Instructions to use DUTAOZHANG/Styele2Code_model2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- pyannote.audio
How to use DUTAOZHANG/Styele2Code_model2 with pyannote.audio:
from pyannote.audio import Model, Inference model = Model.from_pretrained("DUTAOZHANG/Styele2Code_model2") inference = Inference(model) # inference on the whole file inference("file.wav") # inference on an excerpt from pyannote.core import Segment excerpt = Segment(start=2.0, end=5.0) inference.crop("file.wav", excerpt) - Notebooks
- Google Colab
- Kaggle
| base_model: | |
| - google/flan-t5-large | |
| license: apache-2.0 | |
| metrics: | |
| - bleu | |
| tags: | |
| - code | |
| language: | |
| - en | |
| new_version: google/flan-t5-large | |
| pipeline_tag: graph-ml | |
| datasets: | |
| - none-yet/anime-captions | |
| library_name: pyannote-audio | |
| ### 📦 Model Weights Usage | |
| This repository hosts two separate model weights that need to be used together for the full functionality of Style2Code: | |
| ✅ **Style Encoder Checkpoint (first stage)**: | |
| Trained via contrastive learning to extract explicit style embeddings. | |
| ✅ **Flan-T5 Generator Checkpoint (second stage)**: | |
| Fine-tuned to generate code while incorporating the style embeddings. | |
| To **use these weights**: | |
| 1️⃣ Clone the Style2Code repository: | |
| ```bash | |
| git clone https://github.com/zh19980811/Style2Code.git | |
| cd Style2Code | |