semvec/pypi-packages huggingface dataset:
seavad 0.0.8
SeaVAD: Voice Activity Detection module with silero and state machine.
Requires Python: >=3.8

# SeaVAD

SeaVAD is a Python package for Voice Activity Detection (VAD) using a state machine version of SileroVAD os you can control the performance.

## Installation

You can install SeaVAD using pip:

```bash
pip install SeaVAD
```

## Usage

Here is a simple example of how to use SeaVAD:

```python
from seavad.main import SeaVAD

# Load your audio file
audio_path = 'path/to/your/audio/file.wav'
with open(test_audio, "r") as f:
    f.seek(44)
    audio_data = f.read()

# Local VAD onnx model path
model_file_path = 'path/to/vad/onnx/model'

# Create a SeaVAD object with the sample rate and sample width of your audio.
# Only 16000 and 8000 sample_rate supported
vad = SeaVAD(model_file_path=model_file_path, sample_rate=16000, sample_width=2)

# Detect voice activity
segments = vad.get_vad_segments(audio_data)

# Print the detected segments
for (start, end) in segments:
    print(f"Start: {start}, End: {end}")
```

## License

See the [LICENSE](LICENSE) file for details.

## Contact

For any questions or inquiries, please contact us at info@seasalt.ai
promptpy 0.1.0
A Python command line input and validation library
Requires Python: >=3.9
Keywords: cli, command line interface, prompt

# promptpy

**A Python command-line input and validation library.**

![version](https://img.shields.io/badge/version-0.1.0-blue)
![Python Version from PEP 621 TOML](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2Ffourtreestech%2Fpromptpy%2Fmain%2Fpyproject.toml)
![coverage](https://img.shields.io/badge/coverage-100%25-green)

**promptpy** prompts for and validates a range of different data types
on the command line. Out of the box it supports limited character sets,
integers, floats, dates, yes/no, lists and single-character commands:

    from promptpy import Prompt

    prompt = Prompt()
    choice = prompt.integer("Pick a number", min=1, max=10, default=7)
    # Prompt is displayed as:
    # Pick a number (1-10) [7]:

The user will be reprompted with context-specific error messages
until a valid integer is entered.

It is easy to add other types of prompt and validation by creating
custom validators.

For more detail see the [documentation](https://promptpy.readthedocs.io/).

## Installation

    (.venv) $ pip install promptpy

## Examples

### Options

    from promptpy import Prompt

    prompt = Prompt()
    options = {
        's': 'to solve',
        'p': 'to play',
        'q': 'to quit',
    }
    choice = prompt.options(options, default='s')
    # Prompt is displayed as:
    # Enter S to solve, P to play, Q to quit [S]:

### Yes/No

    from promptpy import Prompt

    prompt = Prompt()
    choice = prompt.yes_no("Again", default="y")
    # Prompt is displayed as:
    # Again (Y/N)? [Y]:

### Date

    import datetime
    from promptpy import Prompt

    prompt = Prompt()
    date = prompt.date('Enter a date (dd/mm/yyyy)', '%d/%m/%Y', default=datetime.date(2024, 1, 1))
    # Prompt is displayed as:
    # Enter a date (dd/mm/yyyy) [01/01/2024]:

See the [Usage](https://promptpy.readthedocs.io/en/latest/usage.html) section
of the documentation for a full list of methods, or the
[prompt](https://promptpy.readthedocs.io/en/latest/prompt.html) module for details
of methods and parameters.
fin-stresstest 0.1.3
Herramienta para análisis de escenarios de estrés financiero
Requires Python: >=3.7

# fin-stresstest

[![PyPI version](https://badge.fury.io/py/fin-stresstest.svg)](https://badge.fury.io/py/fin-stresstest)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python 3.7+](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/downloads/)

Una biblioteca Python para realizar pruebas de estrés financiero y análisis de escenarios de riesgo.

## Características

- Generación de escenarios de estrés basados en datos históricos
- Modelado de extremos mediante Teoría de Valores Extremos (EVT)
- Captura de dependencias no lineales con modelos de cópulas
- Simulación histórica y paramétrica
- Cálculo de métricas de riesgo (VaR, Expected Shortfall)
- Visualización avanzada de resultados
- Herramientas para análisis de sensibilidad
- Utilidades para manipulación de datos financieros
- Carga automática de datos desde Yahoo Finance

## Instalación

```bash
pip install fin-stresstest