mirror of https://github.com/coqui-ai/TTS.git
Refactoring the whole bunch
Eren Golge
parent
133ac8e3fd
commit
6385253d8c
|
|
@ -114,4 +114,5 @@ venv.bak/
|
|||
|
||||
# pytorch models
|
||||
*.pth.tar
|
||||
result/
|
||||
|
||||
|
|
|
|||
Binary file not shown.
|
|
@ -5,7 +5,7 @@ import collections
|
|||
from torch.utils.data import Dataset
|
||||
|
||||
import train_config as c
|
||||
from Tacotron.text import text_to_sequence
|
||||
from Tacotron.utils.text import text_to_sequence
|
||||
from Tacotron.utils.audio import *
|
||||
from Tacotron.utils.data import prepare_data, pad_data, pad_per_step
|
||||
|
||||
|
|
@ -53,13 +53,17 @@ class LJSpeechDataset(Dataset):
|
|||
|
||||
magnitude = np.array([spectrogram(w) for w in wav])
|
||||
mel = np.array([melspectrogram(w) for w in wav])
|
||||
timesteps = mel.shape[-1]
|
||||
timesteps = mel.shape[2]
|
||||
|
||||
# PAD with zeros that can be divided by outputs per step
|
||||
if timesteps % self.outputs_per_step != 0:
|
||||
magnitude = pad_per_step(magnitude, self.outputs_per_step)
|
||||
mel = pad_per_step(mel, self.outputs_per_step)
|
||||
|
||||
# reshape jombo
|
||||
magnitude = magnitude.transpose(0, 2, 1)
|
||||
mel = mel.transpose(0, 2, 1)
|
||||
|
||||
return text, magnitude, mel
|
||||
|
||||
raise TypeError(("batch must contain tensors, numbers, dicts or lists;\
|
||||
|
|
|
|||
138
network.py
138
network.py
|
|
@ -1,138 +0,0 @@
|
|||
import random
|
||||
from module import *
|
||||
from text.symbols import symbols
|
||||
|
||||
|
||||
class Encoder(nn.Module):
|
||||
"""
|
||||
Encoder
|
||||
"""
|
||||
|
||||
def __init__(self, embedding_size, hidden_size):
|
||||
"""
|
||||
|
||||
:param embedding_size: dimension of embedding
|
||||
"""
|
||||
super(Encoder, self).__init__()
|
||||
self.embedding_size = embedding_size
|
||||
self.embed = nn.Embedding(len(symbols), embedding_size)
|
||||
self.prenet = Prenet(embedding_size, hidden_size * 2, hidden_size)
|
||||
self.cbhg = CBHG(hidden_size)
|
||||
|
||||
def forward(self, input_):
|
||||
|
||||
input_ = torch.transpose(self.embed(input_), 1, 2)
|
||||
prenet = self.prenet.forward(input_)
|
||||
memory = self.cbhg.forward(prenet)
|
||||
|
||||
return memory
|
||||
|
||||
|
||||
class MelDecoder(nn.Module):
|
||||
"""
|
||||
Decoder
|
||||
"""
|
||||
|
||||
def __init__(self, num_mels, hidden_size, dec_out_per_step,
|
||||
teacher_forcing_ratio):
|
||||
|
||||
super(MelDecoder, self).__init__()
|
||||
self.prenet = Prenet(num_mels, hidden_size * 2, hidden_size)
|
||||
self.attn_decoder = AttentionDecoder(hidden_size * 2, num_mels,
|
||||
dec_out_per_step)
|
||||
self.dec_out_per_step = dec_out_per_step
|
||||
self.teacher_forcing_ratio = teacher_forcing_ratio
|
||||
|
||||
def forward(self, decoder_input, memory):
|
||||
|
||||
# Initialize hidden state of GRUcells
|
||||
attn_hidden, gru1_hidden, gru2_hidden = self.attn_decoder.inithidden(
|
||||
decoder_input.size()[0])
|
||||
outputs = list()
|
||||
|
||||
# Training phase
|
||||
if self.training:
|
||||
# Prenet
|
||||
dec_input = self.prenet.forward(decoder_input)
|
||||
timesteps = dec_input.size()[2] // self.dec_out_per_step
|
||||
|
||||
# [GO] Frame
|
||||
prev_output = dec_input[:, :, 0]
|
||||
|
||||
for i in range(timesteps):
|
||||
prev_output, attn_hidden, gru1_hidden, gru2_hidden = self.attn_decoder.forward(prev_output, memory,
|
||||
attn_hidden=attn_hidden,
|
||||
gru1_hidden=gru1_hidden,
|
||||
gru2_hidden=gru2_hidden)
|
||||
|
||||
outputs.append(prev_output)
|
||||
|
||||
if random.random() < self.teacher_forcing_ratio:
|
||||
# Get spectrum at rth position
|
||||
prev_output = dec_input[:, :, i * self.dec_out_per_step]
|
||||
else:
|
||||
# Get last output
|
||||
prev_output = prev_output[:, :, -1]
|
||||
|
||||
# Concatenate all mel spectrogram
|
||||
outputs = torch.cat(outputs, 2)
|
||||
|
||||
else:
|
||||
# [GO] Frame
|
||||
prev_output = decoder_input
|
||||
|
||||
for i in range(max_iters):
|
||||
prev_output = self.prenet.forward(prev_output)
|
||||
prev_output = prev_output[:, :, 0]
|
||||
prev_output, attn_hidden, gru1_hidden, gru2_hidden = self.attn_decoder.forward(prev_output, memory,
|
||||
attn_hidden=attn_hidden,
|
||||
gru1_hidden=gru1_hidden,
|
||||
gru2_hidden=gru2_hidden)
|
||||
outputs.append(prev_output)
|
||||
prev_output = prev_output[:, :, -1].unsqueeze(2)
|
||||
|
||||
outputs = torch.cat(outputs, 2)
|
||||
|
||||
return outputs
|
||||
|
||||
|
||||
class PostProcessingNet(nn.Module):
|
||||
"""
|
||||
Post-processing Network
|
||||
"""
|
||||
|
||||
def __init__(self, num_mels, num_freq, hidden_size):
|
||||
super(PostProcessingNet, self).__init__()
|
||||
self.postcbhg = CBHG(hidden_size,
|
||||
K=8,
|
||||
projection_size=num_mels,
|
||||
is_post=True)
|
||||
self.linear = SeqLinear(hidden_size * 2,
|
||||
num_freq)
|
||||
|
||||
def forward(self, input_):
|
||||
out = self.postcbhg.forward(input_)
|
||||
out = self.linear.forward(torch.transpose(out, 1, 2))
|
||||
|
||||
return out
|
||||
|
||||
|
||||
class Tacotron(nn.Module):
|
||||
"""
|
||||
End-to-end Tacotron Network
|
||||
"""
|
||||
|
||||
def __init__(self, embedding_size, hidden_size, num_mels, num_freq,
|
||||
dec_out_per_step, teacher_forcing_ratio):
|
||||
super(Tacotron, self).__init__()
|
||||
self.encoder = Encoder(embedding_size, hidden_size)
|
||||
self.decoder1 = MelDecoder(num_mels, hidden_size, dec_out_per_step,
|
||||
teacher_forcing_ratio)
|
||||
self.decoder2 = PostProcessingNet(num_mels, num_freq, hidden_size)
|
||||
|
||||
def forward(self, characters, mel_input):
|
||||
memory = self.encoder.forward(characters)
|
||||
mel_output = self.decoder1.forward(mel_input, memory)
|
||||
linear_output = self.decoder2.forward(mel_output)
|
||||
|
||||
return mel_output, linear_output
|
||||
|
|
@ -1,78 +0,0 @@
|
|||
#-*- coding: utf-8 -*-
|
||||
|
||||
import re
|
||||
from Tacotron.text import cleaners
|
||||
from Tacotron.text.symbols import symbols
|
||||
|
||||
|
||||
# Mappings from symbol to numeric ID and vice versa:
|
||||
_symbol_to_id = {s: i for i, s in enumerate(symbols)}
|
||||
_id_to_symbol = {i: s for i, s in enumerate(symbols)}
|
||||
|
||||
# Regular expression matching text enclosed in curly braces:
|
||||
_curly_re = re.compile(r'(.*?)\{(.+?)\}(.*)')
|
||||
|
||||
|
||||
def text_to_sequence(text, cleaner_names):
|
||||
'''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
|
||||
|
||||
The text can optionally have ARPAbet sequences enclosed in curly braces embedded
|
||||
in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
|
||||
|
||||
Args:
|
||||
text: string to convert to a sequence
|
||||
cleaner_names: names of the cleaner functions to run the text through
|
||||
|
||||
Returns:
|
||||
List of integers corresponding to the symbols in the text
|
||||
'''
|
||||
sequence = []
|
||||
|
||||
# Check for curly braces and treat their contents as ARPAbet:
|
||||
while len(text):
|
||||
m = _curly_re.match(text)
|
||||
if not m:
|
||||
sequence += _symbols_to_sequence(_clean_text(text, cleaner_names))
|
||||
break
|
||||
sequence += _symbols_to_sequence(
|
||||
_clean_text(m.group(1), cleaner_names))
|
||||
sequence += _arpabet_to_sequence(m.group(2))
|
||||
text = m.group(3)
|
||||
|
||||
# Append EOS token
|
||||
sequence.append(_symbol_to_id['~'])
|
||||
return sequence
|
||||
|
||||
|
||||
def sequence_to_text(sequence):
|
||||
'''Converts a sequence of IDs back to a string'''
|
||||
result = ''
|
||||
for symbol_id in sequence:
|
||||
if symbol_id in _id_to_symbol:
|
||||
s = _id_to_symbol[symbol_id]
|
||||
# Enclose ARPAbet back in curly braces:
|
||||
if len(s) > 1 and s[0] == '@':
|
||||
s = '{%s}' % s[1:]
|
||||
result += s
|
||||
return result.replace('}{', ' ')
|
||||
|
||||
|
||||
def _clean_text(text, cleaner_names):
|
||||
for name in cleaner_names:
|
||||
cleaner = getattr(cleaners, name)
|
||||
if not cleaner:
|
||||
raise Exception('Unknown cleaner: %s' % name)
|
||||
text = cleaner(text)
|
||||
return text
|
||||
|
||||
|
||||
def _symbols_to_sequence(symbols):
|
||||
return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)]
|
||||
|
||||
|
||||
def _arpabet_to_sequence(text):
|
||||
return _symbols_to_sequence(['@' + s for s in text.split()])
|
||||
|
||||
|
||||
def _should_keep_symbol(s):
|
||||
return s in _symbol_to_id and s is not '_' and s is not '~'
|
||||
|
|
@ -1,91 +0,0 @@
|
|||
#-*- coding: utf-8 -*-
|
||||
|
||||
|
||||
'''
|
||||
Cleaners are transformations that run over the input text at both training and eval time.
|
||||
|
||||
Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
|
||||
hyperparameter. Some cleaners are English-specific. You'll typically want to use:
|
||||
1. "english_cleaners" for English text
|
||||
2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
|
||||
the Unidecode library (https://pypi.python.org/pypi/Unidecode)
|
||||
3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
|
||||
the symbols in symbols.py to match your data).
|
||||
'''
|
||||
|
||||
import re
|
||||
from unidecode import unidecode
|
||||
from .numbers import normalize_numbers
|
||||
|
||||
|
||||
# Regular expression matching whitespace:
|
||||
_whitespace_re = re.compile(r'\s+')
|
||||
|
||||
# List of (regular expression, replacement) pairs for abbreviations:
|
||||
_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
|
||||
('mrs', 'misess'),
|
||||
('mr', 'mister'),
|
||||
('dr', 'doctor'),
|
||||
('st', 'saint'),
|
||||
('co', 'company'),
|
||||
('jr', 'junior'),
|
||||
('maj', 'major'),
|
||||
('gen', 'general'),
|
||||
('drs', 'doctors'),
|
||||
('rev', 'reverend'),
|
||||
('lt', 'lieutenant'),
|
||||
('hon', 'honorable'),
|
||||
('sgt', 'sergeant'),
|
||||
('capt', 'captain'),
|
||||
('esq', 'esquire'),
|
||||
('ltd', 'limited'),
|
||||
('col', 'colonel'),
|
||||
('ft', 'fort'),
|
||||
]]
|
||||
|
||||
|
||||
def expand_abbreviations(text):
|
||||
for regex, replacement in _abbreviations:
|
||||
text = re.sub(regex, replacement, text)
|
||||
return text
|
||||
|
||||
|
||||
def expand_numbers(text):
|
||||
return normalize_numbers(text)
|
||||
|
||||
|
||||
def lowercase(text):
|
||||
return text.lower()
|
||||
|
||||
|
||||
def collapse_whitespace(text):
|
||||
return re.sub(_whitespace_re, ' ', text)
|
||||
|
||||
|
||||
def convert_to_ascii(text):
|
||||
return unidecode(text)
|
||||
|
||||
|
||||
def basic_cleaners(text):
|
||||
'''Basic pipeline that lowercases and collapses whitespace without transliteration.'''
|
||||
text = lowercase(text)
|
||||
text = collapse_whitespace(text)
|
||||
return text
|
||||
|
||||
|
||||
def transliteration_cleaners(text):
|
||||
'''Pipeline for non-English text that transliterates to ASCII.'''
|
||||
text = convert_to_ascii(text)
|
||||
text = lowercase(text)
|
||||
text = collapse_whitespace(text)
|
||||
return text
|
||||
|
||||
|
||||
def english_cleaners(text):
|
||||
'''Pipeline for English text, including number and abbreviation expansion.'''
|
||||
text = convert_to_ascii(text)
|
||||
text = lowercase(text)
|
||||
text = expand_numbers(text)
|
||||
text = expand_abbreviations(text)
|
||||
text = collapse_whitespace(text)
|
||||
return text
|
||||
|
|
@ -1,65 +0,0 @@
|
|||
#-*- coding: utf-8 -*-
|
||||
|
||||
|
||||
import re
|
||||
|
||||
|
||||
valid_symbols = [
|
||||
'AA', 'AA0', 'AA1', 'AA2', 'AE', 'AE0', 'AE1', 'AE2', 'AH', 'AH0', 'AH1', 'AH2',
|
||||
'AO', 'AO0', 'AO1', 'AO2', 'AW', 'AW0', 'AW1', 'AW2', 'AY', 'AY0', 'AY1', 'AY2',
|
||||
'B', 'CH', 'D', 'DH', 'EH', 'EH0', 'EH1', 'EH2', 'ER', 'ER0', 'ER1', 'ER2', 'EY',
|
||||
'EY0', 'EY1', 'EY2', 'F', 'G', 'HH', 'IH', 'IH0', 'IH1', 'IH2', 'IY', 'IY0', 'IY1',
|
||||
'IY2', 'JH', 'K', 'L', 'M', 'N', 'NG', 'OW', 'OW0', 'OW1', 'OW2', 'OY', 'OY0',
|
||||
'OY1', 'OY2', 'P', 'R', 'S', 'SH', 'T', 'TH', 'UH', 'UH0', 'UH1', 'UH2', 'UW',
|
||||
'UW0', 'UW1', 'UW2', 'V', 'W', 'Y', 'Z', 'ZH'
|
||||
]
|
||||
|
||||
_valid_symbol_set = set(valid_symbols)
|
||||
|
||||
|
||||
class CMUDict:
|
||||
'''Thin wrapper around CMUDict data. http://www.speech.cs.cmu.edu/cgi-bin/cmudict'''
|
||||
|
||||
def __init__(self, file_or_path, keep_ambiguous=True):
|
||||
if isinstance(file_or_path, str):
|
||||
with open(file_or_path, encoding='latin-1') as f:
|
||||
entries = _parse_cmudict(f)
|
||||
else:
|
||||
entries = _parse_cmudict(file_or_path)
|
||||
if not keep_ambiguous:
|
||||
entries = {word: pron for word,
|
||||
pron in entries.items() if len(pron) == 1}
|
||||
self._entries = entries
|
||||
|
||||
def __len__(self):
|
||||
return len(self._entries)
|
||||
|
||||
def lookup(self, word):
|
||||
'''Returns list of ARPAbet pronunciations of the given word.'''
|
||||
return self._entries.get(word.upper())
|
||||
|
||||
|
||||
_alt_re = re.compile(r'\([0-9]+\)')
|
||||
|
||||
|
||||
def _parse_cmudict(file):
|
||||
cmudict = {}
|
||||
for line in file:
|
||||
if len(line) and (line[0] >= 'A' and line[0] <= 'Z' or line[0] == "'"):
|
||||
parts = line.split(' ')
|
||||
word = re.sub(_alt_re, '', parts[0])
|
||||
pronunciation = _get_pronunciation(parts[1])
|
||||
if pronunciation:
|
||||
if word in cmudict:
|
||||
cmudict[word].append(pronunciation)
|
||||
else:
|
||||
cmudict[word] = [pronunciation]
|
||||
return cmudict
|
||||
|
||||
|
||||
def _get_pronunciation(s):
|
||||
parts = s.strip().split(' ')
|
||||
for part in parts:
|
||||
if part not in _valid_symbol_set:
|
||||
return None
|
||||
return ' '.join(parts)
|
||||
|
|
@ -1,71 +0,0 @@
|
|||
#-*- coding: utf-8 -*-
|
||||
|
||||
import inflect
|
||||
import re
|
||||
|
||||
|
||||
_inflect = inflect.engine()
|
||||
_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
|
||||
_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
|
||||
_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
|
||||
_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
|
||||
_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
|
||||
_number_re = re.compile(r'[0-9]+')
|
||||
|
||||
|
||||
def _remove_commas(m):
|
||||
return m.group(1).replace(',', '')
|
||||
|
||||
|
||||
def _expand_decimal_point(m):
|
||||
return m.group(1).replace('.', ' point ')
|
||||
|
||||
|
||||
def _expand_dollars(m):
|
||||
match = m.group(1)
|
||||
parts = match.split('.')
|
||||
if len(parts) > 2:
|
||||
return match + ' dollars' # Unexpected format
|
||||
dollars = int(parts[0]) if parts[0] else 0
|
||||
cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
|
||||
if dollars and cents:
|
||||
dollar_unit = 'dollar' if dollars == 1 else 'dollars'
|
||||
cent_unit = 'cent' if cents == 1 else 'cents'
|
||||
return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
|
||||
elif dollars:
|
||||
dollar_unit = 'dollar' if dollars == 1 else 'dollars'
|
||||
return '%s %s' % (dollars, dollar_unit)
|
||||
elif cents:
|
||||
cent_unit = 'cent' if cents == 1 else 'cents'
|
||||
return '%s %s' % (cents, cent_unit)
|
||||
else:
|
||||
return 'zero dollars'
|
||||
|
||||
|
||||
def _expand_ordinal(m):
|
||||
return _inflect.number_to_words(m.group(0))
|
||||
|
||||
|
||||
def _expand_number(m):
|
||||
num = int(m.group(0))
|
||||
if num > 1000 and num < 3000:
|
||||
if num == 2000:
|
||||
return 'two thousand'
|
||||
elif num > 2000 and num < 2010:
|
||||
return 'two thousand ' + _inflect.number_to_words(num % 100)
|
||||
elif num % 100 == 0:
|
||||
return _inflect.number_to_words(num // 100) + ' hundred'
|
||||
else:
|
||||
return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
|
||||
else:
|
||||
return _inflect.number_to_words(num, andword='')
|
||||
|
||||
|
||||
def normalize_numbers(text):
|
||||
text = re.sub(_comma_number_re, _remove_commas, text)
|
||||
text = re.sub(_pounds_re, r'\1 pounds', text)
|
||||
text = re.sub(_dollars_re, _expand_dollars, text)
|
||||
text = re.sub(_decimal_number_re, _expand_decimal_point, text)
|
||||
text = re.sub(_ordinal_re, _expand_ordinal, text)
|
||||
text = re.sub(_number_re, _expand_number, text)
|
||||
return text
|
||||
|
|
@ -1,24 +0,0 @@
|
|||
#-*- coding: utf-8 -*-
|
||||
|
||||
|
||||
'''
|
||||
Defines the set of symbols used in text input to the model.
|
||||
|
||||
The default is a set of ASCII characters that works well for English or text that has been run
|
||||
through Unidecode. For other data, you can modify _characters. See TRAINING_DATA.md for details.
|
||||
'''
|
||||
from Tacotron.text import cmudict
|
||||
|
||||
_pad = '_'
|
||||
_eos = '~'
|
||||
_characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!\'(),-.:;? '
|
||||
|
||||
# Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as uppercase letters):
|
||||
_arpabet = ['@' + s for s in cmudict.valid_symbols]
|
||||
|
||||
# Export all symbols:
|
||||
symbols = [_pad, _eos] + list(_characters) + _arpabet
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
print(symbols)
|
||||
48
train.py
48
train.py
|
|
@ -8,14 +8,15 @@ import numpy as np
|
|||
|
||||
import torch.nn as nn
|
||||
from torch import optim
|
||||
from torch.autograd import Variable
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
from network import *
|
||||
import train_config as c
|
||||
from utils.generic_utils import (Progbar, remove_experiment_folder,
|
||||
create_experiment_folder, save_checkpoint)
|
||||
from utils.model import get_param_size
|
||||
from datasets.LJSpeech import LJSpeechDataset
|
||||
from models.tacotron import Tacotron
|
||||
|
||||
use_cuda = torch.cuda.is_available()
|
||||
|
||||
|
|
@ -40,8 +41,7 @@ def main(args):
|
|||
c.hidden_size,
|
||||
c.num_mels,
|
||||
c.num_freq,
|
||||
c.dec_out_per_step,
|
||||
c.teacher_forcing_ratio)
|
||||
c.dec_out_per_step)
|
||||
if use_cuda:
|
||||
model = nn.DataParallel(model.cuda())
|
||||
|
||||
|
|
@ -73,10 +73,13 @@ def main(args):
|
|||
|
||||
dataloader = DataLoader(dataset, batch_size=args.batch_size,
|
||||
shuffle=True, collate_fn=dataset.collate_fn,
|
||||
drop_last=True, num_workers=8)
|
||||
drop_last=True, num_workers=32)
|
||||
progbar = Progbar(len(dataset) / args.batch_size)
|
||||
|
||||
for i, data in enumerate(dataloader):
|
||||
text_input = data[0]
|
||||
magnitude_input = data[1]
|
||||
mel_input = data[2]
|
||||
|
||||
current_step = i + args.restore_step + epoch * len(dataloader) + 1
|
||||
|
||||
|
|
@ -84,34 +87,37 @@ def main(args):
|
|||
|
||||
try:
|
||||
mel_input = np.concatenate((np.zeros(
|
||||
[args.batch_size, c.num_mels, 1], dtype=np.float32), data[2][:, :, 1:]), axis=2)
|
||||
[args.batch_size, 1, c.num_mels], dtype=np.float32),
|
||||
mel_input[:, 1:, :]), axis=1)
|
||||
except:
|
||||
raise TypeError("not same dimension")
|
||||
|
||||
if use_cuda:
|
||||
characters = Variable(torch.from_numpy(data[0]).type(
|
||||
text_input_var = Variable(torch.from_numpy(text_input).type(
|
||||
torch.cuda.LongTensor), requires_grad=False).cuda()
|
||||
mel_input = Variable(torch.from_numpy(mel_input).type(
|
||||
mel_input_var = Variable(torch.from_numpy(mel_input).type(
|
||||
torch.cuda.FloatTensor), requires_grad=False).cuda()
|
||||
mel_spectrogram = Variable(torch.from_numpy(data[2]).type(
|
||||
torch.cuda.FloatTensor), requires_grad=False).cuda()
|
||||
linear_spectrogram = Variable(torch.from_numpy(data[1]).type(
|
||||
mel_spec_var = Variable(torch.from_numpy(mel_input).type(
|
||||
torch.cuda.FloatTensor), requires_grad=False).cuda()
|
||||
linear_spec_var = Variable(torch.from_numpy(magnitude_input)
|
||||
.type(torch.cuda.FloatTensor), requires_grad=False).cuda()
|
||||
|
||||
else:
|
||||
characters = Variable(torch.from_numpy(data[0]).type(
|
||||
text_input_var = Variable(torch.from_numpy(text_input).type(
|
||||
torch.LongTensor), requires_grad=False)
|
||||
mel_input = Variable(torch.from_numpy(mel_input).type(
|
||||
mel_input_var = Variable(torch.from_numpy(mel_input).type(
|
||||
torch.FloatTensor), requires_grad=False)
|
||||
mel_spectrogram = Variable(torch.from_numpy(
|
||||
data[2]).type(torch.FloatTensor), requires_grad=False)
|
||||
linear_spectrogram = Variable(torch.from_numpy(
|
||||
data[1]).type(torch.FloatTensor), requires_grad=False)
|
||||
mel_spec_var = Variable(torch.from_numpy(
|
||||
mel_input).type(torch.FloatTensor), requires_grad=False)
|
||||
linear_spec_var = Variable(torch.from_numpy(
|
||||
magnitude_input).type(torch.FloatTensor),
|
||||
requires_grad=False)
|
||||
|
||||
mel_output, linear_output = model.forward(characters, mel_input)
|
||||
mel_output, linear_output, alignments =\
|
||||
model.forward(text_input_var, mel_input_var)
|
||||
|
||||
mel_loss = criterion(mel_output, mel_spectrogram)
|
||||
linear_loss = torch.abs(linear_output - linear_spectrogram)
|
||||
mel_loss = criterion(mel_output, mel_spec_var)
|
||||
linear_loss = torch.abs(linear_output - linear_spec_var)
|
||||
linear_loss = 0.5 * \
|
||||
torch.mean(linear_loss) + 0.5 * \
|
||||
torch.mean(linear_loss[:, :n_priority_freq, :])
|
||||
|
|
@ -168,9 +174,9 @@ def adjust_learning_rate(optimizer, step):
|
|||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--restore_step', type=int,
|
||||
help='Global step to restore checkpoint', default=0)
|
||||
help='Global step to restore checkpoint', default=128)
|
||||
parser.add_argument('--batch_size', type=int,
|
||||
help='Batch size', default=32)
|
||||
help='Batch size', default=128)
|
||||
parser.add_argument('--config', type=str,
|
||||
help='path to config file for training',)
|
||||
args = parser.parse_args()
|
||||
|
|
|
|||
|
|
@ -10,20 +10,24 @@ ref_level_db = 20
|
|||
hidden_size = 128
|
||||
embedding_size = 256
|
||||
|
||||
# training
|
||||
epochs = 10000
|
||||
lr = 0.001
|
||||
decay_step = [500000, 1000000, 2000000]
|
||||
batch_size = 128
|
||||
max_iters = 200
|
||||
griffin_lim_iters = 60
|
||||
power = 1.5
|
||||
dec_out_per_step = 5
|
||||
teacher_forcing_ratio = 1.0
|
||||
#teacher_forcing_ratio = 1.0
|
||||
|
||||
epochs = 10000
|
||||
lr = 0.001
|
||||
decay_step = [500000, 1000000, 2000000]
|
||||
# outputing
|
||||
log_step = 100
|
||||
save_step = 2000
|
||||
|
||||
# text processing
|
||||
cleaners = 'english_cleaners'
|
||||
|
||||
# data settings
|
||||
data_path = '/data/shared/KeithIto/LJSpeech-1.0/'
|
||||
output_path = './result'
|
||||
checkpoint_path = './model_new'
|
||||
|
|
|
|||
Binary file not shown.
Loading…
Reference in New Issue