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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/dc_tts
Modified by sean leary. [email protected]
https://github.com/SeanPLeary/dc_tts-transfer-learning
'''
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
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 text_normalize(text):
text = ''.join(char for char in unicodedata.normalize('NFD', text)
if unicodedata.category(char) != 'Mn') # Strip accents
text = text.lower()
text = re.sub("[^{}]".format(hp.vocab), " ", text)
text = re.sub("[ ]+", " ", text)
return text
def load_data(mode="train"):
'''Loads data
Args:
mode: "train" or "synthesize".
'''
# Load vocabulary
char2idx, idx2char = load_vocab()
if mode=="train":
if "LJ" in hp.data:
# Parse
fpaths, text_lengths, texts = [], [], []
transcript = os.path.join(hp.data, 'transcript.csv')
lines = codecs.open(transcript, 'r', 'utf-8').readlines()
for line in lines:
fname, _, text = line.strip().split("|")
fpath = os.path.join(hp.data, "wavs", fname + ".wav")
fpaths.append(fpath)
text = text_normalize(text) + "E" # E: EOS
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: # nick or kate
# Parse
fpaths, text_lengths, texts = [], [], []
transcript = os.path.join(hp.data, 'transcript.csv')
lines = codecs.open(transcript, 'r', 'utf-8').readlines()
# for line in lines:
# #fname, _, text, is_inside_quotes, duration = line.strip().split("|")
# #duration = float(duration)
# #if duration > 10. : continue
# fname, text = line.strip().split("|")
# fpath = os.path.join(hp.data, fname)
# fpaths.append(fpath)
# text += "E" # E: EOS
# text = [char2idx[char] for char in text]
# text_lengths.append(len(text))
# texts.append(np.array(text, np.int32).tostring())
for line in lines:
#fname, _, text, is_inside_quotes, duration = line.strip().split("|")
#duration = float(duration)
#if duration > 10. : continue
fname, text = line.strip().split("|")
text = text.lower()
text = text.replace('-', ' ')
numbers = re.search('[0-9]+', text)
test1 = re.search('&',text)
if numbers is None and test1 is None:
fpath = os.path.join(hp.data, fname)
fpaths.append(fpath)
text += "E" # E: EOS
#text = text_normalize(text) + "E" # E: EOS
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
lines = codecs.open(hp.test_data, 'r', 'utf-8').readlines()[1:]
sents = [text_normalize(line.split(" ", 1)[-1]).strip() + "E" for line in lines] # text normalization, E: EOS
texts = np.zeros((len(sents), hp.max_N), np.int32)
for i, sent in enumerate(sents):
texts[i, :len(sent)] = [char2idx[char] for char in 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,)
if hp.prepro:
def _load_spectrograms(fpath):
fname = os.path.basename(fpath)
#mel = "mels/{}".format(fname.replace("wav", "npy"))
#mag = "mags/{}".format(fname.replace("wav", "npy"))
mel = "/mels/{}".format(fname.decode("utf-8").replace("wav", "npy"))
mag = "/mags/{}".format(fname.decode("utf-8").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])
else:
fname, mel, mag = tf.py_func(load_spectrograms, [fpath], [tf.string, tf.float32, tf.float32]) # (None, n_mels)
# 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