174 lines
6.1 KiB
Python
174 lines
6.1 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 scipy.io.wavfile import write
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import numpy as np
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from tqdm import tqdm
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from matplotlib import cm
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from tensorboardX import SummaryWriter
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from ruamel import yaml
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from pathlib import Path
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import argparse
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from pprint import pprint
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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.models.transformer_tts.utils import *
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from parakeet import audio
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from parakeet.models.transformer_tts import Vocoder
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from parakeet.models.transformer_tts import TransformerTTS
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from parakeet.utils import io
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def add_config_options_to_parser(parser):
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parser.add_argument("--config", type=str, help="path of the config file")
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parser.add_argument("--use_gpu", type=int, default=0, help="device to use")
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parser.add_argument(
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"--max_len",
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type=int,
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default=200,
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help="The max length of audio when synthsis.")
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parser.add_argument(
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"--checkpoint_transformer",
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type=str,
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help="transformer_tts checkpoint to synthesis")
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parser.add_argument(
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"--checkpoint_vocoder",
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type=str,
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help="vocoder checkpoint to synthesis")
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parser.add_argument(
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"--output",
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type=str,
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default="synthesis",
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help="path to save experiment results")
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def synthesis(text_input, args):
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local_rank = dg.parallel.Env().local_rank
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place = (fluid.CUDAPlace(local_rank) if args.use_gpu else fluid.CPUPlace())
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with open(args.config) as f:
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cfg = yaml.load(f, Loader=yaml.Loader)
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# tensorboard
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if not os.path.exists(args.output):
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os.mkdir(args.output)
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writer = SummaryWriter(os.path.join(args.output, 'log'))
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fluid.enable_dygraph(place)
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with fluid.unique_name.guard():
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network_cfg = cfg['network']
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model = TransformerTTS(
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network_cfg['embedding_size'], network_cfg['hidden_size'],
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network_cfg['encoder_num_head'], network_cfg['encoder_n_layers'],
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cfg['audio']['num_mels'], network_cfg['outputs_per_step'],
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network_cfg['decoder_num_head'], network_cfg['decoder_n_layers'])
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# Load parameters.
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global_step = io.load_parameters(
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model=model, checkpoint_path=args.checkpoint_transformer)
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model.eval()
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with fluid.unique_name.guard():
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model_vocoder = Vocoder(
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cfg['train']['batch_size'], cfg['vocoder']['hidden_size'],
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cfg['audio']['num_mels'], cfg['audio']['n_fft'])
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# Load parameters.
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global_step = io.load_parameters(
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model=model_vocoder, checkpoint_path=args.checkpoint_vocoder)
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model_vocoder.eval()
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# init input
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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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mel_input = dg.to_variable(np.zeros([1, 1, 80])).astype(np.float32)
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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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pbar = tqdm(range(args.max_len))
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for i in pbar:
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pos_mel = np.arange(1, mel_input.shape[1] + 1)
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pos_mel = fluid.layers.unsqueeze(dg.to_variable(pos_mel), [0])
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mel_pred, postnet_pred, attn_probs, stop_preds, attn_enc, attn_dec = model(
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text, mel_input, pos_text, pos_mel)
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mel_input = fluid.layers.concat(
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[mel_input, postnet_pred[:, -1:, :]], axis=1)
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mag_pred = model_vocoder(postnet_pred)
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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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# synthesis with cbhg
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wav = _ljspeech_processor.inv_spectrogram(
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fluid.layers.transpose(fluid.layers.squeeze(mag_pred, [0]), [1, 0])
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.numpy())
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global_step = 0
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for i, prob in enumerate(attn_probs):
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for j in range(4):
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x = np.uint8(cm.viridis(prob.numpy()[j]) * 255)
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writer.add_image(
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'Attention_%d_0' % global_step,
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x,
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i * 4 + j,
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dataformats="HWC")
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writer.add_audio(text_input + '(cbhg)', wav, 0, cfg['audio']['sr'])
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if not os.path.exists(os.path.join(args.output, 'samples')):
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os.mkdir(os.path.join(args.output, 'samples'))
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write(
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os.path.join(os.path.join(args.output, 'samples'), 'cbhg.wav'),
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cfg['audio']['sr'], wav)
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# synthesis with griffin-lim
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wav = _ljspeech_processor.inv_melspectrogram(
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fluid.layers.transpose(
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fluid.layers.squeeze(postnet_pred, [0]), [1, 0]).numpy())
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writer.add_audio(text_input + '(griffin)', wav, 0, cfg['audio']['sr'])
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write(
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os.path.join(os.path.join(args.output, 'samples'), 'griffin.wav'),
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cfg['audio']['sr'], wav)
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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="Synthesis model")
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add_config_options_to_parser(parser)
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args = parser.parse_args()
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# Print the whole config setting.
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pprint(vars(args))
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synthesis("Parakeet stands for Paddle PARAllel text-to-speech toolkit.",
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args)
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