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import yaml
import random
import argparse
import os
import time
from tqdm import tqdm
from pathlib import Path
import torch
from torch.utils.data import DataLoader
from accelerate import Accelerator
from diffusers import DDIMScheduler
from configs.plugin import get_params
from model.p2e_cross import P2E_Cross
# from modules.speaker_encoder.encoder import inference as spk_encoder
from openvoice.api import ToneColorConverter
from transformers import T5Tokenizer, T5EncoderModel
from inference import eval_plugin_light
from dataset.dreamvc import DreamData
# from vc_wrapper import load_diffvc_models
from utils import minmax_norm_diff, reverse_minmax_norm_diff
parser = argparse.ArgumentParser()
# config settings
parser.add_argument('--config-name', type=str, default='Plugin_base')
# training settings
parser.add_argument("--amp", type=str, default='fp16')
parser.add_argument('--epochs', type=int, default=100)
parser.add_argument('--batch-size', type=int, default=32)
parser.add_argument('--num-workers', type=int, default=4)
parser.add_argument('--num-threads', type=int, default=1)
parser.add_argument('--save-every', type=int, default=5)
# log and random seed
parser.add_argument('--random-seed', type=int, default=2023)
parser.add_argument('--log-step', type=int, default=200)
parser.add_argument('--log-dir', type=str, default='../logs/')
parser.add_argument('--save-dir', type=str, default='../ckpts/')
args = parser.parse_args()
params = get_params(args.config_name)
args.log_dir = args.log_dir + args.config_name + '/'
with open('model/p2e_cross.yaml', 'r') as fp:
config = yaml.safe_load(fp)
if os.path.exists(args.save_dir + args.config_name) is False:
os.makedirs(args.save_dir + args.config_name)
if os.path.exists(args.log_dir) is False:
os.makedirs(args.log_dir)
if __name__ == '__main__':
# Fix the random seed
random.seed(args.random_seed)
torch.manual_seed(args.random_seed)
# Set device
torch.set_num_threads(args.num_threads)
if torch.cuda.is_available():
args.device = 'cuda'
torch.cuda.manual_seed(args.random_seed)
torch.cuda.manual_seed_all(args.random_seed)
torch.backends.cuda.matmul.allow_tf32 = True
if torch.backends.cudnn.is_available():
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.allow_tf32 = True
torch.backends.cudnn.benchmark = False
else:
args.device = 'cpu'
train_set = DreamData(data_dir='../prepare/spk/', meta_dir='../prepare/plugin_meta.csv',
subset='train', prompt_dir='../prepare/prompts.csv',)
train_loader = DataLoader(train_set, num_workers=args.num_workers, batch_size=args.batch_size, shuffle=True)
# use accelerator for multi-gpu training
accelerator = Accelerator(mixed_precision=args.amp)
# vc model
ckpt_converter = '../prepare/checkpoints_v2/converter'
vc_model = ToneColorConverter(f'{ckpt_converter}/config.json', device='cuda')
vc_model.load_ckpt(f'{ckpt_converter}/checkpoint.pth')
# text encoder
tokenizer = T5Tokenizer.from_pretrained(params.text_encoder.model)
text_encoder = T5EncoderModel.from_pretrained(params.text_encoder.model).to(accelerator.device)
text_encoder.eval()
# main U-Net
model = P2E_Cross(config['diffwrap']).to(accelerator.device)
# model.load_state_dict(torch.load('64.pt')['model'])
total_params = sum([param.nelement() for param in model.parameters()])
print("Number of parameter: %.2fM" % (total_params / 1e6))
noise_scheduler = DDIMScheduler(num_train_timesteps=params.diff.num_train_steps,
beta_start=params.diff.beta_start, beta_end=params.diff.beta_end,
rescale_betas_zero_snr=True,
timestep_spacing="trailing",
clip_sample=False,
prediction_type='v_prediction')
optimizer = torch.optim.AdamW(model.parameters(),
lr=params.opt.learning_rate,
betas=(params.opt.beta1, params.opt.beta2),
weight_decay=params.opt.weight_decay,
eps=params.opt.adam_epsilon,)
loss_func = torch.nn.MSELoss()
model, optimizer, train_loader = accelerator.prepare(model, optimizer, train_loader)
global_step = 0
losses = 0
if accelerator.is_main_process:
eval_plugin_light(vc_model, [tokenizer, text_encoder],
model, noise_scheduler, (1, 256, 1),
val_meta='../prepare/val_meta.csv',
val_folder='/home/jerry/Projects/Dataset/Speech/vctk_libritts/',
guidance_scale=3, guidance_rescale=0.0,
ddim_steps=100, eta=1, random_seed=2024,
device=accelerator.device,
epoch='test', save_path=args.log_dir + 'output/', val_num=1)
accelerator.wait_for_everyone()
for epoch in range(args.epochs):
model.train()
for step, batch in enumerate(tqdm(train_loader)):
spk_embed, prompt = batch
with torch.no_grad():
# audio_clip = minmax_norm_diff(logmel(audio_clip)).unsqueeze(1)
text_batch = tokenizer(prompt,
max_length=32,
padding='max_length', truncation=True, return_tensors="pt")
text, text_mask = text_batch.input_ids.to(spk_embed.device), \
text_batch.attention_mask.to(spk_embed.device)
text = text_encoder(input_ids=text, attention_mask=text_mask)[0]
# spk_embed = minmax_norm_diff(spk_embed, vmax=0.5, vmin=0.0)
# adding noise
noise = torch.randn(spk_embed.shape).to(accelerator.device)
timesteps = torch.randint(0, params.diff.num_train_steps, (noise.shape[0],),
device=accelerator.device, ).long()
noisy_target = noise_scheduler.add_noise(spk_embed, noise, timesteps)
# v prediction - model output
velocity = noise_scheduler.get_velocity(spk_embed, noise, timesteps)
# inference
pred = model(noisy_target, timesteps, text, text_mask, train_cfg=True, cfg_prob=0.25)
# backward
if params.diff.v_prediction:
loss = loss_func(pred, velocity)
else:
loss = loss_func(pred, noise)
accelerator.backward(loss)
optimizer.step()
optimizer.zero_grad()
global_step += 1
losses += loss.item()
if accelerator.is_main_process:
if global_step % args.log_step == 0:
n = open(args.log_dir + 'diff_vc.txt', mode='a')
n.write(time.asctime(time.localtime(time.time())))
n.write('\n')
n.write('Epoch: [{}][{}] Batch: [{}][{}] Loss: {:.6f}\n'.format(
epoch + 1, args.epochs, step + 1, len(train_loader), losses / args.log_step))
n.close()
losses = 0.0
accelerator.wait_for_everyone()
if (epoch + 1) % args.save_every == 0:
if accelerator.is_main_process:
eval_plugin_light(vc_model, [tokenizer, text_encoder],
model, noise_scheduler, (1, 256, 1),
val_meta='../prepare/val_meta.csv',
val_folder='/home/jerry/Projects/Dataset/Speech/vctk_libritts/',
guidance_scale=3, guidance_rescale=0.0,
ddim_steps=50, eta=1, random_seed=2024,
device=accelerator.device,
epoch=epoch, save_path=args.log_dir + 'output/', val_num=10)
unwrapped_unet = accelerator.unwrap_model(model)
accelerator.save({
"model": unwrapped_unet.state_dict(),
}, args.save_dir + args.config_name + '/' + str(epoch) + '.pt')
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