Update README.md

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README.md

@@ -1,3 +1,82 @@
----
-license: mit
----
+---
+license: mit  # Changed to lowercase
+language: en  # Changed to ISO 639-1 code
+base_model: facebook/musicgen-medium
+pipeline_tag: text-to-audio
+tags:
+- audio
+- music-generation
+- text-to-music
+- musicgen
+- transformers
+library_name: transformers
+metrics:
+- type: audio_quality
+  value: "32000"
+  name: sample_rate
+- type: generation_length
+  value: "30"
+  name: max_duration_seconds
+datasets: []
+---
+
+# MelodyMaster V1
+
+MelodyMaster V1 is an AI music generation model based on Meta's MusicGen-medium architecture. It generates high-quality music from text descriptions.
+
+## Model Description
+
+- **Model Architecture:** MusicGen (3.3B parameters)
+- **Base Model:** facebook/musicgen-medium
+- **Task:** Text-to-Music Generation
+- **Output:** 32kHz audio samples
+- **Max Duration:** 30 seconds
+
+## Usage
+
+```python
+from transformers import AutoProcessor, MusicgenForConditionalGeneration
+
+# Load model and processor
+model = MusicgenForConditionalGeneration.from_pretrained("opentunesai/melodymasterv1")
+processor = AutoProcessor.from_pretrained("opentunesai/melodymasterv1")
+
+# Process text and generate music
+inputs = processor(
+    text=["happy rock song with electric guitar"],
+    padding=True,
+    return_tensors="pt",
+)
+
+audio_values = model.generate(**inputs, max_new_tokens=1500)
+```
+
+## Example Prompts
+
+- "An upbeat electronic dance track with a strong beat"
+- "A peaceful piano melody with soft strings"
+- "A rock song with electric guitar and drums"
+- "Jazz trio with piano, bass and drums"
+
+## Demo
+
+Try the model in our [Gradio Demo Space](https://huggingface.co/spaces/opentunesai/melodymasterv1-demo)
+
+## License
+
+Apache 2.0
+
+## Acknowledgments
+
+Based on Meta's MusicGen model. Original model card: [facebook/musicgen-medium](https://huggingface.co/facebook/musicgen-medium)
+
+## Citation
+
+```bibtex
+@article{copet2023simple,
+      title={Simple and Controllable Music Generation}, 
+      author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez},
+      year={2023},
+      journal={arXiv preprint arXiv:2306.05284},
+}
+```