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linter and test updates for speaker_encoder, gmm_Attention

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Eren Golge
2019-11-12 12:42:42 +01:00
parent 1401a0db6b
commit df1b8b3ec7
14 changed files with 171 additions and 207 deletions
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speaker_encoder/tests.py
+23 -16
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@@ -4,22 +4,21 @@ import torch as T
from TTS.speaker_encoder.model import SpeakerEncoder
from TTS.speaker_encoder.loss import GE2ELoss
from TTS.speaker_encoder.dataset import MyDataset
from TTS.utils.audio import AudioProcessor
from torch.utils.data import DataLoader
from TTS.datasets.preprocess import libri_tts
from TTS.utils.generic_utils import load_config
file_path = os.path.dirname(os.path.realpath(__file__)) + "/../tests/"
c = load_config(os.path.join(file_path, 'test_config.json'))
file_path = os.path.dirname(os.path.realpath(__file__)) + "/../tests/"
c = load_config(os.path.join(file_path, "test_config.json"))
class SpeakerEncoderTests(unittest.TestCase):
# pylint: disable=R0201
def test_in_out(self):
dummy_input = T.rand(4, 20, 80) # B x T x D
dummy_hidden = [T.rand(2, 4, 128), T.rand(2, 4, 128)]
model = SpeakerEncoder(input_dim=80, proj_dim=256, lstm_dim=768, num_lstm_layers=3)
model = SpeakerEncoder(
input_dim=80, proj_dim=256, lstm_dim=768, num_lstm_layers=3
)
# computing d vectors
output = model.forward(dummy_input)
assert output.shape[0] == 4
@@ -35,8 +34,10 @@ class SpeakerEncoderTests(unittest.TestCase):
# check normalization
output_norm = T.nn.functional.normalize(output, dim=1, p=2)
assert_diff = (output_norm - output).sum().item()
assert output.type() == 'torch.FloatTensor'
assert abs(assert_diff) < 1e-4, f" [!] output_norm has wrong values - {assert_diff}"
assert output.type() == "torch.FloatTensor"
assert (
abs(assert_diff) < 1e-4
), f" [!] output_norm has wrong values - {assert_diff}"
# compute d for a given batch
dummy_input = T.rand(1, 240, 80) # B x T x D
output = model.compute_embedding(dummy_input, num_frames=160, overlap=0.5)
@@ -45,23 +46,29 @@ class SpeakerEncoderTests(unittest.TestCase):
assert len(output.shape) == 2
class GE2ELossTests(unittest.TestCase):
# pylint: disable=R0201
def test_in_out(self):
# check random input
dummy_input = T.rand(4, 5, 64) # num_speaker x num_utterance x dim
loss = GE2ELoss(loss_method='softmax')
loss = GE2ELoss(loss_method="softmax")
output = loss.forward(dummy_input)
assert output.item() >= 0.
assert output.item() >= 0.0
# check all zeros
dummy_input = T.ones(4, 5, 64) # num_speaker x num_utterance x dim
loss = GE2ELoss(loss_method='softmax')
loss = GE2ELoss(loss_method="softmax")
output = loss.forward(dummy_input)
# check speaker loss with orthogonal d-vectors
dummy_input = T.empty(3, 64)
dummy_input = T.nn.init.orthogonal(dummy_input)
dummy_input = T.cat([dummy_input[0].repeat(5, 1, 1).transpose(0, 1), dummy_input[1].repeat(5, 1, 1).transpose(0, 1), dummy_input[2].repeat(5, 1, 1).transpose(0, 1)]) # num_speaker x num_utterance x dim
loss = GE2ELoss(loss_method='softmax')
dummy_input = T.cat(
[
dummy_input[0].repeat(5, 1, 1).transpose(0, 1),
dummy_input[1].repeat(5, 1, 1).transpose(0, 1),
dummy_input[2].repeat(5, 1, 1).transpose(0, 1),
]
) # num_speaker x num_utterance x dim
loss = GE2ELoss(loss_method="softmax")
output = loss.forward(dummy_input)
assert output.item() < 0.005
@@ -77,4 +84,4 @@ class GE2ELossTests(unittest.TestCase):
# print(mel.shape)
# if count == 4:
# break
# count += 1
# count += 1