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main.py
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main.py
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from __future__ import absolute_import
from __future__ import print_function
import numpy as np
np.random.seed(42) # for reproducibility
from tqdm import tqdm
import torch
from torch import nn
from torch.autograd import Variable
from torch.utils.data import DataLoader
from stft_dataset import STFTDataset
from residual import ResidualModel
from highway import HighwayModel
from masking import MaskingModel
from baseline import BaselineModel
from pytorch_utils import TrainLoop, load_checkpoint
def load_data(window_size, step_size, use_log):
print("Loading data...")
G_train = STFTDataset(window=window_size, step=step_size, use_log=use_log)
G_train.load_metadata_from_desc_file('ieee_reverb_only_train.json')
G_train.fit_stats()
G_val = STFTDataset(window=window_size, step=step_size, use_log=use_log)
G_val.load_metadata_from_desc_file('ieee_reverb_only_valid.json')
G_val.feats_mean = G_train.feats_mean
G_val.feats_std = G_train.feats_std
return G_train, G_val
def load_noisy_data(window_size, overlap, use_log):
print("Loading data...")
G_train = STFTDataset(window=window_size, step=overlap, use_log=use_log)
G_train.load_metadata_from_desc_file('ieee_noisy_train.json')
G_train.fit_stats()
G_val = STFTDataset(window=window_size, step=overlap, use_log=use_log)
G_val.load_metadata_from_desc_file('ieee_noisy_valid.json')
G_val.feats_mean = G_train.feats_mean
G_val.feats_std = G_train.feats_std
return G_train, G_val
def load_noisy_timit(window_size, overlap, use_log):
print("Loading data...")
G_train = STFTDataset(window=window_size, step=overlap, use_log=use_log)
G_train.load_metadata_from_desc_file('timit_noisy_train.json')
G_train.fit_stats()
G_val = STFTDataset(window=window_size, step=overlap, use_log=use_log)
G_val.load_metadata_from_desc_file('timit_noisy_valid.json')
G_val.feats_mean = G_train.feats_mean
G_val.feats_std = G_train.feats_std
return G_train, G_val
def load_reverb_timit(window_size, overlap, use_log):
print("Loading data...")
G_train = STFTDataset(window=window_size, step=overlap, use_log=use_log)
G_train.load_metadata_from_desc_file('timit_reverb_only_train.json')
G_train.fit_stats()
G_val = STFTDataset(window=window_size, step=overlap, use_log=use_log)
G_val.load_metadata_from_desc_file('timit_reverb_only_valid.json')
G_val.feats_mean = G_train.feats_mean
G_val.feats_std = G_train.feats_std
return G_train, G_val
def train_fn(model, optimizer, criterion, batch):
x, y, lengths = batch
x = Variable(x.cuda())
y = Variable(y.cuda(), requires_grad=False)
mask = Variable(torch.ByteTensor(x.size()).fill_(1).cuda(),
requires_grad=False)
for k, l in enumerate(lengths):
mask[:l, k, :] = 0
y_hat = model(x)
# Apply mask
y_hat.masked_fill_(mask, 0.0)
y.masked_fill_(mask, 0.0)
loss = criterion(y_hat, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
return loss.data.item()
def valid_fn(model, criterion, batch):
x, y, lengths = batch
x = Variable(x.cuda(), volatile=True)
y = Variable(y.cuda(), requires_grad=False)
mask = Variable(torch.ByteTensor(x.size()).fill_(1).cuda(),
requires_grad=False)
for k, l in enumerate(lengths):
mask[:l, k, :] = 0
y_hat = model(x)
# Apply mask
y_hat.masked_fill_(mask, 0.0)
y.masked_fill_(mask, 0.0)
val_loss = criterion(y_hat, y).data.item()
return val_loss
if __name__ == '__main__':
from argparse import ArgumentParser
import os
from glob import glob
parser = ArgumentParser()
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--max_epochs', type=int, default=100)
parser.add_argument('--num_hidden', type=int, default=256)
parser.add_argument('--num_blocks', type=int, default=3)
parser.add_argument('--num_layers_per_block', type=int, default=1)
parser.add_argument('--learning_rate', type=float, default=1e-4)
parser.add_argument('--model_type', default='residual')
parser.add_argument('--window_size', type=int, default=32)
parser.add_argument('--step_size', type=int, default=16)
parser.add_argument('--data_type', default='reverb')
parser.add_argument('--use_log', action='store_true')
parser.add_argument('checkpoint_path')
args = parser.parse_args()
try:
train_loop = load_checkpoint(args.checkpoint_path)
except ValueError:
print('No checkpoints, initializing a model from scratch...')
window_size = args.window_size # in ms
step_size = args.step_size
n_input = int(1e-3*window_size*16000/2 + 1)
n_output = n_input
if args.model_type == 'residual':
model = ResidualModel(n_input,
args.num_blocks,
args.num_hidden,
args.num_layers_per_block).cuda()
elif args.model_type == 'highway':
model = HighwayModel(n_input,
args.num_blocks,
args.num_hidden,
args.num_layers_per_block).cuda()
elif args.model_type == 'masking':
model = MaskingModel(n_input,
args.num_blocks,
args.num_hidden,
args.num_layers_per_block).cuda()
elif args.model_type == 'baseline':
model = BaselineModel(n_input,
args.num_hidden,
args.num_layers_per_block).cuda()
else:
raise ValueError('model_type has to be either "residual", "highway", or "baseline"')
print(model)
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
if args.data_type == 'reverb':
print('Loading reverb dataset')
G_train, G_val = load_data(window_size, step_size, args.use_log)
elif args.data_type == 'noisy':
print('Loading noisy dataset')
G_train, G_val = load_noisy_data(window_size, step_size, args.use_log)
elif args.data_type == 'noisy_timit':
print('Loading noisy_timit dataset')
G_train, G_val = load_noisy_timit(window_size, step_size, args.use_log)
elif args.data_type == 'reverb_timit':
G_train, G_val = load_reverb_timit(window_size, step_size, args.use_log)
else:
raise ValueError('data_type has to be either "reverb" or "noisy"')
train_loader = DataLoader(G_train, batch_size=args.batch_size,
collate_fn=G_train.collate_samples,
num_workers=8, shuffle=True)
valid_loader = DataLoader(G_val, batch_size=args.batch_size,
collate_fn=G_train.collate_samples,
num_workers=4)
train_loop = TrainLoop(model,
optimizer, criterion,
train_fn, train_loader,
valid_fn, valid_loader,
checkpoint_path=args.checkpoint_path)
train_loop.train(args.max_epochs)