def load_am():
# 1.声学模型-----------------------------------
from model_speech.cnn_ctc import Am, am_hparams
am_args = am_hparams()
am_args.vocab_size = len(train_data.am_vocab)
am = Am(am_args)
print('loading acoustic model...')
am.ctc_model.load_weights('logs_am/model.h5')
def train_am(x=None,y=None,fit_epoch=10):
from model_speech.cnn_ctc import Am, am_hparams
am_args = am_hparams()
am_args.vocab_size = len(utils.pny_vocab)
am_args.gpu_nums = 1
am_args.lr = 0.0008
am_args.is_training = True
am = Am(am_args)
if os.path.exists(os.path.join(utils.cur_path,'logs_am','model.h5')):
print('加载声学模型...')
am.ctc_model.load_weights(os.path.join(utils.cur_path,'logs_am','model.h5'))
checkpoint = ModelCheckpoint(os.path.join(utils.cur_path,'checkpoint', "model_{epoch:02d}-{val_loss:.2f}.h5"),
monitor='val_loss',save_best_only=True)
eStop = EarlyStopping()#损失函数不再减小后patience轮停止训练
#tensorboard --logdir=/media/yangjinming/DATA/GitHub/AboutPython/AboutDL/语音识别/logs_am/tbLog/ --host=127.0.0.1
#tensbrd = TensorBoard(log_dir=os.path.join(utils.cur_path,'logs_am','tbLog'))
if x is not None:#利用实时声音训练调整模型，使定制化
size=1
if type(x) == np.ndarray:
x,y = utils.real_time2data([x],[y])
else:
size = len(x)
x,y = utils.real_time2data(x,y)
am.ctc_model.fit(x=x,y=y,batch_size=size,epochs=fit_epoch)
else:#利用训练数据
batch = train_data.get_am_batch()#获取的是生成器
dev_batch = dev_data.get_am_batch()
validate_step = 200#取N个验证的平均结果
history = am.ctc_model.fit_generator(batch, steps_per_epoch=batch_num, epochs=epochs, callbacks=[eStop,checkpoint],
workers=1, use_multiprocessing=False,verbose=1,validation_data=dev_batch, validation_steps=validate_step)
am.ctc_model.save_weights(os.path.join(utils.cur_path,'logs_am','model.h5'))
#写入序列化的 PB 文件
#with keras.backend.get_session() as sess:
sess = keras.backend.get_session()
constant_graph = tf.compat.v1.graph_util.convert_variables_to_constants(sess,
sess.graph_def,output_node_names=['the_inputs','dense_2/truediv'])
with tf.gfile.GFile(os.path.join(utils.cur_path,'logs_am','amModel.pb'), mode='wb') as f:
f.write(constant_graph.SerializeToString())
#保存TF serving用文件
builder = tf.saved_model.builder.SavedModelBuilder(os.path.join(utils.cur_path,'logs_am',modelVersion))
model_signature = tf.saved_model.signature_def_utils.predict_signature_def(
inputs={'input': am.inputs}, outputs={'output': am.outputs})
builder.add_meta_graph_and_variables(sess,[tf.saved_model.tag_constants.SERVING],
{tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: model_signature})
builder.save()
if x is None:
sess.close()
def _model_init_keras(self, model_dir, config):
# 1.声学模型训练-----------------------------------
from model_speech.cnn_ctc import Am, am_hparams
am_args = am_hparams()
am_args.vocab_size = len(self.label_vocabulary)
am_args.gpu_nums = 0
am_args.lr = 0.0008
am_args.is_training = True
am = Am(am_args)
base_model = am.ctc_model
self.ctc_model = tf.keras.estimator.model_to_estimator(
base_model, model_dir=model_dir, config=config)
def __init__(self,test_flag = True):
# 0.准备解码所需字典，参数需和训练一致，也可以将字典保存到本地，直接进行读取
self.test_flag = test_flag
#print('加载声学模型中...')
if K_usePB:
self.AM_sess = tf.Session()
with tf.gfile.GFile(os.path.join(cur_path,'logs_am','amModel.pb'), 'rb') as f:#加载模型
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
self.AM_sess.graph.as_default()
tf.import_graph_def(graph_def, name='') #导入计算图
self.AM_sess.run(tf.global_variables_initializer())#需要有一个初始化的过程
self.AM_x = self.AM_sess.graph.get_tensor_by_name('the_inputs:0') #此处的x一定要和之前保存时输入的名称一致！
self.AM_preds = self.AM_sess.graph.get_tensor_by_name('dense_2/truediv:0')
else:
from model_speech.cnn_ctc import Am, am_hparams
am_args = am_hparams()
am_args.vocab_size = len(pny_vocab)#这里有个坑，需要和训练时的长度一致，需要强烈关注！
self.am = Am(am_args)
self.am.ctc_model.load_weights(os.path.join(cur_path,'logs_am','model.h5'))
#print('加载语言模型中...')
if tf_usePB:
self.sess = tf.Session()
with tf.gfile.GFile(os.path.join(cur_path,'logs_lm','lmModel.pb'), 'rb') as f:#加载模型
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
self.sess.graph.as_default()
tf.import_graph_def(graph_def, name='') # 导入计算图
self.sess.run(tf.global_variables_initializer())# 需要有一个初始化的过程
self.x = self.sess.graph.get_tensor_by_name('x:0') #此处的x一定要和之前保存时输入的名称一致！
self.preds = self.sess.graph.get_tensor_by_name('preds:0')
else:#ckpt
from model_language.transformer import Lm, lm_hparams
lm_args = lm_hparams()
lm_args.input_vocab_size = len(pny_vocab)
lm_args.label_vocab_size = len(han_vocab)
lm_args.dropout_rate = 0.
self.lm = Lm(lm_args)
self.sess = tf.Session(graph=self.lm.graph)
with self.lm.graph.as_default():
saver =tf.train.Saver()
with self.sess.as_default():
lmPath = tf.train.latest_checkpoint(os.path.join(cur_path,'logs_lm'))
saver.restore(self.sess, lmPath)
def train_am(epochs):
# 1.声学模型训练-----------------------------------
from model_speech.cnn_ctc import Am, am_hparams
am_args = am_hparams()
am_args.vocab_size = len(train_data.am_vocab)
am_args.gpu_nums = 1
am_args.lr = 0.0008
am_args.is_training = True
am = Am(am_args)
if os.path.exists('logs_am') and not os.listdir('logs_am'):
model = os.listdir('logs_am')[0]
am.ctc_model.load_weights(os.path.join('logs_am', model))
batch_num = len(train_data.wav_lst) // train_data.batch_size
# checkpoint
ckpt = "model_{val_loss:.3f}_{epoch:04d}.h5"
checkpoint = ModelCheckpoint(os.path.join('logs_am', ckpt),
monitor='val_loss',
save_weights_only=True,
verbose=1,
save_best_only=True)
batch = train_data.get_am_batch()
dev_batch = dev_data.get_am_batch()
am.ctc_model.fit_generator(batch,
steps_per_epoch=batch_num,
epochs=10,
callbacks=[checkpoint],
workers=1,
use_multiprocessing=False,
validation_data=dev_batch,
validation_steps=2)
am.ctc_model.save_weights('logs_am/model.h5')
import difflib
import tensorflow as tf
import numpy as np
from utils import decode_ctc, GetEditDistance
# 0.准备解码所需字典，参数需和训练一致，也可以将字典保存到本地，直接进行读取
from utils import get_data, data_hparams
data_args = data_hparams()
train_data = get_data(data_args)
# 1.声学模型-----------------------------------
from model_speech.cnn_ctc import Am, am_hparams
am_args = am_hparams() # 參數初始化 EX: learning rate
# am_args.vocab_size = 230
am_args.vocab_size = len(train_data.am_vocab) # 設定單字長度
am = Am(am_args) # 利用設定好的參數，建造出一個model
print('loading acoustic model...')
am.ctc_model.load_weights('logs_am/model.h5')
am.ctc_model.summary()
# 2.语言模型-------------------------------------------
from model_language.transformer import Lm, lm_hparams
lm_args = lm_hparams()
lm_args.input_vocab_size = len(train_data.pny_vocab)
lm_args.label_vocab_size = len(train_data.han_vocab)
lm_args.dropout_rate = 0.
print('loading language model...')
import numpy as np
from utils import decode_ctc, GetEditDistance, cal_ctc_acc
# 0.准备解码所需字典，参数需和训练一致，也可以将字典保存到本地，直接进行读取
from utils import get_data, data_hparams
data_args = data_hparams()
data_args.data_length = 20000
train_data = get_data(data_args)
print("xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx")
print("=====train_data_amvocab", len(train_data.pny_vocab))
# 1.声学模型-----------------------------------
from model_speech.cnn_ctc import Am, am_hparams
# from model_speech.gru_ctc import Am, am_hparams
am_args = am_hparams()
am_args.vocab_size = len(train_data.am_vocab)
am = Am(am_args)
print('loading acoustic model...')
am.ctc_model.load_weights('logs_am/model.h5')
# am.ctc_model.load_weights('checkpoint/model_01-0.00.hdf5')
# 2.语言模型-------------------------------------------
# 3. 准备测试所需数据， 不必和训练数据一致，通过设置data_args.data_type测试，
# 此处应设为'test'，我用了'train'因为演示模型较小，如果使用'test'看不出效果，
# 且会出现未出现的词。
data_args = data_hparams()
data_args.data_type = 'test'
data_args.zanghua = True
data_args.data_length = 20000
test_data = get_data(data_args)
def build_model(self):
am_args = am_hparams()
am_args.vocab_size = 230 #len(train_data.am_vocab)
am = Am(am_args)
return am.ctc_model
