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95 lines
3.0 KiB
95 lines
3.0 KiB
3 years ago
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# Copyright (c) 2020 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 pickle
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3 years ago
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from pathlib import Path
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3 years ago
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import numpy as np
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from paddle.io import Dataset
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3 years ago
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from parakeet.data.batch import batch_spec
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from parakeet.data.batch import batch_text_id
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3 years ago
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class LJSpeech(Dataset):
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"""A simple dataset adaptor for the processed ljspeech dataset."""
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def __init__(self, root):
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self.root = Path(root).expanduser()
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records = []
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with open(self.root / "metadata.pkl", 'rb') as f:
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metadata = pickle.load(f)
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for mel_name, text, ids in metadata:
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mel_name = self.root / "mel" / (mel_name + ".npy")
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records.append((mel_name, text, ids))
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self.records = records
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def __getitem__(self, i):
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mel_name, _, ids = self.records[i]
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mel = np.load(mel_name)
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return ids, mel
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def __len__(self):
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return len(self.records)
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class LJSpeechCollector(object):
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"""A simple callable to batch LJSpeech examples."""
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def __init__(self, padding_idx=0, padding_value=0., padding_stop_token=1.0):
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self.padding_idx = padding_idx
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self.padding_value = padding_value
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self.padding_stop_token = padding_stop_token
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def __call__(self, examples):
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texts = []
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mels = []
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text_lens = []
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mel_lens = []
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for data in examples:
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text, mel = data
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text = np.array(text, dtype=np.int64)
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text_lens.append(len(text))
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mels.append(mel)
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texts.append(text)
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mel_lens.append(mel.shape[1])
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# Sort by text_len in descending order
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texts = [
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i
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for i, _ in sorted(
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zip(texts, text_lens), key=lambda x: x[1], reverse=True)
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]
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mels = [
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i
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for i, _ in sorted(
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zip(mels, text_lens), key=lambda x: x[1], reverse=True)
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]
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mel_lens = [
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i
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for i, _ in sorted(
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zip(mel_lens, text_lens), key=lambda x: x[1], reverse=True)
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]
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mel_lens = np.array(mel_lens, dtype=np.int64)
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text_lens = np.array(sorted(text_lens, reverse=True), dtype=np.int64)
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# Pad sequence with largest len of the batch
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texts, _ = batch_text_id(texts, pad_id=self.padding_idx)
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mels, _ = batch_spec(mels, pad_value=self.padding_value)
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mels = np.transpose(mels, axes=(0, 2, 1))
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return texts, mels, text_lens, mel_lens
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