# Copyright (c) 2022 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. import collections import json from paddle.io import Dataset from paddleaudio.backends import load as load_audio from paddleaudio.datasets.dataset import feat_funcs class AMIDataset(Dataset): """ AMI dataset. """ meta_info = collections.namedtuple( 'META_INFO', ('id', 'duration', 'wav', 'start', 'stop', 'record_id')) def __init__(self, json_file: str, feat_type: str='raw', **kwargs): """ Ags: json_file (:obj:`str`): Data prep JSON file. 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. """ 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.json_file = json_file self.feat_type = feat_type self.feat_config = kwargs self._data = self._get_data() super(AMIDataset, self).__init__() def _get_data(self): with open(self.json_file, "r") as f: meta_data = json.load(f) data = [] for key in meta_data: sub_seg = meta_data[key]["wav"] wav = sub_seg["file"] duration = sub_seg["duration"] start = sub_seg["start"] stop = sub_seg["stop"] rec_id = str(key).rsplit("_", 2)[0] data.append( self.meta_info( str(key), float(duration), wav, int(start), int(stop), str(rec_id))) return data def _convert_to_record(self, idx: int): sample = self._data[idx] record = {} # To show all fields in a namedtuple: `type(sample)._fields` for field in type(sample)._fields: record[field] = getattr(sample, field) waveform, sr = load_audio(record['wav']) waveform = waveform[record['start']:record['stop']] feat_func = feat_funcs[self.feat_type] feat = feat_func( waveform, sr=sr, **self.feat_config) if feat_func else waveform record.update({'feat': feat}) return record def __getitem__(self, idx): return self._convert_to_record(idx) def __len__(self): return len(self._data)