codejin / gst_tacotron Goto Github PK

it should be

self.layer.add(tf.keras.layers.Conv1D(
                filters= filters,
                kernel_size= kernel_size,
                strides= stride,
                padding= 'same',
                activation=None,
                use_bias=False
                ))
self.layer.add(tf.keras.layers.BatchNormalization())
self.layer.add(tf.keras.layers.ReLU())

Hello,
I'm gonna train this model with my datasets. How can I start? Where should the files to be placed?
thanks!

Hi,
thanks for the great repo! I noticed that the checkpoint link (https://drive.google.com/open?id=1zhpJt5VM1jpG4NapsDUKGT_BAr_ZEsfM) leads to a "Error 404" page. Would you be able to update the link?
Thanks,
Ewald

Hi,
Is there any chance for a pretrained model(created from the checkpoint) that outputs the Mel Spectrogram from text(without wav)?
Because when I tried to load the checkpoint I get the following output:

==================================================================================================
Total params: 506,720
Trainable params: 505,824
Non-trainable params: 896

Checkpoint 'Checkpoint/S_36000.CHECKPOINT.H5' is loaded.
Inference running...
2020-04-21 10:41:16.278946: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2020-04-21 10:41:17.310366: W tensorflow/stream_executor/gpu/redzone_allocator.cc:312] Internal: Invoking GPU asm compilation is supported on Cuda non-Windows platforms only
Relying on driver to perform ptx compilation. This message will be only logged once.
2020-04-21 10:41:17.599566: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

And then the program just stops without any warning.
Hope for your help

When I try to load the the pretrained weights I get the following Warnings. I tested the model on the provided example in Inference section of README.md and compared the spectrograms, the alignments and the stop tokens with the ones in your Result section, the overall shape is similar but there is some differences. So I think these Warnings are responsible for the differences. Do you have any idea how can these warnings be resolved ?

WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.embeddings.Embedding object at 0x7f22d8be95f8> and <tensorflow.python.keras.layers.convolutional.Conv1D object at 0x7f22d8be9a90>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.Conv1D object at 0x7f22d8be9a90> and <tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7f2334790c18>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7f2334790c18> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x7f22d2d562e8>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.Conv1D object at 0x7f22d2d569b0> and <tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7f22d2d11518>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7f22d2d11518> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x7f22d2d11c18>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.Conv1D object at 0x7f22d2d11cc0> and <tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7f22d2d292b0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7f22d2d292b0> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x7f22d2d29e48>).