* 训练wavernn声码器:

* 训练hifigan声码器:

* Train the wavernn vocoder:

* Train the hifigan vocoder

self.ui.log("set hifigan as vocoder")

self.ui.log("set wavernn as vocoder")

training_files = files[: -int(len(files)*0.05)]

validation_files = files[-int(len(files)*0.05): ]

import warnings

warnings.simplefilter(action='ignore', category=FutureWarning)

import itertools

import os

import time

import argparse

import json

import torch

import torch.nn.functional as F

from torch.utils.tensorboard import SummaryWriter

from torch.utils.data import DistributedSampler, DataLoader

import torch.multiprocessing as mp

from torch.distributed import init_process_group

from torch.nn.parallel import DistributedDataParallel

from vocoder.hifigan.env import AttrDict, build_env

from vocoder.hifigan.meldataset import MelDataset, mel_spectrogram, get_dataset_filelist

from vocoder.hifigan.models import Generator, MultiPeriodDiscriminator, MultiScaleDiscriminator, feature_loss, generator_loss,\

discriminator_loss

from vocoder.hifigan.utils import plot_spectrogram, scan_checkpoint, load_checkpoint, save_checkpoint

torch.backends.cudnn.benchmark = True

def train(rank, a, h):

a.checkpoint_path = a.models_dir.joinpath(a.run_id+'_hifigan')

a.checkpoint_path.mkdir(exist_ok=True)

a.training_epochs = 3100

a.stdout_interval = 5

a.checkpoint_interval = 25000

a.summary_interval = 5000

a.validation_interval = 1000

a.fine_tuning = True

a.input_wavs_dir = a.syn_dir.joinpath("audio")

a.input_mels_dir = a.syn_dir.joinpath("mels")

if h.num_gpus > 1:

init_process_group(backend=h.dist_config['dist_backend'], init_method=h.dist_config['dist_url'],

world_size=h.dist_config['world_size'] * h.num_gpus, rank=rank)

torch.cuda.manual_seed(h.seed)

device = torch.device('cuda:{:d}'.format(rank))

generator = Generator(h).to(device)

mpd = MultiPeriodDiscriminator().to(device)

msd = MultiScaleDiscriminator().to(device)

if rank == 0:

print(generator)

os.makedirs(a.checkpoint_path, exist_ok=True)

print("checkpoints directory : ", a.checkpoint_path)

if os.path.isdir(a.checkpoint_path):

cp_g = scan_checkpoint(a.checkpoint_path, 'g_')

cp_do = scan_checkpoint(a.checkpoint_path, 'do_')

steps = 0

if cp_g is None or cp_do is None:

state_dict_do = None

last_epoch = -1

else:

state_dict_g = load_checkpoint(cp_g, device)

state_dict_do = load_checkpoint(cp_do, device)

generator.load_state_dict(state_dict_g['generator'])

mpd.load_state_dict(state_dict_do['mpd'])

msd.load_state_dict(state_dict_do['msd'])

steps = state_dict_do['steps'] + 1

last_epoch = state_dict_do['epoch']

if h.num_gpus > 1:

generator = DistributedDataParallel(generator, device_ids=[rank]).to(device)

mpd = DistributedDataParallel(mpd, device_ids=[rank]).to(device)

msd = DistributedDataParallel(msd, device_ids=[rank]).to(device)

optim_g = torch.optim.AdamW(generator.parameters(), h.learning_rate, betas=[h.adam_b1, h.adam_b2])

optim_d = torch.optim.AdamW(itertools.chain(msd.parameters(), mpd.parameters()),

h.learning_rate, betas=[h.adam_b1, h.adam_b2])

if state_dict_do is not None:

optim_g.load_state_dict(state_dict_do['optim_g'])

optim_d.load_state_dict(state_dict_do['optim_d'])

scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=h.lr_decay, last_epoch=last_epoch)

scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=h.lr_decay, last_epoch=last_epoch)

training_filelist, validation_filelist = get_dataset_filelist(a)

# print(training_filelist)

# exit()

trainset = MelDataset(training_filelist, h.segment_size, h.n_fft, h.num_mels,

h.hop_size, h.win_size, h.sampling_rate, h.fmin, h.fmax, n_cache_reuse=0,

shuffle=False if h.num_gpus > 1 else True, fmax_loss=h.fmax_for_loss, device=device,

fine_tuning=a.fine_tuning, base_mels_path=a.input_mels_dir)

train_sampler = DistributedSampler(trainset) if h.num_gpus > 1 else None

train_loader = DataLoader(trainset, num_workers=h.num_workers, shuffle=False,

sampler=train_sampler,

batch_size=h.batch_size,

pin_memory=True,

drop_last=True)

if rank == 0:

validset = MelDataset(validation_filelist, h.segment_size, h.n_fft, h.num_mels,

h.hop_size, h.win_size, h.sampling_rate, h.fmin, h.fmax, False, False, n_cache_reuse=0,

fmax_loss=h.fmax_for_loss, device=device, fine_tuning=a.fine_tuning,

base_mels_path=a.input_mels_dir)

validation_loader = DataLoader(validset, num_workers=1, shuffle=False,

sampler=None,

batch_size=1,

pin_memory=True,

drop_last=True)

sw = SummaryWriter(os.path.join(a.checkpoint_path, 'logs'))

generator.train()

mpd.train()

msd.train()

for epoch in range(max(0, last_epoch), a.training_epochs):

if rank == 0:

start = time.time()

print("Epoch: {}".format(epoch+1))

if h.num_gpus > 1:

train_sampler.set_epoch(epoch)

for i, batch in enumerate(train_loader):

if rank == 0:

start_b = time.time()

x, y, _, y_mel = batch

x = torch.autograd.Variable(x.to(device, non_blocking=True))

y = torch.autograd.Variable(y.to(device, non_blocking=True))

y_mel = torch.autograd.Variable(y_mel.to(device, non_blocking=True))

y = y.unsqueeze(1)

y_g_hat = generator(x)

y_g_hat_mel = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, h.win_size,

h.fmin, h.fmax_for_loss)

optim_d.zero_grad()

# MPD

y_df_hat_r, y_df_hat_g, _, _ = mpd(y, y_g_hat.detach())

loss_disc_f, losses_disc_f_r, losses_disc_f_g = discriminator_loss(y_df_hat_r, y_df_hat_g)

# MSD

y_ds_hat_r, y_ds_hat_g, _, _ = msd(y, y_g_hat.detach())

loss_disc_s, losses_disc_s_r, losses_disc_s_g = discriminator_loss(y_ds_hat_r, y_ds_hat_g)

loss_disc_all = loss_disc_s + loss_disc_f

loss_disc_all.backward()

optim_d.step()

# Generator

optim_g.zero_grad()

# L1 Mel-Spectrogram Loss

loss_mel = F.l1_loss(y_mel, y_g_hat_mel) * 45

y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mpd(y, y_g_hat)

y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = msd(y, y_g_hat)

loss_fm_f = feature_loss(fmap_f_r, fmap_f_g)

loss_fm_s = feature_loss(fmap_s_r, fmap_s_g)

loss_gen_f, losses_gen_f = generator_loss(y_df_hat_g)

loss_gen_s, losses_gen_s = generator_loss(y_ds_hat_g)

loss_gen_all = loss_gen_s + loss_gen_f + loss_fm_s + loss_fm_f + loss_mel

loss_gen_all.backward()

optim_g.step()

if rank == 0:

# STDOUT logging

if steps % a.stdout_interval == 0:

with torch.no_grad():

mel_error = F.l1_loss(y_mel, y_g_hat_mel).item()

print('Steps : {:d}, Gen Loss Total : {:4.3f}, Mel-Spec. Error : {:4.3f}, s/b : {:4.3f}'.

format(steps, loss_gen_all, mel_error, time.time() - start_b))

# checkpointing

if steps % a.checkpoint_interval == 0 and steps != 0:

checkpoint_path = "{}/g_{:08d}.pt".format(a.checkpoint_path, steps)

save_checkpoint(checkpoint_path,

{'generator': (generator.module if h.num_gpus > 1 else generator).state_dict()})

checkpoint_path = "{}/do_{:08d}".format(a.checkpoint_path, steps)

save_checkpoint(checkpoint_path,

{'mpd': (mpd.module if h.num_gpus > 1

else mpd).state_dict(),

'msd': (msd.module if h.num_gpus > 1

else msd).state_dict(),

'optim_g': optim_g.state_dict(), 'optim_d': optim_d.state_dict(), 'steps': steps,

'epoch': epoch})

# Tensorboard summary logging

if steps % a.summary_interval == 0:

sw.add_scalar("training/gen_loss_total", loss_gen_all, steps)

sw.add_scalar("training/mel_spec_error", mel_error, steps)

# Validation

if steps % a.validation_interval == 0: # and steps != 0:

generator.eval()

torch.cuda.empty_cache()

val_err_tot = 0

with torch.no_grad():

for j, batch in enumerate(validation_loader):

x, y, _, y_mel = batch

y_g_hat = generator(x.to(device))

y_mel = torch.autograd.Variable(y_mel.to(device, non_blocking=True))

y_g_hat_mel = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels, h.sampling_rate,

h.hop_size, h.win_size,

h.fmin, h.fmax_for_loss)

if j <= 4:

if steps == 0:

sw.add_audio('gt/y_{}'.format(j), y[0], steps, h.sampling_rate)

sw.add_figure('gt/y_spec_{}'.format(j), plot_spectrogram(x[0]), steps)

sw.add_audio('generated/y_hat_{}'.format(j), y_g_hat[0], steps, h.sampling_rate)

y_hat_spec = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels,

h.sampling_rate, h.hop_size, h.win_size,

h.fmin, h.fmax)

sw.add_figure('generated/y_hat_spec_{}'.format(j),

plot_spectrogram(y_hat_spec.squeeze(0).cpu().numpy()), steps)

val_err = val_err_tot / (j+1)

sw.add_scalar("validation/mel_spec_error", val_err, steps)

generator.train()

steps += 1

scheduler_g.step()

scheduler_d.step()

if rank == 0:

print('Time taken for epoch {} is {} sec\n'.format(epoch + 1, int(time.time() - start)))

from vocoder.wavernn import audio

import vocoder.wavernn.hparams as hp

from vocoder.hifigan.train import train as train_hifigan

from vocoder.hifigan.env import AttrDict

import json

parser.add_argument("vocoder_type", type=str, default="wavernn", help= \

"Choose the vocoder type for train. Defaults to wavernn"

"Now, Support <hifigan> and <wavernn> for choose")

parser.add_argument("--config", type=str, default="vocoder/hifigan/config_16k_.json")

# Process the arguments

if args.vocoder_type == "wavernn":

# Run the training wavernn

train(**vars(args))

elif args.vocoder_type == "hifigan":

with open(args.config) as f:

json_config = json.load(f)

h = AttrDict(json_config)

train_hifigan(0, args, h)