Merge pull request #1302 from jerryuhoo/develop

[TTS] Add support for finetuning speedyspeech
pull/1322/head
TianYuan 3 years ago committed by GitHub
commit 8f507ba4ba
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@ -21,7 +21,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
python3 link_wav.py \
python3 ${MAIN_ROOT}/utils/link_wav.py \
--old-dump-dir=dump \
--dump-dir=dump_finetune
fi

@ -21,7 +21,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
python3 link_wav.py \
python3 ${MAIN_ROOT}/utils/link_wav.py \
--old-dump-dir=dump \
--dump-dir=dump_finetune
fi

@ -0,0 +1,246 @@
# 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
# 长度和原本的 mel 不一致怎么办?
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.frontend.zh_frontend import Frontend
from paddlespeech.t2s.models.speedyspeech import SpeedySpeech
from paddlespeech.t2s.models.speedyspeech import SpeedySpeechInference
from paddlespeech.t2s.modules.normalizer import ZScore
def evaluate(args, speedyspeech_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)
with open(args.tones_dict, "r") as f:
tone_id = [line.strip().split() for line in f.readlines()]
tone_size = len(tone_id)
print("tone_size:", tone_size)
frontend = Frontend(
phone_vocab_path=args.phones_dict, tone_vocab_path=args.tones_dict)
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
model = SpeedySpeech(
vocab_size=vocab_size,
tone_size=tone_size,
**speedyspeech_config["model"],
spk_num=spk_num)
model.set_state_dict(
paddle.load(args.speedyspeech_checkpoint)["main_params"])
model.eval()
stat = np.load(args.speedyspeech_stat)
mu, std = stat
mu = paddle.to_tensor(mu)
std = paddle.to_tensor(std)
speedyspeech_normalizer = ZScore(mu, std)
speedyspeech_inference = SpeedySpeechInference(speedyspeech_normalizer,
model)
speedyspeech_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]
phones, tones = frontend._get_phone_tone(phones, get_tone_ids=True)
if tones:
tone_ids = frontend._t2id(tones)
tone_ids = paddle.to_tensor(tone_ids)
if phones:
phone_ids = frontend._p2id(phones)
phone_ids = paddle.to_tensor(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))
durations = paddle.unsqueeze(durations, axis=0)
# 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 = speedyspeech_inference(
phone_ids, tone_ids, durations=durations, spk_id=speaker_id)
np.save(sub_output_dir / (utt_id + "_feats.npy"), mel)
def main():
# parse args and config and redirect to train_sp
parser = argparse.ArgumentParser(
description="Synthesize with speedyspeech & 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(
"--speedyspeech-config", type=str, help="speedyspeech config file.")
parser.add_argument(
"--speedyspeech-checkpoint",
type=str,
help="speedyspeech checkpoint to load.")
parser.add_argument(
"--speedyspeech-stat",
type=str,
help="mean and standard deviation used to normalize spectrogram when training speedyspeech."
)
parser.add_argument(
"--phones-dict",
type=str,
default="phone_id_map.txt",
help="phone vocabulary file.")
parser.add_argument(
"--tones-dict",
type=str,
default="tone_id_map.txt",
help="tone 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.")
def str2bool(str):
return True if str.lower() == 'true' else False
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.speedyspeech_config) as f:
speedyspeech_config = CfgNode(yaml.safe_load(f))
print("========Args========")
print(yaml.safe_dump(vars(args)))
print("========Config========")
print(speedyspeech_config)
evaluate(args, speedyspeech_config)
if __name__ == "__main__":
main()

@ -222,7 +222,7 @@ class SpeedySpeech(nn.Layer):
decoded = self.decoder(encodings)
return decoded, pred_durations
def inference(self, text, tones=None, spk_id=None):
def inference(self, text, tones=None, durations=None, spk_id=None):
# text: [T]
# tones: [T]
# input of embedding must be int64
@ -234,6 +234,7 @@ class SpeedySpeech(nn.Layer):
encodings = self.encoder(text, tones, spk_id)
if type(durations) == type(None):
pred_durations = self.duration_predictor(encodings) # (1, T)
durations_to_expand = paddle.round(pred_durations.exp())
durations_to_expand = (durations_to_expand).astype(paddle.int64)
@ -252,6 +253,9 @@ class SpeedySpeech(nn.Layer):
k += d
encodings = paddle.matmul(M, encodings)
else:
durations_to_expand = durations
encodings = expand(encodings, durations_to_expand)
shape = paddle.shape(encodings)
t_dec, feature_size = shape[1], shape[2]
@ -266,7 +270,8 @@ class SpeedySpeechInference(nn.Layer):
self.normalizer = normalizer
self.acoustic_model = speedyspeech_model
def forward(self, phones, tones, spk_id=None):
normalized_mel = self.acoustic_model.inference(phones, tones, spk_id)
def forward(self, phones, tones, durations=None, spk_id=None):
normalized_mel = self.acoustic_model.inference(
phones, tones, durations=durations, spk_id=spk_id)
logmel = self.normalizer.inverse(normalized_mel)
return logmel

@ -20,6 +20,7 @@ import jsonlines
import numpy as np
from tqdm import tqdm
def main():
# parse config and args
parser = argparse.ArgumentParser(
@ -58,9 +59,18 @@ def main():
mel_path = output_dir / ("raw/" + name)
gen_mel = np.load(mel_path)
wave_name = utt_id + "_wave.npy"
try:
wav = np.load(old_dump_dir / sub / ("raw/" + wave_name))
os.symlink(old_dump_dir / sub / ("raw/" + wave_name),
output_dir / ("raw/" + wave_name))
except FileNotFoundError:
print("delete " + name +
" because it cannot be found in the dump folder")
os.remove(output_dir / "raw" / name)
continue
except FileExistsError:
print("file " + name + " exists, skip.")
continue
num_sample = wav.shape[0]
num_frames = gen_mel.shape[0]
wav_path = output_dir / ("raw/" + wave_name)

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