xiongxinlei
2 years ago
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b75268c588
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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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