semvec/pypi-packages huggingface dataset:
pyometrics 0.1.1
A library for creating and comparing voice biometrics using GMM models.
Requires Python: >=3.6

# pyometrics

A Python library for creating and comparing voice biometrics using Gaussian Mixture Models (GMMs).
You can train a biometric model from a set of audio files and then compare new samples against that model to get a similarity score.

## Features

- MFCC + Delta Feature Extraction  
- GMM Training via scikit-learn  
- User-Defined Minimum Required Audio Files (min_files)  
- Supports .wav, .mp3, .m4a, .flac by default

## Installation

Install from PyPI:

```bash
pip install pyometrics
```

## Usage

1. Train (or create) a voice biometric:

```python
from pyometrics import create_voice_biometric

create_voice_biometric(
    folder_path="path/to/audio/folder",
    model_path="my_biometric.gmm",
    min_files=5  # specify how many audio files must exist
)
```

- The library expects at least ``min_files`` audio files in the folder.
- The GMM model is saved to the file ``my_biometric.gmm``.

2. Compare a new audio sample:

```python
from pyometrics import compare_voice_biometric

score = compare_voice_biometric(
    audio_path="path/to/new_audio.mp3",
    model_path="my_biometric.gmm"
)
print("Similarity Score (log-likelihood):", score)
```

- A higher (less negative) log-likelihood indicates closer similarity.

## Example

Suppose you have at least 10 .mp3 files of the same speaker in ``samples_for_biometric/``. You can do:

```python
from pyometrics import create_voice_biometric, compare_voice_biometric

create_voice_biometric(
    folder_path="samples_for_biometric",
    model_path="example.gmm",
    min_files=10
)

score = compare_voice_biometric(
    audio_path="test_sample.m4a",
    model_path="example.gmm"
)
print("Score:", score)  # The higher, the more similar
```

## Requirements

- Python 3.6+  
- librosa  
- numpy  
- scikit-learn

## License
This project is licensed under the [MIT License](LICENSE).
bytelogger 2.1.3
bytelogger: Is a simple tool for logging and debuging your code. You can easyly turn debug_mode and log mode on or off.
Requires Python: >=3.6

# bytelogger

## What is bytelogger?

`bytelogger` is an very light weight logging tool for any python application. It can log the hole console but also make it prettier and more reliable. 

You can easy turn the logs or the debug_mode on or off with boolean (true and false statments).

## How to use it?

### debug_mode:

In the debug mode you define wich type of ouput you want to have.

Here are the diffrent types:

#### The dev_debug, is ment for just the developer, where you can see wich function or class got executed, for better debuging.
- dev_debug

#### The debung is a normal debuging mode where you can see for example that file X got created or that the execution was succesfully.
- debug

#### The warn is a warn message or debug, that is ment for example mode X is disabled or could not load module X.
- warn

#### The error debug is, as it names says it. Ment for the error messages. It says error loading module X or couldnt finde crusel file or module X.
- error

Here is an code example how to use it:

```python

import bytelogger as log

log.debug("Succesfully created.")
↓
[DEBUG] 22:22Uhr 10.07.2025 = Succesfully created.


log.debug_dev("Entered function show_example_log().")
↓
[DEV DEBUG] 22:22Uhr 10.07.2025 = Entered function show_example_log().


log.warn("The main process is disabled.")
↓
[WARN] 22:22Uhr 10.07.2025 = The main process is disabled.


log.debug("Failed to load or find module show_example_log().")
↓
[ERROR] 22:22Uhr 10.07.2025 = Failed to load or find module show_example_log().

```

When you want to turn the debug_mode off, to use for example you own tool or just normal prints, or when you dont need them right now.

You can disable them following way:
```python
import bytelogger


bytelogger.init(debug_mode = false)
```

### log_mode:

Its very simple. Basicly every console output for example the debug_mode ↑ would be logged. Or in short terms any output. 

When you just import the normal bytelogger, it automaticly logs every thing. 

When you dont want him to log every thing automaticly (for example just for the debug_mode or just so), you just need to turn it off by saying:

```python
import bytelogger

bytelogger.init(log_mode = false)
```
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`made by nobyte`