""" /* Copyright (c) 2023 Amazon Written by Jan Buethe */ /* Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: - Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. - Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ """ import os import argparse import sys import math as m import random import yaml from tqdm import tqdm try: import git has_git = True except: has_git = False import torch from torch.optim.lr_scheduler import LambdaLR import torch.nn.functional as F from scipy.io import wavfile import numpy as np import pesq from data import LPCNetVocodingDataset from models import model_dict from utils.lpcnet_features import load_lpcnet_features from utils.misc import count_parameters from losses.stft_loss import MRSTFTLoss, MRLogMelLoss parser = argparse.ArgumentParser() parser.add_argument('setup', type=str, help='setup yaml file') parser.add_argument('output', type=str, help='output path') parser.add_argument('--device', type=str, help='compute device', default=None) parser.add_argument('--initial-checkpoint', type=str, help='initial checkpoint', default=None) parser.add_argument('--test-features', type=str, help='path to features for testing', default=None) parser.add_argument('--no-redirect', action='store_true', help='disables re-direction of stdout') args = parser.parse_args() torch.set_num_threads(4) with open(args.setup, 'r') as f: setup = yaml.load(f.read(), yaml.FullLoader) checkpoint_prefix = 'checkpoint' output_prefix = 'output' setup_name = 'setup.yml' output_file='out.txt' # check model if not 'name' in setup['model']: print(f'warning: did not find model entry in setup, using default PitchPostFilter') model_name = 'pitchpostfilter' else: model_name = setup['model']['name'] # prepare output folder if os.path.exists(args.output): print("warning: output folder exists") reply = input('continue? (y/n): ') while reply not in {'y', 'n'}: reply = input('continue? (y/n): ') if reply == 'n': os._exit() else: os.makedirs(args.output, exist_ok=True) checkpoint_dir = os.path.join(args.output, 'checkpoints') os.makedirs(checkpoint_dir, exist_ok=True) # add repo info to setup if has_git: working_dir = os.path.split(__file__)[0] try: repo = git.Repo(working_dir, search_parent_directories=True) setup['repo'] = dict() hash = repo.head.object.hexsha urls = list(repo.remote().urls) is_dirty = repo.is_dirty() if is_dirty: print("warning: repo is dirty") setup['repo']['hash'] = hash setup['repo']['urls'] = urls setup['repo']['dirty'] = is_dirty except: has_git = False # dump setup with open(os.path.join(args.output, setup_name), 'w') as f: yaml.dump(setup, f) ref = None # prepare inference test if wanted inference_test = False if type(args.test_features) != type(None): test_features = load_lpcnet_features(args.test_features) features = test_features['features'] periods = test_features['periods'] inference_folder = os.path.join(args.output, 'inference_test') os.makedirs(inference_folder, exist_ok=True) inference_test = True # training parameters batch_size = setup['training']['batch_size'] epochs = setup['training']['epochs'] lr = setup['training']['lr'] lr_decay_factor = setup['training']['lr_decay_factor'] lr_gen = lr * setup['training']['gen_lr_reduction'] lambda_feat = setup['training']['lambda_feat'] lambda_reg = setup['training']['lambda_reg'] adv_target = setup['training'].get('adv_target', 'target') # load training dataset data_config = setup['data'] data = LPCNetVocodingDataset(setup['dataset'], **data_config) # load validation dataset if given if 'validation_dataset' in setup: validation_data = LPCNetVocodingDataset(setup['validation_dataset'], **data_config) validation_dataloader = torch.utils.data.DataLoader(validation_data, batch_size=batch_size, drop_last=True, num_workers=4) run_validation = True else: run_validation = False # create model model = model_dict[model_name](*setup['model']['args'], **setup['model']['kwargs']) # create discriminator disc_name = setup['discriminator']['name'] disc = model_dict[disc_name]( *setup['discriminator']['args'], **setup['discriminator']['kwargs'] ) # set compute device if type(args.device) == type(None): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") else: device = torch.device(args.device) # dataloader dataloader = torch.utils.data.DataLoader(data, batch_size=batch_size, drop_last=True, shuffle=True, num_workers=4) # optimizer is introduced to trainable parameters parameters = [p for p in model.parameters() if p.requires_grad] optimizer = torch.optim.Adam(parameters, lr=lr_gen) # disc optimizer parameters = [p for p in disc.parameters() if p.requires_grad] optimizer_disc = torch.optim.Adam(parameters, lr=lr, betas=[0.5, 0.9]) # learning rate scheduler scheduler = LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay_factor * x)) if args.initial_checkpoint is not None: print(f"loading state dict from {args.initial_checkpoint}...") chkpt = torch.load(args.initial_checkpoint, map_location=device) model.load_state_dict(chkpt['state_dict']) if 'disc_state_dict' in chkpt: print(f"loading discriminator state dict from {args.initial_checkpoint}...") disc.load_state_dict(chkpt['disc_state_dict']) if 'optimizer_state_dict' in chkpt: print(f"loading optimizer state dict from {args.initial_checkpoint}...") optimizer.load_state_dict(chkpt['optimizer_state_dict']) if 'disc_optimizer_state_dict' in chkpt: print(f"loading discriminator optimizer state dict from {args.initial_checkpoint}...") optimizer_disc.load_state_dict(chkpt['disc_optimizer_state_dict']) if 'scheduler_state_disc' in chkpt: print(f"loading scheduler state dict from {args.initial_checkpoint}...") scheduler.load_state_dict(chkpt['scheduler_state_dict']) # if 'torch_rng_state' in chkpt: # print(f"setting torch RNG state from {args.initial_checkpoint}...") # torch.set_rng_state(chkpt['torch_rng_state']) if 'numpy_rng_state' in chkpt: print(f"setting numpy RNG state from {args.initial_checkpoint}...") np.random.set_state(chkpt['numpy_rng_state']) if 'python_rng_state' in chkpt: print(f"setting Python RNG state from {args.initial_checkpoint}...") random.setstate(chkpt['python_rng_state']) # loss w_l1 = setup['training']['loss']['w_l1'] w_lm = setup['training']['loss']['w_lm'] w_slm = setup['training']['loss']['w_slm'] w_sc = setup['training']['loss']['w_sc'] w_logmel = setup['training']['loss']['w_logmel'] w_wsc = setup['training']['loss']['w_wsc'] w_xcorr = setup['training']['loss']['w_xcorr'] w_sxcorr = setup['training']['loss']['w_sxcorr'] w_l2 = setup['training']['loss']['w_l2'] w_sum = w_l1 + w_lm + w_sc + w_logmel + w_wsc + w_slm + w_xcorr + w_sxcorr + w_l2 stftloss = MRSTFTLoss(sc_weight=w_sc, log_mag_weight=w_lm, wsc_weight=w_wsc, smooth_log_mag_weight=w_slm, sxcorr_weight=w_sxcorr).to(device) logmelloss = MRLogMelLoss().to(device) def xcorr_loss(y_true, y_pred): dims = list(range(1, len(y_true.shape))) loss = 1 - torch.sum(y_true * y_pred, dim=dims) / torch.sqrt(torch.sum(y_true ** 2, dim=dims) * torch.sum(y_pred ** 2, dim=dims) + 1e-9) return torch.mean(loss) def td_l2_norm(y_true, y_pred): dims = list(range(1, len(y_true.shape))) loss = torch.mean((y_true - y_pred) ** 2, dim=dims) / (torch.mean(y_pred ** 2, dim=dims) ** .5 + 1e-6) return loss.mean() def td_l1(y_true, y_pred, pow=0): dims = list(range(1, len(y_true.shape))) tmp = torch.mean(torch.abs(y_true - y_pred), dim=dims) / ((torch.mean(torch.abs(y_pred), dim=dims) + 1e-9) ** pow) return torch.mean(tmp) def criterion(x, y): return (w_l1 * td_l1(x, y, pow=1) + stftloss(x, y) + w_logmel * logmelloss(x, y) + w_xcorr * xcorr_loss(x, y) + w_l2 * td_l2_norm(x, y)) / w_sum # model checkpoint checkpoint = { 'setup' : setup, 'state_dict' : model.state_dict(), 'loss' : -1 } if not args.no_redirect: print(f"re-directing output to {os.path.join(args.output, output_file)}") sys.stdout = open(os.path.join(args.output, output_file), "w") print("summary:") print(f"generator: {count_parameters(model.cpu()) / 1e6:5.3f} M parameters") if hasattr(model, 'flop_count'): print(f"generator: {model.flop_count(16000) / 1e6:5.3f} MFLOPS") print(f"discriminator: {count_parameters(disc.cpu()) / 1e6:5.3f} M parameters") if ref is not None: noisy = np.fromfile(os.path.join(args.testdata, 'noisy.s16'), dtype=np.int16) initial_mos = pesq.pesq(16000, ref, noisy, mode='wb') print(f"initial MOS (PESQ): {initial_mos}") best_loss = 1e9 log_interval = 10 m_r = 0 m_f = 0 s_r = 1 s_f = 1 def optimizer_to(optim, device): for param in optim.state.values(): if isinstance(param, torch.Tensor): param.data = param.data.to(device) if param._grad is not None: param._grad.data = param._grad.data.to(device) elif isinstance(param, dict): for subparam in param.values(): if isinstance(subparam, torch.Tensor): subparam.data = subparam.data.to(device) if subparam._grad is not None: subparam._grad.data = subparam._grad.data.to(device) optimizer_to(optimizer, device) optimizer_to(optimizer_disc, device) for ep in range(1, epochs + 1): print(f"training epoch {ep}...") model.to(device) disc.to(device) model.train() disc.train() running_disc_loss = 0 running_adv_loss = 0 running_feature_loss = 0 running_reg_loss = 0 with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch: for i, batch in enumerate(tepoch): # set gradients to zero optimizer.zero_grad() # push batch to device for key in batch: batch[key] = batch[key].to(device) target = batch['target'].to(device) disc_target = batch[adv_target].to(device) # calculate model output output = model(batch['features'], batch['periods']) # discriminator update scores_gen = disc(output.detach()) scores_real = disc(disc_target.unsqueeze(1)) disc_loss = 0 for scale in scores_gen: disc_loss += ((scale[-1]) ** 2).mean() m_f = 0.9 * m_f + 0.1 * scale[-1].detach().mean().cpu().item() s_f = 0.9 * s_f + 0.1 * scale[-1].detach().std().cpu().item() for scale in scores_real: disc_loss += ((1 - scale[-1]) ** 2).mean() m_r = 0.9 * m_r + 0.1 * scale[-1].detach().mean().cpu().item() s_r = 0.9 * s_r + 0.1 * scale[-1].detach().std().cpu().item() disc_loss = 0.5 * disc_loss / len(scores_gen) winning_chance = 0.5 * m.erfc( (m_r - m_f) / m.sqrt(2 * (s_f**2 + s_r**2)) ) disc.zero_grad() disc_loss.backward() optimizer_disc.step() # generator update scores_gen = disc(output) # calculate loss loss_reg = criterion(output.squeeze(1), target) num_discs = len(scores_gen) loss_gen = 0 for scale in scores_gen: loss_gen += ((1 - scale[-1]) ** 2).mean() / num_discs loss_feat = 0 for k in range(num_discs): num_layers = len(scores_gen[k]) - 1 f = 4 / num_discs / num_layers for l in range(num_layers): loss_feat += f * F.l1_loss(scores_gen[k][l], scores_real[k][l].detach()) model.zero_grad() (loss_gen + lambda_feat * loss_feat + lambda_reg * loss_reg).backward() optimizer.step() running_adv_loss += loss_gen.detach().cpu().item() running_disc_loss += disc_loss.detach().cpu().item() running_feature_loss += lambda_feat * loss_feat.detach().cpu().item() running_reg_loss += lambda_reg * loss_reg.detach().cpu().item() # update status bar if i % log_interval == 0: tepoch.set_postfix(adv_loss=f"{running_adv_loss/(i + 1):8.7f}", disc_loss=f"{running_disc_loss/(i + 1):8.7f}", feat_loss=f"{running_feature_loss/(i + 1):8.7f}", reg_loss=f"{running_reg_loss/(i + 1):8.7f}", wc=f"{100*winning_chance:5.2f}%") # save checkpoint checkpoint['state_dict'] = model.state_dict() checkpoint['disc_state_dict'] = disc.state_dict() checkpoint['optimizer_state_dict'] = optimizer.state_dict() checkpoint['disc_optimizer_state_dict'] = optimizer_disc.state_dict() checkpoint['scheduler_state_dict'] = scheduler.state_dict() checkpoint['torch_rng_state'] = torch.get_rng_state() checkpoint['numpy_rng_state'] = np.random.get_state() checkpoint['python_rng_state'] = random.getstate() checkpoint['adv_loss'] = running_adv_loss/(i + 1) checkpoint['disc_loss'] = running_disc_loss/(i + 1) checkpoint['feature_loss'] = running_feature_loss/(i + 1) checkpoint['reg_loss'] = running_reg_loss/(i + 1) if inference_test: print("running inference test...") out = model.process(features, periods).cpu().numpy() wavfile.write(os.path.join(inference_folder, f'{model_name}_epoch_{ep}.wav'), 16000, out) if ref is not None: mos = pesq.pesq(16000, ref, out, mode='wb') print(f"MOS (PESQ): {mos}") torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_epoch_{ep}.pth')) torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_last.pth')) print() print('Done')