dreamvoice/train_utils/src/train.py

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')