# Copyright (c) 2021 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 typing import List
import numpy as np
import paddle
from ..backends.soundfile_backend import soundfile_load as load_audio
from ..compliance.kaldi import fbank as kaldi_fbank
from ..compliance.kaldi import mfcc as kaldi_mfcc
from ..compliance.librosa import melspectrogram
from ..compliance.librosa import mfcc
feat_funcs = {
'raw': None,
'melspectrogram': melspectrogram,
'mfcc': mfcc,
'kaldi_fbank': kaldi_fbank,
'kaldi_mfcc': kaldi_mfcc,
}
[docs]class AudioClassificationDataset(paddle.io.Dataset):
"""
Base class of audio classification dataset.
"""
def __init__(self,
files: List[str],
labels: List[int],
feat_type: str='raw',
sample_rate: int=None,
**kwargs):
"""
Ags:
files (:obj:`List[str]`): A list of absolute path of audio files.
labels (:obj:`List[int]`): Labels of audio files.
feat_type (:obj:`str`, `optional`, defaults to `raw`):
It identifies the feature type that user wants to extrace of an audio file.
"""
super(AudioClassificationDataset, self).__init__()
if feat_type not in feat_funcs.keys():
raise RuntimeError(
f"Unknown feat_type: {feat_type}, it must be one in {list(feat_funcs.keys())}"
)
self.files = files
self.labels = labels
self.feat_type = feat_type
self.sample_rate = sample_rate
self.feat_config = kwargs # Pass keyword arguments to customize feature config
def _get_data(self, input_file: str):
raise NotImplementedError
def _convert_to_record(self, idx):
file, label = self.files[idx], self.labels[idx]
if self.sample_rate is None:
waveform, sample_rate = load_audio(file)
else:
waveform, sample_rate = load_audio(file, sr=self.sample_rate)
feat_func = feat_funcs[self.feat_type]
record = {}
if self.feat_type in ['kaldi_fbank', 'kaldi_mfcc']:
waveform = paddle.to_tensor(waveform).unsqueeze(0) # (C, T)
record['feat'] = feat_func(
waveform=waveform, sr=self.sample_rate, **self.feat_config)
else:
record['feat'] = feat_func(
waveform, sample_rate,
**self.feat_config) if feat_func else waveform
record['label'] = label
return record
def __getitem__(self, idx):
record = self._convert_to_record(idx)
if self.feat_type in ['kaldi_fbank', 'kaldi_mfcc']:
return self.keys[idx], record['feat'], record['label']
else:
return np.array(record['feat']).transpose(), np.array(
record['label'], dtype=np.int64)
def __len__(self):
return len(self.files)