from typing import Collection
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from typing import Dict
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from typing import List
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from typing import Tuple
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from typing import Union
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import numpy as np
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import torch
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from funasr.modules.nets_utils import pad_list, pad_list_all_dim
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class CommonCollateFn:
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"""Functor class of common_collate_fn()"""
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def __init__(
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self,
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float_pad_value: Union[float, int] = 0.0,
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int_pad_value: int = -32768,
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not_sequence: Collection[str] = (),
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max_sample_size=None
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):
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self.float_pad_value = float_pad_value
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self.int_pad_value = int_pad_value
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self.not_sequence = set(not_sequence)
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self.max_sample_size = max_sample_size
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def __repr__(self):
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return (
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f"{self.__class__}(float_pad_value={self.float_pad_value}, "
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f"int_pad_value={self.float_pad_value})"
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)
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def __call__(
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self, data: Collection[Tuple[str, Dict[str, np.ndarray]]]
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) -> Tuple[List[str], Dict[str, torch.Tensor]]:
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return common_collate_fn(
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data,
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float_pad_value=self.float_pad_value,
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int_pad_value=self.int_pad_value,
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not_sequence=self.not_sequence,
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)
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def common_collate_fn(
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data: Collection[Tuple[str, Dict[str, np.ndarray]]],
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float_pad_value: Union[float, int] = 0.0,
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int_pad_value: int = -32768,
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not_sequence: Collection[str] = (),
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) -> Tuple[List[str], Dict[str, torch.Tensor]]:
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"""Concatenate ndarray-list to an array and convert to torch.Tensor.
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"""
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uttids = [u for u, _ in data]
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data = [d for _, d in data]
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assert all(set(data[0]) == set(d) for d in data), "dict-keys mismatching"
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assert all(
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not k.endswith("_lengths") for k in data[0]
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), f"*_lengths is reserved: {list(data[0])}"
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output = {}
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for key in data[0]:
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if data[0][key].dtype.kind == "i":
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pad_value = int_pad_value
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else:
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pad_value = float_pad_value
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array_list = [d[key] for d in data]
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tensor_list = [torch.from_numpy(a) for a in array_list]
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tensor = pad_list(tensor_list, pad_value)
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output[key] = tensor
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if key not in not_sequence:
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lens = torch.tensor([d[key].shape[0] for d in data], dtype=torch.long)
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output[key + "_lengths"] = lens
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output = (uttids, output)
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return output
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class DiarCollateFn:
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"""Functor class of common_collate_fn()"""
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def __init__(
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self,
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float_pad_value: Union[float, int] = 0.0,
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int_pad_value: int = -32768,
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not_sequence: Collection[str] = (),
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max_sample_size=None
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):
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self.float_pad_value = float_pad_value
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self.int_pad_value = int_pad_value
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self.not_sequence = set(not_sequence)
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self.max_sample_size = max_sample_size
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def __repr__(self):
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return (
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f"{self.__class__}(float_pad_value={self.float_pad_value}, "
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f"int_pad_value={self.float_pad_value})"
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)
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def __call__(
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self, data: Collection[Tuple[str, Dict[str, np.ndarray]]]
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) -> Tuple[List[str], Dict[str, torch.Tensor]]:
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return diar_collate_fn(
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data,
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float_pad_value=self.float_pad_value,
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int_pad_value=self.int_pad_value,
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not_sequence=self.not_sequence,
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)
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def diar_collate_fn(
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data: Collection[Tuple[str, Dict[str, np.ndarray]]],
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float_pad_value: Union[float, int] = 0.0,
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int_pad_value: int = -32768,
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not_sequence: Collection[str] = (),
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) -> Tuple[List[str], Dict[str, torch.Tensor]]:
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"""Concatenate ndarray-list to an array and convert to torch.Tensor.
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"""
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uttids = [u for u, _ in data]
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data = [d for _, d in data]
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assert all(set(data[0]) == set(d) for d in data), "dict-keys mismatching"
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assert all(
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not k.endswith("_lengths") for k in data[0]
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), f"*_lengths is reserved: {list(data[0])}"
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output = {}
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for key in data[0]:
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if data[0][key].dtype.kind == "i":
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pad_value = int_pad_value
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else:
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pad_value = float_pad_value
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array_list = [d[key] for d in data]
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tensor_list = [torch.from_numpy(a) for a in array_list]
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tensor = pad_list_all_dim(tensor_list, pad_value)
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output[key] = tensor
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if key not in not_sequence:
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lens = torch.tensor([d[key].shape[0] for d in data], dtype=torch.long)
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output[key + "_lengths"] = lens
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output = (uttids, output)
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return output
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def crop_to_max_size(feature, target_size):
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size = len(feature)
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diff = size - target_size
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if diff <= 0:
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return feature
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start = np.random.randint(0, diff + 1)
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end = size - diff + start
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return feature[start:end]
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def clipping_collate_fn(
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data: Collection[Tuple[str, Dict[str, np.ndarray]]],
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max_sample_size=None,
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not_sequence: Collection[str] = (),
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) -> Tuple[List[str], Dict[str, torch.Tensor]]:
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# mainly for pre-training
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uttids = [u for u, _ in data]
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data = [d for _, d in data]
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assert all(set(data[0]) == set(d) for d in data), "dict-keys mismatching"
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assert all(
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not k.endswith("_lengths") for k in data[0]
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), f"*_lengths is reserved: {list(data[0])}"
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output = {}
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for key in data[0]:
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array_list = [d[key] for d in data]
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tensor_list = [torch.from_numpy(a) for a in array_list]
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sizes = [len(s) for s in tensor_list]
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if max_sample_size is None:
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target_size = min(sizes)
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else:
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target_size = min(min(sizes), max_sample_size)
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tensor = tensor_list[0].new_zeros(len(tensor_list), target_size, tensor_list[0].shape[1])
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for i, (source, size) in enumerate(zip(tensor_list, sizes)):
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diff = size - target_size
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if diff == 0:
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tensor[i] = source
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else:
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tensor[i] = crop_to_max_size(source, target_size)
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output[key] = tensor
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if key not in not_sequence:
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lens = torch.tensor([source.shape[0] for source in tensor], dtype=torch.long)
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output[key + "_lengths"] = lens
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output = (uttids, output)
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return output
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