semvec/pypi-packages huggingface dataset:
vochunk 1.0.0
Audio effects classifiers for voice
License: MIT

# Voice Quality Classifier

What do we want? Clean vocal acapellas! When do we want them? Now!

# Cascaded Sorting

![flowchart](imgs/flowchart.png)

This repository hosts several models that are meant to cascade results. The output of the final classifier is audio ready to be used in training a Singing Voice Conversion (SVC) model.

## State of Research

-   [x] Voice vs. Non-voice classifier
-   [x] Reverb vs. Dry classifier
-   [ ] Polyphony vs. Monophony classifier
-   [ ] Multi-voice unison vs. Single-voice unison

# Installation

This package was developed using Python 3.11.4, and thus that version of python is recommended.

Clone this repository and make it the current directory in the terminal.

The, open a new virtual environment and install the requirements.

```console
pip install -r requirements.txt
```

## Model Weights

Download model weights from my dropbox.

```console
cd voicequalityclassifier
wget https://www.dropbox.com/scl/fi/w6yqkhzpq4l8wpw53zrjk/weights.zip?rlkey=3ju9ofcn93wdklzc3sr8nlezz&dl=1 -O weights.zip
unzip weights.zip
cd ..
```

# Usage

For some example audio download a few files with this command:

```console
wget https://www.dropbox.com/scl/fi/sclv9z73xobw2hbagjqob/audio.zip?rlkey=3cq04ds7ymt5q7d4v1dzkfgib&dl=1 -O audio.zip
unzip audio.zip
```

The `voicequalityclassifier` is a package that can also be run as a module. To run the whole classification scheme, run

```console
./env/bin/python voicequalityclassifier/main.py --input path/to/audio.wav
```

In the console, you will see the classification results. If you see:

```console
for file path/to/audio.mp3 detected nonvoice
```

This means that no voice was detected (i.e. the source is an instrumental stem or stereo mix).
If you see:

```console
for file path/to/audio.mp3 detected voice
for file path/to/audio.mp3 detected reverb
```

This means there was reverb detected on the track.

Finally, if you see:

```console
for file path/to/audio.mp3 detected voice
for file path/to/audio.mp3 detected dry
```

Then we're in business and we have a dry acapella file.

```console
for file path/to/audio.mp3 detected voice
for file path/to/audio.mp3 detected reverb
```

This means there was reverb detected on the track.

Finally, if you see:

```console
for file path/to/audio.mp3 detected voice
for file path/to/audio.mp3 detected dry
```

Then we're in business and we have a dry acapella file.
pyphenopop 0.0.2
Phenotypic deconvolution in heterogeneous cancer cell populations using drug screen data
License: MIT
Requires Python: >=3.8.0

Mixed Cell Population Identification Package
=======================
[![PyPI version](https://badge.fury.io/py/pyphenopop.svg)](https://badge.fury.io/py/pyphenopop)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7323577.svg)](https://doi.org/10.5281/zenodo.7323577)



This package contains methods designed to determine the existance of 
subpopulations with different responses to a given drug from a dataset of 
screenings with that drug on the total cell population. The implementation is
based on the method presented in the article 
["Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data"](https://doi.org/10.1016/j.crmeth.2023.100417).
The dataset should contatin cell viability data tested on C different drug concentrations over 
N time points and containing R replicates. The package can then estimate the 
number of subpopulations, their mixture proportions and a dose-reponse curve 
for each of the subpopulations. A Matlab package of the method is available at https://github.com/ocbe-uio/PhenoPop.

## Install
The package can be easily install via `pip install pyphenopop`. You can also install it from the [Github repository](https://github.com/ocbe-uio/pyPhenoPop) using 

`pip install git+https://github.com/ocbe-uio/pyPhenoPop.git` 

or by cloning the repository

`git clone https://github.com/ocbe-uio/pyPhenoPop` 

and installing from the local repository via

`pip install .`

## Usage

A tutorial using data from the original [publication](https://doi.org/10.1016/j.crmeth.2023.100417) is provided in [`examples/tutorial.ipynb`](https://github.com/ocbe-uio/pyPhenoPop/blob/main/examples/tutorial.ipynb). Additional information can be obtained by executing

```
from pyphenopop.mixpopid import mixture_id
help(mixture_id)
```

## Publication

When using pyPhenoPop in your project, please cite
* Köhn-Luque, A., Myklebust, E. M., Tadele, D. S., Giliberto, M., Schmiester, L., Noory, J., ... & Foo, J. (2023). Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data. Cell Reports Methods, 3(3).
```
@article{kohn2023phenotypic,
  title={Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data},
  author = {Alvaro Köhn-Luque and Even Moa Myklebust and Dagim Shiferaw Tadele and Mariaserena Giliberto and Leonard Schmiester and Jasmine Noory and Elise Harivel and Polina Arsenteva and Shannon M. Mumenthaler and Fredrik Schjesvold and Kjetil Taskén and Jorrit M. Enserink and Kevin Leder and Arnoldo Frigessi and Jasmine Foo},
  journal={Cell Reports Methods},
  volume={3},
  number={3},
  year={2023},
  doi={https://doi.org/10.1016/j.crmeth.2023.100417},
  publisher={Elsevier}
}

```
databricks-azure-ad-sync-provider 0.1.4
Default template for PDM package
License: MIT
Requires Python: ==3.9.*

# databricks-azure-ad-sync-provider

`databricks-azure-ad-sync-provider` is a package that allows to sync users, groups and service principals from Microsoft Entra ID to Databricks.

## Requirements

`databricks-azure-ad-sync-provider` is built on top of:

* python 3.9+

#### Authentication

Authenticate to you Microsoft Entra ID and Azure Databricks to use the package.
There are multiple ways available for that, we suggest the following:

**1. With Azure CLI**

+ Authenticate to your Azure account on your machine with `az login`.
+ Specify necessary variables
  1. With environment variables: Setup for `DATABRICKS_HOST` and `DATABRICKS_ACCOUNT_ID`
  2. With a `.databrickscfg` file: Create the `.databrickscfg` file (~ for Linux or macOS, and %USERPROFILE% for Windows) containing the
     following info:

  ```
  [DEFAULT]
  host = https://accounts.azuredatabricks.net/
  account_id = <Databricks account id>
  ```

**2. With Microsoft Entra ID service principal:**

+ Create a service principal in Microsoft Entra ID and add it to Azure Databricks and grant it target permissions (see [reference documentation](https://learn.microsoft.com/en-gb/azure/databricks/dev-tools/service-principals)).
+ Specify necessary variables

  1. With environment variables

     - For Azure: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET (see [Microsoft Entra ID authentication](https://learn.microsoft.com/en-us/azure/communication-services/quickstarts/identity/service-principal?pivots=platform-azcli))
     - For Databricks: DATABRICKS_HOST, DATABRICKS_ACCOUNT_ID, DATABRICKS_CLIENT_ID, DATABRICKS_CLIENT_SECRET (see [Databricks authentication](https://docs.databricks.com/en/dev-tools/auth/oauth-m2m.html#language-Environment))
  2. With a `.databrickscfg` file: Create a .databrickscfg file (~ for Linux or macOS, and %USERPROFILE% for Windows) containing the following info:

  ```
  [DEFAULT]
  host = https://accounts.azuredatabricks.net/
  account_id = <Databricks account id>
  azure_tenant_id = <Azure tenant id>
  azure_client_id = <Azure service principal application ID>
  azure_client_secret = <Azure service principal secret>
  ```

## Installation

```
pip install databricks-azure-ad-sync-provider
```

## Usage

**Sync execution**
  ```
<fct_name> -f/--file <path to your yaml file> -d/--delete
  ```
+ -f/--file <path to your yaml file>:  it's required to provide the object ID(s) of Azure groups and (optionally) exclude object ID(s) in a yaml file (ex. see test.yaml).
+ -d/--delete: If provided, this enablew to delete identities in Databricks.