rinna / japanese-data2vec-audio-base

rinna/japanese-data2vec-audio-base

Overview

This is a Japanese data2vec Audio Base model trained by rinna Co., Ltd.

• Model summary

  The model architecture is the same as the original data2vec Audio 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

How to use the model

import soundfile as sf
from transformers import AutoFeatureExtractor, AutoModel

model_name = "rinna/japanese-data2vec-audio-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-data2vec-audio-base,
    title = {rinna/japanese-data2vec-audio-base},
    author = {Hono, Yukiya and Mitsui, Kentaro and Sawada, Kei},
    url = {https://huggingface.co/rinna/japanese-data2vec-audio-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

@inproceedings{baevski2022data2vec,
    title={Data2vec: A general framework for self-supervised learning in speech, vision and language},
    author={Baevski, Alexei and Hsu, Wei-Ning and Xu, Qiantong and Babu, Arun and Gu, Jiatao and Auli, Michael},
    booktitle={International Conference on Machine Learning},
    year={2022},
    pages={1298--1312},
    doi={10.48550/arXiv.2202.03555}
}

License

The Apache 2.0 license