| --- |
| 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. |
|
|
| ```bash |
| 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: |
|
|
| ```bash |
| pip install -U hojo-asr |
| ``` |
|
|
| ### Sample Usage |
|
|
| ```python |
| 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 |
| - [x] release Hojo-ASR-4B model and inference engine |
| - [x] 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](https://huggingface.co/Qwen) |
| - [WenetSpeech-Yue](https://github.com/ASLP-lab/WenetSpeech-Yue) |
| - [WenetSpeech-Chuan](https://github.com/ASLP-lab/WenetSpeech-Chuan) |
|
|
|
|
| ## Licence |
| This project is open-sourced under the [Apache 2.0 License](LICENSE.txt), which can be freely used for academic research, personal projects, and commercial secondary development. |
