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 = ResNetSpeakerEncoder(input_dim=80, proj_dim=256)
# computing d vectors
output = model.forward(dummy_input)
assert output.shape[0] == 4
assert output.shape[1] == 256
output = model.forward(dummy_input, l2_norm=True)
assert output.shape[0] == 4
assert output.shape[1] == 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,
num_eval=10)
assert output.shape[0] == 1
assert output.shape[1] == 256
assert len(output.shape) == 2
def setup_model(c):
if c.model_params["model_name"].lower() == "lstm":
model = LSTMSpeakerEncoder(
c.model_params["input_dim"],
c.model_params["proj_dim"],
c.model_params["lstm_dim"],
c.model_params["num_lstm_layers"],
)
elif c.model_params["model_name"].lower() == "resnet":
model = ResNetSpeakerEncoder(input_dim=c.model_params["input_dim"], proj_dim=c.model_params["proj_dim"])
return model
def setup_speaker_encoder_model(config: "Coqpit"):
if config.model_params["model_name"].lower() == "lstm":
model = LSTMSpeakerEncoder(
config.model_params["input_dim"],
config.model_params["proj_dim"],
config.model_params["lstm_dim"],
config.model_params["num_lstm_layers"],
use_torch_spec=config.model_params.get("use_torch_spec", False),
audio_config=config.audio,
)
elif config.model_params["model_name"].lower() == "resnet":
model = ResNetSpeakerEncoder(
input_dim=config.model_params["input_dim"],
proj_dim=config.model_params["proj_dim"],
log_input=config.model_params.get("log_input", False),
use_torch_spec=config.model_params.get("use_torch_spec", False),
audio_config=config.audio,
)
return model