# 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. 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_phn_dur from paddlespeech.t2s.datasets.preprocess_utils import merge_silence from paddlespeech.t2s.utils import str2bool def process_sentence(config: Dict[str, Any], fp: Path, sentences: Dict, output_dir: Path, mel_extractor=None, cut_sil: bool=True): utt_id = fp.stem # for vctk if utt_id.endswith("_mic2"): utt_id = utt_id[:-5] record = None if utt_id in sentences: # reading, resampling may occur y, _ = librosa.load(str(fp), sr=config.fs) if len(y.shape) != 1: return record max_value = np.abs(y).max() if max_value > 1.0: y = y / max_value assert len(y.shape) == 1, f"{utt_id} is not a mono-channel audio." assert np.abs(y).max( ) <= 1.0, f"{utt_id} is seems to be different that 16 bit PCM." phones = sentences[utt_id][0] durations = sentences[utt_id][1] speaker = sentences[utt_id][2] d_cumsum = np.pad(np.array(durations).cumsum(0), (1, 0), 'constant') # little imprecise than use *.TextGrid directly times = librosa.frames_to_time( d_cumsum, sr=config.fs, hop_length=config.n_shift) if cut_sil: start = 0 end = d_cumsum[-1] if phones[0] == "sil" and len(durations) > 1: start = times[1] durations = durations[1:] phones = phones[1:] if phones[-1] == 'sil' and len(durations) > 1: end = times[-2] durations = durations[:-1] phones = phones[:-1] sentences[utt_id][0] = phones sentences[utt_id][1] = durations start, end = librosa.time_to_samples([start, end], sr=config.fs) y = y[start:end] # extract mel feats logmel = mel_extractor.get_log_mel_fbank(y) # adjust time to make num_samples == num_frames * hop_length num_frames = logmel.shape[0] if y.size < num_frames * config.n_shift: y = np.pad( y, (0, num_frames * config.n_shift - y.size), mode="reflect") else: y = y[:num_frames * config.n_shift] num_sample = y.shape[0] mel_path = output_dir / (utt_id + "_feats.npy") wav_path = output_dir / (utt_id + "_wave.npy") np.save(wav_path, y) # (num_samples, ) np.save(mel_path, logmel) # (num_frames, n_mels) record = { "utt_id": utt_id, "num_samples": num_sample, "num_frames": num_frames, "feats": str(mel_path), "wave": str(wav_path), } return record def process_sentences(config, fps: List[Path], sentences: Dict, output_dir: Path, mel_extractor=None, nprocs: int=1, cut_sil: bool=True): if nprocs == 1: results = [] for fp in tqdm.tqdm(fps, total=len(fps)): record = process_sentence(config, fp, sentences, output_dir, mel_extractor, cut_sil) 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, sentences, output_dir, mel_extractor, cut_sil) 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="baker", type=str, help="name of dataset, should in {baker, ljspeech, 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="vocoder config file.") parser.add_argument( "--verbose", type=int, default=1, help="logging level. higher is more logging. (default=1)") parser.add_argument( "--num-cpu", type=int, default=1, help="number of process.") parser.add_argument( "--dur-file", default=None, type=str, help="path to durations.txt.") parser.add_argument( "--cut-sil", type=str2bool, default=True, help="whether cut sil in the edge of audio") 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) dur_file = Path(args.dur_file).expanduser() assert rootdir.is_dir() assert dur_file.is_file() with open(args.config, 'rt') as f: config = CfgNode(yaml.safe_load(f)) if args.verbose > 1: print(vars(args)) print(config) sentences, speaker_set = get_phn_dur(dur_file) merge_silence(sentences) # split data into 3 sections if args.dataset == "baker": wav_files = sorted(list((rootdir / "Wave").rglob("*.wav"))) num_train = 9800 num_dev = 100 train_wav_files = wav_files[:num_train] dev_wav_files = wav_files[num_train:num_train + num_dev] test_wav_files = wav_files[num_train + num_dev:] elif args.dataset == "ljspeech": wav_files = sorted(list((rootdir / "wavs").rglob("*.wav"))) # split data into 3 sections num_train = 12900 num_dev = 100 train_wav_files = wav_files[:num_train] dev_wav_files = wav_files[num_train:num_train + num_dev] test_wav_files = wav_files[num_train + num_dev:] elif args.dataset == "vctk": sub_num_dev = 5 wav_dir = rootdir / "wav48_silence_trimmed" train_wav_files = [] dev_wav_files = [] test_wav_files = [] 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 elif args.dataset == "aishell3": sub_num_dev = 5 wav_dir = rootdir / "train" / "wav" train_wav_files = [] dev_wav_files = [] test_wav_files = [] for speaker in os.listdir(wav_dir): wav_files = sorted(list((wav_dir / speaker).rglob("*.wav"))) 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 {baker, ljspeech, vctk, aishell3} 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) 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) # process for the 3 sections if train_wav_files: process_sentences( config, train_wav_files, sentences, train_dump_dir, mel_extractor=mel_extractor, nprocs=args.num_cpu, cut_sil=args.cut_sil) if dev_wav_files: process_sentences( config, dev_wav_files, sentences, dev_dump_dir, mel_extractor=mel_extractor, nprocs=args.num_cpu, cut_sil=args.cut_sil) if test_wav_files: process_sentences( config, test_wav_files, sentences, test_dump_dir, mel_extractor=mel_extractor, nprocs=args.num_cpu, cut_sil=args.cut_sil) if __name__ == "__main__": main()