Medix-AI/coqui-tts
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coqui-tts
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test fixes

This commit is contained in:
Eren Gölge 2021-03-05 02:51:16 +01:00 committed by Eren Gölge
parent 0a61ea3b2d
commit 2c364c0df8
2 changed files with 5 additions and 4 deletions
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1
run_tests.sh
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@ -13,6 +13,7 @@ nosetests tests -x &&\
./tests/test_vocoder_wavernn_train.sh && \ ./tests/test_vocoder_wavernn_train.sh && \
./tests/test_vocoder_wavegrad_train.sh && \ ./tests/test_vocoder_wavegrad_train.sh && \
./tests/test_speedy_speech_train.sh && \ ./tests/test_speedy_speech_train.sh && \
./tests/test_align_tts_train.sh && \
./tests/test_compute_statistics.sh && \ ./tests/test_compute_statistics.sh && \
# linter check # linter check

8
tests/test_glow_tts.py
View File

@ -59,7 +59,7 @@ class GlowTTSTrainTest(unittest.TestCase):
use_encoder_prenet=True, use_encoder_prenet=True,
num_flow_blocks_dec=12, num_flow_blocks_dec=12,
kernel_size_dec=5, kernel_size_dec=5,
dilation_rate=5, dilation_rate=1,
num_block_layers=4, num_block_layers=4,
dropout_p_dec=0., dropout_p_dec=0.,
num_speakers=0, num_speakers=0,
@ -88,7 +88,7 @@ class GlowTTSTrainTest(unittest.TestCase):
use_encoder_prenet=True, use_encoder_prenet=True,
num_flow_blocks_dec=12, num_flow_blocks_dec=12,
kernel_size_dec=5, kernel_size_dec=5,
dilation_rate=5, dilation_rate=1,
num_block_layers=4, num_block_layers=4,
dropout_p_dec=0., dropout_p_dec=0.,
num_speakers=0, num_speakers=0,
@ -111,11 +111,11 @@ class GlowTTSTrainTest(unittest.TestCase):
assert (param - param_ref).sum() == 0, param assert (param - param_ref).sum() == 0, param
count += 1 count += 1
optimizer = optim.Adam(model.parameters(), lr=c.lr) optimizer = optim.Adam(model.parameters(), lr=0.001)
for _ in range(5): for _ in range(5):
optimizer.zero_grad()
z, logdet, y_mean, y_log_scale, alignments, o_dur_log, o_total_dur = model.forward( z, logdet, y_mean, y_log_scale, alignments, o_dur_log, o_total_dur = model.forward(
input_dummy, input_lengths, mel_spec, mel_lengths, None) input_dummy, input_lengths, mel_spec, mel_lengths, None)
optimizer.zero_grad()
loss_dict = criterion(z, y_mean, y_log_scale, logdet, mel_lengths, loss_dict = criterion(z, y_mean, y_log_scale, logdet, mel_lengths,
o_dur_log, o_total_dur, input_lengths) o_dur_log, o_total_dur, input_lengths)
loss = loss_dict['loss'] loss = loss_dict['loss']