semvec/pypi-packages huggingface dataset:
social-gpt 0.0.19
Social gpt

# Social GPT
This package helps you to build embeddings based on someone social profile. It will scrape the data from the social media and build the embeddings based on the data. The embeddings can be used for further analysis.
Once scraped, you can query it with the use of openai.

## Installation
Install using following command:
`pip install social-gpt`

## Setup
Update the `example.env` and change it's name to `.env`. Add relevant information in the `.env` file.

## Creating embeddings

As of now, only youtube is supported. Also, as of now, we will scrape 50 videos, which we will extend to all of the videos shortly. We will be bringing more social media platforms soon. To create embeddings, run the following command:
To get the channel id, go to this [website](https://commentpicker.com/youtube-channel-id.php) and enter the channel url. It will give you the channel id. 
``` python
from social_gpt.ingestion.ingestion import SocialIngestion
ingestion = SocialIngestion(channel_id, 'youtube')
index_id = ingestion.create_embeddings()
```
Store this `index_id` for querying the embeddings.

## Querying embeddings

``` python
from social_gpt.query.query_helper import QueryHelper
q = QueryHelper(index_id)
response = q.query('What is the best way to learn python?')
print(response)
```

That's it!
syntheon 0.1.0
Parameter inference of music synthesizers to simplify sound design process.
Requires Python: >=3.7

![syntheon_logo](docs/syntheon-logo.png)

# Syntheon

Syntheon - [Pantheon](https://en.wikipedia.org/wiki/Pantheon,_Rome) for music synthesizers. 

Syntheon provides **parameter inference** for music synthesizers using *deep learning models*. Given an audio sample, Syntheon infers the best parameter preset for a given synthesizer that can recreate the audio sample. 

**Check out [this presentation](https://docs.google.com/presentation/d/1PA4fom6QvCW_YG8L0MMVumrAluljcymndNlaK2HW5t0/edit?usp=sharing) on the recent advances of synth parameter inference.

For now: 
- :heavy_check_mark: [Vital](https://vital.audio/) is supported
- [Dexed](https://asb2m10.github.io/dexed/) is work-in-progress

## Installation

```
python3 -m pip install syntheon
```

## Usage

```python
from syntheon import infer_params

output_params_file, eval_dict = infer_params(
    "your_audio.wav", 
    "vital", 
    enable_eval=True
)
```

## Testing

```
python3 -m pytest
```

## Structure

For each synthesizer, we need to define:

- **converter** for preset format conversion: 
    - `serializeToDict`: convert preset file to a Python dictionary to be handled by inferencer
    - `parseToPluginFile`: convert Python dictionary back to preset file, to be loaded by the synthesizer

- **inferencer** for model inference:
    - `convert`: define the workflow of `load_model` -> `inference` -> `convert_to_preset`

## Contribution

Syntheon is actively under development, and contributions are welcomed. Some TODOs we have in mind include:

- Replicating state-of-the-art approaches
- Improving current model performance
- Incorporating new synthesizers 
- Code refactoring 😅
janscalckernel 1.1.0
A jupyter calc kernel.
Requires Python: >=3.7

# janscalckernel

![Logo](https://raw.githubusercontent.com/jans-code/janscalckernel/main/janscalckernel/logo-svg.svg)

A simple and robust jupyter kernel implementation of [calc](http://www.isthe.com/chongo/tech/comp/calc/), because sometimes you just want to calculate.

## Installation

- install calc from your distro's package manager or from [here](https://github.com/lcn2/calc/releases).
- get the kernel module via pip
- `pip install janscalckernel`
- then install kernelspec
- `janscalckernel`

## Dev Installation

- download/clone this project
- open shell in project folder
- `pip install -e ./`
- `jupyter kernelspec install --user janscalckernel`

## Uninstall

- `jupyter kernelspec uninstall janscalckernel`
- `pip uninstall janscalckernel`