def test_inference():
input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
input_lengths = torch.randint(100, 129, (8,)).long().to(device)
input_lengths[-1] = 128
mel_spec = torch.rand(8, 30, c.audio["num_mels"]).to(device)
mel_lengths = torch.randint(20, 30, (8,)).long().to(device)
speaker_ids = torch.randint(0, 5, (8,)).long().to(device)
# create model
config = GlowTTSConfig(num_chars=32)
model = GlowTTS(config).to(device)
model.eval()
print(" > Num parameters for GlowTTS model:%s" % (count_parameters(model)))
# inference encoder and decoder with MAS
y = model.inference_with_MAS(input_dummy, input_lengths, mel_spec, mel_lengths)
y2 = model.decoder_inference(mel_spec, mel_lengths)
assert (
y2["model_outputs"].shape == y["model_outputs"].shape
), "Difference between the shapes of the glowTTS inference with MAS ({}) and the inference using only the decoder ({}) !!".format(
y["model_outputs"].shape, y2["model_outputs"].shape
)
def _test_inference(self, batch_size):
input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids = self._create_inputs(
batch_size)
config = GlowTTSConfig(num_chars=32)
model = GlowTTS(config).to(device)
model.eval()
outputs = model.inference(input_dummy, {"x_lengths": input_lengths})
self._assert_inference_outputs(outputs, input_dummy, mel_spec)
def test_inference():
input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
input_lengths = torch.randint(100, 129, (8, )).long().to(device)
input_lengths[-1] = 128
mel_spec = torch.rand(8, c.audio["num_mels"], 30).to(device)
mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
speaker_ids = torch.randint(0, 5, (8, )).long().to(device)
# create model
model = GlowTTS(
num_chars=32,
hidden_channels_enc=48,
hidden_channels_dec=48,
hidden_channels_dp=32,
out_channels=80,
encoder_type="rel_pos_transformer",
encoder_params={
"kernel_size": 3,
"dropout_p": 0.1,
"num_layers": 6,
"num_heads": 2,
"hidden_channels_ffn": 16, # 4 times the hidden_channels
"input_length": None,
},
use_encoder_prenet=True,
num_flow_blocks_dec=12,
kernel_size_dec=5,
dilation_rate=1,
num_block_layers=4,
dropout_p_dec=0.0,
num_speakers=0,
c_in_channels=0,
num_splits=4,
num_squeeze=1,
sigmoid_scale=False,
mean_only=False,
).to(device)
model.eval()
print(" > Num parameters for GlowTTS model:%s" %
(count_parameters(model)))
# inference encoder and decoder with MAS
y, *_ = model.inference_with_MAS(input_dummy, input_lengths, mel_spec,
mel_lengths, None)
y_dec, _ = model.decoder_inference(mel_spec, mel_lengths)
assert (
y_dec.shape == y.shape
), "Difference between the shapes of the glowTTS inference with MAS ({}) and the inference using only the decoder ({}) !!".format(
y.shape, y_dec.shape)
def _test_inference_with_MAS(self, batch_size):
input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids = self._create_inputs(
batch_size)
# create model
config = GlowTTSConfig(num_chars=32)
model = GlowTTS(config).to(device)
model.eval()
# inference encoder and decoder with MAS
y = model.inference_with_MAS(input_dummy, input_lengths, mel_spec,
mel_lengths)
y2 = model.decoder_inference(mel_spec, mel_lengths)
assert (
y2["model_outputs"].shape == y["model_outputs"].shape
), "Difference between the shapes of the glowTTS inference with MAS ({}) and the inference using only the decoder ({}) !!".format(
y["model_outputs"].shape, y2["model_outputs"].shape)