def load_lm():
# 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...')
lm = Lm(lm_args)
sess = tf.Session(graph=lm.graph)
with lm.graph.as_default():
saver = tf.train.Saver()
with sess.as_default():
latest = tf.train.latest_checkpoint('logs_lm')
saver.restore(sess, latest)
return sess, lm
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)
# 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...')
lm = Lm(lm_args)
sess = tf.Session(graph=lm.graph)
with lm.graph.as_default():
saver =tf.train.Saver()
with sess.as_default():
latest = tf.train.latest_checkpoint('logs_lm')
saver.restore(sess, latest)
# 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。
def train_lm(epochs):
# 2.语言模型训练-------------------------------------------
from model_language.transformer import Lm, lm_hparams
lm_args = lm_hparams()
lm_args.num_heads = 8
lm_args.num_blocks = 6
lm_args.input_vocab_size = len(train_data.pny_vocab)
lm_args.label_vocab_size = len(train_data.han_vocab)
lm_args.max_length = 100
lm_args.hidden_units = 512
lm_args.dropout_rate = 0.2
lm_args.lr = 0.0003
lm_args.is_training = True
lm = Lm(lm_args)
batch_num = len(train_data.wav_lst) // train_data.batch_size
dev_batch_num = len(dev_data.wav_lst) // dev_data.batch_size
pre_epoch = 0
with lm.graph.as_default():
saver = tf.train.Saver(max_to_keep=5)
with tf.Session(graph=lm.graph) as sess:
add_num = -1
if os.path.exists('logs_lm/checkpoint'):
# 恢复变量
import re
model_file = tf.train.latest_checkpoint('logs_lm')
pre_epoch = int(re.findall('\d+', model_file)[0])
min_loss = float(re.findall('\d+\.+\d*', model_file)[0])
print(
"=====================restore latest save model, epoch=%d===loss=%.3f=================="
% (pre_epoch, min_loss))
saver.restore(sess, model_file)
else:
# 初始化变量
sess.run(tf.global_variables_initializer())
merged = tf.summary.merge_all()
min_loss = 100
writer = tf.summary.FileWriter('logs_lm/tensorboard',
tf.get_default_graph())
for k in range(pre_epoch, epochs):
#训练==============================================
total_loss = 0
batch = train_data.get_lm_batch()
for i in range(batch_num):
input_batch, label_batch = next(batch)
feed = {lm.x: input_batch, lm.y: label_batch}
cost, _ = sess.run([lm.mean_loss, lm.train_op], feed_dict=feed)
total_loss += cost
if (k * batch_num + i) % 10 == 0:
rs = sess.run(merged, feed_dict=feed)
writer.add_summary(rs, k * batch_num + i)
if (i + 1) % 500 == 0:
print(
'epoch:{:04d}, step:{:05d}, train_loss:{:0.3f}'.format(
k + 1, i, total_loss / i))
epoch_loss = total_loss / batch_num
print('epochs', k + 1, ': average loss = ', epoch_loss)
#验证==============================================
dev_batch = dev_data.get_lm_batch()
for i in range(dev_batch_num):
total_loss = 0
input_batch, label_batch = next(dev_batch)
feed = {lm.x: input_batch, lm.y: label_batch}
cost, _ = sess.run([lm.mean_loss, lm.train_op], feed_dict=feed)
total_loss += cost
if (i + 1) % 500 == 0:
print("dev_step:{:05d}, dev_loss:{:0.3f}".format(
i, total_loss / i))
print('dev_loss:{:.3f}'.format(total_loss / dev_batch_num))
if total_loss / dev_batch_num < min_loss:
min_loss = total_loss / dev_batch_num
print("save model")
saver.save(
sess, 'logs_lm/model_epoch_{:04d}_val_loss_{:0.3f}'.format(
k + 1, min_loss))
writer.close()
def train_lm(eStop = False):
global epochs
from model_language.transformer import Lm, lm_hparams
import numpy as np
lm_args = lm_hparams()
lm_args.num_heads = 8
lm_args.num_blocks = 6
lm_args.input_vocab_size = len(utils.pny_vocab)
lm_args.label_vocab_size = len(utils.han_vocab)
lm_args.max_length = 100
lm_args.hidden_units = 512
lm_args.dropout_rate = 0.2
lm_args.lr = 0.0003
lm_args.is_training = True
lm = Lm(lm_args)
batch_num_list = [i for i in range(batch_num)]#为进度条显示每一个epoch中的进度用
loss_list=[]#记录每一步平均损失的列表，实现提前终止训练功能：每次取出后N个数据的平均值和当前的平均损失值作比较
with lm.graph.as_default():
saver =tf.train.Saver()
with tf.Session(graph=lm.graph) as sess:
merged = tf.summary.merge_all()
sess.run(tf.global_variables_initializer())
add_num = 0
if os.path.exists(os.path.join(utils.cur_path,'logs_lm','checkpoint')):
print('加载语言模型中...')
latest = tf.train.latest_checkpoint(os.path.join(utils.cur_path,'logs_lm'))
add_num = int(latest.split('_')[-1])
saver.restore(sess, latest)
#tensorboard --logdir=/media/yangjinming/DATA/GitHub/AboutPython/AboutDL/语音识别/logs_lm/tensorboard --host=127.0.0.1
#writer = tf.summary.FileWriter(os.path.join(utils.cur_path,'logs_lm','tensorboard'), tf.get_default_graph())
for k in range(epochs):
total_loss = 0
batch = train_data.get_lm_batch()
for i in tqdm(batch_num_list,ncols=90):
input_batch, label_batch = next(batch)
feed = {lm.x: input_batch, lm.y: label_batch}
cost,_ = sess.run([lm.mean_loss,lm.train_op], feed_dict=feed)
total_loss += cost
if (k * batch_num + i) % 10 == 0:
rs=sess.run(merged, feed_dict=feed)
#writer.add_summary(rs, k * batch_num + i)
avg_loss = total_loss/batch_num
print('步数', k+1, ': 平均损失值 = ', avg_loss)
loss_list.append(avg_loss)
if eStop and len(loss_list)>1 and avg_loss>np.mean(loss_list[-5:])-0.0015:#平均每个epoch下降不到0.0005则终止
#if input('模型性能已无法提升，是否提前结束训练？ yes/no:')=='yes':
epochs = k+1#为后面保存模型时记录名字用
break
saver.save(sess, os.path.join(utils.cur_path,'logs_lm','model_%d' % (epochs + add_num)))
#writer.close()
# 写入序列化的 PB 文件
constant_graph = tf.compat.v1.graph_util.convert_variables_to_constants(sess, sess.graph_def,output_node_names=['x','y','preds'])
with tf.gfile.GFile(os.path.join(utils.cur_path,'logs_lm','lmModel.pb'), mode='wb') as f:
f.write(constant_graph.SerializeToString())
#tf serving 用保存文件，目前保存了三种模型，按需选择一种即可
model_signature = tf.saved_model.signature_def_utils.build_signature_def(
inputs={"pinyin": tf.saved_model.utils.build_tensor_info(lm.x)},
outputs={"hanzi": tf.saved_model.utils.build_tensor_info(lm.preds)},
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
builder = tf.saved_model.builder.SavedModelBuilder(os.path.join(utils.cur_path,'logs_lm',modelVersion))
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()
