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93 lines
2.8 KiB
93 lines
2.8 KiB
# 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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from typing import List
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import librosa
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import numpy as np
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from paddle.io import Dataset
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__all__ = ["AudioSegmentDataset", "AudioDataset", "AudioFolderDataset"]
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class AudioSegmentDataset(Dataset):
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"""A simple dataset adaptor for audio files to train vocoders.
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Read -> trim silence -> normalize -> extract a segment
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"""
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def __init__(self,
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file_paths: List[Path],
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sample_rate: int,
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length: int,
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top_db: float):
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self.file_paths = file_paths
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self.sr = sample_rate
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self.top_db = top_db
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self.length = length # samples in the clip
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def __getitem__(self, i):
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fpath = self.file_paths[i]
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y, sr = librosa.load(fpath, self.sr)
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y, _ = librosa.effects.trim(y, top_db=self.top_db)
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y = librosa.util.normalize(y)
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y = y.astype(np.float32)
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# pad or trim
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if y.size <= self.length:
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y = np.pad(y, [0, self.length - len(y)], mode='constant')
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else:
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start = np.random.randint(0, 1 + len(y) - self.length)
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y = y[start:start + self.length]
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return y
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def __len__(self):
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return len(self.file_paths)
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class AudioDataset(Dataset):
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"""A simple dataset adaptor for the audio files.
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Read -> trim silence -> normalize
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"""
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def __init__(self,
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file_paths: List[Path],
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sample_rate: int,
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top_db: float=60):
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self.file_paths = file_paths
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self.sr = sample_rate
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self.top_db = top_db
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def __getitem__(self, i):
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fpath = self.file_paths[i]
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y, sr = librosa.load(fpath, self.sr)
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y, _ = librosa.effects.trim(y, top_db=self.top_db)
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y = librosa.util.normalize(y)
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y = y.astype(np.float32)
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return y
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def __len__(self):
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return len(self.file_paths)
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class AudioFolderDataset(AudioDataset):
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def __init__(
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self,
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root,
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sample_rate,
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top_db=60,
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extension=".wav", ):
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root = Path(root).expanduser()
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file_paths = sorted(list(root.rglob("*{}".format(extension))))
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super().__init__(file_paths, sample_rate, top_db)
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