Update README.md

README.md

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 sdk: gradio
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-# [SongBloom]: *Coherent Song Generation via Interleaved Autoregressive Sketching and Diffusion Refinement*
-
-We propose **SongBloom**, a novel framework for full-length song generation that leverages an interleaved paradigm of autoregressive sketching and diffusion-based refinement. SongBloom employs an autoregressive diffusion model that combines the high fidelity of diffusion models with the scalability of language models.
-Specifically, it gradually extends a musical sketch from short to long and refines the details from coarse to fine-grained. The interleaved generation paradigm effectively integrates prior semantic and acoustic context to guide the generation process.
-Experimental results demonstrate that SongBloom outperforms existing methods across both subjective and objective metrics and achieves performance comparable to the state-of-the-art commercial music generation platforms.
-
-![img](docs/architecture.png)
-
-Demo page:  [https://cypress-yang.github.io/SongBloom_demo](https://cypress-yang.github.io/SongBloom_demo)
-
-ArXiv: [https://arxiv.org/abs/2506.07634](https://arxiv.org/abs/2506.07634)
-
-## Prepare Environments
-
-```bash
-conda create -n SongBloom python==3.8.12
-conda activate SongBloom
-
-# yum install libsndfile
-# pip install torch==2.2.0 torchaudio==2.2.0 --index-url https://download.pytorch.org/whl/cu118 # For different CUDA version
-pip install -r requirements.txt
-```
-
-## Data Preparation
-
-A  .jsonl file, where each line is a json object:
-
-```json
-{
-	"idx": "The index of each sample", 
-	"lyrics": "The lyrics to be generated",
-	"prompt_wav": "The path of the style prompt audio",
-}
-```
-
-One example can be refered to as: [example/test.jsonl](example/test.jsonl)
-
-The prompt wav should be a 10-second, 48kHz audio clip.
-
-The details about lyric format can be found in [docs/lyric_format.md](docs/lyric_format.md).
-
-## Inference
-
-```bash
-source set_env.sh
-
-python3 infer.py --input-jsonl example/test.jsonl
-
-# For GPUs with low VRAM like RTX4090, you should set the dtype as bfloat16
-python3 infer.py --input-jsonl example/test.jsonl --dtype bfloat16
-
-# SongBloom also supports flash-attn (optional). To enable it, please install flash-attn (v2.6.3 is used during training) manually and set os.environ['DISABLE_FLASH_ATTN'] = "0" in infer.py:8
-```
-
-## Models
-
-| Name                 | Size | Max Length | Prompt type | 🤗                                            |
-| -------------------- | ---- | ---------- | ----------- | -------------------------------------------- |
-| songbloom_full_150s  | 2B   | 2m30s      | 10s wav     | [link](https://huggingface.co/CypressYang/SongBloom) |
-| songbloom_mulan_150s | 2B   | 2m30s      | 10s wav / text description |           coming soon                           |
-| ... |      |            |             |                                              |
-
-
-
-## TODO List
-
-- [ ] Support Text Description
-- [ ] Full version
-
-## Citation
-
-```
-@article{yang2025songbloom,
-title={SongBloom: Coherent Song Generation via Interleaved Autoregressive Sketching and Diffusion Refinement},
-author={Yang, Chenyu and Wang, Shuai and Chen, Hangting and Tan, Wei and Yu, Jianwei and Li, Haizhou},
-journal={arXiv preprint arXiv:2506.07634},
-year={2025}
-}
-```
-
-## License
-
-SongBloom (codes and weights) is released under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). 
+short_description: Online demo for Apple's DiffuCoder-7B-cpGRPO (Diffusion LLM)
+sdk_version: 5.38.0
+---