DeepSpeech is an open source Speech-To-Text engine, using a model trained by machine learning techniques based on Baidu's Deep Speech research paper <https://arxiv.org/abs/1412.5567>. Project DeepSpeech uses Google's TensorFlow <https://www.tensorflow.org/> to make the implementation easier.

To install and use deepspeech all you have to do is:

.. code-block:: bash

Create and activate a virtualenv

virtualenv -p python3 $HOME/tmp/deepspeech-venv/ source $HOME/tmp/deepspeech-venv/bin/activate

Install DeepSpeech

pip3 install deepspeech

Download pre-trained English model and extract

curl -LO https://github.com/mozilla/DeepSpeech/releases/download/v0.6.0/deepspeech-0.6.0-models.tar.gz tar xvf deepspeech-0.6.0-models.tar.gz

Download example audio files

curl -LO https://github.com/mozilla/DeepSpeech/releases/download/v0.6.0/audio-0.6.0.tar.gz tar xvf audio-0.6.0.tar.gz

Transcribe an audio file

deepspeech --model deepspeech-0.6.0-models/output_graph.pbmm --lm deepspeech-0.6.0-models/lm.binary --trie deepspeech-0.6.0-models/trie --audio audio/2830-3980-0043.wav

A pre-trained English model is available for use and can be downloaded using the instructions below <USING.rst#using-a-pre-trained-model>. Currently, only 16-bit, 16 kHz, mono-channel WAVE audio files are supported in the Python client. A package with some example audio files is available for download in our release notes <https://github.com/mozilla/DeepSpeech/releases/latest>.

Quicker inference can be performed using a supported NVIDIA GPU on Linux. See the release notes <https://github.com/mozilla/DeepSpeech/releases/latest>_ to find which GPUs are supported. To run deepspeech on a GPU, install the GPU specific package:

.. code-block:: bash

Create and activate a virtualenv

virtualenv -p python3 $HOME/tmp/deepspeech-gpu-venv/ source $HOME/tmp/deepspeech-gpu-venv/bin/activate

Install DeepSpeech CUDA enabled package

pip3 install deepspeech-gpu

Transcribe an audio file.

deepspeech --model deepspeech-0.6.0-models/output_graph.pbmm --lm deepspeech-0.6.0-models/lm.binary --trie deepspeech-0.6.0-models/trie --audio audio/2830-3980-0043.wav

Please ensure you have the required CUDA dependencies <USING.rst#cuda-dependency>_.

See the output of deepspeech -h for more information on the use of deepspeech. (If you experience problems running deepspeech\ , please check required runtime dependencies <native_client/README.rst#required-dependencies>_\ ).

Table of Contents

Using a Pre-trained Model <USING.rst#using-a-pre-trained-model>_
- CUDA dependency <USING.rst#cuda-dependency>_
- Getting the pre-trained model <USING.rst#getting-the-pre-trained-model>_
- Model compatibility <USING.rst#model-compatibility>_
- Using the Python package <USING.rst#using-the-python-package>_
- Using the Node.JS package <USING.rst#using-the-nodejs-package>_
- Using the Command Line client <USING.rst#using-the-command-line-client>_
- Installing bindings from source <USING.rst#installing-bindings-from-source>_
- Third party bindings <USING.rst#third-party-bindings>_
Trying out DeepSpeech with examples <examples/README.rst>_
- Microphone VAD streaming <examples/mic_vad_streaming/README.rst>_
- FFMPEG VAD streaming <examples/ffmpeg_vad_streaming/README.rst>_
- Net framework <examples/net_framework/README.rst>_
- Nodejs wav <examples/nodejs_wav/README.rst>_
- VAD transcriber <examples/vad_transcriber/README.rst>_
Training your own Model <TRAINING.rst#training-your-own-model>_
- Prerequisites for training a model <TRAINING.rst#prerequisites-for-training-a-model>_
- Getting the training code <TRAINING.rst#getting-the-training-code>_
- Installing Python dependencies <TRAINING.rst#installing-python-dependencies>_
- Recommendations <TRAINING.rst#recommendations>_
- Common Voice training data <TRAINING.rst#common-voice-training-data>_
- Training a model <TRAINING.rst#training-a-model>_
- Checkpointing <TRAINING.rst#checkpointing>_
- Exporting a model for inference <TRAINING.rst#exporting-a-model-for-inference>_
- Exporting a model for TFLite <TRAINING.rst#exporting-a-model-for-tflite>_
- Making a mmap-able model for inference <TRAINING.rst#making-a-mmap-able-model-for-inference>_
- Continuing training from a release model <TRAINING.rst#continuing-training-from-a-release-model>_
- Training with Augmentation <TRAINING.rst#training-with-augmentation>_
Contribution guidelines <CONTRIBUTING.rst>_
Contact/Getting Help <SUPPORT.rst>_

FAQs

What is deepspeech-gpu?

Is deepspeech-gpu well maintained?

Last updated on 03 Dec 2019

Did you know?

Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.

Install