def generate(_unit):
""""""
'''状态集合'''
states = {_: _unit for _ in range(self.__state_num)}
'''观测概率表示(GMM)'''
observations = ['GMM_probability']
'''状态转移矩阵'''
A = np.zeros((self.__state_num, self.__state_num))
'''开始状态，为虚状态，只允许向下一个状态转移'''
A[0][1] = 1.
for j in range(1, self.__state_num - 1):
for k in range(j, j + 2):
A[j][k] = 0.5
'''创建基元文件夹'''
unit_path = PARAMETERS_FILE_PATH + '/%s/%s' % (self.__unit_type,
_unit)
if not os.path.exists(unit_path):
os.mkdir(unit_path)
'''''' '''''' ''''''
log = Log(self.__unit_type, _unit, console=self.__console)
if new_log:
log.generate()
else:
log.append()
'''初始化GMM'''
gmm = [
Clustering.GMM(self.__vector_size, self.__mix_level, log)
for _ in range(self.__state_num - 2)
]
'''初始化虚状态评分类'''
virtual_gmm_1 = AcousticModel.VirtualState(0.)
virtual_gmm_2 = AcousticModel.VirtualState(0.)
gmm.insert(0, virtual_gmm_1)
gmm.append(virtual_gmm_2)
'''生成hmm实例'''
lhmm = LHMM(states,
observations,
log,
T=None,
A=A,
profunc=gmm,
pi=None)
'''数据结构：{基元：HMM,...}'''
self.__unit[_unit] = lhmm
def init_unit(self, unit, new_log=True, fix_code=0):
"""
初始化基元，生成基元的复合数据结构
:param unit: 初始化指定基元
:param new_log: 是否删除先前日志
:param fix_code: 关闭参数更新，000=0 001=1 010=2 100=4...
:return:
"""
""""""
'''状态集合'''
states = {_: unit for _ in range(self.__state_num)}
'''状态转移矩阵'''
transmat = np.zeros((self.__state_num, self.__state_num))
'''开始状态，为虚状态，只允许向下一个状态转移'''
transmat[0][1] = 1.
for j in range(1, self.__state_num - 1):
transmat[j][j] = 0.5 # 第一个转移概率
transmat[j][j + 1] = 0.5 # 第二个转移概率
'''创建基元文件夹'''
unit_path = PARAMETERS_FILE_PATH + '/%s/%s' % (self.__unit_type, unit)
log_hmm_path = unit_path + '/HMM'
log_gmm_path = [
unit_path + '/GMM_%d' % gmm_id
for gmm_id in range(self.__state_num - 2)
]
try:
os.mkdir(unit_path)
except FileExistsError:
pass
try:
os.mkdir(log_hmm_path)
except FileExistsError:
pass
try:
for gmm_id in range(self.__state_num - 2):
os.mkdir(log_gmm_path[gmm_id])
except FileExistsError:
pass
'''''' '''''' ''''''
log_hmm = Log(self.__unit_type, log_hmm_path, console=self.__console)
log_gmm = [
Log(self.__unit_type,
path=log_gmm_path[gmm_id],
console=self.__console)
for gmm_id in range(self.__state_num - 2)
]
if new_log:
log_hmm.generate()
for gmm_id in range(self.__state_num - 2):
log_gmm[gmm_id].generate()
else:
log_hmm.append()
for gmm_id in range(self.__state_num - 2):
log_gmm[gmm_id].append()
'''初始化GMM'''
gmm = []
for gmm_id in range(self.__state_num - 2):
gmm.append(
Clustering.GMM(log_gmm[gmm_id],
dimension=self.__vector_size,
mix_level=self.__mix_level,
gmm_id=gmm_id))
'''初始化虚状态评分类'''
virtual_gmm_1 = AcousticModel.VirtualState(1.)
virtual_gmm_2 = AcousticModel.VirtualState(0.)
gmm.insert(0, virtual_gmm_1)
gmm.append(virtual_gmm_2)
'''生成hmm实例'''
lhmm = LHMM(states,
self.__state_num,
log_hmm,
transmat=transmat,
profunc=gmm,
fix_code=fix_code)
return lhmm
class Task(object):
def __init__(self, num=1, console=False, **kwargs):
"""
:param num: 参与训练的机器数量(单机情况下，num=1)
:param console: 是否显示基元训练信息
:param kwargs: 声学模型参数
"""
if 'task_num' in kwargs.keys():
self.num = kwargs['task_num']
else:
self.num = num
'''检测必要的目录是否存在，不存在则创建'''
path = PARAMETERS_FILE_PATH + '/' + kwargs['unit_type']
if not os.path.exists(path):
os.makedirs(path)
'''''' '''''' '''''' '''''' '''''' ''''''
self.console = console # 当console=True时，每个基元的训练信息将输出到控制台上。在这种情况下，训练进程的数量最好为1。
self.kwargs = kwargs
self.log = Log(kwargs['unit_type'], console=self.console)
self.log.generate()
'''检查最大高斯混合度约束'''
if self.kwargs['mix_level'] > self.kwargs['max_mix_level']:
raise MixtureNumberError(self.kwargs['mix_level'],
self.kwargs['max_mix_level'], self.log)
self.c_covariance = kwargs['c_covariance']
def split_unit(self):
"""
基元分割
:return: 各机器的训练清单
"""
amodel = AcousticModel(self.log,
self.kwargs['unit_type'],
processes=self.kwargs['processes'],
console=self.console,
state_num=self.kwargs['state_num'],
mix_level=self.kwargs['mix_level'],
delta_1=self.kwargs['delta_1'],
delta_2=self.kwargs['delta_2'])
amodel.load_unit()
units = amodel.loaded_units
chunk = len(units) // self.num
traininfo_path = PARAMETERS_FILE_PATH + '/%s' % self.kwargs['unit_type']
for job_id in range(self.num - 1):
trainInfo_file = traininfo_path + '/trainInfo_%d.csv' % job_id
'''删除现存trainInfo文件'''
if os.path.exists(trainInfo_file):
os.remove(trainInfo_file)
units_slice = set(units).difference(
set(units[job_id * chunk:(job_id + 1) * chunk]))
for unit in units_slice:
trainInfo = open(trainInfo_file, 'a+')
trainInfo.writelines('%s\n' % unit)
'''最后一个机器的训练清单'''
units_slice = set(units).difference(set(units[(self.num - 1) *
chunk:]))
trainInfo_file = traininfo_path + '/trainInfo_%d.csv' % (self.num - 1)
'''删除现存trainInfo文件'''
if os.path.exists(trainInfo_file):
os.remove(trainInfo_file)
for unit in units_slice:
trainInfo = open(
traininfo_path + '/trainInfo_%d.csv' % (self.num - 1), 'a+')
trainInfo.writelines('%s\n' % unit)
def split_data(self, audiopath, labelpath):
"""
音频数据、标注地址分割
:param audiopath: 音频路径
:param labelpath: 标注路径
:return:
"""
generator = AcousticModel.init_audio(audiopath, labelpath) # 音频和标注的迭代器
file_count = generator.__next__() # 文件总数
chunk = file_count // self.num
for job_id in range(self.num - 1):
path_file = PARAMETERS_FILE_PATH + '/%s/pathInfo_%d.csv' % (
self.kwargs['unit_type'], job_id)
'''文件已存在'''
if os.path.exists(path_file):
continue
with open(path_file, 'w') as f:
count = 0
while count < chunk:
count += 1
path = generator.__next__()
f.write(path[0]) # 写入音频路径
f.write(path[1]) # 写入标注路径
'''写入最后一个路径列表'''
path_file = PARAMETERS_FILE_PATH + '/%s/pathInfo_%d.csv' % (
self.kwargs['unit_type'], (self.num - 1))
with open(path_file, 'w') as f:
for path in generator:
f.write(path[0]) # 写入音频路径
f.write(path[1]) # 写入标注路径
def parallel_data(self, env, mode=1, init=True):
"""
并行处理数据
:param env: 用于标识机器的环境变量名
:param mode: 训练方案
:param init: 是否初始化
:return:
"""
'''获取作业ID'''
try:
job_id = os.environ[env]
except JobIDExistError:
raise JobIDExistError(self.log)
amodel = AcousticModel(self.log,
self.kwargs['unit_type'],
processes=self.kwargs['processes'],
job_id=job_id,
console=self.console,
state_num=self.kwargs['state_num'],
mix_level=self.kwargs['mix_level'],
delta_1=self.kwargs['delta_1'],
delta_2=self.kwargs['delta_2'])
amodel.load_unit()
amodel.process_data(mode=mode,
load_line=self.kwargs['load_line'],
init=init,
proportion=self.kwargs['proportion'],
step=self.kwargs['step'],
differentiation=self.kwargs['differentiation'],
coefficient=self.kwargs['coefficient'])
def parallel_train(self,
env,
mode=1,
init=True,
show_q=False,
show_a=False):
"""
并行训练
:param env: 用于标识机器的环境变量名
:param mode: 训练方案
:param init: 是否初始化
:param show_q: 显示HMM/GMM当前似然度
:param show_a: 显示HMM重估后状态转移矩阵
:return:
"""
'''获取作业ID'''
try:
job_id = os.environ[env]
except JobIDExistError:
raise JobIDExistError(self.log)
amodel = AcousticModel(self.log,
self.kwargs['unit_type'],
processes=self.kwargs['processes'],
job_id=job_id,
console=self.console,
state_num=self.kwargs['state_num'],
mix_level=self.kwargs['mix_level'],
delta_1=self.kwargs['delta_1'],
delta_2=self.kwargs['delta_2'])
amodel.load_unit()
amodel.training(mode=mode,
init=init,
show_q=show_q,
show_a=show_a,
load_line=self.kwargs['load_line'],
c_covariance=self.c_covariance)
def add_mix_level(self):
"""
增加高斯混合度
:return:
"""
if self.kwargs['mix_level'] < self.kwargs['max_mix_level']:
self.kwargs['mix_level'] += 1
def auto(self,
init=True,
t=1,
mode=1,
add_mix=False,
show_a=False,
show_q=False):
"""
声学模型单机自动训练
:param init: 是否初始化
:param t: 训练次数
:param mode: 训练方案(training scheme):
--方案1(mode=1，适合孤立词识别系统或语料库语速平缓的语音识别系统声学模型的训练):
1、初始化靠均分数据(uniformly segmentation)，每个HMM(state)的数据收集起来，最后集中训练GMM&HMM。
(其中HMM是由Embedded Training训练而得，这里的Embedded Training与方案2不同之处在于，它并不使用
前向后向的累计概率来训练GMM，仅用于HMM中状态概率矩阵和初始概率矩阵的训练。)
2、重估(Re-estimation)依靠声学模型，经过维特比对齐(Viterbi Alignment)后，得出最佳序列，将序列
中不同的数据收集到不同的状态文件夹中，等待最后集中训练；重估训练中，GMM会使用SMEM算法调整空间。
3、每一步训练后，高斯混合度将会增加，在下一轮训练时将使用K-Means算法按照新的混合度重新聚类
--方案2(mode=2，适合一般语料库下的连续语音识别系统声学模型的训练):
1、所有子字HMM使用全局均值和协方差(flat-start)
2、对每一个训练语料建立句子级的HMM(Embedded HMM)，进行嵌入式训练(Embedded Training)，得到每个句子
的参数累加值，最后用各模型的累加器(Accumulator)对模型的各参数进行训练。
3、嵌入式训练中，运行Baum-Welch算法，进行迭代。
:param add_mix: 每轮训练后是否增加高斯混合度
:param show_q: 显示HMM/GMM当前似然度
:param show_a: 显示HMM重估后状态转移矩阵
:return:
"""
current_t = 1
env_key = 'env_id'
self.log.note('训练方案：方案%d' % mode, cls='i', show_console=self.console)
self.split_data(AUDIO_FILE_PATH, LABEL_FILE_PATH)
if mode == 1:
self.split_unit()
while current_t <= t:
current_t += 1
self.parallel_data(env_key, init=init)
self.parallel_train(env_key,
mode=mode,
init=init,
show_q=show_q,
show_a=show_a)
if add_mix:
self.add_mix_level()
init = False
elif mode == 2:
while current_t <= t:
current_t += 1
self.parallel_data(env_key, mode=mode, init=init)
self.parallel_train(env_key,
mode=mode,
init=init,
show_q=show_q,
show_a=show_a)
init = False
def end(self):
self.log.close()
class Task(object):
def __init__(self, num=1, console=False, **kwargs):
"""
:param num: 并行任务数
:param console: 是否显示基元训练信息
:param kwargs: 声学模型参数
"""
if 'task_num' in kwargs.keys():
self.num = kwargs['task_num']
else:
self.num = num
'''检测必要的目录是否存在，不存在则创建'''
path = PARAMETERS_FILE_PATH + '/' + kwargs['unit_type']
if not os.path.exists(path):
os.makedirs(path)
'''''' '''''' '''''' '''''' '''''' ''''''
self.console = console
self.kwargs = kwargs
self.log = Log(kwargs['unit_type'])
self.log.generate()
'''检查最大高斯混合度约束'''
if self.kwargs['mix_level'] > self.kwargs['max_mix_level']:
raise MixtureNumberError(self.kwargs['mix_level'],
self.kwargs['max_mix_level'], self.log)
def split_unit(self):
"""
基元分割
:return: 各机器的训练清单
"""
amodel = AcousticModel(self.log,
self.kwargs['unit_type'],
console=self.console,
state_num=self.kwargs['state_num'],
mix_level=self.kwargs['mix_level'],
delta_1=self.kwargs['delta_1'],
delta_2=self.kwargs['delta_2'])
amodel.load_unit()
units = amodel.loaded_units
chunk = len(units) // self.num
traininfo_path = PARAMETERS_FILE_PATH + '/%s' % self.kwargs['unit_type']
for job_id in range(self.num - 1):
trainInfo_file = traininfo_path + '/trainInfo_%d.csv' % job_id
'''删除现存trainInfo文件'''
if os.path.exists(trainInfo_file):
os.remove(trainInfo_file)
units_slice = set(units).difference(
set(units[job_id * chunk:(job_id + 1) * chunk]))
for unit in units_slice:
trainInfo = open(trainInfo_file, 'a+')
trainInfo.writelines('%s\n' % unit)
'''最后一个机器的训练清单'''
units_slice = set(units).difference(set(units[(self.num - 1) *
chunk:]))
trainInfo_file = traininfo_path + '/trainInfo_%d.csv' % (self.num - 1)
'''删除现存trainInfo文件'''
if os.path.exists(trainInfo_file):
os.remove(trainInfo_file)
for unit in units_slice:
trainInfo = open(
traininfo_path + '/trainInfo_%d.csv' % (self.num - 1), 'a+')
trainInfo.writelines('%s\n' % unit)
def split_data(self, audiopath, labelpath):
"""
音频数据、标注地址分割
:param audiopath: 音频路径
:param labelpath: 标注路径
:return:
"""
generator = AcousticModel.init_audio(audiopath, labelpath) # 音频和标注的迭代器
file_count = generator.__next__() # 文件总数
chunk = file_count // self.num
for job_id in range(self.num - 1):
path_file = PARAMETERS_FILE_PATH + '/%s/pathInfo_%d.csv' % (
self.kwargs['unit_type'], job_id)
'''文件已存在'''
if os.path.exists(path_file):
continue
with open(path_file, 'w') as f:
count = 0
while count < chunk:
count += 1
path = generator.__next__()
f.write(path[0]) # 写入音频路径
f.write(path[1]) # 写入标注路径
'''写入最后一个路径列表'''
path_file = PARAMETERS_FILE_PATH + '/%s/pathInfo_%d.csv' % (
self.kwargs['unit_type'], (self.num - 1))
with open(path_file, 'w') as f:
for path in generator:
f.write(path[0]) # 写入音频路径
f.write(path[1]) # 写入标注路径
def parallel_data(self, env, init=True):
"""
并行处理数据
:param env: 用于标识机器的环境变量名
:param init: 是否初始化
:return:
"""
'''获取作业ID'''
try:
job_id = os.environ[env]
except JobIDExistError:
raise JobIDExistError(self.log)
amodel = AcousticModel(self.log,
self.kwargs['unit_type'],
processes=self.kwargs['processes'],
job_id=job_id,
console=self.console,
state_num=self.kwargs['state_num'],
mix_level=self.kwargs['mix_level'],
delta_1=self.kwargs['delta_1'],
delta_2=self.kwargs['delta_2'])
amodel.load_unit()
amodel.split_data(load_line=self.kwargs['load_line'], init=init)
def parallel_train(self,
env,
init=True,
show_q=False,
show_a=False,
c_covariance=1e-3):
"""
并行训练
:param env: 用于标识机器的环境变量名
:param init: 是否初始化
:param show_q: 显示HMM/GMM当前似然度
:param show_a: 显示HMM重估后状态转移矩阵
:param c_covariance: 修正数值，纠正GMM中的Singular Matrix
:return:
"""
'''获取作业ID'''
try:
job_id = os.environ[env]
except JobIDExistError:
raise JobIDExistError(self.log)
'''从配置项中获取协方差修正数值，获取失败则在参数中获取，默认为1e-3'''
try:
c_covariance_ = os.environ['c_covariance']
except KeyError:
c_covariance_ = c_covariance
amodel = AcousticModel(self.log,
self.kwargs['unit_type'],
processes=self.kwargs['processes'],
job_id=job_id,
console=self.console,
state_num=self.kwargs['state_num'],
mix_level=self.kwargs['mix_level'],
delta_1=self.kwargs['delta_1'],
delta_2=self.kwargs['delta_2'])
amodel.load_unit()
amodel.training(init=init,
show_q=show_q,
show_a=show_a,
c_covariance=c_covariance_)
def add_mix_level(self):
"""
增加高斯混合度
:return:
"""
if self.kwargs['mix_level'] < self.kwargs['max_mix_level']:
self.kwargs['mix_level'] += 1
def auto(self, init=True, t=1):
"""
单机自动训练
:param init: 是否初始化
:param t: 训练次数
:return:
"""
current_t = 1
env_key = 'env_id'
self.split_unit()
self.split_data(AUDIO_FILE_PATH, LABEL_FILE_PATH)
while current_t <= t:
current_t += 1
self.parallel_data(env_key, init=init)
self.parallel_train(env_key, init=init, show_q=True, show_a=True)
self.add_mix_level()
init = False
def end(self):
self.log.close()