# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pathlib import Path
from typing import List
import librosa
import numpy as np
from paddle.io import Dataset
__all__ = ["AudioSegmentDataset", "AudioDataset", "AudioFolderDataset"]
[docs]class AudioSegmentDataset(Dataset):
"""A simple dataset adaptor for audio files to train vocoders.
Read -> trim silence -> normalize -> extract a segment
"""
def __init__(self,
file_paths: List[Path],
sample_rate: int,
length: int,
top_db: float):
self.file_paths = file_paths
self.sr = sample_rate
self.top_db = top_db
self.length = length # samples in the clip
def __getitem__(self, i):
fpath = self.file_paths[i]
y, sr = librosa.load(fpath, self.sr)
y, _ = librosa.effects.trim(y, top_db=self.top_db)
y = librosa.util.normalize(y)
y = y.astype(np.float32)
# pad or trim
if y.size <= self.length:
y = np.pad(y, [0, self.length - len(y)], mode='constant')
else:
start = np.random.randint(0, 1 + len(y) - self.length)
y = y[start:start + self.length]
return y
def __len__(self):
return len(self.file_paths)
[docs]class AudioDataset(Dataset):
"""A simple dataset adaptor for the audio files.
Read -> trim silence -> normalize
"""
def __init__(self,
file_paths: List[Path],
sample_rate: int,
top_db: float=60):
self.file_paths = file_paths
self.sr = sample_rate
self.top_db = top_db
def __getitem__(self, i):
fpath = self.file_paths[i]
y, sr = librosa.load(fpath, self.sr)
y, _ = librosa.effects.trim(y, top_db=self.top_db)
y = librosa.util.normalize(y)
y = y.astype(np.float32)
return y
def __len__(self):
return len(self.file_paths)
[docs]class AudioFolderDataset(AudioDataset):
def __init__(
self,
root,
sample_rate,
top_db=60,
extension=".wav", ):
root = Path(root).expanduser()
file_paths = sorted(list(root.rglob("*{}".format(extension))))
super().__init__(file_paths, sample_rate, top_db)