license: apache-2.0
language:
- zh
- en
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:
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.