train_first.py

import os
import os.path as osp
import yaml
import shutil
import numpy as np
import torch
import click
import warnings

warnings.simplefilter("ignore")

# load packages
import random
import yaml
from munch import Munch
import numpy as np
import torch
import torch.nn.functional as F

from models import *
from meldataset import build_dataloader
from utils import *
from losses import *
from optimizers import build_optimizer
import time

from accelerate import Accelerator
from accelerate import DistributedDataParallelKwargs

from torch.utils.tensorboard import SummaryWriter

import logging
from accelerate.logging import get_logger

logger = get_logger(__name__, log_level="DEBUG")


@click.command()
@click.option("-p", "--config_path", default="Configs/config.yml", type=str)
def main(config_path):
    config = yaml.safe_load(open(config_path))

    log_dir = config["log_dir"]
    if not osp.exists(log_dir):
        os.makedirs(log_dir, exist_ok=True)
    shutil.copy(config_path, osp.join(log_dir, osp.basename(config_path)))
    ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
    accelerator = Accelerator(
        project_dir=log_dir, split_batches=True, kwargs_handlers=[ddp_kwargs]
    )
    if accelerator.is_main_process:
        writer = SummaryWriter(log_dir + "/tensorboard")

    # write logs
    file_handler = logging.FileHandler(osp.join(log_dir, "train.log"))
    file_handler.setLevel(logging.DEBUG)
    file_handler.setFormatter(
        logging.Formatter("%(levelname)s:%(asctime)s: %(message)s")
    )
    logger.logger.addHandler(file_handler)

    batch_size = config.get("batch_size", 10)
    device = accelerator.device

    epochs = config.get("epochs_1st", 200)
    config.get("save_freq", 2)
    log_interval = config.get("log_interval", 10)
    saving_epoch = config.get("save_freq", 2)

    data_params = config.get("data_params", None)
    sr = config["preprocess_params"].get("sr", 24000)
    train_path = data_params["train_data"]
    val_path = data_params["val_data"]
    root_path = data_params["root_path"]
    min_length = data_params["min_length"]
    OOD_data = data_params["OOD_data"]

    max_len = config.get("max_len", 200)

    # load data
    train_list, val_list = get_data_path_list(train_path, val_path)

    train_dataloader = build_dataloader(
        train_list,
        root_path,
        OOD_data=OOD_data,
        min_length=min_length,
        batch_size=batch_size,
        num_workers=2,
        dataset_config={},
        device=device,
    )

    val_dataloader = build_dataloader(
        val_list,
        root_path,
        OOD_data=OOD_data,
        min_length=min_length,
        batch_size=batch_size,
        validation=True,
        num_workers=0,
        device=device,
        dataset_config={},
    )

    with accelerator.main_process_first():
        # load pretrained ASR model
        ASR_config = config.get("ASR_config", False)
        ASR_path = config.get("ASR_path", False)
        text_aligner = load_ASR_models(ASR_path, ASR_config)

        # load pretrained F0 model
        F0_path = config.get("F0_path", False)
        pitch_extractor = load_F0_models(F0_path)

        # load BERT model
        from Utils.PLBERT.util import load_plbert

        BERT_path = config.get("PLBERT_dir", False)
        plbert = load_plbert(BERT_path)

    scheduler_params = {
        "max_lr": float(config["optimizer_params"].get("lr", 1e-4)),
        "pct_start": float(config["optimizer_params"].get("pct_start", 0.0)),
        "epochs": epochs,
        "steps_per_epoch": len(train_dataloader),
    }

    model_params = recursive_munch(config["model_params"])
    multispeaker = model_params.multispeaker
    model = build_model(model_params, text_aligner, pitch_extractor, plbert)

    best_loss = float("inf")  # best test loss
    list([])
    list([])

    loss_params = Munch(config["loss_params"])
    TMA_epoch = loss_params.TMA_epoch

    for k in model:
        model[k] = accelerator.prepare(model[k])

    train_dataloader, val_dataloader = accelerator.prepare(
        train_dataloader, val_dataloader
    )

    _ = [model[key].to(device) for key in model]

    # initialize optimizers after preparing models for compatibility with FSDP
    optimizer = build_optimizer(
        {key: model[key].parameters() for key in model},
        scheduler_params_dict={key: scheduler_params.copy() for key in model},
        lr=float(config["optimizer_params"].get("lr", 1e-4)),
    )

    for k, v in optimizer.optimizers.items():
        optimizer.optimizers[k] = accelerator.prepare(optimizer.optimizers[k])
        optimizer.schedulers[k] = accelerator.prepare(optimizer.schedulers[k])

    with accelerator.main_process_first():
        if config.get("pretrained_model", "") != "":
            model, optimizer, start_epoch, iters = load_checkpoint(
                model,
                optimizer,
                config["pretrained_model"],
                load_only_params=config.get("load_only_params", True),
            )
        else:
            start_epoch = 0
            iters = 0

    # in case not distributed
    try:
        n_down = model.text_aligner.module.n_down
    except:
        n_down = model.text_aligner.n_down

    # wrapped losses for compatibility with mixed precision
    stft_loss = MultiResolutionSTFTLoss().to(device)
    gl = GeneratorLoss(model.mpd, model.msd).to(device)
    dl = DiscriminatorLoss(model.mpd, model.msd).to(device)
    wl = WavLMLoss(model_params.slm.model, model.wd, sr, model_params.slm.sr).to(device)

    for epoch in range(start_epoch, epochs):
        running_loss = 0
        start_time = time.time()

        _ = [model[key].train() for key in model]

        for i, batch in enumerate(train_dataloader):
            waves = batch[0]
            batch = [b.to(device) for b in batch[1:]]
            texts, input_lengths, _, _, mels, mel_input_length, _ = batch

            with torch.no_grad():
                mask = length_to_mask(mel_input_length // (2**n_down)).to("cuda")
                text_mask = length_to_mask(input_lengths).to(texts.device)

            ppgs, s2s_pred, s2s_attn = model.text_aligner(mels, mask, texts)

            s2s_attn = s2s_attn.transpose(-1, -2)
            s2s_attn = s2s_attn[..., 1:]
            s2s_attn = s2s_attn.transpose(-1, -2)

            with torch.no_grad():
                attn_mask = (
                    (~mask)
                    .unsqueeze(-1)
                    .expand(mask.shape[0], mask.shape[1], text_mask.shape[-1])
                    .float()
                    .transpose(-1, -2)
                )
                attn_mask = (
                    attn_mask.float()
                    * (~text_mask)
                    .unsqueeze(-1)
                    .expand(text_mask.shape[0], text_mask.shape[1], mask.shape[-1])
                    .float()
                )
                attn_mask = attn_mask < 1

            s2s_attn.masked_fill_(attn_mask, 0.0)

            with torch.no_grad():
                mask_ST = mask_from_lens(
                    s2s_attn, input_lengths, mel_input_length // (2**n_down)
                )
                s2s_attn_mono = maximum_path(s2s_attn, mask_ST)

            # encode
            t_en = model.text_encoder(texts, input_lengths, text_mask)

            # 50% of chance of using monotonic version
            if bool(random.getrandbits(1)):
                asr = t_en @ s2s_attn
            else:
                asr = t_en @ s2s_attn_mono

            # get clips
            mel_input_length_all = accelerator.gather(
                mel_input_length
            )  # for balanced load
            mel_len = min(
                [int(mel_input_length_all.min().item() / 2 - 1), max_len // 2]
            )
            mel_len_st = int(mel_input_length.min().item() / 2 - 1)

            en = []
            gt = []
            wav = []
            st = []

            for bib in range(len(mel_input_length)):
                mel_length = int(mel_input_length[bib].item() / 2)

                random_start = np.random.randint(0, mel_length - mel_len)
                en.append(asr[bib, :, random_start : random_start + mel_len])
                gt.append(
                    mels[bib, :, (random_start * 2) : ((random_start + mel_len) * 2)]
                )

                y = waves[bib][
                    (random_start * 2) * 300 : ((random_start + mel_len) * 2) * 300
                ]
                wav.append(torch.from_numpy(y).to(device))

                # style reference (better to be different from the GT)
                random_start = np.random.randint(0, mel_length - mel_len_st)
                st.append(
                    mels[bib, :, (random_start * 2) : ((random_start + mel_len_st) * 2)]
                )

            en = torch.stack(en)
            gt = torch.stack(gt).detach()
            st = torch.stack(st).detach()

            wav = torch.stack(wav).float().detach()

            # clip too short to be used by the style encoder
            if gt.shape[-1] < 80:
                continue

            with torch.no_grad():
                real_norm = log_norm(gt.unsqueeze(1)).squeeze(1).detach()
                F0_real, _, _ = model.pitch_extractor(gt.unsqueeze(1))

            s = model.style_encoder(
                st.unsqueeze(1) if multispeaker else gt.unsqueeze(1)
            )

            y_rec = model.decoder(en, F0_real, real_norm, s)

            # discriminator loss

            if epoch >= TMA_epoch:
                optimizer.zero_grad()
                d_loss = dl(wav.detach().unsqueeze(1).float(), y_rec.detach()).mean()
                accelerator.backward(d_loss)
                optimizer.step("msd")
                optimizer.step("mpd")
            else:
                d_loss = 0

            # generator loss
            optimizer.zero_grad()
            loss_mel = stft_loss(y_rec.squeeze(), wav.detach())

            if epoch >= TMA_epoch:  # start TMA training
                loss_s2s = 0
                for _s2s_pred, _text_input, _text_length in zip(
                    s2s_pred, texts, input_lengths
                ):
                    loss_s2s += F.cross_entropy(
                        _s2s_pred[:_text_length], _text_input[:_text_length]
                    )
                loss_s2s /= texts.size(0)

                loss_mono = F.l1_loss(s2s_attn, s2s_attn_mono) * 10

                loss_gen_all = gl(wav.detach().unsqueeze(1).float(), y_rec).mean()
                loss_slm = wl(wav.detach(), y_rec).mean()

                g_loss = (
                    loss_params.lambda_mel * loss_mel
                    + loss_params.lambda_mono * loss_mono
                    + loss_params.lambda_s2s * loss_s2s
                    + loss_params.lambda_gen * loss_gen_all
                    + loss_params.lambda_slm * loss_slm
                )

            else:
                loss_s2s = 0
                loss_mono = 0
                loss_gen_all = 0
                loss_slm = 0
                g_loss = loss_mel

            running_loss += accelerator.gather(loss_mel).mean().item()

            accelerator.backward(g_loss)

            optimizer.step("text_encoder")
            optimizer.step("style_encoder")
            optimizer.step("decoder")

            if epoch >= TMA_epoch:
                optimizer.step("text_aligner")
                optimizer.step("pitch_extractor")

            iters = iters + 1

            if (i + 1) % log_interval == 0 and accelerator.is_main_process:
                log_print(
                    "Epoch [%d/%d], Step [%d/%d], Mel Loss: %.5f, Gen Loss: %.5f, Disc Loss: %.5f, Mono Loss: %.5f, S2S Loss: %.5f, SLM Loss: %.5f"
                    % (
                        epoch + 1,
                        epochs,
                        i + 1,
                        len(train_list) // batch_size,
                        running_loss / log_interval,
                        loss_gen_all,
                        d_loss,
                        loss_mono,
                        loss_s2s,
                        loss_slm,
                    ),
                    logger,
                )

                writer.add_scalar("train/mel_loss", running_loss / log_interval, iters)
                writer.add_scalar("train/gen_loss", loss_gen_all, iters)
                writer.add_scalar("train/d_loss", d_loss, iters)
                writer.add_scalar("train/mono_loss", loss_mono, iters)
                writer.add_scalar("train/s2s_loss", loss_s2s, iters)
                writer.add_scalar("train/slm_loss", loss_slm, iters)

                running_loss = 0

                print("Time elasped:", time.time() - start_time)

        loss_test = 0

        _ = [model[key].eval() for key in model]

        with torch.no_grad():
            iters_test = 0
            for batch_idx, batch in enumerate(val_dataloader):
                optimizer.zero_grad()

                waves = batch[0]
                batch = [b.to(device) for b in batch[1:]]
                texts, input_lengths, _, _, mels, mel_input_length, _ = batch

                with torch.no_grad():
                    mask = length_to_mask(mel_input_length // (2**n_down)).to("cuda")
                    ppgs, s2s_pred, s2s_attn = model.text_aligner(mels, mask, texts)

                    s2s_attn = s2s_attn.transpose(-1, -2)
                    s2s_attn = s2s_attn[..., 1:]
                    s2s_attn = s2s_attn.transpose(-1, -2)

                    text_mask = length_to_mask(input_lengths).to(texts.device)
                    attn_mask = (
                        (~mask)
                        .unsqueeze(-1)
                        .expand(mask.shape[0], mask.shape[1], text_mask.shape[-1])
                        .float()
                        .transpose(-1, -2)
                    )
                    attn_mask = (
                        attn_mask.float()
                        * (~text_mask)
                        .unsqueeze(-1)
                        .expand(text_mask.shape[0], text_mask.shape[1], mask.shape[-1])
                        .float()
                    )
                    attn_mask = attn_mask < 1
                    s2s_attn.masked_fill_(attn_mask, 0.0)

                # encode
                t_en = model.text_encoder(texts, input_lengths, text_mask)

                asr = t_en @ s2s_attn

                # get clips
                mel_input_length_all = accelerator.gather(
                    mel_input_length
                )  # for balanced load
                mel_len = min(
                    [int(mel_input_length.min().item() / 2 - 1), max_len // 2]
                )

                en = []
                gt = []
                wav = []
                for bib in range(len(mel_input_length)):
                    mel_length = int(mel_input_length[bib].item() / 2)

                    random_start = np.random.randint(0, mel_length - mel_len)
                    en.append(asr[bib, :, random_start : random_start + mel_len])
                    gt.append(
                        mels[
                            bib, :, (random_start * 2) : ((random_start + mel_len) * 2)
                        ]
                    )
                    y = waves[bib][
                        (random_start * 2) * 300 : ((random_start + mel_len) * 2) * 300
                    ]
                    wav.append(torch.from_numpy(y).to("cuda"))

                wav = torch.stack(wav).float().detach()

                en = torch.stack(en)
                gt = torch.stack(gt).detach()

                F0_real, _, F0 = model.pitch_extractor(gt.unsqueeze(1))
                s = model.style_encoder(gt.unsqueeze(1))
                real_norm = log_norm(gt.unsqueeze(1)).squeeze(1)
                y_rec = model.decoder(en, F0_real, real_norm, s)

                loss_mel = stft_loss(y_rec.squeeze(), wav.detach())

                loss_test += accelerator.gather(loss_mel).mean().item()
                iters_test += 1

        if accelerator.is_main_process:
            print("Epochs:", epoch + 1)
            log_print(
                "Validation loss: %.3f" % (loss_test / iters_test) + "\n\n\n\n", logger
            )
            print("\n\n\n")
            writer.add_scalar("eval/mel_loss", loss_test / iters_test, epoch + 1)
            attn_image = get_image(s2s_attn[0].cpu().numpy().squeeze())
            writer.add_figure("eval/attn", attn_image, epoch)

            with torch.no_grad():
                for bib in range(len(asr)):
                    mel_length = int(mel_input_length[bib].item())
                    gt = mels[bib, :, :mel_length].unsqueeze(0)
                    en = asr[bib, :, : mel_length // 2].unsqueeze(0)

                    F0_real, _, _ = model.pitch_extractor(gt.unsqueeze(1))
                    F0_real = F0_real.unsqueeze(0)
                    s = model.style_encoder(gt.unsqueeze(1))
                    real_norm = log_norm(gt.unsqueeze(1)).squeeze(1)

                    y_rec = model.decoder(en, F0_real, real_norm, s)

                    writer.add_audio(
                        "eval/y" + str(bib),
                        y_rec.cpu().numpy().squeeze(),
                        epoch,
                        sample_rate=sr,
                    )
                    if epoch == 0:
                        writer.add_audio(
                            "gt/y" + str(bib),
                            waves[bib].squeeze(),
                            epoch,
                            sample_rate=sr,
                        )

                    if bib >= 6:
                        break

            if epoch % saving_epoch == 0:
                if (loss_test / iters_test) < best_loss:
                    best_loss = loss_test / iters_test
                print("Saving..")
                state = {
                    "net": {key: model[key].state_dict() for key in model},
                    "optimizer": optimizer.state_dict(),
                    "iters": iters,
                    "val_loss": loss_test / iters_test,
                    "epoch": epoch,
                }
                save_path = osp.join(log_dir, "epoch_1st_%05d.pth" % epoch)
                torch.save(state, save_path)

    if accelerator.is_main_process:
        print("Saving..")
        state = {
            "net": {key: model[key].state_dict() for key in model},
            "optimizer": optimizer.state_dict(),
            "iters": iters,
            "val_loss": loss_test / iters_test,
            "epoch": epoch,
        }
        save_path = osp.join(log_dir, config.get("first_stage_path", "first_stage.pth"))
        torch.save(state, save_path)


if __name__ == "__main__":
    main()