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.
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:
# 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.wavA pre-trained English model is available for use and can be downloaded using the instructions below. 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-0.6.1-models/output_graph.pbmm --lm deepspeech-0.6.1-models/lm.binary --trie deepspeech-0.6.1-models/trie --audio audio/2830-3980-0043.wavPlease 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).
Table of Contents
- Using a Pre-trained Model
- Trying out DeepSpeech with examples
- 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
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