audiosegment.py
def detect_voice(self, prob_detect_voice=0.5):
        """
        Returns self as a list of tuples:
        [('v', voiced segment), ('u', unvoiced segment), (etc.)]

        The overall order of the AudioSegment is preserved.

        :param prob_detect_voice: The raw probability that any random 20ms window of the audio file
                                  contains voice.
        :returns: The described list.
        """
        assert self.frame_rate in (48000, 32000, 16000, 8000), "Try resampling to one of the allowed frame rates."
        assert self.sample_width == 2, "Try resampling to 16 bit."
        assert self.channels == 1, "Try resampling to one channel."

        class model_class:
            def __init__(self, aggressiveness):
                self.v = webrtcvad.Vad(int(aggressiveness))

            def predict(self, vector):
                if self.v.is_speech(vector.raw_data, vector.frame_rate):
                    return 1
                else:
                    return 0

        model = model_class(aggressiveness=2)
        pyesno = 0.3  # Probability of the next 20 ms being unvoiced given that this 20 ms was voiced
        pnoyes = 0.2  # Probability of the next 20 ms being voiced given that this 20 ms was unvoiced
        p_realyes_outputyes = 0.4  # WebRTCVAD has a very high FP rate - just because it says yes, doesn't mean much
        p_realyes_outputno  = 0.05  # If it says no, we can be very certain that it really is a no
        p_yes_raw = prob_detect_voice
        filtered = self.detect_event(model=model,
                                     ms_per_input=20,
                                     transition_matrix=(pyesno, pnoyes),
                                     model_stats=(p_realyes_outputyes, p_realyes_outputno),
                                     event_length_s=0.25,
                                     prob_raw_yes=p_yes_raw)
        ret = []
        for tup in filtered:
            t = ('v', tup[1]) if tup[0] == 'y' else ('u', tup[1])
            ret.append(t)
        return ret
def detect_voice(self, prob_detect_voice=0.5):
        """
        Returns self as a list of tuples:
        [('v', voiced segment), ('u', unvoiced segment), (etc.)]

        The overall order of the AudioSegment is preserved.

        :param prob_detect_voice: The raw probability that any random 20ms window of the audio file
                                  contains voice.
        :returns: The described list.
        """
        assert self.frame_rate in (48000, 32000, 16000, 8000), "Try resampling to one of the allowed frame rates."
        assert self.sample_width == 2, "Try resampling to 16 bit."
        assert self.channels == 1, "Try resampling to one channel."

        class model_class:
            def __init__(self, aggressiveness):
                self.v = webrtcvad.Vad(int(aggressiveness))

            def predict(self, vector):
                if self.v.is_speech(vector.raw_data, vector.frame_rate):
                    return 1
                else:
                    return 0

        model = model_class(aggressiveness=2)
        pyesno = 0.3  # Probability of the next 20 ms being unvoiced given that this 20 ms was voiced
        pnoyes = 0.2  # Probability of the next 20 ms being voiced given that this 20 ms was unvoiced
        p_realyes_outputyes = 0.4  # WebRTCVAD has a very high FP rate - just because it says yes, doesn't mean much
        p_realyes_outputno  = 0.05  # If it says no, we can be very certain that it really is a no
        p_yes_raw = prob_detect_voice
        filtered = self.detect_event(model=model,
                                     ms_per_input=20,
                                     transition_matrix=(pyesno, pnoyes),
                                     model_stats=(p_realyes_outputyes, p_realyes_outputno),
                                     event_length_s=0.25,
                                     prob_raw_yes=p_yes_raw)
        ret = []
        for tup in filtered:
            t = ('v', tup[1]) if tup[0] == 'y' else ('u', tup[1])
            ret.append(t)
        return ret
def detect_voice ( self , prob_detect_voice = 0.5 ) : assert self . frame_rate in ( 48000 , 32000 , 16000 , 8000 ) , "Try resampling to one of the allowed frame rates." assert self . sample_width == 2 , "Try resampling to 16 bit." assert self . channels == 1 , "Try resampling to one channel." class model_class : def __init__ ( self , aggressiveness ) : self . v = webrtcvad . Vad ( int ( aggressiveness ) ) def predict ( self , vector ) : if self . v . is_speech ( vector . raw_data , vector . frame_rate ) : return 1 else : return 0 model = model_class ( aggressiveness = 2 ) pyesno = 0.3 # Probability of the next 20 ms being unvoiced given that this 20 ms was voiced pnoyes = 0.2 # Probability of the next 20 ms being voiced given that this 20 ms was unvoiced p_realyes_outputyes = 0.4 # WebRTCVAD has a very high FP rate - just because it says yes, doesn't mean much p_realyes_outputno = 0.05 # If it says no, we can be very certain that it really is a no p_yes_raw = prob_detect_voice filtered = self . detect_event ( model = model , ms_per_input = 20 , transition_matrix = ( pyesno , pnoyes ) , model_stats = ( p_realyes_outputyes , p_realyes_outputno ) , event_length_s = 0.25 , prob_raw_yes = p_yes_raw ) ret = [ ] for tup in filtered : t = ( 'v' , tup [ 1 ] ) if tup [ 0 ] == 'y' else ( 'u' , tup [ 1 ] ) ret . append ( t ) return ret
Returns self as a list of tuples:
        [('v', voiced segment), ('u', unvoiced segment), (etc.)]

        The overall order of the AudioSegment is preserved.

        :param prob_detect_voice: The raw probability that any random 20ms window of the audio file
                                  contains voice.
        :returns: The described list.
Returns self as a list of tuples : [ ( v voiced segment ) ( u unvoiced segment ) ( etc . ) ]