103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
# 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 os
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from tensorboardX import SummaryWriter
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from collections import OrderedDict
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import argparse
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from parse import add_config_options_to_parser
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from pprint import pprint
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from ruamel import yaml
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import numpy as np
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import paddle.fluid as fluid
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import paddle.fluid.dygraph as dg
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from parakeet.g2p.en import text_to_sequence
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from parakeet import audio
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from parakeet.models.fastspeech.fastspeech import FastSpeech
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def load_checkpoint(step, model_path):
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model_dict, _ = fluid.dygraph.load_dygraph(os.path.join(model_path, step))
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new_state_dict = OrderedDict()
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for param in model_dict:
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if param.startswith('_layers.'):
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new_state_dict[param[8:]] = model_dict[param]
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else:
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new_state_dict[param] = model_dict[param]
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return new_state_dict
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def synthesis(text_input, args):
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place = (fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace())
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# tensorboard
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if not os.path.exists(args.log_dir):
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os.mkdir(args.log_dir)
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path = os.path.join(args.log_dir, 'synthesis')
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with open(args.config_path) as f:
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cfg = yaml.load(f, Loader=yaml.Loader)
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writer = SummaryWriter(path)
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with dg.guard(place):
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model = FastSpeech(cfg)
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model.set_dict(
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load_checkpoint(
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str(args.fastspeech_step),
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os.path.join(args.checkpoint_path, "fastspeech")))
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model.eval()
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text = np.asarray(text_to_sequence(text_input))
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text = fluid.layers.unsqueeze(dg.to_variable(text), [0])
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pos_text = np.arange(1, text.shape[1] + 1)
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pos_text = fluid.layers.unsqueeze(dg.to_variable(pos_text), [0])
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mel_output, mel_output_postnet = model(
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text, pos_text, alpha=args.alpha)
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_ljspeech_processor = audio.AudioProcessor(
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sample_rate=cfg['audio']['sr'],
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num_mels=cfg['audio']['num_mels'],
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min_level_db=cfg['audio']['min_level_db'],
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ref_level_db=cfg['audio']['ref_level_db'],
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n_fft=cfg['audio']['n_fft'],
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win_length=cfg['audio']['win_length'],
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hop_length=cfg['audio']['hop_length'],
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power=cfg['audio']['power'],
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preemphasis=cfg['audio']['preemphasis'],
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signal_norm=True,
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symmetric_norm=False,
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max_norm=1.,
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mel_fmin=0,
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mel_fmax=None,
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clip_norm=True,
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griffin_lim_iters=60,
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do_trim_silence=False,
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sound_norm=False)
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mel_output_postnet = fluid.layers.transpose(
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fluid.layers.squeeze(mel_output_postnet, [0]), [1, 0])
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wav = _ljspeech_processor.inv_melspectrogram(mel_output_postnet.numpy(
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))
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writer.add_audio(text_input, wav, 0, cfg['audio']['sr'])
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print("Synthesis completed !!!")
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writer.close()
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description="Train Fastspeech model")
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add_config_options_to_parser(parser)
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args = parser.parse_args()
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synthesis("Transformer model is so fast!", args)
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