DeepSpeech/examples/mic_vad_streaming

Microphone VAD Streaming
========================

Stream from microphone to DeepSpeech, using VAD (voice activity detection). A fairly simple example demonstrating the DeepSpeech streaming API in Python. Also useful for quick, real-time testing of models and decoding parameters.

Installation
------------

.. code-block:: bash

   pip install -r requirements.txt

Uses portaudio for microphone access, so on Linux, you may need to install its header files to compile the ``pyaudio`` package:

.. code-block:: bash

   sudo apt install portaudio19-dev

Installation on MacOS may fail due to portaudio, use brew to install it:

.. code-block:: bash

   brew install portaudio

Usage
-----

.. code-block::

   usage: mic_vad_streaming.py [-h] [-v VAD_AGGRESSIVENESS] [--nospinner]
                               [-w SAVEWAV] -m MODEL [-l LM]
                               [-t TRIE] [-nf N_FEATURES] [-nc N_CONTEXT]
                               [-la LM_ALPHA] [-lb LM_BETA]
                               [-bw BEAM_WIDTH]

   Stream from microphone to DeepSpeech using VAD

   optional arguments:
     -h, --help            show this help message and exit
     -v VAD_AGGRESSIVENESS, --vad_aggressiveness VAD_AGGRESSIVENESS
                           Set aggressiveness of VAD: an integer between 0 and 3,
                           0 being the least aggressive about filtering out non-
                           speech, 3 the most aggressive. Default: 3
     --nospinner           Disable spinner
     -w SAVEWAV, --savewav SAVEWAV
                           Save .wav files of utterences to given directory
     -m MODEL, --model MODEL
                           Path to the model (protocol buffer binary file, or
                           entire directory containing all standard-named files
                           for model)
     -l LM, --lm LM        Path to the language model binary file. Default:
                           lm.binary
     -t TRIE, --trie TRIE  Path to the language model trie file created with
                           native_client/generate_trie. Default: trie
     -nf N_FEATURES, --n_features N_FEATURES
                           Number of MFCC features to use. Default: 26
     -nc N_CONTEXT, --n_context N_CONTEXT
                           Size of the context window used for producing
                           timesteps in the input vector. Default: 9
     -la LM_ALPHA, --lm_alpha LM_ALPHA
                           The alpha hyperparameter of the CTC decoder. Language
                           Model weight. Default: 0.75
     -lb LM_BETA, --lm_beta LM_BETA
                           The beta hyperparameter of the CTC decoder. Word insertion
                           bonus. Default: 1.85
     -bw BEAM_WIDTH, --beam_width BEAM_WIDTH
                           Beam width used in the CTC decoder when building
                           candidate transcriptions. Default: 500