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
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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
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