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import os
import unittest
import torch as T
from tests import get_tests_input_path
from TTS.speaker_encoder.losses import GE2ELoss, AngleProtoLoss
from TTS.speaker_encoder.model import SpeakerEncoder
from TTS.utils.io import load_config
file_path = get_tests_input_path()
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
)
# computing d vectors
output = model.forward(dummy_input)
assert output.shape[0] == 4
assert output.shape[1] == 256
output = model.inference(dummy_input)
assert output.shape[0] == 4
assert output.shape[1] == 256
# compute d vectors by passing LSTM hidden
# output = model.forward(dummy_input, dummy_hidden)
# assert output.shape[0] == 4
# assert output.shape[1] == 20
# assert output.shape[2] == 256
# 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}"
# 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)
assert output.shape[0] == 1
assert output.shape[1] == 256
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")
output = loss.forward(dummy_input)
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")
output = loss.forward(dummy_input)
assert output.item() >= 0.0
# 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")
output = loss.forward(dummy_input)
assert output.item() < 0.005
class AngleProtoLossTests(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 = AngleProtoLoss()
output = loss.forward(dummy_input)
assert output.item() >= 0.0
# check all zeros
dummy_input = T.ones(4, 5, 64) # num_speaker x num_utterance x dim
loss = AngleProtoLoss()
output = loss.forward(dummy_input)
assert output.item() >= 0.0
# 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 = AngleProtoLoss()
output = loss.forward(dummy_input)
assert output.item() < 0.005
# class LoaderTest(unittest.TestCase):
# def test_output(self):
# items = libri_tts("/home/erogol/Data/Libri-TTS/train-clean-360/")
# ap = AudioProcessor(**c['audio'])
# dataset = MyDataset(ap, items, 1.6, 64, 10)
# loader = DataLoader(dataset, batch_size=32, shuffle=False, num_workers=0, collate_fn=dataset.collate_fn)
# count = 0
# for mel, spk in loader:
# print(mel.shape)
# if count == 4:
# break
# count += 1
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