# Copyright (c) 2021 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. """Normalize feature files and dump them.""" import argparse import logging from operator import itemgetter from pathlib import Path from typing import List import jsonlines import numpy as np from sklearn.preprocessing import StandardScaler from tqdm import tqdm from paddlespeech.t2s.datasets.data_table import DataTable from paddlespeech.t2s.utils import str2bool INITIALS = [ 'b', 'p', 'm', 'f', 'd', 't', 'n', 'l', 'g', 'k', 'h', 'zh', 'ch', 'sh', 'r', 'z', 'c', 's', 'j', 'q', 'x' ] INITIALS += ['y', 'w', 'sp', 'spl', 'spn', 'sil'] def intersperse(lst, item): result = [item] * (len(lst) * 2 + 1) result[1::2] = lst return result def insert_after_character(lst, item): result = [item] for phone in lst: result.append(phone) if phone not in INITIALS: # finals has tones assert phone[-1] in "12345" result.append(item) return result def add_blank(phones: List[str], filed: str="character", blank_token: str=""): if filed == "phone": """ add blank after phones input: ["n", "i3", "h", "ao3", "m", "a5"] output: ["n", "", "i3", "", "h", "", "ao3", "", "m", "", "a5"] """ phones = intersperse(phones, blank_token) elif filed == "character": """ add blank after characters input: ["n", "i3", "h", "ao3"] output: ["n", "i3", "", "h", "ao3", "", "m", "a5"] """ phones = insert_after_character(phones, blank_token) return phones def main(): """Run preprocessing process.""" parser = argparse.ArgumentParser( description="Normalize dumped raw features (See detail in parallel_wavegan/bin/normalize.py)." ) parser.add_argument( "--metadata", type=str, required=True, help="directory including feature files to be normalized. " "you need to specify either *-scp or rootdir.") parser.add_argument( "--dumpdir", type=str, required=True, help="directory to dump normalized feature files.") parser.add_argument( "--feats-stats", type=str, required=True, help="speech statistics file.") parser.add_argument( "--skip-wav-copy", default=False, action="store_true", help="whether to skip the copy of wav files.") parser.add_argument( "--phones-dict", type=str, default=None, help="phone vocabulary file.") parser.add_argument( "--speaker-dict", type=str, default=None, help="speaker id map file.") parser.add_argument( "--add-blank", type=str2bool, default=True, help="whether to add blank between phones") args = parser.parse_args() dumpdir = Path(args.dumpdir).expanduser() # use absolute path dumpdir = dumpdir.resolve() dumpdir.mkdir(parents=True, exist_ok=True) # get dataset with jsonlines.open(args.metadata, 'r') as reader: metadata = list(reader) dataset = DataTable( metadata, converters={ "feats": np.load, "wave": None if args.skip_wav_copy else np.load, }) logging.info(f"The number of files = {len(dataset)}.") # restore scaler feats_scaler = StandardScaler() feats_scaler.mean_ = np.load(args.feats_stats)[0] feats_scaler.scale_ = np.load(args.feats_stats)[1] feats_scaler.n_features_in_ = feats_scaler.mean_.shape[0] vocab_phones = {} with open(args.phones_dict, 'rt') as f: phn_id = [line.strip().split() for line in f.readlines()] for phn, id in phn_id: vocab_phones[phn] = int(id) vocab_speaker = {} with open(args.speaker_dict, 'rt') as f: spk_id = [line.strip().split() for line in f.readlines()] for spk, id in spk_id: vocab_speaker[spk] = int(id) # process each file output_metadata = [] for item in tqdm(dataset): utt_id = item['utt_id'] feats = item['feats'] wave = item['wave'] # normalize feats = feats_scaler.transform(feats) feats_path = dumpdir / f"{utt_id}_feats.npy" np.save(feats_path, feats.astype(np.float32), allow_pickle=False) if not args.skip_wav_copy: wav_path = dumpdir / f"{utt_id}_wave.npy" np.save(wav_path, wave.astype(np.float32), allow_pickle=False) else: wav_path = wave phones = item['phones'] text_lengths = item['text_lengths'] if args.add_blank: phones = add_blank(phones, filed="character") text_lengths = len(phones) phone_ids = [vocab_phones[p] for p in phones] spk_id = vocab_speaker[item["speaker"]] record = { "utt_id": item['utt_id'], "text": phone_ids, "text_lengths": text_lengths, 'feats': str(feats_path), "feats_lengths": item['feats_lengths'], "wave": str(wav_path), "spk_id": spk_id, } # add spk_emb for voice cloning if "spk_emb" in item: record["spk_emb"] = str(item["spk_emb"]) output_metadata.append(record) output_metadata.sort(key=itemgetter('feats_lengths')) output_metadata_path = Path(args.dumpdir) / "metadata.jsonl" with jsonlines.open(output_metadata_path, 'w') as writer: for item in output_metadata: writer.write(item) logging.info(f"metadata dumped into {output_metadata_path}") if __name__ == "__main__": main()