test1.py

import os
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
from jamo import hangul_to_jamo
from models.tacotron import Tacotron
from util.hparams import *
from util.plot_alignment import plot_alignment
from util.text import sequence_to_text, text_to_sequence


sentences = [
  '정말로 사랑한담 기다려주세요'
]

checkpoint_dir = './checkpoint/1'
save_dir = './output'
os.makedirs(save_dir, exist_ok=True)


def test_step(text, idx):
    seq = text_to_sequence(text)
    enc_input = np.asarray([seq], dtype=np.int32)
    sequence_length = np.asarray([len(seq)], dtype=np.int32)
    dec_input = np.zeros((1, max_iter, mel_dim), dtype=np.float32)

    pred = []
    for i in range(1, max_iter+1):
        mel_out, alignment = model(enc_input, sequence_length, dec_input, is_training=False)
        if i < max_iter:
            dec_input[:, i, :] = mel_out[:, reduction * i - 1, :]
        pred.extend(mel_out[:, reduction * (i-1) : reduction * i, :])

    pred = np.reshape(np.asarray(pred), [-1, mel_dim])
    alignment = np.squeeze(alignment, axis=0)

    np.save(os.path.join(save_dir, 'mel-{}'.format(idx)), pred, allow_pickle=False)

    input_seq = sequence_to_text(seq)
    alignment_dir = os.path.join(save_dir, 'align-{}.png'.format(idx))
    plot_alignment(alignment, alignment_dir, input_seq)


model = Tacotron(K=16, conv_dim=[128, 128])
checkpoint = tf.train.Checkpoint(model=model)
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir)).expect_partial()

for i, text in enumerate(sentences):
    jamo = ''.join(list(hangul_to_jamo(text)))
    test_step(jamo, i)