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
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()
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')
# 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()
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()
