Medix-AI/coqui-tts
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coqui-tts
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test updates

This commit is contained in:
root 2020-01-15 23:10:11 +01:00
parent 8dfedb691e
commit 1c5d3b52cf
3 changed files with 4 additions and 4 deletions
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2
tests/test_layers.py
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@ -119,7 +119,7 @@ class EncoderTests(unittest.TestCase):
class L1LossMaskedTests(unittest.TestCase): class L1LossMaskedTests(unittest.TestCase):
def test_in_out(self): def test_in_out(self):
# test input == target # test input == target
layer = L1LossMasked() layer = L1LossMasked(seq_len_norm=False)
dummy_input = T.ones(4, 8, 128).float() dummy_input = T.ones(4, 8, 128).float()
dummy_target = T.ones(4, 8, 128).float() dummy_target = T.ones(4, 8, 128).float()
dummy_length = (T.ones(4) * 8).long() dummy_length = (T.ones(4) * 8).long()

2
tests/test_tacotron2_model.py
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@ -38,7 +38,7 @@ class TacotronTrainTest(unittest.TestCase):
stop_targets.size(1) // c.r, -1) stop_targets.size(1) // c.r, -1)
stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze() stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
criterion = MSELossMasked().to(device) criterion = MSELossMasked(seq_len_norm=False).to(device)
criterion_st = nn.BCEWithLogitsLoss().to(device) criterion_st = nn.BCEWithLogitsLoss().to(device)
model = Tacotron2(num_chars=24, r=c.r, num_speakers=5).to(device) model = Tacotron2(num_chars=24, r=c.r, num_speakers=5).to(device)
model.train() model.train()

4
tests/test_tacotron_model.py
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@ -44,7 +44,7 @@ class TacotronTrainTest(unittest.TestCase):
stop_targets = (stop_targets.sum(2) > stop_targets = (stop_targets.sum(2) >
0.0).unsqueeze(2).float().squeeze() 0.0).unsqueeze(2).float().squeeze()
criterion = L1LossMasked().to(device) criterion = L1LossMasked(seq_len_norm=False).to(device)
criterion_st = nn.BCEWithLogitsLoss().to(device) criterion_st = nn.BCEWithLogitsLoss().to(device)
model = Tacotron( model = Tacotron(
num_chars=32, num_chars=32,
@ -106,7 +106,7 @@ class TacotronGSTTrainTest(unittest.TestCase):
stop_targets = (stop_targets.sum(2) > stop_targets = (stop_targets.sum(2) >
0.0).unsqueeze(2).float().squeeze() 0.0).unsqueeze(2).float().squeeze()
criterion = L1LossMasked().to(device) criterion = L1LossMasked(seq_len_norm=False).to(device)
criterion_st = nn.BCEWithLogitsLoss().to(device) criterion_st = nn.BCEWithLogitsLoss().to(device)
model = Tacotron( model = Tacotron(
num_chars=32, num_chars=32,