title: How to trigger streaming voice recognition with Google Cloud API and Python
body: <p>First of all I am quite new to both python and this website, so please bear with me.</p>
  
  <p>I have successfully performed voice recognition from a live microphone input using the example code available at the Google Cloud site (<a href="https://cloud.google.com/speech-to-text/docs/streaming-recognize" rel="nofollow noreferrer">https://cloud.google.com/speech-to-text/docs/streaming-recognize</a>), and I was hoping to start and stop the process when it's been triggered by another python script. For this I have tried stopping the program with a While loop before it reaches the "main()" call line at the end of the program that makes it loop, and keeping it there until certain variable is changed. This successfully starts the speech recognition when I want it to, but also generates all sorts of errors and problems (the output is always different, sometimes the program just exits without notice, other times it says that there is not input device or that it cannot connect to the server). </p>
  
  <p>Just running the whole python program when I need it is not an option either, as that takes some time and I can't afford the delay. I have searched the internet for a solution, and couldn't find one. If someone could give me a direction of how to accomplish what I am searching for I would really appreciate it.</p>
  
  <p>This is the example code:</p>
  
  <pre><code>from __future__ import division
  
  import re
  import sys
  
  from google.cloud import speech
  from google.cloud.speech import enums
  from google.cloud.speech import types
  import pyaudio
  from six.moves import queue
  
  # Audio recording parameters
  RATE = 16000
  CHUNK = int(RATE / 10)  # 100ms
  
  
  class MicrophoneStream(object):
      """Opens a recording stream as a generator yielding the audio chunks."""
      def __init__(self, rate, chunk):
          self._rate = rate
          self._chunk = chunk
  
          # Create a thread-safe buffer of audio data
          self._buff = queue.Queue()
          self.closed = True
  
      def __enter__(self):
          self._audio_interface = pyaudio.PyAudio()
          self._audio_stream = self._audio_interface.open(
              format=pyaudio.paInt16,
              # The API currently only supports 1-channel (mono) audio
              channels=1, rate=self._rate,
              input=True, frames_per_buffer=self._chunk,
              # Run the audio stream asynchronously to fill the buffer object.
              # This is necessary so that the input device's buffer doesn't
              # overflow while the calling thread makes network requests, etc.
              stream_callback=self._fill_buffer,
          )
  
          self.closed = False
  
          return self
  
      def __exit__(self, type, value, traceback):
          self._audio_stream.stop_stream()
          self._audio_stream.close()
          self.closed = True
          # Signal the generator to terminate so that the client's
          # streaming_recognize method will not block the process termination.
          self._buff.put(None)
          self._audio_interface.terminate()
  
      def _fill_buffer(self, in_data, frame_count, time_info, status_flags):
          """Continuously collect data from the audio stream, into the buffer."""
          self._buff.put(in_data)
          return None, pyaudio.paContinue
  
      def generator(self):
          while not self.closed:
              # Use a blocking get() to ensure there's at least one chunk of
              # data, and stop iteration if the chunk is None, indicating the
              # end of the audio stream.
              chunk = self._buff.get()
              if chunk is None:
                  return
              data = [chunk]
  
              # Now consume whatever other data's still buffered.
              while True:
                  try:
                      chunk = self._buff.get(block=False)
                      if chunk is None:
                          return
                      data.append(chunk)
                  except queue.Empty:
                      break
  
              yield b''.join(data)
  
  
  def listen_print_loop(responses):
      """Iterates through server responses and prints them.
  
      The responses passed is a generator that will block until a response
      is provided by the server.
  
      Each response may contain multiple results, and each result may contain
      multiple alternatives.  Here we
      print only the transcription for the top alternative of the top result.
  
      In this case, responses are provided for interim results as well. If the
      response is an interim one, print a line feed at the end of it, to allow
      the next result to overwrite it, until the response is a final one. For the
      final one, print a newline to preserve the finalized transcription.
      """
      num_chars_printed = 0
      for response in responses:
          if not response.results:
              continue
  
          # The `results` list is consecutive. For streaming, we only care about
          # the first result being considered, since once it's `is_final`, it
          # moves on to considering the next utterance.
          result = response.results[0]
          if not result.alternatives:
              continue
  
          # Display the transcription of the top alternative.
          transcript = result.alternatives[0].transcript
  
          # Display interim results, but with a carriage return at the end of the
          # line, so subsequent lines will overwrite them.
          #
          # If the previous result was longer than this one, we need to print
          # some extra spaces to overwrite the previous result
          overwrite_chars = ' ' * (num_chars_printed - len(transcript))
  
          if not result.is_final:
              sys.stdout.write(transcript + overwrite_chars + '\r')
              sys.stdout.flush()
  
              num_chars_printed = len(transcript)
  
          else:
              print(transcript + overwrite_chars)
  
              # Exit recognition if any of the transcribed phrases could be
              # one of our keywords.
              if re.search(r'\b(exit|quit)\b', transcript, re.I):
                  print('Exiting..')
                  break
  
              num_chars_printed = 0
  
  
  def main():
      # See http://g.co/cloud/speech/docs/languages
      # for a list of supported languages.
      language_code = 'en-US'  # a BCP-47 language tag
  
      client = speech.SpeechClient()
      config = types.RecognitionConfig(
          encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
          sample_rate_hertz=RATE,
          language_code=language_code)
      streaming_config = types.StreamingRecognitionConfig(
          config=config,
          interim_results=True)
  
      with MicrophoneStream(RATE, CHUNK) as stream:
          audio_generator = stream.generator()
          requests = (types.StreamingRecognizeRequest(audio_content=content)
                      for content in audio_generator)
  
          responses = client.streaming_recognize(streaming_config, requests)
  
          # Now, put the transcription responses to use.
          listen_print_loop(responses)
  
  
  if __name__ == '__main__':
      main()
  </code></pre>