more data utils

deepspeech/io/batchfy.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 import itertools
 
-import logger
 import numpy as np
 
 from deepspeech.utils.log import Log

deepspeech/io/dataloader.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from paddle.io import DataLoader
+
+from deepspeech.frontend.utility import read_manifest
+from deepspeech.io.batchfy import make_batchset
+from deepspeech.io.dataset import TransformDataset
+from deepspeech.io.utility import LoadInputsAndTargets
+from deepspeech.io.utility import pad_list
+from deepspeech.utils.log import Log
+
+__all__ = ["CustomConverter", "BatchDataLoader"]
+
+logger = Log(__name__).getlog()
+
+
+class CustomConverter():
+    """Custom batch converter.
+
+    Args:
+        subsampling_factor (int): The subsampling factor.
+        dtype (paddle.dtype): Data type to convert.
+
+    """
+
+    def __init__(self, subsampling_factor=1, dtype=paddle.float32):
+        """Construct a CustomConverter object."""
+        self.subsampling_factor = subsampling_factor
+        self.ignore_id = -1
+        self.dtype = dtype
+
+    def __call__(self, batch):
+        """Transform a batch and send it to a device.
+
+        Args:
+            batch (list): The batch to transform.
+
+        Returns:
+            tuple(paddle.Tensor, paddle.Tensor, paddle.Tensor)
+
+        """
+        # batch should be located in list
+        assert len(batch) == 1
+        xs, ys = batch[0]
+
+        # perform subsampling
+        if self.subsampling_factor > 1:
+            xs = [x[::self.subsampling_factor, :] for x in xs]
+
+        # get batch of lengths of input sequences
+        ilens = np.array([x.shape[0] for x in xs])
+
+        # perform padding and convert to tensor
+        # currently only support real number
+        if xs[0].dtype.kind == "c":
+            xs_pad_real = pad_list([x.real for x in xs], 0).astype(self.dtype)
+            xs_pad_imag = pad_list([x.imag for x in xs], 0).astype(self.dtype)
+            # Note(kamo):
+            # {'real': ..., 'imag': ...} will be changed to ComplexTensor in E2E.
+            # Don't create ComplexTensor and give it E2E here
+            # because torch.nn.DataParellel can't handle it.
+            xs_pad = {"real": xs_pad_real, "imag": xs_pad_imag}
+        else:
+            xs_pad = pad_list(xs, 0).astype(self.dtype)
+
+        ilens = paddle.to_tensor(ilens)
+
+        # NOTE: this is for multi-output (e.g., speech translation)
+        ys_pad = pad_list(
+            [np.array(y[0][:]) if isinstance(y, tuple) else y for y in ys],
+            self.ignore_id)
+
+        olens = np.array([y.shape[0] for y in ys])
+        return xs_pad, ilens, ys_pad, olens
+
+
+class BatchDataLoader():
+    def __init__(self,
+                 json_file: str,
+                 train_mode: bool,
+                 sortagrad: bool=False,
+                 batch_size: int=0,
+                 maxlen_in: float=float('inf'),
+                 maxlen_out: float=float('inf'),
+                 minibatches: int=0,
+                 mini_batch_size: int=1,
+                 batch_count: str='auto',
+                 batch_bins: int=0,
+                 batch_frames_in: int=0,
+                 batch_frames_out: int=0,
+                 batch_frames_inout: int=0,
+                 preprocess_conf=None,
+                 n_iter_processes: int=1,
+                 subsampling_factor: int=1,
+                 num_encs: int=1):
+        self.json_file = json_file
+        self.train_mode = train_mode
+
+        self.use_sortagrad = sortagrad == -1 or sortagrad > 0
+        self.batch_size = batch_size
+        self.maxlen_in = maxlen_in
+        self.maxlen_out = maxlen_out
+        self.batch_count = batch_count
+        self.batch_bins = batch_bins
+        self.batch_frames_in = batch_frames_in
+        self.batch_frames_out = batch_frames_out
+        self.batch_frames_inout = batch_frames_inout
+
+        self.subsampling_factor = subsampling_factor
+        self.num_encs = num_encs
+        self.preprocess_conf = preprocess_conf
+
+        self.n_iter_processes = n_iter_processes
+
+        # read json data
+        data_json = read_manifest(json_file)
+        logger.info(f"load {json_file} file.")
+
+        # make minibatch list (variable length)
+        self.data = make_batchset(
+            data_json,
+            batch_size,
+            maxlen_in,
+            maxlen_out,
+            minibatches,  # for debug
+            min_batch_size=mini_batch_size,
+            shortest_first=self.use_sortagrad,
+            count=batch_count,
+            batch_bins=batch_bins,
+            batch_frames_in=batch_frames_in,
+            batch_frames_out=batch_frames_out,
+            batch_frames_inout=batch_frames_inout,
+            iaxis=0,
+            oaxis=0, )
+        logger.info(f"batchfy data {json_file}: {len(self.data)}.")
+
+        self.load = LoadInputsAndTargets(
+            mode="asr",
+            load_output=True,
+            preprocess_conf=preprocess_conf,
+            preprocess_args={"train":
+                             train_mode},  # Switch the mode of preprocessing
+        )
+
+        # Setup a converter
+        if num_encs == 1:
+            self.converter = CustomConverter(
+                subsampling_factor=subsampling_factor, dtype=dtype)
+        else:
+            assert NotImplementedError("not impl CustomConverterMulEnc.")
+
+        # hack to make batchsize argument as 1
+        # actual bathsize is included in a list
+        # default collate function converts numpy array to pytorch tensor
+        # we used an empty collate function instead which returns list
+        self.train_loader = DataLoader(
+            dataset=TransformDataset(
+                self.data, lambda data: self.converter([self.load(data)])),
+            batch_size=1,
+            shuffle=not use_sortagrad if train_mode else False,
+            collate_fn=lambda x: x[0],
+            num_workers=n_iter_processes, )
+        logger.info(f"dataloader for {json_file}.")
+
+    def __repr__(self):
+        return f"DataLoader {self.json_file}-{self.train_mode}-{self.use_sortagrad}"

deepspeech/io/dataset.py

@@ -19,7 +19,7 @@ from yacs.config import CfgNode
 from deepspeech.frontend.utility import read_manifest
 from deepspeech.utils.log import Log
 
-__all__ = ["ManifestDataset", "TripletManifestDataset"]
+__all__ = ["ManifestDataset", "TripletManifestDataset", "TransformDataset"]
 
 logger = Log(__name__).getlog()
 
@@ -116,3 +116,27 @@ class TripletManifestDataset(ManifestDataset):
         instance = self._manifest[idx]
         return instance["utt"], instance["feat"], instance["text"], instance[
             "text1"]
+
+
+class TransformDataset(Dataset):
+    """Transform Dataset.
+
+    Args:
+        data: list object from make_batchset
+        transfrom: transform function
+
+    """
+
+    def __init__(self, data, transform):
+        """Init function."""
+        super().__init__()
+        self.data = data
+        self.transform = transform
+
+    def __len__(self):
+        """Len function."""
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        """[] operator."""
+        return self.transform(self.data[idx])

deepspeech/io/utility.py

@@ -11,17 +11,24 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from collections import OrderedDict
 from typing import List
 
 import numpy as np
 
+from deepspeech.frontend.augmentor.augmentation import AugmentationPipeline
 from deepspeech.utils.log import Log
 
-__all__ = ["pad_sequence"]
+__all__ = ["pad_list", "pad_sequence", "LoadInputsAndTargets"]
 
 logger = Log(__name__).getlog()
 
 
+def pad_list(sequences: List[np.ndarray],
+             padding_value: float=0.0) -> np.ndarray:
+    return pad_sequence(sequences, True, padding_value)
+
+
 def pad_sequence(sequences: List[np.ndarray],
                  batch_first: bool=True,
                  padding_value: float=0.0) -> np.ndarray:
@@ -80,3 +87,299 @@ def pad_sequence(sequences: List[np.ndarray],
             out_tensor[:length, i, ...] = tensor
 
     return out_tensor
+
+
+class LoadInputsAndTargets():
+    """Create a mini-batch from a list of dicts
+
+    >>> batch = [('utt1',
+    ...           dict(input=[dict(feat='some.ark:123',
+    ...                            filetype='mat',
+    ...                            name='input1',
+    ...                            shape=[100, 80])],
+    ...                output=[dict(tokenid='1 2 3 4',
+    ...                             name='target1',
+    ...                             shape=[4, 31])]]))
+    >>> l = LoadInputsAndTargets()
+    >>> feat, target = l(batch)
+
+    :param: str mode: Specify the task mode, "asr" or "tts"
+    :param: str preprocess_conf: The path of a json file for pre-processing
+    :param: bool load_input: If False, not to load the input data
+    :param: bool load_output: If False, not to load the output data
+    :param: bool sort_in_input_length: Sort the mini-batch in descending order
+        of the input length
+    :param: bool use_speaker_embedding: Used for tts mode only
+    :param: bool use_second_target: Used for tts mode only
+    :param: dict preprocess_args: Set some optional arguments for preprocessing
+    :param: Optional[dict] preprocess_args: Used for tts mode only
+    """
+
+    def __init__(
+            self,
+            mode="asr",
+            preprocess_conf=None,
+            load_input=True,
+            load_output=True,
+            sort_in_input_length=True,
+            preprocess_args=None,
+            keep_all_data_on_mem=False, ):
+        self._loaders = {}
+
+        if mode not in ["asr"]:
+            raise ValueError("Only asr are allowed: mode={}".format(mode))
+
+        if preprocess_conf is not None:
+            self.preprocessing = AugmentationPipeline(preprocess_conf)
+            logging.warning(
+                "[Experimental feature] Some preprocessing will be done "
+                "for the mini-batch creation using {}".format(
+                    self.preprocessing))
+        else:
+            # If conf doesn't exist, this function don't touch anything.
+            self.preprocessing = None
+
+        self.mode = mode
+        self.load_output = load_output
+        self.load_input = load_input
+        self.sort_in_input_length = sort_in_input_length
+        if preprocess_args is None:
+            self.preprocess_args = {}
+        else:
+            assert isinstance(preprocess_args, dict), type(preprocess_args)
+            self.preprocess_args = dict(preprocess_args)
+
+        self.keep_all_data_on_mem = keep_all_data_on_mem
+
+    def __call__(self, batch, return_uttid=False):
+        """Function to load inputs and targets from list of dicts
+
+        :param List[Tuple[str, dict]] batch: list of dict which is subset of
+            loaded data.json
+        :param bool return_uttid: return utterance ID information for visualization
+        :return: list of input token id sequences [(L_1), (L_2), ..., (L_B)]
+        :return: list of input feature sequences
+            [(T_1, D), (T_2, D), ..., (T_B, D)]
+        :rtype: list of float ndarray
+        :return: list of target token id sequences [(L_1), (L_2), ..., (L_B)]
+        :rtype: list of int ndarray
+
+        """
+        x_feats_dict = OrderedDict()  # OrderedDict[str, List[np.ndarray]]
+        y_feats_dict = OrderedDict()  # OrderedDict[str, List[np.ndarray]]
+        uttid_list = []  # List[str]
+
+        for uttid, info in batch:
+            uttid_list.append(uttid)
+
+            if self.load_input:
+                # Note(kamo): This for-loop is for multiple inputs
+                for idx, inp in enumerate(info["input"]):
+                    # {"input":
+                    #  [{"feat": "some/path.h5:F01_050C0101_PED_REAL",
+                    #    "filetype": "hdf5",
+                    #    "name": "input1", ...}], ...}
+                    x = self._get_from_loader(
+                        filepath=inp["feat"],
+                        filetype=inp.get("filetype", "mat"))
+                    x_feats_dict.setdefault(inp["name"], []).append(x)
+
+            if self.load_output:
+                for idx, inp in enumerate(info["output"]):
+                    if "tokenid" in inp:
+                        # ======= Legacy format for output =======
+                        # {"output": [{"tokenid": "1 2 3 4"}])
+                        x = np.fromiter(
+                            map(int, inp["tokenid"].split()), dtype=np.int64)
+                    else:
+                        # ======= New format =======
+                        # {"input":
+                        #  [{"feat": "some/path.h5:F01_050C0101_PED_REAL",
+                        #    "filetype": "hdf5",
+                        #    "name": "target1", ...}], ...}
+                        x = self._get_from_loader(
+                            filepath=inp["feat"],
+                            filetype=inp.get("filetype", "mat"))
+
+                    y_feats_dict.setdefault(inp["name"], []).append(x)
+
+        if self.mode == "asr":
+            return_batch, uttid_list = self._create_batch_asr(
+                x_feats_dict, y_feats_dict, uttid_list)
+        else:
+            raise NotImplementedError(self.mode)
+
+        if self.preprocessing is not None:
+            # Apply pre-processing all input features
+            for x_name in return_batch.keys():
+                if x_name.startswith("input"):
+                    return_batch[x_name] = self.preprocessing(
+                        return_batch[x_name], uttid_list,
+                        **self.preprocess_args)
+
+        if return_uttid:
+            return tuple(return_batch.values()), uttid_list
+
+        # Doesn't return the names now.
+        return tuple(return_batch.values())
+
+    def _create_batch_asr(self, x_feats_dict, y_feats_dict, uttid_list):
+        """Create a OrderedDict for the mini-batch
+
+        :param OrderedDict x_feats_dict:
+            e.g. {"input1": [ndarray, ndarray, ...],
+                  "input2": [ndarray, ndarray, ...]}
+        :param OrderedDict y_feats_dict:
+            e.g. {"target1": [ndarray, ndarray, ...],
+                  "target2": [ndarray, ndarray, ...]}
+        :param: List[str] uttid_list:
+            Give uttid_list to sort in the same order as the mini-batch
+        :return: batch, uttid_list
+        :rtype: Tuple[OrderedDict, List[str]]
+        """
+        # handle single-input and multi-input (paralell) asr mode
+        xs = list(x_feats_dict.values())
+
+        if self.load_output:
+            ys = list(y_feats_dict.values())
+            assert len(xs[0]) == len(ys[0]), (len(xs[0]), len(ys[0]))
+
+            # get index of non-zero length samples
+            nonzero_idx = list(
+                filter(lambda i: len(ys[0][i]) > 0, range(len(ys[0]))))
+            for n in range(1, len(y_feats_dict)):
+                nonzero_idx = filter(lambda i: len(ys[n][i]) > 0, nonzero_idx)
+        else:
+            # Note(kamo): Be careful not to make nonzero_idx to a generator
+            nonzero_idx = list(range(len(xs[0])))
+
+        if self.sort_in_input_length:
+            # sort in input lengths based on the first input
+            nonzero_sorted_idx = sorted(
+                nonzero_idx, key=lambda i: -len(xs[0][i]))
+        else:
+            nonzero_sorted_idx = nonzero_idx
+
+        if len(nonzero_sorted_idx) != len(xs[0]):
+            logging.warning(
+                "Target sequences include empty tokenid (batch {} -> {}).".
+                format(len(xs[0]), len(nonzero_sorted_idx)))
+
+        # remove zero-length samples
+        xs = [[x[i] for i in nonzero_sorted_idx] for x in xs]
+        uttid_list = [uttid_list[i] for i in nonzero_sorted_idx]
+
+        x_names = list(x_feats_dict.keys())
+        if self.load_output:
+            ys = [[y[i] for i in nonzero_sorted_idx] for y in ys]
+            y_names = list(y_feats_dict.keys())
+
+            # Keeping x_name and y_name, e.g. input1, for future extension
+            return_batch = OrderedDict([
+                * [(x_name, x) for x_name, x in zip(x_names, xs)],
+                * [(y_name, y) for y_name, y in zip(y_names, ys)],
+            ])
+        else:
+            return_batch = OrderedDict(
+                [(x_name, x) for x_name, x in zip(x_names, xs)])
+        return return_batch, uttid_list
+
+    def _get_from_loader(self, filepath, filetype):
+        """Return ndarray
+
+        In order to make the fds to be opened only at the first referring,
+        the loader are stored in self._loaders
+
+        >>> ndarray = loader.get_from_loader(
+        ...     'some/path.h5:F01_050C0101_PED_REAL', filetype='hdf5')
+
+        :param: str filepath:
+        :param: str filetype:
+        :return:
+        :rtype: np.ndarray
+        """
+        if filetype == "hdf5":
+            # e.g.
+            #    {"input": [{"feat": "some/path.h5:F01_050C0101_PED_REAL",
+            #                "filetype": "hdf5",
+            # -> filepath = "some/path.h5", key = "F01_050C0101_PED_REAL"
+            filepath, key = filepath.split(":", 1)
+
+            loader = self._loaders.get(filepath)
+            if loader is None:
+                # To avoid disk access, create loader only for the first time
+                loader = h5py.File(filepath, "r")
+                self._loaders[filepath] = loader
+            return loader[key][()]
+        elif filetype == "sound.hdf5":
+            # e.g.
+            #    {"input": [{"feat": "some/path.h5:F01_050C0101_PED_REAL",
+            #                "filetype": "sound.hdf5",
+            # -> filepath = "some/path.h5", key = "F01_050C0101_PED_REAL"
+            filepath, key = filepath.split(":", 1)
+
+            loader = self._loaders.get(filepath)
+            if loader is None:
+                # To avoid disk access, create loader only for the first time
+                loader = SoundHDF5File(filepath, "r", dtype="int16")
+                self._loaders[filepath] = loader
+            array, rate = loader[key]
+            return array
+        elif filetype == "sound":
+            # e.g.
+            #    {"input": [{"feat": "some/path.wav",
+            #                "filetype": "sound"},
+            # Assume PCM16
+            if not self.keep_all_data_on_mem:
+                array, _ = soundfile.read(filepath, dtype="int16")
+                return array
+            if filepath not in self._loaders:
+                array, _ = soundfile.read(filepath, dtype="int16")
+                self._loaders[filepath] = array
+            return self._loaders[filepath]
+        elif filetype == "npz":
+            # e.g.
+            #    {"input": [{"feat": "some/path.npz:F01_050C0101_PED_REAL",
+            #                "filetype": "npz",
+            filepath, key = filepath.split(":", 1)
+
+            loader = self._loaders.get(filepath)
+            if loader is None:
+                # To avoid disk access, create loader only for the first time
+                loader = np.load(filepath)
+                self._loaders[filepath] = loader
+            return loader[key]
+        elif filetype == "npy":
+            # e.g.
+            #    {"input": [{"feat": "some/path.npy",
+            #                "filetype": "npy"},
+            if not self.keep_all_data_on_mem:
+                return np.load(filepath)
+            if filepath not in self._loaders:
+                self._loaders[filepath] = np.load(filepath)
+            return self._loaders[filepath]
+        elif filetype in ["mat", "vec"]:
+            # e.g.
+            #    {"input": [{"feat": "some/path.ark:123",
+            #                "filetype": "mat"}]},
+            # In this case, "123" indicates the starting points of the matrix
+            # load_mat can load both matrix and vector
+            if not self.keep_all_data_on_mem:
+                return kaldiio.load_mat(filepath)
+            if filepath not in self._loaders:
+                self._loaders[filepath] = kaldiio.load_mat(filepath)
+            return self._loaders[filepath]
+        elif filetype == "scp":
+            # e.g.
+            #    {"input": [{"feat": "some/path.scp:F01_050C0101_PED_REAL",
+            #                "filetype": "scp",
+            filepath, key = filepath.split(":", 1)
+            loader = self._loaders.get(filepath)
+            if loader is None:
+                # To avoid disk access, create loader only for the first time
+                loader = kaldiio.load_scp(filepath)
+                self._loaders[filepath] = loader
+            return loader[key]
+        else:
+            raise NotImplementedError(
+                "Not supported: loader_type={}".format(filetype))