__init__.py

#!/usr/bin/env python3

"""Library for performing speech recognition, with support for several engines and APIs, online and offline."""

__author__ = "Anthony Zhang (Uberi)"
__version__ = "3.4.6"
__license__ = "BSD"

import io, os, subprocess, wave, aifc, base64
import math, audioop, collections, threading
import platform, stat, random, uuid
import json

try: # attempt to use the Python 2 modules
    from urllib import urlencode
    from urllib2 import Request, urlopen, URLError, HTTPError
except ImportError: # use the Python 3 modules
    from urllib.parse import urlencode
    from urllib.request import Request, urlopen
    from urllib.error import URLError, HTTPError

# define exceptions
class WaitTimeoutError(Exception): pass
class RequestError(Exception): pass
class UnknownValueError(Exception): pass

class AudioSource(object):
    def __init__(self):
        raise NotImplementedError("this is an abstract class")

    def __enter__(self):
        raise NotImplementedError("this is an abstract class")

    def __exit__(self, exc_type, exc_value, traceback):
        raise NotImplementedError("this is an abstract class")

class Microphone(AudioSource):
    """
    Creates a new ``Microphone`` instance, which represents a physical microphone on the computer. Subclass of ``AudioSource``.

    This will throw an ``AttributeError`` if you don't have PyAudio 0.2.9 or later installed.

    If ``device_index`` is unspecified or ``None``, the default microphone is used as the audio source. Otherwise, ``device_index`` should be the index of the device to use for audio input.

    A device index is an integer between 0 and ``pyaudio.get_device_count() - 1`` (assume we have used ``import pyaudio`` beforehand) inclusive. It represents an audio device such as a microphone or speaker. See the `PyAudio documentation <http://people.csail.mit.edu/hubert/pyaudio/docs/>`__ for more details.

    The microphone audio is recorded in chunks of ``chunk_size`` samples, at a rate of ``sample_rate`` samples per second (Hertz).

    Higher ``sample_rate`` values result in better audio quality, but also more bandwidth (and therefore, slower recognition). Additionally, some machines, such as some Raspberry Pi models, can't keep up if this value is too high.

    Higher ``chunk_size`` values help avoid triggering on rapidly changing ambient noise, but also makes detection less sensitive. This value, generally, should be left at its default.
    """
    def __init__(self, device_index = None, sample_rate = 16000, chunk_size = 1024):
        # set up PyAudio
        self.pyaudio_module = self.get_pyaudio()

        assert device_index is None or isinstance(device_index, int), "Device index must be None or an integer"
        if device_index is not None: # ensure device index is in range
            audio = self.pyaudio_module.PyAudio()
            try:
                count = audio.get_device_count() # obtain device count
            except:
                audio.terminate()
                raise
            assert 0 <= device_index < count, "Device index out of range ({0} devices available; device index should be between 0 and {1} inclusive)".format(count, count - 1)
        assert isinstance(sample_rate, int) and sample_rate > 0, "Sample rate must be a positive integer"
        assert isinstance(chunk_size, int) and chunk_size > 0, "Chunk size must be a positive integer"
        self.device_index = device_index
        self.format = self.pyaudio_module.paInt16 # 16-bit int sampling
        self.SAMPLE_WIDTH = self.pyaudio_module.get_sample_size(self.format) # size of each sample
        self.SAMPLE_RATE = sample_rate # sampling rate in Hertz
        self.CHUNK = chunk_size # number of frames stored in each buffer

        self.audio = None
        self.stream = None

    @staticmethod
    def get_pyaudio():
        """
        Imports the pyaudio module and checks its version. Throws exceptions if pyaudio can't be found or a wrong version is installed
        """
        try:
            import pyaudio
        except ImportError:
            raise AttributeError("Could not find PyAudio; check installation")
        from distutils.version import LooseVersion
        if LooseVersion(pyaudio.__version__) < LooseVersion("0.2.9"):
            raise AttributeError("PyAudio 0.2.9 or later is required (found version {0})".format(pyaudio.__version__))
        return pyaudio

    @staticmethod
    def list_microphone_names():
        """
        Returns a list of the names of all available microphones. For microphones where the name can't be retrieved, the list entry contains ``None`` instead.

        The index of each microphone's name is the same as its device index when creating a ``Microphone`` instance - indices in this list can be used as values of ``device_index``.
        """
        audio = Microphone.get_pyaudio().PyAudio()
        try:
            result = []
            for i in range(audio.get_device_count()):
                device_info = audio.get_device_info_by_index(i)
                result.append(device_info.get("name"))
        finally:
            audio.terminate()
        return result

    def __enter__(self):
        assert self.stream is None, "This audio source is already inside a context manager"
        self.audio = self.pyaudio_module.PyAudio()
        try:
            self.stream = Microphone.MicrophoneStream(
                self.audio.open(
                    input_device_index = self.device_index, channels = 1,
                    format = self.format, rate = self.SAMPLE_RATE, frames_per_buffer = self.CHUNK,
                    input = True, # stream is an input stream
                )
            )
        except:
            self.audio.terminate()
            raise
        return self

    def __exit__(self, exc_type, exc_value, traceback):
        try:
            self.stream.close()
        finally:
            self.stream = None
            self.audio.terminate()

    class MicrophoneStream(object):
        def __init__(self, pyaudio_stream):
            self.pyaudio_stream = pyaudio_stream

        def read(self, size):
            return self.pyaudio_stream.read(size, exception_on_overflow = False)

        def close(self):
            try:
                # sometimes, if the stream isn't stopped, closing the stream throws an exception
                if not self.pyaudio_stream.is_stopped():
                    self.pyaudio_stream.stop_stream()
            finally:
                self.pyaudio_stream.close()

class AudioFile(AudioSource):
    """
    Creates a new ``AudioFile`` instance given a WAV/AIFF/FLAC audio file `filename_or_fileobject`. Subclass of ``AudioSource``.

    If ``filename_or_fileobject`` is a string, then it is interpreted as a path to an audio file on the filesystem. Otherwise, ``filename_or_fileobject`` should be a file-like object such as ``io.BytesIO`` or similar.

    Note that functions that read from the audio (such as ``recognizer_instance.record`` or ``recognizer_instance.listen``) will move ahead in the stream. For example, if you execute ``recognizer_instance.record(audiofile_instance, duration=10)`` twice, the first time it will return the first 10 seconds of audio, and the second time it will return the 10 seconds of audio right after that. This is always reset to the beginning when entering an ``AudioFile`` context.

    WAV files must be in PCM/LPCM format; WAVE_FORMAT_EXTENSIBLE and compressed WAV are not supported and may result in undefined behaviour.

    Both AIFF and AIFF-C (compressed AIFF) formats are supported.

    FLAC files must be in native FLAC format; OGG-FLAC is not supported and may result in undefined behaviour.
    """

    def __init__(self, filename_or_fileobject):
        if str is bytes: # Python 2 - if a file path is specified, it must either be a `str` instance or a `unicode` instance
            assert isinstance(filename_or_fileobject, (str, unicode)) or hasattr(filename_or_fileobject, "read"), "Given audio file must be a filename string or a file-like object"
        else: # Python 3 - if a file path is specified, it must be a `str` instance
            assert isinstance(filename_or_fileobject, str) or hasattr(filename_or_fileobject, "read"), "Given audio file must be a filename string or a file-like object"
        self.filename_or_fileobject = filename_or_fileobject
        self.stream = None
        self.DURATION = None

    def __enter__(self):
        assert self.stream is None, "This audio source is already inside a context manager"
        try:
            # attempt to read the file as WAV
            self.audio_reader = wave.open(self.filename_or_fileobject, "rb")
            self.little_endian = True # RIFF WAV is a little-endian format (most ``audioop`` operations assume that the frames are stored in little-endian form)
        except wave.Error:
            try:
                # attempt to read the file as AIFF
                self.audio_reader = aifc.open(self.filename_or_fileobject, "rb")
                self.little_endian = False # AIFF is a big-endian format
            except aifc.Error:
                # attempt to read the file as FLAC
                if hasattr(self.filename_or_fileobject, "read"):
                    flac_data = self.filename_or_fileobject.read()
                else:
                    with open(self.filename_or_fileobject, "rb") as f: flac_data = f.read()

                # run the FLAC converter with the FLAC data to get the AIFF data
                flac_converter = get_flac_converter()
                process = subprocess.Popen([
                    flac_converter,
                    "--stdout", "--totally-silent", # put the resulting AIFF file in stdout, and make sure it's not mixed with any program output
                    "--decode", "--force-aiff-format", # decode the FLAC file into an AIFF file
                    "-", # the input FLAC file contents will be given in stdin
                ], stdin=subprocess.PIPE, stdout=subprocess.PIPE)
                aiff_data, stderr = process.communicate(flac_data)
                aiff_file = io.BytesIO(aiff_data)
                try:
                    self.audio_reader = aifc.open(aiff_file, "rb")
                except aifc.Error:
                    assert False, "Audio file could not be read as WAV, AIFF, or FLAC; check if file is corrupted"
                self.little_endian = False # AIFF is a big-endian format
        assert 1 <= self.audio_reader.getnchannels() <= 2, "Audio must be mono or stereo"
        self.SAMPLE_WIDTH = self.audio_reader.getsampwidth()

        # 24-bit audio needs some special handling for old Python versions (workaround for https://bugs.python.org/issue12866)
        samples_24_bit_pretending_to_be_32_bit = False
        if self.SAMPLE_WIDTH == 3: # 24-bit audio
            try: audioop.bias(b"", self.SAMPLE_WIDTH, 0) # test whether this sample width is supported (for example, ``audioop`` in Python 3.3 and below don't support sample width 3, while Python 3.4+ do)
            except audioop.error: # this version of audioop doesn't support 24-bit audio (probably Python 3.3 or less)
                samples_24_bit_pretending_to_be_32_bit = True # while the ``AudioFile`` instance will outwardly appear to be 32-bit, it will actually internally be 24-bit
                self.SAMPLE_WIDTH = 4 # the ``AudioFile`` instance should present itself as a 32-bit stream now, since we'll be converting into 32-bit on the fly when reading

        self.SAMPLE_RATE = self.audio_reader.getframerate()
        self.CHUNK = 4096
        self.FRAME_COUNT = self.audio_reader.getnframes()
        self.DURATION = self.FRAME_COUNT / float(self.SAMPLE_RATE)
        self.stream = AudioFile.AudioFileStream(self.audio_reader, self.little_endian, samples_24_bit_pretending_to_be_32_bit)
        return self

    def __exit__(self, exc_type, exc_value, traceback):
        if not hasattr(self.filename_or_fileobject, "read"): # only close the file if it was opened by this class in the first place (if the file was originally given as a path)
            self.audio_reader.close()
        self.stream = None
        self.DURATION = None

    class AudioFileStream(object):
        def __init__(self, audio_reader, little_endian, samples_24_bit_pretending_to_be_32_bit):
            self.audio_reader = audio_reader # an audio file object (e.g., a `wave.Wave_read` instance)
            self.little_endian = little_endian # whether the audio data is little-endian (when working with big-endian things, we'll have to convert it to little-endian before we process it)
            self.samples_24_bit_pretending_to_be_32_bit = samples_24_bit_pretending_to_be_32_bit # this is true if the audio is 24-bit audio, but 24-bit audio isn't supported, so we have to pretend that this is 32-bit audio and convert it on the fly

        def read(self, size = -1):
            buffer = self.audio_reader.readframes(self.audio_reader.getnframes() if size == -1 else size)
            if not isinstance(buffer, bytes): buffer = b"" # workaround for https://bugs.python.org/issue24608

            sample_width = self.audio_reader.getsampwidth()
            if not self.little_endian: # big endian format, convert to little endian on the fly
                if hasattr(audioop, "byteswap"): # ``audioop.byteswap`` was only added in Python 3.4 (incidentally, that also means that we don't need to worry about 24-bit audio being unsupported, since Python 3.4+ always has that functionality)
                    buffer = audioop.byteswap(buffer, sample_width)
                else: # manually reverse the bytes of each sample, which is slower but works well enough as a fallback
                    buffer = buffer[sample_width - 1::-1] + b"".join(buffer[i + sample_width:i:-1] for i in range(sample_width - 1, len(buffer), sample_width))

            # workaround for https://bugs.python.org/issue12866
            if self.samples_24_bit_pretending_to_be_32_bit: # we need to convert samples from 24-bit to 32-bit before we can process them with ``audioop`` functions
                buffer = b"".join("\x00" + buffer[i:i + sample_width] for i in range(0, len(buffer), sample_width)) # since we're in little endian, we prepend a zero byte to each 24-bit sample to get a 32-bit sample
            if self.audio_reader.getnchannels() != 1: # stereo audio
                buffer = audioop.tomono(buffer, sample_width, 1, 1) # convert stereo audio data to mono
            return buffer

class AudioData(object):
    def __init__(self, frame_data, sample_rate, sample_width):
        assert sample_rate > 0, "Sample rate must be a positive integer"
        assert sample_width % 1 == 0 and 1 <= sample_width <= 4, "Sample width must be between 1 and 4 inclusive"
        self.frame_data = frame_data
        self.sample_rate = sample_rate
        self.sample_width = int(sample_width)

    def get_raw_data(self, convert_rate = None, convert_width = None):
        """
        Returns a byte string representing the raw frame data for the audio represented by the ``AudioData`` instance.

        If ``convert_rate`` is specified and the audio sample rate is not ``convert_rate`` Hz, the resulting audio is resampled to match.

        If ``convert_width`` is specified and the audio samples are not ``convert_width`` bytes each, the resulting audio is converted to match.

        Writing these bytes directly to a file results in a valid `RAW/PCM audio file <https://en.wikipedia.org/wiki/Raw_audio_format>`__.
        """
        assert convert_rate is None or convert_rate > 0, "Sample rate to convert to must be a positive integer"
        assert convert_width is None or (convert_width % 1 == 0 and 1 <= convert_width <= 4), "Sample width to convert to must be between 1 and 4 inclusive"

        raw_data = self.frame_data

        # make sure unsigned 8-bit audio (which uses unsigned samples) is handled like higher sample width audio (which uses signed samples)
        if self.sample_width == 1:
            raw_data = audioop.bias(raw_data, 1, -128) # subtract 128 from every sample to make them act like signed samples

        # resample audio at the desired rate if specified
        if convert_rate is not None and self.sample_rate != convert_rate:
            raw_data, _ = audioop.ratecv(raw_data, self.sample_width, 1, self.sample_rate, convert_rate, None)

        # convert samples to desired sample width if specified
        if convert_width is not None and self.sample_width != convert_width:
            if convert_width == 3: # we're converting the audio into 24-bit (workaround for https://bugs.python.org/issue12866)
                raw_data = audioop.lin2lin(raw_data, self.sample_width, 4) # convert audio into 32-bit first, which is always supported
                try: audioop.bias(b"", 3, 0) # test whether 24-bit audio is supported (for example, ``audioop`` in Python 3.3 and below don't support sample width 3, while Python 3.4+ do)
                except audioop.error: # this version of audioop doesn't support 24-bit audio (probably Python 3.3 or less)
                    raw_data = b"".join(raw_data[i + 1:i + 4] for i in range(0, len(raw_data), 4)) # since we're in little endian, we discard the first byte from each 32-bit sample to get a 24-bit sample
                else: # 24-bit audio fully supported, we don't need to shim anything
                    raw_data = audioop.lin2lin(raw_data, self.sample_width, convert_width)
            else:
                raw_data = audioop.lin2lin(raw_data, self.sample_width, convert_width)

        # if the output is 8-bit audio with unsigned samples, convert the samples we've been treating as signed to unsigned again
        if convert_width == 1:
            raw_data = audioop.bias(raw_data, 1, 128) # add 128 to every sample to make them act like unsigned samples again

        return raw_data

    def get_wav_data(self, convert_rate = None, convert_width = None):
        """
        Returns a byte string representing the contents of a WAV file containing the audio represented by the ``AudioData`` instance.

        If ``convert_width`` is specified and the audio samples are not ``convert_width`` bytes each, the resulting audio is converted to match.

        If ``convert_rate`` is specified and the audio sample rate is not ``convert_rate`` Hz, the resulting audio is resampled to match.

        Writing these bytes directly to a file results in a valid `WAV file <https://en.wikipedia.org/wiki/WAV>`__.
        """
        raw_data = self.get_raw_data(convert_rate, convert_width)
        sample_rate = self.sample_rate if convert_rate is None else convert_rate
        sample_width = self.sample_width if convert_width is None else convert_width

        # generate the WAV file contents
        with io.BytesIO() as wav_file:
            wav_writer = wave.open(wav_file, "wb")
            try: # note that we can't use context manager, since that was only added in Python 3.4
                wav_writer.setframerate(sample_rate)
                wav_writer.setsampwidth(sample_width)
                wav_writer.setnchannels(1)
                wav_writer.writeframes(raw_data)
                wav_data = wav_file.getvalue()
            finally:  # make sure resources are cleaned up
                wav_writer.close()
        return wav_data

    def get_aiff_data(self, convert_rate = None, convert_width = None):
        """
        Returns a byte string representing the contents of an AIFF-C file containing the audio represented by the ``AudioData`` instance.

        If ``convert_width`` is specified and the audio samples are not ``convert_width`` bytes each, the resulting audio is converted to match.

        If ``convert_rate`` is specified and the audio sample rate is not ``convert_rate`` Hz, the resulting audio is resampled to match.

        Writing these bytes directly to a file results in a valid `AIFF-C file <https://en.wikipedia.org/wiki/Audio_Interchange_File_Format>`__.
        """
        raw_data = self.get_raw_data(convert_rate, convert_width)
        sample_rate = self.sample_rate if convert_rate is None else convert_rate
        sample_width = self.sample_width if convert_width is None else convert_width

        # the AIFF format is big-endian, so we need to covnert the little-endian raw data to big-endian
        if hasattr(audioop, "byteswap"): # ``audioop.byteswap`` was only added in Python 3.4
            raw_data = audioop.byteswap(raw_data, sample_width)
        else: # manually reverse the bytes of each sample, which is slower but works well enough as a fallback
            raw_data = raw_data[sample_width - 1::-1] + b"".join(raw_data[i + sample_width:i:-1] for i in range(sample_width - 1, len(raw_data), sample_width))

        # generate the AIFF-C file contents
        with io.BytesIO() as aiff_file:
            aiff_writer = aifc.open(aiff_file, "wb")
            try: # note that we can't use context manager, since that was only added in Python 3.4
                aiff_writer.setframerate(sample_rate)
                aiff_writer.setsampwidth(sample_width)
                aiff_writer.setnchannels(1)
                aiff_writer.writeframes(raw_data)
                aiff_data = aiff_file.getvalue()
            finally:  # make sure resources are cleaned up
                aiff_writer.close()
        return aiff_data

    def get_flac_data(self, convert_rate = None, convert_width = None):
        """
        Returns a byte string representing the contents of a FLAC file containing the audio represented by the ``AudioData`` instance.

        Note that 32-bit FLAC is not supported. If the audio data is 32-bit and ``convert_width`` is not specified, then the resulting FLAC will be a 24-bit FLAC.

        If ``convert_rate`` is specified and the audio sample rate is not ``convert_rate`` Hz, the resulting audio is resampled to match.

        If ``convert_width`` is specified and the audio samples are not ``convert_width`` bytes each, the resulting audio is converted to match.

        Writing these bytes directly to a file results in a valid `FLAC file <https://en.wikipedia.org/wiki/FLAC>`__.
        """
        assert convert_width is None or (convert_width % 1 == 0 and 1 <= convert_width <= 3), "Sample width to convert to must be between 1 and 3 inclusive"

        if self.sample_width > 3 and convert_width is None: # resulting WAV data would be 32-bit, which is not convertable to FLAC using our encoder
            convert_width = 3 # the largest supported sample width is 24-bit, so we'll limit the sample width to that

        # run the FLAC converter with the WAV data to get the FLAC data
        wav_data = self.get_wav_data(convert_rate, convert_width)
        flac_converter = get_flac_converter()
        process = subprocess.Popen([
            flac_converter,
            "--stdout", "--totally-silent", # put the resulting FLAC file in stdout, and make sure it's not mixed with any program output
            "--best", # highest level of compression available
            "-", # the input FLAC file contents will be given in stdin
        ], stdin=subprocess.PIPE, stdout=subprocess.PIPE)
        flac_data, stderr = process.communicate(wav_data)
        return flac_data

class Recognizer(AudioSource):
    def __init__(self):
        """
        Creates a new ``Recognizer`` instance, which represents a collection of speech recognition functionality.
        """
        self.energy_threshold = 300 # minimum audio energy to consider for recording
        self.dynamic_energy_threshold = True
        self.dynamic_energy_adjustment_damping = 0.15
        self.dynamic_energy_ratio = 1.5
        self.pause_threshold = 0.8 # seconds of non-speaking audio before a phrase is considered complete
        self.phrase_threshold = 0.3 # minimum seconds of speaking audio before we consider the speaking audio a phrase - values below this are ignored (for filtering out clicks and pops)
        self.non_speaking_duration = 0.5 # seconds of non-speaking audio to keep on both sides of the recording

    def record(self, source, duration = None, offset = None):
        """
        Records up to ``duration`` seconds of audio from ``source`` (an ``AudioSource`` instance) starting at ``offset`` (or at the beginning if not specified) into an ``AudioData`` instance, which it returns.

        If ``duration`` is not specified, then it will record until there is no more audio input.
        """
        assert isinstance(source, AudioSource), "Source must be an audio source"
        assert source.stream is not None, "Audio source must be entered before recording, see documentation for `AudioSource`; are you using `source` outside of a `with` statement?"

        frames = io.BytesIO()
        seconds_per_buffer = (source.CHUNK + 0.0) / source.SAMPLE_RATE
        elapsed_time = 0
        offset_time = 0
        offset_reached = False
        while True: # loop for the total number of chunks needed
            if offset and not offset_reached:
                offset_time += seconds_per_buffer
                if offset_time > offset:
                    offset_reached = True

            buffer = source.stream.read(source.CHUNK)
            if len(buffer) == 0: break

            if offset_reached or not offset:
                elapsed_time += seconds_per_buffer
                if duration and elapsed_time > duration: break

                frames.write(buffer)

        frame_data = frames.getvalue()
        frames.close()
        return AudioData(frame_data, source.SAMPLE_RATE, source.SAMPLE_WIDTH)

    def adjust_for_ambient_noise(self, source, duration = 1):
        """
        Adjusts the energy threshold dynamically using audio from ``source`` (an ``AudioSource`` instance) to account for ambient noise.

        Intended to calibrate the energy threshold with the ambient energy level. Should be used on periods of audio without speech - will stop early if any speech is detected.

        The ``duration`` parameter is the maximum number of seconds that it will dynamically adjust the threshold for before returning. This value should be at least 0.5 in order to get a representative sample of the ambient noise.
        """
        assert isinstance(source, AudioSource), "Source must be an audio source"
        assert source.stream is not None, "Audio source must be entered before adjusting, see documentation for `AudioSource`; are you using `source` outside of a `with` statement?"
        assert self.pause_threshold >= self.non_speaking_duration >= 0

        seconds_per_buffer = (source.CHUNK + 0.0) / source.SAMPLE_RATE
        elapsed_time = 0

        # adjust energy threshold until a phrase starts
        while True:
            elapsed_time += seconds_per_buffer
            if elapsed_time > duration: break
            buffer = source.stream.read(source.CHUNK)
            energy = audioop.rms(buffer, source.SAMPLE_WIDTH) # energy of the audio signal

            # dynamically adjust the energy threshold using assymmetric weighted average
            damping = self.dynamic_energy_adjustment_damping ** seconds_per_buffer # account for different chunk sizes and rates
            target_energy = energy * self.dynamic_energy_ratio
            self.energy_threshold = self.energy_threshold * damping + target_energy * (1 - damping)

    def listen(self, source, timeout = None):
        """
        Records a single phrase from ``source`` (an ``AudioSource`` instance) into an ``AudioData`` instance, which it returns.

        This is done by waiting until the audio has an energy above ``recognizer_instance.energy_threshold`` (the user has started speaking), and then recording until it encounters ``recognizer_instance.pause_threshold`` seconds of non-speaking or there is no more audio input. The ending silence is not included.

        The ``timeout`` parameter is the maximum number of seconds that it will wait for a phrase to start before giving up and throwing an ``speech_recognition.WaitTimeoutError`` exception. If ``timeout`` is ``None``, it will wait indefinitely.
        """
        assert isinstance(source, AudioSource), "Source must be an audio source"
        assert source.stream is not None, "Audio source must be entered before listening, see documentation for `AudioSource`; are you using `source` outside of a `with` statement?"
        assert self.pause_threshold >= self.non_speaking_duration >= 0

        seconds_per_buffer = (source.CHUNK + 0.0) / source.SAMPLE_RATE
        pause_buffer_count = int(math.ceil(self.pause_threshold / seconds_per_buffer)) # number of buffers of non-speaking audio before the phrase is complete
        phrase_buffer_count = int(math.ceil(self.phrase_threshold / seconds_per_buffer)) # minimum number of buffers of speaking audio before we consider the speaking audio a phrase
        non_speaking_buffer_count = int(math.ceil(self.non_speaking_duration / seconds_per_buffer)) # maximum number of buffers of non-speaking audio to retain before and after

        # read audio input for phrases until there is a phrase that is long enough
        elapsed_time = 0 # number of seconds of audio read
        while True:
            frames = collections.deque()

            # store audio input until the phrase starts
            while True:
                elapsed_time += seconds_per_buffer
                if timeout and elapsed_time > timeout: # handle timeout if specified
                    raise WaitTimeoutError("listening timed out")

                buffer = source.stream.read(source.CHUNK)
                if len(buffer) == 0: break # reached end of the stream
                frames.append(buffer)
                if len(frames) > non_speaking_buffer_count: # ensure we only keep the needed amount of non-speaking buffers
                    frames.popleft()

                # detect whether speaking has started on audio input
                energy = audioop.rms(buffer, source.SAMPLE_WIDTH) # energy of the audio signal
                if energy > self.energy_threshold: break

                # dynamically adjust the energy threshold using assymmetric weighted average
                if self.dynamic_energy_threshold:
                    damping = self.dynamic_energy_adjustment_damping ** seconds_per_buffer # account for different chunk sizes and rates
                    target_energy = energy * self.dynamic_energy_ratio
                    self.energy_threshold = self.energy_threshold * damping + target_energy * (1 - damping)

            # read audio input until the phrase ends
            pause_count, phrase_count = 0, 0
            while True:
                elapsed_time += seconds_per_buffer

                buffer = source.stream.read(source.CHUNK)
                if len(buffer) == 0: break # reached end of the stream
                frames.append(buffer)
                phrase_count += 1

                # check if speaking has stopped for longer than the pause threshold on the audio input
                energy = audioop.rms(buffer, source.SAMPLE_WIDTH) # energy of the audio signal
                if energy > self.energy_threshold:
                    pause_count = 0
                else:
                    pause_count += 1
                if pause_count > pause_buffer_count: # end of the phrase
                    break

            # check how long the detected phrase is, and retry listening if the phrase is too short
            phrase_count -= pause_count
            if phrase_count >= phrase_buffer_count: break # phrase is long enough, stop listening

        # obtain frame data
        for i in range(pause_count - non_speaking_buffer_count): frames.pop() # remove extra non-speaking frames at the end
        frame_data = b"".join(list(frames))

        return AudioData(frame_data, source.SAMPLE_RATE, source.SAMPLE_WIDTH)

    def listen_in_background(self, source, callback):
        """
        Spawns a thread to repeatedly record phrases from ``source`` (an ``AudioSource`` instance) into an ``AudioData`` instance and call ``callback`` with that ``AudioData`` instance as soon as each phrase are detected.

        Returns a function object that, when called, requests that the background listener thread stop, and waits until it does before returning. The background thread is a daemon and will not stop the program from exiting if there are no other non-daemon threads.

        Phrase recognition uses the exact same mechanism as ``recognizer_instance.listen(source)``.

        The ``callback`` parameter is a function that should accept two parameters - the ``recognizer_instance``, and an ``AudioData`` instance representing the captured audio. Note that ``callback`` function will be called from a non-main thread.
        """
        assert isinstance(source, AudioSource), "Source must be an audio source"
        running = [True]
        def threaded_listen():
            with source as s:
                while running[0]:
                    try: # listen for 1 second, then check again if the stop function has been called
                        audio = self.listen(s, 1)
                    except WaitTimeoutError: # listening timed out, just try again
                        pass
                    else:
                        if running[0]: callback(self, audio)
        def stopper():
            running[0] = False
            listener_thread.join() # block until the background thread is done, which can be up to 1 second
        listener_thread = threading.Thread(target=threaded_listen)
        listener_thread.daemon = True
        listener_thread.start()
        return stopper

    def recognize_sphinx(self, audio_data, language = "en-US", show_all = False):
        """
        Performs speech recognition on ``audio_data`` (an ``AudioData`` instance), using CMU Sphinx.

        The recognition language is determined by ``language``, an RFC5646 language tag like ``"en-US"`` or ``"en-GB"``, defaulting to US English. Out of the box, only ``en-US`` is supported. See `Notes on using `PocketSphinx <https://github.com/Uberi/speech_recognition/blob/master/reference/pocketsphinx.rst>`__ for information about installing other languages. This document is also included under ``reference/pocketsphinx.rst``.

        Returns the most likely transcription if ``show_all`` is false (the default). Otherwise, returns the Sphinx ``pocketsphinx.pocketsphinx.Decoder`` object resulting from the recognition.

        Raises a ``speech_recognition.UnknownValueError`` exception if the speech is unintelligible. Raises a ``speech_recognition.RequestError`` exception if there are any issues with the Sphinx installation.
        """
        assert isinstance(audio_data, AudioData), "`audio_data` must be audio data"
        assert isinstance(language, str), "`language` must be a string"
        
        # import the PocketSphinx speech recognition module
        try:
            from pocketsphinx import pocketsphinx
            from sphinxbase import sphinxbase
        except ImportError:
            raise RequestError("missing PocketSphinx module: ensure that PocketSphinx is set up correctly.")
        except ValueError:
            raise RequestError("bad PocketSphinx installation detected; make sure you have PocketSphinx version 0.0.9 or better.")

        language_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "pocketsphinx-data", language)
        if not os.path.isdir(language_directory):
            raise RequestError("missing PocketSphinx language data directory: \"{0}\"".format(language_directory))
        acoustic_parameters_directory = os.path.join(language_directory, "acoustic-model")
        if not os.path.isdir(acoustic_parameters_directory):
            raise RequestError("missing PocketSphinx language model parameters directory: \"{0}\"".format(acoustic_parameters_directory))
        language_model_file = os.path.join(language_directory, "language-model.lm.bin")
        if not os.path.isfile(language_model_file):
            raise RequestError("missing PocketSphinx language model file: \"{0}\"".format(language_model_file))
        phoneme_dictionary_file = os.path.join(language_directory, "pronounciation-dictionary.dict")
        if not os.path.isfile(phoneme_dictionary_file):
            raise RequestError("missing PocketSphinx phoneme dictionary file: \"{0}\"".format(phoneme_dictionary_file))

        # create decoder object
        config = pocketsphinx.Decoder.default_config()
        config.set_string("-hmm", acoustic_parameters_directory) # set the path of the hidden Markov model (HMM) parameter files
        config.set_string("-lm", language_model_file)
        config.set_string("-dict", phoneme_dictionary_file)
        config.set_string("-logfn", os.devnull) # disable logging (logging causes unwanted output in terminal)
        decoder = pocketsphinx.Decoder(config)

        # obtain audio data
        raw_data = audio_data.get_raw_data(convert_rate = 16000, convert_width = 2) # the included language models require audio to be 16-bit mono 16 kHz in little-endian format

        # obtain recognition results
        decoder.start_utt() # begin utterance processing
        decoder.process_raw(raw_data, False, True) # process audio data with recognition enabled (no_search = False), as a full utterance (full_utt = True)
        decoder.end_utt() # stop utterance processing

        if show_all: return decoder

        # return results
        hypothesis = decoder.hyp()
        if hypothesis is not None: return hypothesis.hypstr
        raise UnknownValueError() # no transcriptions available

    def recognize_google(self, audio_data, key = None, language = "en-US", show_all = False):
        """
        Performs speech recognition on ``audio_data`` (an ``AudioData`` instance), using the Google Speech Recognition API.

        The Google Speech Recognition API key is specified by ``key``. If not specified, it uses a generic key that works out of the box. This should generally be used for personal or testing purposes only, as it **may be revoked by Google at any time**.

        To obtain your own API key, simply following the steps on the `API Keys <http://www.chromium.org/developers/how-tos/api-keys>`__ page at the Chromium Developers site. In the Google Developers Console, Google Speech Recognition is listed as "Speech API".

        The recognition language is determined by ``language``, an RFC5646 language tag like ``"en-US"`` (US English) or ``"fr-FR"`` (International French), defaulting to US English. A list of supported language values can be found in this `StackOverflow answer <http://stackoverflow.com/a/14302134>`__.

        Returns the most likely transcription if ``show_all`` is false (the default). Otherwise, returns the raw API response as a JSON dictionary.

        Raises a ``speech_recognition.UnknownValueError`` exception if the speech is unintelligible. Raises a ``speech_recognition.RequestError`` exception if the speech recognition operation failed, if the key isn't valid, or if there is no internet connection.
        """
        assert isinstance(audio_data, AudioData), "`audio_data` must be audio data"
        assert key is None or isinstance(key, str), "`key` must be `None` or a string"
        assert isinstance(language, str), "`language` must be a string"

        flac_data = audio_data.get_flac_data(
            convert_rate = None if audio_data.sample_rate >= 8000 else 8000, # audio samples must be at least 8 kHz
            convert_width = 2 # audio samples must be 16-bit
        )
        if key is None: key = "AIzaSyBOti4mM-6x9WDnZIjIeyEU21OpBXqWBgw"
        url = "http://www.google.com/speech-api/v2/recognize?{0}".format(urlencode({
            "client": "chromium",
            "lang": language,
            "key": key,
        }))
        request = Request(url, data = flac_data, headers = {"Content-Type": "audio/x-flac; rate={0}".format(audio_data.sample_rate)})

        # obtain audio transcription results
        try:
            response = urlopen(request)
        except HTTPError as e:
            raise RequestError("recognition request failed: {0}".format(getattr(e, "reason", "status {0}".format(e.code)))) # use getattr to be compatible with Python 2.6
        except URLError as e:
            raise RequestError("recognition connection failed: {0}".format(e.reason))
        response_text = response.read().decode("utf-8")

        # ignore any blank blocks
        actual_result = []
        for line in response_text.split("\n"):
            if not line: continue
            result = json.loads(line)["result"]
            if len(result) != 0:
                actual_result = result[0]
                break

        # return results
        if show_all: return actual_result
        if "alternative" not in actual_result: raise UnknownValueError()
        for entry in actual_result["alternative"]:
            if "transcript" in entry:
                return entry["transcript"]
        raise UnknownValueError() # no transcriptions available

    def recognize_wit(self, audio_data, key, show_all = False):
        """
        Performs speech recognition on ``audio_data`` (an ``AudioData`` instance), using the Wit.ai API.

        The Wit.ai API key is specified by ``key``. Unfortunately, these are not available without `signing up for an account <https://wit.ai/>`__ and creating an app. You will need to add at least one intent to the app before you can see the API key, though the actual intent settings don't matter.

        To get the API key for a Wit.ai app, go to the app's overview page, go to the section titled "Make an API request", and look for something along the lines of ``Authorization: Bearer XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX``; ``XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX`` is the API key. Wit.ai API keys are 32-character uppercase alphanumeric strings.

        The recognition language is configured in the Wit.ai app settings.

        Returns the most likely transcription if ``show_all`` is false (the default). Otherwise, returns the `raw API response <https://wit.ai/docs/http/20141022#get-intent-via-text-link>`__ as a JSON dictionary.

        Raises a ``speech_recognition.UnknownValueError`` exception if the speech is unintelligible. Raises a ``speech_recognition.RequestError`` exception if the speech recognition operation failed, if the key isn't valid, or if there is no internet connection.
        """
        assert isinstance(audio_data, AudioData), "Data must be audio data"
        assert isinstance(key, str), "`key` must be a string"

        wav_data = audio_data.get_wav_data(
            convert_rate = None if audio_data.sample_rate >= 8000 else 8000, # audio samples must be at least 8 kHz
            convert_width = 2 # audio samples should be 16-bit
        )
        url = "https://api.wit.ai/speech?v=20141022"
        request = Request(url, data = wav_data, headers = {"Authorization": "Bearer {0}".format(key), "Content-Type": "audio/wav"})
        try:
            response = urlopen(request)
        except HTTPError as e:
            raise RequestError("recognition request failed: {0}".format(getattr(e, "reason", "status {0}".format(e.code)))) # use getattr to be compatible with Python 2.6
        except URLError as e:
            raise RequestError("recognition connection failed: {0}".format(e.reason))
        response_text = response.read().decode("utf-8")
        result = json.loads(response_text)

        # return results
        if show_all: return result
        if "_text" not in result or result["_text"] is None: raise UnknownValueError()
        return result["_text"]

    def recognize_bing(self, audio_data, key, language = "en-US", show_all = False):
        """
        Performs speech recognition on ``audio_data`` (an ``AudioData`` instance), using the Microsoft Bing Voice Recognition API.

        The Microsoft Bing Voice Recognition API key is specified by ``key``. Unfortunately, these are not available without `signing up for an account <https://www.microsoft.com/cognitive-services/en-us/speech-api>`__ with Microsoft Cognitive Services.

        To get the API key, go to the `Microsoft Cognitive Services subscriptions overview <https://www.microsoft.com/cognitive-services/en-us/subscriptions>`__, go to the entry titled "Speech", and look for the key under the "Keys" column. Microsoft Bing Voice Recognition API keys are 32-character lowercase hexadecimal strings.

        The recognition language is determined by ``language``, an RFC5646 language tag like ``"en-US"`` (US English) or ``"fr-FR"`` (International French), defaulting to US English. A list of supported language values can be found in the `API documentation <https://www.microsoft.com/cognitive-services/en-us/speech-api/documentation/api-reference-rest/BingVoiceRecognition#user-content-4-supported-locales>`__.

        Returns the most likely transcription if ``show_all`` is false (the default). Otherwise, returns the `raw API response <https://www.microsoft.com/cognitive-services/en-us/speech-api/documentation/api-reference-rest/BingVoiceRecognition#user-content-3-voice-recognition-responses>`__ as a JSON dictionary.

        Raises a ``speech_recognition.UnknownValueError`` exception if the speech is unintelligible. Raises a ``speech_recognition.RequestError`` exception if the speech recognition operation failed, if the key isn't valid, or if there is no internet connection.
        """
        assert isinstance(audio_data, AudioData), "Data must be audio data"
        assert isinstance(key, str), "`key` must be a string"
        assert isinstance(language, str), "`language` must be a string"

        access_token, expire_time = getattr(self, "bing_cached_access_token", None), getattr(self, "bing_cached_access_token_expiry", None)
        allow_caching = True
        try:
            from time import monotonic # we need monotonic time to avoid being affected by system clock changes, but this is only available in Python 3.3+
        except ImportError:
            try:
                from monotonic import monotonic # use time.monotonic backport for Python 2 if available (from https://pypi.python.org/pypi/monotonic)
            except (ImportError, RuntimeError):
                expire_time = None # monotonic time not available, don't cache access tokens
                allow_caching = False # don't allow caching, since monotonic time isn't available
        if expire_time is None or monotonic() > expire_time: # caching not enabled, first credential request, or the access token from the previous one expired
            # get an access token using OAuth
            credential_url = "https://oxford-speech.cloudapp.net/token/issueToken"
            credential_request = Request(credential_url, data = urlencode({
              "grant_type": "client_credentials",
              "client_id": "python",
              "client_secret": key,
              "scope": "https://speech.platform.bing.com"
            }).encode("utf-8"))
            if allow_caching:
                start_time = monotonic()
            try:
                credential_response = urlopen(credential_request)
            except HTTPError as e:
                raise RequestError("recognition request failed: {0}".format(getattr(e, "reason", "status {0}".format(e.code)))) # use getattr to be compatible with Python 2.6
            except URLError as e:
                raise RequestError("recognition connection failed: {0}".format(e.reason))
            credential_text = credential_response.read().decode("utf-8")
            credentials = json.loads(credential_text)
            access_token, expiry_seconds = credentials["access_token"], float(credentials["expires_in"])

            if allow_caching:
                # save the token for the duration it is valid for
                self.bing_cached_access_token = access_token
                self.bing_cached_access_token_expiry = start_time + expiry_seconds

        wav_data = audio_data.get_wav_data(
            convert_rate = 16000, # audio samples must be 8kHz or 16 kHz
            convert_width = 2 # audio samples should be 16-bit
        )
        url = "https://speech.platform.bing.com/recognize/query?{0}".format(urlencode({
            "version": "3.0",
            "requestid": uuid.uuid4(),
            "appID": "D4D52672-91D7-4C74-8AD8-42B1D98141A5",
            "format": "json",
            "locale": language,
            "device.os": "wp7",
            "scenarios": "ulm",
            "instanceid": uuid.uuid4(),
            "result.profanitymarkup": "0",
        }))
        request = Request(url, data = wav_data, headers = {
            "Authorization": "Bearer {0}".format(access_token),
            "Content-Type": "audio/wav; samplerate=16000; sourcerate={0}; trustsourcerate=true".format(audio_data.sample_rate),
        })
        try:
            response = urlopen(request)
        except HTTPError as e:
            raise RequestError("recognition request failed: {0}".format(getattr(e, "reason", "status {0}".format(e.code)))) # use getattr to be compatible with Python 2.6
        except URLError as e:
            raise RequestError("recognition connection failed: {0}".format(e.reason))
        response_text = response.read().decode("utf-8")
        result = json.loads(response_text)

        # return results
        if show_all: return result
        if "header" not in result or "lexical" not in result["header"]: raise UnknownValueError()
        return result["header"]["lexical"]

    def recognize_api(self, audio_data, client_access_token, language = "en", session_id = None, show_all = False):
        """
        Perform speech recognition on ``audio_data`` (an ``AudioData`` instance), using the api.ai Speech to Text API.

        The api.ai API client access token is specified by ``client_access_token``. Unfortunately, this is not available without `signing up for an account <https://console.api.ai/api-client/#/signup>`__ and creating an api.ai agent. To get the API client access token, go to the agent settings, go to the section titled "API keys", and look for "Client access token". API client access tokens are 32-character lowercase hexadecimal strings.

        Although the recognition language is specified when creating the api.ai agent in the web console, it must also be provided in the ``language`` parameter as an RFC5646 language tag like ``"en"`` (US English) or ``"fr"`` (International French), defaulting to US English. A list of supported language values can be found in the `API documentation <https://api.ai/docs/reference/#languages>`__.

        The ``session_id`` is an optional string of up to 36 characters used to identify the client making the requests; api.ai can make use of previous requests that used the same session ID to give more accurate results for future requests. If ``None``, sessions are not used; every query is interpreted as if it is the first one.

        Returns the most likely transcription if ``show_all`` is false (the default). Otherwise, returns the `raw API response <https://api.ai/docs/reference/#a-namepost-multipost-query-multipart>`__ as a JSON dictionary.

        Raises a ``speech_recognition.UnknownValueError`` exception if the speech is unintelligible. Raises a ``speech_recognition.RequestError`` exception if the speech recognition operation failed, if the key isn't valid, or if there is no internet connection.
        """
        assert isinstance(audio_data, AudioData), "Data must be audio data"
        assert isinstance(client_access_token, str), "`username` must be a string"
        assert isinstance(language, str), "`language` must be a string"
        assert session_id is None or (isinstance(session_id, str) and len(session_id) <= 36), "`session_id` must be a string of up to 36 characters"

        wav_data = audio_data.get_wav_data(convert_rate = 16000, convert_width = 2) # audio must be 16-bit mono 16 kHz
        url = "https://api.api.ai/v1/query"

        # pick a good multipart boundary; one that is guaranteed not to be in the text
        while True:
            boundary = uuid.uuid4().hex # generate a random boundary
            if boundary.encode("utf-8") not in wav_data:
                break

        if session_id is None: session_id = uuid.uuid4().hex
        data = (
            b"--" + boundary.encode("utf-8") + b"\r\n" +
            b"Content-Disposition: form-data; name=\"request\"\r\n" +
            b"Content-Type: application/json\r\n" +
            b"\r\n" +
            b"{\"v\": \"20150910\", \"sessionId\": \"" + session_id.encode("utf-8") + b"\", \"lang\": \"" + language.encode("utf-8") + b"\"}\r\n" +
            b"--" + boundary.encode("utf-8") + b"\r\n" +
            b"Content-Disposition: form-data; name=\"voiceData\"; filename=\"audio.wav\"\r\n" +
            b"Content-Type: audio/wav\r\n" +
            b"\r\n" +
            wav_data + b"\r\n" +
            b"--" + boundary.encode("utf-8") + b"--\r\n"
        )

        request = Request(url, data = data, headers = {
            "Authorization": "Bearer {0}".format(client_access_token),
            "Content-Length": str(len(data)),
            "Expect": "100-continue",
            "Content-Type": "multipart/form-data; boundary={0}".format(boundary)
        })
        try:
            response = urlopen(request)
        except HTTPError as e:
            raise RequestError("recognition request failed: {0}".format(getattr(e, "reason", "status {0}".format(e.code)))) # use getattr to be compatible with Python 2.6
        except URLError as e:
            raise RequestError("recognition connection failed: {0}".format(e.reason))
        response_text = response.read().decode("utf-8")
        result = json.loads(response_text)

        # return results
        if show_all: return result
        if "asr" not in result or result["asr"] is None:
            raise UnknownValueError()
        return result["result"]["resolvedQuery"]

    def recognize_ibm(self, audio_data, username, password, language = "en-US", show_all = False):
        """
        Performs speech recognition on ``audio_data`` (an ``AudioData`` instance), using the IBM Speech to Text API.

        The IBM Speech to Text username and password are specified by ``username`` and ``password``, respectively. Unfortunately, these are not available without `signing up for an account <https://console.ng.bluemix.net/registration/>`__. Once logged into the Bluemix console, follow the instructions for `creating an IBM Watson service instance <http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/doc/getting_started/gs-credentials.shtml>`__, where the Watson service is "Speech To Text". IBM Speech to Text usernames are strings of the form XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX, while passwords are mixed-case alphanumeric strings.

        The recognition language is determined by ``language``, an RFC5646 language tag with a dialect like ``"en-US"`` (US English) or ``"zh-CN"`` (Mandarin Chinese), defaulting to US English. The supported language values are listed under the ``model`` parameter of the `audio recognition API documentation <http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/speech-to-text/api/v1/#recognize_audio_sessionless12>`__, in the form ``LANGUAGE_BroadbandModel``, where ``LANGUAGE`` is the language value.

        Returns the most likely transcription if ``show_all`` is false (the default). Otherwise, returns the `raw API response <http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/speech-to-text/api/v1/#recognize_audio_sessionless12>`__ as a JSON dictionary.

        Raises a ``speech_recognition.UnknownValueError`` exception if the speech is unintelligible. Raises a ``speech_recognition.RequestError`` exception if the speech recognition operation failed, if the key isn't valid, or if there is no internet connection.
        """
        assert isinstance(audio_data, AudioData), "Data must be audio data"
        assert isinstance(username, str), "`username` must be a string"
        assert isinstance(password, str), "`password` must be a string"

        flac_data = audio_data.get_flac_data(
            convert_rate = None if audio_data.sample_rate >= 16000 else 16000, # audio samples should be at least 16 kHz
            convert_width = None if audio_data.sample_width >= 2 else 2 # audio samples should be at least 16-bit
        )
        model = "{0}_BroadbandModel".format(language)
        url = "https://stream.watsonplatform.net/speech-to-text/api/v1/recognize?{0}".format(urlencode({
            "profanity_filter": "false",
            "continuous": "true",
            "model": model,
        }))
        request = Request(url, data = flac_data, headers = {"Content-Type": "audio/x-flac"})
        if hasattr("", "encode"):
            authorization_value = base64.standard_b64encode("{0}:{1}".format(username, password).encode("utf-8")).decode("utf-8")
        else:
            authorization_value = base64.standard_b64encode("{0}:{1}".format(username, password))
        request.add_header("Authorization", "Basic {0}".format(authorization_value))
        try:
            response = urlopen(request)
        except HTTPError as e:
            raise RequestError("recognition request failed: {0}".format(getattr(e, "reason", "status {0}".format(e.code)))) # use getattr to be compatible with Python 2.6
        except URLError as e:
            raise RequestError("recognition connection failed: {0}".format(e.reason))
        response_text = response.read().decode("utf-8")
        result = json.loads(response_text)

        # return results
        if show_all: return result
        if "results" not in result or len(result["results"]) < 1 or "alternatives" not in result["results"][0]:
            raise UnknownValueError()

        transcription = []
        for utterance in result["results"]:
            if "alternatives" not in utterance: raise UnknownValueError()
            for hypothesis in utterance["alternatives"]:
                if "transcript" in hypothesis:
                    transcription.append(hypothesis["transcript"])
        return "\n".join(transcription)

def get_flac_converter():
    # determine which converter executable to use
    system = platform.system()
    path = os.path.dirname(os.path.abspath(__file__)) # directory of the current module file, where all the FLAC bundled binaries are stored
    flac_converter = shutil_which("flac") # check for installed version first
    if flac_converter is None: # flac utility is not installed
        compatible_machine_types = ["i686", "i786", "x86", "x86_64", "AMD64"] # whitelist of machine types our bundled binaries are compatible with
        if system == "Windows" and platform.machine() in compatible_machine_types:
            flac_converter = os.path.join(path, "flac-win32.exe")
        elif system == "Linux" and platform.machine() in compatible_machine_types:
            flac_converter = os.path.join(path, "flac-linux-x86")
        elif system == "Darwin" and platform.machine() in compatible_machine_types:
            flac_converter = os.path.join(path, "flac-mac")
        else:
            raise OSError("FLAC conversion utility not available - consider installing the FLAC command line application using `brew install flac` or your operating system's equivalent")

    # mark FLAC converter as executable if possible
    try:
        stat_info = os.stat(flac_converter)
        os.chmod(flac_converter, stat_info.st_mode | stat.S_IEXEC)
    except OSError: pass

    return flac_converter

def shutil_which(pgm):
    """Python 2 backport of ``shutil.which()`` from Python 3"""
    path = os.getenv('PATH')
    for p in path.split(os.path.pathsep):
        p = os.path.join(p, pgm)
        if os.path.exists(p) and os.access(p, os.X_OK):
            return p

# backwards compatibility shims
WavFile = AudioFile # WavFile was renamed to AudioFile in 3.4.1
def recognize_att(self, audio_data, app_key, app_secret, language = "en-US", show_all = False):
    authorization_url = "https://api.att.com/oauth/v4/token"
    authorization_body = "client_id={0}&client_secret={1}&grant_type=client_credentials&scope=SPEECH".format(app_key, app_secret)
    try: authorization_response = urlopen(authorization_url, data = authorization_body.encode("utf-8"))
    except HTTPError as e: raise RequestError("credential request failed: {0}".format(getattr(e, "reason", "status {0}".format(e.code))))
    except URLError as e: raise RequestError("credential connection failed: {0}".format(e.reason))
    authorization_text = authorization_response.read().decode("utf-8")
    authorization_bearer = json.loads(authorization_text).get("access_token")
    if authorization_bearer is None: raise RequestError("missing OAuth access token in requested credentials")
    wav_data = audio_data.get_wav_data(convert_rate = 8000 if audio_data.sample_rate < 16000 else 16000, convert_width = 2)
    request = Request("https://api.att.com/speech/v3/speechToText", data = wav_data, headers = {"Authorization": "Bearer {0}".format(authorization_bearer), "Content-Language": language, "Content-Type": "audio/wav"})
    try: response = urlopen(request)
    except HTTPError as e: raise RequestError("recognition request failed: {0}".format(getattr(e, "reason", "status {0}".format(e.code))))
    except URLError as e: raise RequestError("recognition connection failed: {0}".format(e.reason))
    result = json.loads(response.read().decode("utf-8"))
    if show_all: return result
    if "Recognition" not in result or "NBest" not in result["Recognition"]: raise UnknownValueError()
    for entry in result["Recognition"]["NBest"]:
        if entry.get("Grade") == "accept" and "ResultText" in entry: return entry["ResultText"]
        raise UnknownValueError() # no transcriptions available
Recognizer.recognize_att = classmethod(recognize_att) # AT&T API is deprecated and shutting down as of 3.4.0