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# import logging
# import os
import pathlib
import random
# import sys
# from typing import Dict
#
# import lightning.pytorch as pl
# import matplotlib
import numpy as np
import torch.utils.data
# from lightning.pytorch.utilities.rank_zero import rank_zero_debug, rank_zero_info, rank_zero_only
from matplotlib import pyplot as plt
from torch import nn
from torch.utils.data import Dataset
# from torchmetrics import Metric, MeanMetric
import utils
from models.nsf_univnet.nsfunivnet import nsfUnivNet
from modules.loss.nsf_univloss import nsf_univloss
# from models.ddsp_univnet.ddspunivnet import ddspUnivNet
# from models.univnet.univnet import UnivNet
# from models.lvc_ddspgan.lvc_ddspgan import DDSPgan
# from models.nsf_HiFigan.models import Generator, AttrDict, MultiScaleDiscriminator, MultiPeriodDiscriminator
# from modules.loss.ddsp_univloss import ddsp_univloss
from modules.univ_D.discriminator import MultiPeriodDiscriminator, MultiResSpecDiscriminator
from training.base_task_gan import GanBaseTask
from utils.wav2mel import PitchAdjustableMelSpectrogram
def spec_to_figure(spec, vmin=None, vmax=None):
if isinstance(spec, torch.Tensor):
spec = spec.cpu().numpy()
fig = plt.figure(figsize=(12, 9),dpi=100)
plt.pcolor(spec.T, vmin=vmin, vmax=vmax)
plt.tight_layout()
return fig
class ddsp_univ_dataset(Dataset):
def __init__(self, config: dict, data_dir, infer=False):
super().__init__()
self.config = config
self.data_dir = data_dir if isinstance(data_dir, pathlib.Path) else pathlib.Path(data_dir)
with open(self.data_dir, 'r', encoding='utf8') as f:
fills = f.read().strip().split('\n')
self.data_index = fills
self.infer = infer
self.volume_aug = self.config['volume_aug']
self.volume_aug_prob = self.config['volume_aug_prob'] if not infer else 0
def __getitem__(self, index):
data_path = self.data_index[index]
data = np.load(data_path)
return {'f0':data['f0'],'spectrogram':data['mel'],'audio':data['audio'],'uv':data['uv']}
def __len__(self):
return len(self.data_index)
def collater(self, minibatch):
samples_per_frame = self.config['hop_size']
if self.infer:
crop_mel_frames = 0
else:
crop_mel_frames = self.config['crop_mel_frames']
for record in minibatch:
# Filter out records that aren't long enough.
if len(record['spectrogram']) < crop_mel_frames:
del record['spectrogram']
del record['audio']
del record['f0']
del record['uv']
continue
start = random.randint(0, record['spectrogram'].shape[0] - 1 - crop_mel_frames)
end = start + crop_mel_frames
if self.infer:
record['spectrogram'] = record['spectrogram'].T
record['f0'] = record['f0']
record['uv']=record['uv']
else:
record['spectrogram'] = record['spectrogram'][start:end].T
record['f0'] = record['f0'][start:end]
record['uv']=record['uv'][start:end]
start *= samples_per_frame
end *= samples_per_frame
if self.infer:
cty=(len(record['spectrogram'].T) * samples_per_frame)
record['audio'] = record['audio'][:cty]
record['audio'] = np.pad(record['audio'], (
0, (len(record['spectrogram'].T) * samples_per_frame) - len(record['audio'])),
mode='constant')
pass
else:
# record['spectrogram'] = record['spectrogram'][start:end].T
record['audio'] = record['audio'][start:end]
record['audio'] = np.pad(record['audio'], (0, (end - start) - len(record['audio'])),
mode='constant')
if self.volume_aug:
for record in minibatch:
if random.random() < self.volume_aug_prob:
audio = record['audio']
audio_mel = record['spectrogram']
max_amp = float(np.max(np.abs(audio))) + 1e-5
max_shift = min(3, np.log(1 / max_amp))
log_mel_shift = random.uniform(-3, max_shift)
# audio *= (10 ** log_mel_shift)
audio *= np.exp(log_mel_shift)
audio_mel += log_mel_shift
audio_mel = torch.clamp(torch.from_numpy(audio_mel), min=np.log(1e-5)).numpy()
record['audio'] = audio
record['spectrogram'] = audio_mel
audio = np.stack([record['audio'] for record in minibatch if 'audio' in record])
spectrogram = np.stack([record['spectrogram'] for record in minibatch if 'spectrogram' in record])
f0 = np.stack([record['f0'] for record in minibatch if 'f0' in record])
uv=np.stack([record['uv'] for record in minibatch if 'uv' in record])
return {
'audio': torch.from_numpy(audio).unsqueeze(1),
'mel': torch.from_numpy(spectrogram), 'f0': torch.from_numpy(f0),'uv':torch.from_numpy(uv)
}
class stftlog:
def __init__(self,
n_fft=2048,
win_length=2048,
hop_length=512,
center=False,):
self.hop_length=hop_length
self.win_size=win_length
self.n_fft = n_fft
self.win_size = win_length
self.center = center
self.hann_window = {}
def exc(self,y):
hann_window_key = f"{y.device}"
if hann_window_key not in self.hann_window:
self.hann_window[hann_window_key] = torch.hann_window(
self.win_size, device=y.device
)
y = torch.nn.functional.pad(
y.unsqueeze(1),
(
int((self.win_size - self.hop_length) // 2),
int((self.win_size - self.hop_length+1) // 2),
),
mode="reflect",
)
y = y.squeeze(1)
spec = torch.stft(
y,
self.n_fft,
hop_length=self.hop_length,
win_length=self.win_size,
window=self.hann_window[hann_window_key],
center=self.center,
pad_mode="reflect",
normalized=False,
onesided=True,
return_complex=True,
).abs()
return spec
class nsf_univnet_task(GanBaseTask):
def __init__(self, config):
super().__init__(config)
self.TF = PitchAdjustableMelSpectrogram( f_min=0,
f_max=None,
n_mels=256,)
self.logged_gt_wav = set()
self.stft=stftlog()
upmel = config['model_args'].get('upmel')
self.upmel=upmel
# if upmel is not None:
# self.noisec=config['model_args']['cond_in_channels']*upmel
# else:
self.noisec = config['model_args']['cond_in_channels']
def build_dataset(self):
self.train_dataset = ddsp_univ_dataset(config=self.config,
data_dir=pathlib.Path(self.config['DataIndexPath']) / self.config[
'train_set_name'])
self.valid_dataset = ddsp_univ_dataset(config=self.config,
data_dir=pathlib.Path(self.config['DataIndexPath']) / self.config[
'valid_set_name'], infer=True)
def build_model(self):
# cfg=self.config['model_args']
# cfg.update({'sampling_rate':self.config['audio_sample_rate'],'num_mels':self.config['audio_num_mel_bins'],'hop_size':self.config['hop_size']})
# h=AttrDict(cfg)
self.generator=nsfUnivNet(self.config,use_weight_norm=self.config['model_args'].get('use_weight_norm',True))
self.discriminator=nn.ModuleDict({'mrd':MultiResSpecDiscriminator(fft_sizes=self.config['model_args'].get('mrd_fft_sizes',[1024, 2048, 512]),
hop_sizes=self.config['model_args'].get('mrd_hop_sizes',[120, 240, 50]),
win_lengths= self.config['model_args'].get('mrd_win_lengths',[600, 1200, 240]),), 'mpd':MultiPeriodDiscriminator(periods=self.config['model_args']['discriminator_periods'])})
def build_losses_and_metrics(self):
self.mix_loss=nsf_univloss(self.config)
def Gforward(self, sample, infer=False):
"""
steps:
1. run the full model
2. calculate losses if not infer
"""
mel=sample['mel']
if self.upmel is not None:
x=torch.randn(mel.size()[0],self.noisec,mel.size()[-1]*self.upmel,device=mel.device,dtype=mel.dtype).to(mel)
else:
x = torch.randn(mel.size()[0], self.noisec, mel.size()[-1],device=mel.device,dtype=mel.dtype).to(mel)
wav, nsfwav=self.generator(x=x,c=mel, f0=sample['f0'])
return {'audio':wav,'nsfwav':nsfwav,}
def Dforward(self, Goutput):
mrd_out,mrd_feature=self.discriminator['mrd'](Goutput)
mpd_out,mpd_feature=self.discriminator['mpd'](Goutput)
return (mrd_out,mrd_feature),(mpd_out,mpd_feature)
def _training_step(self, sample, batch_idx):
"""
:return: total loss: torch.Tensor, loss_log: dict, other_log: dict
"""
aux_only = False
if self.aux_step is not None:
if self.aux_step > self.global_step:
aux_only = True
log_diet = {}
opt_g, opt_d = self.optimizers()
Goutput = self.Gforward(sample=sample)
if not aux_only:
Dfake = self.Dforward(Goutput=Goutput['audio'].detach())
Dtrue = self.Dforward(Goutput=sample['audio'])
Dloss, Dlog = self.mix_loss.Dloss(Dfake=Dfake, Dtrue=Dtrue)
log_diet.update(Dlog)
# if self.clip_grad_norm is not None:
# self.manual_backward(Dloss/self.clip_grad_norm)
# else:
opt_d.zero_grad()
self.manual_backward(Dloss)
if self.clip_grad_norm is not None:
self.clip_gradients(opt_d, gradient_clip_val=self.clip_grad_norm, gradient_clip_algorithm="norm")
opt_d.step()
opt_d.zero_grad()
if not aux_only:
GDfake = self.Dforward(Goutput=Goutput['audio'])
GDtrue=self.Dforward(Goutput=sample['audio'])
GDloss, GDlog = self.mix_loss.GDloss(GDfake=GDfake,GDtrue=GDtrue)
log_diet.update(GDlog)
Auxloss, Auxlog = self.mix_loss.Auxloss(Goutput=Goutput, sample=sample,step=self.global_step//2)
log_diet.update(Auxlog)
if not aux_only:
Gloss=GDloss + Auxloss
else:
Gloss=Auxloss
# if self.clip_grad_norm is not None:
# self.manual_backward(Gloss / self.clip_grad_norm)
# else:
# self.manual_backward(Gloss)
# if (batch_idx + 1) % self.accumulate_grad_batches == 0:
opt_g.zero_grad()
self.manual_backward(Gloss)
if self.clip_grad_norm is not None:
self.clip_gradients(opt_g, gradient_clip_val=self.clip_grad_norm, gradient_clip_algorithm="norm")
opt_g.step()
return log_diet
def _validation_step(self, sample, batch_idx):
wav=self.Gforward(sample)['audio']
with torch.no_grad():
# self.TF = self.TF.cpu()
# mels = torch.log10(torch.clamp(self.TF(wav.squeeze(0).cpu().float()), min=1e-5))
# GTmels = torch.log10(torch.clamp(self.TF(sample['audio'].squeeze(0).cpu().float()), min=1e-5))
stfts=self.stft.exc(wav.squeeze(0).cpu().float())
Gstfts=self.stft.exc(sample['audio'].squeeze(0).cpu().float())
Gstfts_log10=torch.log10(torch.clamp(Gstfts, min=1e-7))
Gstfts_log = torch.log(torch.clamp(Gstfts, min=1e-7))
stfts_log10=torch.log10(torch.clamp(stfts, min=1e-7))
stfts_log= torch.log(torch.clamp(stfts, min=1e-7))
# self.plot_mel(batch_idx, GTmels.transpose(1,2), mels.transpose(1,2), name=f'diffmel_{batch_idx}')
self.plot_mel(batch_idx, Gstfts_log10.transpose(1,2), stfts_log10.transpose(1,2), name=f'HIFImel_{batch_idx}/log10')
# self.plot_mel(batch_idx, Gstfts_log.transpose(1, 2), stfts_log.transpose(1, 2), name=f'HIFImel_{batch_idx}/log')
self.logger.experiment.add_audio(f'diff_{batch_idx}_', wav,
sample_rate=self.config['audio_sample_rate'],
global_step=self.global_step)
if batch_idx not in self.logged_gt_wav:
# gt_wav = self.vocoder.spec2wav(gt_mel, f0=f0)
self.logger.experiment.add_audio(f'gt_{batch_idx}_', sample['audio'],
sample_rate=self.config['audio_sample_rate'],
global_step=self.global_step)
self.logged_gt_wav.add(batch_idx)
return {'l1loss':nn.L1Loss()(wav, sample['audio'])}, 1
def plot_mel(self, batch_idx, spec, spec_out, name=None):
name = f'mel_{batch_idx}' if name is None else name
vmin = self.config['mel_vmin']
vmax = self.config['mel_vmax']
spec_cat = torch.cat([(spec_out - spec).abs() + vmin, spec, spec_out], -1)
self.logger.experiment.add_figure(name, spec_to_figure(spec_cat[0], vmin, vmax), self.global_step)
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