scorer deepspeech-0.7.4-models.scorer --audio my_audio_file.wav

The --scorer argument is optional, and represents an external language model to be used when transcribing the audio.

See the Python client for an example of how to use the package programatically.

Using the Node.JS / Electron.JS package

You can download the JS bindings using npm:

npm install deepspeech
Please note that as of now, we support:

  • Node.JS versions 4 to 13.

  • Electron.JS versions 1.6 to 7.1

TypeScript support is also provided.

Alternatively, if you’re using Linux and have a supported NVIDIA GPU, you can install the GPU specific package as follows:

npm install deepspeech-gpu

See the release notes to find which GPUs are supported. Please ensure you have the required CUDA dependency.

See the TypeScript client for an example of how to use the bindings programatically.

Using the command-line client

To download the pre-built binaries for the deepspeech command-line (compiled C++) client, use util/taskcluster.py:

python3 util/taskcluster.py --target .

or if you’re on macOS:

python3 util/taskcluster.py --arch osx --target .

also, 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" --target "."

The script taskcluster.py will download native_client.tar.xz (which includes the deepspeech binary and associated libraries) and extract it into the current folder. Also, taskcluster.py will download binaries for Linux/x86_64 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. Specific branches of DeepSpeech or TensorFlow can be specified as well.

Alternatively you may manually download the native_client.tar.xz from the [releases](https://github.com/mozilla/DeepSpeech/releases).

Note: the following command assumes you downloaded the pre-trained model.

./deepspeech --model deepspeech-0.7.4-models.pbmm --scorer deepspeech-0.7.4-models.scorer --audio audio_input.wav

See the help output with ./deepspeech -h for more details.

Installing bindings from source

If pre-built binaries aren’t available for your system, you’ll need to install them from scratch. Follow the native client build and installation instructions.

Dockerfile for building from source

We provide Dockerfile.build to automatically build libdeepspeech.so, the C++ native client, Python bindings, and KenLM. You need to generate the Dockerfile from the template using:

make Dockerfile.build

If you want to specify a different DeepSpeech repository / branch, you can pass DEEPSPEECH_REPO or DEEPSPEECH_SHA parameters:

make Dockerfile.build DEEPSPEECH_REPO=git://your/fork DEEPSPEECH_SHA=origin/your-branch

Third party bindings

In addition to the bindings above, third party developers have started to provide bindings to other languages: