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import torch
from TTS.tts.layers.speedy_speech.encoder import Encoder
from TTS.tts.layers.speedy_speech.decoder import Decoder
from TTS.tts.layers.speedy_speech.duration_predictor import DurationPredictor
from TTS.tts.utils.generic_utils import sequence_mask
from TTS.tts.models.speedy_speech import SpeedySpeech
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def test_encoder():
input_dummy = torch.rand(8, 14, 37).to(device)
input_lengths = torch.randint(31, 37, (8, )).long().to(device)
input_lengths[-1] = 37
input_mask = torch.unsqueeze(
sequence_mask(input_lengths, input_dummy.size(2)), 1).to(device)
# residual bn conv encoder
layer = Encoder(out_channels=11,
in_hidden_channels=14,
encoder_type='residual_conv_bn').to(device)
output = layer(input_dummy, input_mask)
assert list(output.shape) == [8, 11, 37]
# transformer encoder
layer = Encoder(out_channels=11,
in_hidden_channels=14,
encoder_type='transformer',
encoder_params={
'hidden_channels_ffn': 768,
'num_heads': 2,
"kernel_size": 3,
"dropout_p": 0.1,
"num_layers": 6,
"rel_attn_window_size": 4,
"input_length": None
}).to(device)
output = layer(input_dummy, input_mask)
assert list(output.shape) == [8, 11, 37]
def test_decoder():
input_dummy = torch.rand(8, 128, 37).to(device)
input_lengths = torch.randint(31, 37, (8, )).long().to(device)
input_lengths[-1] = 37
input_mask = torch.unsqueeze(
sequence_mask(input_lengths, input_dummy.size(2)), 1).to(device)
# residual bn conv decoder
layer = Decoder(out_channels=11, in_hidden_channels=128).to(device)
output = layer(input_dummy, input_mask)
assert list(output.shape) == [8, 11, 37]
# transformer decoder
layer = Decoder(out_channels=11,
in_hidden_channels=128,
decoder_type='transformer',
decoder_params={
'hidden_channels_ffn': 128,
'num_heads': 2,
"kernel_size": 3,
"dropout_p": 0.1,
"num_layers": 8,
"rel_attn_window_size": 4,
"input_length": None
}).to(device)
output = layer(input_dummy, input_mask)
assert list(output.shape) == [8, 11, 37]
# wavenet decoder
layer = Decoder(out_channels=11,
in_hidden_channels=128,
decoder_type='wavenet',
decoder_params={
"num_blocks": 12,
"hidden_channels": 192,
"kernel_size": 5,
"dilation_rate": 1,
"num_layers": 4,
"dropout_p": 0.05
}).to(device)
output = layer(input_dummy, input_mask)
assert list(output.shape) == [8, 11, 37]
def test_duration_predictor():
input_dummy = torch.rand(8, 128, 27).to(device)
input_lengths = torch.randint(20, 27, (8, )).long().to(device)
input_lengths[-1] = 27
x_mask = torch.unsqueeze(sequence_mask(input_lengths, input_dummy.size(2)),
1).to(device)
layer = DurationPredictor(hidden_channels=128).to(device)
output = layer(input_dummy, x_mask)
assert list(output.shape) == [8, 1, 27]
def test_speedy_speech():
num_chars = 7
B = 8
T_en = 37
T_de = 74
x_dummy = torch.randint(0, 7, (B, T_en)).long().to(device)
x_lengths = torch.randint(31, T_en, (B, )).long().to(device)
x_lengths[-1] = T_en
# set durations. max total duration should be equal to T_de
durations = torch.randint(1, 4, (B, T_en))
durations = durations * (T_de / durations.sum(1)).unsqueeze(1)
durations = durations.to(torch.long).to(device)
max_dur = durations.sum(1).max()
durations[:, 0] += T_de - max_dur if T_de > max_dur else 0
y_lengths = durations.sum(1)
model = SpeedySpeech(num_chars, out_channels=80, hidden_channels=128)
if use_cuda:
model.cuda()
# forward pass
o_de, o_dr, attn = model(x_dummy, x_lengths, y_lengths, durations)
assert list(o_de.shape) == [B, 80, T_de], f"{list(o_de.shape)}"
assert list(attn.shape) == [B, T_de, T_en]
assert list(o_dr.shape) == [B, T_en]
# with speaker embedding
model = SpeedySpeech(num_chars,
out_channels=80,
hidden_channels=128,
num_speakers=10,
c_in_channels=256).to(device)
model.forward(x_dummy,
x_lengths,
y_lengths,
durations,
g=torch.randint(0, 10, (B,)).to(device))
assert list(o_de.shape) == [B, 80, T_de], f"{list(o_de.shape)}"
assert list(attn.shape) == [B, T_de, T_en]
assert list(o_dr.shape) == [B, T_en]
# with speaker external embedding
model = SpeedySpeech(num_chars,
out_channels=80,
hidden_channels=128,
num_speakers=10,
external_c=True,
c_in_channels=256).to(device)
model.forward(x_dummy,
x_lengths,
y_lengths,
durations,
g=torch.rand((B,256)).to(device))
assert list(o_de.shape) == [B, 80, T_de], f"{list(o_de.shape)}"
assert list(attn.shape) == [B, T_de, T_en]
assert list(o_dr.shape) == [B, T_en] |