import gradio as gr import librosa import numpy as np from pydub import AudioSegment from pydub.effects import compress_dynamic_range import os # Titan Intelligence Core ghost_titan = { "key": "--", "bpm": 0, "crest": 0, "phase": "Mono Compatible", "harmonics": [], "spectral_density": {}, "analyzed": False } def engine_v28_titan(input_audio, ref_audio, pressure, active_fix, active_master): global ghost_titan if input_audio is None: return None, "STATUS: OFFLINE", "--", "0" try: # 1. SONIC DNA SCAN (120 sec limit) y, sr = librosa.load(input_audio, duration=120) # BPM Detection - FIX: Removed non-existent .rhythm attribute onset_env = librosa.onset.onset_strength(y=y, sr=sr) # Version-agnostic Librosa tempo extraction if hasattr(librosa.feature, 'tempo'): tempo = librosa.feature.tempo(onset_envelope=onset_env, sr=sr) else: tempo = librosa.beat.tempo(onset_envelope=onset_env, sr=sr) bpm = int(np.atleast_1d(tempo)[0]) if bpm < 115: bpm *= 2 # DnB/Techno optimizer # KEY Detection via FFT Chromatography chroma = librosa.feature.chroma_stft(y=y, sr=sr, n_fft=4096) notes = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"] detected_key = notes[np.argmax(np.mean(chroma, axis=1))] fund_freq = librosa.note_to_hz(detected_key + '1') # Phase and Crest Factor Analysis (Punch) rms = librosa.feature.rms(y=y) crest = 20 * np.log10(np.max(np.abs(y)) / (np.mean(rms) + 1e-6)) S = np.abs(librosa.stft(y)) freq = librosa.fft_frequencies(sr=sr) ghost_titan.update({ "key": detected_key, "bpm": bpm, "crest": crest, "harmonics": [round(fund_freq * i, 1) for i in range(1, 6)], "spectral_density": { "sub": np.mean(S[freq < 60]), "punch": np.mean(S[(freq >= 100) & (freq < 250)]), "mud": np.mean(S[(freq >= 250) & (freq < 500)]), "harsh": np.mean(S[(freq >= 3000) & (freq < 5000)]), "air": np.mean(S[freq > 12000]) }, "analyzed": True }) # Load audio for processing audio = AudioSegment.from_file(input_audio) log = "--- GHOST TITAN V28 SYSTEM LOG ---\n\n" # ADAPTIVE MIX REPAIR if active_fix: audio = audio.high_pass_filter(34) if ghost_titan["spectral_density"]["mud"] > ghost_titan["spectral_density"]["punch"] * 1.3: audio = audio.low_pass_filter(4500) log += "[!] Mud resonance detected. Adaptive filter applied.\n" log += "[+] MIX REPAIR: High-pass filter @ 34Hz applied.\n" # TITAN MASTERING CHAIN if active_master: audio = audio.normalize(headroom=0.5) target_lufs = -14 + ((pressure / 100) * 8) gain_db = target_lufs - (-20) # FIX: Explicitly call compress_dynamic_range and remove invalid 'window' argument audio = compress_dynamic_range( audio, threshold=-20.0, ratio=4.0, attack=12.0, release=100.0 ) optimal_gain = min(gain_db, pressure / 10) audio = audio.apply_gain(optimal_gain) audio = audio.normalize(headroom=0.1) log += f"[+] MASTER: Titan chain activated (Target: {round(target_lufs, 1)} LUFS).\n" log += f"[+] Applied +{round(optimal_gain, 1)}dB gain with soft limiting.\n" # Export processed audio output_path = "/tmp/ghost_titan_v28_mastered.wav" audio.export(output_path, format="wav", parameters=["-ar", str(sr), "-ac", "2"]) log += f"\nā–¶ DIAGNOSIS: Key {detected_key} | BPM {bpm} | Crest {round(crest, 1)}dB\n" log += f"ā–¶ RECOMMENDATION: Clean {ghost_titan['harmonics'][2]}Hz for vocal space." if active_master: log += "\n\nāœ… MASTERING COMPLETE - Download below!" return output_path, log, detected_key, str(bpm) except Exception as e: return None, f"ERROR: {str(e)}", "!", "0" def titan_mentor_ai(question): if not ghost_titan["analyzed"]: return "Upload a track first." msg = question.lower() k = ghost_titan["key"] b = ghost_titan["bpm"] sd = ghost_titan["spectral_density"] h = ghost_titan["harmonics"] if "bass" in msg or "sub" in msg: return f"Sub at {h[0]}Hz. Punch zone ({h[1]}Hz) is strong. Use sidechain at {h[0]}Hz." if "punch" in msg or "transient" in msg: ms = round(60000 / b / 2, 1) return f"Set Attack 30ms, Release {ms}ms at {b} BPM." if "mud" in msg or "clean" in msg: return f"Cut -3dB at {h[3]}Hz (4th harmonic of {k})." if "key" in msg: return f"Key: {k} | Fundamentals: {h[:2]}" if "bpm" in msg: return f"BPM: {b} (DnB range 160-180)" return f"Status: {k} @ {b} BPM | Crest: {round(ghost_titan['crest'], 1)}dB" css = """ body, .gradio-container { background: #020202 !important; color: #00e5ff !important; font-family: monospace; } .gr-box { border: 2px solid #00e5ff !important; background: #080808 !important; } .gr-button-primary { background: linear-gradient(180deg, #00e5ff, #007cff) !important; color: #000 !important; } """ with gr.Blocks(theme=gr.themes.Monochrome(), css=css) as demo: gr.HTML("

šŸ‘» GHOST TITAN v28

") with gr.Row(): with gr.Column(scale=1): out_key = gr.Label(label="KEY") out_bpm = gr.Label(label="BPM") in_audio = gr.Audio(label="šŸŽµ UPLOAD TRACK", type="filepath") # FIX: Restored reference audio block mapping to 'ref_audio' in engine_v28_titan in_ref = gr.Audio(label="MASTER REFERENCE (Optional)", type="filepath") fix_btn = gr.Checkbox(label="šŸ”§ MIX REPAIR", value=False) mst_btn = gr.Checkbox(label="šŸ”„ AUTO MASTER", value=False) in_pwr = gr.Slider(0, 100, label="GAIN PRESSURE", value=85) exec_btn = gr.Button("šŸš€ PROCESS", variant="primary") with gr.Column(scale=2): out_audio = gr.Audio(label="OUTPUT WAV", type="filepath") out_diag = gr.Textbox(label="LOG", lines=8) with gr.Group(): user_msg = gr.Textbox(placeholder="Ask about bass, punch, mud...", label="Question") chat_btn = gr.Button("ASK AI") chat_out = gr.Textbox(label="ANSWER", lines=3) exec_btn.click( fn=engine_v28_titan, # FIX: Replaced duplicate 'in_audio' with 'in_ref' inputs=[in_audio, in_ref, in_pwr, fix_btn, mst_btn], outputs=[out_audio, out_diag, out_key, out_bpm] ) chat_btn.click( fn=titan_mentor_ai, inputs=[user_msg], outputs=[chat_out] ) if __name__ == "__main__": demo.launch()