ParakeetRebeccaRosario/examples/transformer_tts/synthesis.py

# 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 os
from scipy.io.wavfile import write
from parakeet.g2p.en import text_to_sequence
import numpy as np
from tqdm import tqdm
from matplotlib import cm
from tensorboardX import SummaryWriter
from ruamel import yaml
import paddle.fluid as fluid
import paddle.fluid.dygraph as dg
from pathlib import Path
import argparse
from parse import add_config_options_to_parser
from pprint import pprint
from collections import OrderedDict
from parakeet.models.transformer_tts.utils import *
from parakeet import audio
from parakeet.models.transformer_tts.vocoder import Vocoder
from parakeet.models.transformer_tts.transformer_tts import TransformerTTS


def load_checkpoint(step, model_path):
    model_dict, _ = fluid.dygraph.load_dygraph(os.path.join(model_path, step))
    new_state_dict = OrderedDict()
    for param in model_dict:
        if param.startswith('_layers.'):
            new_state_dict[param[8:]] = model_dict[param]
        else:
            new_state_dict[param] = model_dict[param]
    return new_state_dict


def synthesis(text_input, args):
    place = (fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace())

    with open(args.config_path) as f:
        cfg = yaml.load(f, Loader=yaml.Loader)

    # tensorboard
    if not os.path.exists(args.log_dir):
        os.mkdir(args.log_dir)
    path = os.path.join(args.log_dir, 'synthesis')

    writer = SummaryWriter(path)

    with dg.guard(place):
        with fluid.unique_name.guard():
            model = TransformerTTS(cfg)
            model.set_dict(
                load_checkpoint(
                    str(args.transformer_step),
                    os.path.join(args.checkpoint_path, "transformer")))
            model.eval()

        with fluid.unique_name.guard():
            model_vocoder = Vocoder(cfg, args.batch_size)
            model_vocoder.set_dict(
                load_checkpoint(
                    str(args.vocoder_step),
                    os.path.join(args.checkpoint_path, "vocoder")))
            model_vocoder.eval()
        # init input
        text = np.asarray(text_to_sequence(text_input))
        text = fluid.layers.unsqueeze(dg.to_variable(text), [0])
        mel_input = dg.to_variable(np.zeros([1, 1, 80])).astype(np.float32)
        pos_text = np.arange(1, text.shape[1] + 1)
        pos_text = fluid.layers.unsqueeze(dg.to_variable(pos_text), [0])

        pbar = tqdm(range(args.max_len))
        for i in pbar:
            dec_slf_mask = get_triu_tensor(
                mel_input.numpy(), mel_input.numpy()).astype(np.float32)
            dec_slf_mask = fluid.layers.cast(
                dg.to_variable(dec_slf_mask != 0), np.float32) * (-2**32 + 1)
            pos_mel = np.arange(1, mel_input.shape[1] + 1)
            pos_mel = fluid.layers.unsqueeze(dg.to_variable(pos_mel), [0])
            mel_pred, postnet_pred, attn_probs, stop_preds, attn_enc, attn_dec = model(
                text, mel_input, pos_text, pos_mel, dec_slf_mask)
            mel_input = fluid.layers.concat(
                [mel_input, postnet_pred[:, -1:, :]], axis=1)

        mag_pred = model_vocoder(postnet_pred)

        _ljspeech_processor = audio.AudioProcessor(
            sample_rate=cfg['audio']['sr'],
            num_mels=cfg['audio']['num_mels'],
            min_level_db=cfg['audio']['min_level_db'],
            ref_level_db=cfg['audio']['ref_level_db'],
            n_fft=cfg['audio']['n_fft'],
            win_length=cfg['audio']['win_length'],
            hop_length=cfg['audio']['hop_length'],
            power=cfg['audio']['power'],
            preemphasis=cfg['audio']['preemphasis'],
            signal_norm=True,
            symmetric_norm=False,
            max_norm=1.,
            mel_fmin=0,
            mel_fmax=None,
            clip_norm=True,
            griffin_lim_iters=60,
            do_trim_silence=False,
            sound_norm=False)

        wav = _ljspeech_processor.inv_spectrogram(
            fluid.layers.transpose(
                fluid.layers.squeeze(mag_pred, [0]), [1, 0]).numpy())
        global_step = 0
        for i, prob in enumerate(attn_probs):
            for j in range(4):
                x = np.uint8(cm.viridis(prob.numpy()[j]) * 255)
                writer.add_image(
                    'Attention_%d_0' % global_step,
                    x,
                    i * 4 + j,
                    dataformats="HWC")

        for i, prob in enumerate(attn_enc):
            for j in range(4):
                x = np.uint8(cm.viridis(prob.numpy()[j]) * 255)
                writer.add_image(
                    'Attention_enc_%d_0' % global_step,
                    x,
                    i * 4 + j,
                    dataformats="HWC")

        for i, prob in enumerate(attn_dec):
            for j in range(4):
                x = np.uint8(cm.viridis(prob.numpy()[j]) * 255)
                writer.add_image(
                    'Attention_dec_%d_0' % global_step,
                    x,
                    i * 4 + j,
                    dataformats="HWC")
        writer.add_audio(text_input, wav, 0, cfg['audio']['sr'])
        if not os.path.exists(args.sample_path):
            os.mkdir(args.sample_path)
        write(
            os.path.join(args.sample_path, 'test.wav'), cfg['audio']['sr'],
            wav)
    writer.close()


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description="Synthesis model")
    add_config_options_to_parser(parser)
    args = parser.parse_args()
    synthesis("Parakeet stands for Paddle PARAllel text-to-speech toolkit.",
              args)