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"""
/* 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
import numpy as np
from scipy.io import wavfile
import pesq
from data import SilkEnhancementSet
from models import model_dict
from engine.engine import train_one_epoch, evaluate
from utils.silk_features import load_inference_data
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('--testdata', type=str, help='path to features and signal 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)
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
if args.testdata is not None:
testsignal, features, periods, numbits = load_inference_data(args.testdata, **setup['data'])
inference_test = True
inference_folder = os.path.join(args.output, 'inference_test')
os.makedirs(os.path.join(args.output, 'inference_test'), exist_ok=True)
try:
ref = np.fromfile(os.path.join(args.testdata, 'clean.s16'), dtype=np.int16)
except:
pass
else:
inference_test = False
# 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 = SilkEnhancementSet(setup['dataset'], **data_config)
# load validation dataset if given
if 'validation_dataset' in setup:
validation_data = SilkEnhancementSet(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:
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
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(testsignal, features, periods, numbits).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')
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