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90 lines
3.1 KiB
90 lines
3.1 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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import pickle
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from pathlib import Path
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import numpy as np
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from paddle.io import Dataset
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from parakeet.data import batch_spec
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from parakeet.data import batch_text_id
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from parakeet.exps.voice_cloning.tacotron2_ge2e.preprocess_transcription import _phones
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from parakeet.exps.voice_cloning.tacotron2_ge2e.preprocess_transcription import _tones
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from parakeet.frontend import Vocab
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voc_phones = Vocab(sorted(list(_phones)))
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print("vocab_phones:\n", voc_phones)
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voc_tones = Vocab(sorted(list(_tones)))
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print("vocab_tones:\n", voc_tones)
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class AiShell3(Dataset):
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"""Processed AiShell3 dataset."""
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def __init__(self, root):
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super().__init__()
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self.root = Path(root).expanduser()
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self.embed_dir = self.root / "embed"
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self.mel_dir = self.root / "mel"
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with open(self.root / "metadata.pickle", 'rb') as f:
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self.records = pickle.load(f)
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def __getitem__(self, index):
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metadatum = self.records[index]
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sentence_id = metadatum["sentence_id"]
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speaker_id = sentence_id[:7]
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phones = metadatum["phones"]
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tones = metadatum["tones"]
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phones = np.array(
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[voc_phones.lookup(item) for item in phones], dtype=np.int64)
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tones = np.array(
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[voc_tones.lookup(item) for item in tones], dtype=np.int64)
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mel = np.load(str(self.mel_dir / speaker_id / (sentence_id + ".npy")))
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embed = np.load(
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str(self.embed_dir / speaker_id / (sentence_id + ".npy")))
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return phones, tones, mel, embed
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def __len__(self):
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return len(self.records)
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def collate_aishell3_examples(examples):
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phones, tones, mel, embed = list(zip(*examples))
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text_lengths = np.array([item.shape[0] for item in phones], dtype=np.int64)
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spec_lengths = np.array([item.shape[1] for item in mel], dtype=np.int64)
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T_dec = np.max(spec_lengths)
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stop_tokens = (
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np.arange(T_dec) >= np.expand_dims(spec_lengths, -1)).astype(np.float32)
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phones, _ = batch_text_id(phones)
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tones, _ = batch_text_id(tones)
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mel, _ = batch_spec(mel)
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mel = np.transpose(mel, (0, 2, 1))
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embed = np.stack(embed)
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# 7 fields
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# (B, T), (B, T), (B, T, C), (B, C), (B,), (B,), (B, T)
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return phones, tones, mel, embed, text_lengths, spec_lengths, stop_tokens
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if __name__ == "__main__":
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dataset = AiShell3("~/datasets/aishell3/train")
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example = dataset[0]
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examples = [dataset[i] for i in range(10)]
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batch = collate_aishell3_examples(examples)
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for field in batch:
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print(field.shape, field.dtype)
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