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wavernn_preprocess.py
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wavernn_preprocess.py
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import os
os.environ["CUDA_VISIBLE_DEVICES"] = ""
import wave
from datetime import datetime
import numpy as np
import tensorflow as tf
from tacotron.datasets import audio
from tacotron.utils.infolog import log
from librosa import effects
from tacotron.models import create_model
from tacotron.utils import plot
from tacotron.utils.text import text_to_sequence
import os
from tacotron_hparams import hparams
import shutil
from tacotron.pinyin.parse_text_to_pyin import get_pyin
def padding_targets(target, r, padding_value):
lens = target.shape[0]
if lens % r == 0:
return target
else:
target = np.pad(target, [(0, r - lens % r), (0, 0)], mode='constant', constant_values=padding_value)
return target
class Synthesizer:
def load(self, checkpoint_path, hparams, gta=False, model_name='Tacotron'):
log('Constructing model: %s' % model_name)
#Force the batch size to be known in order to use attention masking in batch synthesis
inputs = tf.placeholder(tf.int32, (1, None), name='inputs')
input_lengths = tf.placeholder(tf.int32, (1), name='input_lengths')
targets = tf.placeholder(tf.float32, (None, None, hparams.num_mels), name='mel_targets')
target_lengths = tf.placeholder(tf.int32, (1), name='target_length')
gta = True
#initialize(self, inputs, input_lengths, mel_targets=None, stop_token_targets=None,
# linear_targets=None, targets_lengths=None, gta=False, global_step=None, is_training=False,
# is_evaluating=False)
with tf.variable_scope('Tacotron_model', reuse=tf.AUTO_REUSE) as scope:
self.model = create_model(model_name, hparams)
self.model.initialize(inputs=inputs, input_lengths=input_lengths, mel_targets=targets,
targets_lengths=target_lengths, gta=gta, is_evaluating=True)
self.mel_outputs = self.model.mel_outputs
self.alignments = self.model.alignments
self._hparams = hparams
self.inputs = inputs
self.input_lengths = input_lengths
self.targets = targets
self.target_lengths = target_lengths
log('Loading checkpoint: %s' % checkpoint_path)
#Memory allocation on the GPUs as needed
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
self.session = tf.Session(config=config)
self.session.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(self.session, checkpoint_path)
def synthesize(self, text, mel, out_dir, idx):
hparams = self._hparams
r = hparams.outputs_per_step
T2_output_range = (-hparams.max_abs_value, hparams.max_abs_value) if hparams.symmetric_mels else (0, hparams.max_abs_value)
target = np.load(mel)
target = np.clip(target, T2_output_range[0], T2_output_range[1])
target_length = target.shape[0]
targets = padding_targets(target, r, T2_output_range[0])
new_target_length = targets.shape[0]
pyin, text = get_pyin(text)
print(text)
inputs = [np.asarray(text_to_sequence(pyin.split(' ')))]
print(inputs)
input_lengths = [len(inputs[0])]
feed_dict = {
self.inputs: np.asarray(inputs, dtype=np.int32),
self.input_lengths: np.asarray(input_lengths, dtype=np.int32),
self.targets: np.asarray([targets], dtype=np.float32),
self.target_lengths: np.asarray([new_target_length], dtype=np.int32),
}
mels, alignments = self.session.run([self.mel_outputs, self.alignments], feed_dict=feed_dict)
mel = mels[0]
print('pred_mel.shape', mel.shape)
mel = np.clip(mel, T2_output_range[0], T2_output_range[1])
mel = mel[:target_length, :]
mel = (mel + T2_output_range[1]) / (2 * T2_output_range[1])
mel = np.clip(mel, 0.0, 1.0) # 0~1.0
print(target_length, new_target_length)
pred_mel_path = os.path.join(out_dir, 'mel-{}-pred.npy'.format(idx))
np.save(pred_mel_path, mel, allow_pickle=False)
plot.plot_spectrogram(mel, pred_mel_path.replace('.npy', '.png'), title='')
alignment = alignments[0]
alignment_path = os.path.join(out_dir, 'align-{}.png'.format(idx))
plot.plot_alignment(alignment, alignment_path, title='')
#alignment_path = os.path.join(out_dir, 'align-{}.npy'.format(idx))
#np.save(alignment_path, alignment, allow_pickle=False)
return pred_mel_path, alignment_path
if __name__ == '__main__':
synth = Synthesizer()
cwd = os.getcwd()
ckpt_path = os.path.join(cwd, 'logs-Tacotron-2/taco_pretrained')
print(cwd, ckpt_path)
checkpoint_path = tf.train.get_checkpoint_state(ckpt_path).model_checkpoint_path
synth.load(checkpoint_path, hparams)
print('succeed in loading checkpoint')
out_dir = os.path.join(cwd, 'predicted_mel')
if os.path.exists(out_dir):
shutil.rmtree(out_dir)
os.makedirs(out_dir)
base_path = '/home/spurs/tts/dataset/bznsyp/training_data_v1'
cnt = 10
res = open(os.path.join(cwd, 'wavernn_training_data.txt'), 'w', encoding='utf-8')
with open(os.path.join(base_path, 'train.txt'), 'r', encoding='utf-8') as f:
for line in f:
#audio_filename, mel_filename, time_steps, mel_frames, text, pyin
line = line.strip().split('|')
audio_name = line[0].strip()
wav_path = os.path.join(base_path, audio_name)
wav = np.load(wav_path)
wav = audio.encode_mu_law(wav)
wav_path = os.path.join(out_dir, audio_name)
np.save(wav_path, wav, allow_pickle=False)
mel_path = os.path.join(base_path, line[1].strip())
mel = np.load(mel_path)
mel = (mel + hparams.max_abs_value) / ( 2 * hparams.max_abs_value)
mel = np.clip(mel, 0, 1.0)
mel_path_new = os.path.join(out_dir, line[1].strip())
np.save(mel_path_new, mel, allow_pickle=False)
text = line[-2].strip()
idx = line[1].strip().split('-')[1].split('.')[0].strip()
print('idx=', idx)
#break
pred_mel_path, alignment_path = synth.synthesize(text, mel_path, out_dir, idx)
log = [wav_path, mel_path_new, pred_mel_path, text]
res.write('|'.join(log) + '\n')
res.close()