updatedSampleCode.py

#!/usr/bin/python
# Copyright (C) 2016 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Sample that streams audio to the Google Cloud Speech API via GRPC."""

from __future__ import division

import contextlib
import functools
import re
import signal
import sys


import google.auth
import google.auth.transport.grpc
import google.auth.transport.requests
from google.cloud.grpc.speech.v1beta1 import cloud_speech_pb2
from google.rpc import code_pb2
from grpc.framework.interfaces.face import face
import pyaudio
from six.moves import queue
from ctypes import *


# [START PyAudio error handling]
ERROR_HANDLER_FUNC = CFUNCTYPE(None, c_char_p, c_int, c_char_p, c_int, c_char_p)

def py_error_handler(filename, line, function, err, fmt):
    pass

c_error_handler = ERROR_HANDLER_FUNC(py_error_handler)

embedded = True;

try:
    asound = cdll.LoadLibrary('libasound.so')
    print("EMBEDDED")
except:
    embedded = False;
    print("NOT EMBEDDED")



@contextlib.contextmanager
def noalsaerr():
    try:
        asound = cdll.LoadLibrary('libasound.so')
        asound.snd_lib_error_set_handler(c_error_handler)
        yield
        asound.snd_lib_error_set_handler(None)
    except:
        pass
# [END PyAudio error handling]


# Audio recording parameters
RATE = 48000
CHUNK = int(RATE / 10)  # 100ms

# The Speech API has a streaming limit of 60 seconds of audio*, so keep the
# connection alive for that long, plus some more to give the API time to figure
# out the transcription.
# * https://g.co/cloud/speech/limits#content
DEADLINE_SECS = 30
SPEECH_SCOPE = 'https://www.googleapis.com/auth/cloud-platform'


def make_channel(host, port):
    """Creates a secure channel with auth credentials from the environment."""
    # Grab application default credentials from the environment
    credentials, _ = google.auth.default(scopes=[SPEECH_SCOPE])

    # Create a secure channel using the credentials.
    http_request = google.auth.transport.requests.Request()
    target = '{}:{}'.format(host, port)

    return google.auth.transport.grpc.secure_authorized_channel(
        credentials, http_request, target)


def _audio_data_generator(buff):
    """A generator that yields all available data in the given buffer.

    Args:
        buff - a Queue object, where each element is a chunk of data.
    Yields:
        A chunk of data that is the aggregate of all chunks of data in `buff`.
        The function will block until at least one data chunk is available.
    """
    stop = False
    while not stop:
        # Use a blocking get() to ensure there's at least one chunk of data.
        data = [buff.get()]

        # Now consume whatever other data's still buffered.
        while True:
            try:
                data.append(buff.get(block=False))
            except queue.Empty:
                break

        # `None` in the buffer signals that the audio stream is closed. Yield
        # the final bit of the buffer and exit the loop.
        if None in data:
            stop = True
            data.remove(None)

        yield b''.join(data)


def _fill_buffer(buff, in_data, frame_count, time_info, status_flags):
    """Continuously collect data from the audio stream, into the buffer."""
    buff.put(in_data)
    return None, pyaudio.paContinue


# [START audio_stream]
@contextlib.contextmanager
def record_audio(rate, chunk):
    """Opens a recording stream in a context manager."""
    # Create a thread-safe buffer of audio data
    buff = queue.Queue()
    if embedded == True:
        print("RUNNING EMBEDDED")
        with noalsaerr():
            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()
    else:
        print("RUNNING NOT EMBEDDED")
        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.
    """
    sContext = cloud_speech_pb2.SpeechContext(
        phrases=['expand','weather','glance','collapse']
    )


    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
        # for a list of supported languages.
        language_code='en-US',  # a BCP-47 language tag
        speech_context=sContext
        #                 # "phrases": [
        #                 #             # "Glance, show me my day",
        #                 #             # "Glance, expand weather",
        #                 #             # "Glance, collapse weather"
        #                 #             ]
        #                 }
    )

    streaming_config = cloud_speech_pb2.StreamingRecognitionConfig(
        interim_results=interim_results,
        config=recognition_config,
    )

    yield cloud_speech_pb2.StreamingRecognizeRequest(
        streaming_config=streaming_config)

    for data in data_stream:
        # Subsequent requests can all just have the content
        yield cloud_speech_pb2.StreamingRecognizeRequest(audio_content=data)


def listen_print_loop(recognize_stream):
    """Iterates through server responses and prints them.

    The recognize_stream passed is a generator that will block until a response
    is provided by the server. When the transcription response comes, print it.

    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 resp in recognize_stream:
        if resp.error.code != code_pb2.OK:
            print('Server error: ' + resp.error.message)
            break;

        if not resp.results:
            continue

        # Display the top transcription
        result = resp.results[0]
        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 not result.is_final:
            # If the previous result was longer than this one, we need to print
            # some extra spaces to overwrite the previous result
            overwrite_chars = ' ' * max(0, num_chars_printed - len(transcript))

            tempResult = transcript + overwrite_chars + '\r'
            # sys.stdout.write(tempResult)
            print("TEMP:" + tempResult);
            sys.stdout.flush()

            num_chars_printed = len(transcript)

        else:
            print("FINAL:" + transcript)
            # sys.exit()
            break;
            # 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():
    service = cloud_speech_pb2.SpeechStub(
        make_channel('speech.googleapis.com', 443))

    answer = ""
    while True:

        # If we're not retrying from a failed attempt,
        # wait for the user to send 'y' to start recording
        if answer != "retry":
            answer = raw_input("Do you want to record? y/n: ")
        #  pass through raw_input block
        #  in an attempt to retry the streaming
        #  request.
        else:
            answer = "y"

        if answer == "y":
            print("Recieved the Y")


            # For streaming audio from the microphone, there are three threads.
            # First, a thread that collects audio data as it comes in
            with record_audio(RATE, CHUNK) as buffered_audio_data:
                # Second, a thread that sends requests with that data
                requests = request_stream(buffered_audio_data, RATE)
                # Third, a thread that listens for transcription responses
                recognize_stream = service.StreamingRecognize(
                    requests, DEADLINE_SECS)

                # Now, put the transcription responses to use.
                try:
                    listen_print_loop(recognize_stream)
                    recognize_stream.cancel()

                except face.CancellationError:
                    # This happens because of the interrupt handler
                    pass
                except face.AbortionError:
                    print("ABORTION ERROR RECEIVED")
                    answer = "retry"



if __name__ == '__main__':
    main()