DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.
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README.rst

Project DeepSpeech
==================


.. image:: https://readthedocs.org/projects/deepspeech/badge/?version=latest
   :target: http://deepspeech.readthedocs.io/?badge=latest
   :alt: Documentation


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   :target: https://community-tc.services.mozilla.com/api/github/v1/repository/mozilla/DeepSpeech/master/latest
   :alt: Task Status


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.

**NOTE:** This documentation applies to the **MASTER version** of DeepSpeech only. If you're using a stable release, you must use the documentation for the corresponding version by using GitHub's branch switcher button above.

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/v0.6.1/deepspeech-0.6.1-models.tar.gz
   tar xvf deepspeech-0.6.1-models.tar.gz

   # Download example audio files
   curl -LO https://github.com/mozilla/DeepSpeech/releases/download/v0.6.1/audio-0.6.1.tar.gz
   tar xvf audio-0.6.1.tar.gz

   # Transcribe an audio file
   deepspeech --model deepspeech-0.6.1-models/output_graph.pbmm --lm deepspeech-0.6.1-models/lm.binary --trie deepspeech-0.6.1-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 <doc/USING.rst#using-a-pre-trained-model>`_. 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-0.6.1-models/output_graph.pbmm --lm deepspeech-0.6.1-models/lm.binary --trie deepspeech-0.6.1-models/trie --audio audio/2830-3980-0043.wav

Please ensure you have the required `CUDA dependencies <doc/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 <doc/USING.rst#using-a-pre-trained-model>`_

  * `CUDA dependency <doc/USING.rst#cuda-dependency>`_
  * `Getting the pre-trained model <doc/USING.rst#getting-the-pre-trained-model>`_
  * `Model compatibility <doc/USING.rst#model-compatibility>`_
  * `Using the Python package <doc/USING.rst#using-the-python-package>`_
  * `Using the Node.JS package <doc/USING.rst#using-the-nodejs-package>`_
  * `Using the Command Line client <doc/USING.rst#using-the-command-line-client>`_
  * `Installing bindings from source <doc/USING.rst#installing-bindings-from-source>`_
  * `Third party bindings <doc/USING.rst#third-party-bindings>`_


* `Trying out DeepSpeech with examples <examples/README.rst>`_

* `Training your own Model <doc/TRAINING.rst#training-your-own-model>`_

  * `Prerequisites for training a model <doc/TRAINING.rst#prerequisites-for-training-a-model>`_
  * `Getting the training code <doc/TRAINING.rst#getting-the-training-code>`_
  * `Installing Python dependencies <doc/TRAINING.rst#installing-python-dependencies>`_
  * `Recommendations <doc/TRAINING.rst#recommendations>`_
  * `Common Voice training data <doc/TRAINING.rst#common-voice-training-data>`_
  * `Training a model <doc/TRAINING.rst#training-a-model>`_
  * `Checkpointing <doc/TRAINING.rst#checkpointing>`_
  * `Exporting a model for inference <doc/TRAINING.rst#exporting-a-model-for-inference>`_
  * `Exporting a model for TFLite <doc/TRAINING.rst#exporting-a-model-for-tflite>`_
  * `Making a mmap-able model for inference <doc/TRAINING.rst#making-a-mmap-able-model-for-inference>`_
  * `Continuing training from a release model <doc/TRAINING.rst#continuing-training-from-a-release-model>`_
  * `Training with Augmentation <doc/TRAINING.rst#training-with-augmentation>`_

* `Contribution guidelines <CONTRIBUTING.rst>`_
* `Contact/Getting Help <SUPPORT.rst>`_