README.md

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title: README
emoji: 🦀
colorFrom: green
colorTo: gray
sdk: static
pinned: true
---

# Team 3 Project - Tone Evaluation

## Overview

Welcome to Team 3's Tone Evaluation project! This repository contains the necessary files and resources for our project, which focuses on data processing, training, testing, and a user interface (UI) demo.

## Project Structure

- **Data Processing File**: [data_processing.py](/path/to/data_processing.py)
  - This script is responsible for processing the raw data and preparing it for training and testing.
  - It takes input audio in wav format, and transfer audio into mel spectrum form and fundamental frequency form. These will be the two main features for the model to analyze.
  - We convert the pinyin and tone into numerical lables by providing a text file and link each pinyin to a index.

- **Train File**: [train.py](/path/to/train.py)
  - This file contains the code for training our tone evaluation model. We use CNN+CTC model for this task. 

- **Test File**: [test.py](/path/to/test.py)
  - Use this script to evaluate the performance of our trained model on test data.
  - Currenty, we set the model to only accepct wav format audio, and after loading the audio, model will predict the tone sequence for the sentence.

- **UI Demo**: [ui_demo.py](/path/to/ui_demo.py)
  - Explore the user interface demo to interact with the tone evaluation model.
  - You can upload wav format audio to our UI and see the evaluation result. We also provided some audio files for you to directly use.

## Dataset

We provide two versions of the dataset:

- **Full Size Version**: Download from Kaggle
- **Small Size Zip Version**: Zip file, Download from [data_mini.txt](/path/to/dara_mini.zip)

Additionally, we offer a text file for Pinyin encoding: [pinyin_encoding.txt](/path/to/pinyin_encoding.txt). This file is crucial for understanding the encoding used in our dataset.

## Getting Started

Follow these steps to get started with our project:

1. Clone this repository to your local machine.
2. Run the data processing script: `python data_processing.py`
3. Train the model using: `python train.py`
4. Evaluate the model with: `python test.py`
5. Explore the UI demo: `python ui_demo.py`

## Additional Information


- If you encounter any issues or have questions, feel free to reach out to our team through the [Issues](/path/to/issues) section.

We hope you find our project useful and insightful! Happy coding!