semvec/pypi-packages huggingface dataset:
tinycrate 0.1.5
A lightweight RO-Crate library
Requires Python: >=3.11

# tinycrate

A minimal RO-Crate library in Python with an emphasis on working transparently
with crates on disk and crates over the network.

## Usage

    from tinycrate.tinycrate import TinyCrate

    tc_from_json = TinyCrate(jsonld)
    tc_from_url = TinyCrate(url)
    tc_from_disk = TinyCrate(crate_path)

    r = tc_from_url.root()

    for entity in tc_from_url.all():
        for prop.value in entity.items():
            print(f"{prop}: {value}"")
        if entity.type == "File":
            contents = entity.fetch()
            print(contents)
upseeker 0.0.9
InsideOpt UpSeeker Linux Distribution
Requires Python: >=3.9.0
Keywords: insideopt, seeker, optimization

===========
upseeker
===========

InsideOpt UpSeeker Linux Distribution

Credits
-------

This package was created with Cookiecutter_.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
soundpadrc 1.0.1
Control Soundpad with Python
Requires Python: >=3.8

![PyPI - Version](https://img.shields.io/pypi/v/soundpadrc)

# soundpadrc

Python library for controlling [Soundpad](https://www.leppsoft.com/soundpad/en/) via the [Soundpad Remote Control API](https://www.leppsoft.com/soundpad/en/rc/).

## Installation

To install soundpadrc from PyPI:

```bash
pip install soundpadrc
```

To install release candidate versions:

```bash
pip install --pre --upgrade -i https://test.pypi.org/simple/ soundpadrc
```

## Example Usage

```python
from soundpadrc import Soundpad

sp = Soundpad()

print(sp.categories())

print(sp.query_sounds("hobbit"))
```

## References

- https://github.com/Ilya-Kokhanovsky/soundpad.py
- https://www.leppsoft.com/soundpad/files/rc/SoundpadRemoteControl.java
- https://www.leppsoft.com/soundpad/en/rc/

## Python packaging

https://packaging.python.org/en/latest/tutorials/packaging-projects/  
https://github.com/pypa/sampleproject  
https://python-semantic-release.readthedocs.io/en/latest/  
https://python-semantic-release.readthedocs.io/en/latest/automatic-releases/github-actions.html  
https://packaging.python.org/en/latest/guides/publishing-package-distribution-releases-using-github-actions-ci-cd-workflows/
ioseeker 0.2.7
InsideOpt Seeker Distribution for Windows, Linux, and Mac
Requires Python: >=3.9.0
Keywords: insideopt, seeker, optimization

================
ioseeker
================

InsideOpt Seeker Windows, Mac, and Linux Distribution

Credits
-------

This package was created with Cookiecutter_.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
ginseng 0.2.1
Efficient and accurate annotation for single-cell sequencing data
Requires Python: >=3.12

# ginseng

`ginseng` is a python package for accurate, and somewhat efficient, cell type annotation using [`jax`](https://github.com/jax-ml/jax) and [`zarr`](https://zarr.dev/).

## Installation

`ginseng` can be installed using `pip`

```bash
pip install ginseng
```

Or, added to your project using `uv`

```bash
uv add ginseng
```

## License

`ginseng` is licensed under the MIT License (see LICENSE).

You may not use this file except in compliance with the license. A copy of the license has been included in the root of the repository. Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the MIT license, shall be licensed as above, without any additional terms or conditions.
WrightTools 3.6.2
Tools for loading, processing, and plotting multidimensional spectroscopy data.
Requires Python: >=3.7
Keywords: spectroscopy, science, multidimensional, visualization

WrightTools
-----------

``WrightTools`` loads, processes, and plots multidimensional spectroscopy data.

"Multidimensional spectroscopy" (MDS) is a family of diverse analytical techniques that record the response of a material to multiple stimuli---typically multiple ultrafast pulses of light.
Due to its diversity and dimensionality, MDS data is challenging to process and visualize.
``WrightTools`` is a freely available and openly licensed Python package that is made specifically for multidimensional spectroscopy.
It aims to be a core toolkit that is general enough to handle all MDS datasets and processing workloads.
Being built for and by MDS practitioners, ``WrightTools`` has an intuitive, high-level, object-oriented interface for spectroscopists.

Documentation at `wright.tools <http://wright.tools>`_.

.. image:: https://anaconda.org/conda-forge/wrighttools/badges/version.svg
   :target: https://anaconda.org/conda-forge/wrighttools

.. image:: https://badge.fury.io/py/WrightTools.svg
   :target: https://badge.fury.io/py/WrightTools

.. image:: https://readthedocs.org/projects/wrighttools/badge/?version=stable
   :target: http://wright.tools/en/stable/?badge=stable

.. image:: https://readthedocs.org/projects/wrighttools/badge/?version=latest
   :target: http://wright.tools/en/latest/?badge=latest

.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
   :target: https://github.com/psf/black

.. image:: http://joss.theoj.org/papers/10.21105/joss.01141/status.svg
   :target: https://doi.org/10.21105/joss.01141

.. image:: https://raw.githubusercontent.com/wright-group/WrightTools/master/logo/logo.png
   :width: 400
   :alt: WrightTools
   :align: center
ebook-reader-supertonic 1.3.3
A high-quality Flow-Matching based Text-to-Speech library using ONNX.
Requires Python: >=3.9

# ebook-reader-supertonic

A high-quality Flow-Matching based Text-to-Speech library using ONNX. This is a Python port of the Supertonic-2 web implementation.

## Features
- **10 Unique Voice Styles**: Professional male and female voices.
- **Auto-Downloader**: Automatically fetches models from HuggingFace to a global cache (`~/.cache/ebook_reader_supertonic`).
- **Word Timestamps**: Heuristic estimation by default, with optional Vosk-based extraction (offline ASR) for better word timing.
- **Adjustable Parameters**: Control speed (0.9 - 1.4) and diffusion steps (3 - 14).
- **Lightweight Inference**: Runs on CPU/GPU via ONNX Runtime.

## Installation

```bash
pip install ebook-reader-supertonic
```

## Quick Start

```python
from ebook_reader_supertonic import SupertonicTTS, VOICE_STYLES, MIN_SPEED, MAX_SPEED

# 1. Initialize engine
# Models are automatically cached in ~/.cache/ebook_reader_supertonic
engine = SupertonicTTS()

# 2. Synthesize
# Returns:
# - audio: np.ndarray (float32, normalized -1 to 1)
# - sample_rate: int (44100)
# - word_timestamps: List[Dict] -> [{'word': str, 'start': float, 'end': float}]
audio, sr, word_timestamps = engine.synthesize(
    text="Hello! Welcome to ebook-reader-supertonic.", 
    voice='F5', 
    speed=1.0, 
    steps=10,
    # timestamps_backend="auto",  # 'estimate' (default), 'vosk', or 'auto'
    # vosk_model_path="path/to/vosk/model",  # or set env VOSK_MODEL_PATH
)
```

### Vosk auto-download (optional)
If you use `timestamps_backend="auto"` or `"vosk"` and no model path is configured, the package can auto-download the pinned model `vosk-model-en-us-0.22-lgraph` into `~/.cache/vosk`.

Environment variables:
- `VOSK_MODEL_PATH`: use an existing local model directory (disables download).
- `VOSK_CACHE_DIR`: override cache base (default `~/.cache/vosk`).
- `VOSK_OFFLINE=1`: forbid downloads (error for `"vosk"`, fallback to estimate for `"auto"`).
- `EBOOK_READER_VOSK_AUTO_DOWNLOAD=0`: disable auto-download behavior.

# 3. Calculate Total Duration
duration = len(audio) / sr
print(f"Generated {duration:.2f}s of audio")

# 4. Access Word Timing
for segment in word_timestamps:
    print(f"{segment['word']}: {segment['start']}s -> {segment['end']}s")

# 5. Save to file
engine.save_wav(audio, "output.wav")
```

## API Reference

### `SupertonicTTS.synthesize(text, voice='M3', steps=10, speed=1.0, lang=None)`
- **Parameters**:
  - `text` (str): Text to synthesize.
  - `voice` (str): Voice ID (`F1-F5`, `M1-M5`).
  - `steps` (int): Diffusion steps (`MIN_STEPS=3` to `MAX_STEPS=14`).
  - `speed` (float): Speed factor (`MIN_SPEED=0.9` to `MAX_SPEED=1.4`).
  - `lang` (str): Manual language override (e.g., 'en', 'ko'). Auto-detects if None.
- **Returns**: `(audio_data, sample_rate, word_timestamps)`

### `VOICE_STYLES`
A list of Pydantic models containing voice metadata:
```python
voice = VOICE_STYLES[0]
print(voice.id)          # 'F1'
print(voice.gender)      # 'female'
print(voice.description) # 'Correct and natural...'
```

## Author
Izzet Sezer <sezer@imsezer.com>