mycroft-core/test/client/audio_consumer_test.py

import unittest
from Queue import Queue

from os.path import dirname, join
from speech_recognition import WavFile, AudioData

from mycroft.client.speech.listener import AudioConsumer, RecognizerLoop
from mycroft.client.speech.recognizer_wrapper import (
    RemoteRecognizerWrapperFactory
)

__author__ = 'seanfitz'


class MockRecognizer(object):
    def __init__(self):
        self.transcriptions = []

    def recognize_google(self, audio, key=None, language=None, show_all=False):
        return self.transcriptions.pop(0)

    def set_transcriptions(self, transcriptions):
        self.transcriptions = transcriptions


class AudioConsumerTest(unittest.TestCase):
    """
    AudioConsumerTest
    """

    def setUp(self):
        self.loop = RecognizerLoop()
        self.queue = Queue()
        self.recognizer = MockRecognizer()

        self.consumer = AudioConsumer(
                self.loop.state,
                self.queue,
                self.loop,
                self.loop.wakeup_recognizer,
                self.loop.mycroft_recognizer,
                RemoteRecognizerWrapperFactory.wrap_recognizer(
                        self.recognizer, 'google'))

    def __create_sample_from_test_file(self, sample_name):
        root_dir = dirname(dirname(dirname(__file__)))
        filename = join(
                root_dir, 'test', 'client', 'data', sample_name + '.wav')
        wavfile = WavFile(filename)
        with wavfile as source:
            return AudioData(
                    source.stream.read(), wavfile.SAMPLE_RATE,
                    wavfile.SAMPLE_WIDTH)

    def test_word_extraction(self):
        """
        This is intended to test the extraction of the word: ``mycroft``.
        The values for ``ideal_begin`` and ``ideal_end`` were found using an
        audio tool like Audacity and they represent a sample value position of
        the audio. ``tolerance`` is an acceptable margin error for the distance
        between the ideal and actual values found by the ``WordExtractor``
        """

        audio = self.__create_sample_from_test_file('weather_mycroft')
        self.queue.put(audio)
        tolerance = 4000
        ideal_begin = 70000
        ideal_end = 92000

        monitor = {}
        self.recognizer.set_transcriptions(["what's the weather next week"])

        def wakeword_callback(message):
            monitor['pos_begin'] = message.get('pos_begin')
            monitor['pos_end'] = message.get('pos_end')

        self.loop.once('recognizer_loop:wakeword', wakeword_callback)
        self.consumer.read_audio()

        actual_begin = monitor.get('pos_begin')
        self.assertIsNotNone(actual_begin)
        diff = abs(actual_begin - ideal_begin)
        self.assertTrue(
                diff <= tolerance,
                str(diff) + " is not less than " + str(tolerance))

        actual_end = monitor.get('pos_end')
        self.assertIsNotNone(actual_end)
        diff = abs(actual_end - ideal_end)
        self.assertTrue(
                diff <= tolerance,
                str(diff) + " is not less than " + str(tolerance))

    def test_wakeword_in_beginning(self):
        self.queue.put(self.__create_sample_from_test_file('weather_mycroft'))
        self.recognizer.set_transcriptions(["what's the weather next week"])
        monitor = {}

        def callback(message):
            monitor['utterances'] = message.get('utterances')

        self.loop.once('recognizer_loop:utterance', callback)
        self.consumer.read_audio()

        utterances = monitor.get('utterances')
        self.assertIsNotNone(utterances)
        self.assertTrue(len(utterances) == 1)
        self.assertEquals("what's the weather next week", utterances[0])

    def test_wakeword(self):
        self.queue.put(self.__create_sample_from_test_file('mycroft'))
        self.recognizer.set_transcriptions(["silence"])
        monitor = {}

        def callback(message):
            monitor['utterances'] = message.get('utterances')

        self.loop.once('recognizer_loop:utterance', callback)
        self.consumer.read_audio()

        utterances = monitor.get('utterances')
        self.assertIsNotNone(utterances)
        self.assertTrue(len(utterances) == 1)
        self.assertEquals("silence", utterances[0])

    def test_ignore_wakeword_when_sleeping(self):
        self.queue.put(self.__create_sample_from_test_file('mycroft'))
        self.recognizer.set_transcriptions(["not detected"])
        self.loop.sleep()
        monitor = {}

        def wakeword_callback(message):
            monitor['wakeword'] = message.get('utterance')

        self.loop.once('recognizer_loop:wakeword', wakeword_callback)
        self.consumer.read_audio()
        self.assertIsNone(monitor.get('wakeword'))
        self.assertTrue(self.loop.state.sleeping)

    def test_wakeup(self):
        self.queue.put(self.__create_sample_from_test_file('mycroft_wakeup'))
        self.loop.sleep()
        self.consumer.read_audio()
        self.assertFalse(self.loop.state.sleeping)

    def test_call_and_response(self):
        self.queue.put(self.__create_sample_from_test_file('mycroft'))
        self.recognizer.set_transcriptions(["silence"])
        monitor = {}

        def wakeword_callback(message):
            monitor['wakeword'] = message.get('utterance')

        self.loop.once('recognizer_loop:wakeword', wakeword_callback)
        self.consumer.read_audio()
        self.assertIsNotNone(monitor.get('wakeword'))

        self.queue.put(self.__create_sample_from_test_file('weather_mycroft'))
        self.recognizer.set_transcriptions(["what's the weather next week"])

        def utterance_callback(message):
            monitor['utterances'] = message.get('utterances')

        self.loop.once('recognizer_loop:utterance', utterance_callback)
        self.consumer.read_audio()

        utterances = monitor.get('utterances')
        self.assertIsNotNone(utterances)
        self.assertTrue(len(utterances) == 1)
        self.assertEquals("what's the weather next week", utterances[0])