---
thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png
language: ja
datasets:
  - reazon-research/reazonspeech
tags:
  - automatic-speech-recognition
  - speech
  - audio
  - hubert
  - gpt_neox
  - asr
  - nlp
license: apache-2.0
---

# `rinna/nue-asr`

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

# Overview
[[Paper]](https://arxiv.org/abs/2312.03668)
[[GitHub]](https://github.com/rinnakk/nue-asr)

We propose a novel end-to-end speech recognition model, `Nue ASR`, which integrates pre-trained speech and language models.

The name `Nue` comes from the Japanese word ([`鵺/ぬえ/Nue`](https://en.wikipedia.org/wiki/Nue)), one of the Japanese legendary creatures ([`妖怪/ようかい/Yōkai`](https://en.wikipedia.org/wiki/Y%C5%8Dkai)).

This model is capable of performing highly accurate Japanese speech recognition.
By utilizing a GPU, it can recognize speech at speeds exceeding real-time.

Benchmark score including our models can be seen at https://rinnakk.github.io/research/benchmarks/asr/

* **Model architecture**

    This model consists of three main components: HuBERT audio encoder, bridge network, and GPT-NeoX decoder.
    The weights of HuBERT and GPT-NeoX were initialized with the pre-trained weights of HuBERT and GPT-NeoX, respectively.
    - [japanese-hubert-base](https://huggingface.co./rinna/japanese-hubert-base)
    - [japanese-gpt-neox-3.6b](https://huggingface.co./rinna/japanese-gpt-neox-3.6b)

* **Training**

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


* **Authors**

    - [Yukiya Hono](https://huggingface.co./yky-h)
    - [Koh Mitsuda](https://huggingface.co./mitsu-koh)
    - [Tianyu Zhao](https://huggingface.co./tianyuz)
    - [Kentaro Mitsui](https://huggingface.co./Kentaro321)
    - [Toshiaki Wakatsuki](https://huggingface.co./t-w)
    - [Kei Sawada](https://huggingface.co./keisawada)

---

# How to use the model

First, install the code for inference this model.

```bash
pip install git+https://github.com/rinnakk/nue-asr.git
```

Command-line interface and python interface are available.

## Command-line usage
The following command will transcribe the audio file via the command line interface.
Audio files will be automatically downsampled to 16kHz.
```bash
nue-asr audio1.wav
```
You can specify multiple audio files.
```bash
nue-asr audio1.wav audio2.flac audio3.mp3
```

We can use DeepSpeed-Inference to accelerate the inference speed of GPT-NeoX module.
If you use DeepSpeed-Inference, you need to install DeepSpeed.
```bash
pip install deepspeed
```

Then, you can use DeepSpeed-Inference as follows:
```bash
nue-asr --use-deepspeed audio1.wav
```

Run `nue-asr --help` for more information.

## Python usage
The example of python interface is as follows:
```python
import nue_asr

model = nue_asr.load_model("rinna/nue-asr")
tokenizer = nue_asr.load_tokenizer("rinna/nue-asr")

result = nue_asr.transcribe(model, tokenizer, "path_to_audio.wav")
print(result.text)
```
`nue_asr.transcribe` function can accept audio data as either a `numpy.array` or a `torch.Tensor`, in addition to traditional audio waveform file paths.

Accelerating the inference speed of models using DeepSpeed-Inference is also available through the python interface.
```python
import nue_asr

model = nue_asr.load_model("rinna/nue-asr", use_deepspeed=True)
tokenizer = nue_asr.load_tokenizer("rinna/nue-asr")

result = nue_asr.transcribe(model, tokenizer, "path_to_audio.wav")
print(result.text)
```

---

# Tokenization
The model uses the same sentencepiece-based tokenizer as [japanese-gpt-neox-3.6b](https://huggingface.co./rinna/japanese-gpt-neox-3.6b).

---

# How to cite
```bibtex
@article{hono2023integration,
    title={An Integration of Pre-Trained Speech and Language Models for End-to-End Speech Recognition},
    author={Hono, Yukiya and Mitsuda, Koh and Zhao, Tianyu and Mitsui, Kentaro and Wakatsuki, Toshiaki and Sawada, Kei},
    journal={arXiv preprint arXiv:2312.03668},
    year={2023}
}

@misc{rinna-nue-asr,
    title={rinna/nue-asr},
    author={Hono, Yukiya and Mitsuda, Koh and Zhao, Tianyu and Mitsui, Kentaro and Wakatsuki, Toshiaki and Sawada, Kei},
    url={https://huggingface.co./rinna/nue-asr}
}
```
---

# Citations
```bibtex
@article{hsu2021hubert,
    title={HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units},
    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},
    year={2021},
    volume={29},
    pages={3451-3460},
    doi={10.1109/TASLP.2021.3122291}
}

@software{andoniangpt2021gpt,
    title={{GPT-NeoX: Large Scale Autoregressive Language Modeling in PyTorch}},
    author={Andonian, Alex and Anthony, Quentin and Biderman, Stella and Black, Sid and Gali, Preetham and Gao, Leo and Hallahan, Eric and Levy-Kramer, Josh and Leahy, Connor and Nestler, Lucas and Parker, Kip and Pieler, Michael and Purohit, Shivanshu and Songz, Tri and Phil, Wang and Weinbach, Samuel},
    url={https://www.github.com/eleutherai/gpt-neox},
    doi={10.5281/zenodo.5879544},
    month={8},
    year={2021},
    version={0.0.1},
}
```
---

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