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410 lines
15 KiB
410 lines
15 KiB
3 years ago
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from collections import OrderedDict
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import kaldiio
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import numpy as np
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import soundfile
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from deepspeech.frontend.augmentor.augmentation import AugmentationPipeline
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from deepspeech.utils.log import Log
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__all__ = ["LoadInputsAndTargets"]
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logger = Log(__name__).getlog()
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class LoadInputsAndTargets():
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"""Create a mini-batch from a list of dicts
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>>> batch = [('utt1',
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... dict(input=[dict(feat='some.ark:123',
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... filetype='mat',
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... name='input1',
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... shape=[100, 80])],
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... output=[dict(tokenid='1 2 3 4',
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... name='target1',
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... shape=[4, 31])]]))
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>>> l = LoadInputsAndTargets()
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>>> feat, target = l(batch)
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:param: str mode: Specify the task mode, "asr" or "tts"
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:param: str preprocess_conf: The path of a json file for pre-processing
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:param: bool load_input: If False, not to load the input data
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:param: bool load_output: If False, not to load the output data
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:param: bool sort_in_input_length: Sort the mini-batch in descending order
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of the input length
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:param: bool use_speaker_embedding: Used for tts mode only
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:param: bool use_second_target: Used for tts mode only
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:param: dict preprocess_args: Set some optional arguments for preprocessing
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:param: Optional[dict] preprocess_args: Used for tts mode only
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"""
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def __init__(
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self,
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mode="asr",
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preprocess_conf=None,
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load_input=True,
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load_output=True,
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sort_in_input_length=True,
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preprocess_args=None,
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keep_all_data_on_mem=False, ):
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self._loaders = {}
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if mode not in ["asr"]:
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raise ValueError("Only asr are allowed: mode={}".format(mode))
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if preprocess_conf is not None:
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self.preprocessing = AugmentationPipeline(preprocess_conf)
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logging.warning(
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"[Experimental feature] Some preprocessing will be done "
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"for the mini-batch creation using {}".format(
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self.preprocessing))
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else:
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# If conf doesn't exist, this function don't touch anything.
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self.preprocessing = None
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self.mode = mode
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self.load_output = load_output
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self.load_input = load_input
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self.sort_in_input_length = sort_in_input_length
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if preprocess_args is None:
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self.preprocess_args = {}
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else:
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assert isinstance(preprocess_args, dict), type(preprocess_args)
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self.preprocess_args = dict(preprocess_args)
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self.keep_all_data_on_mem = keep_all_data_on_mem
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def __call__(self, batch, return_uttid=False):
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"""Function to load inputs and targets from list of dicts
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:param List[Tuple[str, dict]] batch: list of dict which is subset of
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loaded data.json
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:param bool return_uttid: return utterance ID information for visualization
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:return: list of input token id sequences [(L_1), (L_2), ..., (L_B)]
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:return: list of input feature sequences
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[(T_1, D), (T_2, D), ..., (T_B, D)]
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:rtype: list of float ndarray
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:return: list of target token id sequences [(L_1), (L_2), ..., (L_B)]
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:rtype: list of int ndarray
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"""
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x_feats_dict = OrderedDict() # OrderedDict[str, List[np.ndarray]]
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y_feats_dict = OrderedDict() # OrderedDict[str, List[np.ndarray]]
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uttid_list = [] # List[str]
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for uttid, info in batch:
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uttid_list.append(uttid)
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if self.load_input:
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# Note(kamo): This for-loop is for multiple inputs
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for idx, inp in enumerate(info["input"]):
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# {"input":
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# [{"feat": "some/path.h5:F01_050C0101_PED_REAL",
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# "filetype": "hdf5",
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# "name": "input1", ...}], ...}
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x = self._get_from_loader(
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filepath=inp["feat"],
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filetype=inp.get("filetype", "mat"))
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x_feats_dict.setdefault(inp["name"], []).append(x)
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if self.load_output:
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for idx, inp in enumerate(info["output"]):
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if "tokenid" in inp:
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# ======= Legacy format for output =======
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# {"output": [{"tokenid": "1 2 3 4"}])
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x = np.fromiter(
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map(int, inp["tokenid"].split()), dtype=np.int64)
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else:
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# ======= New format =======
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# {"input":
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# [{"feat": "some/path.h5:F01_050C0101_PED_REAL",
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# "filetype": "hdf5",
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# "name": "target1", ...}], ...}
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x = self._get_from_loader(
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filepath=inp["feat"],
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filetype=inp.get("filetype", "mat"))
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y_feats_dict.setdefault(inp["name"], []).append(x)
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if self.mode == "asr":
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return_batch, uttid_list = self._create_batch_asr(
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x_feats_dict, y_feats_dict, uttid_list)
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else:
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raise NotImplementedError(self.mode)
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if self.preprocessing is not None:
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# Apply pre-processing all input features
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for x_name in return_batch.keys():
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if x_name.startswith("input"):
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return_batch[x_name] = self.preprocessing(
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return_batch[x_name], uttid_list,
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**self.preprocess_args)
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if return_uttid:
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return tuple(return_batch.values()), uttid_list
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# Doesn't return the names now.
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return tuple(return_batch.values())
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def _create_batch_asr(self, x_feats_dict, y_feats_dict, uttid_list):
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"""Create a OrderedDict for the mini-batch
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:param OrderedDict x_feats_dict:
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e.g. {"input1": [ndarray, ndarray, ...],
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"input2": [ndarray, ndarray, ...]}
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:param OrderedDict y_feats_dict:
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e.g. {"target1": [ndarray, ndarray, ...],
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"target2": [ndarray, ndarray, ...]}
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:param: List[str] uttid_list:
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Give uttid_list to sort in the same order as the mini-batch
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:return: batch, uttid_list
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:rtype: Tuple[OrderedDict, List[str]]
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"""
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# handle single-input and multi-input (paralell) asr mode
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xs = list(x_feats_dict.values())
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if self.load_output:
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ys = list(y_feats_dict.values())
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assert len(xs[0]) == len(ys[0]), (len(xs[0]), len(ys[0]))
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# get index of non-zero length samples
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nonzero_idx = list(
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filter(lambda i: len(ys[0][i]) > 0, range(len(ys[0]))))
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for n in range(1, len(y_feats_dict)):
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nonzero_idx = filter(lambda i: len(ys[n][i]) > 0, nonzero_idx)
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else:
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# Note(kamo): Be careful not to make nonzero_idx to a generator
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nonzero_idx = list(range(len(xs[0])))
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if self.sort_in_input_length:
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# sort in input lengths based on the first input
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nonzero_sorted_idx = sorted(
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nonzero_idx, key=lambda i: -len(xs[0][i]))
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else:
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nonzero_sorted_idx = nonzero_idx
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if len(nonzero_sorted_idx) != len(xs[0]):
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logging.warning(
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"Target sequences include empty tokenid (batch {} -> {}).".
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format(len(xs[0]), len(nonzero_sorted_idx)))
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# remove zero-length samples
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xs = [[x[i] for i in nonzero_sorted_idx] for x in xs]
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uttid_list = [uttid_list[i] for i in nonzero_sorted_idx]
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x_names = list(x_feats_dict.keys())
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if self.load_output:
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ys = [[y[i] for i in nonzero_sorted_idx] for y in ys]
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y_names = list(y_feats_dict.keys())
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# Keeping x_name and y_name, e.g. input1, for future extension
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return_batch = OrderedDict([
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* [(x_name, x) for x_name, x in zip(x_names, xs)],
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* [(y_name, y) for y_name, y in zip(y_names, ys)],
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])
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else:
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return_batch = OrderedDict(
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[(x_name, x) for x_name, x in zip(x_names, xs)])
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return return_batch, uttid_list
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def _get_from_loader(self, filepath, filetype):
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"""Return ndarray
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In order to make the fds to be opened only at the first referring,
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the loader are stored in self._loaders
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>>> ndarray = loader.get_from_loader(
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... 'some/path.h5:F01_050C0101_PED_REAL', filetype='hdf5')
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:param: str filepath:
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:param: str filetype:
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:return:
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:rtype: np.ndarray
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"""
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if filetype == "hdf5":
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# e.g.
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# {"input": [{"feat": "some/path.h5:F01_050C0101_PED_REAL",
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# "filetype": "hdf5",
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# -> filepath = "some/path.h5", key = "F01_050C0101_PED_REAL"
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filepath, key = filepath.split(":", 1)
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loader = self._loaders.get(filepath)
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if loader is None:
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# To avoid disk access, create loader only for the first time
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loader = h5py.File(filepath, "r")
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self._loaders[filepath] = loader
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return loader[key][()]
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elif filetype == "sound.hdf5":
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# e.g.
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# {"input": [{"feat": "some/path.h5:F01_050C0101_PED_REAL",
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# "filetype": "sound.hdf5",
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# -> filepath = "some/path.h5", key = "F01_050C0101_PED_REAL"
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filepath, key = filepath.split(":", 1)
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loader = self._loaders.get(filepath)
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if loader is None:
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# To avoid disk access, create loader only for the first time
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loader = SoundHDF5File(filepath, "r", dtype="int16")
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self._loaders[filepath] = loader
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array, rate = loader[key]
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return array
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elif filetype == "sound":
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# e.g.
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# {"input": [{"feat": "some/path.wav",
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# "filetype": "sound"},
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# Assume PCM16
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if not self.keep_all_data_on_mem:
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array, _ = soundfile.read(filepath, dtype="int16")
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return array
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if filepath not in self._loaders:
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array, _ = soundfile.read(filepath, dtype="int16")
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self._loaders[filepath] = array
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return self._loaders[filepath]
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elif filetype == "npz":
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# e.g.
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# {"input": [{"feat": "some/path.npz:F01_050C0101_PED_REAL",
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# "filetype": "npz",
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filepath, key = filepath.split(":", 1)
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loader = self._loaders.get(filepath)
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if loader is None:
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# To avoid disk access, create loader only for the first time
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loader = np.load(filepath)
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self._loaders[filepath] = loader
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return loader[key]
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elif filetype == "npy":
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# e.g.
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# {"input": [{"feat": "some/path.npy",
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# "filetype": "npy"},
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if not self.keep_all_data_on_mem:
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return np.load(filepath)
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if filepath not in self._loaders:
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self._loaders[filepath] = np.load(filepath)
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return self._loaders[filepath]
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elif filetype in ["mat", "vec"]:
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# e.g.
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# {"input": [{"feat": "some/path.ark:123",
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# "filetype": "mat"}]},
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# In this case, "123" indicates the starting points of the matrix
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# load_mat can load both matrix and vector
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if not self.keep_all_data_on_mem:
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return kaldiio.load_mat(filepath)
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if filepath not in self._loaders:
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self._loaders[filepath] = kaldiio.load_mat(filepath)
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return self._loaders[filepath]
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elif filetype == "scp":
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# e.g.
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# {"input": [{"feat": "some/path.scp:F01_050C0101_PED_REAL",
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# "filetype": "scp",
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filepath, key = filepath.split(":", 1)
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loader = self._loaders.get(filepath)
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if loader is None:
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# To avoid disk access, create loader only for the first time
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loader = kaldiio.load_scp(filepath)
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self._loaders[filepath] = loader
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return loader[key]
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else:
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raise NotImplementedError(
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"Not supported: loader_type={}".format(filetype))
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class SoundHDF5File():
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"""Collecting sound files to a HDF5 file
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>>> f = SoundHDF5File('a.flac.h5', mode='a')
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>>> array = np.random.randint(0, 100, 100, dtype=np.int16)
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>>> f['id'] = (array, 16000)
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>>> array, rate = f['id']
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:param: str filepath:
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:param: str mode:
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:param: str format: The type used when saving wav. flac, nist, htk, etc.
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:param: str dtype:
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"""
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def __init__(self,
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filepath,
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mode="r+",
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format=None,
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|
dtype="int16",
|
||
|
|
**kwargs):
|
||
|
|
self.filepath = filepath
|
||
|
|
self.mode = mode
|
||
|
|
self.dtype = dtype
|
||
|
|
|
||
|
|
self.file = h5py.File(filepath, mode, **kwargs)
|
||
|
|
if format is None:
|
||
|
|
# filepath = a.flac.h5 -> format = flac
|
||
|
|
second_ext = os.path.splitext(os.path.splitext(filepath)[0])[1]
|
||
|
|
format = second_ext[1:]
|
||
|
|
if format.upper() not in soundfile.available_formats():
|
||
|
|
# If not found, flac is selected
|
||
|
|
format = "flac"
|
||
|
|
|
||
|
|
# This format affects only saving
|
||
|
|
self.format = format
|
||
|
|
|
||
|
|
def __repr__(self):
|
||
|
|
return '<SoundHDF5 file "{}" (mode {}, format {}, type {})>'.format(
|
||
|
|
self.filepath, self.mode, self.format, self.dtype)
|
||
|
|
|
||
|
|
def create_dataset(self, name, shape=None, data=None, **kwds):
|
||
|
|
f = io.BytesIO()
|
||
|
|
array, rate = data
|
||
|
|
soundfile.write(f, array, rate, format=self.format)
|
||
|
|
self.file.create_dataset(
|
||
|
|
name, shape=shape, data=np.void(f.getvalue()), **kwds)
|
||
|
|
|
||
|
|
def __setitem__(self, name, data):
|
||
|
|
self.create_dataset(name, data=data)
|
||
|
|
|
||
|
|
def __getitem__(self, key):
|
||
|
|
data = self.file[key][()]
|
||
|
|
f = io.BytesIO(data.tobytes())
|
||
|
|
array, rate = soundfile.read(f, dtype=self.dtype)
|
||
|
|
return array, rate
|
||
|
|
|
||
|
|
def keys(self):
|
||
|
|
return self.file.keys()
|
||
|
|
|
||
|
|
def values(self):
|
||
|
|
for k in self.file:
|
||
|
|
yield self[k]
|
||
|
|
|
||
|
|
def items(self):
|
||
|
|
for k in self.file:
|
||
|
|
yield k, self[k]
|
||
|
|
|
||
|
|
def __iter__(self):
|
||
|
|
return iter(self.file)
|
||
|
|
|
||
|
|
def __contains__(self, item):
|
||
|
|
return item in self.file
|
||
|
|
|
||
|
|
def __len__(self, item):
|
||
|
|
return len(self.file)
|
||
|
|
|
||
|
|
def __enter__(self):
|
||
|
|
return self
|
||
|
|
|
||
|
|
def __exit__(self, exc_type, exc_val, exc_tb):
|
||
|
|
self.file.close()
|
||
|
|
|
||
|
|
def close(self):
|
||
|
|
self.file.close()
|