test_speaker.py

#test_gender.py
import os
import cPickle
import numpy as np
from scipy.io.wavfile import read
from speakerfeatures import extract_features
import warnings
warnings.filterwarnings("ignore")
import time


#path to training data
source   = "development_set\\"   

modelpath = "speaker_models\\"

test_file = "development_set_test.txt"        

file_paths = open(test_file,'r')


gmm_files = [os.path.join(modelpath,fname) for fname in 
              os.listdir(modelpath) if fname.endswith('.gmm')]

#Load the Gaussian gender Models
models    = [cPickle.load(open(fname,'r')) for fname in gmm_files]
speakers   = [fname.split("\\")[-1].split(".gmm")[0] for fname 
              in gmm_files]

# Read the test directory and get the list of test audio files 
for path in file_paths:   
    
    path = path.strip()   
    print path
    sr,audio = read(source + path)
    vector   = extract_features(audio,sr)
    
    log_likelihood = np.zeros(len(models)) 
    
    for i in range(len(models)):
        gmm    = models[i]         #checking with each model one by one
        scores = np.array(gmm.score(vector))
        log_likelihood[i] = scores.sum()
    
    winner = np.argmax(log_likelihood)
    print "\tdetected as - ", speakers[winner]
    time.sleep(1.0)