semvec/pypi-packages huggingface dataset:
pitchsqueezer 0.1.0
Robust pitch tracker for speech, using synchrosqueezing and spectral autocorrelation
Requires Python: >=3.7
Keywords: speech analysis,pitch,prosody,tts

# PitchSqueezer
A pitch tracker for speech, using synchro-squeezed stft and frequency domain autocorrelation, designed to analyze imperfect recordings of spontaneous speech.
Relies heavily on the nice [ssqueezepy](https://github.com/OverLordGoldDragon/ssqueezepy/tree/master/ssqueezepy]) package.


Features:
* (mostly) better quality than popular Python trackers (pyin, pyaapt) 
* robust to creaky voices and bad recordings
* does not need finetuning for min and max f0, works fine with both low and high pitched voices
* acceptable speed (~ 10x faster than librosa.pyin)
* (alternatively) a continuous pitch track, filling unvoiced gaps relatively naturally
* a wavelet decomposition of the pitch track (reversible, except for mean value) 
* compatible with librosa's pyin and pytorch_audio spectrograms, regarding number of frames
* a command line tool for parallel batch processing of directories (as well as API)

Installation: 
```
soon: pip install pitchsqueezer
```
Meanwhile, download the repository, and in the directory where pyproject.toml resides, do
```
pip install .
```

Examples of basic usage:
```
;; Command line, extract f0 for all wavs in a directory using 10 ms frame shift, save as numpy files
$ pitch_squeezer path/to/wavs/ -r 100 -f npy

;; API
import pitch_squeezer as ps
f0, if0 = ps.track_pitch(input_file, min_hz=50, max_hz=500)
f0_cwt = ps.f0_cwt(if0)
```
docs: https://asuni.github.io/PitchSqueezer/

Visualization of the method: <img src="images/Figure_1.png">



 comparison with librosa.pyin on creaky female voice: <img src="images/squeezer_vs_pyin.png">
korouge-score 0.1.4
Pure python implementation of ROUGE-1.5.5. for korean
Requires Python: >=3.7

# Rouge Implementation for Korean
original source code from https://github.com/google-research/google-research/tree/master/rouge

기존 rouge_scorer는 알파벳과 숫자를 제외하고는 제거했기 때문에 한국어로는 계산하려면 별도로 처리가 필요했습니다.
tokenize.py 에서 해당 부분을 주석처리한 뒤 pypi로 새롭게 배포했습니다.
따라서 불용어나 특수문자 제거, stemmer처리 등은 별도로 진행해야 합니다.
```
pip install korouge_score
```

```python
from korouge_score import rouge_scorer

scorer = rouge_scorer.RougeScorer(["rouge1", "rouge2", "rougeL", "rougeLsum"])

ref = "안녕하세요 반가워요 오늘 뭐 먹을래요?"
pred = "안녕 ㅋ 반가워 오늘 뭐 할래?"

print(scorer.score(ref, pred))
>>> {'rouge1': Score(precision=0.3333333333333333, recall=0.4, fmeasure=0.3636363636363636), 'rouge2': Score(precision=0.2, recall=0.25, fmeasure=0.22222222222222224), 'rougeL': Score(precision=0.3333333333333333, recall=0.4, fmeasure=0.3636363636363636), 'rougeLsum': Score(precision=0.3333333333333333, recall=0.4, fmeasure=0.3636363636363636)}
```
pygoml 0.0.1
PyGOML: A Python Library for Solving Combinatorial Optimization Problems using Decision Diagram based Branch-and-Bound in combination with Machine Learning

# PyGOML: Decision Diagram based Branch-and-Bound in combination with Machine Learning

Under constructio! Not ready for use yet! Currently experimenting and planning.
Developed by Abdelouahed Ben Mhamed, a Ph.D. student at the Moroccan International Center for Artificial Intelligence (AI Movement).