100 lines
3.8 KiB
Python
100 lines
3.8 KiB
Python
# 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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from pathlib import Path
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import pandas as pd
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from ruamel.yaml import YAML
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import io
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import librosa
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import numpy as np
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from parakeet.g2p.en import text_to_sequence
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from parakeet.data.dataset import Dataset
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from parakeet.data.datacargo import DataCargo
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from parakeet.data.batch import TextIDBatcher, WavBatcher
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class VCTK(Dataset):
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def __init__(self, root):
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assert isinstance(root, (
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str, Path)), "root should be a string or Path object"
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self.root = root if isinstance(root, Path) else Path(root)
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self.text_root = self.root.joinpath("txt")
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self.wav_root = self.root.joinpath("wav48")
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if not (self.root.joinpath("metadata.csv").exists() and
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self.root.joinpath("speaker_indices.yaml").exists()):
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self._prepare_metadata()
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self.speaker_indices, self.metadata = self._load_metadata()
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def _load_metadata(self):
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yaml = YAML(typ='safe')
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speaker_indices = yaml.load(self.root.joinpath("speaker_indices.yaml"))
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metadata = pd.read_csv(
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self.root.joinpath("metadata.csv"), sep="|", quoting=3, header=1)
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return speaker_indices, metadata
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def _prepare_metadata(self):
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metadata = []
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speaker_to_index = {}
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for i, speaker_folder in enumerate(self.text_root.iterdir()):
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if speaker_folder.is_dir():
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speaker_to_index[speaker_folder.name] = i
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for text_file in speaker_folder.iterdir():
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if text_file.is_file():
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with io.open(str(text_file)) as f:
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transcription = f.read().strip()
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wav_file = text_file.with_suffix(".wav")
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metadata.append(
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(wav_file.name, speaker_folder.name, transcription))
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metadata = pd.DataFrame.from_records(
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metadata, columns=["wave_file", "speaker", "text"])
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# save them
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yaml = YAML(typ='safe')
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yaml.dump(speaker_to_index, self.root.joinpath("speaker_indices.yaml"))
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metadata.to_csv(
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self.root.joinpath("metadata.csv"),
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sep="|",
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quoting=3,
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index=False)
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def _get_example(self, metadatum):
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wave_file, speaker, text = metadatum
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wav_path = self.wav_root.joinpath(speaker, wave_file)
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wav, sr = librosa.load(str(wav_path), sr=None)
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phoneme_seq = np.array(text_to_sequence(text))
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return wav, self.speaker_indices[speaker], phoneme_seq
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def __getitem__(self, index):
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metadatum = self.metadata.iloc[index]
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example = self._get_example(metadatum)
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return example
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def __len__(self):
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return len(self.metadata)
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def _batch_examples(self, minibatch):
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wav_batch, speaker_batch, phoneme_batch = [], [], []
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for example in minibatch:
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wav, speaker_id, phoneme_seq = example
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wav_batch.append(wav)
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speaker_batch.append(speaker_id)
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phoneme_batch.append(phoneme_seq)
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wav_batch = WavBatcher(pad_value=0.)(wav_batch)
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speaker_batch = np.array(speaker_batch)
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phoneme_batch = TextIDBatcher(pad_id=0)(phoneme_batch)
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return wav_batch, speaker_batch, phoneme_batch
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