semvec/pypi-packages huggingface dataset:
TOPSIS-Ayush-Jain-101803690 0.0.4
Implementation of TOPSIS
Requires Python: >=3.0
Keywords: topsis pandas rank

# TOPSIS Implementation by Ayush Jain 101803690	
This package will get topsis score for selected csv file.

## Important Details
Input/Output Files:

Input File

-> Input file contain three or more columns

-> First column is the object/variable name (e.g. M1, M2, M3, M4...)

-> From 2nd to last columns contain numeric values only

Output Files
-> Result file contains all the columns of input file and two additional columns having TOPSIS SCORE and RANK

The parameters for the topsis functions are as follows:
-> The input file name. It must end with .csv or .txt. You can give the complete file path if you want to read a file from some specific place

-> The weights string which is comma seperated for each weight.
   Example: "1,1,2,3"

-> The impact string which is comma seperated for each impact and determines if weight at that index is to be taken positive or negative.
   Example: "-,+,+,+"

-> The output file name. It must end with .csv or .txt. You can give the complete path along with file name to save the file at specific place. Otherwise, it will save it as default where the package is installed.

The package has only one function topsis(4 parameters(all necessary else exceptions occur)). 

Sample Use case:

```
import TOPSIS_AyushJain_101803690 as t
t.topsis("input.csv","1,1,1,1","+,+,+,+","output.csv")
TOPSIS-Ria-101803043 0.0.1
Find the Topsis Score

# TOPSIS-Ria-101803043

TOPSIS-Ria-101803043 is a package that will provide feature to do multi-criteria decision making in choosing the best models among the data provided.

  - It will provide with the TOPSIS Score.
  - It will do Ranking of Models on the basis of the given data.

# TOPSIS:

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method. TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution. 


### Installation
TOPSIS-Ria-101803043 requires [Python](https://www.python.org/) v2.7+ to run.

Install the package using pip as follows :

```sh
$ pip install TOPSI-Ria-101803043
```

### HOW TO USE THIS PACKAGE

TOPSIS-Ria-101803043 can be run as in the following examples:


**IN PYTHON IDLE :**



### Sample Dataset
The decision matrix (a) will be extracted from the csv file as the pandas dataframe which will contain each row representing a Model alternative, and each column representing a criterion like Accuracy, RSeq, Root Mean Squared Error, Correlation, and many more.

| Model | Correlation | RSeq | RMSE | Accuracy
| ------ | ------ | ----- | ----- | --------
| M1 | 0.79 | 0.62 | 1.25 | 60.89 
| M2 | 0.66 | 0.44 | 2.89 | 63.07
| M3 | 0.56 | 0.31 | 1.57 | 62.87
| M4 | 0.82 | 0.67 | 2.68 | 70.19
| M5 | 0.75 | 0.56 | 1.3 | 80.39

Weights (w) is not already normalised will be normalised later in the code.
Information of impacts positive(+) or negative(-) impact criteria should be provided in (im).

**No of weights and no of impacts should be equal to no. of columns in dataset excluding the first column** 



License
----

MIT License

Copyright (c) 2020 Ria Kapoor

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
univoc 0.2.1
A PyTorch implementation of Towards Achieving Robust Universal Neural Vocoding.
Requires Python: >=3.6
Keywords: Speech Synthesis,Neural Vocoder,Text-to-Speech,PyTorch

# Towards Achieving Robust Universal Neural Vocoding

A PyTorch implementation of [Towards Achieving Robust Universal Neural Vocoding](https://arxiv.org/abs/1811.06292).
Audio samples can be found [here](https://bshall.github.io/UniversalVocoding/).

<div align="center">
    <img width="788" height="508" alt="Architecture of the vocoder." 
      src="https://github.com/bshall/UniversalVocoding/raw/master/univoc.png"><br>
    <sup><strong>Fig 1:</strong>Architecture of the vocoder.</sup>
</div>

## Quick Start

Ensure you have Python 3.6 and PyTorch 1.7 or greater installed. Then install the package with:
```
pip install univoc
```

## Example Usage

```python
import torch
import soundfile as sf
from univoc import Vocoder

# download pretrained weights (and optionally move to GPU)
vocoder = Vocoder.from_pretrained(
    "https://github.com/bshall/UniversalVocoding/releases/download/v0.2/univoc-ljspeech-7mtpaq.pt"
).cuda()

# load log-Mel spectrogram from file or tts
mel = ...

# generate waveform
with torch.no_grad():
    wav, sr = vocoder.generate(mel)

# save output
sf.write("path/to/save.wav", wav, sr)
```

## Train from Scratch

1. Clone the repo:
```
git clone https://github.com/bshall/UniversalVocoding
cd ./UniversalVocoding
```
2. Install requirements:
```
pip install -r requirements.txt
```
3. Download and extract the [LJ-Speech dataset](https://keithito.com/LJ-Speech-Dataset/):
```
wget https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2
tar -xvjf LJSpeech-1.1.tar.bz2
```
4. Download the train split [here](https://github.com/bshall/UniversalVocoding/releases/tag/v0.2) and extract it in the root directory of the repo. 
5. Extract Mel spectrograms and preprocess audio:
```
python preprocess.py in_dir=path/to/LJSpeech-1.1 out_dir=datasets/LJSpeech-1.1
```
6. Train the model:
```
python train.py checkpoint_dir=ljspeech dataset_dir=datasets/LJSpeech-1.1
```

## Pretrained Models

Pretrained weights for the 10-bit LJ-Speech model are available [here](https://github.com/bshall/UniversalVocoding/releases/tag/v0.2).

## Notable Differences from the Paper

1. Trained on 16kHz audio from a single speaker. For an older version trained on 102 different speakers form the [ZeroSpeech 2019: TTS without T](https://zerospeech.com/2019/) English dataset click [here](https://github.com/bshall/UniversalVocoding/releases/tag/v0.1).
2. Uses an embedding layer instead of one-hot encoding.

### Acknowlegements

- https://github.com/fatchord/WaveRNN