add conf for beta-2 and beta-3, update dataloader

configs/data/normal_largecorpus_multiview_cnsl.yaml

@@ -24,6 +24,20 @@ args:
   g_sd: 2
   SNRmin: 10
   SNRmax: 40
+
+  view_padding_configs:
+    '1':
+      padding_type: repeat
+      random_start: False
+    '2':
+      padding_type: repeat
+      random_start: False
+    '3':
+      padding_type: repeat
+      random_start: False
+    '4':
+      padding_type: repeat
+      random_start: False
 
   data:
     augmentation_methods:

configs/experiment/partialspoof/aasistssl_multiview_conf-beta-2.yaml

@@ -6,7 +6,7 @@
 defaults:
   - override /data: normal_largecorpus_multiview_cnsl
   - override /model: xlsr_aasist_multiview
-  - override /callbacks: default_loss_earlystop
+  - override /callbacks: default_loss_earlystop_lts
   - override /trainer: default
 
 # all parameters below will be merged with parameters from default configurations set above
@@ -34,10 +34,14 @@ data:
   batch_size: 14
   num_workers: 8
   pin_memory: true
+
   args:
     protocol_path: ${oc.env:MY_EXTENDED_ASVSPOOF_PROTOCOLS}
     chunk_size: 16000 # 1 sec
     overlap_size: 8000 # 0.5 sec
+    random_start: False
+    trim_length: 64600
+    padding_type: repeat
     view_padding_configs:
       '1':
         padding_type: repeat
@@ -51,10 +55,11 @@ data:
       '4':
         padding_type: repeat
         random_start: False
-
-    data:
+    data:    
       augmentation_methods: ["RawBoostdf"]
-      random_start: False
+      aug_dir: ${oc.env:MY_EXTENDED_ASVSPOOF_PATH}/aug
+      repeat_pad: True
+      online_aug: True
 
 logger:
   wandb:

configs/experiment/partialspoof/aasistssl_multiview_conf-beta-3.yaml

@@ -6,7 +6,7 @@
 defaults:
   - override /data: normal_largecorpus_multiview_cnsl
   - override /model: xlsr_aasist_multiview
-  - override /callbacks: default_loss_earlystop
+  - override /callbacks: default_loss_earlystop_lts
   - override /trainer: default
 
 # all parameters below will be merged with parameters from default configurations set above
@@ -34,10 +34,14 @@ data:
   batch_size: 14
   num_workers: 8
   pin_memory: true
+
   args:
     protocol_path: ${oc.env:MY_EXTENDED_ASVSPOOF_PROTOCOLS}
     chunk_size: 16000 # 1 sec
     overlap_size: 8000 # 0.5 sec
+    random_start: False
+    trim_length: 64600
+    padding_type: repeat
     view_padding_configs:
       '1':
         padding_type: repeat
@@ -51,10 +55,11 @@ data:
       '4':
         padding_type: repeat
         random_start: False
-
-    data:
+    data:    
       augmentation_methods: ["RawBoostdf"]
-      random_start: False
+      aug_dir: ${oc.env:MY_EXTENDED_ASVSPOOF_PATH}/aug
+      repeat_pad: True
+      online_aug: True
 
 logger:
   wandb:

src/data/components/augwrapper.py

@@ -1075,11 +1075,12 @@ def RawBoostFull(x, args, sr=16000, audio_path=None):
 def RawBoostdf(x, args, sr=16000, audio_path=None):
     aug_dir = args.aug_dir
     utt_id = os.path.basename(audio_path).split('.')[0]
-    aug_audio_path = os.path.join(aug_dir, 'RawBoostdf', utt_id + '.wav')
+
     if args.online_aug:
         return process_Rawboost_feature(x, sr, args, algo=3)
     else:
         # check if the augmented file exists
+        aug_audio_path = os.path.join(aug_dir, 'RawBoostdf', utt_id + '.wav')
         if (os.path.exists(aug_audio_path)):
             waveform, _ = librosa.load(aug_audio_path, sr=sr, mono=True)
             return waveform

src/data/components/baseloader.py

@@ -3,10 +3,10 @@
 
 
 class Dataset_base(Dataset):
-    def __init__(self, args, list_IDs, labels, base_dir, algo=5, vocoders=[], 
-                 augmentation_methods=[], eval_augment=None, num_additional_real=2, num_additional_spoof=2, 
-                 trim_length=64000, wav_samp_rate=16000, noise_path=None, rir_path=None, 
-                 aug_dir=None, online_aug=False, repeat_pad=True, is_train=True,
+    def __init__(self, args, list_IDs, labels, base_dir, algo=5, vocoders=[],
+                 augmentation_methods=[], eval_augment=None, num_additional_real=2, num_additional_spoof=2,
+                 trim_length=64000, wav_samp_rate=16000, noise_path=None, rir_path=None,
+                 aug_dir=None, online_aug=True, repeat_pad=True, is_train=True,
                  random_start=False):
         """
         Args:
@@ -23,8 +23,9 @@ def __init__(self, args, list_IDs, labels, base_dir, algo=5, vocoders=[],
         self.algo = algo
         self.vocoders = vocoders
         print("vocoders:", vocoders)
-        
+
         self.augmentation_methods = augmentation_methods
+        print("augmentation_methods:", augmentation_methods)
         if len(augmentation_methods) < 1:
             # using default augmentation method RawBoostWrapper12
             # self.augmentation_methods = ["RawBoost12"]
@@ -34,8 +35,7 @@ def __init__(self, args, list_IDs, labels, base_dir, algo=5, vocoders=[],
         self.num_additional_spoof = num_additional_spoof
         self.trim_length = trim_length
         self.sample_rate = wav_samp_rate
-
-        self.args.noise_path = noise_path
+        self.args.q = noise_path
         self.args.rir_path = rir_path
         self.args.aug_dir = aug_dir
         self.args.online_aug = online_aug
@@ -49,4 +49,3 @@ def __len__(self):
     def __getitem__(self, idx):
         # to be implemented in child classes
         pass
-

src/data/normal_datamodule.py

@@ -22,89 +22,97 @@
 for aug in SUPPORTED_AUGMENTATION:
     exec(f"from src.data.components.augwrapper import {aug}")
 
+
 class Dataset_for(Dataset_base):
     def __init__(self, args, list_IDs, labels, base_dir, algo=5, vocoders=[],
-                    augmentation_methods=[], eval_augment=None, num_additional_real=2, num_additional_spoof=2,
-                    trim_length=64000, wav_samp_rate=16000, noise_path=None, rir_path=None,
-                    aug_dir=None, online_aug=False, repeat_pad=True, is_train=True, random_start=False):
-        super(Dataset_for, self).__init__(args, list_IDs, labels, base_dir, algo, vocoders, 
-                 augmentation_methods, eval_augment, num_additional_real, num_additional_spoof, 
-                 trim_length, wav_samp_rate, noise_path, rir_path, 
-                 aug_dir, online_aug, repeat_pad, is_train, random_start)
+                 augmentation_methods=[], eval_augment=None, num_additional_real=2, num_additional_spoof=2,
+                 trim_length=64000, wav_samp_rate=16000, noise_path=None, rir_path=None,
+                 aug_dir=None, online_aug=False, repeat_pad=True, is_train=True, random_start=False):
+        super(Dataset_for, self).__init__(args, list_IDs, labels, base_dir, algo, vocoders,
+                                          augmentation_methods, eval_augment, num_additional_real, num_additional_spoof,
+                                          trim_length, wav_samp_rate, noise_path, rir_path,
+                                          aug_dir, online_aug, repeat_pad, is_train, random_start)
 
     def __getitem__(self, idx):
         utt_id = self.list_IDs[idx]
         filepath = os.path.join(self.base_dir, utt_id)
         X = load_audio(filepath, self.sample_rate)
-        
+
         # apply augmentation
         # randomly choose an augmentation method
         if self.is_train:
-            augmethod_index = random.choice(range(len(self.augmentation_methods))) if len(self.augmentation_methods) > 0 else -1
+            augmethod_index = random.choice(range(len(self.augmentation_methods))) if len(
+                self.augmentation_methods) > 0 else -1
             if augmethod_index >= 0:
-                X = globals()[self.augmentation_methods[augmethod_index]](X, self.args, self.sample_rate, 
-                                                                        audio_path = filepath)
+                X = globals()[self.augmentation_methods[augmethod_index]](X, self.args, self.sample_rate,
+                                                                          audio_path=filepath)
 
-        X_pad= pad(X,padding_type="repeat" if self.repeat_pad else "zero", max_len=self.trim_length, random_start=True)
-        x_inp= Tensor(X_pad)
+        X_pad = pad(X, padding_type="repeat" if self.repeat_pad else "zero",
+                    max_len=self.trim_length, random_start=True)
+        x_inp = Tensor(X_pad)
         target = self.labels[utt_id]
-        return idx, x_inp, target		
+        return idx, x_inp, target
 
 
 class Dataset_for_dev(Dataset_base):
     def __init__(self, args, list_IDs, labels, base_dir, algo=5, vocoders=[],
-                    augmentation_methods=[], eval_augment=None, num_additional_real=2, num_additional_spoof=2,
-                    trim_length=64000, wav_samp_rate=16000, noise_path=None, rir_path=None,
-                    aug_dir=None, online_aug=False, repeat_pad=False, is_train=True,
-                    random_start=False
-                    ):
-        super(Dataset_for_dev, self).__init__(args, list_IDs, labels, base_dir, algo, vocoders, 
-                 augmentation_methods, eval_augment, num_additional_real, num_additional_spoof, 
-                 trim_length, wav_samp_rate, noise_path, rir_path, 
-                 aug_dir, online_aug, repeat_pad, is_train, random_start)
+                 augmentation_methods=[], eval_augment=None, num_additional_real=2, num_additional_spoof=2,
+                 trim_length=64000, wav_samp_rate=16000, noise_path=None, rir_path=None,
+                 aug_dir=None, online_aug=False, repeat_pad=False, is_train=True,
+                 random_start=False
+                 ):
+        super(Dataset_for_dev, self).__init__(args, list_IDs, labels, base_dir, algo, vocoders,
+                                              augmentation_methods, eval_augment, num_additional_real, num_additional_spoof,
+                                              trim_length, wav_samp_rate, noise_path, rir_path,
+                                              aug_dir, online_aug, repeat_pad, is_train, random_start)
 
         if repeat_pad:
             self.padding_type = "repeat"
         else:
             self.padding_type = "zero"
-    
+
     def __getitem__(self, index):
         utt_id = self.list_IDs[index]
         X, fs = librosa.load(self.base_dir + "/" + utt_id, sr=16000)
-        X_pad = pad(X,self.padding_type,self.trim_length, random_start=self.random_start)
+        X_pad = pad(X, self.padding_type, self.trim_length,
+                    random_start=self.random_start)
         x_inp = Tensor(X_pad)
         target = self.labels[utt_id]
         return index, x_inp, target
 
+
 class Dataset_for_eval(Dataset_base):
     def __init__(self, args, list_IDs, labels, base_dir, algo=5, vocoders=[],
-                    augmentation_methods=[], eval_augment=None, num_additional_real=2, num_additional_spoof=2,
-                    trim_length=64000, wav_samp_rate=16000, noise_path=None, rir_path=None,
-                    aug_dir=None, online_aug=False, repeat_pad=True, is_train=True, enable_chunking=False, random_start=False
-                    ):
-        super(Dataset_for_eval, self).__init__(args, list_IDs, labels, base_dir, algo, vocoders, 
-                 augmentation_methods, eval_augment, num_additional_real, num_additional_spoof, 
-                 trim_length, wav_samp_rate, noise_path, rir_path, 
-                 aug_dir, online_aug, repeat_pad, is_train, random_start)
+                 augmentation_methods=[], eval_augment=None, num_additional_real=2, num_additional_spoof=2,
+                 trim_length=64000, wav_samp_rate=16000, noise_path=None, rir_path=None,
+                 aug_dir=None, online_aug=False, repeat_pad=True, is_train=True, enable_chunking=False, random_start=False
+                 ):
+        super(Dataset_for_eval, self).__init__(args, list_IDs, labels, base_dir, algo, vocoders,
+                                               augmentation_methods, eval_augment, num_additional_real, num_additional_spoof,
+                                               trim_length, wav_samp_rate, noise_path, rir_path,
+                                               aug_dir, online_aug, repeat_pad, is_train, random_start)
         self.enable_chunking = enable_chunking
         if repeat_pad:
             self.padding_type = "repeat"
         else:
             self.padding_type = "zero"
-    
+
     def __getitem__(self, idx):
         utt_id = self.list_IDs[idx]
         filepath = os.path.join(self.base_dir, utt_id)
         X, _ = librosa.load(filepath, sr=16000)
         # apply augmentation at inference time
         if self.eval_augment is not None:
             # print("eval_augment:", self.eval_augment)
-            X = globals()[self.eval_augment](X, self.args, self.sample_rate, audio_path = filepath)
+            X = globals()[self.eval_augment](
+                X, self.args, self.sample_rate, audio_path=filepath)
         if not self.enable_chunking:
-            X= pad(X,padding_type=self.padding_type,max_len=self.trim_length, random_start=self.random_start)
+            X = pad(X, padding_type=self.padding_type,
+                    max_len=self.trim_length, random_start=self.random_start)
         x_inp = Tensor(X)
         return x_inp, utt_id
 
+
 class NormalDataModule(LightningDataModule):
     """`LightningDataModule` for the ASVSpoof dataset.
 
@@ -156,10 +164,6 @@ def __init__(
         num_workers: int = 0,
         pin_memory: bool = False,
         args: Optional[Dict[str, Any]] = None,
-        # list_IDs: list = [], labels: list = [], base_dir: str = '', algo: int = 5, vocoders: list = [],
-        # augmentation_methods: list = [], eval_augment: Optional[str] = None, num_additional_real: int = 2, num_additional_spoof: int = 2,
-        # trim_length: int = 64000, wav_samp_rate: int = 16000, noise_path: Optional[str] = None, rir_path: Optional[str] = None,
-        # aug_dir: Optional[str] = None, online_aug: bool = False, repeat_pad: bool = True, is_train: bool = True, enable_chunking: bool = False
     ) -> None:
         """Initialize a `ASVSpoofDataModule`.
 
@@ -191,7 +195,7 @@ def num_classes(self) -> int:
         """
         return 2
 
-    def prepare_data(self) -> None:        
+    def prepare_data(self) -> None:
         pass
 
     def setup(self, stage: Optional[str] = None) -> None:
@@ -214,27 +218,28 @@ def setup(self, stage: Optional[str] = None) -> None:
 
         # load and split datasets only if not loaded already
         if not self.data_train and not self.data_val and not self.data_test:
-            
+
             # define train dataloader
 
             d_label_trn, file_train = self.genList(dir_meta=os.path.join(
-                    self.data_dir), is_train=True, is_eval=False, is_dev=False)
-            
+                self.data_dir), is_train=True, is_eval=False, is_dev=False)
+
             print('no. of training trials', len(file_train))
 
-            d_label_dev, file_dev = self.genList(dir_meta=os.path.join(self.data_dir), is_train=False, is_eval=False, is_dev=True)
+            d_label_dev, file_dev = self.genList(dir_meta=os.path.join(
+                self.data_dir), is_train=False, is_eval=False, is_dev=True)
             print('no. of validation trials', len(file_dev))
-            file_eval = self.genList(dir_meta=os.path.join(self.data_dir), is_train=False, is_eval=True, is_dev=False)
-
+            file_eval = self.genList(dir_meta=os.path.join(
+                self.data_dir), is_train=False, is_eval=True, is_dev=False)
+
             self.data_train = Dataset_for(self.args, list_IDs=file_train, labels=d_label_trn,
-                            base_dir=self.data_dir+'/',  **self.args['data'])
+                                          base_dir=self.data_dir+'/',  **self.args['data'])
 
             self.data_val = Dataset_for_dev(self.args, list_IDs=file_dev, labels=d_label_dev,
-                                  base_dir=self.data_dir+'/',  is_train=False, **self.args['data'])
+                                            base_dir=self.data_dir+'/',  is_train=False, **self.args['data'])
 
             self.data_test = Dataset_for_eval(self.args, list_IDs=file_eval, labels=None,
-                                    base_dir=self.data_dir+'/',  **self.args['data'])
-
+                                              base_dir=self.data_dir+'/',  **self.args['data'])
 
     def train_dataloader(self) -> DataLoader[Any]:
         """Create and return the train dataloader.
@@ -299,11 +304,11 @@ def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
         :param state_dict: The datamodule state returned by `self.state_dict()`.
         """
         pass
-    
+
     def genList(self, dir_meta, is_train=False, is_eval=False, is_dev=False):
         # bonafide: 1, spoof: 0
         d_meta = {}
-        file_list=[]
+        file_list = []
         protocol = os.path.join(dir_meta, "protocol.txt")
 
         if (is_train):
@@ -325,7 +330,7 @@ def genList(self, dir_meta, is_train=False, is_eval=False, is_dev=False):
                     file_list.append(utt)
                     d_meta[utt] = 1 if label == 'bonafide' else 0
             return d_meta, file_list
-        
+
         if (is_eval):
             # no eval protocol yet
             with open(protocol, 'r') as f:
@@ -338,5 +343,6 @@ def genList(self, dir_meta, is_train=False, is_eval=False, is_dev=False):
             # return d_meta, file_list
             return d_meta, file_list
 
+
 if __name__ == "__main__":
-    _ = NormalDataModule()
+    _ = NormalDataModule()