.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_auto_examples_stream_example.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_stream_example.py:


Stream of audio from your microphone
====================================

This is an example of using your own Microphone to continuously transcribe what is being uttered. Whenever the
recognizer detects a silence in the audio stream from your microphone, it will be transcribed.


.. code-block:: default

    from danspeech import Recognizer
    from danspeech.pretrained_models import TransferLearned
    from danspeech.audio.resources import Microphone
    from danspeech.language_models import DSL3gram


    # Get a list of microphones found by PyAudio
    mic_list = Microphone.list_microphone_names()
    mic_list_with_numbers = list(zip(range(len(mic_list)), mic_list))
    print("Available microphones: {0}".format(mic_list_with_numbers))

    # Choose the microphone
    mic_number = input("Pick the number of the microphone you would like to use: ")

    # Init a microphone object
    m = Microphone(sampling_rate=16000, device_index=int(mic_number))

    # Init a DanSpeech model and create a Recognizer instance
    model = TransferLearned()
    recognizer = Recognizer(model=model)

    # Try using the DSL 3 gram language model
    try:
        lm = DSL3gram()
        recognizer.update_decoder(lm=lm)
    except ImportError:
        print("ctcdecode not installed. Using greedy decoding.")

    print("Speek a lot to adjust silence detection from microphone...")
    with m as source:
        recognizer.adjust_for_speech(source, duration=5)

    # Enable streaming
    recognizer.enable_streaming()

    # Create the streaming generator which runs a background thread listening to the microphone stream
    generator = recognizer.streaming(source=m)

    # The below code runs for a long time. The generator returns transcriptions of spoken speech from your microphone.
    print("Speak")
    for i in range(100000):
        trans = next(generator)
        print(trans)


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_stream_example.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download

     :download:`Download Python source code: stream_example.py <stream_example.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: stream_example.ipynb <stream_example.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_