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funasr_local/samplers/sorted_batch_sampler.py

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+import logging
+from typing import Iterator
+from typing import Tuple
+
+from typeguard import check_argument_types
+
+from funasr_local.fileio.read_text import load_num_sequence_text
+from funasr_local.samplers.abs_sampler import AbsSampler
+
+
+class SortedBatchSampler(AbsSampler):
+    """BatchSampler with sorted samples by length.
+
+    Args:
+        batch_size:
+        shape_file:
+        sort_in_batch: 'descending', 'ascending' or None.
+        sort_batch:
+    """
+
+    def __init__(
+        self,
+        batch_size: int,
+        shape_file: str,
+        sort_in_batch: str = "descending",
+        sort_batch: str = "ascending",
+        drop_last: bool = False,
+    ):
+        assert check_argument_types()
+        assert batch_size > 0
+        self.batch_size = batch_size
+        self.shape_file = shape_file
+        self.sort_in_batch = sort_in_batch
+        self.sort_batch = sort_batch
+        self.drop_last = drop_last
+
+        # utt2shape: (Length, ...)
+        #    uttA 100,...
+        #    uttB 201,...
+        utt2shape = load_num_sequence_text(shape_file, loader_type="csv_int")
+        if sort_in_batch == "descending":
+            # Sort samples in descending order (required by RNN)
+            keys = sorted(utt2shape, key=lambda k: -utt2shape[k][0])
+        elif sort_in_batch == "ascending":
+            # Sort samples in ascending order
+            keys = sorted(utt2shape, key=lambda k: utt2shape[k][0])
+        else:
+            raise ValueError(
+                f"sort_in_batch must be either one of "
+                f"ascending, descending, or None: {sort_in_batch}"
+            )
+        if len(keys) == 0:
+            raise RuntimeError(f"0 lines found: {shape_file}")
+
+        # Apply max(, 1) to avoid 0-batches
+        N = max(len(keys) // batch_size, 1)
+        if not self.drop_last:
+            # Split keys evenly as possible as. Note that If N != 1,
+            # the these batches always have size of batch_size at minimum.
+            self.batch_list = [
+                keys[i * len(keys) // N : (i + 1) * len(keys) // N] for i in range(N)
+            ]
+        else:
+            self.batch_list = [
+                tuple(keys[i * batch_size : (i + 1) * batch_size]) for i in range(N)
+            ]
+
+        if len(self.batch_list) == 0:
+            logging.warning(f"{shape_file} is empty")
+
+        if sort_in_batch != sort_batch:
+            if sort_batch not in ("ascending", "descending"):
+                raise ValueError(
+                    f"sort_batch must be ascending or descending: {sort_batch}"
+                )
+            self.batch_list.reverse()
+
+        if len(self.batch_list) == 0:
+            raise RuntimeError("0 batches")
+
+    def __repr__(self):
+        return (
+            f"{self.__class__.__name__}("
+            f"N-batch={len(self)}, "
+            f"batch_size={self.batch_size}, "
+            f"shape_file={self.shape_file}, "
+            f"sort_in_batch={self.sort_in_batch}, "
+            f"sort_batch={self.sort_batch})"
+        )
+
+    def __len__(self):
+        return len(self.batch_list)
+
+    def __iter__(self) -> Iterator[Tuple[str, ...]]:
+        return iter(self.batch_list)