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small gst config change

pull/10/head
SanjaESC 2020-07-13 08:51:37 +02:00 committed by thllwg
parent 69367bd2ae
commit 18007e389d
2 changed files with 3 additions and 59 deletions
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60
tests/test_tacotron2_model.py
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@ -2,7 +2,6 @@ import os
import copy
import torch
import unittest
import numpy as np
from torch import optim
from torch import nn
@ -21,7 +20,8 @@ c = load_config(os.path.join(file_path, 'test_config.json'))
class TacotronTrainTest(unittest.TestCase):
def test_train_step(self):
@staticmethod
def test_train_step():
input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
input_lengths = torch.randint(100, 128, (8, )).long().to(device)
input_lengths = torch.sort(input_lengths, descending=True)[0]
@ -71,59 +71,3 @@ class TacotronTrainTest(unittest.TestCase):
), "param {} with shape {} not updated!! \n{}\n{}".format(
count, param.shape, param, param_ref)
count += 1
class TacotronGSTTrainTest(unittest.TestCase):
def test_train_step(self):
input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
input_lengths = torch.randint(100, 128, (8, )).long().to(device)
input_lengths = torch.sort(input_lengths, descending=True)[0]
mel_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
mel_postnet_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
mel_lengths[0] = 30
stop_targets = torch.zeros(8, 30, 1).float().to(device)
speaker_ids = torch.randint(0, 5, (8, )).long().to(device)
for idx in mel_lengths:
stop_targets[:, int(idx.item()):, 0] = 1.0
stop_targets = stop_targets.view(input_dummy.shape[0],
stop_targets.size(1) // c.r, -1)
stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
criterion = MSELossMasked(seq_len_norm=False).to(device)
criterion_st = nn.BCEWithLogitsLoss().to(device)
model = Tacotron2(num_chars=24,
gst=True,
r=c.r,
num_speakers=5).to(device)
model.train()
model_ref = copy.deepcopy(model)
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
assert (param - param_ref).sum() == 0, param
count += 1
optimizer = optim.Adam(model.parameters(), lr=c.lr)
for i in range(5):
mel_out, mel_postnet_out, align, stop_tokens = model.forward(
input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids)
assert torch.sigmoid(stop_tokens).data.max() <= 1.0
assert torch.sigmoid(stop_tokens).data.min() >= 0.0
optimizer.zero_grad()
loss = criterion(mel_out, mel_spec, mel_lengths)
stop_loss = criterion_st(stop_tokens, stop_targets)
loss = loss + criterion(mel_postnet_out, mel_postnet_spec, mel_lengths) + stop_loss
loss.backward()
optimizer.step()
# check parameter changes
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
# ignore pre-higway layer since it works conditional
# if count not in [145, 59]:
assert (param != param_ref).any(
), "param {} with shape {} not updated!! \n{}\n{}".format(
count, param.shape, param, param_ref)
count += 1

2
utils/generic_utils.py
View File

@ -359,8 +359,8 @@ def check_config(c):
# GST
_check_argument('use_gst', c, restricted=True, val_type=bool)
_check_argument('gst_style_input', c, restricted=True, val_type=str)
_check_argument('gst', c, restricted=True, val_type=dict)
_check_argument('gst_style_input', c['gst'], restricted=True, val_type=str)
_check_argument('gst_embedding_dim', c['gst'], restricted=True, val_type=int, min_val=1)
_check_argument('gst_num_heads', c['gst'], restricted=True, val_type=int, min_val=1)
_check_argument('gst_style_tokens', c['gst'], restricted=True, val_type=int, min_val=1)