semvec/pypi-packages huggingface dataset:
NetHyTech-STT 1.0
NetHyTech-STT: Convert Speech to Text with Ease!
Requires Python: >=3.8


# 🚀 NetHyTech-STT

NetHyTech-STT is a Python package for seamless speech-to-text (STT) conversion using Selenium. It offers an easy-to-use interface for transcribing speech from various sources into text format.

## ✨ Features

- **Simple Integration**: Easily convert speech to text using Selenium and the webdriver_manager.
- **Versatile Usage**: Suitable for a wide range of applications, including transcription of audio files, live speech-to-text conversion in web applications, and more.
- **Python Compatibility**: Compatible with Python 3.8 and above.
- **Flexible Configuration**: Customize settings such as browser options for optimal performance.

## 🛠️ Installation

You can install NetHyTech-STT via pip:

Pypi link :- [pip](https://pypi.org/project/NetHyTech-STT/).

```bash
pip install NetHyTech-STT
```

## 🚀 Usage

Here's a simple example demonstrating how to use NetHyTech-STT:

```python
from NetHyTech_STT import listen

if __name__ == "__main__":
    listen()
```

For more advanced usage examples and detailed documentation, please visit the [GitHub repository](https://github.com/AnubhavChaturvedi-GitHub/NetHyTech_STT.git).

## 📚 Examples

Explore the `examples` directory for additional usage examples and sample scripts.

## 🤝 Contributing

Contributions are welcome! If you have ideas for improvements, feature requests, or bug reports, please open an issue on GitHub or submit a pull request.

## 📱 Contact

For any questions, feedback, or support, feel free to reach out:

- YouTube: [NetHyTech](https://www.youtube.com/channel/UC7YDMgu0dMRZotLMuB3oEcQ)
- Instagram: [NetHyTech](https://www.instagram.com/_anubhav__chaturvedi_/)

## 📄 License

NetHyTech-STT is licensed under the [MIT License](LICENSE).
mknn 0.0.3
Modified K-Nearest Neighbor
Requires Python: >=3.10

# Modified K-Nearest Neighbors (MKNN)


## Cara Penggunaan

1. **Instalasi**

    ```
    pip install mknn
    ```

2. **Import**



    ```python
    from mknn import MKNN
    ```

3. **Inisialisasi Model**

    Buat objek MKNN dengan parameter yang sesuai, seperti jumlah tetangga (n_neighbors) dan metrik jarak (distance):

    ```python
    mknn = MKNN(n_neighbors=5, distance='euclidean')
    ```

4. **Fit Model**

    Fit model menggunakan data pelatihan (X_train) dan label (y_train):

    ```python
    mknn.fit(X_train, y_train)
    ```

5. **Prediksi**

    Lakukan prediksi pada data uji (X_test):

    ```python
    predictions = mknn.predict(X_test)
    ```

6. **Evaluasi**

    Evaluasi performa model dengan menggunakan metrik-metrik evaluasi yang sesuai seperti akurasi:

    ```python
    accuracy = mknn.score(X_test, y_test)
    ```

7. **Perbandingan dengan KNN Standar**

    Untuk membandingkan performa MKNN dengan KNN standar, gunakan fungsi `compare_with_knn`:

    ```python
    comparison = mknn.compare_with_knn(X_test, y_test)
    ```

    Hasilnya akan berupa sebuah dictionary yang berisi akurasi MKNN dan KNN standar.


## Workflow
![Workflow](workflow.png)

1. **Calculate Distance (Train Data)**

    Menghitung jarak antar data latih (euclidean/manhattan).

2. **Validity**

    Apabila label 𝑥 sama dengan label tetangga, maka nilainya adalah 1, dan jika tidak sama, maka nilainya adalah 0.

3. **Calculate Distance(Test Data)**

    Menghitung Jarak Data uji dengan data latih (euclidean/manhattan).

4. **Calculate Weight**

    Menghitung bobot dengan persamaan: 

    $$ W(x) = Validity(i)  {1 \over d(e) + 0.5} $$


## Catatan

Pastikan bahwa data yang Anda gunakan telah diproses dengan benar sesuai dengan kebutuhan algoritma.
mpu6050 0.0.3
A Python Library for Efficient MPU6050 DMP Access.
Keywords: python, quaternion, vector, XYZVector, IMU, DMP, INS, Accelometer, Gyrometer, MPU6050, Fusion, EKF, Kalman Filter


# MPU6050 DMP Library

## Abstract

This library is primarily derived from the contributions of Geir Istad and has been released as a pip-installable package. This library aims to simplify the use of digital motion processor (DMP) inside inertial motion unit (IMU), along with other motion data. The main focus of this package is on providing orientaion of the device in space as quaternion, which is convertable to euler angles. The resulting data are processed and denoised using extended Kalman filter (EKF), inside the DMP module.



My main contributions to this library are towards enhancing the DMP results, detailed examples, usage description and making the library PyPI-installable. Apart from the great work done by Geir Istad, there were some issues encounterd in practice.



The enhancements are listed below:

- Quaternion to euler angles conversion (roll, pitch, yaw) enhanced using scipy library

- Linear (world-frame) acceleration rewritten using new formulas, based on quaternion

- Better access to DMP frequency

- Comprehensible, Practical examples with detailed explanation

- PyPI installable



**This library is tested on Nvidia Jetson with I2C communication.**



For more details, please refer to my github repository,

[mpu6050](https://github.com/OmidAlek/mpu6050).