semvec/pypi-packages huggingface dataset:
audio-denoiser 0.1.2
A Python library for (speech) audio denoising.
Requires Python: >=3.9

This Python library reduces substantial background noise 
in audio files containing speech. It uses a machine 
learning model (38m parameters) trained to handle 
different types of ambient noise.

## Installation

    pip install audio-denoiser

In Windows, you need the `soundfile` audio backend:

    pip install soundfile

In Linux, both the `soundfile` and `sox` audio backends should be supported. Note that the library is trained with the `soundfile` backend.

## Usage

Basic:

    from audio_denoiser.AudioDenoiser import AudioDenoiser
    
    denoiser = AudioDenoiser()
    in_audio_file = '/content/input-audio-with-noise.wav'
    out_audio_file = '/content/output-denoised-audio.wav'
    denoiser.process_audio_file(in_audio_file, out_audio_file)

With additional options:

    from audio_denoiser.AudioDenoiser import AudioDenoiser
    import torch
    import torchaudio
    
    # Use a CUDA device for inference if available
    device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
    denoiser = AudioDenoiser(device=device)
    in_audio_file = '/content/input-audio-with-noise.wav'
    out_audio_file = '/content/output-denoised-audio.wav'
    auto_scale = True # Recommended for low-volume input audio
    denoiser.process_audio_file(in_audio_file, out_audio_file, auto_scale=auto_scale)

You can also provide your own waveform tensor:

    from audio_denoiser.AudioDenoiser import AudioDenoiser

    noisy_waveform, sample_rate = torchaudio.load('/content/input-audio-with-noise.wav')
    denoiser = AudioDenoiser()
    denoised_waveform = denoiser.process_waveform(noisy_waveform, sample_rate, auto_scale=False)
    print('Tensor shape: ', denoised_waveform.shape)
hoainamcuto 0.0.1
A small example package
Requires Python: >=3.8

-----

<p align="center">
<img class="HuynhKhoiDepTrai" src="https://png.pngtree.com/thumb_back/fw800/background/20230527/pngtree-an-anime-girl-in-a-beautiful-pose-with-beautiful-flowers-image_2698613.jpg" alt="KhoiHuynh1109">
</p>

-----

### <p align="center">🫨 TeleBot Obfuscation 🫨</p>
<p align="center">
<strong>
Hello, with this source code, you can code more accordingly
<br>It's great if you use it
</strong>
</p>

-----

### <p align="center">👽 How To Use? 👽</p>
<p align="center">
<strong>
You Can Use It By Downloading Then Launching<br>
Or Download And Upload It To <a href="https://replit.com">Replit</a>  And Launching
</strong>
</p>

**Setup**:<br>
```
pkg install git
```
**Commands**:<br>
```
git clone https://github.com/huynhminhkhoi888/telebot_obfuscation
```
```
cd telebot_obfuscation
```
```
python main.py
```
**Attention**:<br>
<p align="center">
<strong>Remember Instead API_TOKEN_BOT<br> and USER_ADMIN_BOT</strong>
</p>

-----
### <p align="center">🙉 Help Contact 🙉</p>
<p align="center">
<strong>
Click <a href="https://www.facebook.com/valerie.alvares">Here </a>To Contact!
<br></br>
<img src="https://png.pngtree.com/thumb_back/fw800/background/20230613/pngtree-anime-girl-is-sitting-in-front-of-some-blossoms-image_2923953.jpg"
</strong>
</p>

-----
academic-markdown 0.2.8
CLI for converting Markdown to Latex using pandoc and Docker.
Requires Python: >=3.7

# Academic Markdown CLI

This is the command line tool used in the
[academic_markdown](https://github.com/cochaviz/academic_markdown) project.
While there should be a discrete README for this project, for now I would like
to ask anyone to move there for more information on the project.