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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.
deep-learningdeepspeechembeddedmachine-learningneural-networksofflineon-devicespeech-recognitionspeech-to-texttensorflow
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| examples | ||
| images | ||
| native_client | ||
| taskcluster | ||
| util | ||
| .cardboardlint.yml | ||
| .compute | ||
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| .pylintrc | ||
| .readthedocs.yml | ||
| .taskcluster.yml | ||
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| bazel.patch | ||
| BIBLIOGRAPHY.md | ||
| build-python-wheel.yml-DISABLED_ENABLE_ME_TO_REBUILD_DURING_PR | ||
| CODE_OF_CONDUCT.md | ||
| CONTRIBUTING.rst | ||
| DeepSpeech.py | ||
| Dockerfile | ||
| evaluate_tflite.py | ||
| evaluate.py | ||
| GRAPH_VERSION | ||
| ISSUE_TEMPLATE.md | ||
| LICENSE | ||
| lm_optimizer.py | ||
| README.rst | ||
| RELEASE.rst | ||
| requirements_eval_tflite.txt | ||
| requirements_tests.txt | ||
| requirements_transcribe.txt | ||
| requirements.txt | ||
| stats.py | ||
| SUPPORT.rst | ||
| transcribe.py | ||
| VERSION | ||
Project DeepSpeech ================== .. image:: https://readthedocs.org/projects/deepspeech/badge/?version=latest :target: http://deepspeech.readthedocs.io/?badge=latest :alt: Documentation .. image:: https://community-tc.services.mozilla.com/api/github/v1/repository/mozilla/DeepSpeech/master/badge.svg :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. **Documentation for the latest stable version** is published on `deepspeech.readthedocs.io <http://deepspeech.readthedocs.io/?badge=latest>`_. 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 --scorer deepspeech-0.6.1-models/kenlm.scorer --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 --scorer deepspeech-0.6.1-models/kenlm.scorer --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>`_