fix: dnorm_type defaults to bnorm as should

run_segan+_train.sh

@@ -4,4 +4,4 @@
 python -u train.py --save_path ckpt_segan+ \
 	--clean_trainset data_veu4/expanded_segan1_additive/clean_trainset \
 	--noisy_trainset data_veu4/expanded_segan1_additive/noisy_trainset \
-	--cache_dir data_tmp --no_train_gen --batch_size 300 
+	--cache_dir data_tmp --no_train_gen --batch_size 300 --no-bias

train.py

@@ -3,7 +3,7 @@
 import torch.nn as nn
 from torch.utils.data import DataLoader
 from segan.models import SEGAN, WSEGAN, AEWSEGAN
-from segan.datasets import SEDataset, collate_fn
+from segan.datasets import SEDataset, SEH5Dataset, collate_fn
 from segan.utils import Additive
 import numpy as np
 import random
@@ -38,39 +38,53 @@ def main(opts):
         segan.G.load_pretrained(opts.g_pretrained_ckpt, True)
     if opts.d_pretrained_ckpt is not None:
         segan.D.load_pretrained(opts.d_pretrained_ckpt, True)
-    # create dataset(s) and dataloader(s)
-    dset = SEDataset(opts.clean_trainset, 
-                     opts.noisy_trainset, 
-                     opts.preemph,
-                     do_cache=True,
-                     cache_dir=opts.cache_dir,
-                     split='train',
-                     stride=opts.data_stride,
-                     slice_size=opts.slice_size,
-                     max_samples=opts.max_samples,
-                     verbose=True,
-                     slice_workers=opts.slice_workers,
-                     preemph_norm=opts.preemph_norm,
-                     random_scale=opts.random_scale
-                    )
+    # create Dataset(s) and Dataloader(s)
+    if opts.h5:
+        # H5 Dataset with processed speech chunks
+        if opts.h5_data_root is None:
+            raise ValueError('Please specify an H5 data root')
+        dset = SEH5Dataset(opts.h5_data_root, split='train',
+                           preemph=opts.preemph,
+                           verbose=True,
+                           random_scale=opts.random_scale)
+    else:
+        # Directory Dataset from raw wav files
+        dset = SEDataset(opts.clean_trainset, 
+                         opts.noisy_trainset, 
+                         opts.preemph,
+                         do_cache=True,
+                         cache_dir=opts.cache_dir,
+                         split='train',
+                         stride=opts.data_stride,
+                         slice_size=opts.slice_size,
+                         max_samples=opts.max_samples,
+                         verbose=True,
+                         slice_workers=opts.slice_workers,
+                         preemph_norm=opts.preemph_norm,
+                         random_scale=opts.random_scale
+                        )
     dloader = DataLoader(dset, batch_size=opts.batch_size,
                          shuffle=True, num_workers=opts.num_workers,
                          pin_memory=CUDA,
                          collate_fn=collate_fn)
     if opts.clean_valset is not None:
-        # validation dataset 
-        va_dset = SEDataset(opts.clean_valset, 
-                            opts.noisy_valset, 
-                            opts.preemph,
-                            do_cache=True,
-                            cache_dir=opts.cache_dir,
-                            split='valid',
-                            stride=opts.data_stride,
-                            slice_size=opts.slice_size,
-                            max_samples=opts.max_samples,
-                            verbose=True,
-                            slice_workers=opts.slice_workers,
-                            preemph_norm=opts.preemph_norm)
+        if opts.h5:
+            dset = SEH5Dataset(opts.h5_data_root, split='valid',
+                               preemph=opts.preemph,
+                               verbose=True)
+        else:
+            va_dset = SEDataset(opts.clean_valset, 
+                                opts.noisy_valset, 
+                                opts.preemph,
+                                do_cache=True,
+                                cache_dir=opts.cache_dir,
+                                split='valid',
+                                stride=opts.data_stride,
+                                slice_size=opts.slice_size,
+                                max_samples=opts.max_samples,
+                                verbose=True,
+                                slice_workers=opts.slice_workers,
+                                preemph_norm=opts.preemph_norm)
         va_dloader = DataLoader(va_dset, batch_size=300,
                                 shuffle=False, num_workers=opts.num_workers,
                                 pin_memory=CUDA,
@@ -103,6 +117,12 @@ def main(opts):
                         default=None)#'data/clean_valset')
     parser.add_argument('--noisy_valset', type=str,
                         default=None)#'data/noisy_valset')
+    parser.add_argument('--h5_data_root', type=str, default=None,
+                        help='H5 data root dir (Def: None). The '
+                             'files will be found by split name '
+                             '{train, valid, test}.h5')
+    parser.add_argument('--h5', action='store_true', default=False,
+                        help='Activate H5 dataset mode (Def: False).')
     parser.add_argument('--data_stride', type=float,
                         default=0.5, help='Stride in seconds for data read')
     parser.add_argument('--seed', type=int, default=111, 
@@ -169,7 +189,7 @@ def main(opts):
                         '3) constant: with alpha value, set values to' \
                         ' not learnable, just fixed.\n(Def: alpha)')
     parser.add_argument('--skip_init', type=str, default='one',
-                        help='Way to init skip connections (Def: ones)')
+                        help='Way to init skip connections (Def: one)')
     parser.add_argument('--skip_kwidth', type=int, default=11)
 
     # Generator parameters
@@ -215,7 +235,7 @@ def main(opts):
     parser.add_argument('--denc_poolings', type=int, nargs='+', 
                         default=[4, 4, 4, 4, 4],
                         help='(Def: [4, 4, 4, 4, 4])')
-    parser.add_argument('--dnorm_type', type=str, default=None,
+    parser.add_argument('--dnorm_type', type=str, default='bnorm',
                         help='Normalization to be used in D. Can '
                         'be: (1) snorm, (2) bnorm or (3) none '
                         '(Def: bnorm).')