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| title: Mandarin Tone Evaluation | |
| emoji: 📉 | |
| colorFrom: gray | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 4.7.1 | |
| app_file: app.py | |
| pinned: false | |
| # 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**: [dataset.py](https://huggingface.co/spaces/CS5647Team3/Mandarin_Tone_Evaluation/blob/main/dataset.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](https://huggingface.co/spaces/CS5647Team3/Mandarin_Tone_Evaluation/blob/main/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](https://huggingface.co/spaces/CS5647Team3/Mandarin_Tone_Evaluation/blob/main/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**: [app.py](https://huggingface.co/spaces/CS5647Team3/Mandarin_Tone_Evaluation/blob/main/app.py) | |
| - Run this file to 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 [full_dataset](https://huggingface.co/datasets/CS5647Team3/full_dataset) | |
| - **Small Size Zip Version**: Zip file, Download from [data_mini](https://huggingface.co/datasets/CS5647Team3/data_mini) | |
| Additionally, we offer a text file for Pinyin encoding: [pinyin.txt](https://huggingface.co/spaces/CS5647Team3/Mandarin_Tone_Evaluation/blob/main/pinyin.txt). This file is crucial for understanding the encoding used in our dataset. | |
| ## Getting Started | |
| To directly view the UI demo, just go to our space and click "App" tab on the top right. | |
| Otherwise, 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 emails. | |
| - Dataset and preprocessing | |
| - Shen Siyan shen[email protected] | |
| - Ouyang Yanjia [email protected] | |
| - Model Training | |
| - Zhao Zhengkai [email protected] | |
| - Liu Mingxuan [email protected] | |
| We hope you find our project useful and insightful! Happy coding! | |