glow-tts
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Published
20 hours ago •
jaywalnut310
jaywalnut310
Multi speaker training error
Hi, I am trying to train a multi-speaker, but when I run "train.py" I get the following error:
INFO:korean_base_2:{'train': {'use_cuda': True, 'log_interval': 20, 'seed': 1234, 'epochs': 10000, 'learning_rate': 1.0, 'betas': [0.9, 0.98], 'eps': 1e-09, 'warmup_steps': 4000, 'scheduler': 'noam', 'batch_size': 64, 'ddi': True, 'fp16_run': True, 'save_epoch': 10}, 'data': {'load_mel_from_disk': True, 'training_files': 'filelists/korean_audio_text_train_filelist_suffle.txt', 'validation_files': 'filelists/korean_audio_text_val_filelist_suffle.txt', 'text_cleaners': ['korean_cleaners'], 'max_wav_value': 32768.0, 'sampling_rate': 22050, 'filter_length': 1024, 'hop_length': 256, 'win_length': 1024, 'n_mel_channels': 80, 'mel_fmin': 0.0, 'mel_fmax': 8000.0, 'add_noise': False, 'add_blank': True}, 'model': {'hidden_channels': 192, 'filter_channels': 768, 'filter_channels_dp': 256, 'kernel_size': 3, 'p_dropout': 0.1, 'n_blocks_dec': 12, 'n_layers_enc': 6, 'n_heads': 2, 'p_dropout_dec': 0.05, 'dilation_rate': 1, 'kernel_size_dec': 5, 'n_block_layers': 4, 'n_sqz': 2, 'prenet': True, 'mean_only': True, 'n_speakers': 12, 'gin_channels': 12, 'hidden_channels_enc': 192, 'hidden_channels_dec': 192, 'window_size': 4}, 'model_dir': './logs/korean_base_2'}
WARNING:korean_base_2:/data/tts/glow-tts is not a git repository, therefore hash value comparison will be ignored.
Traceback (most recent call last):
File "init.py", line 58, in <module>
main()
File "init.py", line 51, in main
_ = generator(x, x_lengths, y, y_lengths, gen=False)
File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/data/tts/glow-tts/models.py", line 280, in forward
x_m, x_logs, logw, x_mask = self.encoder(x, x_lengths, g=g)
File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/data/tts/glow-tts/models.py", line 117, in forward
logw = self.proj_w(x_dp, x_mask)
File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/data/tts/glow-tts/models.py", line 29, in forward
x = self.conv_1(x * x_mask)
File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py", line 259, in forward
self.padding, self.dilation, self.groups)
RuntimeError: Given groups=1, weight of size [256, 204, 3], expected input[64, 192, 1] to have 204 channels, but got 192 channels instead
Single-speaker training works fine, but when I add "gin_channels" and start multi-speaker training, I get an error. computer specifications are GPU: RTX 3090 x2 CUDA: 11.1 Pytorch: 1.7.1+cu110
How can I solve this?
Since you specify a gin_channel to be 12, and 204-192 is exactly 12, so I suspect the input to the proj_w layer in the encoder is not concatenated by speaker embedding somehow. You can debug into the code to see the tensor shapes.
It came to me that you probably need to check if the speaker information is correctly fed into the model. In the code provided by the authors, speaker identity (the variable g in model.forward) is not given. So perhaps your problem is mistakenly using a single-speaker data loader.
Thank you for the reply! I got these errors despite changing from init.py and train.py to TextMelSpeakerLoader, TextMelSpeakerCollate. I also attach the code for your reference.
===init,py===
import argparse
import math
import torch
from torch import nn, optim
from torch.nn import functional as F
from torch.utils.data import DataLoader
from data_utils import TextMelSpeakerLoader, TextMelSpeakerCollate
import models
import commons
import utils
from text.symbols import symbols
class FlowGenerator_DDI(models.FlowGenerator):
"""A helper for Data-dependent Initialization"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
for f in self.decoder.flows:
if getattr(f, "set_ddi", False):
f.set_ddi(True)
def main():
hps = utils.get_hparams()
logger = utils.get_logger(hps.model_dir)
logger.info(hps)
utils.check_git_hash(hps.model_dir)
torch.manual_seed(hps.train.seed)
train_dataset = TextMelSpeakerLoader(hps.data.training_files, hps.data)
collate_fn = TextMelSpeakerCollate(1)
train_loader = DataLoader(train_dataset, num_workers=8, shuffle=True,
batch_size=hps.train.batch_size, pin_memory=True,
drop_last=True, collate_fn=collate_fn)
generator = FlowGenerator_DDI(
len(symbols) + getattr(hps.data, "add_blank", False),
out_channels=hps.data.n_mel_channels,
**hps.model).cuda()
optimizer_g = commons.Adam(generator.parameters(), scheduler=hps.train.scheduler, dim_model=hps.model.hidden_channels, warmup_steps=hps.train.warmup_steps, lr=hps.train.learning_rate, betas=hps.train.betas, eps=hps.train.eps)
generator.train()
for batch_idx, (x, x_lengths, y, y_lengths, sid) in enumerate(train_loader):
x, x_lengths = x.cuda(), x_lengths.cuda()
y, y_lengths = y.cuda(), y_lengths.cuda()
sid = sid.cuda()
_ = generator(x, x_lengths, y, y_lengths, gen=False, g=sid)
break
utils.save_checkpoint(generator, optimizer_g, hps.train.learning_rate, 0, os.path.join(hps.model_dir, "ddi_G.pth"))
if __name__ == "__main__":
main()
===train.py===
import os
import json
import argparse
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 apex.parallel import DistributedDataParallel as DDP
from apex import amp
from data_utils import TextMelSpeakerLoader, TextMelSpeakerCollate
import models
import commons
import utils
global_step = 0
def main():
"""Assume Single Node Multi GPUs Training Only"""
assert torch.cuda.is_available(), "CPU training is not allowed."
n_gpus = torch.cuda.device_count()
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '80000'
hps = utils.get_hparams()
mp.spawn(train_and_eval, nprocs=n_gpus, args=(n_gpus, hps,))
def train_and_eval(rank, n_gpus, hps):
global global_step
if rank == 0:
logger = utils.get_logger(hps.model_dir)
logger.info(hps)
utils.check_git_hash(hps.model_dir)
writer = SummaryWriter(log_dir=hps.model_dir)
writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
dist.init_process_group(backend='nccl', init_method='env://', world_size=n_gpus, rank=rank)
torch.manual_seed(hps.train.seed)
torch.cuda.set_device(rank)
train_dataset = TextMelSpeakerLoader(hps.data.training_files, hps.data)
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset,
num_replicas=n_gpus,
rank=rank,
shuffle=True)
collate_fn = TextMelSpeakerCollate(1)
train_loader = DataLoader(train_dataset, num_workers=8, shuffle=False,
batch_size=hps.train.batch_size, pin_memory=True,
drop_last=True, collate_fn=collate_fn, sampler=train_sampler)
if rank == 0:
val_dataset = TextMelSpeakerLoader(hps.data.validation_files, hps.data)
val_loader = DataLoader(val_dataset, num_workers=8, shuffle=False,
batch_size=hps.train.batch_size, pin_memory=True,
drop_last=True, collate_fn=collate_fn)
generator = models.FlowGenerator(
n_vocab=len(hps.data.symbols) + getattr(hps.data, "add_blank", False),
out_channels=hps.data.n_mel_channels,
**hps.model).cuda(rank)
optimizer_g = commons.Adam(generator.parameters(), scheduler=hps.train.scheduler, dim_model=hps.model.hidden_channels, warmup_steps=hps.train.warmup_steps, lr=hps.train.learning_rate, betas=hps.train.betas, eps=hps.train.eps)
if hps.train.fp16_run:
generator, optimizer_g._optim = amp.initialize(generator, optimizer_g._optim, opt_level="O1")
generator = DDP(generator)
epoch_str = 1
global_step = 0
try:
_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), generator, optimizer_g)
epoch_str += 1
optimizer_g.step_num = (epoch_str - 1) * len(train_loader)
optimizer_g._update_learning_rate()
global_step = (epoch_str - 1) * len(train_loader)
except:
if hps.train.ddi and os.path.isfile(os.path.join(hps.model_dir, "ddi_G.pth")):
_ = utils.load_checkpoint(os.path.join(hps.model_dir, "ddi_G.pth"), generator, optimizer_g)
for epoch in range(epoch_str, hps.train.epochs + 1):
if rank==0:
train(rank, epoch, hps, generator, optimizer_g, train_loader, logger, writer)
evaluate(rank, epoch, hps, generator, optimizer_g, val_loader, logger, writer_eval)
if epoch % hps.train.save_epoch == 0:
utils.save_checkpoint(generator, optimizer_g, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "G_{}.pth".format(epoch)))
else:
train(rank, epoch, hps, generator, optimizer_g, train_loader, None, None)
def train(rank, epoch, hps, generator, optimizer_g, train_loader, logger, writer):
train_loader.sampler.set_epoch(epoch)
global global_step
generator.train()
for batch_idx, (x, x_lengths, y, y_lengths, sid) in enumerate(train_loader):
x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)
y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)
sid = sid.cuda(rank, non_blocking=True)
# Train Generator
optimizer_g.zero_grad()
(z, z_m, z_logs, logdet, z_mask), (x_m, x_logs, x_mask), (attn, logw, logw_) = generator(x, x_lengths, y, y_lengths, gen=False, g=sid)
l_mle = commons.mle_loss(z, z_m, z_logs, logdet, z_mask)
l_length = commons.duration_loss(logw, logw_, x_lengths)
loss_gs = [l_mle, l_length]
loss_g = sum(loss_gs)
if hps.train.fp16_run:
with amp.scale_loss(loss_g, optimizer_g._optim) as scaled_loss:
scaled_loss.backward()
grad_norm = commons.clip_grad_value_(amp.master_params(optimizer_g._optim), 5)
else:
loss_g.backward()
grad_norm = commons.clip_grad_value_(generator.parameters(), 5)
optimizer_g.step()
if rank==0:
if batch_idx % hps.train.log_interval == 0:
(y_gen, *_), *_ = generator.module(x[:1], x_lengths[:1], gen=True, g=sid[:1])
logger.info('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(x), len(train_loader.dataset),
100. * batch_idx / len(train_loader),
loss_g.item()))
logger.info([x.item() for x in loss_gs] + [global_step, optimizer_g.get_lr()])
scalar_dict = {"loss/g/total": loss_g, "learning_rate": optimizer_g.get_lr(), "grad_norm": grad_norm}
scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(loss_gs)})
utils.summarize(
writer=writer,
global_step=global_step,
images={"y_org": utils.plot_spectrogram_to_numpy(y[0].data.cpu().numpy()),
"y_gen": utils.plot_spectrogram_to_numpy(y_gen[0].data.cpu().numpy()),
"attn": utils.plot_alignment_to_numpy(attn[0,0].data.cpu().numpy()),
},
scalars=scalar_dict)
global_step += 1
if rank == 0:
logger.info('====> Epoch: {}'.format(epoch))
def evaluate(rank, epoch, hps, generator, optimizer_g, val_loader, logger, writer_eval):
if rank == 0:
global global_step
generator.eval()
losses_tot = []
with torch.no_grad():
for batch_idx, (x, x_lengths, y, y_lengths, sid) in enumerate(val_loader):
x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)
y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)
sid = sid.cuda(rank, non_blocking=True)
(z, z_m, z_logs, logdet, z_mask), (x_m, x_logs, x_mask), (attn, logw, logw_) = generator(x, x_lengths, y, y_lengths, gen=False, g=sid)
l_mle = commons.mle_loss(z, z_m, z_logs, logdet, z_mask)
l_length = commons.duration_loss(logw, logw_, x_lengths)
loss_gs = [l_mle, l_length]
loss_g = sum(loss_gs)
if batch_idx == 0:
losses_tot = loss_gs
else:
losses_tot = [x + y for (x, y) in zip(losses_tot, loss_gs)]
if batch_idx % hps.train.log_interval == 0:
logger.info('Eval Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(x), len(val_loader.dataset),
100. * batch_idx / len(val_loader),
loss_g.item()))
logger.info([x.item() for x in loss_gs])
losses_tot = [x/len(val_loader) for x in losses_tot]
loss_tot = sum(losses_tot)
scalar_dict = {"loss/g/total": loss_tot}
scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_tot)})
utils.summarize(
writer=writer_eval,
global_step=global_step,
scalars=scalar_dict)
logger.info('====> Epoch: {}'.format(epoch))
if __name__ == "__main__":
main()
I don't see any problems from the code you attached, but I discover that the code in your error log (the error messages that you provided at the first of this post) differs from the init.py with a line _ = generator(x, x_lengths, y, y_lengths, gen=False, g=sid). Just to check, does this bug still exist with the codes you attached?
Yes I am still getting the error. Is there any code I can refer to?
Or maybe it's a CUDA or GPU issue?
I do not think this is related to device or hardware, as CUDA/GPU won't change the data attributes (values, shapes, etc.) themselves. By the error messages, I guess this is closely related to the TextEncoder part. Specifically at here https://github.com/jaywalnut310/glow-tts/blob/13e997689d643410f5d9f1f9a73877ae85e19bc2/models.py#L105, when we are trying to predict the durations based on encoded text hidden vectors, we choose to either concatenate g or not. I think the problem is caused around there as your error message hinted. Maybe a more careful look into the code (together with tensor shapes) is necessary.
Thank you so much for your kind reply. After making some modifications, a new error occurred.
INFO:korean_base_ms:{'train': {'use_cuda': True, 'log_interval': 20, 'seed': 1234, 'epochs': 10000, 'learning_rate': 1.0, 'betas': [0.9, 0.98], 'eps': 1e-09, 'warmup_steps': 4000, 'scheduler': 'noam', 'batch_size': 32, 'ddi': True, 'fp16_run': True, 'save_epoch': 10}, 'data': {'load_mel_from_disk': True, 'training_files': 'filelists/korean_kss_train.txt', 'validation_files': 'filelists/korean_kss_val.txt', 'symbols': 'korean', 'text_cleaners': ['korean_cleaners'], 'max_wav_value': 32768.0, 'sampling_rate': 22050, 'filter_length': 1024, 'hop_length': 256, 'win_length': 1024, 'n_mel_channels': 80, 'mel_fmin': 0.0, 'mel_fmax': 8000.0, 'add_noise': False, 'add_blank': True}, 'model': {'hidden_channels': 192, 'filter_channels': 768, 'filter_channels_dp': 256, 'kernel_size': 3, 'p_dropout': 0.1, 'n_blocks_dec': 12, 'n_layers_enc': 6, 'n_heads': 2, 'p_dropout_dec': 0.05, 'dilation_rate': 1, 'kernel_size_dec': 5, 'n_block_layers': 4, 'n_sqz': 2, 'prenet': True, 'mean_only': True, 'n_speakers': 2, 'gin_channels': 256, 'hidden_channels_enc': 192, 'hidden_channels_dec': 192, 'window_size': 4}, 'model_dir': './logs/korean_base_ms'}
WARNING:korean_base_ms:/data/tts/glow-tts_ver2 is not a git repository, therefore hash value comparison will be ignored.
THCudaCheck FAIL file=/pytorch/aten/src/THC/THCCachingHostAllocator.cpp line=278 error=710 : device-side assert triggered
Traceback (most recent call last):
File "init.py", line 58, in <module>
main()
File "init.py", line 51, in main
_ = generator(x, x_lengths, y, y_lengths, gen=False, g=sid)
File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/data/tts/glow-tts_ver2/models.py", line 280, in forward
x_m, x_logs, logw, x_mask = self.encoder(x, x_lengths, g=g)
File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/data/tts/glow-tts_ver2/models.py", line 102, in forward
x = self.pre(x, x_mask)
File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/data/tts/glow-tts_ver2/modules.py", line 61, in forward
x = self.conv_layers[i](x * x_mask)
File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py", line 259, in forward
self.padding, self.dilation, self.groups)
RuntimeError: cuDNN error: CUDNN_STATUS_ALLOC_FAILED
INFO:korean_base_ms:{'train': {'use_cuda': True, 'log_interval': 20, 'seed': 1234, 'epochs': 10000, 'learning_rate': 1.0, 'betas': [0.9, 0.98], 'eps': 1e-09, 'warmup_steps': 4000, 'scheduler': 'noam', 'batch_size': 32, 'ddi': True, 'fp16_run': True, 'save_epoch': 10}, 'data': {'load_mel_from_disk': True, 'training_files': 'filelists/korean_kss_train.txt', 'validation_files': 'filelists/korean_kss_val.txt', 'symbols': 'korean', 'text_cleaners': ['korean_cleaners'], 'max_wav_value': 32768.0, 'sampling_rate': 22050, 'filter_length': 1024, 'hop_length': 256, 'win_length': 1024, 'n_mel_channels': 80, 'mel_fmin': 0.0, 'mel_fmax': 8000.0, 'add_noise': False, 'add_blank': True}, 'model': {'hidden_channels': 192, 'filter_channels': 768, 'filter_channels_dp': 256, 'kernel_size': 3, 'p_dropout': 0.1, 'n_blocks_dec': 12, 'n_layers_enc': 6, 'n_heads': 2, 'p_dropout_dec': 0.05, 'dilation_rate': 1, 'kernel_size_dec': 5, 'n_block_layers': 4, 'n_sqz': 2, 'prenet': True, 'mean_only': True, 'n_speakers': 2, 'gin_channels': 256, 'hidden_channels_enc': 192, 'hidden_channels_dec': 192, 'window_size': 4}, 'model_dir': './logs/korean_base_ms'}
WARNING:korean_base_ms:/data/tts/glow-tts_ver2 is not a git repository, therefore hash value comparison will be ignored.
Warning: apex was installed without --cpp_ext. Falling back to Python flatten and unflatten.
Selected optimization level O1: Insert automatic casts around Pytorch functions and Tensor methods.
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THCudaCheck FAIL file=/pytorch/aten/src/THC/THCCachingHostAllocator.cpp line=278 error=710 : device-side assert triggered
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THCudaCheck FAIL file=/pytorch/aten/src/THC/THCCachingHostAllocator.cpp line=278 error=710 : device-side assert triggered
terminate called after throwing an instance of 'std::runtime_error'
what(): NCCL error in: /pytorch/torch/lib/c10d/../c10d/NCCLUtils.hpp:136, unhandled cuda error, NCCL version 2.7.8
terminate called after throwing an instance of 'std::runtime_error'
what(): NCCL error in: /pytorch/torch/lib/c10d/../c10d/NCCLUtils.hpp:136, unhandled cuda error, NCCL version 2.7.8
Traceback (most recent call last):
File "train.py", line 193, in <module>
main()
File "train.py", line 33, in main
mp.spawn(train_and_eval, nprocs=n_gpus, args=(n_gpus, hps,))
File "/opt/conda/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 199, in spawn
return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
File "/opt/conda/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 157, in start_processes
while not context.join():
File "/opt/conda/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 118, in join
raise Exception(msg)
Exception:
And when you see tensor shapes while debugging, you mean visualization?
Usually this kind of errors are caused from nn.Embedding. I mean, the index of embedding layer exceeds what this layer can support. Check the following may help:
- What is the number of speakers in your dataset? Is it larger than what you specify in the configurations?
- If not, then does your speaker index begin from 0?
As for the tensor shapes, I mean the shape (dimensions) of a Pytorch tensor object. In other words, the .shape attribute of a tensor.
I understand now about tensor shapes.
The number of speakers is 12, and starting with number 1. Below is the speaker data I have configured.
DUMMY_KSS/1/1_0000.wav|1|그는 괜찮은 척하려고 애쓰는 것 같았다.
DUMMY_KSS/1/1_0001.wav|1|그녀의 사랑을 얻기 위해 애썼지만 헛수고였다.
DUMMY_KSS/1/1_0002.wav|1|용돈을 아껴 써라.
DUMMY_KSS/1/1_0003.wav|1|그는 아내를 많이 아낀다.
DUMMY_KSS/1/1_0004.wav|1|그 애 전화번호 알아?
DUMMY_KSS/1/1_0005.wav|1|차에 대해 잘 아세요?
DUMMY_KSS/1/1_0006.wav|1|거기 도착하면 나한테 알려 줘.
DUMMY_KSS/1/1_0007.wav|1|그들은 내가 시험에 떨어졌다고 알려 왔다.
DUMMY_KSS/1/1_0008.wav|1|나는 살아오면서 감기를 앓은 적이 한 번도 없다.
DUMMY_KSS/1/1_0009.wav|1|사흘 동안 심하게 몸살을 앓았어요.
DUMMY_KSS/1/1_0010.wav|1|요즘 공부가 안돼요.
DUMMY_KSS/1/1_0011.wav|1|장사가 잘 안돼서 우울해.
DUMMY_KSS/1/1_0012.wav|1|아무 것도 먹지 않으면 죽게 되어 있다.
DUMMY_KSS/1/1_0013.wav|1|그녀는 이유를 묻지 않고 돈을 빌려 주었다.
DUMMY_KSS/1/1_0014.wav|1|내가 아기를 안고 있을게.
DUMMY_KSS/1/1_0015.wav|1|엄마는 말없이 나를 꼭 안아 주셨다.
Yes, then you should begin the index with 0, because an Embedding layer of size n supports index from 0 to n-1.
Thank you so much. Learning goes on! I never even thought that the speaker ID would start with 0.