ref: 35ee397e060283d30c098ae5e17836316bbec08b
dir: /dnn/torch/lpcnet/train_lpcnet.py/
import os
import argparse
import sys
try:
import git
has_git = True
except:
has_git = False
import yaml
import torch
from torch.optim.lr_scheduler import LambdaLR
from data import LPCNetDataset
from models import model_dict
from engine.lpcnet_engine import train_one_epoch, evaluate
from utils.data import load_features
from utils.wav import wavwrite16
debug = False
if debug:
args = type('dummy', (object,),
{
'setup' : 'setup.yml',
'output' : 'testout',
'device' : None,
'test_features' : None,
'finalize': False,
'initial_checkpoint': None,
'no-redirect': False
})()
else:
parser = argparse.ArgumentParser("train_lpcnet.py")
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('--test-features', type=str, help='test feature file in v2 format', default=None)
parser.add_argument('--finalize', action='store_true', help='run single training round with lr=1e-5')
parser.add_argument('--initial-checkpoint', type=str, help='initial checkpoint', default=None)
parser.add_argument('--no-redirect', action='store_true', help='disables re-direction of output')
args = parser.parse_args()
torch.set_num_threads(4)
with open(args.setup, 'r') as f:
setup = yaml.load(f.read(), yaml.FullLoader)
if args.finalize:
if args.initial_checkpoint is None:
raise ValueError('finalization requires initial checkpoint')
if 'sparsification' in setup['lpcnet']['config']:
for sp_job in setup['lpcnet']['config']['sparsification'].values():
sp_job['start'], sp_job['stop'] = 0, 0
setup['training']['lr'] = 1.0e-5
setup['training']['lr_decay_factor'] = 0.0
setup['training']['epochs'] = 1
checkpoint_prefix = 'checkpoint_finalize'
output_prefix = 'output_finalize'
setup_name = 'setup_finalize.yml'
output_file='out_finalize.txt'
else:
checkpoint_prefix = 'checkpoint'
output_prefix = 'output'
setup_name = 'setup.yml'
output_file='out.txt'
# check model
if not 'model' in setup['lpcnet']:
print(f'warning: did not find model entry in setup, using default lpcnet')
model_name = 'lpcnet'
else:
model_name = setup['lpcnet']['model']
# prepare output folder
if os.path.exists(args.output) and not debug and not args.finalize:
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)
# prepare inference test if wanted
run_inference_test = False
if type(args.test_features) != type(None):
test_features = load_features(args.test_features)
inference_test_dir = os.path.join(args.output, 'inference_test')
os.makedirs(inference_test_dir, exist_ok=True)
run_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
lpcnet_config = setup['lpcnet']['config']
data = LPCNetDataset( setup['dataset'],
features=lpcnet_config['features'],
input_signals=lpcnet_config['signals'],
target=lpcnet_config['target'],
frames_per_sample=setup['training']['frames_per_sample'],
feature_history=lpcnet_config['feature_history'],
feature_lookahead=lpcnet_config['feature_lookahead'],
lpc_gamma=lpcnet_config.get('lpc_gamma', 1))
# load validation dataset if given
if 'validation_dataset' in setup:
validation_data = LPCNetDataset( setup['validation_dataset'],
features=lpcnet_config['features'],
input_signals=lpcnet_config['signals'],
target=lpcnet_config['target'],
frames_per_sample=setup['training']['frames_per_sample'],
feature_history=lpcnet_config['feature_history'],
feature_lookahead=lpcnet_config['feature_lookahead'],
lpc_gamma=lpcnet_config.get('lpc_gamma', 1))
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['lpcnet']['config'])
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=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)
# learning rate scheduler
scheduler = LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay_factor * x))
# loss
criterion = torch.nn.NLLLoss()
# 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")
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
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'))
# run inference test
if run_inference_test:
model.to("cpu")
print("running inference test...")
output = model.generate(test_features['features'], test_features['periods'], test_features['lpcs'])
testfilename = os.path.join(inference_test_dir, output_prefix + f'_epoch_{ep}.wav')
wavwrite16(testfilename, output.numpy(), 16000)
model.to(device)
print()