semvec/pypi-packages huggingface dataset:
sbcli-expand 1.0.2
CLI for managing SimplyBlock cluster


# SBCLI (SimplyBlock CLI)

Simplyblock provides a feature-rich CLI (command line interface) to deploy SimplyBlock Cluster and to manage the SimplyBlock Control plane

## Install

The CLI can be installed using pypi package [sbctl](https://pypi.org/project/sbctl/)

```
pip install --upgrade sbctl
```

# Components

## Simply Block Core
Contains core logic and controllers for the simplyblock cluster

## Simply Block CLI
The list of all the available CLI options can be here [CLI docs](./simplyblock_cli/README.md)

## SimplyBlock Web API

Web app that provides API to manage the cluster. More about this in [README.md](./simplyblock_web/README.md)


### local development

FoundationDB requires a client library (libfdb_c.dylib) for the Python bindings to interact with the database.
Depending on the OS architecture, please install the appropriate version from the official github repo

```
wget https://github.com/apple/foundationdb/releases/download/7.3.3/FoundationDB-7.3.3_arm64.pkg
```
setup the code on a management node and the webApp code can be developed by building the `docker-compose-dev.yml` file.

```
sudo docker compose -f docker-compose-dev.yml up --build -d
```
maestro-mcp 0.1.4
The Official Maestro MCP for Claude Desktop, Cursor and other MCP clients.
Requires Python: >=3.11
Keywords: automation, maestro, mcp, mobile, testing

# Maestro MCP

A Python package for Maestro MCP.

## Installation

You can install the package via pip:

```bash
pip install maestro-mcp
```

Refer to the full [Maestro MCP installation documentation](https://docs.maestro.dev/mcp) for more details.
dmel 1.0.1
A python package for dmel log mel-filterbanks discretization.
Requires Python: >3.8

<div align="center">
  <h1>dMel: Discretized Log Mel-Filterbanks</h1>

  [![Paper](https://img.shields.io/badge/Paper-Arxiv%20Link-green)](https://arxiv.org/pdf/2407.15835)
  [![Blog](https://img.shields.io/badge/Blog-Apple%20ML-blue)](https://apple.github.io/dmel-demo)
</div>

This software project accompanies the research paper, [dMel: Speech Tokenization Made Simple](https://arxiv.org/pdf/2407.15835) by *Bai, He and Likhomanenko, Tatiana and Zhang, Ruixiang and Gu, Zijin and Aldeneh, Zakaria and Jaitly, Navdeep* on speech tokenization for speech generation and speech recognition.



Repository contains the `dmel` pytorch-based package which performs discretization of the log mel-filterbanks for the given audio to prepare speech representations for decoder model training which will be generative model of speech.

## Installation
- from pypi
```bash
pip install dmel
```
- from source
```bash
pip install .
```

## Example of usage
We have a snipped of code to run feature extraction for both dMel and Mel and plotting their representations.
To run example:

```bash
pip install torchaudio matplotlib dmel
python run_example.py
```

The example will generate `example_mel.png` and `example_dmel.png`

## License
Repository is under [LICENSE](LICENSE).

## Citation

```
@article{bai2024dmel,
  title={dMel: Speech Tokenization Made Simple},
  author={Bai, He and Likhomanenko, Tatiana and Zhang, Ruixiang and Gu, Zijin and Aldeneh, Zakaria and Jaitly, Navdeep},
  journal={arXiv preprint arXiv:2407.15835},
  year={2024}
}
```