ccrrong
2 years ago
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7a03f36548
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# Copyright (c) 2022 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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import json
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from dataclasses import dataclass
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from dataclasses import fields
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from paddle.io import Dataset
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from paddleaudio import load as load_audio
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from paddleaudio.compliance.librosa import melspectrogram
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from paddleaudio.compliance.librosa import mfcc
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@dataclass
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class meta_info:
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"""the audio meta info in the vector JSONDataset
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Args:
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id (str): the segment name
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duration (float): segment time
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wav (str): wav file path
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start (int): start point in the original wav file
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stop (int): stop point in the original wav file
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lab_id (str): the record id
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"""
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id: str
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duration: float
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wav: str
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start: int
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stop: int
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record_id: str
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# json dataset support feature type
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feat_funcs = {
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'raw': None,
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'melspectrogram': melspectrogram,
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'mfcc': mfcc,
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}
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class JSONDataset(Dataset):
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"""
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dataset from json file.
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"""
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def __init__(self, json_file: str, feat_type: str='raw', **kwargs):
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"""
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Ags:
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json_file (:obj:`str`): Data prep JSON file.
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labels (:obj:`List[int]`): Labels of audio files.
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feat_type (:obj:`str`, `optional`, defaults to `raw`):
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It identifies the feature type that user wants to extrace of an audio file.
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"""
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if feat_type not in feat_funcs.keys():
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raise RuntimeError(
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f"Unknown feat_type: {feat_type}, it must be one in {list(feat_funcs.keys())}"
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)
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self.json_file = json_file
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self.feat_type = feat_type
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self.feat_config = kwargs
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self._data = self._get_data()
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super(JSONDataset, self).__init__()
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def _get_data(self):
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with open(self.json_file, "r") as f:
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meta_data = json.load(f)
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data = []
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for key in meta_data:
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sub_seg = meta_data[key]["wav"]
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wav = sub_seg["file"]
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duration = sub_seg["duration"]
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start = sub_seg["start"]
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stop = sub_seg["stop"]
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rec_id = str(key).rsplit("_", 2)[0]
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data.append(
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meta_info(
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str(key),
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float(duration), wav, int(start), int(stop), str(rec_id)))
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return data
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def _convert_to_record(self, idx: int):
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sample = self._data[idx]
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record = {}
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# To show all fields in a namedtuple
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for field in fields(sample):
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record[field.name] = getattr(sample, field.name)
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waveform, sr = load_audio(record['wav'])
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waveform = waveform[record['start']:record['stop']]
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feat_func = feat_funcs[self.feat_type]
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feat = feat_func(
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waveform, sr=sr, **self.feat_config) if feat_func else waveform
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record.update({'feat': feat})
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return record
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def __getitem__(self, idx):
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return self._convert_to_record(idx)
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def __len__(self):
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return len(self._data)
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