thuhcsi / SpanPSP

SpanPSP

This repository contains code accompanying the paper "A CHARACTER-LEVEL SPAN-BASED MODEL FOR MANDARIN PROSODIC STRUCTURE PREDICTION" published on ICASSP 2022.

TTS Samples | Paper

Environment

• Python 3.7 or higher.
• Pytorch 1.6.0, or any compatible version.
• NLTK 3.2, torch-struct 0.4, transformers 4.3.0, or compatible.
• pytokenizations 0.7.2 or compatible.

Repository structure

SpanPSP
├──bert-base-chinese
|   ├──config.json
|   ├──pytorch_model.bin
|   └──vocab.txt
├──data
|   ├──train
|   |   ├──raw_data
|   |   |   └──raw_data.txt
|   |   └──tree_data
|   |       ├──tree_train.txt
|   |       ├──tree_validate.txt
|   |       └──tree_test.txt
|   └──inference
|       ├──raw_data
|       |   └──raw_data.txt
|       ├──tree_data
|           └──tree_data.txt
├──models
|   ├──pretrained_model
|   |   └──pretrained_SpanPSP_Databaker.pt
|   └──yours
├──src
|   ├──benepar
|       ├── ...
|   ├──count_fscore.py
|   ├──evaluate.py
|   ├──export.py
|   ├──inference_seq2tree.py
|   ├──learning_rate.py
|   ├──main.py
|   ├──seq_with_label.py
|   ├──train_raw2tree.py
|   ├──transliterate.py
|   ├──treebank.py
├──README.md

Download pretrained model

You can download the pre-trained models from the link below and put them in the right place as shown in the repository structure.

• bert-base-chinese

Link: https://huggingface.co/bert-base-chinese

• SpanPSP_Databaker，SpanPSP_PeopleDaily

Link: https://pan.baidu.com/s/1bwwFbyP1WoEr3fLbbGeXpQ

Password: 9r2h

Training and test with your dataset

Data preprocessing

First prepare your own dataset into the following format, and put it (raw_data.txt) in the right place as shown in the above repository structure.

猴子#2用#1尾巴#2荡秋千#3。

Then use the following command to convert the data of the above raw file from sequence format to tree format, and devide it into training, validation, and test with the ratio of 8:1:1.

$ python src/train_raw2tree.py

After that, you can get the tree_train.txt, tree_validate.txt and tree_test.txt.

Training

Train your model using:

$ python src/main.py  train  --train-path [your_training_data_path]  --dev-path [your_dev_data_path]  --model-path-base [your_saving_model_path] 

For example:

$ python src/main.py  train  --train-path data/train/tree_data/tree_train.txt  --dev-path data/train/tree_data/tree_validate.txt  --model-path-base models/my_model 

Test

Test your model using:

$ python src/main.py  test  --model-path [your_trained_model_path]  --test-path [your_test_data_path]

For example:

$ python src/main.py  test  --model-path models/my_model.pt  --test-path data/train/tree_data/tree_test.txt

Inference

Data preprocessing

First prepare your own dataset into the following format, and put it (raw_data.txt) in the right place as shown in the repository structure.

猴子用尾巴荡秋千。

Then use the following command to convert the dataset from sequence format to tree format:

$ python src/inference_seq2tree.py

After that, you can get the tree_data.txt.

inference

Inference with your data using:

$ python src/main.py  inference  --model-path [your_pretrained_model_path]  --test-path [your_test_data_path]  --output-path [your_output_data_path]

For example:

$ python src/main.py  inference  --model-path models/pretrained_model/pretrained_SpanPSP_Databaker.pt  --test-path data/inference/tree_data/tree_data.txt  --output-path data/inference/output_data.txt