In [1]:

    

# import Speaker Recognition Module
import speaker.recognition as SR
Gender = SR.GMMRec()


    

In [2]:

    

import scipy.io.wavfile as wav
from speaker.silence import remove_silence
from features import mfcc
def get_mfcc(audio_path):    
    (sr, sig) = wav.read(audio_path)
    if len(sig.shape) > 1:
        sig = sig[:, 0]    
    cleansig = remove_silence(sr, sig)
    mfcc_vecs = mfcc(cleansig, sr, numcep = 15)
    return mfcc_vecs


    

In [3]:

    

import numpy as np

# Here we use mfcc as the audio features, but in theory, other audio features should work as well, e.g. lpc
female_mfcc = np.array(get_mfcc('./Audio/female.wav')) # female_mfcc.shape = (N1, D); N1 vectors and D dimension
male_mfcc = np.array(get_mfcc('./Audio/male.wav')) # male_mfcc.shape = (N2, D);
Gender.enroll('Female', female_mfcc) # enroll the female audio features
Gender.enroll('Male', male_mfcc) # enroll the male audio features
Gender.train() # train the GMMs with PyCASP
Gender.dump('gender.model') # save the trained model into a file named "gender.model" for future use


    

In [4]:

    

Gender = SR.GMMRec.load('gender.model') # this is not necessary if you just trained the model
test_mfcc = np.array(get_mfcc('/Users/xuhe/Downloads/SpectrogramInversion1.02b/tapestr_rec.wav')) # test_mfcc.shape = (N3, D)
Gender.predict(test_mfcc) # predict the speaker, where result is the most porbabel speaker label, and log_lkld is the log likelihood for test_mfcc to be from the recognized speaker.


    

    
Out[4]:





('Female', -22.373874909185876)

In [5]:

    

def totime(secs):
    m, s = divmod(secs, 60)
    h, m = divmod(m, 60)
    return h, m, s

def showresult(recognizer, sig, sr, head):
    cleansig = remove_silence(sr, sig)
    mfcc_vecs = mfcc(cleansig, sr, numcep = 15)    
    print("%d:%02d:%02d" % (totime(head)), recognizer.predict(
                mfcc_vecs))

def recognize(recognizer, audio_path, step = 1, duration = 1.5):
    (fs, signal) = wav.read(audio_path)
    if len(signal.shape) > 1:
        signal = signal[:, 0]
    head = 0
    totallen = np.round(signal.shape[0] / fs).astype(int)
    print('Recognition results:')
    while head < totallen:
        tail = head + duration
        if tail > totallen:
            tail = totallen
        signali = signal[fs * head : np.min([fs * tail, fs * totallen])]           
        showresult(recognizer, signali, fs, head)
        head += step


    

In [6]:

    

recognize(Gender, './Audio/female-male.wav', step = 5, duration = 5)


    

    




Recognition results:
('0:00:00', ('Male', -19.65672572544716))
('0:00:05', ('Male', -19.389260191396541))
('0:00:10', ('Male', -19.886238792273502))
('0:00:15', ('Male', -19.988046642253273))
('0:00:20', ('Male', -20.857762606257122))
('0:00:25', ('Female', -20.056092628403363))
('0:00:30', ('Female', -19.888043075692561))
('0:00:35', ('Female', -19.657557661472801))
('0:00:40', ('Female', -19.745223859738523))
('0:00:45', ('Female', -19.680926940400678))
('0:00:50', ('Female', -19.458031006355842))
('0:00:55', ('Female', -19.553981803248707))
('0:01:00', ('Female', -20.053499089615951))
('0:01:05', ('Male', -19.686199644242794))
('0:01:10', ('Male', -19.852808517223))
('0:01:15', ('Male', -20.039521601708593))
('0:01:20', ('Male', -19.904757723357431))
('0:01:25', ('Male', -20.143563372546421))
('0:01:30', ('Male', -19.966010831665649))
('0:01:35', ('Male', -19.826530139561765))
('0:01:40', ('Male', -19.912105539081182))
('0:01:45', ('Male', -19.848151795975433))
('0:01:50', ('Male', -19.684047168185359))
('0:01:55', ('Male', -19.983463014416124))
('0:02:00', ('Male', -19.397841075840084))
('0:02:05', ('Male', -19.766136825379665))
('0:02:10', ('Female', -19.768935512293602))

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