Instructions to use ICTNLP/StreamUni-Phi4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use ICTNLP/StreamUni-Phi4 with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("undefined") model.load_adapter("ICTNLP/StreamUni-Phi4", set_active=True) - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| datasets: | |
| - ICTNLP/StreamUni | |
| base_model: | |
| - microsoft/Phi-4-multimodal-instruct | |
| pipeline_tag: audio-text-to-text | |
| library_name: adapter-transformers | |
| # The model for the paper '[StreamUni: Achieving Streaming Speech Translation with a Unified Large Speech-Language Model](https://arxiv.org/abs/2507.07803v1)' | |
| ## Usage | |
| Please refer to [Github Page](https://github.com/ictnlp/StreamUni) | |
| ### Requirements | |
| Phi-4 family has been integrated in the `4.48.2` version of `transformers`. The current `transformers` version can be verified with: `pip list | grep transformers`. | |
| We suggest to run with Python 3.10. | |
| Examples of required packages: | |
| ``` | |
| flash_attn==2.7.4.post1 | |
| torch==2.6.0 | |
| transformers==4.48.2 | |
| accelerate==1.3.0 | |
| soundfile==0.13.1 | |
| pillow==11.1.0 | |
| scipy==1.15.2 | |
| torchvision==0.21.0 | |
| backoff==2.2.1 | |
| peft==0.13.2 | |
| ``` | |
| ## Training Datasets | |
| - https://huggingface.co/datasets/ICTNLP/StreamUni | |
| ## Github Pages | |
| - https://github.com/ictnlp/StreamUni | |