Update model card with paper info, links and sample usage (#1)

- Update model card with paper info, links and sample usage (9405350ac6d354df3abe67381cd0610c9c127242)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,6 +1,120 @@
 ---
-license: cc-by-nc-4.0
 base_model:
 - HKUSTAudio/AudioX
+license: cc-by-nc-4.0
 pipeline_tag: text-to-audio
----
+tags:
+- audio-generation
+- music-generation
+---
+
+# AudioX: A Unified Framework for Anything-to-Audio Generation
+
+AudioX is a unified framework for generating audio and music from diverse multimodal control signals, including text, video, and audio. It features a Multimodal Adaptive Fusion (MAF) module to effectively align and fuse these inputs.
+
+- **Paper:** [AudioX: A Unified Framework for Anything-to-Audio Generation](https://huggingface.co/papers/2503.10522)
+- **Project Page:** [https://zeyuet.github.io/AudioX/](https://zeyuet.github.io/AudioX/)
+- **Repository:** [https://github.com/ZeyueT/AudioX](https://github.com/ZeyueT/AudioX)
+- **Demo:** [Hugging Face Spaces](https://huggingface.co/spaces/Zeyue7/AudioX)
+
+## Installation
+
+To use AudioX, first install the required dependencies and the package from the official repository:
+
+```bash
+# Clone the repository
+git clone https://github.com/ZeyueT/AudioX.git
+cd AudioX
+
+# Install dependencies
+pip install git+https://github.com/ZeyueT/AudioX.git
+conda install -c conda-forge ffmpeg libsndfile
+```
+
+## Sample Usage
+
+Below is an example of how to perform Video-to-Music generation programmatically:
+
+```python
+import torch
+import torchaudio
+from einops import rearrange
+from audiox import get_pretrained_model
+from audiox.inference.generation import generate_diffusion_cond
+from audiox.data.utils import read_video, merge_video_audio, load_and_process_audio, encode_video_with_synchformer
+import os
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load pretrained model
+# Choose one: "HKUSTAudio/AudioX", "HKUSTAudio/AudioX-MAF", or "HKUSTAudio/AudioX-MAF-MMDiT"
+model_name = "HKUSTAudio/AudioX"
+model, model_config = get_pretrained_model(model_name)
+sample_rate = model_config["sample_rate"]
+sample_size = model_config["sample_size"]
+target_fps = model_config["video_fps"]
+seconds_start = 0
+seconds_total = 10
+
+model = model.to(device)
+
+# Example: Video-to-Music generation
+video_path = "example/V2M_sample-1.mp4"
+text_prompt = "Generate music for the video" 
+audio_path = None
+
+# Prepare inputs
+video_tensor = read_video(video_path, seek_time=seconds_start, duration=seconds_total, target_fps=target_fps)
+if audio_path:
+    audio_tensor = load_and_process_audio(audio_path, sample_rate, seconds_start, seconds_total)
+else:
+    # Use zero tensor when no audio is provided
+    audio_tensor = torch.zeros((2, int(sample_rate * seconds_total)))
+
+# For AudioX-MAF and AudioX-MAF-MMDiT: encode video with synchformer
+video_sync_frames = None
+if "MAF" in model_name:
+    video_sync_frames = encode_video_with_synchformer(
+        video_path, model_name, seconds_start, seconds_total, device
+    )
+
+# Create conditioning
+conditioning = [{
+    "video_prompt": {"video_tensors": video_tensor.unsqueeze(0), "video_sync_frames": video_sync_frames},        
+    "text_prompt": text_prompt,
+    "audio_prompt": audio_tensor.unsqueeze(0),
+    "seconds_start": seconds_start,
+    "seconds_total": seconds_total
+}]
+    
+# Generate audio
+output = generate_diffusion_cond(
+    model,
+    steps=250,
+    cfg_scale=7,
+    conditioning=conditioning,
+    sample_size=sample_size,
+    sigma_min=0.3,
+    sigma_max=500,
+    sampler_type="dpmpp-3m-sde",
+    device=device
+)
+
+# Post-process and save audio
+output = rearrange(output, "b d n -> d (b n)")
+output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+torchaudio.save("output.wav", output, sample_rate)
+```
+
+## Citation
+
+If you find AudioX useful in your research, please consider citing the following:
+
+```bibtex
+@article{tian2025audiox,
+  title={AudioX: Diffusion Transformer for Anything-to-Audio Generation},
+  author={Tian, Zeyue and Jin, Yizhu and Liu, Zhaoyang and Yuan, Ruibin and Tan, Xu and Chen, Qifeng and Xue, Wei and Guo, Yike},
+  journal={arXiv preprint arXiv:2503.10522},
+  year={2025}
+}
+```