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Merge pull request #2282 from Dexterp37/update_docs
Add the wasm docs
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doc/DEPLOYMENT.rst

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* :ref:`The Android libstt AAR package <android-usage>`
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* :ref:`The command-line client <cli-usage>`
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* :ref:`The C API <c-usage>`
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* :ref:`Using the WebAssembly package <wasm-usage>`
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In some use cases, you might want to use the inference facilities built into the training code, for example for faster prototyping of new features. They are not production-ready, but because it's all Python code you won't need to recompile in order to test code changes, which can be much faster. See :ref:`checkpoint-inference` for more details.
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Then, simply include the header file and link against the shared libraries in your project, and you should be able to use the C API. Reference documentation is available in :ref:`c-api`.
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.. _wasm-usage:
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Using the WebAssembly package
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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A pre-built ES5 libstt WebAssembly package can be `downloaded from GitHub Releases <https://github.com/coqui-ai/STT/releases/latest/download/libstt.tflite.wasm.zip>`_ .
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See the `wasm` directory in `STT-examples <https://github.com/coqui-ai/STT-examples/>_` for an example of how to use the package programmatically in a web page.
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A pre-built ES6 libstt WebAssembly package can be downloaded using `npm`:
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.. code-block:: bash
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npm install stt-wasm
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The ES6 package can be used in projects using bundlers.
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Installing bindings from source
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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doc/NodeJS-API.rst

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JavaScript (NodeJS / ElectronJS)
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JavaScript (NodeJS / ElectronJS / WASM)
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================================
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Model

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