Delete data_utils.py

data_utils.py

@@ -1,94 +0,0 @@
-import os
-import numpy as np
-import torch
-from torch import Tensor
-import librosa
-from torch.utils.data import Dataset
-
-
-# Audioni padding qilish
-def pad(x, max_len=64600):
-    x_len = x.shape[0]
-    if x_len >= max_len:
-        return x[:max_len]
-    # Padding kerak
-    num_repeats = (max_len // x_len) + 1
-    padded_x = np.tile(x, (1, num_repeats))[:, :max_len][0]
-    return padded_x
-
-def genSpoof_list(dir_meta, is_train=False, is_eval=False):
-    d_meta = {}
-    file_list = []
-    with open(dir_meta, 'r') as f:
-        l_meta = f.readlines()
-
-    if is_train:
-        for line in l_meta:
-            _, key, _, _, label = line.strip().split(' ')
-            file_list.append(key)
-            d_meta[key] = 1 if label == 'bonafide' else 0
-        return d_meta, file_list
-    elif is_eval:
-        for line in l_meta:
-            key = line.strip()
-            file_list.append(key)
-        return file_list
-    else:
-        for line in l_meta:
-            _, key, _, _, label = line.strip().split(' ')
-            file_list.append(key)
-            d_meta[key] = 1 if label == 'bonafide' else 0
-        return d_meta, file_list
-
-class Dataset_ASVspoof2019_train(Dataset):
-    def __init__(self, list_IDs, labels, base_dir, cut=64600):
-        """
-        Args:
-            list_IDs: Utts kalitlari ro'yxati (string).
-            labels: Kalitlar va tegishli yorliqlar lug'ati.
-            base_dir: Ma'lumotlar joylashgan katalog (flac katalogsiz).
-            cut: Maksimal uzunlik (standart: 64600).
-        """
-        self.list_IDs = list_IDs
-        self.labels = labels
-        self.base_dir = base_dir
-        self.cut = cut
-
-    def __len__(self):
-        return len(self.list_IDs)
-
-    def __getitem__(self, index):
-        key = self.list_IDs[index]
-        file_path = os.path.join(self.base_dir, f"{key}.flac")  # flac ni qayta qo‘shmang
-        if not os.path.exists(file_path):
-            raise FileNotFoundError(f"File not found: {file_path}")
-
-        X, fs = librosa.load(file_path, sr=16000)
-        X_pad = pad(X, self.cut)
-        x_inp = Tensor(X_pad)
-        y = self.labels[key]
-        return x_inp, y
-
-
-# ASVspoof2021 baholash ma'lumotlar to'plami uchun Dataset sinfi
-class Dataset_ASVspoof2021_eval(Dataset):
-    def __init__(self, list_IDs, base_dir, cut=64600):
-        self.list_IDs = [x.replace(' ', '_') for x in list_IDs]  # Bo'sh joylarni almashtirish
-        self.base_dir = base_dir
-        self.cut = cut
-
-    def __len__(self):
-        return len(self.list_IDs)
-
-    def __getitem__(self, index):
-        key = self.list_IDs[index]
-        file_path = os.path.join(self.base_dir, f"{key}.flac")
-        if not os.path.exists(file_path):
-            print(f"Checking file: {file_path}, Exists: {os.path.exists(file_path)}")  # Fayl mavjudligini tekshirish
-            raise FileNotFoundError(f"File not found: {file_path}")
-
-        X, fs = librosa.load(file_path, sr=16000)
-        X_pad = pad(X, self.cut)
-        x_inp = Tensor(X_pad)
-        return x_inp, key
-