semvec/pypi-packages huggingface dataset:
geass 0.2.2
Opinionated CLI for generating transcripts to use with LLMs
Requires Python: >=3.10

# Geass

CLI tool for audio transcription using `faster-whisper`. Supports local and remote
transcription (via [Modal](https://modal.com)), caching, and multiple output formats.

![PyPI](https://img.shields.io/pypi/v/geass)
![License](https://img.shields.io/badge/license-MIT-blue)
![Python Version](https://img.shields.io/badge/python-3.10%2B-blue?logo=python)
![wakapi.dev](https://wakapi.dev/api/badge/Kabilan108/interval:any/project:geass)

## Features

- Transcribes audio files using `faster-whisper` models (e.g., `base`, `large-v3`).
- Local transcription with CPU/GPU support or remote via Modal with GPU options (T4, A100, etc.).
- Output formats: plain text, JSON, SRT.
- Aggregates segments into larger chunks with `--interval`.

## Installation

```bash
pip install geass
```

Requires Python 3.10+. For local GPU use, ensure CUDA and PyTorch are set up.

## Usage

### List Available Models

```bash
geass list-models
```

Shows all `faster-whisper` models (e.g., `tiny`, `large-v3-turbo`).

### Transcribe Audio

```bash
geass transcribe audio.wav
```

Transcribes `audio.wav` with default settings (`base` model, text output).

#### Options

- `-m, --model`: Specify model (e.g., `-m large-v3`).
- `-f, --format`: Output format (`text`, `json`, `srt`). Default: `text`.
- `-s, --save`: Save transcription to database.
- `-r, --remote`: Use Modal for remote transcription.
- `-g, --gpu`: GPU type for remote (e.g., `-g A100`). Default: `A100`.
- `-i, --interval`: Aggregate segments into intervals (e.g., `-i 10` for 10s chunks).

#### Examples

Transcribe with a specific model and JSON output:
```bash
geass transcribe audio.mp3 -m large-v2 -f json
```

Remote transcription with saving:
```bash
geass transcribe audio.wav -r -s
```

Multiple files with SRT output:
```bash
geass transcribe file1.wav file2.mp3 -f srt
```

## Database

Transcriptions are cached in `~/.geass.db`. Tables:
- `transcripts`: Stores file metadata (path, duration, timings).
- `segments`: Stores transcription segments (start, end, text).
- `cache`: Maps file hashes to transcript IDs.
- `defaults`: Stores settings (model, etc.).

## Notes

- Local GPU transcription uses `float16` on CUDA, `int8` on CPU.
- Remote transcription requires a Modal account and setup.
- The output is wrapped in XML `<transcript>` tags.
toy-lang-fun 0.1.2
A toy functional programming language
Requires Python: >=3.8

# Toy Language Interpreter

A minimalist Scheme-like programming language interpreter written in Python. This project implements a custom programming language with support for basic arithmetic operations, conditionals, and functional programming concepts.

## Features

- Lambda functions and closures
- Basic arithmetic operations `(+, -, *, /)`
- Comparison operators `(<, >, <=, >=)`
- Boolean operations `(and, or, not)`
- Conditional statements `(if)`
- Local variable bindings
- Comments support

## Installation

```bash
# Using pip
pip install toy_lang
```

## Quick start

```lisp
; Hello World
(display "Hello, World!")

; Basic arithmetic
(+ 1 2 3)  ; => 6
(* 4 5)    ; => 20

; Lambda functions
(lambda (x) (* x x))

; Conditional statements
(if (> 5 3)
    "yes"
    "no")
```

## Language Features

Arithmetic Operations

* Addition: `(+ 1 2 3)`
* Subtraction: `(- 10 5)`
* Multiplication: `(* 2 3 4)`
* Division: `(/ 10 2)`

Comparison Operators

* Less than: `(< 5 10)`
* Greater than: `(> 10 5)`
* Less than or equal: `(<= 5 5)`
* Greater than or equal: `(>= 10 10)`


Boolean Operations

* And: `(and #t #f)`
* Or: `(or #t #f)`
* Not: `(not #t)`


Functions

```lisp
; Define a function
(lambda (x) (* x x))

; Using begin for multiple expressions
(begin
  (define square (lambda (x) (* x x)))
  (square 5))
```

## Development

To set up the development environment:
```bash
git clone https://github.com/M-krishna/toy_lang.git
cd toy_lang
```

## Contributing
* Fork the repository
* Create your feature branch (`git checkout -b feature/amazing-feature`)
* Commit your changes (`git commit -m 'Add some amazing feature'`)
* Push to the branch (`git push origin feature/amazing-feature`)
* Open a Pull Request


## License

This project is licensed under the MIT License - see the LICENSE file for details.
xlnumba 0.1.0
A package for converting excel files to Numba optimized python code
Requires Python: >=3.12
Keywords: excel, numba, optimization

# xlNumba

xlNumba is utility to convert Excel spreadsheets into optimized Python code using Numba.  This idea is to enable taking 
complex models built in Excel and converting them into high-performance Python code.

This library does not attempt to provide full Excel compatability.  Instead it focusses on providing a subset of capabilities
which can be compiled by Numba and optimized to provide high-performance execution.

In general numerical (and in the future date) related functions are supported while text functions have very limited 
support.  Excel has some fairly complex exception handling and type coercion rules which are not supported. 


## Basic Example

```
from xlnumba import Compiler

ctx = Compiler("tests/fixtures/basic")
ctx.add_input('Raw', 'A1')
ctx.add_output('Adjusted', 'B1')
fn = ctx.compile()
adjusted = fn(Raw=3.0)
```


## User Defined Functions example.

User defined functions can be created using the `@xlnumba_function` decorator. This decorator takes the name of the 
excel function.  Note that the function must be numba compilable. 

```
@xlnumba_function("EXCEL_FNC_NAME")
def custom_func(x):
    return 3 * x + 5
```

## Acknowledgements

The original idea for this was inspired from the excellent [pycel](https://github.com/dgorissen/pycel) library.
dictionary-en-gcide 0.1.0
A Python module for english dictionary lookup, based on gcide
Requires Python: >=3.6

# Dictionary Module

A Python module for English dictionary lookups based on free dictionary GCIDE.

## Installation

```bash
pip install dictionary_en_gcide
```

## Extraction of dictionary

The dictionary was extracted using the script extract_dict.py and the files from the GCIDE project, available in https://gcide.gnu.org.ua/.