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194 lines
6.6 KiB
194 lines
6.6 KiB
# 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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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 paddlespeech.s2t.utils.log import Log
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logger = Log(__name__).getlog()
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# the audio meta info in the vector CSVDataset
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# utt_id: the utterance segment name
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# duration: utterance segment time
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# wav: utterance file path
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# start: start point in the original wav file
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# stop: stop point in the original wav file
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# label: the utterance segment's label id
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@dataclass
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class meta_info:
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"""the audio meta info in the vector CSVDataset
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Args:
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utt_id (str): the utterance segment name
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duration (float): utterance segment time
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wav (str): utterance 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 utterance segment's label id
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"""
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utt_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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label: str
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# csv dataset support feature type
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# raw: return the pcm data sample point
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# melspectrogram: fbank feature
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feat_funcs = {
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'raw': None,
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'melspectrogram': melspectrogram,
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}
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class CSVDataset(Dataset):
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def __init__(self,
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csv_path,
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label2id_path=None,
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config=None,
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random_chunk=True,
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feat_type: str="raw",
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n_train_snts: int=-1,
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**kwargs):
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"""Implement the CSV Dataset
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Args:
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csv_path (str): csv dataset file path
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label2id_path (str): the utterance label to integer id map file path
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config (CfgNode): yaml config
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feat_type (str): dataset feature type. if it is raw, it return pcm data.
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n_train_snts (int): select the n_train_snts sample from the dataset.
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if n_train_snts = -1, dataset will load all the sample.
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Default value is -1.
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kwargs : feature type args
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"""
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super().__init__()
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self.csv_path = csv_path
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self.label2id_path = label2id_path
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self.config = config
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self.random_chunk = random_chunk
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self.feat_type = feat_type
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self.n_train_snts = n_train_snts
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self.feat_config = kwargs
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self.id2label = {}
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self.label2id = {}
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self.data = self.load_data_csv()
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self.load_speaker_to_label()
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def load_data_csv(self):
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"""Load the csv dataset content and store them in the data property
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the csv dataset's format has six fields,
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that is audio_id or utt_id, audio duration, segment start point, segment stop point
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and utterance label.
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Note in training period, the utterance label must has a map to integer id in label2id_path
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Returns:
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list: the csv data with meta_info type
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"""
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data = []
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with open(self.csv_path, 'r') as rf:
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for line in rf.readlines()[1:]:
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audio_id, duration, wav, start, stop, spk_id = line.strip(
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).split(',')
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data.append(
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meta_info(audio_id,
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float(duration), wav,
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int(start), int(stop), spk_id))
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if self.n_train_snts > 0:
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sample_num = min(self.n_train_snts, len(data))
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data = data[0:sample_num]
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return data
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def load_speaker_to_label(self):
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"""Load the utterance label map content.
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In vector domain, we call the utterance label as speaker label.
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The speaker label is real speaker label in speaker verification domain,
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and in language identification is language label.
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"""
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if not self.label2id_path:
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logger.warning("No speaker id to label file")
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return
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with open(self.label2id_path, 'r') as f:
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for line in f.readlines():
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label_name, label_id = line.strip().split(' ')
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self.label2id[label_name] = int(label_id)
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self.id2label[int(label_id)] = label_name
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def convert_to_record(self, idx: int):
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"""convert the dataset sample to training record the CSV Dataset
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Args:
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idx (int) : the request index in all the dataset
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"""
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sample = self.data[idx]
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record = {}
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# To show all fields in a namedtuple: `type(sample)._fields`
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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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# random select a chunk audio samples from the audio
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if self.config and self.config.random_chunk:
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num_wav_samples = waveform.shape[0]
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num_chunk_samples = int(self.config.chunk_duration * sr)
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start = random.randint(0, num_wav_samples - num_chunk_samples - 1)
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stop = start + num_chunk_samples
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else:
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start = record['start']
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stop = record['stop']
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# we only return the waveform as feat
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waveform = waveform[start:stop]
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# all availabel feature type is in feat_funcs
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assert self.feat_type in feat_funcs.keys(), \
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f"Unknown feat_type: {self.feat_type}, it must be one in {list(feat_funcs.keys())}"
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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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if self.label2id:
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record.update({'label': self.label2id[record['label']]})
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return record
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def __getitem__(self, idx):
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"""Return the specific index sample
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Args:
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idx (int) : the request index in all the dataset
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"""
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return self.convert_to_record(idx)
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def __len__(self):
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"""Return the dataset length
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Returns:
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int: the length num of the dataset
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"""
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return len(self.data)
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