# 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_samples = y.shape[0]
mel_path = output_dir / (utt_id + "_feats.npy")
wav_path = output_dir / (utt_id + "_wave.npy")
# (num_samples, )
np.save(wav_path, y)
# (num_frames, n_mels)
np.save(mel_path, logmel)
record = {
"utt_id": utt_id,
"num_samples": num_samples,
"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=config,
fp=fp,
sentences=sentences,
output_dir=output_dir,
mel_extractor=mel_extractor,
cut_sil=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, aishell3, 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=config,
fps=train_wav_files,
sentences=sentences,
output_dir=train_dump_dir,
mel_extractor=mel_extractor,
nprocs=args.num_cpu,
cut_sil=args.cut_sil)
if dev_wav_files:
process_sentences(
config=config,
fps=dev_wav_files,
sentences=sentences,
output_dir=dev_dump_dir,
mel_extractor=mel_extractor,
nprocs=args.num_cpu,
cut_sil=args.cut_sil)
if test_wav_files:
process_sentences(
config=config,
fps=test_wav_files,
sentences=sentences,
output_dir=test_dump_dir,
mel_extractor=mel_extractor,
nprocs=args.num_cpu,
cut_sil=args.cut_sil)
if __name__ == "__main__":
main()