Merge pull request #1451 from yt605155624/fix_ci_waveflow

zh794390558 · web-flow · commit 4de48ca2020e · 2022-02-16T14:43:21.000+08:00
[TTS]Fix ci for waveflow
diff --git a/paddlespeech/t2s/datasets/dataset.py b/paddlespeech/t2s/datasets/dataset.py
@@ -0,0 +1,261 @@
+# Copyright (c) 2020 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.
+import six
+from paddle.io import Dataset
+
+__all__ = [
+    "split",
+    "TransformDataset",
+    "CacheDataset",
+    "TupleDataset",
+    "DictDataset",
+    "SliceDataset",
+    "SubsetDataset",
+    "FilterDataset",
+    "ChainDataset",
+]
+
+
+def split(dataset, first_size):
+    """A utility function to split a dataset into two datasets."""
+    first = SliceDataset(dataset, 0, first_size)
+    second = SliceDataset(dataset, first_size, len(dataset))
+    return first, second
+
+
+class TransformDataset(Dataset):
+    def __init__(self, dataset, transform):
+        """Dataset which is transformed from another with a transform.
+
+        Args:
+            dataset (Dataset): the base dataset.
+            transform (callable): the transform which takes an example of the base dataset as parameter and return a new example.
+        """
+        self._dataset = dataset
+        self._transform = transform
+
+    def __len__(self):
+        return len(self._dataset)
+
+    def __getitem__(self, i):
+        in_data = self._dataset[i]
+        return self._transform(in_data)
+
+
+class CacheDataset(Dataset):
+    def __init__(self, dataset):
+        """A lazy cache of the base dataset.
+
+        Args:
+            dataset (Dataset): the base dataset to cache.
+        """
+        self._dataset = dataset
+        self._cache = dict()
+
+    def __len__(self):
+        return len(self._dataset)
+
+    def __getitem__(self, i):
+        if i not in self._cache:
+            self._cache[i] = self._dataset[i]
+        return self._cache[i]
+
+
+class TupleDataset(Dataset):
+    def __init__(self, *datasets):
+        """A compound dataset made from several datasets of the same length. An example of the `TupleDataset` is a tuple of examples from the constituent datasets.
+
+        Args:
+            datasets: tuple[Dataset], the constituent datasets.
+        """
+        if not datasets:
+            raise ValueError("no datasets are given")
+        length = len(datasets[0])
+        for i, dataset in enumerate(datasets):
+            if len(dataset) != length:
+                raise ValueError("all the datasets should have the same length."
+                                 "dataset {} has a different length".format(i))
+        self._datasets = datasets
+        self._length = length
+
+    def __getitem__(self, index):
+        # SOA
+        batches = [dataset[index] for dataset in self._datasets]
+        if isinstance(index, slice):
+            length = len(batches[0])
+            # AOS
+            return [
+                tuple([batch[i] for batch in batches])
+                for i in six.moves.range(length)
+            ]
+        else:
+            return tuple(batches)
+
+    def __len__(self):
+        return self._length
+
+
+class DictDataset(Dataset):
+    def __init__(self, **datasets):
+        """
+        A compound dataset made from several datasets of the same length. An 
+        example of the `DictDataset` is a dict of examples from the constituent 
+        datasets.
+
+        WARNING: paddle does not have a good support for DictDataset, because
+        every batch yield from a DataLoader is a list, but it cannot be a dict.
+        So you have to provide a collate function because you cannot use the
+        default one.
+
+        Args:
+            datasets: Dict[Dataset], the constituent datasets.
+        """
+        if not datasets:
+            raise ValueError("no datasets are given")
+        length = None
+        for key, dataset in six.iteritems(datasets):
+            if length is None:
+                length = len(dataset)
+            elif len(dataset) != length:
+                raise ValueError(
+                    "all the datasets should have the same length."
+                    "dataset {} has a different length".format(key))
+        self._datasets = datasets
+        self._length = length
+
+    def __getitem__(self, index):
+        batches = {
+            key: dataset[index]
+            for key, dataset in six.iteritems(self._datasets)
+        }
+        if isinstance(index, slice):
+            length = len(six.next(six.itervalues(batches)))
+            return [{key: batch[i]
+                     for key, batch in six.iteritems(batches)}
+                    for i in six.moves.range(length)]
+        else:
+            return batches
+
+    def __len__(self):
+        return self._length
+
+
+class SliceDataset(Dataset):
+    def __init__(self, dataset, start, finish, order=None):
+        """A Dataset which is a slice of the base dataset.
+
+        Args:
+            dataset (Dataset): the base dataset.
+            start (int): the start of the slice.
+            finish (int): the end of the slice, not inclusive.
+            order (List[int], optional): the order, it is a permutation of the valid example ids of the base dataset. If `order` is provided, the slice is taken in `order`. Defaults to None.
+        """
+        if start < 0 or finish > len(dataset):
+            raise ValueError("subset overruns the dataset.")
+        self._dataset = dataset
+        self._start = start
+        self._finish = finish
+        self._size = finish - start
+
+        if order is not None and len(order) != len(dataset):
+            raise ValueError(
+                "order should have the same length as the dataset"
+                "len(order) = {} which does not euqals len(dataset) = {} ".
+                format(len(order), len(dataset)))
+        self._order = order
+
+    def __len__(self):
+        return self._size
+
+    def __getitem__(self, i):
+        if i >= 0:
+            if i >= self._size:
+                raise IndexError('dataset index out of range')
+            index = self._start + i
+        else:
+            if i < -self._size:
+                raise IndexError('dataset index out of range')
+            index = self._finish + i
+
+        if self._order is not None:
+            index = self._order[index]
+        return self._dataset[index]
+
+
+class SubsetDataset(Dataset):
+    def __init__(self, dataset, indices):
+        """A Dataset which is a subset of the base dataset.
+
+        Args:
+            dataset (Dataset): the base dataset.
+            indices (Iterable[int]): the indices of the examples to pick.
+        """
+        self._dataset = dataset
+        if len(indices) > len(dataset):
+            raise ValueError("subset's size larger that dataset's size!")
+        self._indices = indices
+        self._size = len(indices)
+
+    def __len__(self):
+        return self._size
+
+    def __getitem__(self, i):
+        index = self._indices[i]
+        return self._dataset[index]
+
+
+class FilterDataset(Dataset):
+    def __init__(self, dataset, filter_fn):
+        """A filtered dataset.
+
+        Args:
+            dataset (Dataset): the base dataset.
+            filter_fn (callable): a callable which takes an example of the base dataset and return a boolean.
+        """
+        self._dataset = dataset
+        self._indices = [
+            i for i in range(len(dataset)) if filter_fn(dataset[i])
+        ]
+        self._size = len(self._indices)
+
+    def __len__(self):
+        return self._size
+
+    def __getitem__(self, i):
+        index = self._indices[i]
+        return self._dataset[index]
+
+
+class ChainDataset(Dataset):
+    def __init__(self, *datasets):
+        """A concatenation of the several datasets which the same structure.
+
+        Args:
+            datasets (Iterable[Dataset]): datasets to concat.
+        """
+        self._datasets = datasets
+
+    def __len__(self):
+        return sum(len(dataset) for dataset in self._datasets)
+
+    def __getitem__(self, i):
+        if i < 0:
+            raise IndexError("ChainDataset doesnot support negative indexing.")
+
+        for dataset in self._datasets:
+            if i < len(dataset):
+                return dataset[i]
+            i -= len(dataset)
+
+        raise IndexError("dataset index out of range")
diff --git a/paddlespeech/t2s/exps/waveflow/train.py b/paddlespeech/t2s/exps/waveflow/train.py
@@ -19,7 +19,7 @@
 from paddle.io import DataLoader
 from paddle.io import DistributedBatchSampler
 
-from paddlespeech.t2s.data import dataset
+from paddlespeech.t2s.datasets import dataset
 from paddlespeech.t2s.exps.waveflow.config import get_cfg_defaults
 from paddlespeech.t2s.exps.waveflow.ljspeech import LJSpeech
 from paddlespeech.t2s.exps.waveflow.ljspeech import LJSpeechClipCollector
diff --git a/paddlespeech/t2s/frontend/zh_normalization/num.py b/paddlespeech/t2s/frontend/zh_normalization/num.py
@@ -208,7 +208,7 @@ def verbalize_digit(value_string: str, alt_one=False) -> str:
     result_symbols = [DIGITS[digit] for digit in value_string]
     result = ''.join(result_symbols)
     if alt_one:
-        result.replace("一", "幺")
+        result = result.replace("一", "幺")
     return result
 
 
diff --git a/paddlespeech/t2s/models/waveflow.py b/paddlespeech/t2s/models/waveflow.py
@@ -33,11 +33,11 @@ def fold(x, n_group):
     """Fold audio or spectrogram's temporal dimension in to groups.
 
     Args:
-        x(Tensor): The input tensor. shape=(\*, time_steps)
+        x(Tensor): The input tensor. shape=(*, time_steps)
         n_group(int): The size of a group.
 
     Returns:
-        Tensor: Folded tensor. shape=(\*, time_steps // n_group, group)
+        Tensor: Folded tensor. shape=(*, time_steps // n_group, group)
     """
     spatial_shape = list(x.shape[:-1])
     time_steps = paddle.shape(x)[-1]
@@ -98,11 +98,11 @@ def forward(self, x, trim_conv_artifact=False):
             trim_conv_artifact(bool, optional, optional): Trim deconvolution artifact at each layer. Defaults to False.
 
         Returns:
-           Tensor: The upsampled spectrogram. shape=(batch_size, input_channels, time_steps \* upsample_factor)
+           Tensor: The upsampled spectrogram. shape=(batch_size, input_channels, time_steps * upsample_factor)
 
         Notes:
             If trim_conv_artifact is ``True``, the output time steps is less
-            than ``time_steps \* upsample_factors``.
+            than ``time_steps * upsample_factors``.
         """
         x = paddle.unsqueeze(x, 1)  # (B, C, T) -> (B, 1, C, T)
         for layer in self:
@@ -641,7 +641,7 @@ def infer(self, mel):
             mel(np.ndarray): Mel spectrogram of an utterance(in log-magnitude). shape=(C_mel, T_mel)
 
         Returns:
-            Tensor: The synthesized audio, where``T <= T_mel \* upsample_factors``. shape=(B, T)
+            Tensor: The synthesized audio, where``T <= T_mel * upsample_factors``. shape=(B, T)
         """
         start = time.time()
         condition = self.encoder(mel, trim_conv_artifact=True)  # (B, C, T)
