Tacotron-2 | DeepMind's Tacotron-2 Tensorflow implementation | Speech library
kandi X-RAY | Tacotron-2 Summary
kandi X-RAY | Tacotron-2 Summary
DeepMind's Tacotron-2 Tensorflow implementation
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Top functions reviewed by kandi - BETA
- Create training
- Overriding replace method
- Return a debug string for debugging
- Start background threads
- Performs a training step
- Expand global features
- Sample from the discretized mixture of logistics
- Sample from a Gaussian distribution
- Inverse of a linear spectrogram
- Enqueue next train
- Expand a number
- Inverse of Mel spectrogram
- Parse a CMU file
- Inverse convolution of mel spectrogram
- Synthesize audio from text
- Prepare checkpoint
- Create a WaveNet model
- Plot the alignment
- Adds tower loss
- Inverse of magnitude
- Build utterance from path
- Compute the loss
- Process an utterance
- Add an optimizer to the optimizer
- Adds an optimizer
- Forward a single step
Tacotron-2 Key Features
Tacotron-2 Examples and Code Snippets
Lines of Code : 23License : Permissive (MIT)
CopyTacotron-2
├── datasets
├── LJSpeech-1.1 (0)
│ └── wavs
├── logs-Tacotron (2)
│ ├── mel-spectrograms
│ ├── plots
│ ├── pretrained
│ └── wavs
├── papers
├── tacotron
│ ├── models
License : No LicenseCopyProblem med OOM (får slut på minne i grafikkortet):
tacotron_batch_size
outputs_per_step (kallas för "reduction factor = r" i Tacotron-artikeln)
Manlig vs kvinnlig talare:
fmin
fmax
Griffin_lim:
power
Annat:
cleaners
tacotron_test_size
hop_ style-token_tacotron // project root dir
├── datasets
│ └── __init__.py
│ └── audio.py
│ └── preprocessor.py
│ └── wavenet_preprocessor.py
...
├── data_thchs30 // THCHS-30 dataset, this is *a fol Community Discussions
Trending Discussions on Tacotron-2
QUESTION
I'm trying to run tacotron2 on docker within Ubuntu WSL2 (v.20.04) on Win10 2004 build. Docker is installed and running and I can run hello world successfully.
(There's a nearly identical question here, but nobody has answered it.)
When I try to run docker build -t tacotron-2_image docker/ I get the error:
unable to prepare context: unable to evaluate symlinks in Dockerfile path: lstat /home/nate/docker/Dockerfile: no such file or directory
So then I navigated in bash to where docker is installed (/var/lib/docker) and tried to run it there, and got the same error. In both cases I created a docker directory, but kept getting that error in all cases.
How can I get this to work?
...ANSWER
Answered 2020-Aug-16 at 16:52As mentioned here, the error might have nothing to do with "symlinks", and everything with the lack of Dockerfile, which should be in the Tacotron-2/docker folder.
docker build does mention:
The docker build command builds Docker images from a Dockerfile and a “context”.
A build’s context is the set of files located in the specifiedPATHor URL.
In your case, docker build -t tacotron-2_image docker/ is supposed to be executed in the path you have cloned the Rayhane-mamah/Tacotron-2 repository.
To be sure, you could specify said Dockerfile, but that should not be needed:
QUESTION
I was earlier able to browse the github repo at https://github.com/r9y9/Tacotron-2/blob/master/wavenet_vocoder/models/wavenet.py easily in browser, so that when I put cursor on top of jResidualConv1dGLU at Line84, it'd highlight and let me click on "Definition" and "References" of class ResidualConv1dGLU.
But I used the same repo in the same browser today, and it doesn't do anything. It doesn't highlight ResidualConv1dGLU or show links for Definition/References of it. It's as if it doesn't know that it's a class.
Is there some default setting needed to enable that? What am I missing?
PS: (It was working a few days ago, so I am not sure what changed in just a few days)
...ANSWER
Answered 2020-Jun-24 at 06:09What might have changed yesteraday (June 23, 2020) is "Design updates to repositories and GitHub UI"
Try and make sure to clear the cache of your browser and reload everything.
That being said, when clicking on "Jump to", I see:
"Code navigation not available for this commit", which is expected for a fork.
But I see the same issue on the original repository Rayhane-mamah/Tacotron-2.
Those repositories needs to be re-scanned by GitHub, as I mentioned here.
QUESTION
I am trying to implement Tacotron speech synthesis with Tensorflow in Google Colab using this code form a repo in Github, below is my code and working good till the step of using localhost server, how I can to run a localhost server in a notebook in Google Colab?
My code:
...ANSWER
Answered 2020-Mar-09 at 02:50You can do this by using tools like ngrok or remote.it
They give you a URL that you can access from any browser to access your web server running on 8888
Example 1: Tunneling tensorboard running on
QUESTION
My folder structure:
...ANSWER
Answered 2019-Jun-03 at 00:47This is because, when running ttsTacotron.py, Python looks up all non-relative imported modules in the directory containing ttsTacotron.py (and in the system module directories, which isn't relevant here), yet hparams.py is in the Tacotron-2 directory. The simplest fix is probably to add Tacotron-2 to the list of directories in which modules are looked up; this also eliminates the need to use importlib.
QUESTION
I want to ask you how we can effectively re-train a trained seq2seq model to remove/mitigate a specific observed error output. I'm going to give an example about Speech Synthesis, but any idea from different domains, such as Machine Translation and Speech Recognition, using seq2seq model will be appreciated.
I learned the basics of seq2seq with attention model, especially for Speech Synthesis such as Tacotron-2. Using a distributed well-trained model showed me how naturally our computer could speak with the seq2seq (end-to-end) model (you can listen to some audio samples here). But still, the model fails to read some words properly, e.g., it fails to read "obey [əˈbā]" in multiple ways like [əˈbī] and [əˈbē].
The reason is obvious because the word "obey" appears too little, only three times out of 225,715 words, in our dataset (LJ Speech), and the model had no luck.
So, how can we re-train the model to overcome the error? Adding extra audio clips containing the "obey" pronunciation sounds impractical, but reusing the three audio clips has the danger of overfitting. And also, I suppose we use a well-trained model and "simply training more" is not an effective solution.
Now, this is one of the drawbacks of seq2seq model, which is not talked much. The model successfully simplified the pipelines of the traditional models, e.g., for Speech Synthesis, it replaced an acoustic model and a text analysis frontend etc by a single neural network. But we lost the controllability of our model at all. It's impossible to make the system read in a specific way.
Again, if you use a seq2seq model in any field and get an undesirable output, how do you fix that? Is there a data-scientific workaround to this problem, or maybe a cutting-edge Neural Network mechanism to gain more controllability in seq2seq model?
Thanks.
...ANSWER
Answered 2018-Jun-21 at 08:09I found an answer to my own question in Section 3.2 of the paper (Deep Voice 3). So, they trained both of phoneme-based model and character-based model, using phoneme inputs mainly except that character-based model is used if words cannot be converted to their phoneme representations.
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Install Tacotron-2
You can use Tacotron-2 like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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