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License: Apache License 2.0
CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages
License: Apache License 2.0
Hi,
I would like to freeze the pretrained Tacotron model (French) but I can't figure out what the output node names are. I tried various tools to visualize the model but none of them succeeded on my (old) machine because of the model size.
Would you mind sharing this information?
Thank you for your support and for publishing your work publicly.
Hi there, I am new to this project.
Would you please give an example of using pre-trained model to synthesize a new audio?
Dear @Kyubyong
I found another mixed up link.
The link for the Finnish audio samples for tacotron points to the Dutch ones (https://soundcloud.com/kyubyong-park/sets/ms10_nl_t).
The correct link should be: https://soundcloud.com/kyubyong-park/sets/ms10_fi_t
Regards!
Hi, I have a problem with russian model
FailedPreconditionError (see above for traceback): Attempting to use uninitialized value embedding/lookup_table
[[Node: embedding/lookup_table/read = IdentityT=DT_FLOAT, _class=["loc:@embedding/lookup_table"], _device="/job:localhost/replica:0/task:0/cpu:0"]]
Manual alignment of audiobooks is a waste of time.
Could you make a database available in Brazilian Portuguese? If not, could you guide me on how to train one like the databases you made available?
In automatically extracted sentences, both can appear. Looks like numbers can be handled by NTLK but "(" seems harder to handle, since they are associated to an "inflection" in the intonation.
Very small issue, but the links to the models and samples are swapped in in readme for Spanish.
i'm not found vocab in model DCTSS for japanese, you can share with me, thank.
Hi,
I was able to test the synthesize function (synthesize.py) on CPU with success.
But when I tried to use GPU, I have faced with different issues.
First, I tried to use tensorflow-gpu==1.3.0, but according to this chart: https://www.tensorflow.org/install/source#gpu, it requires CUDA 8, and according to this list: https://gitlab.com/nvidia/container-images/cuda/blob/master/doc/supported-tags.md, I could use only Ubuntu 16.04 with an nvidia docker base image for CUDA 8.0, but I have failed with the installation of the requirements on Ubuntu 16.04.
As a second step, I have tried to use tensorflow-gpu==1.5.0 with CUDA 9, but the Nvidia base image for Ubuntu 18.04 support only CUDA 9.2, and not 9.0, and those looks uncompatible...
As a third step, I have tried tensorflow-gpu==1.13.1 with CUDA 10.0, with a CUDA 10.0 based Ubuntu 18.04 base docker image.
Finally, tensorflow can detect the GPU, but the session initialization (sess,run()) takes forever, and eats up all the GPU memory.
I have tried to limit the memory usage, and then the session initialization could finish after more than 4 minutes, but the Feed Forward just stuck at the very beginning, no progress at all within a few minutes.
Any ideas or suggestion? What am I doing wrong?
Thanks!
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