semvec/pypi-packages huggingface dataset:
vehiclemapperscript 50.0
Vehicle Mapper
License: MIT

#### Installation
Install python3 using the following article
[Install python](https://realpython.com/installing-python/)
#### Run these commands after installing python3
```sh
python3.7 -m pip install vehiclemapperscript
python3 -m vehiclemapperscript   
```
#### Inputs
* __Enter base sheet name__ -  Name of the first sheet eg: Digit
* __Enter Comparing sheet name__ - Name of the sheet to be compared with the base sheet Eg: HDFC
* __Enter sheet full path__ - Full path of the excel sheet located in your system
* __Enter base sheet index__ -  Index of the base sheet. If sheet is the first one, index will be 0, and 1 for second sheet
* __Enter index of sheet to be compared__ - Index of the sheet to be compared with the base sheet
* __Enter base sheet make__ - Make column name of the base sheet
* __Enter base sheet model__ - Model column name of the base sheet
* __Enter base sheet variant__ - Variant column name of the base sheet
* __Enter base sheet fuel type__  - Fuel type column nae of the base sheet
* __Enter base sheet vehicle code__ - Vehicle code column name of the base sheet
* __Enter comparing sheet make__ - Make column name of the comparing sheet
* __Enter comparing sheet model__ - Model column name of the comparing sheet
* __Enter comparing sheet variant__ - Variant column name of the comparing sheet
* __Enter comparing sheet fuel type__  - Fuel type column name of the comparing sheet
* __Enter comparing sheet vehile code__ - Vehicle code column name of the comparing sheet
voiceClassifier 1.1.2.1
A simple package for voice classifiaction
License: MIT

# voiceClassifier
A module for classifying voice samples. The layout/Class diagram of package is as follows.

![Package Layout](/images/layout.JPG)

## Installation:
Run this command in terminal
```
pip install voiceclassifier
```
This package has dependancy on librosa and other audio related packages. Please check the requirement.txt for further details.

**Please note that this package is extensively tested on Linux enviourment(Ubuntu.)**

## Examples:
Let's have a look at some examples to gain more understanding.

### 1. Trainning on your own data :

* For this you can simply run evaluate.py file from the package if you downloaded zip from github. For this please keep your voice_samples ready. The name of the voice file will be considerd as a class label.Pass list of all your voice_samples path when asked.
![Train Samples](/images/train.png)


* If you have downloaded the package using pip, then you can train as follow...
```
from voiceClassifier import evaluate
evaluate.train(
               ["/home/rshinde/Desktop/data/Shekhar.m4a","/home/rshinde/Desktop/data/Shubham.m4a",
                "/home/rshinde/Desktop/data/Dhruvesh.m4a","/home/rshinde/Desktop/data/Rohit.m4a"]
```

### 2. classifying the voice sample
Herewith the code...

```
from voiceClassifier.voiceClassify import VoiceClassify

vc = VoiceClassify()
# to get classes
print(vc.classes_())

# pass voice_samples for actual prediciton...
prediciton,probabilities,classes_ = vc.predict("/home/rshinde/Desktop/Dhruvesh.wav")
```

Output

```
Audio preprocessed..
(array(['Dhruvesh'], dtype=object),
 array([[0.55461039, 0.05390575, 0.03683959, 0.35464427]]),
 array(['Dhruvesh', 'Rohit', 'Shekhar', 'Shubham'], dtype=object))
 
```