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PaddleSpeech/paddlespeech/t2s/exps/tacotron2/synthesize.py

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3.5 KiB

# Copyright (c) 2020 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
from pathlib import Path
import numpy as np
import paddle
from matplotlib import pyplot as plt
from paddlespeech.t2s.exps.tacotron2.config import get_cfg_defaults
from paddlespeech.t2s.frontend import EnglishCharacter
from paddlespeech.t2s.models.tacotron2 import Tacotron2
from paddlespeech.t2s.utils import display
def main(config, args):
if args.ngpu == 0:
paddle.set_device("cpu")
elif args.ngpu > 0:
paddle.set_device("gpu")
else:
print("ngpu should >= 0 !")
# model
frontend = EnglishCharacter()
model = Tacotron2.from_pretrained(config, args.checkpoint_path)
model.eval()
# inputs
input_path = Path(args.input).expanduser()
sentences = []
with open(input_path, "rt") as f:
for line in f:
line_list = line.strip().split()
utt_id = line_list[0]
sentence = " ".join(line_list[1:])
sentences.append((utt_id, sentence))
if args.output is None:
output_dir = input_path.parent / "synthesis"
else:
output_dir = Path(args.output).expanduser()
output_dir.mkdir(exist_ok=True)
for i, sentence in enumerate(sentences):
sentence = paddle.to_tensor(frontend(sentence)).unsqueeze(0)
outputs = model.infer(sentence)
mel_output = outputs["mel_outputs_postnet"][0].numpy().T
alignment = outputs["alignments"][0].numpy().T
np.save(str(output_dir / f"sentence_{i}"), mel_output)
display.plot_alignment(alignment)
plt.savefig(str(output_dir / f"sentence_{i}.png"))
if args.verbose:
print("spectrogram saved at {}".format(output_dir /
f"sentence_{i}.npy"))
if __name__ == "__main__":
config = get_cfg_defaults()
parser = argparse.ArgumentParser(
description="generate mel spectrogram with TransformerTTS.")
parser.add_argument(
"--config",
type=str,
metavar="FILE",
help="extra config to overwrite the default config")
parser.add_argument(
"--checkpoint_path", type=str, help="path of the checkpoint to load.")
parser.add_argument("--input", type=str, help="path of the text sentences")
parser.add_argument("--output", type=str, help="path to save outputs")
parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
parser.add_argument(
"--opts",
nargs=argparse.REMAINDER,
help="options to overwrite --config file and the default config, passing in KEY VALUE pairs"
)
parser.add_argument(
"-v", "--verbose", action="store_true", help="print msg")
args = parser.parse_args()
if args.config:
config.merge_from_file(args.config)
if args.opts:
config.merge_from_list(args.opts)
config.freeze()
print(config)
print(args)
main(config, args)