deepspeech-gpu

Project DeepSpeech

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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/v0.5.1/deepspeech-0.5.1-models.tar.gz tar xvf deepspeech-0.5.1-models.tar.gz

Download example audio files

curl -LO https://github.com/mozilla/DeepSpeech/releases/download/v0.5.1/audio-0.5.1.tar.gz tar xvf audio-0.5.1.tar.gz

Transcribe an audio file

deepspeech --model deepspeech-0.5.1-models/output_graph.pbmm --lm deepspeech-0.5.1-models/lm.binary --trie deepspeech-0.5.1-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 <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-0.5.1-models/output_graph.pbmm --lm deepspeech-0.5.1-models/lm.binary --trie deepspeech-0.5.1-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/EXAMPLES.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>_