imprt/kushinada-hubert-base
This is a Japanese HuBERT Base model pre-trained using 62215 hours of audio extracted from large-scale Japanese TV broadcast audio data by voice activity detection.
This model was trained using code from the official repository.
Usage
import soundfile as sf
from transformers import AutoFeatureExtractor
model = "imprt/kushinada-hubert-base"
feature_extractor = AutoFeatureExtractor.from_pretrained(model)
audio_file="/path/to/16k_audio_file"
audio_input, sr = sf.read(audio_file)
feature_extractor(audio_input, sampling_rate=sr)
References
@article{journals/corr/abs-2106-07447,
added-at = {2021-06-16T00:00:00.000+0200},
author = {Hsu, Wei-Ning and Bolte, Benjamin and Tsai, Yao-Hung Hubert and Lakhotia, Kushal and Salakhutdinov, Ruslan and Mohamed, Abdelrahman},
biburl = {https://www.bibsonomy.org/bibtex/2435bd8c9ac37a4eab204ded15e9f8918/dblp},
ee = {https://arxiv.org/abs/2106.07447},
interhash = {c85407653eddc9c9256c261afe8d6954},
intrahash = {435bd8c9ac37a4eab204ded15e9f8918},
journal = {CoRR},
keywords = {dblp},
timestamp = {2024-04-08T22:55:35.000+0200},
title = {HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units.},
url = {http://dblp.uni-trier.de/db/journals/corr/corr2106.html#abs-2106-07447},
volume = {abs/2106.07447},
year = 2021
}