python学习之实现语音的简单训练及识别
语音识别是一门交叉学科。近二十年来，语音识别技术取得显著进步，开始从实验室走向市场。人们预计，未来10年内，语音识别技术将进入工业、家电、通信、汽车电子、医疗、家庭服务、消费电子产品等各个领域。 语音识别听写机在一些领域的应用被美国新闻界评为1997年计算机发展十件大事之一。很多专家都认为语音识别技术是2000年至2010年间信息技术领域十大重要的科技发展技术之一。 语音识别技术所涉及的领域包括：信号处理、模式识别、概率论和信息论、发声机理和听觉机理、人工智能等等。
此次用的是python3.6的版本实现的，安装一些相关模块sklearn:
pip install sklearn
会自动下载相关的包，好像一般来说，是下载最新的（不敢确认）。
中间也可能会存在一定的错误，导致失败，但可以重新，继续下载，反正我是这样的。
知道成功安装。
gParam的代码：
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
u'''
Created on 2019年4月27日
@author: wuluo
'''
__author__ = 'wuluo'
__version__ = '1.0.0'
__company__ = u'重庆交大'
__updated__ = '2019-04-27'
TRAIN_DATA_PATH = 'G:/2018and2019two/duomeitijishu/data/train/'
TEST_DATA_PATH = 'G:/2018and2019two/duomeitijishu/data/test/'
NSTATE = 4
NPDF = 3
MAX_ITER_CNT = 100
NUM = 10
if __name__ == "__main__":
pass
对于读入的路径，我用的是绝对路径，也就是写死。
test的代码：
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
u'''
Created on 2019年4月27日
@author: wuluo
'''
__author__ = 'wuluo'
__version__ = '1.0.0'
__company__ = u'重庆交大'
__updated__ = '2019-04-27'
import numpy as np
from numpy import *
from duomeiti import gParam
from duomeiti import my_hmm
from my_hmm import gmm_hmm #可会出现报错，但实际运行起来，没有问题
my_gmm_hmm = gmm_hmm()
my_gmm_hmm.loadWav(gParam.TRAIN_DATA_PATH)
my_gmm_hmm.hmm_start_train()
my_gmm_hmm.recog(gParam.TEST_DATA_PATH)
if __name__ == "__main__":
pass
在编译与修改代码的边缘，疯狂试探；
主要还是python版本的原因，版本的不同，有些函数的用法也不同。具体详细请见这个网址：https://www.runoob.com/python3/python3-att-list-extend.html
my_hmm的代码：
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
u'''
Created on 2019年4月27日
@author: wuluo
'''
__author__ = 'wuluo'
__version__ = '1.0.0'
__company__ = u'重庆交大'
__updated__ = '2019-04-27'
import numpy as np
from numpy import *
from sklearn.cluster import KMeans
from scipy import sparse
import scipy.io as sio
from scipy import signal
import wave
import math
from duomeiti import gParam
import copy
def pdf(m, v, x):
est_v = np.prod(v, axis=0)
test_x = np.dot((x - m) / v, x - m)
p = (2 * math.pi * np.prod(v, axis=0))**-0.5 * \
np.exp(-0.5 * np.dot((x - m) / v, x - m))
return p
class sampleInfo:
def __init__(self):
self.smpl_wav = []
self.smpl_data = []
self.seg = []
def set_smpl_wav(self, wav):
self.smpl_wav.append(wav)
def set_smpl_data(self, data):
self.smpl_data.append(data)
def set_segment(self, seg_list):
self.seg = seg_list
class mixInfo:
def __init__(self):
self.Cmean = []
self.Cvar = []
self.Cweight = []
self.CM = []
class hmmInfo:
def __init__(self):
self.init = [] # 初始矩阵
self.trans = [] # 转移概率矩阵
self.mix = [] # 高斯混合模型参数
self.N = 0 # 状态数
class gmm_hmm:
def __init__(self):
self.hmm = [] # 单个hmm序列,
self.gmm_hmm_model = [] # 把所有的训练好的gmm-hmm写入到这个队列
self.samples = [] # 0-9 所有的音频数据
self.smplInfo = [] # 这里面主要是单个数字的音频数据和对应mfcc数据
# 每一个HMM对应len(stateInfo)个状态,每个状态指定高斯个数(3)
self.stateInfo = [gParam.NPDF, gParam.NPDF, gParam.NPDF, gParam.NPDF]
def loadWav(self, pathTop):
for i in range(gParam.NUM):
tmp_data = []
for j in range(gParam.NUM):
wavPath = pathTop + str(i) + str(j) + '.wav'
f = wave.open(wavPath, 'rb')
params = f.getparams()
nchannels, sampwidth, framerate, nframes = params[:4]
str_data = f.readframes(nframes)
# print shape(str_data)
f.close()
wave_data = np.fromstring(str_data, dtype=short) / 32767.0
#wave_data.shape = -1,2
#wave_data = wave_data.T
#wave_data = wave_data.reshape(1,wave_data.shape[0]*wave_data.shape[1])
# print shape(wave_data),type(wave_data)
tmp_data.append(wave_data)
self.samples.append(tmp_data)
# 循环读数据,然后进行训练
def hmm_start_train(self):
Nsmpls = len(self.samples)
for i in range(Nsmpls):
tmpSmplInfo0 = []
n = len(self.samples[i])
for j in range(n):
tmpSmplInfo1 = sampleInfo()
tmpSmplInfo1.set_smpl_wav(self.samples[i][j])
tmpSmplInfo0.append(tmpSmplInfo1)
# self.smplInfo.append(tmpSmplInfo0)
print('现在训练第' + str(i) + '个HMM模型')
hmm0 = self.trainhmm(tmpSmplInfo0, self.stateInfo)
print('第' + str(i) + '个模型已经训练完毕')
# self.gmm_hmm_model.append(hmm0)
# 训练hmm
def trainhmm(self, sample, state):
K = len(sample)
print('首先进行语音参数计算-MFCC')
for k in range(K):
tmp = self.mfcc(sample[k].smpl_wav)
sample[k].set_smpl_data(tmp) # 设置MFCCdata
hmm = self.inithmm(sample, state)
pout = zeros((gParam.MAX_ITER_CNT, 1))
for my_iter in range(gParam.MAX_ITER_CNT):
print('第' + str(my_iter) + '训练')
hmm = self.baum(hmm, sample)
for k in range(K):
pout[my_iter, 0] = pout[my_iter, 0] + \
self.viterbi(hmm, sample[k].smpl_data[0])
if my_iter > 0:
if(abs((pout[my_iter, 0] - pout[my_iter - 1, 0]) / pout[my_iter, 0]) < 5e-6):
print('收敛')
self.gmm_hmm_model.append(hmm)
return hmm
self.gmm_hmm_model.append(hmm)
# 获取MFCC参数
def mfcc(self, k):
M = 24 # 滤波器的个数
N = 256 # 一帧语音的采样点数
arr_mel_bank = self.melbank(M, N, 8000, 0, 0.5, 'm')
arr_mel_bank = arr_mel_bank / np.amax(arr_mel_bank)
#计算DCT系数, 12*24
rDCT = 12
cDCT = 24
dctcoef = []
for i in range(1, rDCT + 1):
tmp = [np.cos((2 * j + 1) * i * math.pi * 1.0 / (2.0 * cDCT))
for j in range(cDCT)]
dctcoef.append(tmp)
# 归一化倒谱提升窗口
w = [1 + 6 * np.sin(math.pi * i * 1.0 / rDCT)
for i in range(1, rDCT + 1)]
w = w / np.amax(w)
# 预加重
AggrK = double(k)
AggrK = signal.lfilter([1, -0.9375], 1, AggrK) # ndarray
#AggrK = AggrK.tolist()
# 分帧
FrameK = self.enframe(AggrK[0], N, 80)
n0, m0 = FrameK.shape
for i in range(n0):
#temp = multiply(FrameK[i,:],np.hamming(N))
# print shape(temp)
FrameK[i, :] = multiply(FrameK[i, :], np.hamming(N))
FrameK = FrameK.T
# 计算功率谱
S = (abs(np.fft.fft(FrameK, axis=0)))**2
# 将功率谱通过滤波器组
P = np.dot(arr_mel_bank, S[0:129, :])
# 取对数后做余弦变换
D = np.dot(dctcoef, log(P))
n0, m0 = D.shape
m = []
for i in range(m0):
m.append(np.multiply(D[:, i], w))
n0, m0 = shape(m)
dtm = zeros((n0, m0))
for i in range(2, n0 - 2):
dtm[i, :] = -2 * m[i - 2][:] - m[i - 1][:] + \
m[i + 1][:] + 2 * m[i + 2][:]
dtm = dtm / 3.0
# cc = [m,dtm]
cc = np.column_stack((m, dtm))
# cc.extend(list(dtm))
cc = cc[2:n0 - 2][:]
# print shape(cc)
return cc
# melbank
def melbank(self, p, n, fs, f1, fh, w):
f0 = 700.0 / (1.0 * fs)
fn2 = floor(n / 2.0)
lr = math.log((float)(f0 + fh) / (float)(f0 + f1)) / (float)(p + 1)
tmpList = [0, 1, p, p + 1]
bbl = []
for i in range(len(tmpList)):
bbl.append(n * ((f0 + f1) * math.exp(tmpList[i] * lr) - f0))
#b1 = n*((f0+f1) * math.exp([x*lr for x in tmpList]) - f0)
# print bbl
b2 = ceil(bbl[1])
b3 = floor(bbl[2])
if(w == 'y'):
pf = np.log((f0 + range(b2, b3) / n) / (f0 + f1)) / lr # note
fp = floor(pf)
r = [ones((1, b2)), fp, fp + 1, p * ones((1, fn2 - b3))]
c = [range(0, b3), range(b2, fn2)]
v = 2 * [0.5, ones((1, b2 - 1)), 1 - pf + fp,
pf - fp, ones((1, fn2 - b3 - 1)), 0.5]
mn = 1
mx = fn2 + 1
else:
b1 = floor(bbl[0]) + 1
b4 = min([fn2, ceil(bbl[3])]) - 1
pf = []
for i in range(int(b1), int(b4 + 1), 1):
pf.append(math.log((f0 + (1.0 * i) / n) / (f0 + f1)) / lr)
fp = floor(pf)
pm = pf - fp
k2 = b2 - b1 + 1
k3 = b3 - b1 + 1
k4 = b4 - b1 + 1
r = fp[int(k2 - 1):int(k4)]
r1 = 1 + fp[0:int(k3)]
r = r.tolist()
r1 = r1.tolist()
r.extend(r1)
#r = [fp[int(k2-1):int(k4)],1+fp[0:int(k3)]]
c = list(range(int(k2), int(k4 + 1)))
c2 = list(range(1, int(k3 + 1)))
# c = c.tolist()
# c2 = c2.tolist()
c.extend(c2)
#c = [range(int(k2),int(k4+1)),range(0,int(k3))]
v = 1 - pm[int(k2 - 1):int(k4)]
v = v.tolist()
v1 = pm[0:int(k3)]
v1 = v1.tolist()
v.extend(v1) # [1-pm[int(k2-1):int(k4)],pm[0:int(k3)]]
v = [2 * x for x in v]
mn = b1 + 1
mx = b4 + 1
if(w == 'n'):
v = 1 - math.cos(v * math.pi / 2)
elif (w == 'm'):
tmpV = []
# for i in range(v):
# tmpV.append(1-0.92/1.08*math.cos(v[i]*math))
v = [1 - 0.92 / 1.08 * math.cos(x * math.pi / 2) for x in v]
# print type(c),type(mn)
col_list = [x + int(mn) - 2 for x in c]
r = [x - 1 for x in r]
x = sparse.coo_matrix((v, (r, col_list)), shape=(p, 1 + int(fn2)))
matX = x.toarray()
#np.savetxt('./data.csv',matX, delimiter=' ')
return matX # x.toarray()
# 分帧函数
def enframe(self, x, win, inc):
nx = len(x)
try:
nwin = len(win)
except Exception as err:
# print err
nwin = 1
if (nwin == 1):
wlen = win
else:
wlen = nwin
# print inc,wlen,nx
# here has a bug that nf maybe less than 0
nf = fix(1.0 * (nx - wlen + inc) / inc)
f = zeros((int(nf), wlen))
indf = [inc * j for j in range(int(nf))]
indf = (mat(indf)).T
inds = mat(range(wlen))
indf_tile = tile(indf, wlen)
inds_tile = tile(inds, (int(nf), 1))
mix_tile = indf_tile + inds_tile
for i in range(int(nf)):
for j in range(wlen):
f[i, j] = x[mix_tile[i, j]]
# print x[mix_tile[i,j]]
if nwin > 1: # TODOd
w = win.tolist()
#w_tile = tile(w,(int))
return f
# init hmm
def inithmm(self, sample, M):
K = len(sample)
N0 = len(M)
self.N = N0
# 初始概率矩阵
hmm = hmmInfo()
hmm.init = zeros((N0, 1))
hmm.init[0] = 1
hmm.trans = zeros((N0, N0))
hmm.N = N0
# 初始化转移概率矩阵
for i in range(self.N - 1):
hmm.trans[i, i] = 0.5
hmm.trans[i, i + 1] = 0.5
hmm.trans[self.N - 1, self.N - 1] = 1
# 概率密度函数的初始聚类
# 分段
for k in range(K):
T = len(sample[k].smpl_data[0])
#seg0 = []
seg0 = np.floor(arange(0, T, 1.0 * T / N0))
#seg0 = int(seg0.tolist())
seg0 = np.concatenate((seg0, [T]))
# seg0.append(T)
sample[k].seg = seg0
# 对属于每个状态的向量进行K均值聚类,得到连续混合正态分布
mix = []
for i in range(N0):
vector = []
for k in range(K):
seg1 = int(sample[k].seg[i])
seg2 = int(sample[k].seg[i + 1])
tmp = []
tmp = sample[k].smpl_data[0][seg1:seg2][:]
if k == 0:
vector = np.array(tmp)
else:
vector = np.concatenate((vector, np.array(tmp)))
# vector.append(tmp)
# tmp_mix = mixInfo()
# print id(tmp_mix)
tmp_mix = self.get_mix(vector, M[i], mix)
# mix.append(tmp_mix)
hmm.mix = mix
return hmm
# get mix data
def get_mix(self, vector, K, mix0):
kmeans = KMeans(n_clusters=K, random_state=0).fit(np.array(vector))
# 计算每个聚类的标准差,对角阵,只保存对角线上的元素
mix = mixInfo()
var0 = []
mean0 = []
#ind = []
for j in range(K):
#ind = [i for i in kmeans.labels_ if i==j]
ind = []
ind1 = 0
for i in kmeans.labels_:
if i == j:
ind.append(ind1)
ind1 = ind1 + 1
tmp = [vector[i][:] for i in ind]
var0.append(np.std(tmp, axis=0))
mean0.append(np.mean(tmp, axis=0))
weight0 = zeros((K, 1))
for j in range(K):
tmp = 0
ind1 = 0
for i in kmeans.labels_:
if i == j:
tmp = tmp + ind1
ind1 = ind1 + 1
weight0[j] = tmp
weight0 = weight0 / weight0.sum()
mix.Cvar = multiply(var0, var0)
mix.Cmean = mean0
mix.CM = K
mix.Cweight = weight0
mix0.append(mix)
return mix0
# baum-welch 算法实现函数体
def baum(self, hmm, sample):
mix = copy.deepcopy(hmm.mix) # 高斯混合
N = len(mix) # HMM状态数
K = len(sample) # 语音样本数
SIZE = shape(sample[0].smpl_data[0])[1] # 参数阶数,MFCC维数
print('计算样本参数.....')
c = []
alpha = []
beta = []
ksai = []
gama = []
for k in range(K):
c0, alpha0, beta0, ksai0, gama0 = self.getparam(
hmm, sample[k].smpl_data[0])
c.append(c0)
alpha.append(alpha0)
beta.append(beta0)
ksai.append(ksai0)
gama.append(gama0)
# 重新估算概率转移矩阵
print('----- 重新估算概率转移矩阵 -----')
for i in range(N - 1):
denom = 0
for k in range(K):
ksai0 = ksai[k]
tmp = ksai0[:, i, :] # ksai0[:][i][:]
denom = denom + sum(tmp)
for j in range(i, i + 2):
norm = 0
for k in range(K):
ksai0 = ksai[k]
tmp = ksai0[:, i, j] # [:][i][j]
norm = norm + sum(tmp)
hmm.trans[i, j] = norm / denom
# 重新估算发射概率矩阵,即GMM的参数
print('----- 重新估算输出概率矩阵,即GMM的参数 -----')
for i in range(N):
for j in range(mix[i].CM):
nommean = zeros((1, SIZE))
nomvar = zeros((1, SIZE))
denom = 0
for k in range(K):
gama0 = gama[k]
T = shape(sample[k].smpl_data[0])[0]
for t in range(T):
x = sample[k].smpl_data[0][t][:]
nommean = nommean + gama0[t, i, j] * x
nomvar = nomvar + gama0[t, i, j] * \
(x - mix[i].Cmean[j][:])**2
denom = denom + gama0[t, i, j]
hmm.mix[i].Cmean[j][:] = nommean / denom
hmm.mix[i].Cvar[j][:] = nomvar / denom
nom = 0
denom = 0
# 计算pdf权值
for k in range(K):
gama0 = gama[k]
tmp = gama0[:, i, j]
nom = nom + sum(tmp)
tmp = gama0[:, i, :]
denom = denom + sum(tmp)
hmm.mix[i].Cweight[j] = nom / denom
return hmm
# 前向-后向算法
def getparam(self, hmm, O):
T = shape(O)[0]
init = hmm.init # 初始概率
trans = copy.deepcopy(hmm.trans) # 转移概率
mix = copy.deepcopy(hmm.mix) # 高斯混合
N = hmm.N # 状态数
# 给定观测序列,计算前向概率alpha
x = O[0][:]
alpha = zeros((T, N))
#----- 计算前向概率alpha -----#
for i in range(N): # t=0
tmp = hmm.init[i] * self.mixture(mix[i], x)
alpha[0, i] = tmp # hmm.init[i]*self.mixture(mix[i],x)
# 标定t=0时刻的前向概率
c = zeros((T, 1))
c[0] = 1.0 / sum(alpha[0][:])
alpha[0][:] = c[0] * alpha[0][:]
for t in range(1, T, 1): # t = 1~T
for i in range(N):
temp = 0.0
for j in range(N):
temp = temp + alpha[t - 1, j] * trans[j, i]
alpha[t, i] = temp * self.mixture(mix[i], O[t][:])
c[t] = 1.0 / sum(alpha[t][:])
alpha[t][:] = c[t] * alpha[t][:]
#----- 计算后向概率 -----#
beta = zeros((T, N))
for i in range(N): # T时刻
beta[T - 1, i] = c[T - 1]
for t in range(T - 2, -1, -1):
x = O[t + 1][:]
for i in range(N):
for j in range(N):
beta[t, i] = beta[t, i] + beta[t + 1, j] * \
self.mixture(mix[j], x) * trans[i, j]
beta[t][:] = c[t] * beta[t][:]
# 过渡概率ksai
ksai = zeros((T - 1, N, N))
for t in range(0, T - 1):
denom = sum(np.multiply(alpha[t][:], beta[t][:]))
for i in range(N - 1):
for j in range(i, i + 2, 1):
norm = alpha[t, i] * trans[i, j] * \
self.mixture(mix[j], O[t + 1][:]) * beta[t + 1, j]
ksai[t, i, j] = c[t] * norm / denom
# 混合输出概率 gama
gama = zeros((T, N, max(self.stateInfo)))
for t in range(T):
pab = zeros((N, 1))
for i in range(N):
pab[i] = alpha[t, i] * beta[t, i]
x = O[t][:]
for i in range(N):
prob = zeros((mix[i].CM, 1))
for j in range(mix[i].CM):
m = mix[i].Cmean[j][:]
v = mix[i].Cvar[j][:]
prob[j] = mix[i].Cweight[j] * pdf(m, v, x)
if mix[i].Cweight[j] == 0.0:
print(pdf(m, v, x))
tmp = pab[i] / pab.sum()
tmp = tmp[0]
temp_sum = prob.sum()
for j in range(mix[i].CM):
gama[t, i, j] = tmp * prob[j] / temp_sum
return c, alpha, beta, ksai, gama
def mixture(self, mix, x):
prob = 0.0
for i in range(mix.CM):
m = mix.Cmean[i][:]
v = mix.Cvar[i][:]
w = mix.Cweight[i]
tmp = pdf(m, v, x)
# print tmp
prob = prob + w * tmp # * pdf(m,v,x)
if prob == 0.0:
prob = 2e-100
return prob
# 维特比算法
def viterbi(self, hmm, O):
init = copy.deepcopy(hmm.init)
trans = copy.deepcopy(hmm.trans) # hmm.trans
mix = hmm.mix
N = hmm.N
T = shape(O)[0]
# 计算Log(init)
n_init = len(init)
for i in range(n_init):
if init[i] <= 0:
init[i] = -inf
else:
init[i] = log(init[i])
# 计算log(trans)
m, n = shape(trans)
for i in range(m):
for j in range(n):
if trans[i, j] <= 0:
trans[i, j] = -inf
else:
trans[i, j] = log(trans[i, j])
# 初始化
delta = zeros((T, N))
fai = zeros((T, N))
q = zeros((T, 1))
# t=0
x = O[0][:]
for i in range(N):
delta[0, i] = init[i] + log(self.mixture(mix[i], x))
# t=2:T
for t in range(1, T):
for j in range(N):
tmp = delta[t - 1][:] + trans[:][j].T
tmp = tmp.tolist()
delta[t, j] = max(tmp)
fai[t, j] = tmp.index(max(tmp))
x = O[t][:]
delta[t, j] = delta[t, j] + log(self.mixture(mix[j], x))
tmp = delta[T - 1][:]
tmp = tmp.tolist()
prob = max(tmp)
q[T - 1] = tmp.index(max(tmp))
for t in range(T - 2, -1, -1):
q[t] = fai[t + 1, int(q[t + 1, 0])]
return prob
# 用于测试的程序
def vad(self, k, fs):
k = double(k)
k = multiply(k, 1.0 / max(abs(k)))
# 计算短时过零率
FrameLen = 240
FrameInc = 80
FrameTemp1 = self.enframe(k[0:-2], FrameLen, FrameInc)
FrameTemp2 = self.enframe(k[1:], FrameLen, FrameInc)
signs = np.sign(multiply(FrameTemp1, FrameTemp2))
signs = list(map(lambda x: [[i, 0][i > 0] for i in x], signs))
signs = list(map(lambda x: [[i, 1][i < 0] for i in x], signs))
diffs = np.sign(abs(FrameTemp1 - FrameTemp2) - 0.01)
diffs = list(map(lambda x: [[i, 0][i < 0] for i in x], diffs))
zcr = sum(multiply(signs, diffs), 1)
# 计算短时能量
amp = sum(abs(self.enframe(signal.lfilter(
[1, -0.9375], 1, k), FrameLen, FrameInc)), 1)
# print '短时能量%f' %amp
# 设置门限
print('设置门限')
ZcrLow = max([round(mean(zcr) * 0.1), 3]) # 过零率低门限
ZcrHigh = max([round(max(zcr) * 0.1), 5]) # 过零率高门限
AmpLow = min([min(amp) * 10, mean(amp) * 0.2, max(amp) * 0.1]) # 能量低门限
AmpHigh = max([min(amp) * 10, mean(amp) *
0.2, max(amp) * 0.1]) # 能量高门限
# 端点检测
MaxSilence = 8 # 最长语音间隙时间
MinAudio = 16 # 最短语音时间
Status = 0 # 状态0:静音段,1:过渡段,2:语音段,3:结束段
HoldTime = 0 # 语音持续时间
SilenceTime = 0 # 语音间隙时间
print('开始端点检测')
StartPoint = 0
for n in range(len(zcr)):
if Status == 0 or Status == 1:
if amp[n] > AmpHigh or zcr[n] > ZcrHigh:
StartPoint = n - HoldTime
Status = 2
HoldTime = HoldTime + 1
SilenceTime = 0
elif amp[n] > AmpLow or zcr[n] > ZcrLow:
Status = 1
HoldTime = HoldTime + 1
else:
Status = 0
HoldTime = 0
elif Status == 2:
if amp[n] > AmpLow or zcr[n] > ZcrLow:
HoldTime = HoldTime + 1
else:
SilenceTime = SilenceTime + 1
if SilenceTime < MaxSilence:
HoldTime = HoldTime + 1
elif (HoldTime - SilenceTime) < MinAudio:
Status = 0
HoldTime = 0
SilenceTime = 0
else:
Status = 3
elif Status == 3:
break
if Status == 3:
break
HoldTime = HoldTime - SilenceTime
EndPoint = StartPoint + HoldTime
return StartPoint, EndPoint
def recog(self, pathTop):
N = gParam.NUM
for i in range(N):
wavPath = pathTop + str(i) + '.wav'
f = wave.open(wavPath, 'rb')
params = f.getparams()
nchannels, sampwidth, framerate, nframes = params[:4]
str_data = f.readframes(nframes)
# print shape(str_data)
f.close()
wave_data = np.fromstring(str_data, dtype=short) / 32767.0
x1, x2 = self.vad(wave_data, framerate)
O = self.mfcc([wave_data])
O = O[x1 - 3:x2 - 3][:]
print('第' + str(i) + '个词的观察矢量是:' + str(i))
pout = []
for j in range(N):
pout.append(self.viterbi(self.gmm_hmm_model[j], O))
n = pout.index(max(pout))
print('第' + str(i) + '个词,识别是:' + str(n))
if __name__ == "__main__":
pass
这里是运行结果：
