def load(self, checkpoint_path, model_name='tacotron'):
print('Constructing model: %s' % model_name)
inputs = tf.placeholder(tf.int32, [1, None], 'inputs')
input_lengths = tf.placeholder(tf.int32, [1], 'input_lengths')
identity = tf.placeholder(tf.int32, [1], 'identity')
with tf.variable_scope('model') as scope:
hparams.chinese_symbol = True
hparams.max_iters = 400
self.model = create_model(model_name, hparams)
reader2 = pywrap_tensorflow.NewCheckpointReader(checkpoint_path)
var_to_shape_map = reader2.get_variable_to_shape_map()
id_num = var_to_shape_map['model/inference/embedding_id'][0]
self.model.initialize(inputs,
input_lengths,
identities=identity,
id_num=id_num)
self.wav_output = audio.inv_spectrogram_tensorflow(
self.model.linear_outputs[0])
self.alignment = self.model.alignments[0]
print('Loading checkpoint: %s' % checkpoint_path)
self.session = tf.Session()
self.session.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(self.session, checkpoint_path)
def load(self, checkpoint_path, reference_mel=None, model_name='tacotron'):
print('Constructing model: %s' % model_name)
inputs = tf.placeholder(tf.int32, [1, None], 'inputs')
input_lengths = tf.placeholder(tf.int32, [1], 'input_lengths')
if reference_mel is not None:
reference_mel = tf.placeholder(tf.float32,
[1, None, hparams.num_mels],
'reference_mel')
# Only used in teacher-forcing generating mode
if self.teacher_forcing_generating:
mel_targets = tf.placeholder(tf.float32,
[1, None, hparams.num_mels],
'mel_targets')
else:
mel_targets = None
with tf.variable_scope('model') as scope:
self.model = create_model(model_name, hparams)
self.model.initialize(inputs,
input_lengths,
mel_targets=mel_targets,
reference_mel=reference_mel)
self.wav_output = audio.inv_spectrogram_tensorflow(
self.model.linear_outputs[0])
self.alignments = self.model.alignments[0]
print('Loading checkpoint: %s' % checkpoint_path)
self.session = tf.Session()
self.session.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(self.session, checkpoint_path)
def load(self, checkpoint_path, model_name='tacotron'):
print('Constructing model: %s' % model_name)
inputs = tf.placeholder(tf.int32, [1, None], 'inputs')
input_lengths = tf.placeholder(tf.int32, [1], 'input_lengths')
mel_spec = tf.placeholder(tf.float32,
[None, None, self.hparams.num_mels],
'mel_targets')
with tf.variable_scope('model') as scope:
if self.hparams.enable_fv1 or self.hparams.enable_fv2:
self.net = ResCNN(data=mel_spec, hyparam=self.hparams)
self.net.inference()
voice_print_feature = tf.reduce_mean(self.net.features, 0)
else:
voice_print_feature = None
self.model = create_model(model_name, self.hparams)
self.model.initialize(inputs=inputs,
input_lengths=input_lengths,
voice_print_feature=voice_print_feature)
self.wav_output = audio.inv_spectrogram_tensorflow(
self.model.linear_outputs[0])
print('Loading checkpoint: %s' % checkpoint_path)
self.session = tf.Session()
self.session.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(self.session, checkpoint_path)
def update(self):
with tf.variable_scope('model') as scope:
self.model.update(hparams)
self.wav_output = audio.inv_spectrogram_tensorflow(
self.model.linear_outputs[0])
print('Loading checkpoint: %s' % self.checkpoint_path)
self.session = tf.Session()
self.session.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(self.session, self.checkpoint_path)
def load(self, checkpoint_path, model_name='tacotron'):
inputs = tf.placeholder(tf.int32, [1, None], 'inputs')
input_lengths = tf.placeholder(tf.int32, [1], 'input_lengths')
with tf.variable_scope('model') as scope:
self.model = Tacotron(hparams)
self.model.initialize(inputs, input_lengths)
self.wav_output = audio.inv_spectrogram_tensorflow(self.model.linear_outputs[0])
# 读取已有模型
self.session = tf.Session()
self.session.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(self.session, checkpoint_path)
def load(self, checkpoint_path, model_name='tacotron'):
print('Constructing model: %s' % model_name)
inputs = tf.placeholder(tf.int32, [1, None], 'inputs')
input_lengths = tf.placeholder(tf.int32, [1], 'input_lengths')
with tf.variable_scope('model') as scope:
self.model = create_model(model_name, hparams)
self.model.initialize(inputs, input_lengths)
self.wav_output = audio.inv_spectrogram_tensorflow(self.model.linear_outputs[0])
print('Loading checkpoint: %s' % checkpoint_path)
self.session = tf.Session()
self.session.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(self.session, checkpoint_path)
def load(self, checkpoint_path, model_name='tacotron'):
print('Constructing model: %s' % model_name)
inputs = tf.placeholder(tf.int32, [1, None], 'inputs')
input_lengths = tf.placeholder(tf.int32, [1], 'input_lengths')
with tf.variable_scope('model') as scope:
self.model = create_model(model_name, hparams)
self.model.initialize(inputs, input_lengths)
self.wav_output = audio.inv_spectrogram_tensorflow(self.model.linear_outputs[0])
print('Loading checkpoint: %s' % checkpoint_path)
self.session = tf.Session()
self.session.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(self.session, checkpoint_path)
def load(self, checkpoint_path, vgg19_path, model_name='tacotron'):
print('Constructing model: %s' % model_name)
inputs = tf.placeholder(tf.float32,
[1, hparams.image_dim, hparams.image_dim, 3],
'inputs')
with tf.variable_scope('model') as _:
self.model = create_model(model_name, hparams)
self.model.initialize(inputs, vgg19_path)
self.wav_output = audio.inv_spectrogram_tensorflow(
self.model.linear_outputs[0])
print('Loading checkpoint: %s' % checkpoint_path)
self.session = tf.Session()
self.session.run(tf.global_variables_initializer())
checkpoint_saver = tf.train.import_meta_graph(
'%s.%s' % (checkpoint_path, 'meta'))
checkpoint_saver.restore(self.session, checkpoint_path)
def load(self, checkpoint_path, model_name='tacotron'):
print('Constructing model: %s' % model_name)
inputs = tf.placeholder(tf.int32, [1, None], 'inputs')
input_lengths = tf.placeholder(tf.int32, [1], 'input_lengths')
with tf.variable_scope('model') as scope:
self.model = create_model(model_name, hparams)
self.model.initialize(inputs, input_lengths)
self.wav_output = audio.inv_spectrogram_tensorflow(
self.model.linear_outputs[0])
print('Loading checkpoint: %s' % checkpoint_path)
config = tf.ConfigProto()
# making core utilization optimal, low value more optimaztion
config.gpu_options.per_process_gpu_memory_fraction = 0.2
self.session = tf.Session(config=config)
self.session.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(self.session, checkpoint_path)
import tensorflow as tf
from models import create_model
from util import audio
from hparams import hparams
checkpoint_path = "/tacotron-20180906/model.ckpt"
model_name = 'tacotron'
inputs = tf.placeholder(tf.int32, [1, None], 'inputs')
input_lengths = tf.placeholder(tf.int32, [1], 'input_lengths')
with tf.variable_scope('model') as scope:
model = create_model(model_name, hparams)
model.initialize(inputs, input_lengths)
wav_output = audio.inv_spectrogram_tensorflow(model.linear_outputs[0])
print('Loading checkpoint: %s' % checkpoint_path)
session = tf.Session()
session.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(session, checkpoint_path)
with open("eval.pb", 'w') as f:
g = tf.get_default_graph()
f.write(str(g.as_graph_def()))
