Hojo-ASR-V1

Overview

Introduction

Hojo-ASR-V1 is a high-performance conversational speech recognition model powered by the Qwen3 LLM decoder. It adopts the classic Encoder-Adapter-LLM framework with a customized multi-frame acoustic fusion architecture, fully leveraging acoustic fine-grained features and strong LLM semantic capabilities.

Optimized with multi-stage modular training and reinforcement learning, the model specializes in complex real-world scenarios including noisy environments, informal pronunciation, oral correction and Chinese-English code-switching. It currently supports accurate recognition of Mandarin, English, Cantonese, and Sichuan dialect, delivering balanced accuracy and inference efficiency for industrial deployment.

Quickstart

Environment Setup

The easiest way to use Hojo-ASR is to install the hojo-asr Python package from PyPI.

conda create -n hojo-asr python=3.10
conda activate hojo-asr

Run the following command to get the minimal installation with transformers-backend support:

pip install -U hojo-asr

Sample Usage

from hojo_asr import HOJO_ASR

parser = argparse.ArgumentParser()
parser.add_argument(
    "--batch_size", type=int, default=10, help="batch size for inference"
)
parser.add_argument("--device", type=str, default="cuda:0")
args = parser.parse_args()

model = HOJO_ASR.load_model("/path/to/model_folder", device=args.device)

# Transcribe
# List of wav paths; for a single scp file pass a str (see dataset.resolve_infer_audio_input)
wav_paths = [
    "/path/to/audio.wav",
]
wav_scp = "test.scp"

with open(wav_paths[0], "rb") as f:
    wav_bytes = f.read()

#LIST OF BYTES
wav_bytes_list = [wav_bytes, wav_bytes]

res = model.run_infer(wav_scp, batch_size=args.batch_size)
# res = model.run_infer(wav_paths, batch_size=args.batch_size)
# res = model.run_infer(wav_bytes_list, batch_size=args.batch_size)

for val in res:
    print("key :", val["key"], " text :", val["text"])

Evaluation

ASR Benchmarks on Public English Datasets (WER ↓)

Dataset	Hojo-ASR 4B
AMI	8.64
Earnings22	8.54
Gigaspeech	7.6
LibriSpeech Clean	1.74
LibriSpeech Other	3.66
SPGISpeech	1.92
Tedlium	3.13
Voxpopuli	7.02

Roadmap

• release Hojo-ASR-4B model and inference engine
• support Mandarin, English, Cantonese, and Sichuan dialect
• support multi-lingual and multi-dialect

Commercial Support

We offer commercial support for teams integrating Hojo ASR into their products. This includes integration assistance, custom voice development, and enterprise licensing.

Contact us or email developer@hojoai.com to discuss your requirements.

Credits

Thanks to the following open-source works:

• Qwen
• WenetSpeech-Yue
• WenetSpeech-Chuan

Licence

This project is open-sourced under the Apache 2.0 License, which can be freely used for academic research, personal projects, and commercial secondary development.