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data_load.py
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data_load.py
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#-*- coding: utf-8 -*-
#/usr/bin/python2
'''
By kyubyong park. [email protected].
https://www.github.com/kyubyong/kss
'''
from __future__ import print_function
from hyperparams import Hyperparams as hp
import numpy as np
import tensorflow as tf
from utils import *
import codecs
import re
import os
import unicodedata
from itertools import chain
from g2p import runKoG2P
def load_vocab():
char2idx = {char: idx for idx, char in enumerate(hp.vocab)}
idx2char = {idx: char for idx, char in enumerate(hp.vocab)}
return char2idx, idx2char
def load_data(mode="train"):
'''Loads data
Args:
mode: "train" or "synthesize".
'''
# Load vocabulary
char2idx, idx2char = load_vocab()
# load conversion dictionaries
j2hcj, j2sj, j2shcj = load_j2hcj(), load_j2sj(), load_j2shcj()
if mode=="train":
# Parse
fpaths, text_lengths, texts = [], [], []
transcript = os.path.join(hp.data, 'transcript.v.1.1.txt')
lines = codecs.open(transcript, 'r', 'utf-8').readlines()
for line in lines:
fname, _, expanded, text, _ = line.strip().split("|")
fpath = os.path.join(hp.data, fname)
fpaths.append(fpath)
if hp.num_exp==0:
text = expanded + u"␃" # ␃: EOS
text = runKoG2P(text, "rulebook.txt")
else:
text += u"␃" # ␃: EOS
if hp.num_exp==2:
text = [j2hcj[char] for char in text]
elif hp.num_exp==3:
text = [j2sj[char] for char in text]
elif hp.num_exp==4:
text = [j2shcj[char] for char in text]
text = chain.from_iterable(text)
text = [char2idx[char] for char in text]
text_lengths.append(len(text))
texts.append(np.array(text, np.int32).tostring())
return fpaths, text_lengths, texts
else: # synthesize on unseen test text.
# Parse
def _normalize(line):
_, expanded, text = line.strip().split("|")
if hp.num_exp==0:
text = expanded + u"␃" # ␃: EOS
text = runKoG2P(text, "rulebook.txt")
else:
text += u"␃"
if hp.num_exp==2:
text = [j2hcj[char] for char in text]
elif hp.num_exp==3:
text = [j2sj[char] for char in text]
elif hp.num_exp==4:
text = [j2shcj[char] for char in text]
text = chain.from_iterable(text)
text = [char2idx[char] for char in text]
return text
lines = codecs.open(hp.test_data, 'r', 'utf8').read().splitlines()
sents = [_normalize(line) for line in lines[1:]]
texts = np.zeros((len(sents), hp.max_N), np.int32)
for i, sent in enumerate(sents):
texts[i, :len(sent)] = sent
return texts
def get_batch():
"""Loads training data and put them in queues"""
with tf.device('/cpu:0'):
# Load data
fpaths, text_lengths, texts = load_data() # list
maxlen, minlen = max(text_lengths), min(text_lengths)
# Calc total batch count
num_batch = len(fpaths) // hp.B
# Create Queues
fpath, text_length, text = tf.train.slice_input_producer([fpaths, text_lengths, texts], shuffle=True)
# Parse
text = tf.decode_raw(text, tf.int32) # (None,)
def _load_spectrograms(fpath):
fname = os.path.basename(fpath)
mel = "/data/private/kss/dc_tts/mels/{}".format(fname.replace("wav", "npy"))
mag = "/data/private/kss/dc_tts/mags/{}".format(fname.replace("wav", "npy"))
return fname, np.load(mel), np.load(mag)
fname, mel, mag = tf.py_func(_load_spectrograms, [fpath], [tf.string, tf.float32, tf.float32])
# Add shape information
fname.set_shape(())
text.set_shape((None,))
mel.set_shape((None, hp.n_mels))
mag.set_shape((None, hp.n_fft//2+1))
# Batching
_, (texts, mels, mags, fnames) = tf.contrib.training.bucket_by_sequence_length(
input_length=text_length,
tensors=[text, mel, mag, fname],
batch_size=hp.B,
bucket_boundaries=[i for i in range(minlen + 1, maxlen - 1, 20)],
num_threads=8,
capacity=hp.B*4,
dynamic_pad=True)
return texts, mels, mags, fnames, num_batch