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bf356c4 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 | import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import numpy as np
import pickle
MODEL_PATH = "genre_model.pkl"
SCALER_PATH = "genre_scaler.pkl"
ENCODER_PATH = "genre_encoder.pkl"
NUMERICAL_FEATURES = [
"melody_complexity (vocals)",
"melody_range (vocals)",
"melody_variability (vocals)",
"tempo_bpm_original (mix)",
"danceability custom (mix)",
"loudness_integrated_lufs custom (mix)",
"loudness_range_lu custom (mix)",
"energy_librosa (mix)",
"energy_librosa_std (mix)",
"energy_essentia (mix)",
"energy_essentia_std (mix)",
"energy_combined (mix)",
"spectral_centroid_mean custom (mix)",
"mfcc_mean_1 (mix)",
"mfcc_mean_2 (mix)",
"chroma_mean (mix)",
"spectral_contrast_mean (mix)",
"repetition_score custom (mix)",
"pitch_mean (mix)",
"pitch_std (mix)",
"rms_energy_mean (mix)",
"rms_energy_std (mix)",
"zero_crossing_rate (mix)",
]
def engineer_features(df, feature_cols):
df = df.copy()
df['energy_per_tempo'] = df['energy_combined (mix)'] / (df['tempo_bpm_original (mix)'] + 1)
df['dance_energy_ratio'] = df['danceability custom (mix)'] * df['energy_combined (mix)']
df['loudness_range_ratio'] = df['loudness_range_lu custom (mix)'] / (abs(df['loudness_integrated_lufs custom (mix)']) + 1)
df['melody_energy'] = df['melody_variability (vocals)'] * df['energy_combined (mix)']
df['spectral_complexity'] = df['spectral_centroid_mean custom (mix)'] * df['spectral_contrast_mean (mix)']
df['mfcc_ratio'] = df['mfcc_mean_1 (mix)'] / (abs(df['mfcc_mean_2 (mix)']) + 1)
df['rhythm_strength'] = df['tempo_bpm_original (mix)'] * df['danceability custom (mix)']
df['pitch_variation'] = df['pitch_std (mix)'] / (df['pitch_mean (mix)'] + 1)
df['rms_energy_ratio'] = df['rms_energy_mean (mix)'] / (df['rms_energy_std (mix)'] + 1)
df['chroma_energy'] = df['chroma_mean (mix)'] * df['energy_combined (mix)']
df['zero_tempo'] = df['zero_crossing_rate (mix)'] * df['tempo_bpm_original (mix)']
df['tempo_category'] = np.where(df['tempo_bpm_original (mix)'] < 100, 0, np.where(df['tempo_bpm_original (mix)'] < 130, 1, 2))
df['energy_category'] = np.where(df['energy_combined (mix)'] < 0.3, 0, np.where(df['energy_combined (mix)'] < 0.6, 1, 2))
df['dance_category'] = np.where(df['danceability custom (mix)'] < 0.5, 0, np.where(df['danceability custom (mix)'] < 0.75, 1, 2))
engineered = ['energy_per_tempo', 'dance_energy_ratio', 'loudness_range_ratio', 'melody_energy', 'spectral_complexity',
'mfcc_ratio', 'rhythm_strength', 'pitch_variation', 'rms_energy_ratio', 'chroma_energy', 'zero_tempo',
'tempo_category', 'energy_category', 'dance_category']
return df, feature_cols + engineered
class GenrePredictor:
def __init__(self):
with open(MODEL_PATH, "rb") as f:
self.model = pickle.load(f)
with open(SCALER_PATH, "rb") as f:
self.scaler = pickle.load(f)
with open(ENCODER_PATH, "rb") as f:
self.genre_encoder, self.all_subgenres, self.all_features = pickle.load(f)
def predict(self, feature_dict):
df = pd.DataFrame([feature_dict])
df, _ = engineer_features(df, NUMERICAL_FEATURES)
for col in self.all_features:
if col not in df.columns:
df[col] = 0
values = df[self.all_features].values[0]
values = np.nan_to_num(values, nan=0, posinf=0, neginf=0)
input_scaled = self.scaler.transform(values.reshape(1, -1))
genre_idx = self.model.predict(input_scaled)[0]
genre = self.genre_encoder.inverse_transform([genre_idx])[0]
genre_probs = self.model.predict_proba(input_scaled)[0]
top_indices = np.argsort(genre_probs)[::-1][:5]
similar = [(self.genre_encoder.classes_[i], genre_probs[i]) for i in top_indices]
related_subs = [s for s in self.all_subgenres if genre.lower() in s.lower()]
if not related_subs:
related_subs = self.all_subgenres[:10]
return {
"genre": genre,
"similar_genres": similar,
"subgenres": related_subs
}
if __name__ == "__main__":
predictor = GenrePredictor()
# Example: Pass a row dict to predict
song_features = {
"melody_complexity (vocals)": 2.5,
"melody_range (vocals)": 30.0,
"melody_variability (vocals)": 0.55,
"tempo_bpm_original (mix)": 140.0,
"danceability custom (mix)": 0.70,
"loudness_integrated_lufs custom (mix)": -12.0,
"loudness_range_lu custom (mix)": 5.0,
"energy_librosa (mix)": 0.5,
"energy_librosa_std (mix)": 0.15,
"energy_essentia (mix)": 0.3,
"energy_essentia_std (mix)": 0.15,
"energy_combined (mix)": 0.45,
"spectral_centroid_mean custom (mix)": 0.13,
"mfcc_mean_1 (mix)": 150.0,
"mfcc_mean_2 (mix)": -20.0,
"chroma_mean (mix)": 0.45,
"spectral_contrast_mean (mix)": 20.0,
"repetition_score custom (mix)": 0.007,
"pitch_mean (mix)": 200.0,
"pitch_std (mix)": 80.0,
"rms_energy_mean (mix)": 0.5,
"rms_energy_std (mix)": 0.15,
"zero_crossing_rate (mix)": 0.05,
}
print("\n" + "=" * 60)
print("GENRE PREDICTION WITH DICT INPUT")
print("=" * 60)
print("\nInput Dictionary:")
for k, v in song_features.items():
print(f" {k}: {v}")
result = predictor.predict(song_features)
print("\n" + "=" * 60)
print("PREDICTION RESULT")
print("=" * 60)
print(f"\n GENRE: {result['genre']}")
print("\n Similar Genres:")
for g, prob in result['similar_genres']:
print(f" - {g}: {prob:.1%}")
print("\n Sub-genres:")
for sub in result['subgenres'][:8]:
print(f" - {sub}")
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