diff --git a/custom_nodes/rvc_nodes.py b/custom_nodes/rvc_nodes.py index 39c7b5f..0a26224 100644 --- a/custom_nodes/rvc_nodes.py +++ b/custom_nodes/rvc_nodes.py @@ -408,7 +408,7 @@ class RVCTrainModelNode: retrain=("BOOLEAN",{"default": False}), save_best_model=("BOOLEAN",{"default": True}), best_model_threshold=("INT",dict(default=50,min=20,max=80)), - log_every_epoch=("FLOAT",dict(default=1.,min=0.,max=2.,step=.5)), + log_every_epoch=("FLOAT",dict(default=1.,min=0.,max=2.,step=.1)), num_workers=("INT",dict(default=1,min=1,max=16)) ) } diff --git a/lib/infer_pack/commons.py b/lib/infer_pack/commons.py index c41f483..842339f 100644 --- a/lib/infer_pack/commons.py +++ b/lib/infer_pack/commons.py @@ -1,9 +1,20 @@ import math -import numpy as np import torch -from torch import nn from torch.nn import functional as F +def median_pool1d(signal, kernel_size, dim=-1,stride=1): + # Pad the input to handle edge cases + padding = (kernel_size - 1) // 2 + + input_padded = F.pad(signal, (padding, padding), mode='reflect') + + # Unfold the input to create sliding windows + unfolded = input_padded.unfold(-1, kernel_size, stride) + + # Compute the median along the last dimension + median = unfolded.median(dim=dim).values + + return median def init_weights(m, mean=0.0, std=0.01): classname = m.__class__.__name__ diff --git a/lib/train/losses.py b/lib/train/losses.py index 9792fb1..ff0967b 100644 --- a/lib/train/losses.py +++ b/lib/train/losses.py @@ -1,31 +1,161 @@ import torch import torchaudio +import torch.nn.functional as F +from ..infer_pack.commons import median_pool1d -def mfcc_loss(original_audio, generated_audio, sample_rate, n_mfcc=13, **kwargs): - """ - Computes the loss between the original and generated audio based on MFCCs. - - Parameters: - original_audio (Tensor): The original audio waveform. - generated_audio (Tensor): The generated audio waveform. - sample_rate (int): The sample rate of the audio. - n_mfcc (int): Number of MFCC coefficients to compute (default is 13). - - Returns: - Tensor: The computed MFCC loss. - """ - - mfcc_transform = torchaudio.transforms.MFCC(sample_rate=sample_rate,n_mfcc=n_mfcc,melkwargs=kwargs) - mfcc_transform.to(original_audio.device) +def compute_temporal_envelope(log_magnitude: torch.Tensor, kernel_size=31): - # Compute MFCCs for original and generated audio - original_mfcc = mfcc_transform(original_audio) - generated_mfcc = mfcc_transform(generated_audio) + # Apply median pooling to the generated audio + pooled_stft = median_pool1d(log_magnitude, kernel_size, stride=kernel_size//2+1) - # Compute the L2 loss between the MFCCs of the original and generated audio - loss = torch.nn.functional.smooth_l1_loss(generated_mfcc, original_mfcc) + # Compute differences on the median-pooled audio + diff = F.normalize(torch.diff(pooled_stft, dim=-1, n=1),dim=-1) - return loss + # Return the mean of the differences as the loss + return diff.sum(dim=-2).nan_to_num(0) + +def compute_cepstrals(log_magnitude: torch.Tensor, kernel_size=31): + + pooled_magnitude = median_pool1d(log_magnitude, kernel_size, stride=kernel_size//2+1) + + # Compute the cepstrum + cepstral_coefficients = torch.fft.ifft(pooled_magnitude, dim=-2).real + + return cepstral_coefficients.sum(dim=-2).nan_to_num(0) + +def compute_sts_loss(gen_stft: torch.Tensor, org_stft: torch.Tensor, kernel_size=31): + + # B?N?T + org_mag = torch.log1p(org_stft.abs()) + gen_mag = torch.log1p(gen_stft.abs()) + + gen_temp = compute_temporal_envelope(gen_mag, kernel_size=kernel_size) + org_temp = compute_temporal_envelope(org_mag, kernel_size=kernel_size) + temporal_loss = 1-F.cosine_similarity(gen_temp, org_temp, dim=-1).mean() + + gen_spec = compute_cepstrals(org_mag,kernel_size=kernel_size) + org_spec = compute_cepstrals(gen_mag,kernel_size=kernel_size) + + spectral_loss = F.smooth_l1_loss(gen_spec, org_spec) + + org_phase = org_stft.angle() + gen_phase = gen_stft.angle() + + # temporal invariant phase + org_phase = median_pool1d(org_stft.angle(),3,stride=3).mean(-1) + gen_phase = median_pool1d(gen_stft.angle(),3,stride=3).mean(-1) + phase_loss = F.smooth_l1_loss(gen_phase,org_phase)+F.smooth_l1_loss(1-torch.cos(gen_phase-org_phase),torch.zeros_like(gen_phase)) + + return temporal_loss, spectral_loss, phase_loss.nan_to_num(0) + +def compute_harmonics(stft_matrix: torch.Tensor, kernel_size=31): + # Calculate log-magnitude spectrograms + mag = torch.log1p(stft_matrix.abs()) + + # Apply median filtering to separate harmonics and percussives + harmonic = median_pool1d(mag, kernel_size, stride=kernel_size//2+1) + percussive = median_pool1d(mag.transpose(-2,-1), kernel_size, stride=kernel_size//2+1).transpose(-2,-1) + + return harmonic.nan_to_num(0), percussive.nan_to_num(0) + +def combined_aux_loss( + original_audio: torch.Tensor, generated_audio: torch.Tensor, sample_rate: int, + c_mfcc=1., c_lfcc=1., c_hd=1., c_sts=1., + n_fft=1024, hop_length=320, win_length=1024, + n_mfcc=13, n_lfcc=13, n_filter=80, + f_min=0., f_max=None): + + kernel_size = n_fft//hop_length+1 + if kernel_size % 2 == 0: kernel_size+=1 + + # Compute STFT once + if c_hd+c_sts>0: + hann_window = torch.hann_window(win_length).to( + dtype=original_audio.dtype, device=original_audio.device + ) + generated_stft = torch.stft( + generated_audio.view(-1,generated_audio.size(-1)), + n_fft=n_fft, + hop_length=hop_length, + win_length=win_length, + window=hann_window, + return_complex=True, + center=False) + original_stft = torch.stft( + original_audio.view(-1,original_audio.size(-1)), + n_fft=n_fft, + hop_length=hop_length, + win_length=win_length, + window=hann_window, + return_complex=True, + center=False) + + # Harmonic Loss + if c_hd>0: + original_harmonics, original_percussives = compute_harmonics(original_stft,kernel_size) + generated_harmonics, generated_percussives = compute_harmonics(generated_stft,kernel_size) + # Define loss terms + harmonic_loss = F.smooth_l1_loss(generated_harmonics, original_harmonics) + harmonic_loss += F.smooth_l1_loss(generated_percussives, original_percussives) + harmonic_loss *= c_hd + harmonic_loss + else: harmonic_loss = 0 + + # Spectral Temporal Smoothness Loss + if c_sts>0: + temporal_loss, spectral_loss, phase_loss = compute_sts_loss(generated_stft,original_stft,kernel_size) + temporal_loss *= c_sts + spectral_loss *= c_sts + phase_loss *= c_sts + else: spectral_loss = temporal_loss = phase_loss = 0 + + # MFCC Loss + if c_mfcc>0: + mfcc_transform = torchaudio.transforms.MFCC( + sample_rate=sample_rate, n_mfcc=n_mfcc, + melkwargs=dict(n_fft=n_fft, hop_length=hop_length, win_length=win_length, n_mels=n_filter, f_min=f_min, f_max=f_max) + ).to(original_audio.device) + original_mfcc = mfcc_transform(original_audio) + generated_mfcc = mfcc_transform(generated_audio) + mfcc_loss = F.smooth_l1_loss(generated_mfcc, original_mfcc) * c_mfcc + else: mfcc_loss = 0 + + # LFCC Loss + if c_lfcc>0: + lfcc_transform = torchaudio.transforms.LFCC( + sample_rate=sample_rate, n_lfcc=n_lfcc, n_filter=n_filter, f_min=f_min, f_max=f_max, + speckwargs=dict(n_fft=n_fft, hop_length=hop_length, win_length=win_length) + ).to(original_audio.device) + original_lfcc = lfcc_transform(original_audio) + generated_lfcc = lfcc_transform(generated_audio) + lfcc_loss = F.smooth_l1_loss(generated_lfcc, original_lfcc) * c_lfcc + else: lfcc_loss = 0 + + return harmonic_loss, temporal_loss, spectral_loss, mfcc_loss, lfcc_loss, phase_loss + +def gradient_norm_loss(original_audio: torch.Tensor, generated_audio: torch.Tensor, net_d): + loss=0 + # Compute the gradient penalty + # Randomly interpolate between real and generated data + size = [1]*original_audio.ndim + size[0] = original_audio.size(0) + alpha = torch.rand(*size, device=original_audio.device) + interpolated = alpha * original_audio + (1 - alpha) * generated_audio + interpolated.requires_grad_(True) + # Get the discriminator output for the interpolated data + _, disc_interpolated_output, _, _ = net_d(original_audio, interpolated) + # Compute gradients of discriminator output w.r.t. interpolated data + for output in disc_interpolated_output: + gradient = torch.autograd.grad( + outputs=output, + inputs=interpolated, + grad_outputs=torch.ones(output.size(), device=original_audio.device), + create_graph=True, + only_inputs=True + )[0] + gradient = gradient.view(gradient.size(0), -1) + loss += torch.log(gradient.norm(2,dim=-1)).abs().mean() # force gradnorm=1 + return loss/len(disc_interpolated_output) def feature_loss(fmap_r, fmap_g): loss = 0 @@ -50,7 +180,7 @@ def discriminator_loss(disc_real_outputs, disc_generated_outputs): loss += r_loss + g_loss r_losses.append(r_loss.item()) g_losses.append(g_loss.item()) - + # loss /= len(disc_generated_outputs) #average return loss, r_losses, g_losses @@ -62,7 +192,7 @@ def generator_loss(disc_outputs): l = torch.mean((1 - dg) ** 2) gen_losses.append(l) loss += l - + # loss /= len(disc_outputs) #average return loss, gen_losses diff --git a/training_cli.py b/training_cli.py index 98e172f..ed611d0 100644 --- a/training_cli.py +++ b/training_cli.py @@ -29,7 +29,7 @@ from .lib.train.data_utils import ( TextAudioCollate, DistributedBucketSampler, ) -from .lib.train.losses import generator_loss, discriminator_loss, feature_loss, kl_loss, mfcc_loss +from .lib.train.losses import combined_aux_loss, generator_loss, discriminator_loss, feature_loss, gradient_norm_loss, kl_loss from .lib.train.mel_processing import mel_spectrogram_torch, spec_to_mel_torch def save_checkpoint(ckpt, name, epoch, hps, model_path=None): @@ -327,7 +327,7 @@ def train_and_evaluate( # Make new cache for batch_idx, info in enumerate(train_loader): # Unpack - if hps.if_f0 == 1: + if hps.if_f0: ( phone, phone_lengths, @@ -353,7 +353,7 @@ def train_and_evaluate( if torch.cuda.is_available(): phone = phone.cuda(rank, non_blocking=True) phone_lengths = phone_lengths.cuda(rank, non_blocking=True) - if hps.if_f0 == 1: + if hps.if_f0: pitch = pitch.cuda(rank, non_blocking=True) pitchf = pitchf.cuda(rank, non_blocking=True) sid = sid.cuda(rank, non_blocking=True) @@ -362,7 +362,7 @@ def train_and_evaluate( wave = wave.cuda(rank, non_blocking=True) wave_lengths = wave_lengths.cuda(rank, non_blocking=True) # Cache on list - if hps.if_f0 == 1: + if hps.if_f0: cache.append( ( batch_idx, @@ -406,7 +406,7 @@ def train_and_evaluate( for batch_idx, info in tqdm(data_iterator,desc=f"[Epoch {epoch}]: "): # Data ## Unpack - if hps.if_f0 == 1: + if hps.if_f0: ( phone, phone_lengths, @@ -424,7 +424,7 @@ def train_and_evaluate( if (not hps.if_cache_data_in_gpu) and torch.cuda.is_available(): phone = phone.cuda(rank, non_blocking=True) phone_lengths = phone_lengths.cuda(rank, non_blocking=True) - if hps.if_f0 == 1: + if hps.if_f0: pitch = pitch.cuda(rank, non_blocking=True) pitchf = pitchf.cuda(rank, non_blocking=True) sid = sid.cuda(rank, non_blocking=True) @@ -434,7 +434,7 @@ def train_and_evaluate( # Calculate with autocast(enabled=hps.train.fp16_run): - if hps.if_f0 == 1: + if hps.if_f0: ( y_hat, ids_slice, @@ -473,6 +473,7 @@ def train_and_evaluate( hps.data.mel_fmax, ) if hps.train.fp16_run: y_hat_mel = y_hat_mel.half() + wave_orig = wave.clone() wave = commons.slice_segments(wave, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice # Discriminator @@ -480,46 +481,9 @@ def train_and_evaluate( y_d_hat_r, y_d_hat_g, _, _ = net_d(wave, gen_wave) with autocast(enabled=False): - gradient_penalty = torch.Tensor([0.]).to(wave.device) - if hps.train.get("c_gp",0.)>0: - # Compute the gradient penalty - # Randomly interpolate between real and generated data - size = [1]*wave.ndim - size[0] = wave.size(0) - alpha = torch.rand(*size, device=wave.device) - interpolated = alpha * wave + (1 - alpha) * gen_wave - interpolated.requires_grad_(True) - # Get the discriminator output for the interpolated data - _, disc_interpolated_output, _, _ = net_d(wave, interpolated) - # Compute gradients of discriminator output w.r.t. interpolated data - for output in disc_interpolated_output: - gradient = torch.autograd.grad( - outputs=output, - inputs=interpolated, - grad_outputs=torch.ones(output.size(), device=wave.device), - create_graph=True, - only_inputs=True - )[0] - gradient = gradient.view(gradient.size(0), -1) - gradient_penalty += torch.log(gradient.norm(2,dim=-1)).abs().mean() # force gradnorm=1 - gradient_penalty *= hps.train.c_gp/len(disc_interpolated_output) - - loss_mfcc = torch.Tensor([0.]).to(wave.device) - if hps.train.get("c_mfcc",0)>0: - loss_mfcc = mfcc_loss(wave, gen_wave, SR_MAP[hps.sample_rate], - n_fft=hps.data.filter_length, - hop_length=hps.data.hop_length, - win_length=hps.data.win_length, - n_mels=hps.data.n_mel_channels, - norm="slaney", - f_min=hps.data.mel_fmin, - f_max=hps.data.mel_fmax - ) * hps.train.c_mfcc - - loss_mfcc = loss_mfcc.squeeze() - gradient_penalty = gradient_penalty.squeeze() + gradient_penalty = gradient_norm_loss(wave,gen_wave, net_d)*hps.train.c_gp if hps.train.get("c_gp",0.)>0 else 0 loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g) - loss_disc_all = loss_disc+gradient_penalty+loss_mfcc + loss_disc_all = loss_disc+gradient_penalty optim_d.zero_grad() scaler.scale(loss_disc_all).backward() @@ -531,11 +495,25 @@ def train_and_evaluate( # Generator y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(wave, y_hat) with autocast(enabled=False): - loss_mel = F.smooth_l1_loss(y_mel, y_hat_mel) * hps.train.get("c_mel",45.) + loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.get("c_mel",45.) loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.get("c_kl",1.) loss_fm = feature_loss(fmap_r, fmap_g) * hps.train.get("c_fm",2.) + harmonic_loss, temporal_loss, spectral_loss, mfcc_loss, lfcc_loss, phase_loss = combined_aux_loss( + wave, y_hat, SR_MAP[hps.sample_rate], + c_lfcc=hps.train.get("c_lfcc",0.), + c_mfcc=hps.train.get("c_mfcc",0.), + c_hd=hps.train.get("c_hd",0.), + c_sts=hps.train.get("c_sts",0.), + n_fft=hps.data.filter_length, + hop_length=hps.data.hop_length, + win_length=hps.data.win_length, + n_filter=hps.data.n_mel_channels, + f_min=hps.data.mel_fmin, + f_max=hps.data.mel_fmax + ) loss_gen, losses_gen = generator_loss(y_d_hat_g) - loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl + aux_loss = harmonic_loss + temporal_loss + spectral_loss + mfcc_loss + lfcc_loss + phase_loss + loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl + aux_loss optim_g.zero_grad() scaler.scale(loss_gen_all).backward() @@ -548,13 +526,12 @@ def train_and_evaluate( # if epoch>=max(hps.total_epoch*.2,20): #start saving best model after 20% or 20 epochs if loss_gen_all+loss_disc_all {loss_gen=:.3f} {loss_fm=:.3f} {loss_mel=:.3f} {loss_kl=:.3f}") + 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}") if hps.save_best_model: - if hasattr(net_g, "module"): - ckpt = net_g.module.state_dict() - else: - ckpt = net_g.state_dict() + if hasattr(net_g, "module"): ckpt = net_g.module.state_dict() + else: ckpt = net_g.state_dict() 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}" status = save_checkpoint(ckpt,best_model_name,epoch,hps) @@ -575,16 +552,22 @@ def train_and_evaluate( scalar_dict = { "total/loss/all": loss_gen_all+loss_disc_all, "total/loss/gen_all": loss_gen_all, + "total/loss/aux": aux_loss, "total/loss/disc_all": loss_disc_all, "total/loss/gen": loss_gen, "total/loss/disc": loss_disc, "total/loss/fm": loss_fm, "total/loss/mel": loss_mel, "total/loss/kl": loss_kl, - "total/loss/mfcc": loss_mfcc, - "learning_rate": lr, - "gradient/grad_norm_d": grad_norm_d, - "gradient/grad_norm_g": grad_norm_g, + "aux/loss/harmonic": harmonic_loss, + "aux/loss/temporal": temporal_loss, + "aux/loss/spectral": spectral_loss, + "aux/loss/mfcc": mfcc_loss, + "aux/loss/lfcc": lfcc_loss, + "aux/loss/phase_loss": phase_loss, + "gradient/lr": lr, + "gradient/grad_norm_disc": grad_norm_d, + "gradient/grad_norm_gen": grad_norm_g, "gradient/gradient_penalty": gradient_penalty, **{f"loss/g/{i}": v for i, v in enumerate(losses_gen)}, **{f"loss/d_r/{i}": v for i, v in enumerate(losses_disc_r)}, @@ -597,9 +580,15 @@ def train_and_evaluate( "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()), } + with torch.no_grad(): + if hasattr(net_g, "module"): inference = net_g.module.infer + else: inference = net_g.infer + if hps.if_f0: wave_gen = inference(phone, phone_lengths, pitch, pitchf, sid)[0][0, 0].data + else: wave_gen = inference(phone, phone_lengths, sid)[0][0, 0].data + audio_dict = { - "slice/wave_org": wave.flatten(), - "slice/wave_gen": y_hat.flatten() + "slice/wave_org": wave_orig[0][0], + "slice/wave_gen": wave_gen } utils.summarize( writer=writer, @@ -637,14 +626,15 @@ def train_and_evaluate( if rank == 0: logger.info(f"====> Epoch {epoch} (Total Loss={(loss_disc_all+loss_gen_all).item():.3f}): {global_step=} {lr=:.2E} {epoch_recorder.record()}") - logger.info(f"|| {loss_disc_all=:.3f}: {loss_disc=:.3f}, {loss_mfcc=:.3f}, {gradient_penalty=:.3f}") + logger.info(f"|| {loss_disc_all=:.3f}: {loss_disc=:.3f}, {gradient_penalty=:.3f}") logger.info(f"|| {loss_gen_all=:.3f}: {loss_gen=:.3f}, {loss_fm=:.3f}, {loss_mel=:.3f}, {loss_kl=:.3f}") + logger.info(f"|| {harmonic_loss=:.3f}, {temporal_loss=:.3f}, {spectral_loss=:.3f}, {mfcc_loss=:.3f}, {lfcc_loss=:.3f}, {phase_loss=:.3f}") if epoch >= hps.total_epoch and rank == 0: logger.info("Training is done. The program is closed.") ckpt = net_g.module.state_dict() if hasattr(net_g, "module") else net_g.state_dict() - if hps.save_best_model: + if hps.save_best_model and os.path.isfile(loss_file): with open(loss_file,"r") as f: data = json.load(f) best_model_name = data.get("best_model_name","")