You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
205 lines
7.0 KiB
205 lines
7.0 KiB
# 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.
|
|
# remain for chains
|
|
import argparse
|
|
import logging
|
|
import os
|
|
from pathlib import Path
|
|
|
|
import numpy as np
|
|
import paddle
|
|
import soundfile as sf
|
|
import yaml
|
|
from paddle import jit
|
|
from paddle.static import InputSpec
|
|
from yacs.config import CfgNode
|
|
|
|
from paddlespeech.t2s.frontend.zh_frontend import Frontend
|
|
from paddlespeech.t2s.models.parallel_wavegan import PWGGenerator
|
|
from paddlespeech.t2s.models.parallel_wavegan import PWGInference
|
|
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, pwg_config):
|
|
# dataloader has been too verbose
|
|
logging.getLogger("DataLoader").disabled = True
|
|
|
|
# construct dataset for evaluation
|
|
sentences = []
|
|
with open(args.text, 'rt') as f:
|
|
for line in f:
|
|
items = line.strip().split()
|
|
utt_id = items[0]
|
|
sentence = "".join(items[1:])
|
|
sentences.append((utt_id, sentence))
|
|
|
|
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)
|
|
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)
|
|
|
|
model = SpeedySpeech(
|
|
vocab_size=vocab_size,
|
|
tone_size=tone_size,
|
|
**speedyspeech_config["model"])
|
|
model.set_state_dict(
|
|
paddle.load(args.speedyspeech_checkpoint)["main_params"])
|
|
model.eval()
|
|
|
|
vocoder = PWGGenerator(**pwg_config["generator_params"])
|
|
vocoder.set_state_dict(paddle.load(args.pwg_checkpoint)["generator_params"])
|
|
vocoder.remove_weight_norm()
|
|
vocoder.eval()
|
|
print("model done!")
|
|
|
|
stat = np.load(args.speedyspeech_stat)
|
|
mu, std = stat
|
|
mu = paddle.to_tensor(mu)
|
|
std = paddle.to_tensor(std)
|
|
speedyspeech_normalizer = ZScore(mu, std)
|
|
|
|
stat = np.load(args.pwg_stat)
|
|
mu, std = stat
|
|
mu = paddle.to_tensor(mu)
|
|
std = paddle.to_tensor(std)
|
|
pwg_normalizer = ZScore(mu, std)
|
|
|
|
speedyspeech_inference = SpeedySpeechInference(speedyspeech_normalizer,
|
|
model)
|
|
speedyspeech_inference.eval()
|
|
speedyspeech_inference = jit.to_static(
|
|
speedyspeech_inference,
|
|
input_spec=[
|
|
InputSpec([-1], dtype=paddle.int64), InputSpec(
|
|
[-1], dtype=paddle.int64)
|
|
])
|
|
paddle.jit.save(speedyspeech_inference,
|
|
os.path.join(args.inference_dir, "speedyspeech"))
|
|
speedyspeech_inference = paddle.jit.load(
|
|
os.path.join(args.inference_dir, "speedyspeech"))
|
|
|
|
pwg_inference = PWGInference(pwg_normalizer, vocoder)
|
|
pwg_inference.eval()
|
|
pwg_inference = jit.to_static(
|
|
pwg_inference, input_spec=[
|
|
InputSpec([-1, 80], dtype=paddle.float32),
|
|
])
|
|
paddle.jit.save(pwg_inference, os.path.join(args.inference_dir, "pwg"))
|
|
pwg_inference = paddle.jit.load(os.path.join(args.inference_dir, "pwg"))
|
|
|
|
frontend = Frontend(
|
|
phone_vocab_path=args.phones_dict, tone_vocab_path=args.tones_dict)
|
|
print("frontend done!")
|
|
|
|
output_dir = Path(args.output_dir)
|
|
output_dir.mkdir(parents=True, exist_ok=True)
|
|
|
|
for utt_id, sentence in sentences:
|
|
input_ids = frontend.get_input_ids(
|
|
sentence, merge_sentences=True, get_tone_ids=True)
|
|
phone_ids = input_ids["phone_ids"]
|
|
tone_ids = input_ids["tone_ids"]
|
|
|
|
flags = 0
|
|
for i in range(len(phone_ids)):
|
|
part_phone_ids = phone_ids[i]
|
|
part_tone_ids = tone_ids[i]
|
|
with paddle.no_grad():
|
|
mel = speedyspeech_inference(part_phone_ids, part_tone_ids)
|
|
temp_wav = pwg_inference(mel)
|
|
if flags == 0:
|
|
wav = temp_wav
|
|
flags = 1
|
|
else:
|
|
wav = paddle.concat([wav, temp_wav])
|
|
sf.write(
|
|
output_dir / (utt_id + ".wav"),
|
|
wav.numpy(),
|
|
samplerate=speedyspeech_config.fs)
|
|
print(f"{utt_id} done!")
|
|
|
|
|
|
def main():
|
|
# parse args and config and redirect to train_sp
|
|
parser = argparse.ArgumentParser(
|
|
description="Synthesize with speedyspeech & parallel wavegan.")
|
|
parser.add_argument(
|
|
"--speedyspeech-config", type=str, help="config file for speedyspeech.")
|
|
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(
|
|
"--pwg-config", type=str, help="config file for parallelwavegan.")
|
|
parser.add_argument(
|
|
"--pwg-checkpoint",
|
|
type=str,
|
|
help="parallel wavegan checkpoint to load.")
|
|
parser.add_argument(
|
|
"--pwg-stat",
|
|
type=str,
|
|
help="mean and standard deviation used to normalize spectrogram when training speedyspeech."
|
|
)
|
|
parser.add_argument(
|
|
"--text",
|
|
type=str,
|
|
help="text to synthesize, a 'utt_id sentence' pair per line")
|
|
parser.add_argument(
|
|
"--phones-dict", type=str, default=None, help="phone vocabulary file.")
|
|
parser.add_argument(
|
|
"--tones-dict", type=str, default=None, help="tone vocabulary file.")
|
|
parser.add_argument("--output-dir", type=str, help="output dir")
|
|
parser.add_argument(
|
|
"--inference-dir", type=str, help="dir to save inference models")
|
|
parser.add_argument("--verbose", type=int, default=1, help="verbose")
|
|
parser.add_argument(
|
|
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
|
|
|
|
args, _ = parser.parse_known_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))
|
|
with open(args.pwg_config) as f:
|
|
pwg_config = CfgNode(yaml.safe_load(f))
|
|
|
|
print("========Args========")
|
|
print(yaml.safe_dump(vars(args)))
|
|
print("========Config========")
|
|
print(speedyspeech_config)
|
|
print(pwg_config)
|
|
|
|
evaluate(args, speedyspeech_config, pwg_config)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|