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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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import numpy as np
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from paddle.io import Dataset
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from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
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from paddlespeech.s2t.io.utility import pad_list
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class TextDataset(Dataset):
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@classmethod
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def from_file(cls, file_path):
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dataset = cls(file_path)
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return dataset
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def __init__(self, file_path):
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self._manifest = []
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with open(file_path) as f:
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for line in f:
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self._manifest.append(line.strip())
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def __len__(self):
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return len(self._manifest)
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def __getitem__(self, idx):
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return self._manifest[idx]
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class TextCollatorSpm():
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def __init__(self, unit_type, vocab_filepath, spm_model_prefix):
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assert (vocab_filepath is not None)
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self.text_featurizer = TextFeaturizer(
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unit_type=unit_type,
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vocab=vocab_filepath,
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spm_model_prefix=spm_model_prefix)
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self.eos_id = self.text_featurizer.eos_id
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self.blank_id = self.text_featurizer.blank_id
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def __call__(self, batch):
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"""
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return type [List, np.array [B, T], np.array [B, T], np.array[B]]
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"""
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keys = []
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texts = []
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texts_input = []
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texts_output = []
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text_lens = []
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for idx, item in enumerate(batch):
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key = item.split(" ")[0].strip()
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text = " ".join(item.split(" ")[1:])
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keys.append(key)
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token_ids = self.text_featurizer.featurize(text)
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texts_input.append(
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np.array([self.eos_id] + token_ids).astype(np.int64))
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texts_output.append(
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np.array(token_ids + [self.eos_id]).astype(np.int64))
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text_lens.append(len(token_ids) + 1)
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ys_input_pad = pad_list(texts_input, self.blank_id).astype(np.int64)
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ys_output_pad = pad_list(texts_output, self.blank_id).astype(np.int64)
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y_lens = np.array(text_lens).astype(np.int64)
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return keys, ys_input_pad, ys_output_pad, y_lens
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