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81882f8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 | import gradio as gr
import numpy as np
import scipy.signal as signal
import librosa
import plotly.graph_objects as go
from plotly.subplots import make_subplots
def run_forensic_analysis(vocal_file, master_file):
if not vocal_file or not master_file:
return None, None, None, "❌ Error: Please upload both assets to run diagnostics."
# Load audio clips (Optimized downsampling & 60-second execution cap)
y_v, sr_v = librosa.load(vocal_file, sr=22050, duration=60.0)
y_m, sr_m = librosa.load(master_file, sr=22050, duration=60.0)
# =================================================================
# MODULE 1: STRUCTURAL RECURRENCE MAPPER (SSM)
# =================================================================
# Extract chroma features to map harmonic patterns
chroma = librosa.feature.chroma_stft(y=y_m, sr=sr_m, hop_length=1024)
chroma = librosa.util.normalize(chroma, axis=0)
ssm = np.dot(chroma.T, chroma)
fig_ssm = go.Figure(data=gr.Plot(go.Heatmap(
z=ssm, colorscale='Viridis', showscale=False,
x0=0, dx=1024/sr_m, y0=0, dy=1024/sr_m
)))
fig_ssm.update_layout(
title="Structural Recurrence Matrix (Pattern Matches Over Time)",
xaxis_title="Timeline (Seconds)",
yaxis_title="Timeline (Seconds)",
height=400, margin=dict(l=10, r=10, t=40, b=10)
)
# =================================================================
# MODULE 2: MIX FORENSICS & FREQUENCY HEALTH
# =================================================================
centroid = librosa.feature.spectral_centroid(y=y_m, sr=sr_m, hop_length=512)[0]
flatness = librosa.feature.spectral_flatness(y=y_m, hop_length=512)[0]
t_m = librosa.times_like(centroid, sr=sr_m, hop_length=512)
rms_m = librosa.feature.rms(y=y_m)[0]
crest_factor = np.max(np.abs(y_m)) / (np.mean(rms_m) + 1e-6)
fig_mix = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.1)
fig_mix.add_trace(go.Scatter(x=t_m, y=centroid, mode='lines', name='Centroid (Brightness)', line=dict(color='#FF4B4B')), row=1, col=1)
fig_mix.add_trace(go.Scatter(x=t_m, y=flatness, mode='lines', name='Flatness (Noise/Harsh)', line=dict(color='#00E5FF')), row=2, col=1)
fig_mix.update_layout(
title="Spectral Diagnostic Vectors",
xaxis2_title="Timeline (Seconds)",
height=400, showlegend=False, margin=dict(l=10, r=10, t=40, b=10)
)
# =================================================================
# MODULE 3: SYLLABLE VELOCITY & PHRASING DENSITY
# =================================================================
onset_v = librosa.onset.onset_strength(y=y_v, sr=sr_v, hop_length=512)
t_v = librosa.times_like(onset_v, sr=sr_v, hop_length=512)
peaks, _ = signal.find_peaks(onset_v, height=0.2, distance=10)
peak_times = t_v[peaks]
# Calculate local density (syllables per second window)
window_sz = 2.0
density_y = []
for t in t_v:
count = np.sum((peak_times >= t - window_sz/2) & (peak_times <= t + window_sz/2))
density_y.append(count / window_sz)
fig_vocal = go.Figure()
fig_vocal.add_trace(go.Scatter(x=t_v, y=density_y, mode='lines', fill='tozeroy', line=dict(color='#00FF66', width=2)))
fig_vocal.update_layout(
title="Lyrical Delivery Velocity (Syllables Per Second)",
xaxis_title="Timeline (Seconds)",
yaxis_title="Syllables/Sec",
height=350, margin=dict(l=10, r=10, t=40, b=10)
)
# =================================================================
# DIAGNOSTIC SUMMARY GENERATOR
# =================================================================
avg_flatness = np.mean(flatness)
mix_critique = "Balanced clean spectrum." if avg_flatness < 0.01 else "High harshness/noise ratios detected."
if crest_factor > 5.0:
dynamics_status = "Dynamic, punchy transient preservation."
else:
dynamics_status = "Highly compressed / squashed brickwall mix."
report = f"""### 📊 Forensic Lab Insights (First 60s)
* **Dynamic Crest Factor:** {crest_factor:.2f} $\\rightarrow$ *{dynamics_status}*
* **Total Vocal Artifact Onsets:** {len(peaks)} individual syllables mapped.
* **Peak Delivery Speed:** {max(density_y):.1f} syllables per second.
* **Acoustic Blueprint Profile:** {mix_critique}
> **How to interpret charts:** > * **Recurrence Map:** Look for solid diagonal block shapes. These explicitly flag identical choruses or looping sample patterns.
> * **Spectral Vectors:** Sharp dips or valleys reveal filtering tricks, transitions, or frequency drops.
> * **Delivery Velocity:** Waves illustrate the emotional phrasing of a performance, showing where the artist rushes or drags words."""
return fig_ssm, fig_mix, fig_vocal, report
# =====================================================================
# INTERFACE LAYOUT
# =====================================================================
with gr.Blocks(title="Audio Forensic Analytics Lab") as demo:
gr.Markdown("# 🔬 Audio Forensic & Structural Intelligence Lab")
gr.Markdown("Drop clean stems extracted from Space 1 to extract advanced frequency blueprints, structural maps, and vocal data matrices.")
with gr.Row():
vocal_input = gr.Audio(type="filepath", label="Upload Isolated Vocal Stem (vocals.wav)")
master_input = gr.Audio(type="filepath", label="Upload Instrumental or Full Mix Master")
analyze_btn = gr.Button("Deploy Forensic Diagnosis Engines", variant="primary")
with gr.Tab("1. Structural Recurrence Map"):
plot_ssm = gr.Plot()
with gr.Tab("2. Mix Health & Spectral Vectors"):
plot_mix = gr.Plot()
with gr.Tab("3. Lyrical Velocity Matrix"):
plot_vocal = gr.Plot()
text_report = gr.Markdown()
analyze_btn.click(
fn=run_forensic_analysis,
inputs=[vocal_input, master_input],
outputs=[plot_ssm, plot_mix, plot_vocal, text_report]
)
if __name__ == "__main__":
demo.launch() |