add

README.md

@@ -1,13 +1,40 @@
 ---
 title: Elastic Musicgen Large
-emoji: 🦀
+emoji: 🎵
 colorFrom: pink
 colorTo: blue
 sdk: gradio
 sdk_version: 5.34.2
 app_file: app.py
 pinned: false
-short_description: Playground for music generation using Elastic-musicgen-large
+short_description: Generate music from text descriptions using Facebook's MusicGen Large model
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🎵 Elastic MusicGen Large
+
+This Hugging Face Space provides a user-friendly interface for generating music from text descriptions using Facebook's MusicGen Large model with Elastic's optimizations.
+
+## Features
+
+- **Text-to-Music Generation**: Convert text descriptions into high-quality audio
+- **Customizable Parameters**: Control creativity, duration, and generation parameters
+- **Interactive Interface**: Easy-to-use Gradio interface with real-time controls
+- **Example Prompts**: Pre-loaded examples to get you started
+
+## How to Use
+
+1. Enter a detailed description of the music you want to generate
+2. Adjust the duration (5-30 seconds) and creativity settings
+3. Click "Generate Music" and wait for your audio to be created
+4. Listen to and download your generated music
+
+## Tips for Better Results
+
+- Be specific in your descriptions (e.g., "slow blues guitar with harmonica")
+- Higher temperature values create more creative/unpredictable results
+- Lower temperature values produce more consistent, predictable music
+- Try different combinations of instruments and genres
+
+---
+
+*Powered by Facebook's MusicGen Large model with Elastic optimizations*

app.py

@@ -0,0 +1,133 @@
+import gradio as gr
+import torch
+from transformers import AutoProcessor, MusicgenForConditionalGeneration
+import scipy.io.wavfile
+import numpy as np
+
+# Load model and processor
+@gr.cache()
+def load_model():
+    """Load the musicgen model and processor"""
+    processor = AutoProcessor.from_pretrained("facebook/musicgen-large")
+    model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-large")
+    return processor, model
+
+def generate_music(text_prompt, duration=10, temperature=1.0, top_k=250, top_p=0.0):
+    """Generate music based on text prompt"""
+    try:
+        processor, model = load_model()
+        
+        # Process the text prompt
+        inputs = processor(
+            text=[text_prompt],
+            padding=True,
+            return_tensors="pt",
+        )
+        
+        # Generate audio
+        with torch.no_grad():
+            audio_values = model.generate(
+                **inputs,
+                max_new_tokens=duration * 50,  # Approximate tokens per second
+                do_sample=True,
+                temperature=temperature,
+                top_k=top_k,
+                top_p=top_p,
+            )
+        
+        # Convert to numpy array and prepare for output
+        audio_data = audio_values[0, 0].cpu().numpy()
+        sample_rate = model.config.sample_rate
+        
+        # Normalize audio
+        audio_data = audio_data / np.max(np.abs(audio_data))
+        
+        return sample_rate, audio_data
+        
+    except Exception as e:
+        return None, f"Error generating music: {str(e)}"
+
+# Create Gradio interface
+with gr.Blocks(title="MusicGen Large - Music Generation") as demo:
+    gr.Markdown("# 🎵 MusicGen Large Music Generator")
+    gr.Markdown("Generate music from text descriptions using Facebook's MusicGen Large model.")
+    
+    with gr.Row():
+        with gr.Column():
+            text_input = gr.Textbox(
+                label="Music Description",
+                placeholder="Enter a description of the music you want to generate (e.g., 'upbeat jazz with piano and drums')",
+                lines=3
+            )
+            
+            with gr.Row():
+                duration = gr.Slider(
+                    minimum=5,
+                    maximum=30,
+                    value=10,
+                    step=1,
+                    label="Duration (seconds)"
+                )
+                temperature = gr.Slider(
+                    minimum=0.1,
+                    maximum=2.0,
+                    value=1.0,
+                    step=0.1,
+                    label="Temperature (creativity)"
+                )
+            
+            with gr.Row():
+                top_k = gr.Slider(
+                    minimum=1,
+                    maximum=500,
+                    value=250,
+                    step=1,
+                    label="Top-k"
+                )
+                top_p = gr.Slider(
+                    minimum=0.0,
+                    maximum=1.0,
+                    value=0.0,
+                    step=0.1,
+                    label="Top-p"
+                )
+            
+            generate_btn = gr.Button("🎵 Generate Music", variant="primary")
+        
+        with gr.Column():
+            audio_output = gr.Audio(
+                label="Generated Music",
+                type="numpy"
+            )
+            
+            gr.Markdown("### Tips:")
+            gr.Markdown("""
+            - Be specific in your descriptions (e.g., "slow blues guitar with harmonica")
+            - Higher temperature = more creative/random results
+            - Lower temperature = more predictable results
+            - Duration is limited to 30 seconds for faster generation
+            """)
+    
+    # Example prompts
+    gr.Examples(
+        examples=[
+            ["upbeat jazz with piano and drums"],
+            ["relaxing acoustic guitar melody"],
+            ["electronic dance music with heavy bass"],
+            ["classical violin concerto"],
+            ["reggae with steel drums and bass"],
+            ["rock ballad with electric guitar solo"],
+        ],
+        inputs=text_input,
+        label="Example Prompts"
+    )
+    
+    # Connect the generate button to the function
+    generate_btn.click(
+        fn=generate_music,
+        inputs=[text_input, duration, temperature, top_k, top_p],
+        outputs=audio_output
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+--index-url https://thestage.jfrog.io/artifactory/api/pypi/pypi-thestage-ai-production/simple
+--extra-index-url https://pypi.nvidia.com
+--extra-index-url https://pypi.org/simple
+
+thestage
+elastic_models[nvidia]
+flash-attn==2.7.3
+torch
+scipy
+transformers
+gradio