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252 lines
9.6 KiB
252 lines
9.6 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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"""
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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 tqdm
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from paddleaudio.backends import soundfile_load as load_audio
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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.utils.vector_utils import get_chunks
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logger = Log(__name__).getlog()
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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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wav_files (list): all the audio list to prepare the csv file
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output_file (str): the output csv file
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config (CfgNode): yaml configuration content
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split_chunks (bool, optional): audio split flag. Defaults to True.
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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 = ["utt_id", "duration", "wav", "start", "stop", "label"]
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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 info means as follows:
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# utt_id: the utterance segment name, which is uniq in training dataset
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# duration: the total utterance time
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# wav: utterance file path, which should be absoulute path
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# start: start point in the original wav file sample point range
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# stop: stop point in the original wav file sample point range
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# label: the utterance segment's label name,
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# which is speaker name in speaker verification domain
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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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"") # we remove the wav suffix name
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audio_duration = item['feat_shape'][0]
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wav_file = item['feat']
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label = audio_id.split('-')[
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0] # speaker name in speaker verification domain
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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, label
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# in vector, the label in speaker 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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label
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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], label
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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:
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0 a.wav b.wav
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1 a.wav a.wav
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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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logger.info(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 dataset in dataset_list:
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with open(dataset, 'r') as f:
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for line in f:
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# audio_id may be in enroll and test at the same time
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# eg: 1 a.wav a.wav
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# the audio a.wav is enroll and test file at the same time
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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(dataset_list, target_dir, split_ratio):
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"""Get the train and dev utterance list from all the training utterance dataset.
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Generally, we use the split_ratio as the train dataset ratio,
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and the remaining utterance (ratio is 1 - split_ratio) is the dev dataset
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Args:
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dataset_list (list): all the dataset to get the all utterances
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target_dir (str): the target train and dev directory,
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we will create the csv directory to store the {train,dev}.csv file
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split_ratio (float): train dataset ratio in all utterance list
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"""
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logger.info("start to get train and dev utt list")
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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 dataset in dataset_list:
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with open(dataset, 'r') as f:
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for line in f:
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# the label is speaker name
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label_name = json.loads(line.strip())['utt2spk']
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speakers.add(label_name)
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audio_files.append(line.strip())
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speakers = sorted(speakers)
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logger.info(f"we get {len(speakers)} speakers from all the train dataset")
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with open(os.path.join(target_dir, "meta", "label2id.txt"), 'w') as f:
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for label_id, label_name in enumerate(speakers):
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f.write(f'{label_name} {label_id}\n')
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logger.info(
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f'we store the speakers to {os.path.join(target_dir, "meta", "label2id.txt")}'
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)
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# the split_ratio is for train dataset
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# the remaining is for dev dataset
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split_idx = int(split_ratio * len(audio_files))
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audio_files = sorted(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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logger.info(
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f"we get train utterances: {len(train_files)}, dev utterance: {len(dev_files)}"
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)
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return train_files, dev_files
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def prepare_data(args, config):
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"""Convert the jsonline format to csv format
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Args:
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args (argparse.Namespace): scripts args
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config (CfgNode): yaml configuration content
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"""
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# stage0: set the random seed
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random.seed(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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# stage 1: prepare the enroll and test csv file
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# And we generate the speaker to label file label2id.txt
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logger.info("start to prepare the data csv file")
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enroll_files, test_files = get_enroll_test_list(
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[args.test], verification_file=config.verification_file)
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prepare_csv(
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enroll_files,
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os.path.join(args.target_dir, "csv", "enroll.csv"),
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config,
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split_chunks=False)
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prepare_csv(
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test_files,
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os.path.join(args.target_dir, "csv", "test.csv"),
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config,
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split_chunks=False)
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# stage 2: prepare the train and dev csv file
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# we get the train dataset ratio as config.split_ratio
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# and the remaining is dev dataset
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logger.info("start to prepare the data csv file")
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train_files, dev_files = get_train_dev_list(
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args.train, target_dir=args.target_dir, split_ratio=config.split_ratio)
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prepare_csv(train_files,
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os.path.join(args.target_dir, "csv", "train.csv"), config)
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prepare_csv(dev_files,
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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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default=None,
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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(
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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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