semvec/pypi-packages huggingface dataset:
simple-diarizer 0.0.13
Simplified diarization pipeline using some pretrained models - audio file to diarized segments in a few lines of code
Requires Python: >=3.7

# simple_diarizer


[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1nMKHOTTROwQitOXQEYq35lvv7nyTOlpe?usp=sharing)
[![](https://shields.io/badge/Trello-simple__diarizer-blue?logo=Trello&style=flat)](https://trello.com/b/2ZN9ybC1/simplediarizer)

Simplified diarization pipeline using some pretrained models. 

Made to be a simple as possible to go from an input audio file to diarized segments.

```python
import soundfile as sf
import matplotlib.pyplot as plt

from simple_diarizer.diarizer import Diarizer
from simple_diarizer.utils import combined_waveplot

diar = Diarizer(
                  embed_model='xvec', # 'xvec' and 'ecapa' supported
                  cluster_method='sc' # 'ahc' and 'sc' supported
               )

segments = diar.diarize(WAV_FILE, num_speakers=NUM_SPEAKERS)

signal, fs = sf.read(WAV_FILE)
combined_waveplot(signal, fs, segments)
plt.show()
```

<p align="center">
  <img src="media/diarized_waveplot.png?raw=true">
</p>

### Install

Simplified diarization is available on PyPI:

```
pip install simple-diarizer
```

### Source Video

"[Some Quick Advice from Barack Obama!](https://youtu.be/I49VNQ6lmKk)"

[![YouTube Thumbnail](https://img.youtube.com/vi/I49VNQ6lmKk/0.jpg)](https://www.youtube.com/watch?v=I49VNQ6lmKk)


# Pre-trained Models

The following pretrained models are used:

 - Voice Activity Detection (VAD)
     - [Silero VAD](https://github.com/snakers4/silero-vad)
 - Deep speaker embedding extraction
     - [SpeechBrain](https://github.com/speechbrain/speechbrain)
        - [X-Vector](https://huggingface.co/speechbrain/spkrec-xvect-voxceleb)
        - [ECAPA-TDNN](https://huggingface.co/speechbrain/spkrec-ecapa-voxceleb)
 - (Optional/Experimental) Speech-to-text
     - [ESPnet Model Zoo](https://github.com/espnet/espnet_model_zoo)
        - English ASR model

# **[Demo](https://colab.research.google.com/drive/1nMKHOTTROwQitOXQEYq35lvv7nyTOlpe?usp=sharing)**

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1nMKHOTTROwQitOXQEYq35lvv7nyTOlpe?usp=sharing)

It can be checked out in the above link, where it will try and diarize any input YouTube URL.

# Other References

- Spectral clustering methods lifted from [https://github.com/wq2012/SpectralCluster](https://github.com/wq2012/SpectralCluster)


# Planned Features

[![](https://shields.io/badge/simple__diarizer-Trello-blue?logo=Trello&style=flat)](https://trello.com/b/2ZN9ybC1/simplediarizer)
backto1 0.0.3
A small command line tool for converting openLCA 2 JSON packages back to version 1.
Requires Python: >=3.11
Keywords: openLCA,life cycle assessment,LCA

# backto1

`backto1` is a small command line tool for converting JSON export packages
from openLCA 2 back to the version 1 format.

## Usage
You need to have Python >= 3.11 installed in order to run this tool. It can
be installed then via `pip`:

```bash
$ pip install backto1
```

To see if it is correctly installed, run

```bash
$ backto1 help

# or

$ python -m backto1 help
```

The usage is quite simple, you pass an openLCA 2 package as input into the
tool and optionally, after the `-o` flag the path of the output file that should
be created (by default the output is stored under `[input file]_backto1.zip`):

```bash
backto1 -o [output file] [input file]
```
flake8-import-order-pep8app 0.2.1
Flake8 Import Order's PEP 8 style with application packages enabled.

# Flake8 Import Order PEP 8 App

A simple package that allows application packages to be separated from third-party packages when using [Flake8 Import Order](https://github.com/PyCQA/flake8-import-order)'s PEP 8 style.
xpath-identifier 0.0.9
Identify Xpath expressions in a given document
Requires Python: >=3.7
Keywords: xpath,identifier,parser

# xpath-identifier
Given an html or similar input xpath-identifier returns the xpath of it's location in the input

## Usuage
```python
from xpath_identifier import search_html
test_html = open("src/tests/fixtures/test_html.html").read()
xpaths = search_html(html_fixture, "Send them a gift tracking link")
print(xpaths)
```
unique-characters-count-VS 0.0.1
A small app, that count unique character in string
Requires Python: >=3.7

# Unique_characters_count

App that count unique characters in the string.

Works as an imported module and also through command line arguments.

As an imported module usage:
number_of_uni_char('your_string')
number_of_uni_chars('list_of_your_string')

Command line usage:
"--string 'your string'"
"--file 'patch to you file, that contains the string'"

-- Added by Vitaly Solomatin 12.12.2022. Thanks to Eugene Chernyshov for finding the correct way.
moesif-blog-zh 2022.12.12.0
Moesif 博客中文翻译
Requires Python: >=3.6

# Moesif 博客中文翻译

> 原文：[Moesif Blog](https://www.moesif.com/blog/)
> 
> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)
> 
> 阶段：机翻（1）
> 
> 勇敢的人你可愿，为这份信念肩并肩？在这街垒的那一边，光明的未来可望见。你可听到这歌声？听鼓声在远方回响。未来与新希望同在，曙光在前方。——《悲惨世界》终曲
> 
> 列に入れよ、我らの味方に。砦の向こうに、憧れの世界。皆聞こえるか？ドラムの響が。彼ら夢見た明日が来るよ。
> 
> Will you joinin our crusade? Who will be strong and stand with me? Somewhere beyond the barricade, is there a world you long to see? Do you hear the people sing? Say, do youhear the distant drums? It is the future that they bring when tomorrow comes!

* [在线阅读](https://moesif.apachecn.org)
* [在线阅读（Gitee）](https://apachecn.gitee.io/doc-template/)
* [ApacheCN 学习资源](http://docs.apachecn.org/)
* [ApacheCN 翻译校对兼职群 713436582](https://jq.qq.com/?_wv=1027&k=VSNtgpjb)

## 贡献指南

为了不断改进翻译质量，我们特此启动了【翻译、校对、笔记整理活动】，开设了多个校对项目。贡献者校对一章之后可以领取千字2\~4元的奖励。进行中的校对活动请见[活动列表](https://home.apachecn.org/#/docs/activity/docs-activity)。更多详情请联系飞龙（Q562826179，V:wizardforcel）。

## 联系方式

### 负责人

* [飞龙](https://github.com/wizardforcel): 562826179

### 其他

*   在我们的 [apachecn/moesif-blog-zh](https://github.com/apachecn/moesif-blog-zh) github 上提 issue.
*   发邮件到 Email: `apachecn@163.com`.
*   在我们的 [组织学习交流群](https://www.apachecn.org/#/docs/join) 中联系群主/管理员即可.

## 下载

### Docker

```
docker pull apachecn0/moesif-blog-zh
docker run -tid -p <port>:80 apachecn0/moesif-blog-zh
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install moesif-blog-zh
moesif-blog-zh <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g moesif-blog-zh
moesif-blog-zh <port>
# 访问 http://localhost:{port} 查看文档
```

## 其它协议

霞鹜文楷采用 [SIL 开放字体协议 1.1](https://github.com/lxgw/LxgwWenKai/blob/main/SIL_Open_Font_License_1.1.txt)。

## 赞助我们

![](http://data.apachecn.org/img/about/donate.jpg)