examples/frcrn/step_2_train_model.py

#!/usr/bin/python3
# -*- coding: utf-8 -*-
"""
FRCRN 论文中:
在 WSJ0 数据集上训练了 120 个 epoch 得到 pesq 3.62, stoi 98.24, si-snr 21.33

WSJ0 包含约 80小时的纯净英语语音录音.

我的音频大约是 1300 小时, 则预期大约需要 10个 epoch
"""
import argparse
import json
import logging
from logging.handlers import TimedRotatingFileHandler
import os
import platform
from pathlib import Path
import random
import sys
import shutil
from typing import List

pwd = os.path.abspath(os.path.dirname(__file__))
sys.path.append(os.path.join(pwd, "../../"))

import numpy as np
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm

from toolbox.torch.utils.data.dataset.denoise_jsonl_dataset import DenoiseJsonlDataset
from toolbox.torchaudio.losses.snr import NegativeSISNRLoss
from toolbox.torchaudio.metrics.pesq import run_pesq_score
from toolbox.torchaudio.models.frcrn.configuration_frcrn import FRCRNConfig
from toolbox.torchaudio.models.frcrn.modeling_frcrn import FRCRN, FRCRNPretrainedModel


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--train_dataset", default="train.xlsx", type=str)
    parser.add_argument("--valid_dataset", default="valid.xlsx", type=str)

    parser.add_argument("--num_serialized_models_to_keep", default=15, type=int)
    parser.add_argument("--patience", default=10, type=int)
    parser.add_argument("--serialization_dir", default="serialization_dir", type=str)

    parser.add_argument("--config_file", default="config.yaml", type=str)

    args = parser.parse_args()
    return args


def logging_config(file_dir: str):
    fmt = "%(asctime)s - %(name)s - %(levelname)s  %(filename)s:%(lineno)d >  %(message)s"

    logging.basicConfig(format=fmt,
                        datefmt="%m/%d/%Y %H:%M:%S",
                        level=logging.INFO)
    file_handler = TimedRotatingFileHandler(
        filename=os.path.join(file_dir, "main.log"),
        encoding="utf-8",
        when="D",
        interval=1,
        backupCount=7
    )
    file_handler.setLevel(logging.INFO)
    file_handler.setFormatter(logging.Formatter(fmt))
    logger = logging.getLogger(__name__)
    logger.addHandler(file_handler)

    return logger


class CollateFunction(object):
    def __init__(self):
        pass

    def __call__(self, batch: List[dict]):
        clean_audios = list()
        noisy_audios = list()

        for sample in batch:
            # noise_wave: torch.Tensor = sample["noise_wave"]
            clean_audio: torch.Tensor = sample["speech_wave"]
            noisy_audio: torch.Tensor = sample["mix_wave"]
            # snr_db: float = sample["snr_db"]

            clean_audios.append(clean_audio)
            noisy_audios.append(noisy_audio)

        clean_audios = torch.stack(clean_audios)
        noisy_audios = torch.stack(noisy_audios)

        # assert
        if torch.any(torch.isnan(clean_audios)) or torch.any(torch.isinf(clean_audios)):
            raise AssertionError("nan or inf in clean_audios")
        if torch.any(torch.isnan(noisy_audios)) or torch.any(torch.isinf(noisy_audios)):
            raise AssertionError("nan or inf in noisy_audios")
        return clean_audios, noisy_audios


collate_fn = CollateFunction()


def main():
    args = get_args()

    config = FRCRNConfig.from_pretrained(
        pretrained_model_name_or_path=args.config_file,
    )

    serialization_dir = Path(args.serialization_dir)
    serialization_dir.mkdir(parents=True, exist_ok=True)

    logger = logging_config(serialization_dir)

    random.seed(config.seed)
    np.random.seed(config.seed)
    torch.manual_seed(config.seed)
    logger.info(f"set seed: {config.seed}")

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    n_gpu = torch.cuda.device_count()
    logger.info(f"GPU available count: {n_gpu}; device: {device}")

    # datasets
    train_dataset = DenoiseJsonlDataset(
        jsonl_file=args.train_dataset,
        expected_sample_rate=config.sample_rate,
        max_wave_value=32768.0,
        min_snr_db=config.min_snr_db,
        max_snr_db=config.max_snr_db,
        # skip=225000,
    )
    valid_dataset = DenoiseJsonlDataset(
        jsonl_file=args.valid_dataset,
        expected_sample_rate=config.sample_rate,
        max_wave_value=32768.0,
        min_snr_db=config.min_snr_db,
        max_snr_db=config.max_snr_db,
    )
    train_data_loader = DataLoader(
        dataset=train_dataset,
        batch_size=config.batch_size,
        # shuffle=True,
        sampler=None,
        # Linux 系统中可以使用多个子进程加载数据, 而在 Windows 系统中不能.
        num_workers=0 if platform.system() == "Windows" else os.cpu_count() // 2,
        collate_fn=collate_fn,
        pin_memory=False,
        prefetch_factor=2,
    )
    valid_data_loader = DataLoader(
        dataset=valid_dataset,
        batch_size=config.batch_size,
        # shuffle=True,
        sampler=None,
        # Linux 系统中可以使用多个子进程加载数据, 而在 Windows 系统中不能.
        num_workers=0 if platform.system() == "Windows" else os.cpu_count() // 2,
        collate_fn=collate_fn,
        pin_memory=False,
        prefetch_factor=2,
    )

    # models
    logger.info(f"prepare models. config_file: {args.config_file}")
    model = FRCRNPretrainedModel(config).to(device)
    model.to(device)
    model.train()

    # optimizer
    logger.info("prepare optimizer, lr_scheduler, loss_fn, evaluation_metric")
    optimizer = torch.optim.AdamW(model.get_params(weight_decay=config.weight_decay), config.lr)

    # resume training
    last_step_idx = -1
    last_epoch = -1
    for step_idx_str in serialization_dir.glob("steps-*"):
        step_idx_str = Path(step_idx_str)
        step_idx = step_idx_str.stem.split("-")[1]
        step_idx = int(step_idx)
        if step_idx > last_step_idx:
            last_step_idx = step_idx
    last_epoch = 0

    if last_step_idx != -1:
        logger.info(f"resume from steps-{last_step_idx}.")
        model_pt = serialization_dir / f"steps-{last_step_idx}/model.pt"
        optimizer_pth = serialization_dir / f"steps-{last_step_idx}/optimizer.pth"

        logger.info(f"load state dict for model.")
        with open(model_pt.as_posix(), "rb") as f:
            state_dict = torch.load(f, map_location="cpu", weights_only=True)
        model.load_state_dict(state_dict, strict=True)

        logger.info(f"load state dict for optimizer.")
        with open(optimizer_pth.as_posix(), "rb") as f:
            state_dict = torch.load(f, map_location="cpu", weights_only=True)
        optimizer.load_state_dict(state_dict)

    if config.lr_scheduler == "CosineAnnealingLR":
        lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
            optimizer,
            last_epoch=last_epoch,
            # T_max=10 * config.eval_steps,
            # eta_min=0.01 * config.lr,
            **config.lr_scheduler_kwargs,
        )
    elif config.lr_scheduler == "MultiStepLR":
        lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
            optimizer,
            last_epoch=last_epoch,
            milestones=[10000, 20000, 30000, 40000, 50000], gamma=0.5
        )
    else:
        raise AssertionError(f"invalid lr_scheduler: {config.lr_scheduler}")

    neg_si_snr_loss_fn = NegativeSISNRLoss(reduction="mean").to(device)

    # training loop

    # state
    average_pesq_score = 1000000000
    average_loss = 1000000000
    average_neg_si_snr_loss = 1000000000
    average_mask_loss = 1000000000

    model_list = list()
    best_epoch_idx = None
    best_step_idx = None
    best_metric = None
    patience_count = 0

    step_idx = 0 if last_step_idx == -1 else last_step_idx

    logger.info("training")
    for epoch_idx in range(max(0, last_epoch+1), config.max_epochs):
        # train
        model.train()

        total_pesq_score = 0.
        total_loss = 0.
        total_neg_si_snr_loss = 0.
        total_mask_loss = 0.
        total_batches = 0.

        progress_bar_train = tqdm(
            initial=step_idx,
            desc="Training; epoch-{}".format(epoch_idx),
        )
        for train_batch in train_data_loader:
            clean_audios, noisy_audios = train_batch
            clean_audios: torch.Tensor = clean_audios.to(device)
            noisy_audios: torch.Tensor = noisy_audios.to(device)

            est_spec, est_wav, est_mask = model.forward(noisy_audios)
            denoise_audios = est_wav

            neg_si_snr_loss = neg_si_snr_loss_fn.forward(denoise_audios, clean_audios)
            mask_loss = model.mask_loss_fn(est_mask, clean_audios, noisy_audios)

            loss = 1.0 * neg_si_snr_loss + 1.0 * mask_loss
            if torch.any(torch.isnan(loss)) or torch.any(torch.isinf(loss)):
                logger.info(f"find nan or inf in loss.")
                continue

            denoise_audios_list_r = list(denoise_audios.detach().cpu().numpy())
            clean_audios_list_r = list(clean_audios.detach().cpu().numpy())
            pesq_score = run_pesq_score(clean_audios_list_r, denoise_audios_list_r, sample_rate=config.sample_rate, mode="nb")

            optimizer.zero_grad()
            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config.clip_grad_norm)
            optimizer.step()
            lr_scheduler.step()

            total_pesq_score += pesq_score
            total_loss += loss.item()
            total_neg_si_snr_loss += neg_si_snr_loss.item()
            total_mask_loss += mask_loss.item()
            total_batches += 1

            average_pesq_score = round(total_pesq_score / total_batches, 4)
            average_loss = round(total_loss / total_batches, 4)
            average_neg_si_snr_loss = round(total_neg_si_snr_loss / total_batches, 4)
            average_mask_loss = round(total_mask_loss / total_batches, 4)

            progress_bar_train.update(1)
            progress_bar_train.set_postfix({
                "lr": lr_scheduler.get_last_lr()[0],
                "pesq_score": average_pesq_score,
                "loss": average_loss,
                "neg_si_snr_loss": average_neg_si_snr_loss,
                "mask_loss": average_mask_loss,
            })

            # evaluation
            step_idx += 1
            if step_idx % config.eval_steps == 0:
                with torch.no_grad():
                    torch.cuda.empty_cache()

                    total_pesq_score = 0.
                    total_loss = 0.
                    total_neg_si_snr_loss = 0.
                    total_mask_loss = 0.
                    total_batches = 0.

                    progress_bar_train.close()
                    progress_bar_eval = tqdm(
                        desc="Evaluation; steps-{}k".format(int(step_idx/1000)),
                    )
                    for eval_batch in valid_data_loader:
                        clean_audios, noisy_audios = eval_batch
                        clean_audios = clean_audios.to(device)
                        noisy_audios = noisy_audios.to(device)

                        est_spec, est_wav, est_mask = model.forward(noisy_audios)
                        denoise_audios = est_wav

                        neg_si_snr_loss = neg_si_snr_loss_fn.forward(denoise_audios, clean_audios)
                        mask_loss = model.mask_loss_fn(est_mask, clean_audios, noisy_audios)

                        loss = 1.0 * neg_si_snr_loss + 1.0 * mask_loss
                        if torch.any(torch.isnan(loss)) or torch.any(torch.isinf(loss)):
                            logger.info(f"find nan or inf in loss.")
                            continue

                        denoise_audios_list_r = list(denoise_audios.detach().cpu().numpy())
                        clean_audios_list_r = list(clean_audios.detach().cpu().numpy())
                        pesq_score = run_pesq_score(clean_audios_list_r, denoise_audios_list_r, sample_rate=config.sample_rate, mode="nb")

                        total_pesq_score += pesq_score
                        total_loss += loss.item()
                        total_neg_si_snr_loss += neg_si_snr_loss.item()
                        total_mask_loss += mask_loss.item()
                        total_batches += 1

                        average_pesq_score = round(total_pesq_score / total_batches, 4)
                        average_loss = round(total_loss / total_batches, 4)
                        average_neg_si_snr_loss = round(total_neg_si_snr_loss / total_batches, 4)
                        average_mask_loss = round(total_mask_loss / total_batches, 4)

                        progress_bar_eval.update(1)
                        progress_bar_eval.set_postfix({
                            "lr": lr_scheduler.get_last_lr()[0],
                            "pesq_score": average_pesq_score,
                            "loss": average_loss,
                            "neg_si_snr_loss": average_neg_si_snr_loss,
                            "mask_loss": average_mask_loss,
                        })

                    total_pesq_score = 0.
                    total_loss = 0.
                    total_neg_si_snr_loss = 0.
                    total_mask_loss = 0.
                    total_batches = 0.

                    progress_bar_eval.close()
                    progress_bar_train = tqdm(
                        initial=progress_bar_train.n,
                        postfix=progress_bar_train.postfix,
                        desc=progress_bar_train.desc,
                    )

                    # save path
                    save_dir = serialization_dir / "steps-{}".format(step_idx)
                    save_dir.mkdir(parents=True, exist_ok=False)

                    # save models
                    model.save_pretrained(save_dir.as_posix())

                    model_list.append(save_dir)
                    if len(model_list) >= args.num_serialized_models_to_keep:
                        model_to_delete: Path = model_list.pop(0)
                        shutil.rmtree(model_to_delete.as_posix())

                    # save optim
                    torch.save(optimizer.state_dict(), (save_dir / "optimizer.pth").as_posix())

                    # save metric
                    if best_metric is None:
                        best_epoch_idx = epoch_idx
                        best_step_idx = step_idx
                        best_metric = average_pesq_score
                    elif average_pesq_score > best_metric:
                        # great is better.
                        best_epoch_idx = epoch_idx
                        best_step_idx = step_idx
                        best_metric = average_pesq_score
                    else:
                        pass

                    metrics = {
                        "epoch_idx": epoch_idx,
                        "best_epoch_idx": best_epoch_idx,
                        "best_step_idx": best_step_idx,
                        "pesq_score": average_pesq_score,
                        "loss": average_loss,
                        "neg_si_snr_loss": average_neg_si_snr_loss,
                        "mask_loss": average_mask_loss,
                    }
                    metrics_filename = save_dir / "metrics_epoch.json"
                    with open(metrics_filename, "w", encoding="utf-8") as f:
                        json.dump(metrics, f, indent=4, ensure_ascii=False)

                    # save best
                    best_dir = serialization_dir / "best"
                    if best_epoch_idx == epoch_idx and best_step_idx == step_idx:
                        if best_dir.exists():
                            shutil.rmtree(best_dir)
                        shutil.copytree(save_dir, best_dir)

                    # early stop
                    early_stop_flag = False
                    if best_epoch_idx == epoch_idx and best_step_idx == step_idx:
                        patience_count = 0
                    else:
                        patience_count += 1
                    if patience_count >= args.patience:
                        early_stop_flag = True

                    # early stop
                    if early_stop_flag:
                        break

    return


if __name__ == "__main__":
    main()