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Format & polish training and webui

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This commit is contained in:
Lengyue
2023-09-12 02:21:48 -04:00
parent c99131fcd6
commit f63acb913f
18 changed files with 91 additions and 115 deletions
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5
attentions.py
View File

@@ -1,4 +1,3 @@
import copy
import math
import torch
from torch import nn
@@ -341,7 +340,7 @@ class MultiHeadAttention(nn.Module):
return ret
def _get_relative_embeddings(self, relative_embeddings, length):
max_relative_position = 2 * self.window_size + 1
2 * self.window_size + 1
# Pad first before slice to avoid using cond ops.
pad_length = max(length - (self.window_size + 1), 0)
slice_start_position = max((self.window_size + 1) - length, 0)
@@ -385,7 +384,7 @@ class MultiHeadAttention(nn.Module):
ret: [b, h, l, 2*l-1]
"""
batch, heads, length, _ = x.size()
# padd along column
# pad along column
x = F.pad(
x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
)

11
commons.py
View File

@@ -1,7 +1,5 @@
import math
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
@@ -16,8 +14,8 @@ def get_padding(kernel_size, dilation=1):
def convert_pad_shape(pad_shape):
l = pad_shape[::-1]
pad_shape = [item for sublist in l for item in sublist]
layer = pad_shape[::-1]
pad_shape = [item for sublist in layer for item in sublist]
return pad_shape
@@ -110,8 +108,8 @@ def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
def convert_pad_shape(pad_shape):
l = pad_shape[::-1]
pad_shape = [item for sublist in l for item in sublist]
layer = pad_shape[::-1]
pad_shape = [item for sublist in layer for item in sublist]
return pad_shape
@@ -132,7 +130,6 @@ def generate_path(duration, mask):
duration: [b, 1, t_x]
mask: [b, 1, t_y, t_x]
"""
device = duration.device
b, _, t_y, t_x = mask.shape
cum_duration = torch.cumsum(duration, -1)

3
configs/config.json
View File

@@ -17,7 +17,8 @@
"init_lr_ratio": 1,
"warmup_epochs": 0,
"c_mel": 45,
"c_kl": 1.0
"c_kl": 1.0,
"skip_optimizer": true
},
"data": {
"training_files": "filelists/train.list",

6
data_utils.py
View File

@@ -1,13 +1,11 @@
import time
import os
import random
import numpy as np
import torch
import torch.utils.data
from tqdm import tqdm
from loguru import logger
import commons
from mel_processing import spectrogram_torch, mel_spectrogram_torch, spec_to_mel_torch
from mel_processing import spectrogram_torch, mel_spectrogram_torch
from utils import load_wav_to_torch, load_filepaths_and_text
from text import cleaned_text_to_sequence, get_bert
@@ -100,7 +98,7 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
if sampling_rate != self.sampling_rate:
raise ValueError(
"{} {} SR doesn't match target {} SR".format(
sampling_rate, self.sampling_rate
filename, sampling_rate, self.sampling_rate
)
)
audio_norm = audio / self.max_wav_value

3
losses.py
View File

@@ -1,7 +1,4 @@
import torch
from torch.nn import functional as F
import commons
def feature_loss(fmap_r, fmap_g):

11
mel_processing.py
View File

@@ -1,16 +1,5 @@
import math
import os
import random
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data
import numpy as np
import librosa
import librosa.util as librosa_util
from librosa.util import normalize, pad_center, tiny
from scipy.signal import get_window
from scipy.io.wavfile import read
from librosa.filters import mel as librosa_mel_fn
MAX_WAV_VALUE = 32768.0

5
modules.py
View File

@@ -1,12 +1,9 @@
import copy
import math
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
from torch.nn import Conv1d
from torch.nn.utils import weight_norm, remove_weight_norm
import commons

8
resample.py
View File

@@ -1,11 +1,9 @@
import os
import argparse
import librosa
import numpy as np
from multiprocessing import Pool, cpu_count
import soundfile
from scipy.io import wavfile
from tqdm import tqdm
@@ -29,9 +27,9 @@ if __name__ == "__main__":
"--out_dir", type=str, default="./dataset", help="path to target dir"
)
args = parser.parse_args()
# processs = 8
processs = cpu_count() - 2 if cpu_count() > 4 else 1
pool = Pool(processes=processs)
# processes = 8
processes = cpu_count() - 2 if cpu_count() > 4 else 1
pool = Pool(processes=processes)
for speaker in os.listdir(args.in_dir):
spk_dir = os.path.join(args.in_dir, speaker)

19
server.py
View File

@@ -40,20 +40,9 @@ def get_text(text, language_str, hps):
else:
bert = torch.zeros(1024, len(phone))
ja_bert = torch.zeros(768, len(phone))
assert bert.shape[-1] == len(phone), (
bert.shape,
len(phone),
sum(word2ph),
p1,
p2,
t1,
t2,
pold,
pold2,
word2ph,
text,
w2pho,
)
assert bert.shape[-1] == len(
phone
), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
phone = torch.LongTensor(phone)
tone = torch.LongTensor(tone)
language = torch.LongTensor(language)
@@ -126,7 +115,7 @@ net_g = SynthesizerTrn(
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
**hps.model
**hps.model,
).to(dev)
_ = net_g.eval()

2
text/chinese.py
View File

@@ -4,7 +4,6 @@ import re
import cn2an
from pypinyin import lazy_pinyin, Style
from text import symbols
from text.symbols import punctuation
from text.tone_sandhi import ToneSandhi
@@ -96,7 +95,6 @@ def _g2p(segments):
tones_list = []
word2ph = []
for seg in segments:
pinyins = []
# Replace all English words in the sentence
seg = re.sub("[a-zA-Z]+", "", seg)
seg_cut = psg.lcut(seg)

1
text/english.py
View File

@@ -2,7 +2,6 @@ import pickle
import os
import re
from g2p_en import G2p
from string import punctuation
from text import symbols

1
text/japanese.py
View File

@@ -1,6 +1,5 @@
# Convert Japanese text to phonemes which is
# compatible with Julius https://github.com/julius-speech/segmentation-kit
import math
import re
import unicodedata

2
text/tone_sandhi.py
View File

@@ -536,7 +536,7 @@ class ToneSandhi:
[item.isnumeric() for item in word if item != "一"]
):
return finals
# "一" between reduplication words shold be yi5, e.g. 看一看
# "一" between reduplication words should be yi5, e.g. 看一看
elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]:
finals[1] = finals[1][:-1] + "5"
# when "一" is ordinal word, it should be yi1

37
train_ms.py
View File

@@ -1,14 +1,10 @@
# flake8: noqa: E402
import os
import json
import argparse
import itertools
import math
import torch
from torch import nn, optim
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import torch.multiprocessing as mp
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.cuda.amp import autocast, GradScaler
@@ -42,7 +38,7 @@ torch.backends.cuda.sdp_kernel("flash")
torch.backends.cuda.enable_flash_sdp(True)
torch.backends.cuda.enable_mem_efficient_sdp(
True
) # Not avaliable if torch version is lower than 2.0
) # Not available if torch version is lower than 2.0
torch.backends.cuda.enable_math_sdp(True)
global_step = 0
@@ -97,23 +93,20 @@ def run():
)
if (
"use_noise_scaled_mas" in hps.model.keys()
and hps.model.use_noise_scaled_mas == True
and hps.model.use_noise_scaled_mas is True
):
print("Using noise scaled MAS for VITS2")
use_noise_scaled_mas = True
mas_noise_scale_initial = 0.01
noise_scale_delta = 2e-6
else:
print("Using normal MAS for VITS1")
use_noise_scaled_mas = False
mas_noise_scale_initial = 0.0
noise_scale_delta = 0.0
if (
"use_duration_discriminator" in hps.model.keys()
and hps.model.use_duration_discriminator == True
and hps.model.use_duration_discriminator is True
):
print("Using duration discriminator for VITS2")
use_duration_discriminator = True
net_dur_disc = DurationDiscriminator(
hps.model.hidden_channels,
hps.model.hidden_channels,
@@ -123,16 +116,14 @@ def run():
).cuda(rank)
if (
"use_spk_conditioned_encoder" in hps.model.keys()
and hps.model.use_spk_conditioned_encoder == True
and hps.model.use_spk_conditioned_encoder is True
):
if hps.data.n_speakers == 0:
raise ValueError(
"n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model"
)
use_spk_conditioned_encoder = True
else:
print("Using normal encoder for VITS1")
use_spk_conditioned_encoder = False
net_g = SynthesizerTrn(
len(symbols),
@@ -176,19 +167,25 @@ def run():
utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"),
net_dur_disc,
optim_dur_disc,
skip_optimizer=True,
skip_optimizer=hps.train.skip_optimizer
if "skip_optimizer" in hps.train
else True,
)
_, optim_g, g_resume_lr, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"),
net_g,
optim_g,
skip_optimizer=True,
skip_optimizer=hps.train.skip_optimizer
if "skip_optimizer" in hps.train
else True,
)
_, optim_d, d_resume_lr, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"),
net_d,
optim_d,
skip_optimizer=True,
skip_optimizer=hps.train.skip_optimizer
if "skip_optimizer" in hps.train
else True,
)
if not optim_g.param_groups[0].get("initial_lr"):
optim_g.param_groups[0]["initial_lr"] = g_resume_lr
@@ -371,9 +368,7 @@ def train_and_evaluate(
optim_dur_disc.zero_grad()
scaler.scale(loss_dur_disc_all).backward()
scaler.unscale_(optim_dur_disc)
grad_norm_dur_disc = commons.clip_grad_value_(
net_dur_disc.parameters(), None
)
commons.clip_grad_value_(net_dur_disc.parameters(), None)
scaler.step(optim_dur_disc)
optim_d.zero_grad()

41
utils.py
View File

@@ -1,6 +1,5 @@
import os
import glob
import sys
import argparse
import logging
import json
@@ -32,11 +31,13 @@ def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False
new_opt_dict["param_groups"] = checkpoint_dict["optimizer"]["param_groups"]
new_opt_dict["param_groups"][0]["params"] = new_opt_dict_params
optimizer.load_state_dict(new_opt_dict)
saved_state_dict = checkpoint_dict["model"]
if hasattr(model, "module"):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
new_state_dict = {}
for k, v in state_dict.items():
try:
@@ -47,15 +48,26 @@ def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False
v.shape,
)
except:
logger.error("%s is not in the checkpoint" % k)
# For upgrading from the old version
if "ja_bert_proj" in k:
v = torch.zeros_like(v)
logger.warn(
f"Seems you are using the old version of the model, the {k} is automatically set to zero for backward compatibility"
)
else:
logger.error(f"{k} is not in the checkpoint")
new_state_dict[k] = v
if hasattr(model, "module"):
model.module.load_state_dict(new_state_dict, strict=False)
else:
model.load_state_dict(new_state_dict, strict=False)
logger.info(
"Loaded checkpoint '{}' (iteration {})".format(checkpoint_path, iteration)
)
return model, optimizer, learning_rate, iteration
@@ -224,20 +236,33 @@ def clean_checkpoints(path_to_models="logs/44k/", n_ckpts_to_keep=2, sort_by_tim
for f in os.listdir(path_to_models)
if os.path.isfile(os.path.join(path_to_models, f))
]
name_key = lambda _f: int(re.compile("._(\d+)\.pth").match(_f).group(1))
time_key = lambda _f: os.path.getmtime(os.path.join(path_to_models, _f))
def name_key(_f):
return int(re.compile("._(\\d+)\\.pth").match(_f).group(1))
def time_key(_f):
return os.path.getmtime(os.path.join(path_to_models, _f))
sort_key = time_key if sort_by_time else name_key
x_sorted = lambda _x: sorted(
def x_sorted(_x):
return sorted(
[f for f in ckpts_files if f.startswith(_x) and not f.endswith("_0.pth")],
key=sort_key,
)
to_del = [
os.path.join(path_to_models, fn)
for fn in (x_sorted("G")[:-n_ckpts_to_keep] + x_sorted("D")[:-n_ckpts_to_keep])
]
del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}")
del_routine = lambda x: [os.remove(x), del_info(x)]
rs = [del_routine(fn) for fn in to_del]
def del_info(fn):
return logger.info(f".. Free up space by deleting ckpt {fn}")
def del_routine(x):
return [os.remove(x), del_info(x)]
[del_routine(fn) for fn in to_del]
def get_hparams_from_dir(model_dir):

27
webui.py
View File

@@ -1,3 +1,5 @@
# flake8: noqa: E402
import sys, os
import logging
@@ -56,20 +58,11 @@ def get_text(text, language_str, hps):
else:
bert = torch.zeros(1024, len(phone))
ja_bert = torch.zeros(768, len(phone))
assert bert.shape[-1] == len(phone), (
bert.shape,
len(phone),
sum(word2ph),
p1,
p2,
t1,
t2,
pold,
pold2,
word2ph,
text,
w2pho,
)
assert bert.shape[-1] == len(
phone
), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
phone = torch.LongTensor(phone)
tone = torch.LongTensor(tone)
language = torch.LongTensor(language)
@@ -138,7 +131,9 @@ if __name__ == "__main__":
default="./configs/config.json",
help="path of your config file",
)
parser.add_argument("--share", default=False, help="make link public")
parser.add_argument(
"--share", default=False, help="make link public", action="store_true"
)
parser.add_argument(
"-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log"
)
@@ -163,7 +158,7 @@ if __name__ == "__main__":
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
**hps.model
**hps.model,
).to(device)
_ = net_g.eval()