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同步操作将从 Gitee 极速下载/Tacotron 强制同步,此操作会覆盖自 Fork 仓库以来所做的任何修改,且无法恢复!!!
确定后同步将在后台操作,完成时将刷新页面,请耐心等待。
import io
import numpy as np
import tensorflow as tf
from hparams import hparams
from librosa import effects
from models import create_model
from text import text_to_sequence
from util import audio
class Synthesizer:
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 synthesize(self, text):
cleaner_names = [x.strip() for x in hparams.cleaners.split(',')]
seq = text_to_sequence(text, cleaner_names)
feed_dict = {
self.model.inputs: [np.asarray(seq, dtype=np.int32)],
self.model.input_lengths: np.asarray([len(seq)], dtype=np.int32)
}
wav = self.session.run(self.wav_output, feed_dict=feed_dict)
wav = audio.inv_preemphasis(wav)
wav = wav[:audio.find_endpoint(wav)]
out = io.BytesIO()
audio.save_wav(wav, out)
return out.getvalue()
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