xiongxinlei
2 years ago
parent
ec24a169ee
commit
9944fec3d4
@ -1,170 +0,0 @@
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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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"""
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Convert the PaddleSpeech jsonline format to csv format
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Currently, Speaker Identificaton Training process need csv format.
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"""
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import argparse
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import os
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import jsonlines
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import collections
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import json
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import csv
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from yacs.config import CfgNode
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import tqdm
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from paddleaudio import load as load_audio
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import random
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from paddlespeech.vector.training.seeding import seed_everything
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# voxceleb meta info for each training utterance segment
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# we extract a segment from a utterance to train
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# and the segment' period is between start and stop time point in the original wav file
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# each field in the meta means as follows:
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# 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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# spk_id: the utterance segment's speaker name
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meta_info = collections.namedtuple(
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'META_INFO', ('id', 'duration', 'wav', 'start', 'stop', 'spk_id'))
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def get_chunks(seg_dur, audio_id, audio_duration):
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num_chunks = int(audio_duration / seg_dur) # all in milliseconds
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chunk_lst = [
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audio_id + "_" + str(i * seg_dur) + "_" + str(i * seg_dur + seg_dur)
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for i in range(num_chunks)
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]
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return chunk_lst
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def prepare_csv(wav_files, output_file, config, split_chunks=True):
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if not os.path.exists(os.path.dirname(output_file)):
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os.makedirs(os.path.dirname(output_file))
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csv_lines = []
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header = ["id", "duration", "wav", "start", "stop", "spk_id"]
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for item in wav_files:
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item = json.loads(item.strip())
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audio_id = item['utt'].replace(".wav", "")
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audio_duration = item['feat_shape'][0]
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wav_file = item['feat']
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spk_id = audio_id.split('-')[0]
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waveform, sr = load_audio(wav_file)
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if split_chunks:
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uniq_chunks_list = get_chunks(config.chunk_duration, audio_id, audio_duration)
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for chunk in uniq_chunks_list:
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s, e = chunk.split("_")[-2:] # Timestamps of start and end
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start_sample = int(float(s) * sr)
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end_sample = int(float(e) * sr)
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# id, duration, wav, start, stop, spk_id
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csv_lines.append([
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chunk, audio_duration, wav_file, start_sample, end_sample,
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spk_id
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])
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else:
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csv_lines.append([audio_id, audio_duration, wav_file, 0, waveform.shape[0], spk_id])
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with open(output_file, mode="w") as csv_f:
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csv_writer = csv.writer(csv_f, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
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csv_writer.writerow(header)
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for line in csv_lines:
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csv_writer.writerow(line)
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def get_enroll_test_list(filelist, verification_file):
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print(f"verification file: {verification_file}")
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enroll_audios = set()
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test_audios = set()
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with open(verification_file, 'r') as f:
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for line in f:
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_, enroll_file, test_file = line.strip().split(' ')
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enroll_audios.add('-'.join(enroll_file.split('/')))
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test_audios.add('-'.join(test_file.split('/')))
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enroll_files = []
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test_files = []
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for item in filelist:
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with open(item, 'r') as f:
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for line in f:
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audio_id = json.loads(line.strip())['utt']
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if audio_id in enroll_audios:
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enroll_files.append(line)
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if audio_id in test_audios:
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test_files.append(line)
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enroll_files = sorted(enroll_files)
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test_files = sorted(test_files)
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return enroll_files, test_files
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def get_train_dev_list(filelist, target_dir, split_ratio):
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if not os.path.exists(os.path.join(target_dir, "meta")):
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os.makedirs(os.path.join(target_dir, "meta"))
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audio_files = []
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speakers = set()
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for item in filelist:
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with open(item, 'r') as f:
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for line in f:
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spk_id = json.loads(line.strip())['utt2spk']
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speakers.add(spk_id)
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audio_files.append(line.strip())
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speakers = sorted(speakers)
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with open(os.path.join(target_dir, "meta", "spk_id2label.txt"), 'w') as f:
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for label, spk_id in enumerate(speakers):
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f.write(f'{spk_id} {label}\n')
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split_idx = int(split_ratio * len(audio_files))
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random.shuffle(audio_files)
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train_files, dev_files = audio_files[:split_idx], audio_files[split_idx:]
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return train_files, dev_files
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def prepare_data(args, config):
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paddle.set_device("cpu")
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seed_everything(config.seed)
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enroll_files, test_files = get_enroll_test_list([args.test], verification_file=config.verification_file)
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prepare_csv(enroll_files, os.path.join(args.target_dir, "csv", "enroll.csv"), config, split_chunks=False)
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prepare_csv(test_files, os.path.join(args.target_dir, "csv", "test.csv"), config, split_chunks=False)
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train_files, dev_files = get_train_dev_list(args.train, target_dir=args.target_dir, split_ratio=config.split_ratio)
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prepare_csv(train_files, os.path.join(args.target_dir, "csv", "train.csv"), config)
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prepare_csv(dev_files, os.path.join(args.target_dir, "csv", "dev.csv"), config)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument(
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"--train",
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required=True,
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nargs='+',
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help="The jsonline files list for train")
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parser.add_argument(
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"--test", required=True, help="The jsonline file for test")
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parser.add_argument(
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"--target_dir",
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required=True,
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help="The target directory stores the csv files and meta file")
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parser.add_argument("--config",
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default=None,
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required=True,
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type=str,
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help="configuration file")
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args = parser.parse_args()
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# parse the yaml config file
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config = CfgNode(new_allowed=True)
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if args.config:
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config.merge_from_file(args.config)
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prepare_data(args, config)
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@ -0,0 +1,154 @@
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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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"""
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Convert the PaddleSpeech jsonline format data to csv format data in voxceleb experiment.
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Currently, Speaker Identificaton Training process use csv format.
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"""
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import argparse
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import csv
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import os
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from typing import List
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import paddle
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import tqdm
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from yacs.config import CfgNode
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from paddlespeech.s2t.utils.log import Log
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from paddlespeech.vector.training.seeding import seed_everything
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logger = Log(__name__).getlog()
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from paddleaudio import load as load_audio
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from paddleaudio import save as save_wav
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def get_chunks(seg_dur, audio_id, audio_duration):
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num_chunks = int(audio_duration / seg_dur) # all in milliseconds
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chunk_lst = [
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audio_id + "_" + str(i * seg_dur) + "_" + str(i * seg_dur + seg_dur)
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for i in range(num_chunks)
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]
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return chunk_lst
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def get_audio_info(wav_file: str,
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split_chunks: bool,
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base_path: str,
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chunk_duration: float=3.0) -> List[List[str]]:
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waveform, sr = load_audio(wav_file)
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audio_id = wav_file.split("/rir_noise/")[-1].split(".")[0]
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audio_duration = waveform.shape[0] / sr
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ret = []
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if split_chunks and audio_duration > chunk_duration: # Split into pieces of self.chunk_duration seconds.
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uniq_chunks_list = get_chunks(chunk_duration, audio_id, audio_duration)
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for idx, chunk in enumerate(uniq_chunks_list):
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s, e = chunk.split("_")[-2:] # Timestamps of start and end
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start_sample = int(float(s) * sr)
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end_sample = int(float(e) * sr)
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new_wav_file = os.path.join(base_path,
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audio_id + f'_chunk_{idx+1:02}.wav')
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save_wav(waveform[start_sample:end_sample], sr, new_wav_file)
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# id, duration, new_wav
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ret.append([chunk, chunk_duration, new_wav_file])
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else: # Keep whole audio.
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ret.append([audio_id, audio_duration, wav_file])
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return ret
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def generate_csv(wav_files,
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output_file: str,
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base_path: str,
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split_chunks: bool=True):
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print(f'Generating csv: {output_file}')
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header = ["id", "duration", "wav"]
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csv_lines = []
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for item in tqdm.tqdm(wav_files):
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csv_lines.extend(
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get_audio_info(
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item, base_path=base_path, split_chunks=split_chunks))
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if not os.path.exists(os.path.dirname(output_file)):
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os.makedirs(os.path.dirname(output_file))
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with open(output_file, mode="w") as csv_f:
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csv_writer = csv.writer(
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csv_f, delimiter=",", quotechar='"', quoting=csv.QUOTE_MINIMAL)
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csv_writer.writerow(header)
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for line in csv_lines:
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csv_writer.writerow(line)
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def prepare_data(args, config):
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# stage0: set the cpu device,
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# all data prepare process will be done in cpu mode
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paddle.device.set_device("cpu")
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# set the random seed, it is a must for multiprocess training
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seed_everything(config.seed)
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# if external config set the skip_prep flat, we will do nothing
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if config.skip_prep:
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return
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base_path = args.noise_dir
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wav_path = os.path.join(base_path, "RIRS_NOISES")
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logger.info(f"base path: {base_path}")
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logger.info(f"wav path: {wav_path}")
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rir_list = os.path.join(wav_path, "real_rirs_isotropic_noises", "rir_list")
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rir_files = []
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with open(rir_list, 'r') as f:
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for line in f.readlines():
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rir_file = line.strip().split(' ')[-1]
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rir_files.append(os.path.join(base_path, rir_file))
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noise_list = os.path.join(wav_path, "pointsource_noises", "noise_list")
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noise_files = []
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with open(noise_list, 'r') as f:
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for line in f.readlines():
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noise_file = line.strip().split(' ')[-1]
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noise_files.append(os.path.join(base_path, noise_file))
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csv_path = os.path.join(args.data_dir, 'csv')
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generate_csv(
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rir_files, os.path.join(csv_path, 'rir.csv'), base_path=base_path)
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generate_csv(
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noise_files, os.path.join(csv_path, 'noise.csv'), base_path=base_path)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument(
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"--noise_dir",
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default=None,
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required=True,
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help="The noise dataset dataset directory.")
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parser.add_argument(
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"--data_dir",
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default=None,
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required=True,
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help="The target directory stores the csv files")
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parser.add_argument(
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"--config",
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default=None,
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required=True,
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type=str,
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help="configuration file")
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args = parser.parse_args()
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# parse the yaml config file
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config = CfgNode(new_allowed=True)
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if args.config:
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config.merge_from_file(args.config)
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# prepare the csv file from jsonlines files
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prepare_data(args, config)
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@ -0,0 +1,262 @@
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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.
|
||||
# See the License for the specific language governing permissions and
|
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# limitations under the License.
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"""
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Convert the PaddleSpeech jsonline format data to csv format data in voxceleb experiment.
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Currently, Speaker Identificaton Training process use csv format.
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"""
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import argparse
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import csv
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import json
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import os
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import random
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import paddle
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import tqdm
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from yacs.config import CfgNode
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from paddleaudio import load as load_audio
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from paddlespeech.s2t.utils.log import Log
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from paddlespeech.vector.training.seeding import seed_everything
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logger = Log(__name__).getlog()
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def get_chunks(seg_dur, audio_id, audio_duration):
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"""Get all chunk segments from a utterance
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Args:
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seg_dur (float): segment chunk duration
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audio_id (str): utterance name
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audio_duration (float): utterance duration
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Returns:
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List: all the chunk segments
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"""
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num_chunks = int(audio_duration / seg_dur) # all in milliseconds
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chunk_lst = [
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audio_id + "_" + str(i * seg_dur) + "_" + str(i * seg_dur + seg_dur)
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for i in range(num_chunks)
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]
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return chunk_lst
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def prepare_csv(wav_files, output_file, config, split_chunks=True):
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"""Prepare the csv file according the wav files
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Args:
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dataset_list (list): all the dataset to get the test utterances
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verification_file (str): voxceleb1 trial file
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"""
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if not os.path.exists(os.path.dirname(output_file)):
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os.makedirs(os.path.dirname(output_file))
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csv_lines = []
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header = ["id", "duration", "wav", "start", "stop", "spk_id"]
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# voxceleb meta info for each training utterance segment
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# we extract a segment from a utterance to train
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# and the segment' period is between start and stop time point in the original wav file
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# each field in the meta means as follows:
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# 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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# spk_id: the utterance segment's speaker name
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for item in tqdm.tqdm(wav_files, total=len(wav_files)):
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item = json.loads(item.strip())
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audio_id = item['utt'].replace(".wav", "")
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audio_duration = item['feat_shape'][0]
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wav_file = item['feat']
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spk_id = audio_id.split('-')[0]
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waveform, sr = load_audio(wav_file)
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if split_chunks:
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uniq_chunks_list = get_chunks(config.chunk_duration, audio_id,
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audio_duration)
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for chunk in uniq_chunks_list:
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s, e = chunk.split("_")[-2:] # Timestamps of start and end
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start_sample = int(float(s) * sr)
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end_sample = int(float(e) * sr)
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# id, duration, wav, start, stop, spk_id
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csv_lines.append([
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chunk, audio_duration, wav_file, start_sample, end_sample,
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spk_id
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])
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else:
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csv_lines.append([
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audio_id, audio_duration, wav_file, 0, waveform.shape[0], spk_id
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])
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with open(output_file, mode="w") as csv_f:
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csv_writer = csv.writer(
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csv_f, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
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csv_writer.writerow(header)
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for line in csv_lines:
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csv_writer.writerow(line)
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def get_enroll_test_list(dataset_list, verification_file):
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"""Get the enroll and test utterance list from all the voxceleb1 test utterance dataset.
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Generally, we get the enroll and test utterances from the verfification file.
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The verification file format as follows:
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target/nontarget enroll-utt test-utt,
|
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we set 0 as nontarget and 1 as target, eg:
|
||||
0 a.wav b.wav
|
||||
1 a.wav a.wav
|
||||
|
||||
Args:
|
||||
dataset_list (list): all the dataset to get the test utterances
|
||||
verification_file (str): voxceleb1 trial file
|
||||
"""
|
||||
logger.info(f"verification file: {verification_file}")
|
||||
enroll_audios = set()
|
||||
test_audios = set()
|
||||
with open(verification_file, 'r') as f:
|
||||
for line in f:
|
||||
_, enroll_file, test_file = line.strip().split(' ')
|
||||
enroll_audios.add('-'.join(enroll_file.split('/')))
|
||||
test_audios.add('-'.join(test_file.split('/')))
|
||||
|
||||
enroll_files = []
|
||||
test_files = []
|
||||
for dataset in dataset_list:
|
||||
with open(dataset, 'r') as f:
|
||||
for line in f:
|
||||
audio_id = json.loads(line.strip())['utt']
|
||||
if audio_id in enroll_audios:
|
||||
enroll_files.append(line)
|
||||
if audio_id in test_audios:
|
||||
test_files.append(line)
|
||||
|
||||
enroll_files = sorted(enroll_files)
|
||||
test_files = sorted(test_files)
|
||||
|
||||
return enroll_files, test_files
|
||||
|
||||
|
||||
def get_train_dev_list(dataset_list, target_dir, split_ratio):
|
||||
"""Get the train and dev utterance list from all the training utterance dataset.
|
||||
Generally, we use the split_ratio as the train dataset ratio,
|
||||
and the remaining utterance (ratio is 1 - split_ratio) is the dev dataset
|
||||
|
||||
Args:
|
||||
dataset_list (list): all the dataset to get the all utterances
|
||||
target_dir (str): the target train and dev directory,
|
||||
we will create the csv directory to store the {train,dev}.csv file
|
||||
split_ratio (float): train dataset ratio in all utterance list
|
||||
"""
|
||||
logger.info("start to get train and dev utt list")
|
||||
if not os.path.exists(os.path.join(target_dir, "meta")):
|
||||
os.makedirs(os.path.join(target_dir, "meta"))
|
||||
|
||||
audio_files = []
|
||||
speakers = set()
|
||||
for dataset in dataset_list:
|
||||
with open(dataset, 'r') as f:
|
||||
for line in f:
|
||||
spk_id = json.loads(line.strip())['utt2spk']
|
||||
speakers.add(spk_id)
|
||||
audio_files.append(line.strip())
|
||||
speakers = sorted(speakers)
|
||||
logger.info(f"we get {len(speakers)} speakers from all the train dataset")
|
||||
|
||||
with open(os.path.join(target_dir, "meta", "spk_id2label.txt"), 'w') as f:
|
||||
for label, spk_id in enumerate(speakers):
|
||||
f.write(f'{spk_id} {label}\n')
|
||||
logger.info(
|
||||
f'we store the speakers to {os.path.join(target_dir, "meta", "spk_id2label.txt")}'
|
||||
)
|
||||
|
||||
# the split_ratio is for train dataset
|
||||
# the remaining is for dev dataset
|
||||
split_idx = int(split_ratio * len(audio_files))
|
||||
audio_files = sorted(audio_files)
|
||||
random.shuffle(audio_files)
|
||||
train_files, dev_files = audio_files[:split_idx], audio_files[split_idx:]
|
||||
logger.info(
|
||||
f"we get train utterances: {len(train_files)}, dev utterance: {len(dev_files)}"
|
||||
)
|
||||
return train_files, dev_files
|
||||
|
||||
|
||||
def prepare_data(args, config):
|
||||
"""Convert the jsonline format to csv format
|
||||
|
||||
Args:
|
||||
args (argparse.Namespace): scripts args
|
||||
config (CfgNode): yaml configuration content
|
||||
"""
|
||||
# stage0: set the cpu device,
|
||||
# all data prepare process will be done in cpu mode
|
||||
paddle.device.set_device("cpu")
|
||||
# set the random seed, it is a must for multiprocess training
|
||||
seed_everything(config.seed)
|
||||
# if external config set the skip_prep flat, we will do nothing
|
||||
if config.skip_prep:
|
||||
return
|
||||
|
||||
# stage 1: prepare the enroll and test csv file
|
||||
# And we generate the speaker to label file spk_id2label.txt
|
||||
logger.info("start to prepare the data csv file")
|
||||
enroll_files, test_files = get_enroll_test_list(
|
||||
[args.test], verification_file=config.verification_file)
|
||||
prepare_csv(
|
||||
enroll_files,
|
||||
os.path.join(args.target_dir, "csv", "enroll.csv"),
|
||||
config,
|
||||
split_chunks=False)
|
||||
prepare_csv(
|
||||
test_files,
|
||||
os.path.join(args.target_dir, "csv", "test.csv"),
|
||||
config,
|
||||
split_chunks=False)
|
||||
|
||||
# stage 2: prepare the train and dev csv file
|
||||
# we get the train dataset ratio as config.split_ratio
|
||||
# and the remaining is dev dataset
|
||||
logger.info("start to prepare the data csv file")
|
||||
train_files, dev_files = get_train_dev_list(
|
||||
args.train, target_dir=args.target_dir, split_ratio=config.split_ratio)
|
||||
prepare_csv(train_files,
|
||||
os.path.join(args.target_dir, "csv", "train.csv"), config)
|
||||
prepare_csv(dev_files,
|
||||
os.path.join(args.target_dir, "csv", "dev.csv"), config)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument(
|
||||
"--train",
|
||||
required=True,
|
||||
nargs='+',
|
||||
help="The jsonline files list for train.")
|
||||
parser.add_argument(
|
||||
"--test", required=True, help="The jsonline file for test")
|
||||
parser.add_argument(
|
||||
"--target_dir",
|
||||
default=None,
|
||||
required=True,
|
||||
help="The target directory stores the csv files and meta file.")
|
||||
parser.add_argument(
|
||||
"--config",
|
||||
default=None,
|
||||
required=True,
|
||||
type=str,
|
||||
help="configuration file")
|
||||
args = parser.parse_args()
|
||||
|
||||
# parse the yaml config file
|
||||
config = CfgNode(new_allowed=True)
|
||||
if args.config:
|
||||
config.merge_from_file(args.config)
|
||||
|
||||
# prepare the csv file from jsonlines files
|
||||
prepare_data(args, config)
|
||||
Loading…
Reference in new issue