TensorFlowTTS | Time State-of-the-art Speech Synthesis | Speech library

by TensorSpeech Python Version: v1.8 License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | TensorFlowTTS Summary

TensorFlowTTS is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Speech, Deep Learning, Tensorflow applications. TensorFlowTTS has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

:stuck_out_tongue_closed_eyes: TensorFlowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2 (supported including English, French, Korean, Chinese, German and Easy to adapt for other languages)

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Quality

Security

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Support

TensorFlowTTS has a medium active ecosystem.

It has 3375 star(s) with 732 fork(s). There are 79 watchers for this library.

It had no major release in the last 12 months.

There are 3 open issues and 671 have been closed. On average issues are closed in 78 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of TensorFlowTTS is v1.8

Quality

TensorFlowTTS has 0 bugs and 0 code smells.

Security

TensorFlowTTS has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

TensorFlowTTS code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

TensorFlowTTS is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

TensorFlowTTS releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

TensorFlowTTS saves you 7128 person hours of effort in developing the same functionality from scratch.

It has 15236 lines of code, 720 functions and 118 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed TensorFlowTTS and discovered the below as its top functions. This is intended to give you an instant insight into TensorFlowTTS implemented functionality, and help decide if they suit your requirements.

Preprocess the input data
Save features to file
Save the statistics to a file
Parse arguments
Generate audio features
Performs ph - based trims
Removes outliers from a series
Check if x is outliers
Generate and save intermediate results
Build the model
Creates a new dataset
Computes the per - example loss for each generator
Inverse inference step
Load a model from pretrained
Compute the per - example loss
Collates audio data
Calculate per - example loss
Call the encoder
Create a tensorflow
Call encoder step
Computes the per - example metrics for each sample
Generate intermediate results
Fix training and validation
Create dataset
Performs the inference step
One step per iteration

Get all kandi verified functions for this library.

TensorFlowTTS Key Features

No Key Features are available at this moment for TensorFlowTTS.

TensorFlowTTS Examples and Code Snippets

No Code Snippets are available at this moment for TensorFlowTTS.

Community Discussions

Trending Discussions on TensorFlowTTS

Sending Protocol Buffer encoded message from Python Server to Java Client

QUESTION

Sending Protocol Buffer encoded message from Python Server to Java Client

Asked 2020-Nov-04 at 21:37

I'm writing a little server that uses protocol buffer to encode some data.

TCP Socket is opened between Android Client and Python Server
Android Client sends string for processing as normal newline delimited utf-8.
Python Server does some processing to generate a response, which gives an Array of Int Arrays: [[int]]. This is encoded in the protocol buffer file:

...

ANSWER

Answered 2020-Nov-04 at 21:37

OK, I worked this out...

In the case where you have a short-lived connection, the socket closing would signify the end of the payload, so no extra logic is required.

In my case, I have a long-lived connection, so closing the socket to signify the end of the payload wouldn't work.

With a Java Client & Server, you could get around this by using:

MessageLite.writeDelimitedTo(OutputStream)

then on the recipient side:

MessageLite.parseDelimitedFrom(InputStream).

Easy enough...

But in the Python API, there is no writeDelimitedTo() function. So instead we must recreate what writeDelimitedTo() is doing. Fortunately, it's simple. It simply adds a _VarintBytes equal to the payload size to the beginning of the message!

Source https://stackoverflow.com/questions/64669310

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install TensorFlowTTS

You can download it from GitHub.
You can use TensorFlowTTS 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.