# 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.
# generate mels using durations.txt
# for mb melgan finetune
import argparse
import os
from pathlib import Path
import numpy as np
import paddle
import yaml
from tqdm import tqdm
from yacs.config import CfgNode
from paddlespeech.t2s.datasets.preprocess_utils import get_phn_dur
from paddlespeech.t2s.datasets.preprocess_utils import merge_silence
from paddlespeech.t2s.models.fastspeech2 import FastSpeech2
from paddlespeech.t2s.models.fastspeech2 import StyleFastSpeech2Inference
from paddlespeech.t2s.modules.normalizer import ZScore
from paddlespeech.t2s.utils import str2bool
[docs]def evaluate(args, fastspeech2_config):
rootdir = Path(args.rootdir).expanduser()
assert rootdir.is_dir()
# construct dataset for evaluation
with open(args.phones_dict, "r") as f:
phn_id = [line.strip().split() for line in f.readlines()]
vocab_size = len(phn_id)
print("vocab_size:", vocab_size)
phone_dict = {}
for phn, id in phn_id:
phone_dict[phn] = int(id)
if args.speaker_dict:
with open(args.speaker_dict, 'rt') as f:
spk_id_list = [line.strip().split() for line in f.readlines()]
spk_num = len(spk_id_list)
else:
spk_num = None
odim = fastspeech2_config.n_mels
model = FastSpeech2(
idim=vocab_size,
odim=odim,
**fastspeech2_config["model"],
spk_num=spk_num)
model.set_state_dict(
paddle.load(args.fastspeech2_checkpoint)["main_params"])
model.eval()
stat = np.load(args.fastspeech2_stat)
mu, std = stat
mu = paddle.to_tensor(mu)
std = paddle.to_tensor(std)
fastspeech2_normalizer = ZScore(mu, std)
fastspeech2_inference = StyleFastSpeech2Inference(fastspeech2_normalizer,
model)
fastspeech2_inference.eval()
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
sentences, speaker_set = get_phn_dur(args.dur_file)
merge_silence(sentences)
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:]
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
train_wav_files = [
os.path.basename(str(str_path)) for str_path in train_wav_files
]
dev_wav_files = [
os.path.basename(str(str_path)) for str_path in dev_wav_files
]
test_wav_files = [
os.path.basename(str(str_path)) for str_path in test_wav_files
]
for i, utt_id in enumerate(tqdm(sentences)):
phones = sentences[utt_id][0]
durations = sentences[utt_id][1]
speaker = sentences[utt_id][2]
# 裁剪掉开头和结尾的 sil
if args.cut_sil:
if phones[0] == "sil" and len(durations) > 1:
durations = durations[1:]
phones = phones[1:]
if phones[-1] == 'sil' and len(durations) > 1:
durations = durations[:-1]
phones = phones[:-1]
# sentences[utt_id][0] = phones
# sentences[utt_id][1] = durations
phone_ids = [phone_dict[phn] for phn in phones]
phone_ids = paddle.to_tensor(np.array(phone_ids))
if args.speaker_dict:
speaker_id = int(
[item[1] for item in spk_id_list if speaker == item[0]][0])
speaker_id = paddle.to_tensor(speaker_id)
else:
speaker_id = None
durations = paddle.to_tensor(np.array(durations))
# 生成的和真实的可能有 1, 2 帧的差距，但是 batch_fn 会修复
# split data into 3 sections
wav_path = utt_id + ".wav"
if wav_path in train_wav_files:
sub_output_dir = output_dir / ("train/raw")
elif wav_path in dev_wav_files:
sub_output_dir = output_dir / ("dev/raw")
elif wav_path in test_wav_files:
sub_output_dir = output_dir / ("test/raw")
sub_output_dir.mkdir(parents=True, exist_ok=True)
with paddle.no_grad():
mel = fastspeech2_inference(
phone_ids, durations=durations, spk_id=speaker_id)
np.save(sub_output_dir / (utt_id + "_feats.npy"), mel)
[docs]def main():
# parse args and config and redirect to train_sp
parser = argparse.ArgumentParser(
description="Synthesize with fastspeech2 & parallel wavegan.")
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(
"--fastspeech2-config", type=str, help="fastspeech2 config file.")
parser.add_argument(
"--fastspeech2-checkpoint",
type=str,
help="fastspeech2 checkpoint to load.")
parser.add_argument(
"--fastspeech2-stat",
type=str,
help="mean and standard deviation used to normalize spectrogram when training fastspeech2."
)
parser.add_argument(
"--phones-dict",
type=str,
default="phone_id_map.txt",
help="phone vocabulary file.")
parser.add_argument(
"--speaker-dict", type=str, default=None, help="speaker id map file.")
parser.add_argument(
"--dur-file", default=None, type=str, help="path to durations.txt.")
parser.add_argument("--output-dir", type=str, help="output dir.")
parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
parser.add_argument(
"--cut-sil",
type=str2bool,
default=True,
help="whether cut sil in the edge of audio")
args = parser.parse_args()
if args.ngpu == 0:
paddle.set_device("cpu")
elif args.ngpu > 0:
paddle.set_device("gpu")
else:
print("ngpu should >= 0 !")
with open(args.fastspeech2_config) as f:
fastspeech2_config = CfgNode(yaml.safe_load(f))
print("========Args========")
print(yaml.safe_dump(vars(args)))
print("========Config========")
print(fastspeech2_config)
evaluate(args, fastspeech2_config)
if __name__ == "__main__":
main()