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bbd6985 6aaf6b3 61fff87 bbd6985 6aaf6b3 bbd6985 6aaf6b3 bbd6985 6aaf6b3 bbd6985 6aaf6b3 420d7ba bbd6985 cca7013 61fff87 cca7013 61fff87 cca7013 61fff87 659cb5a 61fff87 bbd6985 | 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 | from imports import *
import json
import os
import os
import json
import numpy as np
import librosa
import matplotlib.pyplot as plt
import tensorflow as tf
save_path = ""
def audio_to_mel_spec(audio, save_path, sr=22050, n_mels=128, hop_length=512):
y, sr = librosa.load(audio, sr=sr, duration=30)
melspec = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=n_mels, hop_length=hop_length)
melspec_db = librosa.power_to_db(melspec, ref=np.max)
"""plt.figure(figsize=(4.32, 2.88), dpi=100)"""
plt.figure(figsize=(1.28, 1.28), dpi=100) #taille crnn
plt.imshow(melspec_db, aspect='auto', origin='lower', cmap='magma', vmin=-42, vmax=0)
plt.axis('off')
plt.tight_layout(pad=0)
plt.savefig(save_path, bbox_inches='tight', pad_inches=0)
"""
genre_map = {
0: "blues",
1: "classical",
2: "country",
3: "disco",
4: "hiphop",
5: "jazz",
6: "metal",
7: "pop",
8: "reggae",
9: "rock"
}"""
# mapping crnn
with open("models/genre_mapping.json", 'r') as f:
mapping_data = json.load(f)
genre_map = {v: k for k, v in mapping_data.items()}
def load_image(img_path):
"""img = image.load_img(img_path, target_size=(224, 224))"""
img = image.load_img(img_path, target_size=(224, 224)) #crnn
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array = img_array / 255.
return img_array
MODEL_PATH = os.path.join("models", "modele_crnn_hd_final_13genres.keras")
MAPPING_PATH = os.path.join("models", "genre_mapping.json")
# ==========================================
# 2. CHARGEMENT DU MODÈLE ET DU MAPPING
# ==========================================
print("⏳ Chargement du modèle...")
model_CNN = tf.keras.models.load_model("models/modele_crnn_hd_final_13genres.keras")
print("✅ Modèle chargé !")
print("⏳ Chargement des genres...")
with open(MAPPING_PATH, 'r') as f:
mapping = json.load(f)
classes = {v: k for k, v in mapping.items()}
print(f"✅ {len(classes)} genres connus.")
def predict_genre(audio_path):
try:
# 1. Chargement audio
y, sr = librosa.load(audio_path, sr=22050, duration=30)
if len(y) < 22050 * 30:
y = np.pad(y, (0, 22050 * 30 - len(y)), mode='wrap')
else:
y = y[:22050 * 30]
# 2. Mel-spectrogramme (244 bandes)
melspec = librosa.feature.melspectrogram(
y=y,
sr=sr,
n_mels=224
)
melspec_db = librosa.power_to_db(melspec, ref=np.max)
# 3. Sauvegarde image temporaire (244x244)
temp_img_path = "temp_pred.png"
plt.figure(figsize=(2.24, 2.24), dpi=100) # 244x244 pixels
plt.imshow(melspec_db, aspect='auto', origin='lower', cmap='magma')
plt.axis('off')
plt.savefig(temp_img_path, bbox_inches='tight', pad_inches=0)
plt.close()
# 4. Chargement image pour le modèle
img = tf.keras.utils.load_img(
temp_img_path,
target_size=(224, 224)
)
img_array = tf.keras.utils.img_to_array(img)
img_array = tf.expand_dims(img_array, axis=0)
# 5. Prédiction
predictions = model_CNN.predict(img_array)
return predictions#test
except Exception as e:
print(f"❌ Erreur lors de l'analyse : {e}")
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