Update doc to include huggingface space.

README.md

@@ -9,6 +9,9 @@
 
 **Documentation**: <https://k2-fsa.github.io/sherpa/>
 
+Try `sherpa` from within your browser without installing anything:
+<https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition>
+
 ## Introduction
 
 An ASR server framework in **Python**, supporting both streaming
@@ -351,4 +354,4 @@ After running the following commands:
     $ pip install pre-commit
     $ pre-commit install
 
-it will run the checks whenever you run ``git commit`` **automatically**
+it will run the checks whenever you run ``git commit`` **automatically**

docs/source/conf.py

@@ -113,5 +113,7 @@ def get_version():
 .. _transducer: https://arxiv.org/pdf/1211.3711.pdf
 .. _asyncio: https://docs.python.org/3/library/asyncio.html
 .. _k2: https://github.com/k2-fsa/k2
+.. _icefall: https://github.com/k2-fsa/icefall
 .. _PyTorch: https://pytorch.org/
+.. _Huggingface: https://huggingface.co
 """

docs/source/huggingface/index.rst

@@ -0,0 +1,63 @@
+.. _try sherpa with huggingface:
+
+Try sherpa with Huggingface
+===========================
+
+This page describes how to use `sherpa`_ for automatic speech recognition
+with `Huggingface`_.
+
+.. hint::
+
+  You don't need to download or install anything. All you need is a browser.
+
+
+The server is running on CPU within a docker container provided by
+`Huggingface`_ and you use a browser to interact with it. The browser
+can be run on Windows, macOS, Linux, or even on your phone or iPad.
+
+You can either upload a file for recognition or record your speech via
+a microphone from within the browser and submit it for recognition.
+
+Now let's get started.
+
+Visit our Huggingface space
+---------------------------
+
+Start your browser and visit the following address:
+
+`<https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition>`_
+
+and you will see a page like the following screenshot:
+
+.. image:: ./pic/hugging-face-sherpa.png
+   :alt: screenshot of `<https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition>`_
+   :target: https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition
+
+You can:
+
+  1. Select a language for recognition. Currently, we provide pre-trained models
+     from `icefall`_ for the following languages: ``Chinese``, ``English``, and
+     ``Chinese+English``.
+  2. After selecting the target language, you can select a pre-trained model
+     corresponding to the language.
+  3. Select the decoding method. Currently, it provides ``greedy search``
+     and ``modified_beam_search``.
+  4. If you selected ``modified_beam_search``, you can choose the number of
+     active paths during the search.
+  5. Either upload a file or record your speech for recognition.
+  6. Click the button ``Submit for recognition``.
+  7. Wait for a moment and you will get the recognition results.
+
+The following screenshot shows an example when selecting ``Chinese+English``:
+
+.. image:: ./pic/hugging-face-sherpa-3.png
+   :alt: screenshot of `<https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition>`_
+   :target: https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition
+
+
+In the bottom part of the page, you can find a table of examples. You can click
+one of them and then click ``Submit for recognition``.
+
+.. image:: ./pic/hugging-face-sherpa-2.png
+   :alt: screenshot of `<https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition>`_
+   :target: https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition

docs/source/index.rst

@@ -24,6 +24,7 @@ for both streaming ASR and offline ASR (i.e., non-streaming ASR).
    :maxdepth: 2
    :caption: Contents:
 
+   ./huggingface/index
    ./installation/index
    ./streaming_asr/index
    ./offline_asr/index

docs/source/streaming_asr/conv_emformer/index.rst

@@ -1,5 +1,5 @@
 ConvEmformer transducer based streaming ASR
-=======================================
+===========================================
 
 This page describes how to use `sherpa`_ for streaming
 ASR with `ConvEmformer`_ transducer models