semvec/pypi-packages huggingface dataset:
pprint-torrent 1.0.0
Pretty-print the contents of torrent files!
Requires Python: >=3.8, <4
Keywords: torrents,commandline,shell,script

# pprint_torrent

Pretty-print the contents of torrent files!

pprint_torrent can be used to inspect the contents of torrent files. The metainfo dict is pretty-printed
with indendation of sub-levels. The maximum depth can be limited as well. To keep the output compact, the long
binary `pieces` hash sequence is hidden from the output automatically. `pieces root` hashes from v2 torrents are
hexlified (both in the `file tree` and in the `piece layers` dict).

## Usage

Commandline parameters:

    usage: pprint_torrent.py [-h] [-i INDENT] [-w WIDTH] [-d DEPTH] torrent_file

    Pretty-print a given torrent file's metainfo dict to stdout.

    positional arguments:
    torrent_file          Path to the torrent file

    options:
    -h, --help            show this help message and exit
    -i INDENT, --indent INDENT
                            Indentation width (default: 2)
    -w WIDTH, --width WIDTH
                            Maximum width of a line (default: 200)
    -d DEPTH, --depth DEPTH
                            Maximum depth to show (default: unlimited)

Example command:

    $ pprint_torrent.py kubuntu-20.04-desktop-amd64.iso.torrent -w 100

Output:

    { b'announce': b'https://torrent.ubuntu.com/announce',
      b'comment': b'Kubuntu CD cdimage.ubuntu.com',
      b'creation date': 1587648815,
      b'info': { b'length': 2354036736,
                 b'name': b'kubuntu-20.04-desktop-amd64.iso',
                 b'piece length': 1048576,
                 b'pieces': '44900 bytes (hidden in this output)'}}

## Installation

Execute:

    pip install pprint_torrent
audclas 0.0.6
Package that uses extracted audio classification model from neonbjb/DL-Art-School - for filtering fine audio files.
Requires Python: >=3.7

# AudClas

Package that uses audio classification model extracted from neonbjb/DL-Art-School - for filtering fine audio files.

# Classes

Classifier model returns one of 6 possible labels:

| label | class name |
| ----- | ---------- |
| 0     | fine       |
| 1     | env_noise  |
| 2     | music      |
| 3     | two_voices |
| 4     | reverb     |
| -     | unknown    |

# Installation

`pip install audclas`

# examples of usage

For single audio file:

```py
    from audclas.tortoise_audio_classifier import TortoiseAudioClassifier
    classifier = TortoiseAudioClassifier()

    label = classifier('wavs/test.wav')

    print(label)
```

For directory containing audio files (it searches recursively for all .wav / .mp3 files):

```py
    from tqdm import tqdm
    from audclas.tortoise_audio_classifier import TortoiseAudioClassifier
    classifier = TortoiseAudioClassifier()

    batch_size = 32
    do_classify, total = classifier.prepare_classify_dir_job('/content/wavs', batch_size)

    fine_audio_paths = []

    for result in tqdm(do_classify(), total=total):
        for audio_path, audio_label in result:
            if audio_label == 'fine':
                fine_audio_paths.append(audio_path)

    print(f'directory contains {len(fine_audio_paths)} fine files (total files: {total * batch_size})')
```
gold-miner-ui 1.5
A UI plugin that adds graphical extensions to gold-miner
Requires Python: >=3.6

# About gold-miner-ui

This package merely adds additional modules to the [gold-miner] package
for generating live graphs.  It is distributed separately only to
ensure base gold-miner package contains less installation requirements.

[gold-miner]: https://github.com/hardaker/gold-miner