Source code for psychopy_legacy_mic.microphone

#!/usr/bin/env python
# -*- coding: utf-8 -*-

# Part of the PsychoPy library
# Copyright (C) 2002-2018 Jonathan Peirce (C) 2019-2022 Open Science Tools Ltd.
# Distributed under the terms of the GNU General Public License (GPL).

"""Audio capture and analysis using pyo"""

# Author: Jeremy R. Gray, March 2012, March 2013

import os
import glob
import threading
from import pathToString
import urllib.request
import urllib.error
import urllib.parse

import json
import numpy as np
from import wavfile
from psychopy import core, logging, web, prefs
from psychopy.sound import backend_pyo
from psychopy.constants import NOT_STARTED, PLAYING, PSYCHOPY_USERAGENT
# import pyo is done within switchOn to better encapsulate it, can be very
# slow and don't want to delay up to 3 sec when importing microphone
# downside: to make this work requires some trickiness with globals

haveMic = False  # goes True in switchOn, if can import pyo

# flac is used for audio compression; user needs to install it
FLAC_PATH = None  # set on first call to _getFlacPath()

class AudioCapture:
    """Capture sound sample from the default sound input, and save to a file.

    Untested whether you can have two recordings going on simultaneously.

    .. code-block:: python

        from psychopy import microphone
        from psychopy import event, visual  # for key events

        microphone.switchOn(sampleRate=16000)  # do once

        # Record for 1.000 seconds, save to mic.savedFile
        mic = microphone.AudioCapture()

        # Resample, creates a new file discards orig
        mic.resample(48000, keep=False)

        # Record new file for 60 sec or until key 'q'
        w = visual.Window()  # needed for key-events
        mic.record(60, block=False)
        while mic.recorder.running:
            if 'q' in event.getKeys():

    Also see Builder Demo "voiceCapture".

    :Author: Jeremy R. Gray, March 2012

    class _Recorder():
        """Class for internal object to make an audio recording using pyo.

        Never needed by end-users; only used internally in __init__::

            self.recorder = _Recorder(None) # instantiate, global

        Then in record(), do::

  , sec)

        This sets recording parameters, starts recording.
        To stop a recording that is in progress, do::


        This class never handles blocking; AudioCapture has to do that.

        Motivation: Doing pyo Record from within a function worked most of
        the time, but failed catastrophically ~1% of time with a bus error.
        Seemed to be due to a namespace scoping issue, which using globals
        seemed to fix; see pyo mailing list, 7 April 2012. This draws
        heavily on Olivier Belanger's solution.

        def __init__(self):
            self.running = False

        def run(self, filename, sec, sampletype=0, buffering=16,
                chnl=0, chnls=2):
            filename = pathToString(filename)
            self.running = True
            # chnl from psychopy.backend_pyo.get_input_devices()
            inputter = pyo.Input(chnl=chnl, mul=1)
            self.recorder = pyo.Record(inputter, filename, chnls=chnls,
                                       fileformat=0, sampletype=sampletype,
            # handles recording offset
            pyo.Clean_objects(sec, self.recorder).start()
            threading.Timer(sec, self._stop).start()  # set running flag False

        def stop(self):

        def _stop(self):
            self.running = False

    def __init__(self, name='mic', filename='', saveDir='', sampletype=0,
                 buffering=16, chnl=0, stereo=True, autoLog=True):

        name :
            Stem for the output file, also used in logging.
        filename :
            optional file name to use; default = 'name-onsetTimeEpoch.wav'
        saveDir :
            Directory to use for output .wav files. If a saveDir is given, it will return 'saveDir/file'.
            If no saveDir, then return abspath(file)
        sampletype : bit depth
            pyo recording option: 0=16 bits int, 1=24 bits int; 2=32 bits int
        buffering : pyo argument
            Controls the buffering argument for pyo if necessary
        chnl : int (default=0)
            which audio input channel to record (default=0)
        stereo : bool or nChannels (default = True)
            how many channels to record
        if not haveMic:
            raise MicrophoneError('Need to call microphone.switchOn()'
                                  ' before AudioCapture or AdvancedCapture') = name
        self.saveDir = saveDir
        filename = pathToString(filename)
        if filename:
            self.wavOutFilename = filename
            self.wavOutFilename = os.path.join(self.saveDir, name + '.wav')
        if not self.saveDir:
            self.wavOutFilename = os.path.abspath(self.wavOutFilename)
            if not os.path.isdir(self.saveDir):
                os.makedirs(self.saveDir, 0o770)

        self.onset = None  # becomes onset time, used in filename
        self.savedFile = False  # becomes saved file name
        self.status = NOT_STARTED  # for Builder component

        # pyo server good to go?
        if not pyo.serverCreated():
            raise AttributeError('pyo server not created')
        if not pyo.serverBooted():
            raise AttributeError('pyo server not booted')

        self.autoLog = autoLog
        self.loggingId = self.__class__.__name__
            self.loggingId += ' ' +

        if type(chnl) != int:
                chnl = int(chnl)
            except (TypeError, ValueError):
                raise TypeError("AudioCapture argument 'chnl' needs to be an int but received {}".format(repr(chnl)))

        # the recorder object needs to persist, or else get bus errors:
        self.recorder = self._Recorder()
        self.options = {'sampletype': sampletype, 'buffering': buffering,
                        'chnl': chnl, 'chnls': 1 + int(stereo == True)}

    def stop(self, log=True):
        """Interrupt a recording that is in progress; close & keep the file.

        Ends the recording before the duration that was initially specified.
        The same file name is retained, with the same onset time but a
        shorter duration.

        The same recording cannot be resumed after a stop (it is not a pause),
        but you can start a new one.
        if not self.recorder.running:
            if log and self.autoLog:
                msg = '%s: Stop requested, but no record() in progress'
                logging.exp(msg % self.loggingId)
        self.duration = core.getTime() - self.onset  # new shorter duration
        if log and self.autoLog:
            msg = '%s: Record stopped early, new duration %.3fs'
   % (self.loggingId, self.duration))

    def reset(self, log=True):
        """Restores to fresh state, ready to record again
        if log and self.autoLog:
            msg = '%s: resetting at %.3f'
            logging.exp(msg % (self.loggingId, core.getTime()))
        self.__init__(, saveDir=self.saveDir)

    def record(self, sec, filename='', block=True):
        """Capture sound input for duration <sec>, save to a file.

        Return the path/name to the new file. Uses onset time (epoch) as
        a meaningful identifier for filename and log.
        return self._record(sec, filename=filename, block=block)

    def _record(self, sec, filename='', block=True, log=True):
        filename = pathToString(filename)
        while self.recorder.running:
        self.duration = float(sec)
        # for duration estimation, high precision:
        self.onset = core.getTime()
        # use time for unique log and filename, 1 sec precision
        self.fileOnset = core.getAbsTime()
        ms = "%.3f" % (core.getTime() - int(core.getTime()))
        if log and self.autoLog:
            msg = '%s: Record: onset %d, capture %.3fs'
   % (self.loggingId, self.fileOnset,
        if not filename:
            onsettime = '-%d' % self.fileOnset + ms[1:]
            self.savedFile = onsettime.join(
            self.savedFile = os.path.abspath(filename)
            if not self.savedFile.endswith('.wav'):
                self.savedFile += '.wav'

        t0 = core.getTime(), self.duration, **self.options)

        self.rate = backend_pyo.pyoSndServer.getSamplingRate()
        if block:
            core.wait(self.duration, 0)
            if log and self.autoLog:
                msg = '%s: Record: stop. %.3f, capture %.3fs (est)'
                logging.exp(msg % (self.loggingId, core.getTime(),
                                   core.getTime() - t0))
            while self.recorder.running:
                core.wait(.001, 0)
            if log and self.autoLog:
                msg = '%s: Record: return immediately, no blocking'
                logging.exp(msg % (self.loggingId))

        return self.savedFile

    def playback(self, block=True, loops=0, stop=False, log=True):
        """Plays the saved .wav file, as just recorded or resampled. Execution
        blocks by default, but can return immediately with `block=False`.

        `loops` : number of extra repetitions; 0 = play once

        `stop` : True = immediately stop ongoing playback (if there is one),
        and return
        if not self.savedFile or not os.path.isfile(self.savedFile):
            msg = '%s: Playback requested but no saved file' % self.loggingId
            raise ValueError(msg)

        if stop:
            if (hasattr(self, 'current_recording') and
                    self.current_recording.status == PLAYING):

        # play this file:
        name = + '.current_recording'
        self.current_recording = backend_pyo.SoundPyo(
            self.savedFile, name=name, loops=loops)
        if block:
            core.wait(self.duration * (loops + 1))  # set during record()

        if log and self.autoLog:
            if loops:
                msg = '%s: Playback: play %.3fs x %d (est) %s'
                vals = (self.loggingId, self.duration, loops + 1,
                logging.exp(msg % vals)
                msg = '%s: Playback: play %.3fs (est) %s'
                logging.exp(msg % (self.loggingId, self.duration,

    def resample(self, newRate=16000, keep=True, log=True):
        """Re-sample the saved file to a new rate, return the full path.

        Can take several visual frames to resample a 2s recording.

        The default values for resample() are for Google-speech, keeping the
        original (presumably recorded at 48kHz) to archive.
        A warning is generated if the new rate not an integer factor /
        multiple of the old rate.

        To control anti-aliasing, use pyo.downsamp() or upsamp() directly.
        if not self.savedFile or not os.path.isfile(self.savedFile):
            msg = '%s: Re-sample requested but no saved file' % self.loggingId
            raise ValueError(msg)
        if newRate <= 0 or type(newRate) != int:
            msg = '%s: Re-sample bad new rate = %s' % (self.loggingId,
            raise ValueError(msg)

        # set-up:
        if self.rate >= newRate:
            ratio = float(self.rate) / newRate
            info = '-ds%i' % ratio
            ratio = float(newRate) / self.rate
            info = '-us%i' % ratio
        if ratio != int(ratio):
            msg = '%s: old rate is not an integer factor of new rate'
            logging.warn(msg % self.loggingId)
        ratio = int(ratio)
        newFile = info.join(os.path.splitext(self.savedFile))

        # use pyo's downsamp or upsamp based on relative rates:
        if not ratio:
            msg = '%s: Re-sample by %sx is undefined, skipping'
            logging.warn(msg % (self.loggingId, str(ratio)))
        elif self.rate >= newRate:
            t0 = core.getTime()
            # default 128-sample anti-aliasing
            pyo.downsamp(self.savedFile, newFile, ratio)
            if log and self.autoLog:
                msg = '%s: Down-sampled %sx in %.3fs to %s'
                vals = (self.loggingId, str(ratio), core.getTime() - t0,
                logging.exp(msg % vals)
            t0 = core.getTime()
            # default 128-sample anti-aliasing
            pyo.upsamp(self.savedFile, newFile, ratio)
            if log and self.autoLog:
                msg = '%s: Up-sampled %sx in %.3fs to %s'
                vals = (self.loggingId, str(ratio), core.getTime() - t0,
                logging.exp(msg % vals)

        # clean-up:
        if not keep:
            self.savedFile = newFile
            self.rate = newRate

        return os.path.abspath(newFile)

[docs]class AdvAudioCapture(AudioCapture): """Class extends AudioCapture, plays marker sound as a "start" indicator. Has method for retrieving the marker onset time from the file, to allow calculation of vocal RT (or other sound-based RT). See Coder demo > input > """ def __init__(self, name='advMic', filename='', saveDir='', sampletype=0, buffering=16, chnl=0, stereo=True, autoLog=True): AudioCapture.__init__(self, name=name, filename=filename, saveDir=saveDir, sampletype=sampletype, buffering=buffering, chnl=chnl, stereo=stereo) self.setMarker() self.autoLog = autoLog
[docs] def record(self, sec, filename='', block=False): """Starts recording and plays an onset marker tone just prior to returning. The idea is that the start of the tone in the recording indicates when this method returned, to enable you to sync a known recording onset with other events. """ # get effectively the same timing if play after starting the record self.playMarker() self.filename = self._record(sec, filename=filename, block=block) return self.filename
[docs] def setFile(self, filename): """Sets the name of the file to work with. """ self.filename = filename
[docs] def setMarker(self, tone=19000, secs=0.015, volume=0.03, log=True): """Sets the onset marker, where `tone` is either in hz or a custom sound. The default tone (19000 Hz) is recommended for auto-detection, as being easier to isolate from speech sounds (and so reliable to detect). The default duration and volume are appropriate for a quiet setting such as a lab testing room. A louder volume, longer duration, or both may give better results when recording loud sounds or in noisy environments, and will be auto-detected just fine (even more easily). If the hardware microphone in use is not physically near the speaker hardware, a louder volume is likely to be required. Custom sounds cannot be auto-detected, but are supported anyway for presentation purposes. E.g., a recording of someone saying "go" or "stop" could be passed as the onset marker. """ if hasattr(tone, 'play'): self.marker_hz = 0 self.marker = tone if log and self.autoLog: logging.exp('custom sound set as marker; getMarkerOnset()' ' will not be able to auto-detect onset') else: self.marker_hz = float(tone) sampleRate = backend_pyo.pyoSndServer.getSamplingRate() if sampleRate < 2 * self.marker_hz: # NyquistError msg = ("Recording rate (%i Hz) too slow for %i Hz-based" " marker detection.") logging.warning(msg % (int(sampleRate), self.marker_hz)) if log and self.autoLog: msg = 'frequency of recording onset marker: %.1f' logging.exp(msg % self.marker_hz) self.marker = backend_pyo.SoundPyo(self.marker_hz, secs, volume=volume, + '.marker_tone')
[docs] def playMarker(self): """Plays the current marker sound. This is automatically called at the start of recording, but can be called anytime to insert a marker. """
[docs] def getMarkerInfo(self): """Returns (hz, duration, volume) of the marker sound. Custom markers always return 0 hz (regardless of the sound). """ dur, vol = self.marker.getDuration(), self.marker.getVolume() return self.marker_hz, dur, vol
[docs] def getMarkerOnset(self, chunk=128, secs=0.5, filename=''): """Return (onset, offset) time of the first marker within the first `secs` of the saved recording. Has approx ~1.33ms resolution at 48000Hz, chunk=64. Larger chunks can speed up processing times, at a sacrifice of some resolution, e.g., to pre-process long recordings with multiple markers. If given a filename, it will first set that file as the one to work with, and then try to detect the onset marker. """ while self.recorder.running: core.wait(0.10, 0) if filename: self.setFile(filename) else: filename = self.filename return getMarkerOnset(chunk=chunk, secs=secs, filename=filename, marker_hz=self.marker_hz, marker_duration=self.marker.getDuration())
[docs] def getLoudness(self): """Return the RMS loudness of the saved recording. """ # use cached value unless the file has changed, based on its mod time: try: mtime = os.path.getmtime(self.savedFile) except (OSError, TypeError): logging.error('%s no .savedFile, try again' % core.wait(0.01, 0) if not self.savedFile or not os.path.exists(self.savedFile): raise ValueError('no such file') mtime = os.path.getmtime(self.savedFile) if not hasattr(self, 'rms') or self.mtime != mtime: self.rms = getRMS(self.savedFile) # ~3ms for 2s file self.mtime = mtime return self.rms
[docs] def compress(self, keep=False): """Compress using FLAC (lossless compression). """ if os.path.isfile(self.savedFile) and self.savedFile.endswith('.wav'): self.savedFile = wav2flac(self.savedFile, keep=keep)
[docs] def uncompress(self, keep=False): """Uncompress from FLAC to .wav format. """ isFlac = self.savedFile.endswith('.flac') if os.path.isfile(self.savedFile) and isFlac: self.savedFile = flac2wav(self.savedFile, keep=keep)
def getMarkerOnset(filename, chunk=128, secs=0.5, marker_hz=19000, marker_duration=0.015): """Returns marker sound (onset, offset) in sec, as read from filename. """ def thresh2SD(data, mult=2, thr=None): """Return index of first value in abs(data) exceeding 2 * std(data), or length of the data + 1 if nothing > threshold Return threshold so can re-use the same threshold later """ # this algorithm could use improvement data = abs(data) if not thr: thr = mult * np.std(data) val = data[(data > thr)] if not len(val): return len(data) + 1, thr first = val[0] for i, v in enumerate(data): if v == first: return i, thr # read data from file: data, sampleRate = readWavFile(filename) if marker_hz == 0: raise ValueError("Custom marker sounds cannot be auto-detected.") if sampleRate < 2 * marker_hz: # NyquistError msg = "Recording rate (%i Hz) too slow for %i Hz-based marker detection." raise ValueError(msg % (int(sampleRate), marker_hz)) # extract onset: chunk = max(16, chunk) # trades-off against size of bandpass filter # precision in time-domain (= smaller chunks) requires wider freq # {16: 2400, 32: 1200, 64: 600, 128: 300} bandSize = 150 * 2 ** (8 - int(np.log2(chunk))) dataToUse = data[:int(sampleRate * secs)] # only look at first secs lo = max(0, marker_hz - bandSize) # for bandpass filter hi = marker_hz + bandSize dftProfile = getDftBins(dataToUse, sampleRate, lo, hi, chunk) # leading edge of startMarker in chunks onsetChunks, thr = thresh2SD(dftProfile) onsetSecs = onsetChunks * chunk / sampleRate # in secs # extract offset: ratio = chunk / sampleRate start = onsetChunks - 4 stop = int(onsetChunks + marker_duration / ratio) + 4 backwards = dftProfile[max(start, 0):min(stop, len(dftProfile))] offChunks, _junk = thresh2SD(backwards[::-1], thr=thr) offSecs = (start + len(backwards) - offChunks) * ratio # in secs return onsetSecs, offSecs def readWavFile(filename): """Return (data, sampleRate) as read from a wav file, expects int16 data. """ filename = pathToString(filename) try: sampleRate, data = except Exception: if os.path.exists(filename) and os.path.isfile(filename): core.wait(0.01, 0) try: sampleRate, data = except Exception: msg = 'Failed to open wav sound file "%s"' raise SoundFileError(msg % filename) if data.dtype != 'int16': msg = 'expected `int16` data in .wav file %s' raise AttributeError(msg % filename) if len(data.shape) == 2 and data.shape[1] == 2: data = data.transpose() data = data[0] # left channel only? depends on how the file was made return data, sampleRate def getDftBins(data=None, sampleRate=None, low=100, high=8000, chunk=64): """Return DFT (discrete Fourier transform) of ``data``, doing so in time-domain bins, each of size ``chunk`` samples. e.g., for getting FFT magnitudes in a ms-by-ms manner. If given a sampleRate, the data are bandpass filtered (low, high). """ # good to reshape & vectorize data rather than use a python loop if data is None: data = [] bins = [] i = chunk if sampleRate: # just to get freq vector _junk, freq = getDft(data[:chunk], sampleRate) band = (freq > low) & (freq < high) # band (frequency range) while i <= len(data): magn = getDft(data[i - chunk:i]) if sampleRate: bins.append(np.std(magn[band])) # filtered by frequency else: bins.append(np.std(magn)) # unfiltered i += chunk return np.array(bins)
[docs]def getDft(data, sampleRate=None, wantPhase=False): """Compute and return magnitudes of numpy.fft.fft() of the data. If given a sample rate (samples/sec), will return (magn, freq). If wantPhase is True, phase in radians is also returned (magn, freq, phase). data should have power-of-2 samples, or will be truncated. """ # # and .../python/ # truncate to power-of-2 slice; zero-padding to round up is ok too samples = 2 ** int(np.log2(len(data))) samplesHalf = samples // 2 dataSlice = data[:samples] # get magn & phase from the DFT: dft = np.fft.fft(dataSlice) dftHalf = dft[:samplesHalf] / samples magn = abs(dftHalf) * 2 magn[0] /= 2. if wantPhase: phase = np.arctan2(dftHalf.real, dftHalf.imag) # in radians if sampleRate: deltaf = sampleRate / samplesHalf / 2. freq = np.linspace(0, samplesHalf * deltaf, samplesHalf, endpoint=False) if wantPhase: return magn, freq, phase return magn, freq else: if wantPhase: return magn, phase return magn
def getRMSBins(data, chunk=64): """Return RMS (loudness) in bins of ``chunk`` samples """ # better to vectorize bins = [] i = chunk while i <= len(data): r = getRMS(data[i - chunk:i]) bins.append(r) i += chunk return np.array(bins)
[docs]def getRMS(data): """Compute and return the audio power ("loudness"). Uses numpy.std() as RMS. std() is same as RMS if the mean is 0, and .wav data should have a mean of 0. Returns an array if given stereo data (RMS computed within-channel). `data` can be an array (1D, 2D) or filename; .wav format only. data from .wav files will be normalized to -1..+1 before RMS is computed. """ def _rms(data): """Audio loudness / power, as rms; ~2x faster than std() """ if len(data.shape) > 1: return np.std(data, axis=1) # np.sqrt(np.mean(data ** 2, axis=1)) return np.std(data) # np.sqrt(np.mean(data ** 2)) if isinstance(data, str): if not os.path.isfile(data): raise ValueError('getRMS: could not find file %s' % data) _junk, data = data_tr = np.transpose(data) data = data_tr / 32768. elif not isinstance(data, np.ndarray): data = np.array(data).astype(float) return _rms(data)
class SoundFormatNotSupported(Exception): """Class to report an unsupported sound format""" class SoundFileError(Exception): """Class to report sound file failed to load""" class MicrophoneError(Exception): """Class to report a microphone error""" class _GSQueryThread(threading.Thread): """Internal thread class to send a sound file to Google, stash the response. """ def __init__(self, request): threading.Thread.__init__(self, None, 'GoogleSpeechQuery', None) # request is a previously established urllib2.request() obj, namely: # request = urllib2.Request(url, audio, header) at end of # Speech2Text.__init__ self.request = request # set vars and flags: self.t0 = None self.response = None self.duration = None self.stopflag = False self.running = False self.timedout = False self._reset() def _reset(self): # whether run() has been started, not thread start(): self.started = False # initialize data fields that will be exposed: self.confidence = None self.json = None self.raw = '' self.word = '' self.detailed = '' self.words = [] def elapsed(self): # report duration depending on the state of the thread: if self.started is False: return None elif self.running: return core.getTime() - self.t0 else: # whether timed-out or not: return self.duration def _unpackRaw(self): # parse raw url response from google, expose via data fields (see # _reset): if type(self.raw) != str: self.json = json.load(self.raw) else: self._reset() self.status = 'FAILED' self.stop() return self.status = self.json['status'] report = [] for utter_list in self.json["hypotheses"]: for k in utter_list: report.append("%-10s : %s" % (k, utter_list[k])) if k == 'confidence': self.conf = self.confidence = float(utter_list[k]) for key in self.json: if key != "hypotheses": report.append("%-10s : %s" % (key, self.json[key])) self.detailed = '\n'.join(report) self.words = tuple([line.split(':')[1].lstrip() for line in report if line.startswith('utterance')]) if len(self.words): self.word = self.words[0] else: self.word = '' def run(self): self.t0 = core.getTime() # before .running goes True self.running = True self.started = True self.duration = 0 try: self.raw = urllib.request.urlopen(self.request) except Exception: # pragma: no cover # yeah, its the internet, stuff happens # maybe temporary HTTPError: HTTP Error 502: Bad Gateway try: self.raw = urllib.request.urlopen(self.request) except Exception as ex: # or maybe a dropped connection, etc logging.error(str(ex)) self.running = False # proceeds as if "timedout" self.duration = core.getTime() - self.t0 # if no one called .stop() in the meantime, unpack the data: if self.running: self._unpackRaw() self.running = False self.timedout = False else: self.timedout = True def stop(self): self.running = False class Speech2Text(): """Class for speech-recognition (voice to text), using Google's public API. Google's speech API is currently free to use, and seems to work well. Intended for within-experiment processing (near real-time, 1-2s delayed), in which it's often important to skip a slow or failed response, and not wait a long time; `BatchSpeech2Text()` reverses these priorities. It is possible (and perhaps even likely) that Google will start charging for usage. In addition, they can change the interface at any time, including in the middle of an experiment. (If so, please post to the user list and we'll try to develop a fix, but there could still be some downtime.) Presumably, confidential or otherwise sensitive voice data should not be sent to google. :Note: Requires that flac is installed (free download from If you download and install flac, but get an error that flac is missing, try setting the full path to flac in preferences -> general -> flac. :Usage: a) Always import and make an object; no data are available yet:: from microphone import Speech2Text gs = Speech2Text('speech_clip.wav') # set-up only b) Then, either: Initiate a query and wait for response from google (or until the time-out limit is reached). This is "blocking" mode, and is the easiest to do:: resp = gs.getResponse() # execution blocks here print(resp.word, resp.confidence) c) Or instead (advanced usage): Initiate a query, but do not wait for a response ("thread" mode: no blocking, no timeout, more control). `running` will change to False when a response is received (or hang indefinitely if something goes wrong--so you might want to implement a time-out as well):: resp = gs.getThread() # returns immediately while resp.running: print('.',) # displays dots while waiting sys.stdout.flush() core.wait(0.1) print(resp.words) d) Options: Set-up with a different language for the same speech clip; you'll get a different response (possibly having UTF-8 characters):: gs = Speech2Text('speech_clip.wav', lang='ja-JP') resp = gs.getResponse() :Example: See Coder demos / input / :Known limitations: Availability is subject to the whims of google. Any changes google makes along the way could either cause complete failure (disruptive), or could cause slightly different results to be obtained (without it being readily obvious that something had changed). For this reason, it's probably a good idea to re-run speech samples through `Speech2Text` at the end of a study; see `BatchSpeech2Text()`. :Author: Jeremy R. Gray, with thanks to Lefteris Zafiris for his help and excellent command-line perl script at (GPLv2) """ def __init__(self, filename, lang='en-US', timeout=10, samplingrate=16000, pro_filter=2, level=0): """ :Parameters: `filename` : <required> name of the speech file (.flac, .wav, or .spx) to process. wav files will be converted to flac, and for this to work you need to have flac (as an executable). spx format is speex-with-headerbyte (for Google). `lang` : the presumed language of the speaker, as a locale code; default 'en-US' `timeout` : seconds to wait before giving up, default 10 `samplingrate` : the sampling rate of the speech clip in Hz, either 16000 or 8000. You can record at a higher rate, and then down-sample to 16000 for speech recognition. `file` is the down-sampled file, not the original. the sampling rate is auto-detected for .wav files. `pro_filter` : profanity filter level; default 2 (e.g., f***) `level` : flac compression level (0 less compression but fastest) """ # set up some key parameters: results = 5 # how many words wanted self.timeout = timeout useragent = PSYCHOPY_USERAGENT host = "" # determine file type, convert wav to flac if needed: if not os.path.isfile(filename): raise IOError("Cannot find file: %s" % filename) ext = os.path.splitext(filename)[1] if ext not in ['.flac', '.wav']: raise SoundFormatNotSupported("Unsupported filetype: %s\n" % ext) if ext == '.wav': _junk, samplingrate = readWavFile(filename) if samplingrate not in [16000, 8000]: raise SoundFormatNotSupported( 'Speech2Text sample rate must be 16000 or 8000 Hz') self.filename = filename if ext == ".flac": filetype = "x-flac" elif ext == ".wav": # convert to .flac filetype = "x-flac" filename = wav2flac(filename, level=level) # opt for speed"Loading: %s as %s, audio/%s" % (self.filename, lang, filetype)) # occasional error; core.wait(.1) is not always enough; better slow # than fail c = 0 while not os.path.isfile(filename) and c < 10: core.wait(.1, 0) c += 1 audio = open(filename, 'rb').read() if ext == '.wav' and filename.endswith('.flac'): try: os.remove(filename) except Exception: pass # urllib2 makes no attempt to validate the server certificate. here's an idea: # # set up the https request: url = 'https://' + host + '?xjerr=1&' +\ 'client=psychopy3&' +\ 'lang=' + lang + '&'\ 'pfilter=%d' % pro_filter + '&'\ 'maxresults=%d' % results header = {'Content-Type': 'audio/%s; rate=%d' % (filetype, samplingrate), 'User-Agent': useragent} web.requireInternetAccess() # needed to access google's speech API try: self.request = urllib.request.Request(url, audio, header) except Exception: # pragma: no cover # try again before accepting defeat"https request failed. %s, %s. trying again..." % (filename, self.filename)) core.wait(0.2, 0) self.request = urllib.request.Request(url, audio, header) def getThread(self): """Send a query to Google using a new thread, no blocking or timeout. Returns a thread which will **eventually** (not immediately) have the speech data in its namespace; see getResponse. In theory, you could have several threads going simultaneously (almost all the time is spent waiting for a response), rather than doing them sequentially (not tested). """ gsqthread = _GSQueryThread(self.request) gsqthread.start()"Sending speech-recognition https request to google") gsqthread.file = self.filename while not gsqthread.running: # can return too quickly if thread is slow to start core.wait(0.001, 0) return gsqthread # word and time data will eventually be in the namespace def getResponse(self): """Calls `getThread()`, and then polls the thread until there's a response. Will time-out if no response comes within `timeout` seconds. Returns an object having the speech data in its namespace. If there's no match, generally the values will be equivalent to `None` (e.g., an empty string). If you do `resp = getResponse()`, you'll be able to access the data in several ways: `resp.word` : the best match, i.e., the most probably word, or `None` `resp.confidence` : Google's confidence about `.word`, ranging 0 to 1 `resp.words` : tuple of up to 5 guesses; so `.word` == `.words[0]` `resp.raw` : the raw response from Google (just a string) `resp.json` : a parsed version of raw, from `json.load(raw)` """ gsqthread = self.getThread() while gsqthread.elapsed() < self.timeout: # don't need precise timing to poll an http connection core.wait(0.05, 0) if not gsqthread.running: break if gsqthread.running: # timed out gsqthread.status = 408 # same as http code return gsqthread # word and time data are already in the namespace class BatchSpeech2Text(list): def __init__(self, files, threads=3, verbose=False): """Like `Speech2Text()`, but takes a list of sound files or a directory name to search for matching sound files, and returns a list of `(filename, response)` tuples. `response`'s are described in `Speech2Text.getResponse()`. Can use up to 5 concurrent threads. Intended for post-experiment processing of multiple files, in which waiting for a slow response is not a problem (better to get the data). If `files` is a string, it will be used as a directory name for glob (matching all `*.wav`, `*.flac`, and `*.spx` files). There's currently no re-try on http error.""" list.__init__(self) # [ (file1, resp1), (file2, resp2), ...] maxThreads = min(threads, 5) # I get http errors with 6 self.timeout = 30 if type(files) == str and os.path.isdir(files): f = glob.glob(os.path.join(files, '*.wav')) f += glob.glob(os.path.join(files, '*.flac')) f += glob.glob(os.path.join(files, '*.spx')) fileList = f else: fileList = list(files) web.requireInternetAccess() # needed to access google's speech API for i, filename in enumerate(fileList): gs = Speech2Text(filename, level=5) self.append((filename, gs.getThread())) # tuple if verbose:"%i %s" % (i, filename)) while self._activeCount() >= maxThreads: core.wait(.1, 0) # idle at max count def _activeCount(self): # self is a list of (name, thread) tuples; count active threads count = len( [f for f, t in self if t.running and t.elapsed() <= self.timeout]) return count def _getFlacPath(path=None): """Return a path to flac binary. Log flac version (if flac was found). """ global FLAC_PATH if FLAC_PATH is None: if path: FLAC_PATH = path elif prefs.general['flac']: FLAC_PATH = prefs.general['flac'] else: FLAC_PATH = 'flac' try: version, se = core.shellCall([FLAC_PATH, '-v'], stderr=True) if se: raise MicrophoneError except Exception: msg = ("flac not installed (or wrong path in prefs); " "download from") logging.error(msg) raise MicrophoneError(msg)'Using ' + version) return FLAC_PATH
[docs]def flac2wav(path, keep=True): """Uncompress: convert .flac file (on disk) to .wav format (new file). If `path` is a directory name, convert all .flac files in the directory. `keep` to retain the original .flac file(s), default `True`. """ flac_path = _getFlacPath() flac_files = [] path = pathToString(path) if path.endswith('.flac'): flac_files = [path] elif type(path) == str and os.path.isdir(path): flac_files = glob.glob(os.path.join(path, '*.flac')) if len(flac_files) == 0: logging.warn('failed to find .flac file(s) from %s' % path) return None wav_files = [] for flacfile in flac_files: wavname = flacfile.strip('.flac') + '.wav' flac_cmd = [flac_path, "-d", "--totally-silent", "-f", "-o", wavname, flacfile] _junk, se = core.shellCall(flac_cmd, stderr=True) if se: logging.error(se) if not keep: os.unlink(flacfile) wav_files.append(wavname) if len(wav_files) == 1: return wav_files[0] else: return wav_files
[docs]def wav2flac(path, keep=True, level=5): """Lossless compression: convert .wav file (on disk) to .flac format. If `path` is a directory name, convert all .wav files in the directory. `keep` to retain the original .wav file(s), default `True`. `level` is compression level: 0 is fastest but larger, 8 is slightly smaller but much slower. """ flac_path = _getFlacPath() wav_files = [] path = pathToString(path) if path.endswith('.wav'): wav_files = [path] elif type(path) == str and os.path.isdir(path): wav_files = glob.glob(os.path.join(path, '*.wav')) if len(wav_files) == 0: logging.warn('failed to find .wav file(s) from %s' % path) return None flac_files = [] for wavname in wav_files: flacfile = wavname.replace('.wav', '.flac') flac_cmd = [flac_path, "-%d" % level, "-f", "--totally-silent", "-o", flacfile, wavname] _junk, se = core.shellCall(flac_cmd, stderr=True) if se or not os.path.isfile(flacfile): # just try again # ~2% incidence when recording for 1s, 650+ trials # never got two in a row; core.wait() does not help logging.warn('Failed to convert to .flac; trying again') _junk, se = core.shellCall(flac_cmd, stderr=True) if se: logging.error(se) if not keep: os.unlink(wavname) flac_files.append(flacfile) if len(wav_files) == 1: return flac_files[0] else: return flac_files
[docs]def switchOn(sampleRate=48000, outputDevice=None, bufferSize=None): """You need to switch on the microphone before use, which can take several seconds. The only time you can specify the sample rate (in Hz) is during switchOn(). Considerations on the default sample rate 48kHz:: DVD or video = 48,000 CD-quality = 44,100 / 24 bit human hearing: ~15,000 (adult); children & young adult higher human speech: 100-8,000 (useful for telephone: 100-3,300) Google speech API: 16,000 or 8,000 only Nyquist frequency: twice the highest rate, good to oversample a bit pyo's downsamp() function can reduce 48,000 to 16,000 in about 0.02s (uses integer steps sizes). So recording at 48kHz will generate high-quality archival data, and permit easy downsampling. outputDevice, bufferSize: set these parameters on the pyoSndServer before booting; None means use pyo's default values """ # imports pyo, creates sound.pyoSndServer using sound.initPyo() if not yet # created t0 = core.getTime() if prefs.hardware['audioLib'][0] != 'pyo': logging.warning("Starting Microphone but sound lib preference is set to be {}. " "Clashes might occur since 'pyo' is not " "preferred lib but is needed for Microphone" .format(prefs.hardware['audioLib'])) try: global pyo import pyo global haveMic haveMic = True except ImportError: # pragma: no cover msg = ('Microphone class not available, needs pyo; ' 'see') logging.error(msg) raise ImportError(msg) if pyo.serverCreated(): backend_pyo.pyoSndServer.setSamplingRate(sampleRate) else: # backend_pyo.init() will create pyoSndServer. We want there only # ever to be one server # will automatically use duplex=1 and stereo if poss backend_pyo.init(rate=sampleRate) if outputDevice: backend_pyo.pyoSndServer.setOutputDevice(outputDevice) if bufferSize: backend_pyo.pyoSndServer.setBufferSize(bufferSize) logging.exp('%s: switch on (%dhz) took %.3fs' % (__file__.strip('.py'), sampleRate, core.getTime() - t0))
def switchOff(): """(Not needed as of v1.76.00; kept for backwards compatibility only.) """"deprecated: microphone.switchOff() is no longer needed.")

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