Done some changes that are required.

egs/grid/AVSR/audionet_ctc_asr/decode.py

@@ -30,7 +30,7 @@ import torch
 import torch.nn as nn
 
 from torch.utils.data import DataLoader
-from local.dataset_audio import dataset_audio
+from local.dataset_audio import AudioDataSet
 from model import AudioNet
 
 from icefall.checkpoint import average_checkpoints, load_checkpoint
@@ -467,7 +467,7 @@ def main():
     model.to(device)
     model.eval()
 
-    grid = dataset_audio(
+    grid = AudioDataSet(
         params.video_path,
         params.anno_path,
         params.val_list,

egs/grid/AVSR/audionet_ctc_asr/model.py

@@ -1,4 +1,5 @@
-# Copyright      2021  Xiaomi Corp.        (authors: Fangjun Kuang)
+# Copyright      2021  Xiaomi Corp.        (authors: Fangjun Kuang
+#                                                    Mingshuang Luo)
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #

egs/grid/AVSR/audionet_ctc_asr/pretrained.py

@@ -209,7 +209,7 @@ def main():
 
     logging.info("Decoding started")
     features = fbank(waves)
-    # aud_padding = 480
+    # Here , we set aud_padding as 480.
     features_new = torch.zeros(len(features), 480, params.feature_dim).to(
         device
     )

egs/grid/AVSR/audionet_ctc_asr/train.py

@@ -32,7 +32,7 @@ import torch.nn as nn
 import torch.optim as optim
 from torch.utils.data import DataLoader
 
-from local.dataset_audio import dataset_audio
+from local.dataset_audio import AudioDataSet
 from lhotse.utils import fix_random_seed
 from model import AudioNet
 from torch import Tensor
@@ -533,7 +533,7 @@ def run(rank, world_size, args):
         optimizer.load_state_dict(checkpoints["optimizer"])
         scheduler.load_state_dict(checkpoints["scheduler"])
 
-    grid = dataset_audio(
+    grid = AudioDataSet(
         params.video_path,
         params.anno_path,
         params.train_list,

egs/grid/AVSR/audionet_ctc_asr/utils.py

@@ -14,27 +14,32 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+"""
+This script is to encodes the supervisions as Tuple list.
+The supervision tensor has shape ``(batch_size, 3)``.
+Its second dimension contains information about sequence index [0],
+start frames [1] and num frames [2].
+In GRID, the start frame of each audio sample is 0.
+"""
 import torch
 
 
-def encode_supervisions(nnet_output_shape, batch):
+def encode_supervisions(nnet_output_shape: int, batch: dict):
     """
-    Encodes the output of net and texts into
+    Args:
-    a pair of torch Tensor, and a list of transcription strings.
+      nnet_output_shape:
-
+        The shape of nnet_output, e.g: (N, T, D).
-    The supervision tensor has shape ``(batch_size, 3)``.
+      batch:
-    Its second dimension contains information about sequence index [0],
+        A batch of dataloader, it's a dict file
-    start frames [1] and num frames [2].
+        including text and aud/vid arrays.
-
+    Return:
-    In GRID, the start frame of each audio sample is 0.
+      The tuple list of supervisions and the text in batch.
     """
     N, T, D = nnet_output_shape
 
-    supervisions_idx = torch.arange(0, N).to(torch.int32)
+    supervisions_idx = torch.arange(0, N, dtype=torch.int32)
-    start_frames = [0 for _ in range(N)]
+    supervisions_start_frame = torch.full((1, N), 0, dtype=torch.int32)[0]
-    supervisions_start_frame = torch.tensor(start_frames).to(torch.int32)
+    supervisions_num_frames = torch.full((1, N), T, dtype=torch.int32)[0]
-    num_frames = [T for _ in range(N)]
-    supervisions_num_frames = torch.tensor(num_frames).to(torch.int32)
 
     supervision_segments = torch.stack(
         (
@@ -43,7 +48,7 @@ def encode_supervisions(nnet_output_shape, batch):
             supervisions_num_frames,
         ),
         1,
-    ).to(torch.int32)
+    )
     texts = batch["txt"]
 
     return supervision_segments, texts

egs/grid/AVSR/combinenet_ctc_avsr/decode.py

@@ -30,7 +30,7 @@ import torch
 import torch.nn as nn
 
 from torch.utils.data import DataLoader
-from local.dataset_av import dataset_av
+from local.dataset_av import AudioVisualDataset
 from model import CombineNet
 
 from icefall.checkpoint import average_checkpoints, load_checkpoint
@@ -475,7 +475,7 @@ def main():
     model.to(device)
     model.eval()
 
-    grid = dataset_av(
+    grid = AudioVisualDataset(
         params.video_path,
         params.anno_path,
         params.val_list,

egs/grid/AVSR/combinenet_ctc_avsr/model.py

@@ -1,4 +1,4 @@
-# Copyright      2021  Xiaomi Corp.        (authors: Fangjun Kuang)
+# Copyright      2021  Xiaomi Corp.        (authors: Mingshuang Luo)
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #

egs/grid/AVSR/combinenet_ctc_avsr/train.py

@@ -32,7 +32,7 @@ import torch.nn as nn
 import torch.optim as optim
 from torch.utils.data import DataLoader
 
-from local.dataset_av import dataset_av
+from local.dataset_av import AudioVisualDataset
 from lhotse.utils import fix_random_seed
 from model import CombineNet
 from torch import Tensor
@@ -544,7 +544,7 @@ def run(rank, world_size, args):
         optimizer.load_state_dict(checkpoints["optimizer"])
         scheduler.load_state_dict(checkpoints["scheduler"])
 
-    grid = dataset_av(
+    grid = AudioVisualDataset(
         params.video_path,
         params.anno_path,
         params.train_list,

egs/grid/AVSR/combinenet_ctc_avsr/utils.py

@@ -19,24 +19,20 @@ import torch
 
 def encode_supervisions(nnet_output_shape, batch):
     """
-    Encodes Lhotse's ``batch["supervisions"]`` dict into
+    Encodes the output of net and texts into
     a pair of torch Tensor, and a list of transcription strings.
 
     The supervision tensor has shape ``(batch_size, 3)``.
     Its second dimension contains information about sequence index [0],
     start frames [1] and num frames [2].
 
-    The batch items might become re-ordered during this operation -- the
+    In GRID, the start frame of each audio sample is 0.
-    returned tensor and list of strings are guaranteed to be consistent with
-    each other.
     """
     N, T, D = nnet_output_shape
 
-    supervisions_idx = torch.arange(0, N).to(torch.int32)
+    supervisions_idx = torch.arange(0, N, dtype=torch.int32)
-    start_frames = [0 for _ in range(N)]
+    supervisions_start_frame = torch.full((1, N), 0, dtype=torch.int32)[0]
-    supervisions_start_frame = torch.tensor(start_frames).to(torch.int32)
+    supervisions_num_frames = torch.full((1, N), T, dtype=torch.int32)[0]
-    num_frames = [T for _ in range(N)]
-    supervisions_num_frames = torch.tensor(num_frames).to(torch.int32)
 
     supervision_segments = torch.stack(
         (
@@ -45,7 +41,7 @@ def encode_supervisions(nnet_output_shape, batch):
             supervisions_num_frames,
         ),
         1,
-    ).to(torch.int32)
+    )
     texts = batch["txt"]
 
     return supervision_segments, texts

egs/grid/AVSR/local/cvtransforms.py

@@ -26,13 +26,31 @@ The input for the above functions is a sequence of images.
 import random
 
 
-def HorizontalFlip(batch_img, p=0.5):
+def horizontal_flip(batch_img: float, p: float):
-    # (T, H, W, C)
+    """
+    Args:
+      batch_img:
+        The float array of a sequence of images, the shape of the
+        arrat is (T, H, W, C).
+      p:
+        The probability of implementing horizontal flip, the defaults
+        value is 0.5.
+    Return:
+      A new float array of the sequence of images after flipping.
+    """
     if random.random() > p:
         batch_img = batch_img[:, :, ::-1, ...]
     return batch_img
 
 
-def ColorNormalize(batch_img):
+def color_normalize(batch_img: float):
+    """
+    Args:
+      batch_img:
+        The float array of a sequence of images, the shape of the
+        arrat is (T, H, W, C).
+    Return:
+      A new float array of the sequence of images after normalizing.
+    """
     batch_img = batch_img / 255.0
     return batch_img

egs/grid/AVSR/local/dataset_audio.py

@@ -19,7 +19,6 @@
 This script is to load the audio data in GRID.
 The class dataset_audio makes each audio batch data have the same shape.
 """
-import kaldifeat
 import numpy as np
 import os
 
@@ -27,8 +26,10 @@ import torch
 import torchaudio
 from torch.utils.data import Dataset
 
+import kaldifeat
 
-class dataset_audio(Dataset):
+
+class AudioDataSet(Dataset):
     def __init__(
         self,
         video_path: str,
@@ -46,7 +47,7 @@ class dataset_audio(Dataset):
           anno_path:
             The dir path of the texts data.
           file_list:
-            The file which listing all samples for training or testing.
+            A txt file which listing all samples for training or testing.
           aud_padding:
             The padding for each audio sample.
           sample_rate:
@@ -61,6 +62,15 @@ class dataset_audio(Dataset):
         self.sample_rate = sample_rate
         self.feature_dim = feature_dim
         self.phase = phase
+
+        opts = kaldifeat.FbankOptions()
+        opts.device = "cpu"
+        opts.frame_opts.dither = 0
+        opts.frame_opts.snip_edges = False
+        opts.frame_opts.samp_freq = self.sample_rate
+        opts.mel_opts.num_bins = self.feature_dim
+        self.fbank = kaldifeat.Fbank(opts)
+
         with open(file_list, "r") as f:
             self.videos = [
                 os.path.join(video_path, line.strip()) for line in f.readlines()
@@ -92,19 +102,26 @@ class dataset_audio(Dataset):
         return len(self.data)
 
     def _load_aud(self, filename):
-        opts = kaldifeat.FbankOptions()
+        """Load the audio data.
-        opts.device = "cpu"
+        Args:
-        opts.frame_opts.dither = 0
+            filename:
-        opts.frame_opts.snip_edges = False
+                The full path of a wav file.
-        opts.frame_opts.samp_freq = self.sample_rate
+        Return:
-        opts.mel_opts.num_bins = self.feature_dim
+            The fbank feature array.
-        fbank = kaldifeat.Fbank(opts)
+        """
-        wave, sr = torchaudio.load(filename)
+        wave, _ = torchaudio.load(filename)
         wave = wave[0]
-        features = fbank(wave)
+        features = self.fbank(wave)
         return features
 
     def _load_anno(self, name):
+        """Load the text file.
+        Args:
+            name:
+                The file which records the text.
+        Return:
+            A sequence of words.
+        """
         with open(name, "r") as f:
             lines = [line.strip().split(" ") for line in f.readlines()]
             txt = [line[2] for line in lines]
@@ -113,6 +130,15 @@ class dataset_audio(Dataset):
         return txt
 
     def _padding(self, array, length):
+        """Pad zeros for the feature array.
+        Args:
+            array:
+                The feature arry. (Audio or Visual feature)
+            length:
+                The length for padding.
+        Return:
+            A new feature array after padding.
+        """
         array = [array[_] for _ in range(array.shape[0])]
         size = array[0].shape
         for i in range(length - len(array)):

egs/grid/AVSR/local/dataset_av.py

@@ -20,7 +20,6 @@ This script is to load the pair of audio-visual data in GRID.
 The class dataset_av makes each audio-visual batch data have the same shape.
 """
 import cv2
-import kaldifeat
 import numpy as np
 import os
 
@@ -28,10 +27,11 @@ import torch
 import torchaudio
 from torch.utils.data import Dataset
 
-from .cvtransforms import HorizontalFlip, ColorNormalize
+import kaldifeat
+from .cvtransforms import horizontal_flip, color_normalize
 
 
-class dataset_av(Dataset):
+class AudioVisualDataset(Dataset):
     def __init__(
         self,
         video_path,
@@ -94,8 +94,8 @@ class dataset_av(Dataset):
         )
 
         if self.phase == "train":
-            vid = HorizontalFlip(vid)
+            vid = horizontal_flip(vid)
-        vid = ColorNormalize(vid)
+        vid = color_normalize(vid)
 
         vid = self._padding(vid, self.vid_pading)
         aud = self._padding(aud, self.aud_pading)
@@ -110,6 +110,14 @@ class dataset_av(Dataset):
         return len(self.data)
 
     def _load_vid(self, p):
+        """Load the visual data.
+        Args:
+            p:
+                A directory which contains a sequence of frames
+            for a visual sample.
+        Return:
+            The array of a visual sample.
+        """
         files = os.listdir(p)
         files = list(filter(lambda file: file.find(".jpg") != -1, files))
         files = sorted(files, key=lambda file: int(os.path.splitext(file)[0]))
@@ -123,6 +131,13 @@ class dataset_av(Dataset):
         return array
 
     def _load_aud(self, filename):
+        """Load the audio data.
+        Args:
+            filename:
+                The full path of a wav file.
+        Return:
+            The fbank feature array.
+        """
         opts = kaldifeat.FbankOptions()
         opts.frame_opts.dither = 0
         opts.frame_opts.snip_edges = False
@@ -135,6 +150,13 @@ class dataset_av(Dataset):
         return features
 
     def _load_anno(self, name):
+        """Load the text file.
+        Args:
+            name:
+                The file which records the text.
+        Return:
+            A sequence of words.
+        """
         with open(name, "r") as f:
             lines = [line.strip().split(" ") for line in f.readlines()]
             txt = [line[2] for line in lines]
@@ -143,6 +165,15 @@ class dataset_av(Dataset):
         return txt
 
     def _padding(self, array, length):
+        """Pad zeros for the feature array.
+        Args:
+            array:
+                The feature arry. (Audio or Visual feature)
+            length:
+                The length for padding.
+        Return:
+            A new feature array after padding.
+        """
         array = [array[_] for _ in range(array.shape[0])]
         size = array[0].shape
         for i in range(length - len(array)):

egs/grid/AVSR/local/dataset_visual.py

@@ -24,10 +24,13 @@ import os
 import numpy as np
 import torch
 from torch.utils.data import Dataset
-from .cvtransforms import HorizontalFlip, ColorNormalize
+from .cvtransforms import (
+    color_normalize,
+    horizontal_flip,
+)
 
 
-class dataset_visual(Dataset):
+class VisualDataset(Dataset):
     def __init__(
         self,
         video_path: str,
@@ -74,8 +77,8 @@ class dataset_visual(Dataset):
         )
 
         if self.phase == "train":
-            vid = HorizontalFlip(vid)
+            vid = horizontal_flip(vid, p=0.5)
-        vid = ColorNormalize(vid)
+        vid = color_normalize(vid)
 
         vid = self._padding(vid, self.vid_padding)
 
@@ -88,6 +91,14 @@ class dataset_visual(Dataset):
         return len(self.data)
 
     def _load_vid(self, p):
+        """Load the visual data.
+        Args:
+            p:
+                A directory which contains a sequence of frames
+            for a visual sample.
+        Return:
+            The array of a visual sample.
+        """
         files = os.listdir(p)
         files = list(filter(lambda file: file.find(".jpg") != -1, files))
         files = sorted(files, key=lambda file: int(os.path.splitext(file)[0]))
@@ -101,6 +112,13 @@ class dataset_visual(Dataset):
         return array
 
     def _load_anno(self, name):
+        """Load the text file.
+        Args:
+            name:
+                The file which records the text.
+        Return:
+            A sequence of words.
+        """
         with open(name, "r") as f:
             lines = [line.strip().split(" ") for line in f.readlines()]
             txt = [line[2] for line in lines]
@@ -109,6 +127,15 @@ class dataset_visual(Dataset):
         return txt
 
     def _padding(self, array, length):
+        """Pad zeros for the feature array.
+        Args:
+            array:
+                The feature arry. (Audio or Visual feature)
+            length:
+                The length for padding.
+        Return:
+            A new feature array after padding.
+        """
         array = [array[_] for _ in range(array.shape[0])]
         size = array[0].shape
         for i in range(length - len(array)):

egs/grid/AVSR/visualnet2_ctc_vsr/decode.py

@@ -30,7 +30,7 @@ import torch
 import torch.nn as nn
 
 from torch.utils.data import DataLoader
-from local.dataset_visual import dataset_visual
+from local.dataset_visual import VisualDataset
 
 from model import VisualNet2
 
@@ -463,7 +463,7 @@ def main():
     model.to(device)
     model.eval()
 
-    grid = dataset_visual(
+    grid = VisualDataset(
         params.video_path,
         params.anno_path,
         params.val_list,

egs/grid/AVSR/visualnet2_ctc_vsr/train.py

@@ -32,7 +32,7 @@ import torch.nn as nn
 import torch.optim as optim
 from torch.utils.data import DataLoader
 
-from local.dataset_visual import dataset_visual
+from local.dataset_visual import VisualDataset
 from lhotse.utils import fix_random_seed
 
 from model import VisualNet2
@@ -529,7 +529,7 @@ def run(rank, world_size, args):
         optimizer.load_state_dict(checkpoints["optimizer"])
         scheduler.load_state_dict(checkpoints["scheduler"])
 
-    grid = dataset_visual(
+    grid = VisualDataset(
         params.video_path,
         params.anno_path,
         params.train_list,

egs/grid/AVSR/visualnet2_ctc_vsr/utils.py

@@ -14,22 +14,32 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+"""
+This script is to encodes the supervisions as Tuple list.
+The supervision tensor has shape ``(batch_size, 3)``.
+Its second dimension contains information about sequence index [0],
+start frames [1] and num frames [2].
+In GRID, the start frame of each audio sample is 0.
+"""
 import torch
 
 
-def encode_supervisions(nnet_output_shape, batch):
+def encode_supervisions(nnet_output_shape: int, batch: dict):
     """
-    In GRID, the lengths of all samples are same.
+    Args:
-    And here, we don't deploy cut operation on it.
+      nnet_output_shape:
-    So, the start frame is always 0 among all samples.
+        The shape of nnet_output, e.g: (N, T, D).
+      batch:
+        A batch of dataloader, it's a dict file
+        including text and aud/vid arrays.
+    Return:
+      The tuple list of supervisions and the text in batch.
     """
     N, T, D = nnet_output_shape
 
-    supervisions_idx = torch.arange(0, N).to(torch.int32)
+    supervisions_idx = torch.arange(0, N, dtype=torch.int32)
-    start_frames = [0 for _ in range(N)]
+    supervisions_start_frame = torch.full((1, N), 0, dtype=torch.int32)[0]
-    supervisions_start_frame = torch.tensor(start_frames).to(torch.int32)
+    supervisions_num_frames = torch.full((1, N), T, dtype=torch.int32)[0]
-    num_frames = [T for _ in range(N)]
-    supervisions_num_frames = torch.tensor(num_frames).to(torch.int32)
 
     supervision_segments = torch.stack(
         (
@@ -38,8 +48,7 @@ def encode_supervisions(nnet_output_shape, batch):
             supervisions_num_frames,
         ),
         1,
-    ).to(torch.int32)
+    )
-
     texts = batch["txt"]
 
     return supervision_segments, texts

egs/grid/AVSR/visualnet_ctc_vsr/decode.py

@@ -30,7 +30,7 @@ import torch
 import torch.nn as nn
 
 from torch.utils.data import DataLoader
-from local.dataset_visual import dataset_visual
+from local.dataset_visual import VisualDataset
 from model import VisualNet
 
 from icefall.checkpoint import average_checkpoints, load_checkpoint
@@ -462,7 +462,7 @@ def main():
     model.to(device)
     model.eval()
 
-    grid = dataset_visual(
+    grid = VisualDataset(
         params.video_path,
         params.anno_path,
         params.val_list,

egs/grid/AVSR/visualnet_ctc_vsr/train.py

@@ -32,7 +32,7 @@ import torch.nn as nn
 import torch.optim as optim
 from torch.utils.data import DataLoader
 
-from local.dataset_visual import dataset_visual
+from local.dataset_visual import VisualDataset
 from lhotse.utils import fix_random_seed
 from model import VisualNet
 from torch import Tensor
@@ -528,7 +528,7 @@ def run(rank, world_size, args):
         optimizer.load_state_dict(checkpoints["optimizer"])
         scheduler.load_state_dict(checkpoints["scheduler"])
 
-    grid = dataset_visual(
+    grid = VisualDataset(
         params.video_path,
         params.anno_path,
         params.train_list,

egs/grid/AVSR/visualnet_ctc_vsr/utils.py

@@ -14,22 +14,32 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+"""
+This script is to encodes the supervisions as Tuple list.
+The supervision tensor has shape ``(batch_size, 3)``.
+Its second dimension contains information about sequence index [0],
+start frames [1] and num frames [2].
+In GRID, the start frame of each audio sample is 0.
+"""
 import torch
 
 
-def encode_supervisions(nnet_output_shape, batch):
+def encode_supervisions(nnet_output_shape: int, batch: dict):
     """
-    In GRID, the lengths of all samples are same.
+    Args:
-    And here, we don't deploy cut operation on it.
+      nnet_output_shape:
-    So, the start frame is always 0 among all samples.
+        The shape of nnet_output, e.g: (N, T, D).
+      batch:
+        A batch of dataloader, it's a dict file
+        including text and aud/vid arrays.
+    Return:
+      The tuple list of supervisions and the text in batch.
     """
     N, T, D = nnet_output_shape
 
-    supervisions_idx = torch.arange(0, N).to(torch.int32)
+    supervisions_idx = torch.arange(0, N, dtype=torch.int32)
-    start_frames = [0 for _ in range(N)]
+    supervisions_start_frame = torch.full((1, N), 0, dtype=torch.int32)[0]
-    supervisions_start_frame = torch.tensor(start_frames).to(torch.int32)
+    supervisions_num_frames = torch.full((1, N), T, dtype=torch.int32)[0]
-    num_frames = [T for _ in range(N)]
-    supervisions_num_frames = torch.tensor(num_frames).to(torch.int32)
 
     supervision_segments = torch.stack(
         (
@@ -38,8 +48,7 @@ def encode_supervisions(nnet_output_shape, batch):
             supervisions_num_frames,
         ),
         1,
-    ).to(torch.int32)
+    )
-
     texts = batch["txt"]
 
     return supervision_segments, texts