semvec/pypi-packages huggingface dataset:
puretalk 0.1.2
Text-to-Speech (TTS) with natural human voice involves converting written text into spoken words using advanced machine learning models. These models are trained to produce speech that closely mimics the nuances, intonations, and rhythms of human speech, making the output sound more natural and lifelike.
Requires Python: >=3.9

# Puretalk-TTS

## Installation

To install the necessary dependencies, run the following command:

```sh
make install
```
This will install poetry and the required dependencies for the project.  

## Running the Script
To run the script, use the following command:
```angular2html
make run
```
You will be prompted to enter the text and voice ID. The script will then execute with the provided inputs.

install: Installs poetry and the project dependencies.
run: Prompts for text and voice ID.
commonutil-net-fileservice 0.1.9
Helper routine for file service
Requires Python: >=3.8

# commonutil-net-fileservice

This package provides helper routines for setting up file upload-and-process service.

## Limitation

### rsync over ssh

Nested rsync target folder is not supported. File update detection will not work correctly.

### moduli for DH-KEX

Pre-generated moduli will be needed for old clients.

Use system default one or generate your own copy with the following command:

```
ssh-keygen -M generate -O bits=4096 /tmp/moduli-4096.candidate
ssh-keygen -M screen -f /tmp/moduli-4096.candidate moduli-4096.passed
```
interaction-inference 0.0.1
Infer gene interaction via constrained optimization
Requires Python: >=3.8
Keywords: inference, optimization, scRNA-seq

# interaction-inference

Python package implementing an optimization based inference method to detect interaction between pairs of genes from cellular observations, together with tools for simulation of synthetic datasets and visualizations of performance.

For current research on the method applied to interaction detection see the repository [here](https://github.com/willjgh/M5R), and for an in depth explanation of the general optimization approach see my [MSci thesis](https://github.com/willjgh/Year-4-Project/blob/master/Report/01844345_WH.pdf).

## Installation

Package is available on PyPi and installable through pip via

```
pip install interaction-inference
```

## Background and Examples

For a brief overiew of biological background and example usage of the package see the [example notebook](https://github.com/willjgh/interaction-inference/blob/main/examples/example_notebook.ipynb), which shows how to load and simulate datasets, run analysis methods and compute performance metrics on simulated data.