train_eng_ppg_emo_loss.py

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
print("import tensorboard")
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

#有speaker consistency loss#python train_eng_ppg_emo_loss.py -c configs/freevc-eng-ppgs-three-emo.json -m freevc-eng-ppgs-three-emo#-cycleloss  
import commons
import utils
from data_utils_engppg import (
  TextAudioSpeakerLoader,
  TextAudioSpeakerCollate,
  DistributedBucketSampler
)
from models import (
  SynthesizerTrn,
  MultiPeriodDiscriminator,
)
from losses import (
  generator_loss,
  discriminator_loss,
  feature_loss,
  kl_loss
)
from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
"""
from transformers import Wav2Vec2Processor
from transformers.models.wav2vec2.modeling_wav2vec2 import (
    Wav2Vec2Model,
    Wav2Vec2PreTrainedModel,
)
class RegressionHead(nn.Module):
    

    def __init__(self, config):
        super().__init__()

        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.dropout = nn.Dropout(config.final_dropout)
        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)

    def forward(self, features, **kwargs):
        x = features
        x = self.dropout(x)
        x = self.dense(x)
        x = torch.tanh(x)
        x = self.dropout(x)
        x = self.out_proj(x)

        return x


class EmotionModel(Wav2Vec2PreTrainedModel):
    

    def __init__(self, config):
        super().__init__(config)

        self.config = config
        self.wav2vec2 = Wav2Vec2Model(config)
        self.classifier = RegressionHead(config)
        self.init_weights()

    def forward(
            self,
            input_values,
    ):
        outputs = self.wav2vec2(input_values)
        hidden_states = outputs[0]
        hidden_states = torch.mean(hidden_states, dim=1)
        logits = self.classifier(hidden_states)

        return hidden_states, logits
import torchvision
print("load emotion model")
device = 'cuda' if torch.cuda.is_available() else "cpu"
model_name = 'audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim'
processor = Wav2Vec2Processor.from_pretrained(model_name)
model = EmotionModel.from_pretrained(model_name).to(device)
# 方法1： 设置requires_grad = False
for param in model.parameters():
    param.requires_grad = False
print("模型参数冻结")

def process_func(y_input,embeddings):
    

    # run through processor to normalize signal
    # always returns a batch, so we just get the first entry
    # then we put it on the device
    #print(torch.max(y_input),torch.min(y_input),y_input.size())
    #y = processor(y_input, sampling_rate=16000,return_tensors='pt' )
    #print(y)
    #print(y['input_values'])
    #y = y['input_values'][0]
    #y = torch.from_numpy(y).to(device)
    #print(torch.max(y),torch.min(y),y.size())
    #srd=torch.std(y_input, dim=-1, keepdim=True)
    #print(srd.size())
    y = (y_input - torch.mean(y_input, dim=-1, keepdim=True))/(torch.std(y_input, dim=-1, keepdim=True)+ 1e-7)
    #print(torch.max(y),torch.min(y),y.size())
    #preprocess = torchvision.transforms.Normalize(mean=torch.mean(y_input, dim=0), std=torch.std(y_input, dim=0))
    #y = preprocess(y_input)
    #print(torch.max(y),torch.min(y),y.size())
    # run through model
    #y=y_input.squeeze()
    y=y.squeeze()
    #print("input",y.size())
    #with torch.no_grad():
    for param in model.parameters():
      if param.requires_grad == False:
        print("依旧冻结")
      else:
        print("不冻结")
    y = model(y)[0 if embeddings else 1]

    #print("emo embedding",torch.max(y),torch.min(y),y.size())
    # convert to numpy
    #y = y.detach().cpu().numpy()

    return y
"""
torch.backends.cudnn.benchmark = True
global_step = 0
#os.environ['TORCH_DISTRIBUTED_DEBUG'] = 'INFO'


def main():
  """Assume Single Node Multi GPUs Training Only"""
  assert torch.cuda.is_available(), "CPU training is not allowed."
  hps = utils.get_hparams()

  n_gpus = torch.cuda.device_count()
  #os.environ['MASTER_ADDR'] = 'localhost'
  #os.environ['MASTER_PORT'] = hps.train.port
  print("start run")
  run(0,n_gpus, hps)
  #mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))


def run(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 = TextAudioSpeakerLoader(hps.data.training_files, hps)
  train_sampler = DistributedBucketSampler(
      train_dataset,
      hps.train.batch_size,
      [75,100,150,200,250,300,350,400,450,500,550,600,650,700,750,800,850,900,950,1000,1100,1200,1300,1400,1500,2000,3000,4000,5000],
      num_replicas=n_gpus,
      rank=rank,
      shuffle=True)
  collate_fn = TextAudioSpeakerCollate(hps)
  train_loader = DataLoader(train_dataset, num_workers=12, shuffle=False, pin_memory=True,
      collate_fn=collate_fn, batch_sampler=train_sampler)
  if rank == 0:
    eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps)
    eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=True,
        batch_size=hps.train.batch_size, pin_memory=False,
        drop_last=False, collate_fn=collate_fn)

  net_g = SynthesizerTrn(
      hps.data.filter_length // 2 + 1,
      hps.train.segment_size // hps.data.hop_length,
      **hps.model).cuda(rank)
  net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
  optim_g = torch.optim.AdamW(
      net_g.parameters(), 
      hps.train.learning_rate, 
      betas=hps.train.betas, 
      eps=hps.train.eps)
  optim_d = torch.optim.AdamW(
      net_d.parameters(),
      hps.train.learning_rate, 
      betas=hps.train.betas, 
      eps=hps.train.eps)
  #net_g = DDP(net_g, device_ids=[rank])#, find_unused_parameters=True)
  #net_d = DDP(net_d, device_ids=[rank])

  try:
    _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g)
    _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d)
    global_step = (epoch_str - 1) * len(train_loader)
  except:
    epoch_str = 1
    global_step = 0

  scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str-2)
  scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str-2)

  scaler = GradScaler(enabled=hps.train.fp16_run)

  for epoch in range(epoch_str, hps.train.epochs + 1):
    if rank==0:
      train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, eval_loader], logger, [writer, writer_eval])
    else:
      train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, None], None, None)
    scheduler_g.step()
    scheduler_d.step()


def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
  
  net_g, net_d = nets
  optim_g, optim_d = optims
  scheduler_g, scheduler_d = schedulers
  train_loader, eval_loader = loaders
  if writers is not None:
    writer, writer_eval = writers

  train_loader.batch_sampler.set_epoch(epoch)
  global global_step

  net_g.train()
  net_d.train()
  for batch_idx, items in enumerate(train_loader):
    if hps.model.use_spk:
      c, spec, y, spk = items
      g = spk.cuda(rank, non_blocking=True)
    else:
      c, spec, y = items
      g = None
    spec, y = spec.cuda(rank, non_blocking=True), y.cuda(rank, non_blocking=True)
    c = c.cuda(rank, non_blocking=True)
    mel = spec_to_mel_torch(
          spec, 
          hps.data.filter_length, 
          hps.data.n_mel_channels, 
          hps.data.sampling_rate,
          hps.data.mel_fmin, 
          hps.data.mel_fmax)
    real_mel = mel_spectrogram_torch(
          y.squeeze(1), 
          hps.data.filter_length, 
          hps.data.n_mel_channels, 
          hps.data.sampling_rate, 
          hps.data.hop_length, 
          hps.data.win_length, 
          hps.data.mel_fmin, 
          hps.data.mel_fmax
      )
    #print(torch.max(mel),torch.min(mel),torch.max(real_mel),torch.min(real_mel))
    with autocast(enabled=hps.train.fp16_run):
      y_hat, ids_slice, z_mask,\
      (z, z_p, m_p, logs_p, m_q, logs_q),emo_y = net_g(c, spec, g=g, mel=mel)
      #print(torch.max(y),torch.min(y),torch.max(y_hat),torch.min(y_hat))
      y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
      y_hat_mel = mel_spectrogram_torch(
          y_hat.squeeze(1), 
          hps.data.filter_length, 
          hps.data.n_mel_channels, 
          hps.data.sampling_rate, 
          hps.data.hop_length, 
          hps.data.win_length, 
          hps.data.mel_fmin, 
          hps.data.mel_fmax
      )
      y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice 

      # Discriminator
      y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
      with autocast(enabled=False):
        loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
        loss_disc_all = loss_disc
    optim_d.zero_grad()
    scaler.scale(loss_disc_all).backward()
    scaler.unscale_(optim_d)
    grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
    scaler.step(optim_d)

    with autocast(enabled=hps.train.fp16_run):
      # Generator
      y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
      #print(torch.max(mel),torch.min(mel),mel.size(),torch.max(y_hat_mel),torch.min(y_hat_mel),y_hat_mel.size())
      #y_hat_mel=torch.rand_like(y_hat_mel)
      #emo_y_hat=net_g.enc_spk(y_hat_mel.transpose(1,2))#process_func(y_input=y_hat,embeddings=True)
      #y=torch.rand_like(y)
      emo_y_hat=net_g.enc_spk(y_hat_mel.transpose(1,2))#process_func(y_input=y,embeddings=True)
      #print(torch.max(emo_y),torch.min(emo_y),emo_y.size(),torch.max(emo_y_hat),torch.min(emo_y_hat),emo_y_hat.size())
      #print(emo_y_hat.size(),emo_y.size())
      with autocast(enabled=False):
        loss_mel = F.l1_loss(y_hat_mel, y_mel) * hps.train.c_mel
        #print("loss_mel",loss_mel)
        loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl# * 1.5
        #print("loss_kl",loss_kl)
        loss_fm = feature_loss(fmap_r, fmap_g) * 0.5
        #print("loss_fm",loss_fm)
        loss_gen, losses_gen = generator_loss(y_d_hat_g)
        #print("loss_gen, losses_gen",loss_gen, losses_gen)
        loss_emo=F.l1_loss(emo_y_hat.detach(),emo_y.detach()) * hps.train.c_mel * 0.5
        #print("loss_emo",loss_emo)
        loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl + loss_emo
    optim_g.zero_grad()
    scaler.scale(loss_gen_all).backward()
    scaler.unscale_(optim_g)
    grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
    scaler.step(optim_g)
    scaler.update()

    if rank==0:
      if global_step % hps.train.log_interval == 0:
        lr = optim_g.param_groups[0]['lr']
        losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_kl,loss_emo]
        logger.info('Train Epoch: {} [{:.0f}%]'.format(
          epoch,
          100. * batch_idx / len(train_loader)))
        logger.info([x.item() for x in losses] + [global_step, lr])
        
        scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr, "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
        scalar_dict.update({"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/kl": loss_kl, "loss/g/emo": loss_emo})

        scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
        scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
        scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
        image_dict = { 
            "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
            "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()), 
            "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
        }
        utils.summarize(
          writer=writer,
          global_step=global_step, 
          images=image_dict,
          scalars=scalar_dict)

      if global_step % hps.train.eval_interval == 0:
        evaluate(hps, net_g, eval_loader, writer_eval)
        utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
        utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
    global_step += 1
  
  if rank == 0:
    logger.info('====> Epoch: {}'.format(epoch))

 
def evaluate(hps, generator, eval_loader, writer_eval):
    generator.eval()
    with torch.no_grad():
      for batch_idx, items in enumerate(eval_loader):
        if hps.model.use_spk:
          c, spec, y, spk = items
          g = spk[:1].cuda(0)
        else:
          c, spec, y = items
          g = None
        spec, y = spec[:1].cuda(0), y[:1].cuda(0)
        c = c[:1].cuda(0)
        break
      mel = spec_to_mel_torch(
        spec, 
        hps.data.filter_length, 
        hps.data.n_mel_channels, 
        hps.data.sampling_rate,
        hps.data.mel_fmin, 
        hps.data.mel_fmax)
      y_hat = generator.infer(c, g=g, mel=mel)#generator.module.infer(c, g=g, mel=mel)
      print(torch.max(y_hat),torch.min(y_hat))
      y_hat_mel = mel_spectrogram_torch(
        y_hat.squeeze(1).float(),
        hps.data.filter_length,
        hps.data.n_mel_channels,
        hps.data.sampling_rate,
        hps.data.hop_length,
        hps.data.win_length,
        hps.data.mel_fmin,
        hps.data.mel_fmax
      )
    image_dict = {
      "gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy()),
      "gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())
    }
    audio_dict = {
      "gen/audio": y_hat[0],
      "gt/audio": y[0]
    }
    utils.summarize(
      writer=writer_eval,
      global_step=global_step, 
      images=image_dict,
      audios=audio_dict,
      audio_sampling_rate=hps.data.sampling_rate
    )
    generator.train()

                           
if __name__ == "__main__":
  main()