# -*- coding: utf-8-*- """ The Mic class handles all interactions with the microphone and speaker. """ import os import tempfile import wave import audioop import pyaudio import alteration import jasperpath from stt import TranscriptionMode class Mic: speechRec = None speechRec_persona = None def __init__(self, speaker, passive_stt_engine, active_stt_engine): """ Initiates the pocketsphinx instance. Arguments: speaker -- handles platform-independent audio output passive_stt_engine -- performs STT while Jasper is in passive listen mode acive_stt_engine -- performs STT while Jasper is in active listen mode """ self.speaker = speaker self.passive_stt_engine = passive_stt_engine self.active_stt_engine = active_stt_engine self._audio = pyaudio.PyAudio() def __del__(self): self._audio.terminate() def getScore(self, data): rms = audioop.rms(data, 2) score = rms / 3 return score def fetchThreshold(self): # TODO: Consolidate variables from the next three functions THRESHOLD_MULTIPLIER = 1.8 RATE = 16000 CHUNK = 1024 # number of seconds to allow to establish threshold THRESHOLD_TIME = 1 # prepare recording stream stream = self._audio.open(format=pyaudio.paInt16, channels=1, rate=RATE, input=True, frames_per_buffer=CHUNK) # stores the audio data frames = [] # stores the lastN score values lastN = [i for i in range(20)] # calculate the long run average, and thereby the proper threshold for i in range(0, RATE / CHUNK * THRESHOLD_TIME): data = stream.read(CHUNK) frames.append(data) # save this data point as a score lastN.pop(0) lastN.append(self.getScore(data)) average = sum(lastN) / len(lastN) stream.stop_stream() stream.close() # this will be the benchmark to cause a disturbance over! THRESHOLD = average * THRESHOLD_MULTIPLIER return THRESHOLD def passiveListen(self, PERSONA): """ Listens for PERSONA in everyday sound. Times out after LISTEN_TIME, so needs to be restarted. """ THRESHOLD_MULTIPLIER = 1.8 RATE = 16000 CHUNK = 1024 # number of seconds to allow to establish threshold THRESHOLD_TIME = 1 # number of seconds to listen before forcing restart LISTEN_TIME = 10 # prepare recording stream stream = self._audio.open(format=pyaudio.paInt16, channels=1, rate=RATE, input=True, frames_per_buffer=CHUNK) # stores the audio data frames = [] # stores the lastN score values lastN = [i for i in range(30)] # calculate the long run average, and thereby the proper threshold for i in range(0, RATE / CHUNK * THRESHOLD_TIME): data = stream.read(CHUNK) frames.append(data) # save this data point as a score lastN.pop(0) lastN.append(self.getScore(data)) average = sum(lastN) / len(lastN) # this will be the benchmark to cause a disturbance over! THRESHOLD = average * THRESHOLD_MULTIPLIER # save some memory for sound data frames = [] # flag raised when sound disturbance detected didDetect = False # start passively listening for disturbance above threshold for i in range(0, RATE / CHUNK * LISTEN_TIME): data = stream.read(CHUNK) frames.append(data) score = self.getScore(data) if score > THRESHOLD: didDetect = True break # no use continuing if no flag raised if not didDetect: print "No disturbance detected" stream.stop_stream() stream.close() return (None, None) # cutoff any recording before this disturbance was detected frames = frames[-20:] # otherwise, let's keep recording for few seconds and save the file DELAY_MULTIPLIER = 1 for i in range(0, RATE / CHUNK * DELAY_MULTIPLIER): data = stream.read(CHUNK) frames.append(data) # save the audio data stream.stop_stream() stream.close() with tempfile.NamedTemporaryFile(mode='w+b') as f: wav_fp = wave.open(f, 'wb') wav_fp.setnchannels(1) wav_fp.setsampwidth(pyaudio.get_sample_size(pyaudio.paInt16)) wav_fp.setframerate(RATE) wav_fp.writeframes(''.join(frames)) wav_fp.close() f.seek(0) # check if PERSONA was said transcribed = self.passive_stt_engine.transcribe(f, mode=TranscriptionMode.KEYWORD) if PERSONA in transcribed: return (THRESHOLD, PERSONA) return (False, transcribed) def activeListen(self, THRESHOLD=None, LISTEN=True, MUSIC=False): """ Records until a second of silence or times out after 12 seconds Returns the first matching string or None """ options = self.activeListenToAllOptions(THRESHOLD, LISTEN, MUSIC) if options: return options[0] def activeListenToAllOptions(self, THRESHOLD=None, LISTEN=True, MUSIC=False): """ Records until a second of silence or times out after 12 seconds Returns a list of the matching options or None """ RATE = 16000 CHUNK = 1024 LISTEN_TIME = 12 # check if no threshold provided if THRESHOLD == None: THRESHOLD = self.fetchThreshold() self.speaker.play(jasperpath.data('audio', 'beep_hi.wav')) # prepare recording stream stream = self._audio.open(format=pyaudio.paInt16, channels=1, rate=RATE, input=True, frames_per_buffer=CHUNK) frames = [] # increasing the range # results in longer pause after command generation lastN = [THRESHOLD * 1.2 for i in range(30)] for i in range(0, RATE / CHUNK * LISTEN_TIME): data = stream.read(CHUNK) frames.append(data) score = self.getScore(data) lastN.pop(0) lastN.append(score) average = sum(lastN) / float(len(lastN)) # TODO: 0.8 should not be a MAGIC NUMBER! if average < THRESHOLD * 0.8: break self.speaker.play(jasperpath.data('audio', 'beep_lo.wav')) # save the audio data stream.stop_stream() stream.close() with tempfile.SpooledTemporaryFile(mode='w+b') as f: wav_fp = wave.open(f, 'wb') wav_fp.setnchannels(1) wav_fp.setsampwidth(pyaudio.get_sample_size(pyaudio.paInt16)) wav_fp.setframerate(RATE) wav_fp.writeframes(''.join(frames)) wav_fp.close() f.seek(0) mode = TranscriptionMode.MUSIC if MUSIC else TranscriptionMode.NORMAL transcribed = self.active_stt_engine.transcribe(f, mode=mode) return transcribed def say(self, phrase, OPTIONS=" -vdefault+m3 -p 40 -s 160 --stdout > say.wav"): # alter phrase before speaking phrase = alteration.clean(phrase) self.speaker.say(phrase)