semvec/pypi-packages huggingface dataset:
tts-webui-extension.demucs 0.0.4
Demucs is a music source separation model that can separate drums, bass, vocals, and other instruments

# Extension adapter for Demucs

License - the source code within this repository is licensed under the MIT license.

This extension provides a music source separation model that can separate drums, bass, vocals, and other instruments from audio tracks.

## Features

- Separate audio tracks into four components:
  - Drums
  - Bass
  - Vocals
  - Other instruments
- High-quality separation using the HT-Demucs model
- Simple and intuitive interface

## Usage

1. Upload an audio file (MP3, WAV, etc.)
2. Click "Separate" to process the audio
3. Download or listen to the separated components

## About Demucs

Demucs (Deep Extractor for Music Sources) is a state-of-the-art music source separation model developed by Facebook Research. It uses a hybrid architecture combining U-Net and Transformer components to effectively separate music into its constituent parts.

For more information, visit the [official Demucs repository](https://github.com/facebookresearch/demucs).
mp_reader 0.1.2
Add your description here
Requires Python: >=3.13

# mp_reader: an analyzer for [mem_profile](https://github.com/codeinred/mem_profile)

This repo holds the implementation of `mp_reader`, which is an analyzer for the
files output by `mem_profile`.

## mem_profile: an Ownership-Aware Memory Profiler

mem_profile is an ownership-aware memory profiler, designed for direct
measurement of objects and their members.

What does this mean?

When a program is executed with mem_profile, we can see the number of bytes
allocated by types within the program.

![alt text](https://raw.githubusercontent.com/codeinred/mp_reader/main/images/objects_output.png)

Above is some sample output, produced by the analyzer (mp_reader, this project).

This is a toy example, but mem_profile is designed for use with complex
workloads, and has been tested on projects involving hundreds or even thousands
of translation units.

For instance, here's an output of type-level stats from profiling an FTXUI demo
application:

![alt text](https://raw.githubusercontent.com/codeinred/mp_reader/main/images/ftxui-demo.png)

And here's output from profiling CMake:

![alt text](https://raw.githubusercontent.com/codeinred/mp_reader/main/images/cmake-demo-types.png)

For more information, see the
[mem_profile repo](https://github.com/codeinred/mem_profile)!
biobb-adapters 5.1.2
Biobb_adapters is the Biobb module collection to use the building blocks with several workflow managers.
Requires Python: <=3.12,>=3.9
Keywords: Bioinformatics Workflows BioExcel Compatibility

# biobb_adapters

### Introduction
Biobb_adapters is the Biobb module collection to use the building blocks with several workflow managers.
Biobb (BioExcel building blocks) packages are Python building blocks that
create new layer of compatibility and interoperability over popular
bioinformatics tools.
<!-- The latest documentation of this package can be found in our readthedocs site:
[latest API documentation](http://biobb_adapters.readthedocs.io/en/latest/). -->

### Version
5.1.2 2025.1

### Copyright & Licensing
This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU Horizon Europe [101093290](https://cordis.europa.eu/project/id/101093290), EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).

* (c) 2015-2025 [Barcelona Supercomputing Center](https://www.bsc.es/)
* (c) 2015-2025 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/)

Licensed under the
[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.

![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")