MARINE : Multi-task leaRnIng-based JapaNese accent Estimation

September 20, 2022 · View on GitHub

PyPI Python package License DOI

marine is a tool kit for building the Japanese accent estimation model proposed in our paper (demo).

For academic use, please cite the following paper (ISCA archive).

@inproceedings{park22b_interspeech,
  author={Byeongseon Park and Ryuichi Yamamoto and Kentaro Tachibana},
  title={{A Unified Accent Estimation Method Based on Multi-Task Learning for Japanese Text-to-Speech}},
  year=2022,
  booktitle={Proc. Interspeech 2022},
  pages={1931--1935},
  doi={10.21437/Interspeech.2022-334}
}

Notice

The model included in this package is trained using JSUT corpus, which is not the same as the dataset in our paper. Therefore, the model's performance is also not equal to the performance introduced in our paper.

Get started

Installation

$ pip install marine

For development

$ pip install -e ".[dev]"

Quick demo

In [1]: from marine.predict import Predictor

In [2]: nodes = [{"surface": "こんにちは", "pos": "感動詞:*:*:*", "pron": "コンニチワ", "c_type": "*", "c_form": "*", "accent_type": 0, "accent_con_type": "-1", "chain_flag": -1}]

In [3]: predictor = Predictor()

In [4]: predictor.predict([nodes])
Out[4]:
{'mora': [['コ', 'ン', 'ニ', 'チ', 'ワ']],
 'intonation_phrase_boundary': [[0, 0, 0, 0, 0]],
 'accent_phrase_boundary': [[0, 0, 0, 0, 0]],
 'accent_status': [[0, 0, 0, 0, 0]]}

In [5]: predictor.predict([nodes], accent_represent_mode="high_low")
Out[5]:
{'mora': [['コ', 'ン', 'ニ', 'チ', 'ワ']],
 'intonation_phrase_boundary': [[0, 0, 0, 0, 0]],
 'accent_phrase_boundary': [[0, 0, 0, 0, 0]],
 'accent_status': [[0, 1, 1, 1, 1]]}

Build model yourself

Coming soon...

LICENSE