You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
101 lines
3.4 KiB
101 lines
3.4 KiB
# 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 import 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,
|
|
}
|
|
|
|
|
|
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)
|