def __flat_start(self, label_data):
"""
均一起步
(停止使用)
计算全局均值和方差
:param label_data: 所有数据
:return:
"""
'''data为所有数据的合集'''
data = list(label_data.values())
_data = None
size = 0
for index in range(len(data)):
size += sum(data[index][1])
tmp_data = data[index][0][0]
for d in range(1, len(data[index][0])):
tmp_data = np.append(tmp_data, data[index][0][d], axis=0)
if _data is None:
_data = tmp_data
else:
_data = np.append(_data, tmp_data, axis=0)
label = list(label_data.keys())
_label = []
for l in label:
_label.extend(l.split(','))
'''取不重复基元'''
label = list(set(_label))
cluster = Clustering.ClusterInitialization(_data, self.__mix_level,
self.__vector_size,
self.log)
mean, covariance, alpha, clustered_data = cluster.kmeans(algorithm=1)
'''训练GMM'''
tmp_gmm = Clustering.GMM(None, self.__vector_size, self.__mix_level)
tmp_gmm.data = data
tmp_gmm.mean = mean
tmp_gmm.covariance = covariance
tmp_gmm.alpha = alpha
'''GMM Baulm-Welch迭代'''
tmp_gmm.baulm_welch()
'''获取均值、协方差和权重值'''
mean = tmp_gmm.mean
covariance = tmp_gmm.covariance
alpha = tmp_gmm.alpha
for i in range(len(label)):
hmm = self.__unit[label[i]][1]
for j in range(1, len(hmm.profunction) - 1):
'''除去前后两个虚方法'''
gmm = hmm.profunction[j]
gmm.mean = mean
gmm.covariance = covariance
gmm.alpha = alpha
def __flat_start(self,
path_list,
file_count,
proportion=0.25,
step=1,
differentiation=True,
coefficient=1.):
"""
均一起步
计算全局均值和方差
:param path_list: 数据路径列表
:param file_count: 数据总量
:param proportion: 训练数据中，用于计算全局均值和协方差的数据占比
:param step: 在帧中跳跃选取的跳跃步长
:param differentiation: GMM中各分模型参数差异化处理
:param coefficient: 差异化程度，区间[0,1]
:return:
"""
self.log.note('flat starting...', cls='i')
p_file_count = int(file_count * proportion)
p_data = self.__load_audio(path_list[0][0])
p_data = p_data[::step]
for index in range(1, p_file_count):
data = self.__load_audio(path_list[index][0]) # 加载音频数据
data = data[::step]
p_data = np.append(p_data, data, axis=0)
cluster = Clustering.ClusterInitialization(p_data, 1,
self.__vector_size,
self.log)
mean, covariance, alpha, clustered_data = cluster.kmeans(
algorithm=1, cov_matrix=True)
covariance_diagonal = covariance[0].diagonal()
units = self.__loaded_units
''''''
diff_coefficient = np.zeros((self.__mix_level, 1))
if differentiation:
'''差异化处理'''
assert 0 <= coefficient <= 1, '差异化系数不满足区间[0,1]'
diff_coefficient = (np.random.random(
(self.__mix_level, 1)) - np.random.random(
(self.__mix_level, 1))) * coefficient
for unit in units:
hmm = self.init_unit(unit, new_log=True)
gmms = hmm.profunction[1:-1]
for g in gmms:
g.mean = mean.repeat(
self.__mix_level,
axis=0) + diff_coefficient * covariance_diagonal
g.covariance = covariance.repeat(self.__mix_level, axis=0)
self.__save_parameter(unit, hmm)
self.delete_trainInfo()
def __cal_gmm(self,
unit,
unit_data,
init=False,
smem=False,
show_q=False,
c_covariance=1e-3):
"""
计算GMM
:param unit: 当前基元
:param unit_data: 基元数据
:param init: 是否初始化
:param smem: 是否进行SMEM算法
:param show_q: 显示当前似然度
:param c_covariance: 修正数值，纠正GMM中的Singular Matrix
:return:
"""
hmm = self.__unit[unit]
gmms_num = len(hmm.profunction) - 2 # 高斯混合模型数
for i in range(1, len(hmm.profunction) - 1):
'''除去前后两个虚方法'''
gmm = hmm.profunction[i]
gmm.log.note('正在训练GMM%d，共 %d GMM，混合度为 %d' %
(i, gmms_num, self.__mix_level),
cls='i')
data = unit_data[i - 1] # 对应高斯模型的数据
gmm.add_data(data)
if len(data) < self.__mix_level:
gmm.log.note('数据过少，忽略该组数据', cls='w')
continue
if init or gmm.mixture != self.__mix_level: # 当初始化模型或高斯混合度变化时，重新聚类
cluster = Clustering.ClusterInitialization(
data, self.__mix_level, self.__vector_size, gmm.log)
mean, covariance, alpha, clustered_data = cluster.kmeans(
algorithm=1, cov_matrix=True)
gmm.mixture = self.__mix_level
gmm.mean = mean
gmm.covariance = covariance
gmm.alpha = alpha
'''GMM Baulm-Welch迭代'''
gmm.baulm_welch(show_q=show_q,
smem=smem,
c_covariance=c_covariance)
gmm.clear_data() # 清空数据内存
def __cal_gmm(self,
hmm,
unit_data,
init=False,
smem=False,
show_q=False,
c_covariance=1e-3):
"""
计算GMM
:param hmm: 外部传入HMM实例
:param unit_data: 基元数据
:param init: 是否初始化
:param smem: 是否进行SMEM算法
:param show_q: 显示当前似然度
:param c_covariance: 修正数值，纠正GMM中协方差值过小问题
:return:
"""
for i in range(1, self.__state_num - 1):
'''除去前后两个虚方法'''
gmm = hmm.profunction[i]
data = unit_data[i - 1] # 对应高斯模型的数据
gmm.add_data(data)
if len(data) < self.__mix_level:
gmm.log.note('数据过少，忽略该组数据', cls='w')
continue
if init or gmm.mixture != self.__mix_level: # 当初始化模型或高斯混合度变化时，重新聚类
cluster = Clustering.ClusterInitialization(
data, self.__mix_level, self.__vector_size, gmm.log)
mean, covariance, alpha, clustered_data = cluster.kmeans(
algorithm=1, cov_matrix=True)
gmm.mixture = self.__mix_level
gmm.mean = mean
gmm.covariance = covariance
gmm.alpha = alpha
'''GMM Baulm-Welch迭代'''
gmm.em(show_q=show_q, smem=smem, c_covariance=c_covariance)
gmm.clear_data() # 清空数据内存
def __cal_gmm(self, label, c=True):
"""初始化GMM"""
hmm = self.__unit[label][1]
for i in range(1, len(hmm.profunction) - 1):
'''除去前后两个虚方法'''
gmm = hmm.profunction[i]
data = gmm.data
if len(data) < self.__mix_level:
continue
if c:
'''重新聚类'''
cluster = Clustering.ClusterInitialization(
data, self.__mix_level, self.__vector_size)
μ, σ, alpha, clustered_data = cluster.kmeans(algorithm=1)
gmm.set_k(self.__mix_level)
gmm.set_μ(μ)
gmm.set_sigma(σ=σ)
gmm.set_alpha(alpha)
'''GMM Baulm-Welch迭代'''
gmm.baulm_welch(show_q=True, smem=True)
else:
gmm.baulm_welch(show_q=True)
gmm.clear_data() # 清空数据内存