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# coding: utf-8
import sys
from os.path import dirname, join
tacotron_lib_dir = join(dirname(__file__), "..", "lib", "tacotron")
sys.path.append(tacotron_lib_dir)
from text import text_to_sequence, symbols
import torch
from torch.autograd import Variable
from tacotron_pytorch import Tacotron
import numpy as np
def _pad(seq, max_len):
return np.pad(seq, (0, max_len - len(seq)),
mode='constant', constant_values=0)
def test_taco():
B, T_out, D_out = 2, 400, 80
r = 5
T_encoder = T_out // r
texts = ["Thank you very much.", "Hello"]
seqs = [np.array(text_to_sequence(
t, ["english_cleaners"]), dtype=np.int) for t in texts]
input_lengths = np.array([len(s) for s in seqs])
max_len = np.max(input_lengths)
seqs = np.array([_pad(s, max_len) for s in seqs])
x = torch.LongTensor(seqs)
y = torch.rand(B, T_out, D_out)
x = Variable(x)
y = Variable(y)
model = Tacotron(n_vocab=len(symbols), r=r)
print("Encoder input shape: ", x.size())
print("Decoder input shape: ", y.size())
a, b, c = model(x, y, input_lengths=input_lengths)
print("Mel shape:", a.size())
print("Linear shape:", b.size())
print("Attention shape:", c.size())
assert c.size() == (B, T_encoder, max_len)
# Test greddy decoding
a, b, c = model(x, input_lengths=input_lengths)
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