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implemented grad norm penalty+moved logs to dataset

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SayanoAI
2024-08-30 11:42:35 -04:00
parent 9136e1dfe3
commit 847ef98a06
15 changed files with 138 additions and 96 deletions
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custom_nodes/rvc_nodes.py
+42 -31
View File
@@ -19,10 +19,10 @@ from ..config import config
from ..lib.model_utils import load_hubert
from .utils import MultipleTypeProxy, increment_filename_no_overwrite, model_downloader
from .settings import PITCH_EXTRACTION_OPTIONS, SR_MAP
from .settings import PITCH_EXTRACTION_OPTIONS
from .settings.downloader import PRETRAINED_MODELS, RVC_DOWNLOAD_LINK, RVC_INDEX, RVC_MODELS, download_file, extract_zip_without_structure
from ..lib.audio import SUPPORTED_AUDIO, audio_to_bytes, load_input_audio, save_input_audio, get_audio
from ..lib.audio import SUPPORTED_AUDIO, audio_to_bytes, load_input_audio, save_input_audio, get_audio, SR_MAP
from ..vc_infer_pipeline import get_vc, vc_single
import folder_paths
@@ -38,7 +38,7 @@ weights_path = os.path.join(BASE_MODELS_DIR, "uvr5")
dataset_path = os.path.join(input_path,"datasets")
device = get_optimal_torch_device()
CATEGORY = "🌺RVC-Studio/rvc"
LOG_DIR = os.path.join(BASE_DIR,"logs")
MUTE_DIR = os.path.join(BASE_DIR,"dataset")
class LoadPitchExtractionParams:
@classmethod
@@ -259,31 +259,31 @@ class RVCProcessDatasetNode:
if "crepe" in f0_method: cached_params.append(crepe_hop_length)
cache_name = get_hash(*cached_params)
model_log_dir = os.path.join(output_path,"logs",cache_name)
os.makedirs(model_log_dir,exist_ok=True)
dataset_dir = os.path.join(output_path,"dataset",cache_name)
os.makedirs(dataset_dir,exist_ok=True)
filelist_path = os.path.join(model_log_dir, "filelist.txt")
filelist_path = os.path.join(dataset_dir, "filelist.txt")
if not os.path.isfile(filelist_path):
dataset_dir = os.path.join(input_path,"datasets",dataset.split(".")[0])
input_dir = os.path.join(dataset_path,dataset.split(".")[0])
if dataset.endswith("zip"):
files = extract_zip_without_structure(os.path.join(dataset_path,dataset),dataset_dir)
files = extract_zip_without_structure(os.path.join(dataset_path,dataset),input_dir)
assert len(files), "Failed to extract zip file..."
assert preprocess_trainset(dataset_dir,SR_MAP[sr],n_threads,model_log_dir,audio_processor,period,overlap,max_volume), "Failed to preprocess audio..."
assert preprocess_trainset(input_dir,SR_MAP[sr],n_threads,dataset_dir,audio_processor,period,overlap,max_volume), "Failed to preprocess audio..."
assert extract_features_trainset(hubert_model(), model_log_dir,n_p=n_threads,f0method=f0_method,device=device,if_f0=bool(f0_method),version="v2",crepe_hop_length=crepe_hop_length), "Failed to extract features..."
assert extract_features_trainset(hubert_model(), dataset_dir,n_p=n_threads,f0method=f0_method,device=device,if_f0=bool(f0_method),version="v2",crepe_hop_length=crepe_hop_length), "Failed to extract features..."
gt_wavs_dir = os.path.join(model_log_dir,"0_gt_wavs")
feature_dir = os.path.join(model_log_dir,"3_feature768")
gt_wavs_dir = os.path.join(dataset_dir,"0_gt_wavs")
feature_dir = os.path.join(dataset_dir,"3_feature768")
os.makedirs(gt_wavs_dir, exist_ok=True)
os.makedirs(feature_dir, exist_ok=True)
# add training data
if f0_method:
f0_dir = os.path.join(model_log_dir,"2a_f0")
f0nsf_dir = os.path.join(model_log_dir,"2b-f0nsf")
f0_dir = os.path.join(dataset_dir,"2a_f0")
f0nsf_dir = os.path.join(dataset_dir,"2b-f0nsf")
names = (
set([os.path.splitext(name)[0] for name in os.listdir(feature_dir)])
& set([os.path.splitext(name)[0] for name in os.listdir(f0_dir)])
@@ -328,16 +328,16 @@ class RVCProcessDatasetNode:
for _ in range(num_mute):
if f0_method:
data = "|".join([
os.path.join(LOG_DIR,"mute","0_gt_wavs",f"mute{sr}.wav"),
os.path.join(LOG_DIR,"mute",f"3_feature{fea_dim}","mute.npy"),
os.path.join(LOG_DIR,"mute","2a_f0","mute.wav.npy"),
os.path.join(LOG_DIR,"mute","2b-f0nsf","mute.wav.npy"),
os.path.join(MUTE_DIR,"mute","0_gt_wavs",f"mute{sr}.wav"),
os.path.join(MUTE_DIR,"mute",f"3_feature{fea_dim}","mute.npy"),
os.path.join(MUTE_DIR,"mute","2a_f0","mute.wav.npy"),
os.path.join(MUTE_DIR,"mute","2b-f0nsf","mute.wav.npy"),
str(0)
])
else:
data = "|".join([
os.path.join(LOG_DIR,"mute","0_gt_wavs",f"mute{sr}.wav"),
os.path.join(LOG_DIR,"mute",f"3_feature{fea_dim}","mute.npy"),
os.path.join(MUTE_DIR,"mute","0_gt_wavs",f"mute{sr}.wav"),
os.path.join(MUTE_DIR,"mute",f"3_feature{fea_dim}","mute.npy"),
str(0)
])
opt.append(data)
@@ -349,7 +349,7 @@ class RVCProcessDatasetNode:
print("write filelist done")
return (dict(
sample_rate=sr,
model_dir=model_log_dir,
dataset_dir=dataset_dir,
name=model_name,
training_files=filelist_path,
if_f0=bool(f0_method),
@@ -377,7 +377,7 @@ class RVCTrainModelNode:
},
"optional": dict(
gpu=(DEVICES, {"default": DEVICES[0]}),
batch_size=("INT",dict(default=4,min=0,max=64,step=4)),
batch_size=("INT",dict(default=4,min=1,max=64,step=1)),
total_epoch=("INT",dict(default=100,min=10,max=1000,step=10)),
save_every_epoch=("INT",dict(default=0,min=0,max=100)),
pretrained_G=(PRETRAINED_G,{"default": PRETRAINED_G[0]}),
@@ -388,7 +388,10 @@ class RVCTrainModelNode:
train_index=("BOOLEAN",{"default": True}),
retrain=("BOOLEAN",{"default": False}),
save_best_model=("BOOLEAN",{"default": True}),
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=.5)),
gradient_lambda=("FLOAT",dict(default=0.,min=0.,max=100.,step=.1)),
timbre_lambda=("FLOAT",dict(default=0.,min=0.,max=100.,step=.1)),
num_workers=("INT",dict(default=1,min=1,max=16))
)
}
@@ -415,11 +418,16 @@ class RVCTrainModelNode:
train_index=True,
retrain=False,
save_best_model=True,
log_every_epoch=1.):
log_every_epoch=1.,
gradient_lambda=0.,
timbre_lambda=0.,
num_workers=1):
sample_rate = rvc_dataset_pipe["sample_rate"]
name = rvc_dataset_pipe["name"]
model_dir = rvc_dataset_pipe["model_dir"]
dataset_dir = rvc_dataset_pipe["dataset_dir"]
cache_name = get_hash(batch_size,pretrained_G,pretrained_D,gradient_lambda,timbre_lambda)
model_dir = os.path.join(output_path,"logs",cache_name)
if_f0 = rvc_dataset_pipe["if_f0"]
config_path = os.path.join(BASE_DIR,"configs",f"{sample_rate}{'' if sample_rate=='40k' else '_v2'}.json")
@@ -427,7 +435,8 @@ class RVCTrainModelNode:
config = json.load(f)
hparams = HParams(**config)
hparams.model_dir = hparams.experiment_dir = model_dir
hparams.experiment_dir = dataset_dir
hparams.model_dir = model_dir
hparams.save_every_epoch = save_every_epoch
hparams.name = name
hparams.total_epoch = total_epoch
@@ -444,8 +453,11 @@ class RVCTrainModelNode:
hparams.data.training_files = rvc_dataset_pipe["training_files"]
hparams.save_best_model = save_best_model
hparams.log_every_epoch = log_every_epoch
hparams.train.gradient_lambda = gradient_lambda
hparams.train.num_workers = num_workers
hparams.train.timbre_lambda = timbre_lambda
file_index = self.train_index(model_dir, sample_rate, name) if train_index else None
file_index = self.train_index(dataset_dir, sample_rate, name) if train_index else None
model_path = os.path.join(BASE_MODELS_DIR,"RVC",f"{name}_{sample_rate}.pth")
if os.path.isfile(model_path) and retrain: model_path = increment_filename_no_overwrite(model_path)
hparams.model_path = model_path
@@ -455,9 +467,9 @@ class RVCTrainModelNode:
return (lambda: get_vc(model_path, file_index), name, rvc_dataset_pipe["hubert_model"], rvc_dataset_pipe["pitch_extraction_params"])
def train_index(self, model_log_dir, sr, name):
def train_index(self, dataset_dir, sr, name):
key = get_hash(model_log_dir, sr, name)
key = get_hash(dataset_dir, sr, name)
index_file = os.path.join(BASE_MODELS_DIR,"RVC",".index",f"{name}_v2_{sr}_{key}.index")
try:
@@ -466,8 +478,7 @@ class RVCTrainModelNode:
from sklearn.cluster import MiniBatchKMeans
import faiss
feature_dir = os.sep.join([model_log_dir, "3_feature768"])
os.makedirs(feature_dir, exist_ok=True)
feature_dir = os.path.join(dataset_dir, "3_feature768")
npys = []
listdir_res = list(os.listdir(feature_dir))
custom_nodes/settings/__init__.py
-1
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@@ -11,5 +11,4 @@ PITCH_EXTRACTION_OPTIONS = ["crepe","mangio-crepe","rmvpe","rmvpe+"]
MERGE_OPTIONS=["median","mean","min","max"]
TTS_MODELS = ["edge","speecht5"]
N_THREADS_OPTIONS=[1,2,4,8,12,16]
SR_MAP = {"32k": 32000,"40k": 40000, "48k": 48000}
SUPPORTED_LANGUAGES = ['en', 'fr', 'es', "ja", "zh"]
logs/.gitignore → dataset/.gitignore
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logs/mute/.gitignore → dataset/mute/.gitignore
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logs/mute/0_gt_wavs/mute32k.wav → dataset/mute/0_gt_wavs/mute32k.wav
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logs/mute/0_gt_wavs/mute40k.wav → dataset/mute/0_gt_wavs/mute40k.wav
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logs/mute/0_gt_wavs/mute48k.wav → dataset/mute/0_gt_wavs/mute48k.wav
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logs/mute/1_16k_wavs/mute.wav → dataset/mute/1_16k_wavs/mute.wav
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logs/mute/2a_f0/mute.wav.npy → dataset/mute/2a_f0/mute.wav.npy
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logs/mute/2b-f0nsf/mute.wav.npy → dataset/mute/2b-f0nsf/mute.wav.npy
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logs/mute/3_feature256/mute.npy → dataset/mute/3_feature256/mute.npy
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logs/mute/3_feature768/mute.npy → dataset/mute/3_feature768/mute.npy
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lib/audio.py
+1
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@@ -27,6 +27,7 @@ AUTOTUNE_NOTES = np.array([
2093.00, 2217.46, 2349.32, 2489.02, 2637.02, 2793.83,
2959.96, 3135.96, 3322.44, 3520.00, 3729.31, 3951.07
])
SR_MAP = {"32k": 32000,"40k": 40000, "48k": 48000}
class AudioProcessor:
def __init__(self, normalize=True, threshold_silence=True, dynamic_threshold=True, sample_size=16000, multiplier=2.0, fill_method="median", kernel_size=5, silence_threshold_db=-50, normalize_threshold_db=-1):
lib/train/utils.py
+9 -1
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@@ -382,6 +382,10 @@ def get_hparams(init=True):
hparams.if_cache_data_in_gpu = args.if_cache_data_in_gpu
hparams.data.training_files = os.path.join(experiment_dir,"filelist.txt")
hparams.save_best_model = True
hparams.train.gradient_lambda = 0.
hparams.train.num_workers = 4
hparams.train.timbre_alpha = 0.
hparams.model_path = None
return hparams
@@ -476,6 +480,10 @@ class HParams:
def __repr__(self):
return self.__dict__.__repr__()
def get(self, key, default=None):
try: return self[key]
except: return default
def sync_log_interval(self, dataset_length):
log_interval = getattr(self,"log_every_epoch",1)
self.train.log_interval = int(self.train.batch_size*dataset_length*log_interval)
self.train.log_interval = int(dataset_length*log_interval)
training_cli.py
+86 -63
View File
@@ -3,14 +3,11 @@ import json
import os
import shutil
import traceback
from .lib.audio import SR_MAP
from .lib.train import utils
import datetime
from random import shuffle, randint
import torch
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = False
from torch.nn import functional as F
@@ -30,7 +27,6 @@ from .lib.train.data_utils import (
TextAudioCollate,
DistributedBucketSampler,
)
from .lib.train.losses import generator_loss, discriminator_loss, feature_loss, kl_loss
from .lib.train.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
@@ -122,7 +118,7 @@ def train_model(hps: "utils.HParams"):
def run(rank, n_gpus, hps, device):
print(f"{__name__=}")
global global_step, least_loss, loss_file
global global_step, least_loss, loss_file, best_model_name
global_step = 0
loss_file = os.path.join(hps.model_dir,"losses.json")
@@ -130,7 +126,10 @@ def run(rank, n_gpus, hps, device):
with open(loss_file,"r") as f:
data: dict = json.load(f)
least_loss = data.get("least_loss",40)
else: least_loss = 40
best_model_name = data.get("best_model_name","")
else:
least_loss = 40
best_model_name = ""
if hps.version == "v1":
from .lib.infer_pack.models import (
@@ -304,16 +303,16 @@ def run(rank, n_gpus, hps, device):
def train_and_evaluate(
rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers, cache
rank, epoch, hps, nets, optims, _, scaler, loaders, logger, writers, cache
):
net_g, net_d = nets
optim_g, optim_d = optims
train_loader, eval_loader = loaders
train_loader, _ = loaders
if writers is not None:
writer, writer_eval = writers
writer, _ = writers
train_loader.batch_sampler.set_epoch(epoch)
global global_step, least_loss, loss_file
global global_step, least_loss, loss_file, best_model_name
net_g.train()
net_d.train()
@@ -479,13 +478,41 @@ def train_and_evaluate(
) # slice
# Discriminator
y_d_hat_r, y_d_hat_g, _, _ = net_d(wave, y_hat.detach())
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
optim_d.zero_grad()
scaler.scale(loss_disc).backward()
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)
@@ -507,38 +534,35 @@ def train_and_evaluate(
scaler.update()
if rank == 0:
if loss_gen_all<least_loss:
least_loss = loss_gen_all
if hps.save_best_model and epoch>min(hps.total_epoch*.2,20): #start saving after 20% or 20 epochs
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{loss_gen_all:2.2f}"
status = save_checkpoint(ckpt,best_model_name,epoch,hps)
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 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=} ===")
with open(loss_file,"w") as f:
json.dump(dict(least_loss=least_loss.item(),best_model=os.path.join(hps.model_dir,best_model_name+".pth")),f)
logger.info(f"=== saving best model: epoch {hps.name}_e{epoch}_{loss_gen_all:2.2f}: {status=} ===")
logger.info(f"[lowest loss] loss_disc={loss_disc:.3f}, loss_gen={loss_gen:.3f}, loss_fm={loss_fm:.3f},loss_mel={loss_mel:.3f}, loss_kl={loss_kl:.3f}")
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(
"Train Epoch: {} [{:.0f}%]".format(epoch, 100.0 * batch_idx / len(train_loader))
)
logger.info(f"Train Epoch: {epoch} [{100.0 * batch_idx / len(train_loader):.0f}%]")
# Amor For Tensorboard display
if loss_mel > 75:
loss_mel = 75
if loss_kl > 9:
loss_kl = 9
logger.info(f"epoch={epoch} steps={global_step} lr={lr} total_loss={loss_gen_all}")
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}")
logger.info(
f"loss_disc={loss_disc:.3f}, loss_gen={loss_gen:.3f}, loss_fm={loss_fm:.3f},loss_mel={loss_mel:.3f}, loss_kl={loss_kl:.3f}"
)
scalar_dict = {
"total/loss/all": loss_gen_all,
"total/loss/g": loss_gen,
@@ -547,35 +571,31 @@ def train_and_evaluate(
"total/loss/mel": loss_mel,
"total/loss/kl": loss_kl,
"learning_rate": lr,
"grad_norm_d": grad_norm_d,
"grad_norm_g": grad_norm_g,
"gradient/grad_norm_d": grad_norm_d,
"gradient/grad_norm_g": 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)},
**{f"loss/d_g/{i}": v for i, v in enumerate(losses_disc_g)}
}
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()
),
"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()),
}
audio_dict = {
"slice/wave_org": wave.flatten(),
"slice/wave_gen": y_hat.flatten()
}
utils.summarize(
writer=writer,
global_step=global_step,
images=image_dict,
scalars=scalar_dict,
audios=audio_dict,
audio_sampling_rate=SR_MAP[hps.sample_rate]
)
global_step += 1
# /Run steps
@@ -588,14 +608,14 @@ def train_and_evaluate(
optim_g,
hps.train.learning_rate,
epoch,
os.path.join(hps.model_dir, f"G_23333{hps.total_epoch}.pth"),
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, f"D_23333{hps.total_epoch}.pth"),
os.path.join(hps.model_dir, "D_23333.pth"),
)
else:
utils.save_checkpoint(
@@ -603,14 +623,14 @@ def train_and_evaluate(
optim_g,
hps.train.learning_rate,
epoch,
os.path.join(hps.model_dir, f"G_{global_step}.pth"),
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_{global_step}.pth"),
os.path.join(hps.model_dir, f"D_{epoch}.pth"),
)
if rank == 0 and hps.save_every_weights:
if hasattr(net_g, "module"):
@@ -621,7 +641,7 @@ def train_and_evaluate(
logger.info(f"saving ckpt {hps.name}_e{epoch}: {status}")
if rank == 0:
logger.info("====> Epoch: {} {}".format(epoch, epoch_recorder.record()))
logger.info(f"====> Epoch {epoch}: {loss_gen_all=:.3f} {loss_disc=:.3f} {epoch_recorder.record()}")
if epoch >= hps.total_epoch and rank == 0:
logger.info("Training is done. The program is closed.")
@@ -632,12 +652,15 @@ def train_and_evaluate(
if hps.save_best_model:
with open(loss_file,"r") as f:
data = json.load(f)
best_model = data["best_model"]
if os.path.isfile(best_model):
shutil.copy(best_model,os.path.join(os.path.dirname(hps.model_path),f"{hps.name}_{hps.sample_rate}-best.pth"))
best_model_name = data.get("best_model_name","")
best_model_path = os.path.join(hps.model_dir,f"{best_model_name}.pth")
if os.path.isfile(best_model_path):
shutil.copy(best_model_path,os.path.join(
os.path.dirname(hps.model_path),
f"{os.path.basename(hps.model_path).split('.')[0]}-best.pth"))
status = save_checkpoint(ckpt,hps.name,epoch,hps,model_path=hps.model_path)
logger.info(f"saving final ckpt: {status}")
logger.info(f"saving final ckpt {hps.model_path}: {status}")
sleep(1)
os._exit(0)