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def record_audio(rate, chunk):
"""Opens a recording stream in a context manager."""
# Create a thread-safe buffer of audio data
buff = queue.Queue()

audio_interface = pyaudio.PyAudio()
audio_stream = audio_interface.open(
format=pyaudio.paInt16,
# The API currently only supports 1-channel (mono) audio
# https://goo.gl/z757pE
channels=1, rate=rate,
input=True, frames_per_buffer=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=functools.partial(_fill_buffer, buff),
)

yield _audio_data_generator(buff)

audio_stream.stop_stream()
audio_stream.close()
# Signal the _audio_data_generator to finish
buff.put(None)
audio_interface.terminate()
# [END audio_stream]

def request_stream(data_stream, rate, interim_results=True):
"""Yields `StreamingRecognizeRequest`s constructed from a recording audio
stream.
Args:
data_stream: A generator that yields raw audio data to send.
rate: The sampling rate in hertz.
interim_results: Whether to return intermediate results, before the
transcription is finalized.
"""
# The initial request must contain metadata about the stream, so the
# server knows how to interpret it.
recognition_config = cloud_speech_pb2.RecognitionConfig(
# There are a bunch of config options you can specify. See
# https://goo.gl/KPZn97 for the full list.
encoding='LINEAR16', # raw 16-bit signed LE samples
sample_rate=rate, # the rate in hertz
# See http://g.co/cloud/speech/docs/languages

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