def test_forward_attention(
self,
):
(
chars_seq,
chars_seq_lengths,
mel_spec,
mel_postnet_spec,
mel_lengths,
stop_targets,
speaker_ids,
) = self.generate_dummy_inputs()
for idx in mel_lengths:
stop_targets[:, int(idx.item()) :, 0] = 1.0
stop_targets = stop_targets.view(chars_seq.shape[0], stop_targets.size(1) // c.r, -1)
stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
model = Tacotron2(num_chars=24, r=c.r, num_speakers=5, forward_attn=True)
# training pass
output = model(chars_seq, chars_seq_lengths, mel_spec, training=True)
# check model output shapes
assert np.all(output[0].shape == mel_spec.shape)
assert np.all(output[1].shape == mel_spec.shape)
assert output[2].shape[2] == chars_seq.shape[1]
assert output[2].shape[1] == (mel_spec.shape[1] // model.decoder.r)
assert output[3].shape[1] == (mel_spec.shape[1] // model.decoder.r)
# inference pass
output = model(chars_seq, training=False)
def test_tflite_conversion(
self,
): # pylint:disable=no-self-use
model = Tacotron2(
num_chars=24,
num_speakers=0,
r=3,
out_channels=80,
decoder_output_dim=80,
attn_type="original",
attn_win=False,
attn_norm="sigmoid",
prenet_type="original",
prenet_dropout=True,
forward_attn=False,
trans_agent=False,
forward_attn_mask=False,
location_attn=True,
attn_K=0,
separate_stopnet=True,
bidirectional_decoder=False,
enable_tflite=True,
)
model.build_inference()
convert_tacotron2_to_tflite(model, output_path="test_tacotron2.tflite", experimental_converter=True)
# init tflite model
tflite_model = load_tflite_model("test_tacotron2.tflite")
# fake input
inputs = tf.random.uniform([1, 4], maxval=10, dtype=tf.int32) # pylint:disable=unexpected-keyword-arg
# run inference
# get input and output details
input_details = tflite_model.get_input_details()
output_details = tflite_model.get_output_details()
# reshape input tensor for the new input shape
tflite_model.resize_tensor_input(
input_details[0]["index"], inputs.shape
) # pylint:disable=unexpected-keyword-arg
tflite_model.allocate_tensors()
detail = input_details[0]
input_shape = detail["shape"]
tflite_model.set_tensor(detail["index"], inputs)
# run the tflite_model
tflite_model.invoke()
# collect outputs
decoder_output = tflite_model.get_tensor(output_details[0]["index"])
postnet_output = tflite_model.get_tensor(output_details[1]["index"])
# remove tflite binary
os.remove("test_tacotron2.tflite")
model = setup_model(num_chars, num_speakers, c)
checkpoint = torch.load(args.torch_model_path,
map_location=torch.device('cpu'))
state_dict = checkpoint['model']
model.load_state_dict(state_dict)
# init tf model
model_tf = Tacotron2(num_chars=num_chars,
num_speakers=num_speakers,
r=model.decoder.r,
postnet_output_dim=c.audio['num_mels'],
decoder_output_dim=c.audio['num_mels'],
attn_type=c.attention_type,
attn_win=c.windowing,
attn_norm=c.attention_norm,
prenet_type=c.prenet_type,
prenet_dropout=c.prenet_dropout,
forward_attn=c.use_forward_attn,
trans_agent=c.transition_agent,
forward_attn_mask=c.forward_attn_mask,
location_attn=c.location_attn,
attn_K=c.attention_heads,
separate_stopnet=c.separate_stopnet,
bidirectional_decoder=c.bidirectional_decoder)
# set initial layer mapping - these are not captured by the below heuristic approach
# TODO: set layer names so that we can remove these manual matching
common_sufix = '/.ATTRIBUTES/VARIABLE_VALUE'
var_map = [
('embedding/embeddings:0', 'embedding.weight'),
('encoder/lstm/forward_lstm/lstm_cell_1/kernel:0',
