UrbanSounds | SONYC project - UrbanSounds - dataset of sound recordings | Dataset library

by cyrta Python Version: Current License: AGPL-3.0

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | UrbanSounds Summary

UrbanSounds is a Python library typically used in Artificial Intelligence, Dataset applications. UrbanSounds has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However UrbanSounds build file is not available. You can download it from GitHub.

Repository to recreate methods and results of SONYC project Author: Justin Salomon - github profile Juan P. Bello Charlie Mydlarz. Further information including news updates, publications, and project collaborators is available at the SONYC website:

Support

Quality

Security

License

Reuse

Support

UrbanSounds has a low active ecosystem.

It has 9 star(s) with 4 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 1814 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of UrbanSounds is current.

Quality

UrbanSounds has no bugs reported.

Security

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

License

UrbanSounds is licensed under the AGPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

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

UrbanSounds 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.

Top functions reviewed by kandi - BETA

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

Sort the centroids .
Normalise and normalise the data .
Runs the training process .
Prepare the data according to the norm .
Computes the log likelihood function .
Prepare_a_a_a .
The main function .
Prepare a data array .
Randomly sample a random vector .
Unit normalise a vector .

Get all kandi verified functions for this library.

UrbanSounds Key Features

No Key Features are available at this moment for UrbanSounds.

UrbanSounds Examples and Code Snippets

No Code Snippets are available at this moment for UrbanSounds.

Community Discussions

Trending Discussions on UrbanSounds

Import wav file in Tensorflow 2

Distributed Tensorflow: Internal Error - Blas GEMM launch failed

QUESTION

Import wav file in Tensorflow 2

Asked 2019-Oct-18 at 10:17

Using Python 3.7 and Tensorflow 2.0, I'm having a hard time reading wav files from the UrbanSounds dataset. This question and answer are helpful because they explain that the input has to be a string tensor, but it seems to be having a hard time getting past the initial metadata encoded in the file, and getting to the real data. Do I have to preprocess the string before being able to load it as a float32 tensor? I already had to preprocess the data by downsampling it from 24-bit wav to 16-bit wav, so the data-input pipeline is turning out to be much more cumbersome than I would have expected. The required downsampling is particularly frustrating. Here's what I'm trying so far:

...

ANSWER

Answered 2019-Oct-16 at 09:22

It seems that your code fails for dual channel audio file. The code works for mono channel wav file. In your case you can try using scipy.

Source https://stackoverflow.com/questions/58295764

QUESTION

Distributed Tensorflow: Internal Error - Blas GEMM launch failed

Asked 2018-Sep-03 at 10:32

I am experimenting with distributed Tensorflow and started with two processes on localhost (Windows 10, Python 3.6.6, Tensorflow 1.8.0). Each process runs a replica of simple Neural Network (1-hidden layer), modeled for a subset of UrbanSounds dataset (5268 samples with 193 features each).

Following this well-written post: https://learningtensorflow.com/lesson11/ I could repeat their basic example, calculating mean from results of two distinct processes. For my dataset, I modified the code as follows, to divide the total samples into two half and let two distinct processes compute the cost function separately. But after the RPC server is started successfully, both processes end up in following error:

InternalError (see above for traceback): Blas GEMM launch failed : a.shape=(263, 193), b.shape=(193, 200), m=263, n=200, k=193

[[Node: MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:local/replica:0/task:0/device:GPU:0"](_recv_Placeholder_0_G7, w1/read)]]

It appears to me some basic mistake with neural network configuration OR preparing datasets for feed_dict, but I am unable to see that so need another pair of eyes. Another observation during this experiment is that GPU mostly shooted to max and code aborted. Please assist me with any mistake in code or strategy to distribute the Tensorflow?

Thank you.

...

ANSWER

Answered 2018-Sep-03 at 10:32

Simply moving the given code to Ubuntu 16.04.4 LTS, solved the said problem for me.

I am not sure but this seems to be something related to GRPC+Fiewall on Windows 10.

If anybody come across BLASS error on Windows and could solve it on Windows, then please post the solution for rest of us.

Cheers.

Source https://stackoverflow.com/questions/52041077

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

Vulnerabilities

No vulnerabilities reported

Install UrbanSounds

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

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: