""" /* 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 yaml try: import git has_git = True except: has_git = False import torch from torch.optim.lr_scheduler import LambdaLR from scipy.io import wavfile import pesq from data import LPCNetVocodingDataset from models import model_dict from engine.vocoder_engine import train_one_epoch, evaluate 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'] # 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=8) run_validation = True else: run_validation = False # create model model = model_dict[model_name](*setup['model']['args'], **setup['model']['kwargs']) if args.initial_checkpoint is not None: print(f"loading state dict from {args.initial_checkpoint}...") chkpt = torch.load(args.initial_checkpoint, map_location='cpu') model.load_state_dict(chkpt['state_dict']) # 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) # push model to device model.to(device) # dataloader dataloader = torch.utils.data.DataLoader(data, batch_size=batch_size, drop_last=True, shuffle=True, num_workers=8) # optimizer is introduced to trainable parameters parameters = [p for p in model.parameters() if p.requires_grad] optimizer = torch.optim.Adam(parameters, lr=lr) # learning rate scheduler scheduler = LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay_factor * x)) # 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"{count_parameters(model.cpu()) / 1e6:5.3f} M parameters") if hasattr(model, 'flop_count'): print(f"{model.flop_count(16000) / 1e6:5.3f} MFLOPS") if ref is not None: pass best_loss = 1e9 for ep in range(1, epochs + 1): print(f"training epoch {ep}...") new_loss = train_one_epoch(model, criterion, optimizer, dataloader, device, scheduler) # save checkpoint checkpoint['state_dict'] = model.state_dict() checkpoint['loss'] = new_loss if run_validation: print("running validation...") validation_loss = evaluate(model, criterion, validation_dataloader, device) checkpoint['validation_loss'] = validation_loss if validation_loss < best_loss: torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_best.pth')) best_loss = validation_loss 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')