让机器听声音识别男女（机器学习的方法）

人能够很容易的听出说话人的性别，我们能不能让机器也像人一样，听声辨别性别？这个答案是肯定的，特别是随着人工智能算法的发展，识别性能是不断的提升。本实验就是通过声音识别男女性别。主要分为三个部分，第一是对声音文件进行特征提取，第二是通过机器学习方法建立男女性别分类模型，第三则是加载模型进行声音文件测试。

import pandas as pd
import rpy2.robjects as robjects
from rpy2.robjects import r, pandas2ri
import os
os.chdir('home/qlmx')

data_list = []

#获取特征文件
def get_feature(fname):
    pandas2ri.activate()
    robjects.r.source('feature_extract.R')  	#利用rpy2读取R脚本
    data_read = robjects.r.processFolder(fname) #得到数据文件
    data_read = pandas2ri.ri2py(data_read)		#转化为python可以使用的数据
    return data_read

if __name__ == '__main__':
    file_name_list = os.listdir('data')			#存放.wav格式声音的文件夹
    for file_name in file_name_list:
	data = get_feature(file_name)
        data_list.append(data)
    result = pd.concat(data_list)			
    result['label'] = 'male'
    result.to_csv("male.csv", index=False)
    #result['label'] = 'female'
    #result.to_csv("female.csv", index=False)

#-*- coding:utf-8 _*-  

import xgboost as xgb
import pandas as pd
import numpy as np
import sklearn
import pickle
import pprint

def xgb_score(preds, dtrain):
    labels = dtrain.get_label()
    return 'log_loss', sklearn.metrics.log_loss(labels, preds)


input_data = pd.read_csv('train.csv')
input_data = input_data.sample(frac=1) 
gender = {'male' : 0, 'female' : 1}
input_data['label'] = input_data['label'].map(gender)
cols = [c for c in input_data.columns if c not in ['label']]
print cols
train = input_data.iloc[0 :3300]
test = input_data.iloc[3300 : ]
test_label = test['label']
test_label = np.array(test_label).reshape([-1 , 1])
del(test['label'])

fold = 1
for i in range(fold):
    params = {
        'eta': 0.01, #use 0.002
        'max_depth': 5,
        'objective': 'binary:logistic',
        'eval_metric': 'logloss',
        'lambda':0.1,
        'gamma':0.1,
        'seed': i,
        'silent': True
    }
    x1 = train[cols][0:3000]
    x2 = train[cols][3000:]
    y1 = train['label'][0:3000]
    y2 = train['label'][3000 : ]
    watchlist = [(xgb.DMatrix(x1, y1), 'train'), (xgb.DMatrix(x2, y2), 'valid')]
    model = xgb.train(params, xgb.DMatrix(x1, y1), 1500,  watchlist, feval=xgb_score, maximize=False, verbose_eval=50, early_stopping_rounds=50) #use 1500
    if i != 0:
        pred += model.predict(xgb.DMatrix(test[cols]), ntree_limit=model.best_ntree_limit)
    else:
        pred = model.predict(xgb.DMatrix(test[cols]), ntree_limit=model.best_ntree_limit)
        
pred /= fold
pre_label = np.zeros([pred.shape[0], 1])
for i in range(pred.shape[0]):
    if pred[i] >= 0.5:
        pre_label[i] = 1
    else:
        pre_label[i] = 0

acc = np.mean(np.equal(pre_label, test_label).astype(np.float))
print("the test acc is:", acc)

model_save = open('model.pkl', 'wb')    #保存模型
pickle.dump(model, model_save)
model_save.close()

import xgboost as xgb
import pandas as pd
import numpy as np
import sklearn
import pickle
import pprint
import rpy2.robjects as robjects
from rpy2.robjects import r, pandas2ri
import os
os.chdir('/home/qlmx')

#get feature file
def get_feature(fname):
    pandas2ri.activate()
    robjects.r.source('feature_extract.R')
    data_read = robjects.r.processFolder(fname)
    data_read = pandas2ri.ri2py(data_read)
    return data_read

if __name__ == '__main__':
    data_list = []
    model_save = open('model.pkl', 'rb')
    model = pickle.load(model_save)
    model_save.close()

    file_name_list = os.listdir('data')				#读取声音文件
    for file_name in file_name_list:
        data = get_feature(file_name)
        data_list.append(data)
    test = pd.concat(data_list)
   
    pred = model.predict(xgb.DMatrix(test), ntree_limit=model.best_ntree_limit)
    print pred
    pre_label = np.zeros([pred.shape[0], 1])
    for i in range(pred.shape[0]):
        if pred[i] >= 0.5:
            pre_label[i] = 1
        else:
            pre_label[i] = 0
    num = 0
    tlen = len(pre_label)
    for i in pre_label:
        num += i
    print 'female is;'+str(num)
    print 'male is:'+str(tlen-num)
    print (tlen-num)/tlen
    print num/tlen