TensorflowTTS | 超快的中文普通话TTS

by Z-yq Python Version: Current 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. TensorflowTTS has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However TensorflowTTS build file is not available. You can download it from GitHub.

超快的中文普通话TTS

Support

Quality

Security

License

Reuse

Support

TensorflowTTS has a low active ecosystem.

It has 11 star(s) with 4 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. On average issues are closed in 2 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of TensorflowTTS is current.

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 not available. You will need to build from source code and install.

TensorflowTTS has no build file. You will be need to create the build yourself to build the component from source.

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

It has 6712 lines of code, 530 functions and 51 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.

Performs dynamic decoding
Performs a single step
Run the decoder
Extract data from train
Call the encoder
Setup window
Setup target
Extract textors from the audio data
Smooth the energy of the given wavelength
Read an audio file
Load a wav file
Performs a training step
Inverse inference function
One step for one step
Plot a spectrogram
Call the model
Synthesize the input texts
Synthesize a sentence
Synthesize the given text using the speech model
Performs a single training step
Loads the model
Build the neural network
Merge two arrays
Inverse of mel spectrogram
Compiles the model
Compute the weight and output

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.