new loss functions to stabilize training
This commit is contained in:
SayanoAI
+51
-61
@@ -29,7 +29,7 @@ from .lib.train.data_utils import (
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TextAudioCollate,
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DistributedBucketSampler,
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)
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from .lib.train.losses import generator_loss, discriminator_loss, feature_loss, kl_loss, mfcc_loss
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from .lib.train.losses import combined_aux_loss, generator_loss, discriminator_loss, feature_loss, gradient_norm_loss, kl_loss
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from .lib.train.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
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def save_checkpoint(ckpt, name, epoch, hps, model_path=None):
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@@ -327,7 +327,7 @@ def train_and_evaluate(
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# Make new cache
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for batch_idx, info in enumerate(train_loader):
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# Unpack
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if hps.if_f0 == 1:
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if hps.if_f0:
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(
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phone,
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phone_lengths,
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@@ -353,7 +353,7 @@ def train_and_evaluate(
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if torch.cuda.is_available():
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phone = phone.cuda(rank, non_blocking=True)
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phone_lengths = phone_lengths.cuda(rank, non_blocking=True)
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if hps.if_f0 == 1:
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if hps.if_f0:
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pitch = pitch.cuda(rank, non_blocking=True)
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pitchf = pitchf.cuda(rank, non_blocking=True)
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sid = sid.cuda(rank, non_blocking=True)
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@@ -362,7 +362,7 @@ def train_and_evaluate(
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wave = wave.cuda(rank, non_blocking=True)
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wave_lengths = wave_lengths.cuda(rank, non_blocking=True)
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# Cache on list
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if hps.if_f0 == 1:
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if hps.if_f0:
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cache.append(
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(
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batch_idx,
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@@ -406,7 +406,7 @@ def train_and_evaluate(
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for batch_idx, info in tqdm(data_iterator,desc=f"[Epoch {epoch}]: "):
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# Data
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## Unpack
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if hps.if_f0 == 1:
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if hps.if_f0:
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(
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phone,
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phone_lengths,
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@@ -424,7 +424,7 @@ def train_and_evaluate(
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if (not hps.if_cache_data_in_gpu) and torch.cuda.is_available():
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phone = phone.cuda(rank, non_blocking=True)
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phone_lengths = phone_lengths.cuda(rank, non_blocking=True)
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if hps.if_f0 == 1:
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if hps.if_f0:
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pitch = pitch.cuda(rank, non_blocking=True)
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pitchf = pitchf.cuda(rank, non_blocking=True)
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sid = sid.cuda(rank, non_blocking=True)
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@@ -434,7 +434,7 @@ def train_and_evaluate(
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# Calculate
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with autocast(enabled=hps.train.fp16_run):
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if hps.if_f0 == 1:
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if hps.if_f0:
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(
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y_hat,
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ids_slice,
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@@ -473,6 +473,7 @@ def train_and_evaluate(
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hps.data.mel_fmax,
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)
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if hps.train.fp16_run: y_hat_mel = y_hat_mel.half()
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wave_orig = wave.clone()
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wave = commons.slice_segments(wave, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice
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# Discriminator
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@@ -480,46 +481,9 @@ def train_and_evaluate(
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y_d_hat_r, y_d_hat_g, _, _ = net_d(wave, gen_wave)
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with autocast(enabled=False):
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gradient_penalty = torch.Tensor([0.]).to(wave.device)
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if hps.train.get("c_gp",0.)>0:
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# Compute the gradient penalty
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# Randomly interpolate between real and generated data
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size = [1]*wave.ndim
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size[0] = wave.size(0)
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alpha = torch.rand(*size, device=wave.device)
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interpolated = alpha * wave + (1 - alpha) * gen_wave
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interpolated.requires_grad_(True)
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# Get the discriminator output for the interpolated data
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_, disc_interpolated_output, _, _ = net_d(wave, interpolated)
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# Compute gradients of discriminator output w.r.t. interpolated data
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for output in disc_interpolated_output:
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gradient = torch.autograd.grad(
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outputs=output,
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inputs=interpolated,
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grad_outputs=torch.ones(output.size(), device=wave.device),
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create_graph=True,
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only_inputs=True
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)[0]
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gradient = gradient.view(gradient.size(0), -1)
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gradient_penalty += torch.log(gradient.norm(2,dim=-1)).abs().mean() # force gradnorm=1
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gradient_penalty *= hps.train.c_gp/len(disc_interpolated_output)
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loss_mfcc = torch.Tensor([0.]).to(wave.device)
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if hps.train.get("c_mfcc",0)>0:
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loss_mfcc = mfcc_loss(wave, gen_wave, SR_MAP[hps.sample_rate],
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n_fft=hps.data.filter_length,
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hop_length=hps.data.hop_length,
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win_length=hps.data.win_length,
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n_mels=hps.data.n_mel_channels,
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norm="slaney",
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f_min=hps.data.mel_fmin,
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f_max=hps.data.mel_fmax
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) * hps.train.c_mfcc
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loss_mfcc = loss_mfcc.squeeze()
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gradient_penalty = gradient_penalty.squeeze()
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gradient_penalty = gradient_norm_loss(wave,gen_wave, net_d)*hps.train.c_gp if hps.train.get("c_gp",0.)>0 else 0
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loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
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loss_disc_all = loss_disc+gradient_penalty+loss_mfcc
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loss_disc_all = loss_disc+gradient_penalty
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optim_d.zero_grad()
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scaler.scale(loss_disc_all).backward()
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@@ -531,11 +495,25 @@ def train_and_evaluate(
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# Generator
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y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(wave, y_hat)
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with autocast(enabled=False):
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loss_mel = F.smooth_l1_loss(y_mel, y_hat_mel) * hps.train.get("c_mel",45.)
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loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.get("c_mel",45.)
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loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.get("c_kl",1.)
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loss_fm = feature_loss(fmap_r, fmap_g) * hps.train.get("c_fm",2.)
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harmonic_loss, temporal_loss, spectral_loss, mfcc_loss, lfcc_loss, phase_loss = combined_aux_loss(
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wave, y_hat, SR_MAP[hps.sample_rate],
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c_lfcc=hps.train.get("c_lfcc",0.),
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c_mfcc=hps.train.get("c_mfcc",0.),
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c_hd=hps.train.get("c_hd",0.),
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c_sts=hps.train.get("c_sts",0.),
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n_fft=hps.data.filter_length,
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hop_length=hps.data.hop_length,
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win_length=hps.data.win_length,
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n_filter=hps.data.n_mel_channels,
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f_min=hps.data.mel_fmin,
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f_max=hps.data.mel_fmax
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)
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loss_gen, losses_gen = generator_loss(y_d_hat_g)
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loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl
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aux_loss = harmonic_loss + temporal_loss + spectral_loss + mfcc_loss + lfcc_loss + phase_loss
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loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl + aux_loss
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optim_g.zero_grad()
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scaler.scale(loss_gen_all).backward()
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@@ -548,13 +526,12 @@ def train_and_evaluate(
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# if epoch>=max(hps.total_epoch*.2,20): #start saving best model after 20% or 20 epochs
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if loss_gen_all+loss_disc_all<least_loss:
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least_loss = loss_gen_all+loss_disc_all
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logger.info(f"[lowest loss] {least_loss=:.3f}: {loss_disc=:.3f} {loss_mfcc=:.3f} {gradient_penalty=:.3f}, {loss_gen=:.3f}, {loss_fm=:.3f}, {loss_mel=:.3f}, {loss_kl=:.3f}")
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logger.info(f"[lowest loss] {least_loss=:.3f}: {loss_disc=:.3f} {gradient_penalty=:.3f} <==> {loss_gen=:.3f} {loss_fm=:.3f} {loss_mel=:.3f} {loss_kl=:.3f}")
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logger.info(f"[aux] {aux_loss=:.3f}: {harmonic_loss=:.3f}, {temporal_loss=:.3f}, {spectral_loss=:.3f}, {mfcc_loss=:.3f}, {lfcc_loss=:.3f} {phase_loss=:.3f}")
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if hps.save_best_model:
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if hasattr(net_g, "module"):
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ckpt = net_g.module.state_dict()
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else:
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ckpt = net_g.state_dict()
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if hasattr(net_g, "module"): ckpt = net_g.module.state_dict()
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else: ckpt = net_g.state_dict()
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best_model_name = f"{hps.name}_e{epoch}_s{global_step}_loss{least_loss:2.0f}" if hps.save_every_weights else f"{hps.name}_loss{least_loss:2.0f}"
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status = save_checkpoint(ckpt,best_model_name,epoch,hps)
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@@ -575,16 +552,22 @@ def train_and_evaluate(
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scalar_dict = {
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"total/loss/all": loss_gen_all+loss_disc_all,
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"total/loss/gen_all": loss_gen_all,
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"total/loss/aux": aux_loss,
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"total/loss/disc_all": loss_disc_all,
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"total/loss/gen": loss_gen,
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"total/loss/disc": loss_disc,
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"total/loss/fm": loss_fm,
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"total/loss/mel": loss_mel,
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"total/loss/kl": loss_kl,
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"total/loss/mfcc": loss_mfcc,
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"learning_rate": lr,
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"gradient/grad_norm_d": grad_norm_d,
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"gradient/grad_norm_g": grad_norm_g,
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"aux/loss/harmonic": harmonic_loss,
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"aux/loss/temporal": temporal_loss,
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"aux/loss/spectral": spectral_loss,
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"aux/loss/mfcc": mfcc_loss,
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"aux/loss/lfcc": lfcc_loss,
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"aux/loss/phase_loss": phase_loss,
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"gradient/lr": lr,
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"gradient/grad_norm_disc": grad_norm_d,
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"gradient/grad_norm_gen": grad_norm_g,
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"gradient/gradient_penalty": gradient_penalty,
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**{f"loss/g/{i}": v for i, v in enumerate(losses_gen)},
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**{f"loss/d_r/{i}": v for i, v in enumerate(losses_disc_r)},
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@@ -597,9 +580,15 @@ def train_and_evaluate(
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"all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
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}
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with torch.no_grad():
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if hasattr(net_g, "module"): inference = net_g.module.infer
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else: inference = net_g.infer
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if hps.if_f0: wave_gen = inference(phone, phone_lengths, pitch, pitchf, sid)[0][0, 0].data
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else: wave_gen = inference(phone, phone_lengths, sid)[0][0, 0].data
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audio_dict = {
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"slice/wave_org": wave.flatten(),
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"slice/wave_gen": y_hat.flatten()
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"slice/wave_org": wave_orig[0][0],
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"slice/wave_gen": wave_gen
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}
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utils.summarize(
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writer=writer,
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@@ -637,14 +626,15 @@ def train_and_evaluate(
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if rank == 0:
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logger.info(f"====> Epoch {epoch} (Total Loss={(loss_disc_all+loss_gen_all).item():.3f}): {global_step=} {lr=:.2E} {epoch_recorder.record()}")
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logger.info(f"|| {loss_disc_all=:.3f}: {loss_disc=:.3f}, {loss_mfcc=:.3f}, {gradient_penalty=:.3f}")
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logger.info(f"|| {loss_disc_all=:.3f}: {loss_disc=:.3f}, {gradient_penalty=:.3f}")
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logger.info(f"|| {loss_gen_all=:.3f}: {loss_gen=:.3f}, {loss_fm=:.3f}, {loss_mel=:.3f}, {loss_kl=:.3f}")
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logger.info(f"|| {harmonic_loss=:.3f}, {temporal_loss=:.3f}, {spectral_loss=:.3f}, {mfcc_loss=:.3f}, {lfcc_loss=:.3f}, {phase_loss=:.3f}")
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if epoch >= hps.total_epoch and rank == 0:
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logger.info("Training is done. The program is closed.")
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ckpt = net_g.module.state_dict() if hasattr(net_g, "module") else net_g.state_dict()
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if hps.save_best_model:
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if hps.save_best_model and os.path.isfile(loss_file):
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with open(loss_file,"r") as f:
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data = json.load(f)
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best_model_name = data.get("best_model_name","")
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