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PaddleSpeech/paddlespeech/vector/io/dataset_from_json.py

117 lines
3.5 KiB

# 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 json
from dataclasses import dataclass
from dataclasses import fields
from paddle.io import Dataset
from paddleaudio import load as load_audio
from paddleaudio.compliance.librosa import melspectrogram
from paddleaudio.compliance.librosa import mfcc
@dataclass
class meta_info:
"""the audio meta info in the vector JSONDataset
Args:
id (str): the segment name
duration (float): segment time
wav (str): wav file path
start (int): start point in the original wav file
stop (int): stop point in the original wav file
lab_id (str): the record id
"""
id: str
duration: float
wav: str
start: int
stop: int
record_id: str
# json dataset support feature type
feat_funcs = {
'raw': None,
'melspectrogram': melspectrogram,
'mfcc': mfcc,
}
class JSONDataset(Dataset):
"""
dataset from json file.
"""
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(JSONDataset, 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(
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
for field in fields(sample):
record[field.name] = getattr(sample, field.name)
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)