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Oqimli audio bilan ishlash
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| bet | 150/182 | | Sana | 19.05.2024 | | Hajmi | 5,69 Mb. | | #244351 |
Bog'liq Python sun\'iy intellekt texnologiyasi Dasrlik 2024Oqimli audio bilan ishlash:
Agar Real vaqtda tahlil qilish zarur bo‘lsa, audio oqimini qayta ishlash uchun funksionallikni ishlab chiqing.
Optimallashtirish va deployment:
Ishlash va model hajmini minimallashtirish uchun modelni optimallashtiring.
AWS, Azure yoki Heroku kabi platformadan foydalanib, dasturni serverda kengaytiring.
Misol kodi quyidagicha ko‘rinishi mumkin (bu soddalashtirilgan misol, loyihangizning o‘ziga xos talablariga qarab qayta ko‘rib chiqishni talab qiladi):
import librosa
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score # Ma’lumotlarni yuklab olish va qayta ishlash
def load_and_preprocess_data(file_path):
audio, _ = librosa.load(file_path)
features = librosa.feature.mfcc(audio, sr=44100)
return features.flatten()# Misol: o‘quv va test to‘plamlarini yaratish
X = []
y = []# Har bir sinf uchun ma’lumotlarni qo‘shing (masalan, nutq, musiqa, shovqin)
X_class1 = [load_and_preprocess_data("speech_file1.wav") for _ in range(100)]
X_class2 = [load_and_preprocess_data("music_file1.wav") for _ in range(100)]
X_class3 = [load_and_preprocess_data("noise_file1.wav") for _ in range(100)]
X.extend(X_class1 + X_class2 + X_class3)
y.extend([1] * 100 + [2] * 100 + [3] * 100)
# Ma’lumotlarni o‘quv va test to‘plamlariga bo‘lish
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# Modelni o‘qitish
model = RandomForestClassifier()
model.fit(X_train, y_train)# Modelning ishlashini baholash
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")
Bu shunchaki asosiy misol va yanada murakkab audio analitik dasturlarni yaratish uchun yanada puxta sozlash va optimallashtirish talab qilinishi mumkin.
Ovozni qayta ishlashda sun’iy intellekt (AI) audio analitik dasturlarni yaratish uchun keng imkoniyatlarni taqdim etadi. Shu nuqtai nazardan, Python eng mashhur dasturlash tillaridan biridir. Sun’iy intellekt yordamida Python-da audio tahlil dasturini yaratish uchun bir necha qadamlar:
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