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104 lines
3.5 KiB
104 lines
3.5 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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from pathlib import Path
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import numpy as np
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import paddle
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from matplotlib import pyplot as plt
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from paddlespeech.t2s.exps.tacotron2.config import get_cfg_defaults
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from paddlespeech.t2s.frontend import EnglishCharacter
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from paddlespeech.t2s.models.tacotron2 import Tacotron2
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from paddlespeech.t2s.utils import display
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def main(config, args):
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if args.ngpu == 0:
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paddle.set_device("cpu")
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elif args.ngpu > 0:
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paddle.set_device("gpu")
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else:
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print("ngpu should >= 0 !")
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# model
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frontend = EnglishCharacter()
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model = Tacotron2.from_pretrained(config, args.checkpoint_path)
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model.eval()
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# inputs
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input_path = Path(args.input).expanduser()
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sentences = []
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with open(input_path, "rt") as f:
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for line in f:
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line_list = line.strip().split()
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utt_id = line_list[0]
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sentence = " ".join(line_list[1:])
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sentences.append((utt_id, sentence))
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if args.output is None:
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output_dir = input_path.parent / "synthesis"
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else:
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output_dir = Path(args.output).expanduser()
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output_dir.mkdir(exist_ok=True)
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for i, sentence in enumerate(sentences):
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sentence = paddle.to_tensor(frontend(sentence)).unsqueeze(0)
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outputs = model.infer(sentence)
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mel_output = outputs["mel_outputs_postnet"][0].numpy().T
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alignment = outputs["alignments"][0].numpy().T
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np.save(str(output_dir / f"sentence_{i}"), mel_output)
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display.plot_alignment(alignment)
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plt.savefig(str(output_dir / f"sentence_{i}.png"))
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if args.verbose:
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print("spectrogram saved at {}".format(output_dir /
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f"sentence_{i}.npy"))
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if __name__ == "__main__":
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config = get_cfg_defaults()
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parser = argparse.ArgumentParser(
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description="generate mel spectrogram with TransformerTTS.")
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parser.add_argument(
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"--config",
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type=str,
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metavar="FILE",
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help="extra config to overwrite the default config")
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parser.add_argument(
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"--checkpoint_path", type=str, help="path of the checkpoint to load.")
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parser.add_argument("--input", type=str, help="path of the text sentences")
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parser.add_argument("--output", type=str, help="path to save outputs")
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parser.add_argument(
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"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
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parser.add_argument(
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"--opts",
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nargs=argparse.REMAINDER,
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help="options to overwrite --config file and the default config, passing in KEY VALUE pairs"
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)
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parser.add_argument(
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"-v", "--verbose", action="store_true", help="print msg")
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args = parser.parse_args()
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if args.config:
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config.merge_from_file(args.config)
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if args.opts:
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config.merge_from_list(args.opts)
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config.freeze()
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print(config)
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print(args)
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main(config, args)
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