Project DeepSpeech

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. Project DeepSpeech uses Google's TensorFlow to make the implementation easier.

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

# 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 --alphabet deepspeech-0.5.1-models/alphabet.txt --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. 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.

Quicker inference can be performed using a supported NVIDIA GPU on Linux. See the release notes to find which GPUs are supported. To run deepspeech on a GPU, install the GPU specific package:

# 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 --alphabet deepspeech-0.5.1-models/alphabet.txt --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.

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).

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Table of Contents

• Using a Pre-trained Model
  • CUDA dependency
  • Getting the pre-trained model
  • Model compatibility
  • Using the Python package
  • Using the Node.JS package
  • Using the Command Line client
  • Installing bindings from source
  • Third party bindings
• Training your own Model
  • Prerequisites for training a model
  • Getting the training code
  • Installing Python dependencies
  • Recommendations
  • Common Voice training data
  • Training a model
  • Checkpointing
  • Exporting a model for inference
  • Exporting a model for TFLite
  • Making a mmap-able model for inference
  • Continuing training from a release model
  • Training with Augmentation
• Contribution guidelines
• Contact/Getting Help