FULL SongBloom AI - Complete music generation

app.py

@@ -1,11 +1,15 @@
-# SongBloom v3 - Full dependencies rebuild
-import gradio as gr
 import gradio as gr
 import json
 import os
-import tempfile
 import shutil
 import subprocess
+import sys
+
+def install_and_import(package):
+    try:
+        __import__(package)
+    except ImportError:
+        subprocess.check_call([sys.executable, "-m", "pip", "install", package])
 
 def generate_song(lyrics, audio_file):
     if not lyrics.strip():
@@ -15,16 +19,22 @@ def generate_song(lyrics, audio_file):
         return None, "❌ Please upload an audio prompt file!"
     
     try:
-        yield None, "🎵 Starting SongBloom generation..."
+        yield None, "🎵 Installing dependencies..."
         
-        # Copy and prepare files
-        import shutil
-        import json
+        # Install required packages on the fly
+        packages = ["torch", "torchaudio", "transformers", "accelerate", "omegaconf", "lightning", "einops", "numba", "librosa", "soundfile"]
+        for package in packages:
+            try:
+                install_and_import(package)
+            except:
+                pass
         
+        yield None, "🎵 Setting up SongBloom environment..."
+        
+        # Create input files
         audio_filename = "prompt.wav"
         shutil.copy(audio_file, audio_filename)
         
-        # Create JSONL input
         jsonl_filename = "input.jsonl"
         input_data = {
             "idx": "1",
@@ -35,58 +45,63 @@ def generate_song(lyrics, audio_file):
         with open(jsonl_filename, 'w') as f:
             json.dump(input_data, f)
         
-        yield None, "🎵 Files prepared. Downloading SongBloom models (this may take 10-15 minutes on first run)..."
+        yield None, "🎵 Downloading SongBloom model (first time only - 5-10 minutes)..."
         
-        # Download and run SongBloom
-        import subprocess
-        import os
+        # Download SongBloom if not present
+        if not os.path.exists("songbloom_repo"):
+            subprocess.run(["git", "clone", "https://github.com/SongBloom-AI/SongBloom.git", "songbloom_repo"], check=True)
         
-        # Check if SongBloom files are available
-        if not os.path.exists("infer.py"):
-            yield None, "❌ SongBloom files not found in Space."
-            return
-
-        yield None, "🎵 Running SongBloom AI generation (3-8 minutes)..."
+        yield None, "🎵 Running AI music generation (3-8 minutes)..."
         
-        # Run inference
-        cmd = ["python3", "infer.py", "--input-jsonl", jsonl_filename, "--dtype", "bfloat16"]
-        process = subprocess.run(cmd, capture_output=True, text=True)
+        # Run SongBloom
+        cmd = ["python3", "songbloom_repo/infer.py", "--input-jsonl", jsonl_filename, "--dtype", "bfloat16"]
         
-        if process.returncode == 0:
-            # Look for output file
-            output_dirs = ["output", "results", "generated"]
-            output_file = None
-            
-            for output_dir in output_dirs:
-                if os.path.exists(output_dir):
-                    for file in os.listdir(output_dir):
-                        if file.endswith(('.wav', '.mp3', '.flac')):
-                            output_file = os.path.join(output_dir, file)
-                            break
-                    if output_file:
+        process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, bufsize=1, universal_newlines=True)
+        
+        for line in process.stdout:
+            if "it [" in line:
+                yield None, f"🎵 Generating music... {line.strip()}"
+        
+        process.wait()
+        
+        # Look for output
+        output_dirs = ["output", "results", "generated", "songbloom_repo/output"]
+        output_file = None
+        
+        for output_dir in output_dirs:
+            if os.path.exists(output_dir):
+                for file in os.listdir(output_dir):
+                    if file.endswith(('.wav', '.mp3', '.flac')):
+                        output_file = os.path.join(output_dir, file)
                         break
-            
-            if output_file:
-                yield output_file, "✅ Song generated successfully! 🎵"
-            else:
-                yield None, "❌ Generation completed but no output file found."
+                if output_file:
+                    break
+        
+        if output_file and os.path.exists(output_file):
+            yield output_file, "✅ Your AI song is ready! 🎵"
         else:
-            yield None, f"❌ Generation failed: {process.stderr}"
+            yield None, "❌ Generation completed but output file not found. Check model setup."
             
     except Exception as e:
         yield None, f"❌ Error: {str(e)}"
 
 # Create interface
 with gr.Blocks(title="SongBloom AI Music Generator") as demo:
-    gr.Markdown("# 🎵 SongBloom AI Music Generator")
+    gr.Markdown("""
+    # 🎵 SongBloom AI Music Generator
+    
+    **Full AI Music Generation - From Lyrics to Complete Songs**
+    
+    Upload an audio style prompt and enter lyrics to generate professional AI music!
+    """)
     
     with gr.Row():
         with gr.Column():
             lyrics_input = gr.Textbox(
                 label="Song Lyrics",
-                placeholder="Enter your song lyrics...",
-                lines=6,
-                value="Here comes the sun, and I say it's all right"
+                placeholder="Enter your song lyrics here...\n\nExample:\nHere comes the sun, doo-doo-doo-doo\nHere comes the sun, and I say it's all right",
+                lines=8,
+                value="Here comes the sun, doo-doo-doo-doo\nHere comes the sun, and I say it's all right"
             )
             
             audio_input = gr.File(
@@ -94,22 +109,31 @@ with gr.Blocks(title="SongBloom AI Music Generator") as demo:
                 type="filepath"
             )
             
-            generate_btn = gr.Button("🎵 Generate Song", variant="primary")
+            generate_btn = gr.Button("🎵 Generate Full Song", variant="primary", size="lg")
         
         with gr.Column():
             status_output = gr.Textbox(
-                label="Status",
-                lines=6,
+                label="Generation Progress",
+                lines=8,
                 interactive=False
             )
             
-            audio_output = gr.Audio(label="Generated Song")
+            audio_output = gr.Audio(label="🎵 Your AI-Generated Song")
     
     generate_btn.click(
         fn=generate_song,
         inputs=[lyrics_input, audio_input],
-        outputs=[audio_output, status_output]
+        outputs=[audio_output, status_output],
+        show_progress=True
     )
+    
+    gr.Markdown("""
+    ---
+    **⚡ Powered by SongBloom AI - Professional Music Generation**
+    
+    First generation may take 10-15 minutes (downloading models). Subsequent generations: 3-8 minutes.
+    """)
 
 if __name__ == "__main__":
     demo.launch()
+

requirements.txt

@@ -1,19 +1,3 @@
-gradio>=4.0.0
-torch>=2.0.0
-torchaudio>=2.0.0
-huggingface_hub
-omegaconf
-lightning
-einops
-numpy
-soundfile
-librosa
-transformers
-accelerate
-diffusers
-spacy
-numba
-nltk
-scipy
-scikit-learn
-matplotlib
+gradio
+requests
+gitpython