mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
96 lines
3.1 KiB
Python
96 lines
3.1 KiB
Python
import logging
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from typing import Iterator
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from typing import Tuple
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from typeguard import check_argument_types
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from funasr.fileio.read_text import load_num_sequence_text
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from funasr.samplers.abs_sampler import AbsSampler
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class SortedBatchSampler(AbsSampler):
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"""BatchSampler with sorted samples by length.
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Args:
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batch_size:
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shape_file:
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sort_in_batch: 'descending', 'ascending' or None.
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sort_batch:
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"""
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def __init__(
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self,
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batch_size: int,
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shape_file: str,
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sort_in_batch: str = "descending",
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sort_batch: str = "ascending",
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drop_last: bool = False,
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):
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assert check_argument_types()
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assert batch_size > 0
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self.batch_size = batch_size
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self.shape_file = shape_file
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self.sort_in_batch = sort_in_batch
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self.sort_batch = sort_batch
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self.drop_last = drop_last
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# utt2shape: (Length, ...)
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# uttA 100,...
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# uttB 201,...
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utt2shape = load_num_sequence_text(shape_file, loader_type="csv_int")
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if sort_in_batch == "descending":
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# Sort samples in descending order (required by RNN)
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keys = sorted(utt2shape, key=lambda k: -utt2shape[k][0])
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elif sort_in_batch == "ascending":
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# Sort samples in ascending order
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keys = sorted(utt2shape, key=lambda k: utt2shape[k][0])
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else:
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raise ValueError(
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f"sort_in_batch must be either one of "
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f"ascending, descending, or None: {sort_in_batch}"
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)
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if len(keys) == 0:
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raise RuntimeError(f"0 lines found: {shape_file}")
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# Apply max(, 1) to avoid 0-batches
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N = max(len(keys) // batch_size, 1)
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if not self.drop_last:
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# Split keys evenly as possible as. Note that If N != 1,
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# the these batches always have size of batch_size at minimum.
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self.batch_list = [
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keys[i * len(keys) // N : (i + 1) * len(keys) // N] for i in range(N)
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]
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else:
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self.batch_list = [
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tuple(keys[i * batch_size : (i + 1) * batch_size]) for i in range(N)
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]
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if len(self.batch_list) == 0:
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logging.warning(f"{shape_file} is empty")
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if sort_in_batch != sort_batch:
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if sort_batch not in ("ascending", "descending"):
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raise ValueError(
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f"sort_batch must be ascending or descending: {sort_batch}"
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)
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self.batch_list.reverse()
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if len(self.batch_list) == 0:
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raise RuntimeError("0 batches")
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def __repr__(self):
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return (
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f"{self.__class__.__name__}("
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f"N-batch={len(self)}, "
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f"batch_size={self.batch_size}, "
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f"shape_file={self.shape_file}, "
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f"sort_in_batch={self.sort_in_batch}, "
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f"sort_batch={self.sort_batch})"
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)
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def __len__(self):
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return len(self.batch_list)
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def __iter__(self) -> Iterator[Tuple[str, ...]]:
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return iter(self.batch_list)
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