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import torch
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import sys
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import os
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import datetime
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import glob
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import json
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import re
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from utils import (
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get_hparams,
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plot_spectrogram_to_numpy,
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summarize,
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load_checkpoint,
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save_checkpoint,
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latest_checkpoint_path,
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)
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from random import randint, shuffle
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from time import sleep
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from time import time as ttime
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from torch.cuda.amp import GradScaler, autocast
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from torch.nn import functional as F
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.utils.data import DataLoader
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from torch.utils.tensorboard import SummaryWriter
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import torch.distributed as dist
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import torch.multiprocessing as mp
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now_dir = os.getcwd()
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sys.path.append(os.path.join(now_dir))
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from data_utils import (
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DistributedBucketSampler,
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TextAudioCollate,
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TextAudioCollateMultiNSFsid,
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TextAudioLoader,
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TextAudioLoaderMultiNSFsid,
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)
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from losses import (
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discriminator_loss,
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feature_loss,
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generator_loss,
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kl_loss,
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)
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from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
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from rvc.train.process.extract_model import extract_model
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from rvc.lib.infer_pack import commons
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hps = get_hparams()
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if hps.version == "v1":
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from rvc.lib.infer_pack.models import MultiPeriodDiscriminator
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from rvc.lib.infer_pack.models import SynthesizerTrnMs256NSFsid as RVC_Model_f0
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from rvc.lib.infer_pack.models import (
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SynthesizerTrnMs256NSFsid_nono as RVC_Model_nof0,
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)
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elif hps.version == "v2":
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from rvc.lib.infer_pack.models import (
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SynthesizerTrnMs768NSFsid as RVC_Model_f0,
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SynthesizerTrnMs768NSFsid_nono as RVC_Model_nof0,
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MultiPeriodDiscriminatorV2 as MultiPeriodDiscriminator,
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)
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os.environ["CUDA_VISIBLE_DEVICES"] = hps.gpus.replace("-", ",")
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n_gpus = len(hps.gpus.split("-"))
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torch.backends.cudnn.deterministic = False
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torch.backends.cudnn.benchmark = False
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global_step = 0
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lowest_value = {"step": 0, "value": float("inf"), "epoch": 0}
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last_loss_gen_all = 0
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class EpochRecorder:
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def __init__(self):
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self.last_time = ttime()
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def record(self):
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now_time = ttime()
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elapsed_time = now_time - self.last_time
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self.last_time = now_time
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elapsed_time = round(elapsed_time, 1)
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elapsed_time_str = str(datetime.timedelta(seconds=int(elapsed_time)))
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current_time = datetime.datetime.now().strftime("%H:%M:%S")
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return f"time={current_time} | training_speed={elapsed_time_str}"
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def main():
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def start():
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children = []
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pid_file_path = os.path.join(now_dir, "rvc", "train", "train_pid.txt")
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with open(pid_file_path, "w") as pid_file:
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for i in range(n_gpus):
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subproc = mp.Process(
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target=run,
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args=(i, n_gpus, hps),
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)
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children.append(subproc)
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subproc.start()
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pid_file.write(str(subproc.pid) + "\n")
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for i in range(n_gpus):
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children[i].join()
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n_gpus = torch.cuda.device_count()
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if torch.cuda.is_available() == False and torch.backends.mps.is_available() == True:
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n_gpus = 1
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if n_gpus < 1:
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print("GPU not detected, reverting to CPU (not recommended)")
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n_gpus = 1
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if hps.sync_graph == 1:
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print(
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"Sync graph is now activated! With sync graph enabled, the model undergoes a single epoch of training. Once the graphs are synchronized, training proceeds for the previously specified number of epochs."
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)
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hps.custom_total_epoch = 1
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hps.custom_save_every_weights = "1"
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start()
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logs_path = os.path.join(now_dir, "logs")
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model_config_file = os.path.join(now_dir, "logs", hps.name, "config.json")
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rvc_config_file = os.path.join(
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now_dir, "rvc", "configs", hps.version, str(hps.sample_rate) + ".json"
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)
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if not os.path.exists(rvc_config_file):
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rvc_config_file = os.path.join(
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now_dir, "rvc", "configs", "v1", str(hps.sample_rate) + ".json"
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)
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pattern = rf"{os.path.basename(hps.name)}_1e_(\d+)s\.pth"
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for filename in os.listdir(logs_path):
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match = re.match(pattern, filename)
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if match:
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steps = int(match.group(1))
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def edit_config(config_file):
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with open(config_file, "r", encoding="utf8") as json_file:
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config_data = json.load(json_file)
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config_data["train"]["log_interval"] = steps
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with open(config_file, "w", encoding="utf8") as json_file:
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json.dump(
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config_data,
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json_file,
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indent=2,
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separators=(",", ": "),
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ensure_ascii=False,
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)
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edit_config(model_config_file)
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edit_config(rvc_config_file)
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for root, dirs, files in os.walk(
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os.path.join(now_dir, "logs", hps.name), topdown=False
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):
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for name in files:
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file_path = os.path.join(root, name)
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file_name, file_extension = os.path.splitext(name)
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if file_extension == ".0":
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os.remove(file_path)
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elif ("D" in name or "G" in name) and file_extension == ".pth":
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os.remove(file_path)
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elif (
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"added" in name or "trained" in name
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) and file_extension == ".index":
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os.remove(file_path)
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for name in dirs:
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if name == "eval":
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folder_path = os.path.join(root, name)
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for item in os.listdir(folder_path):
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item_path = os.path.join(folder_path, item)
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if os.path.isfile(item_path):
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os.remove(item_path)
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os.rmdir(folder_path)
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print("Successfully synchronized graphs!")
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hps.custom_total_epoch = hps.total_epoch
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hps.custom_save_every_weights = hps.save_every_weights
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start()
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else:
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hps.custom_total_epoch = hps.total_epoch
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hps.custom_save_every_weights = hps.save_every_weights
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start()
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def run(
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rank,
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n_gpus,
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hps,
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):
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global global_step
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if rank == 0:
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writer = SummaryWriter(log_dir=hps.model_dir)
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writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
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os.environ["MASTER_ADDR"] = "localhost"
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os.environ["MASTER_PORT"] = str(randint(20000, 55555))
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dist.init_process_group(
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backend="gloo", init_method="env://", world_size=n_gpus, rank=rank
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)
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torch.manual_seed(hps.train.seed)
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if torch.cuda.is_available():
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torch.cuda.set_device(rank)
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if hps.if_f0 == 1:
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train_dataset = TextAudioLoaderMultiNSFsid(hps.data)
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else:
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train_dataset = TextAudioLoader(hps.data)
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train_sampler = DistributedBucketSampler(
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train_dataset,
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hps.train.batch_size * n_gpus,
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[100, 200, 300, 400, 500, 600, 700, 800, 900],
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num_replicas=n_gpus,
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rank=rank,
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shuffle=True,
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)
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if hps.if_f0 == 1:
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collate_fn = TextAudioCollateMultiNSFsid()
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else:
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collate_fn = TextAudioCollate()
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train_loader = DataLoader(
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train_dataset,
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num_workers=4,
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shuffle=False,
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pin_memory=True,
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collate_fn=collate_fn,
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batch_sampler=train_sampler,
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persistent_workers=True,
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prefetch_factor=8,
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)
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if hps.if_f0 == 1:
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net_g = RVC_Model_f0(
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hps.data.filter_length // 2 + 1,
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hps.train.segment_size // hps.data.hop_length,
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**hps.model,
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is_half=hps.train.fp16_run,
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sr=hps.sample_rate,
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)
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else:
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net_g = RVC_Model_nof0(
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hps.data.filter_length // 2 + 1,
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hps.train.segment_size // hps.data.hop_length,
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**hps.model,
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is_half=hps.train.fp16_run,
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)
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if torch.cuda.is_available():
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net_g = net_g.cuda(rank)
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net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm)
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if torch.cuda.is_available():
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net_d = net_d.cuda(rank)
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optim_g = torch.optim.AdamW(
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net_g.parameters(),
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hps.train.learning_rate,
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betas=hps.train.betas,
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eps=hps.train.eps,
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)
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optim_d = torch.optim.AdamW(
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net_d.parameters(),
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hps.train.learning_rate,
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betas=hps.train.betas,
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eps=hps.train.eps,
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)
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if torch.cuda.is_available():
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net_g = DDP(net_g, device_ids=[rank])
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net_d = DDP(net_d, device_ids=[rank])
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else:
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net_g = DDP(net_g)
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net_d = DDP(net_d)
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try:
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print("Starting training...")
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_, _, _, epoch_str = load_checkpoint(
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latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d
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)
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_, _, _, epoch_str = load_checkpoint(
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latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g
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)
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global_step = (epoch_str - 1) * len(train_loader)
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except:
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epoch_str = 1
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global_step = 0
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if hps.pretrainG != "":
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if rank == 0:
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print(f"Loaded pretrained_G {hps.pretrainG}")
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if hasattr(net_g, "module"):
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print(
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net_g.module.load_state_dict(
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torch.load(hps.pretrainG, map_location="cpu")["model"]
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)
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)
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else:
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print(
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net_g.load_state_dict(
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torch.load(hps.pretrainG, map_location="cpu")["model"]
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)
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)
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if hps.pretrainD != "":
|
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if rank == 0:
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print(f"Loaded pretrained_D {hps.pretrainD}")
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if hasattr(net_d, "module"):
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print(
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net_d.module.load_state_dict(
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torch.load(hps.pretrainD, map_location="cpu")["model"]
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)
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)
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else:
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print(
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net_d.load_state_dict(
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torch.load(hps.pretrainD, map_location="cpu")["model"]
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)
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)
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scheduler_g = torch.optim.lr_scheduler.ExponentialLR(
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optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
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)
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scheduler_d = torch.optim.lr_scheduler.ExponentialLR(
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optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
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)
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scaler = GradScaler(enabled=hps.train.fp16_run)
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cache = []
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for epoch in range(epoch_str, hps.train.epochs + 1):
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if rank == 0:
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train_and_evaluate(
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rank,
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epoch,
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hps,
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[net_g, net_d],
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[optim_g, optim_d],
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scaler,
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[train_loader, None],
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[writer, writer_eval],
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cache,
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)
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else:
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train_and_evaluate(
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rank,
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epoch,
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hps,
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[net_g, net_d],
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[optim_g, optim_d],
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scaler,
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[train_loader, None],
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None,
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cache,
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)
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scheduler_g.step()
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scheduler_d.step()
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def train_and_evaluate(rank, epoch, hps, nets, optims, scaler, loaders, writers, cache):
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global global_step, last_loss_gen_all, lowest_value
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if epoch == 1:
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lowest_value = {"step": 0, "value": float("inf"), "epoch": 0}
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last_loss_gen_all = 0.0
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net_g, net_d = nets
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optim_g, optim_d = optims
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train_loader = loaders[0] if loaders is not None else None
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if writers is not None:
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writer = writers[0]
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train_loader.batch_sampler.set_epoch(epoch)
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net_g.train()
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net_d.train()
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if hps.if_cache_data_in_gpu == True:
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data_iterator = cache
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if cache == []:
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for batch_idx, info in enumerate(train_loader):
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if hps.if_f0 == 1:
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(
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phone,
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phone_lengths,
|
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pitch,
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pitchf,
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spec,
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spec_lengths,
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wave,
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wave_lengths,
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sid,
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) = info
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else:
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(
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phone,
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phone_lengths,
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spec,
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spec_lengths,
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wave,
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wave_lengths,
|
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sid,
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) = info
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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)
|
|
if hps.if_f0 == 1:
|
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pitch = pitch.cuda(rank, non_blocking=True)
|
|
pitchf = pitchf.cuda(rank, non_blocking=True)
|
|
sid = sid.cuda(rank, non_blocking=True)
|
|
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)
|
|
if hps.if_f0 == 1:
|
|
cache.append(
|
|
(
|
|
batch_idx,
|
|
(
|
|
phone,
|
|
phone_lengths,
|
|
pitch,
|
|
pitchf,
|
|
spec,
|
|
spec_lengths,
|
|
wave,
|
|
wave_lengths,
|
|
sid,
|
|
),
|
|
)
|
|
)
|
|
else:
|
|
cache.append(
|
|
(
|
|
batch_idx,
|
|
(
|
|
phone,
|
|
phone_lengths,
|
|
spec,
|
|
spec_lengths,
|
|
wave,
|
|
wave_lengths,
|
|
sid,
|
|
),
|
|
)
|
|
)
|
|
else:
|
|
shuffle(cache)
|
|
else:
|
|
data_iterator = enumerate(train_loader)
|
|
|
|
epoch_recorder = EpochRecorder()
|
|
for batch_idx, info in data_iterator:
|
|
if hps.if_f0 == 1:
|
|
(
|
|
phone,
|
|
phone_lengths,
|
|
pitch,
|
|
pitchf,
|
|
spec,
|
|
spec_lengths,
|
|
wave,
|
|
wave_lengths,
|
|
sid,
|
|
) = info
|
|
else:
|
|
phone, phone_lengths, spec, spec_lengths, wave, wave_lengths, sid = info
|
|
if (hps.if_cache_data_in_gpu == False) 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:
|
|
pitch = pitch.cuda(rank, non_blocking=True)
|
|
pitchf = pitchf.cuda(rank, non_blocking=True)
|
|
sid = sid.cuda(rank, non_blocking=True)
|
|
spec = spec.cuda(rank, non_blocking=True)
|
|
spec_lengths = spec_lengths.cuda(rank, non_blocking=True)
|
|
wave = wave.cuda(rank, non_blocking=True)
|
|
|
|
with autocast(enabled=hps.train.fp16_run):
|
|
if hps.if_f0 == 1:
|
|
(
|
|
y_hat,
|
|
ids_slice,
|
|
x_mask,
|
|
z_mask,
|
|
(z, z_p, m_p, logs_p, m_q, logs_q),
|
|
) = net_g(phone, phone_lengths, pitch, pitchf, spec, spec_lengths, sid)
|
|
else:
|
|
(
|
|
y_hat,
|
|
ids_slice,
|
|
x_mask,
|
|
z_mask,
|
|
(z, z_p, m_p, logs_p, m_q, logs_q),
|
|
) = net_g(phone, phone_lengths, spec, spec_lengths, sid)
|
|
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_mel = commons.slice_segments(
|
|
mel, ids_slice, hps.train.segment_size // hps.data.hop_length
|
|
)
|
|
with autocast(enabled=False):
|
|
y_hat_mel = mel_spectrogram_torch(
|
|
y_hat.float().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,
|
|
)
|
|
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
|
|
)
|
|
|
|
y_d_hat_r, y_d_hat_g, _, _ = net_d(wave, 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
|
|
)
|
|
optim_d.zero_grad()
|
|
scaler.scale(loss_disc).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):
|
|
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_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
|
|
|
|
if loss_gen_all < lowest_value["value"]:
|
|
lowest_value["value"] = loss_gen_all
|
|
lowest_value["step"] = global_step
|
|
lowest_value["epoch"] = epoch
|
|
|
|
if epoch > lowest_value["epoch"]:
|
|
print(
|
|
"Alert: The lower generating loss has been exceeded by a lower loss in a subsequent epoch."
|
|
)
|
|
|
|
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"]
|
|
|
|
|
|
if loss_mel > 75:
|
|
loss_mel = 75
|
|
if loss_kl > 9:
|
|
loss_kl = 9
|
|
|
|
scalar_dict = {
|
|
"loss/g/total": loss_gen_all,
|
|
"loss/d/total": loss_disc,
|
|
"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,
|
|
}
|
|
)
|
|
|
|
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": plot_spectrogram_to_numpy(
|
|
y_mel[0].data.cpu().numpy()
|
|
),
|
|
"slice/mel_gen": plot_spectrogram_to_numpy(
|
|
y_hat_mel[0].data.cpu().numpy()
|
|
),
|
|
"all/mel": plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
|
|
}
|
|
summarize(
|
|
writer=writer,
|
|
global_step=global_step,
|
|
images=image_dict,
|
|
scalars=scalar_dict,
|
|
)
|
|
|
|
|
|
|
|
|
|
global_step += 1
|
|
|
|
if epoch % hps.save_every_epoch == 0 and rank == 0:
|
|
checkpoint_suffix = "{}.pth".format(
|
|
global_step if hps.if_latest == 0 else 2333333
|
|
)
|
|
save_checkpoint(
|
|
net_g,
|
|
optim_g,
|
|
hps.train.learning_rate,
|
|
epoch,
|
|
os.path.join(hps.model_dir, "G_" + checkpoint_suffix),
|
|
)
|
|
save_checkpoint(
|
|
net_d,
|
|
optim_d,
|
|
hps.train.learning_rate,
|
|
epoch,
|
|
os.path.join(hps.model_dir, "D_" + checkpoint_suffix),
|
|
)
|
|
|
|
if rank == 0 and hps.custom_save_every_weights == "1":
|
|
if hasattr(net_g, "module"):
|
|
ckpt = net_g.module.state_dict()
|
|
else:
|
|
ckpt = net_g.state_dict()
|
|
extract_model(
|
|
ckpt,
|
|
hps.sample_rate,
|
|
hps.if_f0,
|
|
hps.name,
|
|
os.path.join(
|
|
hps.model_dir, "{}_{}e_{}s.pth".format(hps.name, epoch, global_step)
|
|
),
|
|
epoch,
|
|
global_step,
|
|
hps.version,
|
|
hps,
|
|
)
|
|
|
|
if hps.overtraining_detector == 1:
|
|
if epoch >= (lowest_value["epoch"] + hps.overtraining_threshold):
|
|
print(
|
|
"Stopping training due to possible overtraining. Lowest generator loss: {} at epoch {}, step {}".format(
|
|
lowest_value["value"], lowest_value["epoch"], lowest_value["step"]
|
|
)
|
|
)
|
|
os._exit(2333333)
|
|
|
|
best_epoch = lowest_value["epoch"] + hps.overtraining_threshold - epoch
|
|
|
|
if rank == 0:
|
|
if epoch > 1:
|
|
print(
|
|
f"{hps.name} | epoch={epoch} | step={global_step} | {epoch_recorder.record()} | lowest_value={lowest_value['value']} (epoch {lowest_value['epoch']} and step {lowest_value['step']}) | Number of epochs remaining for overtraining: {lowest_value['epoch'] + hps.overtraining_threshold - epoch}"
|
|
)
|
|
else:
|
|
print(
|
|
f"{hps.name} | epoch={epoch} | step={global_step} | {epoch_recorder.record()}"
|
|
)
|
|
last_loss_gen_all = loss_gen_all
|
|
|
|
if best_epoch == hps.overtraining_threshold:
|
|
old_model_files = glob.glob(
|
|
os.path.join(
|
|
hps.model_dir,
|
|
"{}_{}e_{}s_best_epoch.pth".format(hps.name, "*", "*"),
|
|
)
|
|
)
|
|
for file in old_model_files:
|
|
os.remove(file)
|
|
|
|
if hasattr(net_g, "module"):
|
|
ckpt = net_g.module.state_dict()
|
|
else:
|
|
ckpt = net_g.state_dict()
|
|
|
|
extract_model(
|
|
ckpt,
|
|
hps.sample_rate,
|
|
hps.if_f0,
|
|
hps.name,
|
|
os.path.join(
|
|
hps.model_dir,
|
|
"{}_{}e_{}s_best_epoch.pth".format(hps.name, epoch, global_step),
|
|
),
|
|
epoch,
|
|
global_step,
|
|
hps.version,
|
|
hps,
|
|
)
|
|
|
|
if epoch >= hps.custom_total_epoch and rank == 0:
|
|
print(
|
|
f"Training has been successfully completed with {epoch} epoch, {global_step} steps and {round(loss_gen_all.item(), 3)} loss gen."
|
|
)
|
|
print(
|
|
f"Lowest generator loss: {lowest_value['value']} at epoch {lowest_value['epoch']}, step {lowest_value['step']}"
|
|
)
|
|
|
|
pid_file_path = os.path.join(now_dir, "rvc", "train", "train_pid.txt")
|
|
os.remove(pid_file_path)
|
|
|
|
if hasattr(net_g, "module"):
|
|
ckpt = net_g.module.state_dict()
|
|
else:
|
|
ckpt = net_g.state_dict()
|
|
|
|
extract_model(
|
|
ckpt,
|
|
hps.sample_rate,
|
|
hps.if_f0,
|
|
hps.name,
|
|
os.path.join(
|
|
hps.model_dir, "{}_{}e_{}s.pth".format(hps.name, epoch, global_step)
|
|
),
|
|
epoch,
|
|
global_step,
|
|
hps.version,
|
|
hps,
|
|
)
|
|
sleep(1)
|
|
os._exit(2333333)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
torch.multiprocessing.set_start_method("spawn")
|
|
main()
|
|
|