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added mfcc loss term

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SayanoAI
2024-08-30 12:47:12 -04:00
parent 847ef98a06
commit b52bae0be0
3 changed files with 141 additions and 120 deletions
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lib/train/losses.py
+27 -1
View File
@@ -1,11 +1,37 @@
import torch
import torchaudio
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)
# Compute MFCCs for original and generated audio
original_mfcc = mfcc_transform(original_audio)
generated_mfcc = mfcc_transform(generated_audio)
# Compute the L2 loss between the MFCCs of the original and generated audio
loss = torch.nn.functional.smooth_l1_loss(generated_mfcc, original_mfcc)
return loss
def feature_loss(fmap_r, fmap_g):
loss = 0
for dr, dg in zip(fmap_r, fmap_g):
for rl, gl in zip(dr, dg):
rl = rl.float().detach()
rl = rl.float()
gl = gl.float()
loss += torch.mean(torch.abs(rl - gl))
lib/train/utils.py
+1 -1
View File
@@ -384,7 +384,7 @@ def get_hparams(init=True):
hparams.save_best_model = True
hparams.train.gradient_lambda = 0.
hparams.train.num_workers = 4
hparams.train.timbre_alpha = 0.
hparams.train.timbre_lambda = 0.
hparams.model_path = None
return hparams
training_cli.py
+113 -118
View File
@@ -3,6 +3,8 @@ import json
import os
import shutil
import traceback
from tqdm import tqdm
from .lib.audio import SR_MAP
from .lib.train import utils
import datetime
@@ -27,7 +29,7 @@ from .lib.train.data_utils import (
TextAudioCollate,
DistributedBucketSampler,
)
from .lib.train.losses import generator_loss, discriminator_loss, feature_loss, kl_loss
from .lib.train.losses import generator_loss, discriminator_loss, feature_loss, kl_loss, mfcc_loss
from .lib.train.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
def save_checkpoint(ckpt, name, epoch, hps, model_path=None):
@@ -84,7 +86,7 @@ def train_model(hps: "utils.HParams"):
os.environ["CUDA_VISIBLE_DEVICES"] = hps.gpus.replace("-", ",")
os.environ["NCCL_P2P_DISABLE"] = "1"
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = str(randint(8189, 8200))
os.environ["MASTER_PORT"] = str(randint(8189, 8205+hps.train.num_workers**2))
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128,garbage_collection_threshold:0.8"
n_gpus = len(hps.gpus.split("-")) if hps.gpus else torch.cuda.device_count()
@@ -125,10 +127,10 @@ def run(rank, n_gpus, hps, device):
if os.path.isfile(loss_file):
with open(loss_file,"r") as f:
data: dict = json.load(f)
least_loss = data.get("least_loss",40)
least_loss = data.get("least_loss",50)
best_model_name = data.get("best_model_name","")
else:
least_loss = 40
least_loss = 50
best_model_name = ""
if hps.version == "v1":
@@ -180,7 +182,7 @@ def run(rank, n_gpus, hps, device):
collate_fn = TextAudioCollate()
train_loader = DataLoader(
train_dataset,
num_workers=4,
num_workers=hps.train.num_workers,
shuffle=False,
pin_memory=True,
collate_fn=collate_fn,
@@ -318,7 +320,7 @@ def train_and_evaluate(
net_d.train()
# Prepare data iterator
if hps.if_cache_data_in_gpu == True:
if hps.if_cache_data_in_gpu:
# Use Cache
data_iterator = cache
if cache == []:
@@ -401,7 +403,7 @@ def train_and_evaluate(
# Run steps
epoch_recorder = EpochRecorder()
for batch_idx, info in data_iterator:
for batch_idx, info in tqdm(data_iterator,desc=f"[Epoch {epoch}]: "):
# Data
## Unpack
if hps.if_f0 == 1:
@@ -419,7 +421,7 @@ def train_and_evaluate(
else:
phone, phone_lengths, spec, spec_lengths, wave, wave_lengths, sid = info
## Load on CUDA
if (hps.if_cache_data_in_gpu == False) and torch.cuda.is_available():
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:
@@ -429,7 +431,6 @@ def train_and_evaluate(
spec = spec.cuda(rank, non_blocking=True)
spec_lengths = spec_lengths.cuda(rank, non_blocking=True)
wave = wave.cuda(rank, non_blocking=True)
# wave_lengths = wave_lengths.cuda(rank, non_blocking=True)
# Calculate
with autocast(enabled=hps.train.fp16_run):
@@ -471,105 +472,116 @@ def train_and_evaluate(
hps.data.mel_fmin,
hps.data.mel_fmax,
)
if hps.train.fp16_run == True:
y_hat_mel = y_hat_mel.half()
wave = commons.slice_segments(
wave, ids_slice * hps.data.hop_length, hps.train.segment_size
) # slice
if hps.train.fp16_run: y_hat_mel = y_hat_mel.half()
wave = commons.slice_segments(wave, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice
# Discriminator
gen_wave = y_hat.detach()
y_d_hat_r, y_d_hat_g, _, _ = net_d(wave, gen_wave)
gradient_penalty = torch.Tensor([0.]).to(wave.device)
if hps.train.get("gradient_lambda",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 += ((gradient.norm(2,dim=-1) - 1) ** 2).mean()
gradient_penalty = gradient_penalty.squeeze()
with autocast(enabled=False):
loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
y_d_hat_r, y_d_hat_g
)
if hps.train.get("gradient_lambda",0.)>0: gradient_penalty*=hps.train.gradient_lambda
gradient_penalty = torch.Tensor([0.]).to(wave.device)
if hps.train.get("gradient_lambda",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.gradient_lambda/len(disc_interpolated_output)
optim_d.zero_grad()
scaler.scale(loss_disc+gradient_penalty).backward()
scaler.unscale_(optim_d)
grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
scaler.step(optim_d)
loss_mfcc = torch.Tensor([0.]).to(wave.device)
if hps.train.get("timbre_lambda",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.timbre_lambda
loss_mfcc = loss_mfcc.squeeze()
gradient_penalty = gradient_penalty.squeeze()
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
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(wave, y_hat)
with autocast(enabled=False):
loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
loss_mel = F.smooth_l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
loss_fm = feature_loss(fmap_r, fmap_g)
loss_gen, losses_gen = generator_loss(y_d_hat_g)
loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl
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()
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 epoch>=max(hps.total_epoch*.2,20): #start saving best model after 20% or 20 epochs
if loss_gen_all+loss_disc<least_loss:
least_loss = loss_gen_all+loss_disc
logger.info(f"[lowest loss] {least_loss=:.3f}: {loss_disc=:.3f}, {loss_gen=:.3f}, {loss_fm=:.3f}, {loss_mel=:.3f}, {loss_kl=:.3f}")
# if epoch>=max(hps.total_epoch*.2,20): #start saving best model after 20% or 20 epochs
if loss_gen_all+loss_disc_all<least_loss:
least_loss = loss_gen_all+loss_disc_all
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}")
if hps.save_best_model:
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:2.2f}"
status = save_checkpoint(ckpt,best_model_name,epoch,hps)
logger.info(f"=== saving best model {best_model_name}: {status=} ===")
if hps.save_best_model:
if hasattr(net_g, "module"):
ckpt = net_g.module.state_dict()
else:
ckpt = net_g.state_dict()
with open(loss_file,"w") as f:
json.dump(dict(least_loss=least_loss.item(),best_model_name=best_model_name),f)
best_model_name = f"{hps.name}_loss{least_loss:2.0f}" if hps.if_save_latest else f"{hps.name}_e{epoch}_s{global_step}_loss{least_loss:2.0f}"
status = save_checkpoint(ckpt,best_model_name,epoch,hps)
logger.info(f"=== saving best model {best_model_name}: {status=} ===")
with open(loss_file,"w") as f:
json.dump(dict(least_loss=least_loss.item(),best_model_name=best_model_name),f)
if hps.train.log_interval>0 and global_step % hps.train.log_interval == 0:
lr = optim_g.param_groups[0]["lr"]
logger.info(f"Train Epoch: {epoch} [{100.0 * batch_idx / len(train_loader):.0f}%]")
logger.info(f"Train Epoch: {epoch} [{100.0 * epoch / hps.total_epoch:.2f}% complete]")
# Amor For Tensorboard display
if loss_mel > 75:
loss_mel = 75
if loss_kl > 9:
loss_kl = 9
logger.info(f"{epoch=} {global_step=} {lr=:.2E} {loss_disc=:.3f} {gradient_penalty.item()=:.2E}")
logger.info(f"{loss_gen_all=:.3f}: {loss_gen=:.3f}, {loss_fm=:.3f}, {loss_mel=:.3f}, {loss_kl=:.3f}")
scalar_dict = {
"total/loss/all": loss_gen_all,
"total/loss/g": loss_gen,
"total/loss/d": loss_disc,
"total/loss/all": loss_gen_all+loss_disc_all,
"total/loss/gen_all": loss_gen_all,
"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,
@@ -602,53 +614,36 @@ def train_and_evaluate(
if hps.save_every_epoch>0 and (epoch % hps.save_every_epoch == 0) and rank == 0:
if hps.if_latest:
utils.save_checkpoint(
net_g,
optim_g,
hps.train.learning_rate,
epoch,
os.path.join(hps.model_dir, "G_23333.pth"),
)
utils.save_checkpoint(
net_d,
optim_d,
hps.train.learning_rate,
epoch,
os.path.join(hps.model_dir, "D_23333.pth"),
)
else:
utils.save_checkpoint(
net_g,
optim_g,
hps.train.learning_rate,
epoch,
os.path.join(hps.model_dir, f"G_{epoch}.pth"),
)
utils.save_checkpoint(
net_d,
optim_d,
hps.train.learning_rate,
epoch,
os.path.join(hps.model_dir, f"D_{epoch}.pth"),
)
if rank == 0 and hps.save_every_weights:
if hasattr(net_g, "module"):
ckpt = net_g.module.state_dict()
else:
ckpt = net_g.state_dict()
status = save_checkpoint(ckpt,f"{hps.name}_e{epoch}_s{global_step}",epoch,hps)
logger.info(f"saving ckpt {hps.name}_e{epoch}: {status}")
saved_epoch = 23333 if hps.if_latest else epoch
utils.save_checkpoint(
net_g,
optim_g,
hps.train.learning_rate,
epoch,
os.path.join(hps.model_dir, f"G_{saved_epoch}.pth"),
)
utils.save_checkpoint(
net_d,
optim_d,
hps.train.learning_rate,
epoch,
os.path.join(hps.model_dir, f"D_{saved_epoch}.pth"),
)
if hps.save_every_weights:
ckpt = net_g.module.state_dict() if hasattr(net_g, "module") else net_g.state_dict()
save_name = f"{hps.name}_e{epoch}_s{global_step}"
status = save_checkpoint(ckpt,save_name,epoch,hps)
logger.info(f"saving ckpt {save_name}: {status}")
if rank == 0:
logger.info(f"====> Epoch {epoch}: {loss_gen_all=:.3f} {loss_disc=:.3f} {epoch_recorder.record()}")
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_gen_all=:.3f}: {loss_gen=:.3f}, {loss_fm=:.3f}, {loss_mel=:.3f}, {loss_kl=:.3f}")
if epoch >= hps.total_epoch and rank == 0:
logger.info("Training is done. The program is closed.")
if hasattr(net_g, "module"):
ckpt = net_g.module.state_dict()
else:
ckpt = net_g.state_dict()
ckpt = net_g.module.state_dict() if hasattr(net_g, "module") else net_g.state_dict()
if hps.save_best_model:
with open(loss_file,"r") as f:
data = json.load(f)