Refactoring LM loader

egs/librispeech/ASR/rnn_lm/dataset.py

@@ -18,8 +18,10 @@ from typing import List, Tuple
 
 import k2
 import torch
+from torch.utils.data import DataLoader
+from torch.utils.data.distributed import DistributedSampler
 
-from icefall.utils import AttributeDict
+from icefall.utils import AttributeDict, add_eos, add_sos
 
 
 class LmDataset(torch.utils.data.Dataset):
@@ -113,104 +115,6 @@ class LmDataset(torch.utils.data.Dataset):
         return sentence_tokens
 
 
-def concat(
-    ragged: k2.RaggedTensor, value: int, direction: str
-) -> k2.RaggedTensor:
-    """Prepend a value to the beginning of each sublist or append a value.
-    to the end of each sublist.
-
-    Args:
-      ragged:
-        A ragged tensor with two axes.
-      value:
-        The value to prepend or append.
-      direction:
-        It can be either "left" or "right". If it is "left", we
-        prepend the value to the beginning of each sublist;
-        if it is "right", we append the value to the end of each
-        sublist.
-
-    Returns:
-      Return a new ragged tensor, whose sublists either start with
-      or end with the given value.
-
-    >>> a = k2.RaggedTensor([[1, 3], [5]])
-    >>> a
-    [ [ 1 3 ] [ 5 ] ]
-    >>> concat(a, value=0, direction="left")
-    [ [ 0 1 3 ] [ 0 5 ] ]
-    >>> concat(a, value=0, direction="right")
-    [ [ 1 3 0 ] [ 5 0 ] ]
-
-    """
-    dtype = ragged.dtype
-    device = ragged.device
-
-    assert ragged.num_axes == 2, f"num_axes: {ragged.num_axes}"
-    pad_values = torch.full(
-        size=(ragged.tot_size(0), 1),
-        fill_value=value,
-        device=device,
-        dtype=dtype,
-    )
-    pad = k2.RaggedTensor(pad_values)
-
-    if direction == "left":
-        ans = k2.ragged.cat([pad, ragged], axis=1)
-    elif direction == "right":
-        ans = k2.ragged.cat([ragged, pad], axis=1)
-    else:
-        raise ValueError(
-            f'Unsupported direction: {direction}. " \
-            "Expect either "left" or "right"'
-        )
-    return ans
-
-
-def add_sos(ragged: k2.RaggedTensor, sos_id: int) -> k2.RaggedTensor:
-    """Add SOS to each sublist.
-
-    Args:
-      ragged:
-        A ragged tensor with two axes.
-      sos_id:
-        The ID of the SOS symbol.
-
-    Returns:
-      Return a new ragged tensor, where each sublist starts with SOS.
-
-    >>> a = k2.RaggedTensor([[1, 3], [5]])
-    >>> a
-    [ [ 1 3 ] [ 5 ] ]
-    >>> add_sos(a, sos_id=0)
-    [ [ 0 1 3 ] [ 0 5 ] ]
-
-    """
-    return concat(ragged, sos_id, direction="left")
-
-
-def add_eos(ragged: k2.RaggedTensor, eos_id: int) -> k2.RaggedTensor:
-    """Add EOS to each sublist.
-
-    Args:
-      ragged:
-        A ragged tensor with two axes.
-      eos_id:
-        The ID of the EOS symbol.
-
-    Returns:
-      Return a new ragged tensor, where each sublist ends with EOS.
-
-    >>> a = k2.RaggedTensor([[1, 3], [5]])
-    >>> a
-    [ [ 1 3 ] [ 5 ] ]
-    >>> add_eos(a, eos_id=0)
-    [ [ 1 3 0 ] [ 5 0 ] ]
-
-    """
-    return concat(ragged, eos_id, direction="right")
-
-
 class LmDatasetCollate:
     def __init__(self, sos_id: int, eos_id: int, blank_id: int):
         """
@@ -294,9 +198,7 @@ def get_dataloader(
         batch_size=params.batch_size,
     )
     if is_distributed:
-        sampler = torch.utils.data.distributed.DistributedSampler(
-            dataset, shuffle=True, drop_last=False
-        )
+        sampler = DistributedSampler(dataset, shuffle=True, drop_last=False)
     else:
         sampler = None
 
@@ -306,7 +208,7 @@ def get_dataloader(
         blank_id=params.blank_id,
     )
 
-    dataloader = torch.utils.data.DataLoader(
+    dataloader = DataLoader(
         dataset,
         batch_size=1,
         collate_fn=collate_fn,