# Copyright (c) 2023 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. import argparse import os from concurrent.futures import ThreadPoolExecutor from operator import itemgetter from pathlib import Path from typing import Any from typing import Dict from typing import List import jsonlines import librosa import numpy as np import tqdm import yaml from yacs.config import CfgNode from paddlespeech.t2s.datasets.get_feats import LogMelFBank from paddlespeech.t2s.datasets.preprocess_utils import get_spk_id_map speaker_set = set() def process_sentence(config: Dict[str, Any], fp: Path, output_dir: Path, mel_extractor=None): utt_id = fp.stem # for vctk if utt_id.endswith("_mic2"): utt_id = utt_id[:-5] speaker = utt_id.split('_')[0] speaker_set.add(speaker) # 需要额外获取 speaker record = None # reading, resampling may occur # 源码的 bug, 读取的时候按照 24000 读取，但是提取 mel 的时候按照 16000 提取 # 具体参考 https://github.com/PaddlePaddle/PaddleSpeech/blob/c7d24ba42c377fe4c0765c6b1faa202a9aeb136f/paddlespeech/t2s/exps/starganv2_vc/vc.py#L165 # 之后需要换成按照 24000 读取和按照 24000 提取 mel wav, _ = librosa.load(str(fp), sr=24000) max_value = np.abs(wav).max() if max_value > 1.0: wav = wav / max_value assert len(wav.shape) == 1, f"{utt_id} is not a mono-channel audio." assert np.abs( wav).max() <= 1.0, f"{utt_id} is seems to be different that 16 bit PCM." # extract mel feats # 注意这里 base = 'e', 后续需要换成 base='10', 我们其他 TTS 模型都是 base='10' logmel = mel_extractor.get_log_mel_fbank(wav, base='e') mel_path = output_dir / (utt_id + "_speech.npy") np.save(mel_path, logmel) record = {"utt_id": utt_id, "speech": str(mel_path), "speaker": speaker} return record def process_sentences( config, fps: List[Path], output_dir: Path, mel_extractor=None, nprocs: int=1, ): if nprocs == 1: results = [] for fp in tqdm.tqdm(fps, total=len(fps)): record = process_sentence( config=config, fp=fp, output_dir=output_dir, mel_extractor=mel_extractor) if record: results.append(record) else: with ThreadPoolExecutor(nprocs) as pool: futures = [] with tqdm.tqdm(total=len(fps)) as progress: for fp in fps: future = pool.submit(process_sentence, config, fp, output_dir, mel_extractor) future.add_done_callback(lambda p: progress.update()) futures.append(future) results = [] for ft in futures: record = ft.result() if record: results.append(record) results.sort(key=itemgetter("utt_id")) with jsonlines.open(output_dir / "metadata.jsonl", 'w') as writer: for item in results: writer.write(item) print("Done") def main(): # parse config and args parser = argparse.ArgumentParser( description="Preprocess audio and then extract features.") parser.add_argument( "--dataset", default="vctk", type=str, help="name of dataset, should in {vctk} now") parser.add_argument( "--rootdir", default=None, type=str, help="directory to dataset.") parser.add_argument( "--dumpdir", type=str, required=True, help="directory to dump feature files.") parser.add_argument("--config", type=str, help="StarGANv2VC config file.") parser.add_argument( "--num-cpu", type=int, default=1, help="number of process.") args = parser.parse_args() rootdir = Path(args.rootdir).expanduser() dumpdir = Path(args.dumpdir).expanduser() # use absolute path dumpdir = dumpdir.resolve() dumpdir.mkdir(parents=True, exist_ok=True) assert rootdir.is_dir() with open(args.config, 'rt') as f: config = CfgNode(yaml.safe_load(f)) if args.dataset == "vctk": sub_num_dev = 5 wav_dir = rootdir / "wav48_silence_trimmed" train_wav_files = [] dev_wav_files = [] test_wav_files = [] # only for test for speaker in os.listdir(wav_dir): wav_files = sorted(list((wav_dir / speaker).rglob("*_mic2.flac"))) if len(wav_files) > 100: train_wav_files += wav_files[:-sub_num_dev * 2] dev_wav_files += wav_files[-sub_num_dev * 2:-sub_num_dev] test_wav_files += wav_files[-sub_num_dev:] else: train_wav_files += wav_files else: print("dataset should in {vctk} now!") train_dump_dir = dumpdir / "train" / "raw" train_dump_dir.mkdir(parents=True, exist_ok=True) dev_dump_dir = dumpdir / "dev" / "raw" dev_dump_dir.mkdir(parents=True, exist_ok=True) test_dump_dir = dumpdir / "test" / "raw" test_dump_dir.mkdir(parents=True, exist_ok=True) # Extractor mel_extractor = LogMelFBank( sr=config.fs, n_fft=config.n_fft, hop_length=config.n_shift, win_length=config.win_length, window=config.window, n_mels=config.n_mels, fmin=config.fmin, fmax=config.fmax, # None here norm=config.norm, htk=config.htk, power=config.power) # process for the 3 sections if train_wav_files: process_sentences( config=config, fps=train_wav_files, output_dir=train_dump_dir, mel_extractor=mel_extractor, nprocs=args.num_cpu) if dev_wav_files: process_sentences( config=config, fps=dev_wav_files, output_dir=dev_dump_dir, mel_extractor=mel_extractor, nprocs=args.num_cpu) if test_wav_files: process_sentences( config=config, fps=test_wav_files, output_dir=test_dump_dir, mel_extractor=mel_extractor, nprocs=args.num_cpu) speaker_id_map_path = dumpdir / "speaker_id_map.txt" get_spk_id_map(speaker_set, speaker_id_map_path) if __name__ == "__main__": main()