def test_multispeaker_inference(self):
num_speakers = 10
config = VitsConfig(num_speakers=num_speakers,
use_speaker_embedding=True)
model = Vits(config).to(device)
batch_size = 1
input_dummy, *_ = self._create_inputs(config, batch_size=batch_size)
speaker_ids = torch.randint(0, num_speakers,
(batch_size, )).long().to(device)
outputs = model.inference(input_dummy, {"speaker_ids": speaker_ids})
self._check_inference_outputs(config,
outputs,
input_dummy,
batch_size=batch_size)
batch_size = 2
input_dummy, input_lengths, *_ = self._create_inputs(
config, batch_size=batch_size)
speaker_ids = torch.randint(0, num_speakers,
(batch_size, )).long().to(device)
outputs = model.inference(input_dummy, {
"x_lengths": input_lengths,
"speaker_ids": speaker_ids
})
self._check_inference_outputs(config,
outputs,
input_dummy,
batch_size=batch_size)
def test_multilingual_inference(self):
num_speakers = 10
num_langs = 3
args = VitsArgs(language_ids_file=LANG_FILE,
use_language_embedding=True,
spec_segment_size=10)
config = VitsConfig(num_speakers=num_speakers,
use_speaker_embedding=True,
model_args=args)
model = Vits(config).to(device)
input_dummy = torch.randint(0, 24, (1, 128)).long().to(device)
speaker_ids = torch.randint(0, num_speakers, (1, )).long().to(device)
lang_ids = torch.randint(0, num_langs, (1, )).long().to(device)
_ = model.inference(input_dummy, {
"speaker_ids": speaker_ids,
"language_ids": lang_ids
})
batch_size = 1
input_dummy, *_ = self._create_inputs(config, batch_size=batch_size)
speaker_ids = torch.randint(0, num_speakers,
(batch_size, )).long().to(device)
lang_ids = torch.randint(0, num_langs,
(batch_size, )).long().to(device)
outputs = model.inference(input_dummy, {
"speaker_ids": speaker_ids,
"language_ids": lang_ids
})
self._check_inference_outputs(config,
outputs,
input_dummy,
batch_size=batch_size)
batch_size = 2
input_dummy, input_lengths, *_ = self._create_inputs(
config, batch_size=batch_size)
speaker_ids = torch.randint(0, num_speakers,
(batch_size, )).long().to(device)
lang_ids = torch.randint(0, num_langs,
(batch_size, )).long().to(device)
outputs = model.inference(
input_dummy, {
"x_lengths": input_lengths,
"speaker_ids": speaker_ids,
"language_ids": lang_ids
})
self._check_inference_outputs(config,
outputs,
input_dummy,
batch_size=batch_size)
def test_inference(self):
num_speakers = 0
config = VitsConfig(num_speakers=num_speakers,
use_speaker_embedding=True)
input_dummy = torch.randint(0, 24, (1, 128)).long().to(device)
model = Vits(config).to(device)
_ = model.inference(input_dummy)
def test_inference(self):
num_speakers = 0
config = VitsConfig(num_speakers=num_speakers,
use_speaker_embedding=True)
model = Vits(config).to(device)
batch_size = 1
input_dummy, *_ = self._create_inputs(config, batch_size=batch_size)
outputs = model.inference(input_dummy)
self._check_inference_outputs(config,
outputs,
input_dummy,
batch_size=batch_size)
batch_size = 2
input_dummy, input_lengths, *_ = self._create_inputs(
config, batch_size=batch_size)
outputs = model.inference(input_dummy,
aux_input={"x_lengths": input_lengths})
self._check_inference_outputs(config,
outputs,
input_dummy,
batch_size=batch_size)
def test_multilingual_inference(self):
num_speakers = 10
num_langs = 3
args = VitsArgs(language_ids_file=LANG_FILE,
use_language_embedding=True,
spec_segment_size=10)
config = VitsConfig(num_speakers=num_speakers,
use_speaker_embedding=True,
model_args=args)
input_dummy = torch.randint(0, 24, (1, 128)).long().to(device)
speaker_ids = torch.randint(0, num_speakers, (1, )).long().to(device)
lang_ids = torch.randint(0, num_langs, (1, )).long().to(device)
model = Vits(config).to(device)
_ = model.inference(input_dummy, {
"speaker_ids": speaker_ids,
"language_ids": lang_ids
})
