Create app.py

API SDK from FB medium music added

app.py

@@ -0,0 +1,428 @@
+import os
+import torch
+import torchaudio
+import psutil
+import time
+import sys
+import numpy as np
+import gc
+import gradio as gr
+from pydub import AudioSegment
+from audiocraft.models import MusicGen
+from torch.cuda.amp import autocast
+import warnings
+
+# Suppress warnings for cleaner output
+warnings.filterwarnings("ignore")
+
+# Set PYTORCH_CUDA_ALLOC_CONF to manage memory fragmentation
+os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"
+
+# Check critical dependencies
+if np.__version__ != "1.23.5":
+    print(f"WARNING: NumPy version {np.__version__} is being used. Tested with numpy==1.23.5.")
+if not torch.__version__.startswith(("2.1.0", "2.3.1")):
+    print(f"WARNING: PyTorch version {torch.__version__} may not be compatible. Expected torch==2.1.0 or 2.3.1.")
+
+# 1) DEVICE SETUP
+device = "cuda" if torch.cuda.is_available() else "cpu"
+if device != "cuda":
+    print("ERROR: CUDA is required for GPU rendering. CPU rendering is disabled.")
+    sys.exit(1)
+print(f"CUDA is available. Using GPU: {torch.cuda.get_device_name(0)}")
+
+# 2) LOAD MUSICGEN INTO VRAM
+try:
+    print("Loading MusicGen medium model into VRAM...")
+    local_model_path = "./models/musicgen-medium"
+    if not os.path.exists(local_model_path):
+        print(f"ERROR: Local model path {local_model_path} does not exist.")
+        print("Please download the MusicGen medium model weights and place them in the correct directory.")
+        sys.exit(1)
+    musicgen_model = MusicGen.get_pretrained(local_model_path, device=device)
+    musicgen_model.set_generation_params(
+        duration=15,  # Default chunk duration
+        two_step_cfg=False  # Disable two-step CFG for stability
+    )
+except Exception as e:
+    print(f"ERROR: Failed to load MusicGen model: {e}")
+    print("Ensure model weights are correctly placed and dependencies are installed.")
+    sys.exit(1)
+
+# 3) RESOURCE MONITORING FUNCTION
+def print_resource_usage(stage: str):
+    print(f"--- {stage} ---")
+    print(f"GPU Memory Allocated: {torch.cuda.memory_allocated() / (1024**3):.2f} GB")
+    print(f"GPU Memory Reserved: {torch.cuda.memory_reserved() / (1024**3):.2f} GB")
+    print(f"CPU Memory Used: {psutil.virtual_memory().percent}%")
+    print("---------------")
+
+# 4) GENRE PROMPT FUNCTIONS
+def set_classic_rock_prompt():
+    return "Classic rock with bluesy electric guitars, steady drums, groovy bass, Hammond organ fills, and a Led Zeppelin-inspired raw energy."
+
+def set_alternative_rock_prompt():
+    return "Alternative rock with distorted guitar riffs, punchy drums, melodic basslines, atmospheric synths, and a Nirvana-inspired grunge vibe."
+
+def set_detroit_techno_prompt():
+    return "Detroit techno with deep pulsing synths, driving basslines, crisp hi-hats, and a rhythmic groove inspired by Juan Atkins."
+
+def set_deep_house_prompt():
+    return "Deep house with warm analog synth chords, soulful vocal chops, deep basslines, and a laid-back groove inspired by Larry Heard."
+
+def set_smooth_jazz_prompt():
+    return "Smooth jazz with warm saxophone leads, expressive Rhodes piano chords, soft bossa nova drums, and a George Benson-inspired feel."
+
+def set_bebop_jazz_prompt():
+    return "Bebop jazz with fast-paced saxophone solos, intricate piano runs, walking basslines, and a Charlie Parker-inspired style."
+
+def set_baroque_classical_prompt():
+    return "Baroque classical with harpsichord, delicate violin, cello, and a Vivaldi-inspired melodic structure."
+
+def set_romantic_classical_prompt():
+    return "Romantic classical with lush strings, expressive piano, dramatic brass, and a Chopin-inspired melodic flow."
+
+def set_boom_bap_hiphop_prompt():
+    return "Boom bap hip-hop with gritty sampled drums, deep basslines, jazzy piano loops, and a J Dilla-inspired groove."
+
+def set_trap_hiphop_prompt():
+    return "Trap hip-hop with hard-hitting 808 bass, snappy snares, rapid hi-hats, and eerie synth melodies."
+
+def set_pop_rock_prompt():
+    return "Pop rock with catchy electric guitar riffs, uplifting synths, steady drums, and a Coldplay-inspired anthemic feel."
+
+def set_fusion_jazz_prompt():
+    return "Fusion jazz with electric piano, funky basslines, intricate drum patterns, and a Herbie Hancock-inspired groove."
+
+def set_edm_prompt():
+    return "EDM with high-energy synth leads, pounding basslines, four-on-the-floor kicks, and a festival-ready drop."
+
+def set_indie_folk_prompt():
+    return "Indie folk with acoustic guitars, heartfelt vocals, gentle percussion, and a Bon Iver-inspired atmosphere."
+
+# 5) AUDIO PROCESSING FUNCTIONS
+def apply_chorus(segment):
+    delayed = segment - 6
+    delayed = delayed.set_frame_rate(segment.frame_rate)
+    return segment.overlay(delayed, position=20)
+
+def apply_eq(segment):
+    segment = segment.low_pass_filter(8000)
+    segment = segment.high_pass_filter(80)
+    return segment
+
+def apply_limiter(segment, max_db=-3.hibit0):
+    if segment.dBFS > max_db:
+        segment = segment - (segment.dBFS - max_db)
+    return segment
+
+def apply_final_gain(segment, target_db=-12.0):
+    gain_adjustment = target_db - segment.dBFS
+    return segment + gain_adjustment
+
+def apply_fade(segment, fade_in_duration=2000, fade_out_duration=2000):
+    segment = segment.fade_in(fade_in_duration)
+    segment = segment.fade_out(fade_out_duration)
+    return segment
+
+# 6) GENERATION & I/O FUNCTIONS
+def generate_music(instrumental_prompt: str, cfg_scale: float, top_k: int, top_p: float, temperature: float, total_duration: int, crossfade_duration: int, num_variations: int = 1):
+    global musicgen_model
+    if not instrumental_prompt.strip():
+        return None, "⚠️ Please enter a valid instrumental prompt!"
+    try:
+        start_time = time.time()
+        total_duration = min(max(total_duration, 10), 90)
+        chunk_duration = 15
+        num_chunks = max(1, total_duration // chunk_duration)
+        chunk_duration = total_duration / num_chunks
+        overlap_duration = min(1.0, crossfade_duration / 1000.0)
+        generation_duration = chunk_duration + overlap_duration
+
+        output_files = []
+        sample_rate = musicgen_model.sample_rate
+
+        for var in range(num_variations):
+            print(f"Generating variation {var+1}/{num_variations}...")
+            audio_chunks = []
+            seed = 42 + var  # Use different seeds for variations
+            torch.manual_seed(seed)
+            np.random.seed(seed)
+
+            for i in range(num_chunks):
+                chunk_prompt = instrumental_prompt
+                print(f"Generating chunk {i+1}/{num_chunks} for variation {var+1} on GPU (prompt: {chunk_prompt})...")
+                musicgen_model.set_generation_params(
+                    duration=generation_duration,
+                    use_sampling=True,
+                    top_k=top_k,
+                    top_p=top_p,
+                    temperature=temperature,
+                    cfg_coef=cfg_scale
+                )
+
+                print_resource_usage(f"Before Chunk {i+1} Generation (Variation {var+1})")
+
+                with torch.no_grad():
+                    with autocast():
+                        audio_chunk = musicgen_model.generate([chunk_prompt], progress=True)[0]
+
+                audio_chunk = audio_chunk.cpu().to(dtype=torch.float32)
+                if audio_chunk.dim() == 1:
+                    audio_chunk = torch.stack([audio_chunk, audio_chunk], dim=0)
+                elif audio_chunk.dim() == 2 and audio_chunk.shape[0] == 1:
+                    audio_chunk = torch.cat([audio_chunk, audio_chunk], dim=0)
+                elif audio_chunk.dim() == 2 and audio_chunk.shape[0] != 2:
+                    audio_chunk = audio_chunk[:1, :]
+                    audio_chunk = torch.cat([audio_chunk, audio_chunk], dim=0)
+                elif audio_chunk.dim() > 2:
+                    audio_chunk = audio_chunk.view(2, -1)
+
+                if audio_chunk.shape[0] != 2:
+                    raise ValueError(f"Expected stereo audio with shape (2, samples), got shape {audio_chunk.shape}")
+
+                temp_wav_path = f"temp_chunk_{var}_{i}.wav"
+                chunk_path = f"chunk_{var}_{i}.mp3"
+                torchaudio.save(temp_wav_path, audio_chunk, sample_rate, bits_per_sample=24)
+                segment = AudioSegment.from_wav(temp_wav_path)
+                segment.export(chunk_path, format="mp3", bitrate="320k")
+                os.remove(temp_wav_path)
+                audio_chunks.append(chunk_path)
+
+                torch.cuda.empty_cache()
+                gc.collect()
+                time.sleep(0.5)
+                print_resource_usage(f"After Chunk {i+1} Generation (Variation {var+1})")
+
+            print(f"Combining audio chunks for variation {var+1}...")
+            final_segment = AudioSegment.from_mp3(audio_chunks[0])
+            for i in range(1, len(audio_chunks)):
+                next_segment = AudioSegment.from_mp3(audio_chunks[i])
+                next_segment = next_segment + 1
+                final_segment = final_segment.append(next_segment, crossfade=crossfade_duration)
+
+            final_segment = final_segment[:total_duration * 1000]
+
+            print(f"Post-processing final track for variation {var+1}...")
+            final_segment = apply_eq(final_segment)
+            final_segment = apply_chorus(final_segment)
+            final_segment = apply_limiter(final_segment, max_db=-3.0)
+            final_segment = final_segment.normalize(headroom=-6.0)
+            final_segment = apply_final_gain(final_segment, target_db=-12.0)
+
+            mp3_path = f"output_cleaned_variation_{var+1}.mp3"
+            final_segment.export(
+                mp3_path,
+                format="mp3",
+                bitrate="320k",
+                tags={"title": f"GhostAI Instrumental Variation {var+1}", "artist": "GhostAI"}
+            )
+            print(f"Saved final audio to {mp3_path}")
+            output_files.append(mp3_path)
+
+            for chunk_path in audio_chunks:
+                os.remove(chunk_path)
+
+        print_resource_usage("After Final Generation")
+        print(f"Total Generation Time: {time.time() - start_time:.2f} seconds")
+
+        # Return the first variation for Gradio display; others are saved to disk
+        return output_files[0], f"✅ Done! Generated {num_variations} variations."
+    except Exception as e:
+        return None, f"❌ Generation failed: {e}"
+    finally:
+        torch.cuda.empty_cache()
+        gc.collect()
+
+def clear_inputs():
+    return "", 3.0, 250, 0.9, 1.0, 30, 500, 1
+
+# 7) CUSTOM CSS
+css = """
+body { 
+    background: linear-gradient(135deg, #0A0A0A 0%, #1C2526 100%); 
+    color: #E0E0E0; 
+    font-family: 'Orbitron', sans-serif; 
+}
+.header-container { 
+    text-align: center; 
+    padding: 15px 20px; 
+    background: rgba(0, 0, 0, 0.9); 
+    border-bottom: 1px solid #00FF9F; 
+}
+#ghost-logo { 
+    font-size: 60px; 
+    animation: glitch-ghost 1.5s infinite; 
+}
+h1 { 
+    color: #A100FF; 
+    font-size: 28px; 
+    animation: glitch-text 2s infinite; 
+}
+.input-container, .settings-container, .output-container { 
+    max-width: 1000px; 
+    margin: 20px auto; 
+    padding: 20px; 
+    background: rgba(28, 37, 38, 0.8); 
+    border-radius: 10px; 
+}
+.textbox { 
+    background: #1A1A1A; 
+    border: 1px solid #A100FF; 
+    color: #E0E0E0; 
+}
+.genre-buttons { 
+    display: flex; 
+    justify-content: center; 
+    gap: 15px; 
+}
+.genre-btn, button { 
+    background: linear-gradient(45deg, #A100FF, #00FF9F); 
+    border: none; 
+    color: #0A0A0A; 
+    padding: 10px 20px; 
+    border-radius: 5px; 
+}
+@keyframes glitch-ghost { 
+    0% { transform: translate(0, 0); opacity: 1; }
+    20% { transform: translate(-5px, 2px); opacity: 0.8; }
+    100% { transform: translate(0, 0); opacity: 1; }
+}
+@keyframes glitch-text { 
+    0% { transform: translate(0, 0); }
+    20% { transform: translate(-2px, 1px); }
+    100% { transform: translate(0, 0); }
+}
+@font-face { 
+    font-family: 'Orbitron'; 
+    src: url('https://fonts.gstatic.com/s/orbitron/v29/yMJRMIlzdpvBhQQL_Qq7dy0.woff2') format('woff2'); 
+}
+"""
+
+# 8) BUILD WITH BLOCKS
+with gr.Blocks(css=css) as demo:
+    gr.Markdown("""
+        <div class="header-container">
+            <div id="ghost-logo">👻</div>
+            <h1>GhostAI Music Generator</h1>
+            <p>Summon the Sound of the Unknown</p>
+        </div>
+    """)
+    
+    with gr.Column(elem_classes="input-container"):
+        instrumental_prompt = gr.Textbox(
+            label="Instrumental Prompt",
+            placeholder="Click a genre button or type your own prompt",
+            lines=4,
+            elem_classes="textbox"
+        )
+        with gr.Row(elem_classes="genre-buttons"):
+            classic_rock_btn = gr.Button("Classic Rock", elem_classes="genre-btn")
+            alternative_rock_btn = gr.Button("Alternative Rock", elem_classes="genre-btn")
+            detroit_techno_btn = gr.Button("Detroit Techno", elem_classes="genre-btn")
+            deep_house_btn = gr.Button("Deep House", elem_classes="genre-btn")
+            smooth_jazz_btn = gr.Button("Smooth Jazz", elem_classes="genre-btn")
+            bebop_jazz_btn = gr.Button("Bebop Jazz", elem_classes="genre-btn")
+            baroque_classical_btn = gr.Button("Baroque Classical", elem_classes="genre-btn")
+            romantic_classical_btn = gr.Button("Romantic Classical", elem_classes="genre-btn")
+            boom_bap_hiphop_btn = gr.Button("Boom Bap Hip-Hop", elem_classes="genre-btn")
+            trap_hiphop_btn = gr.Button("Trap Hip-Hop", elem_classes="genre-btn")
+            pop_rock_btn = gr.Button("Pop Rock", elem_classes="genre-btn")
+            fusion_jazz_btn = gr.Button("Fusion Jazz", elem_classes="genre-btn")
+            edm_btn = gr.Button("EDM", elem_classes="genre-btn")
+            indie_folk_btn = gr.Button("Indie Folk", elem_classes="genre-btn")
+    
+    with gr.Column(elem_classes="settings-container"):
+        cfg_scale = gr.Slider(
+            label="Guidance Scale (CFG)",
+            minimum=1.0,
+            maximum=10.0,
+            value=3.0,
+            step=0.1,
+            info="Higher values make the instrumental more closely follow the prompt."
+        )
+        top_k = gr.Slider(
+            label="Top-K Sampling",
+            minimum=10,
+            maximum=500,
+            value=250,
+            step=10,
+            info="Limits sampling to the top k most likely tokens."
+        )
+        top_p = gr.Slider(
+            label="Top-P Sampling",
+            minimum=0.0,
+            maximum=1.0,
+            value=0.9,
+            step=0.05,
+            info="Keeps tokens with cumulative probability above p."
+        )
+        temperature = gr.Slider(
+            label="Temperature",
+            minimum=0.1,
+            maximum=2.0,
+            value=1.0,
+            step=0.1,
+            info="Controls randomness. Higher values make output more diverse."
+        )
+        total_duration = gr.Slider(
+            label="Total Duration (seconds)",
+            minimum=10,
+            maximum=90,
+            value=30,
+            step=1,
+            info="Total duration of the track (10 to 90 seconds)."
+        )
+        crossfade_duration = gr.Slider(
+            label="Crossfade Duration (ms)",
+            minimum=100,
+            maximum=2000,
+            value=500,
+            step=100,
+            info="Crossfade duration between chunks."
+        )
+        num_variations = gr.Slider(
+            label="Number of Variations",
+            minimum=1,
+            maximum=4,
+            value=1,
+            step=1,
+            info="Number of different versions to generate with varying random seeds."
+        )
+        with gr.Row(elem_classes="action-buttons"):
+            gen_btn = gr.Button("Generate Music")
+            clr_btn = gr.Button("Clear Inputs")
+    
+    with gr.Column(elem_classes="output-container"):
+        out_audio = gr.Audio(label="Generated Stereo Instrumental Track", type="filepath")
+        status = gr.Textbox(label="Status", interactive=False)
+    
+    classic_rock_btn.click(set_classic_rock_prompt, inputs=None, outputs=[instrumental_prompt])
+    alternative_rock_btn.click(set_alternative_rock_prompt, inputs=None, outputs=[instrumental_prompt])
+    detroit_techno_btn.click(set_detroit_techno_prompt, inputs=None, outputs=[instrumental_prompt])
+    deep_house_btn.click(set_deep_house_prompt, inputs=None, outputs=[instrumental_prompt])
+    smooth_jazz_btn.click(set_smooth_jazz_prompt, inputs=None, outputs=[instrumental_prompt])
+   _scale=3.0,
+    top_k=250,
+    top_p=0.9,
+    temperature=1.0,
+    total_duration=30,
+    crossfade_duration=500,
+    num_variations=1
+)
+
+# 9) TURN OFF OPENAPI/DOCS
+app = demo.launch(
+    server_name="0.0.0.0",
+    server_port=9999,
+    share=False,
+    inbrowser=False,
+    show_error=True
+)
+try:
+    fastapi_app = demo._server.app
+    fastapi_app.docs_url = None
+    fastapi_app.redoc_url = None
+    fastapi_app.openapi_url = None
+except Exception:
+    pass