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PaddleSpeech/paddlespeech/t2s/exps/speedyspeech/preprocess.py

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# 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 re
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 compare_duration_and_mel_length
from paddlespeech.t2s.datasets.preprocess_utils import get_phn_dur
from paddlespeech.t2s.datasets.preprocess_utils import get_phones_tones
from paddlespeech.t2s.datasets.preprocess_utils import get_spk_id_map
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
record = None
if utt_id in sentences:
# reading, resampling may occur
wav, _ = librosa.load(str(fp), sr=config.fs)
if len(wav.shape) != 1 or np.abs(wav).max() > 1.0:
return record
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."
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)
wav = wav[start:end]
# extract mel feats
logmel = mel_extractor.get_log_mel_fbank(wav)
# change duration according to mel_length
compare_duration_and_mel_length(sentences, utt_id, logmel)
# utt_id may be popped in compare_duration_and_mel_length
if utt_id not in sentences:
return None
labels = sentences[utt_id][0]
# extract phone and duration
phones = []
tones = []
for label in labels:
# split tone from finals
match = re.match(r'^(\w+)([012345])$', label)
if match:
phones.append(match.group(1))
tones.append(match.group(2))
else:
phones.append(label)
tones.append('0')
durations = sentences[utt_id][1]
num_frames = logmel.shape[0]
assert sum(durations) == num_frames
assert len(phones) == len(tones) == len(durations)
mel_path = output_dir / (utt_id + "_feats.npy")
np.save(mel_path, logmel) # (num_frames, n_mels)
record = {
"utt_id": utt_id,
"phones": phones,
"tones": tones,
"speaker": speaker,
"num_phones": len(phones),
"num_frames": num_frames,
"durations": durations,
"feats": str(mel_path), # Path object
}
return record
def process_sentences(config,
fps: List[Path],
sentences: Dict,
output_dir: Path,
mel_extractor=None,
nprocs: int=1,
cut_sil: bool=True,
use_relative_path: bool=False):
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"))
output_dir = Path(output_dir)
metadata_path = output_dir / "metadata.jsonl"
# NOTE: use relative path to the meta jsonlines file for Full Chain Project
with jsonlines.open(metadata_path, 'w') as writer:
for item in results:
if use_relative_path:
item["feats"] = str(Path(item["feats"]).relative_to(output_dir))
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} 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(
"--dur-file",
default=None,
type=str,
help="path to baker durations.txt.")
parser.add_argument("--config", type=str, help="fastspeech2 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(
"--cut-sil",
type=str2bool,
default=True,
help="whether cut sil in the edge of audio")
parser.add_argument(
"--use-relative-path",
type=str2bool,
default=False,
help="whether use relative path in metadata")
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)
phone_id_map_path = dumpdir / "phone_id_map.txt"
tone_id_map_path = dumpdir / "tone_id_map.txt"
get_phones_tones(sentences, phone_id_map_path, tone_id_map_path,
args.dataset)
speaker_id_map_path = dumpdir / "speaker_id_map.txt"
get_spk_id_map(speaker_set, speaker_id_map_path)
if args.dataset == "baker":
wav_files = sorted(list((rootdir / "Wave").rglob("*.wav")))
# split data into 3 sections
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:]
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)
# process for the 3 sections
if train_wav_files:
process_sentences(
config,
train_wav_files,
sentences,
train_dump_dir,
mel_extractor,
nprocs=args.num_cpu,
cut_sil=args.cut_sil,
use_relative_path=args.use_relative_path)
if dev_wav_files:
process_sentences(
config,
dev_wav_files,
sentences,
dev_dump_dir,
mel_extractor,
cut_sil=args.cut_sil,
use_relative_path=args.use_relative_path)
if test_wav_files:
process_sentences(
config,
test_wav_files,
sentences,
test_dump_dir,
mel_extractor,
nprocs=args.num_cpu,
cut_sil=args.cut_sil,
use_relative_path=args.use_relative_path)
if __name__ == "__main__":
main()