japanese-hubert-base

Japanese HuBERT Base

This is a mirror of japanese-hubert-base, originally released by rinna Co., Ltd. The original model is licensed under the Apache License 2.0. This mirror follows the same license terms. All copyrights remain with the original authors.

---

Overview

This is a Japanese HuBERT Base model trained by rinna Co., Ltd.

• Model summary

  The model architecture is the same as the original HuBERT Base model, which contains 12 transformer layers with 12 attention heads. The model was trained using code from the official repository, and the detailed training configuration can be found in the same repository and the original paper.

• Training

  The model was trained on approximately 19,000 hours of following Japanese speech corpus ReazonSpeech v1.

  • ReazonSpeech

• Contributors

  • Yukiya Hono
  • Kentaro Mitsui
  • Kei Sawada

• Release date

  April 28, 2023

---

How to use the model

import soundfile as sf
from transformers import AutoFeatureExtractor, AutoModel

model_name = "yky-h/japanese-hubert-base"
feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)
model.eval()

raw_speech_16kHz, sr = sf.read(audio_file)
inputs = feature_extractor(
    raw_speech_16kHz,
    return_tensors="pt",
    sampling_rate=sr,
)
outputs = model(**inputs)

print(f"Input:  {inputs.input_values.size()}")  # [1, #samples]
print(f"Output: {outputs.last_hidden_state.size()}")  # [1, #frames, 768]

A fairseq checkpoint file can also be available here.

---

How to cite

@misc{rinna-japanese-hubert-base,
    title = {rinna/japanese-hubert-base},
    author = {Hono, Yukiya and Mitsui, Kentaro and Sawada, Kei},
    url = {https://huggingface.co/rinna/japanese-hubert-base}
}

@inproceedings{sawada2024release,
    title = {Release of Pre-Trained Models for the {J}apanese Language},
    author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh},
    booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
    month = {5},
    year = {2024},
    pages = {13898--13905},
    url = {https://aclanthology.org/2024.lrec-main.1213},
    note = {\url{https://arxiv.org/abs/2404.01657}}
}

---

References

@article{hsu2021hubert,
    author = {Hsu, Wei-Ning and Bolte, Benjamin and Tsai, Yao-Hung Hubert and Lakhotia, Kushal and Salakhutdinov, Ruslan and Mohamed, Abdelrahman},
    journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},
    title = {HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units},
    year = {2021},
    volume = {29},
    pages = {3451-3460},
    doi = {10.1109/TASLP.2021.3122291}
}

---

License

The Apache 2.0 license