
# japanese-hubert-base

![rinna-icon](./rinna.png)

This is a Japanese HuBERT (Hidden Unit Bidirectional Encoder Representations from Transformers) model trained by [rinna Co., Ltd.](https://rinna.co.jp/)

This model was traind using a large-scale Japanese audio dataset, [ReazonSpeech](https://huggingface.co/datasets/reazon-research/reazonspeech) corpus.

## How to use the model

```python
import torch
from transformers import HubertModel

model = HubertModel.from_pretrained("rinna/japanese-hubert-base")
model.eval()

wav_input_16khz = torch.randn(1, 10000)
outputs = model(wav_input_16khz)
print(f"Input:   {wav_input_16khz.size()}")  # [1, 10000]
print(f"Output:  {outputs.last_hidden_state.size()}")  # [1, 31, 768]
```

## Model summary

The model architecture is the same as the [original HuBERT base model](https://huggingface.co/facebook/hubert-base-ls960), which contains 12 transformer layers with 8 attention heads.
The model was trained using code from the [official repository](https://github.com/facebookresearch/fairseq/tree/main/examples/hubert), and the detailed training configuration can be found in the same repository and the [original paper](https://ieeexplore.ieee.org/document/9585401).

A fairseq checkpoint file can also be available [here](https://huggingface.co/rinna/japanese-hubert-base/tree/main/fairseq).

## Training

The model was trained on approximately 19,000 hours of [ReazonSpeech](https://huggingface.co/datasets/reazon-research/reazonspeech) corpus.

## License

[The Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0)


## Citation
```bibtex
@article{hubert2021hsu,
  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},
  number={},
  pages={3451-3460},
  doi={10.1109/TASLP.2021.3122291}
}
```

{"language": "ja", "license": "apache-2.0", "tags": ["hubert", "speech"], "datasets": ["reazon-research/reazonspeech"]}
transformers pytorch safetensors hubert feature-extraction speech ja dataset:reazon-research/reazonspeech license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #hubert #feature-extraction #speech #ja #dataset-reazon-research/reazonspeech #license-apache-2.0 #endpoints_compatible #has_space #region-us 


# japanese-hubert-base

!rinna-icon

This is a Japanese HuBERT (Hidden Unit Bidirectional Encoder Representations from Transformers) model trained by rinna Co., Ltd.

This model was traind using a large-scale Japanese audio dataset, ReazonSpeech corpus.

## How to use the model



## Model summary

The model architecture is the same as the original HuBERT base model, which contains 12 transformer layers with 8 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.

A fairseq checkpoint file can also be available here.

## Training

The model was trained on approximately 19,000 hours of ReazonSpeech corpus.

## License

The Apache 2.0 license



# japanese-hubert-base

!rinna-icon

This is a Japanese HuBERT (Hidden Unit Bidirectional Encoder Representations from Transformers) model trained by rinna Co., Ltd.

This model was traind using a large-scale Japanese audio dataset, ReazonSpeech corpus. ## How to use the model ## Model summary

The model architecture is the same as the original HuBERT base model, which contains 12 transformer layers with 8 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.

A fairseq checkpoint file can also be available here. ## Training

The model was trained on approximately 19,000 hours of ReazonSpeech corpus. ## License

The Apache 2.0 license
TAGS
#transformers #pytorch #safetensors #hubert #feature-extraction #speech #ja #dataset-reazon-research/reazonspeech #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 # japanese-hubert-base

!rinna-icon

This is a Japanese HuBERT (Hidden Unit Bidirectional Encoder Representations from Transformers) model trained by rinna Co., Ltd.

This model was traind using a large-scale Japanese audio dataset, ReazonSpeech corpus. ## How to use the model ## Model summary

The model architecture is the same as the original HuBERT base model, which contains 12 transformer layers with 8 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.

A fairseq checkpoint file can also be available here. ## Training

The model was trained on approximately 19,000 hours of ReazonSpeech corpus. ## License

The Apache 2.0 license
TAGS
#transformers #pytorch #safetensors #hubert #feature-extraction #speech #ja #dataset-reazon-research/reazonspeech #license-apache-2.0 #endpoints_compatible #has_space #region-us 
# japanese-hubert-base

!rinna-icon

This is a Japanese HuBERT (Hidden Unit Bidirectional Encoder Representations from Transformers) model trained by rinna Co., Ltd.

This model was traind using a large-scale Japanese audio dataset, ReazonSpeech corpus.## How to use the model## Model summary

The model architecture is the same as the original HuBERT base model, which contains 12 transformer layers with 8 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.

A fairseq checkpoint file can also be available here.## Training

The model was trained on approximately 19,000 hours of ReazonSpeech corpus.## License

The Apache 2.0 license