some FakeVD

FakeVD/code_test/Text_Feature_Extractor/calc.py

@@ -0,0 +1,12 @@
+import threading
+import os
+video_path = "./videos"
+files = os.listdir(video_path)
+thread_num = 1
+i = 0
+for i in range(thread_num):
+    open("videos-%d.txt" % (i), "w").write('')
+for file in files:
+    if file.endswith('.mp4'):
+        open("videos-%d.txt" % (i % thread_num), "a").write(file + '\n')
+        i += 1

FakeVD/code_test/Text_Feature_Extractor/data/test.py

@@ -0,0 +1,14 @@
+import json
+
+# 假设这是你的 JSON 文件路径
+json_file_path = 'FakeSV/code_test/Text_Feature/data/douyin_6700861687563570439.json'
+
+# 打开并读取 JSON 文件内容
+with open(json_file_path, 'r', encoding='utf-8') as file:
+    data = json.load(file)  # 解析 JSON 数据为字典
+
+# 获取 'wav_result' 列表中第一个元素的 'text' 字段
+text = data['wav_result'][0]['text']
+
+# 打印获取的文本
+print(text)

FakeVD/code_test/Text_Feature_Extractor/main.py

@@ -0,0 +1,149 @@
+from multiprocessing import Pool
+from PIL import Image
+import sys
+
+# sys.path.append('/mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/code_test')
+
+# from imageocr import imageocr
+# from Extract_audio import extract_audio
+from FakeVD.code_test.VGGish_Feature_Extractor.my_vggish_fun import vggish_audio as extract_audio
+from FakeVD.code_test.VGG19_Feature_Extractor.vgg19_feature import load_model_vgg19
+
+# from Imghash import imghash
+# from Extract_frame import V2frame
+from FakeVD.code_test.Text_Feature_Extractor.wav2text import wav2text
+import os
+import shutil
+# from Sentence_distance import sentence_distance
+import time
+import json
+import cv2
+# audio_path = '/mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/dataset/1.wav'
+# frame_path = ''
+# datasave_path = '/mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/code_test/Text_Feature/data'
+# video_path = '/mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/code_test/Text_Feature/videos'
+
+# def get_string_from_list(A):
+#     if type(A) == list:
+#         ret = ''
+#         for i in A:
+#             ret += get_string_from_list(i)
+#         return ret
+#     if type(A) == str:
+#         return A
+#     return ""
+
+
+# ocr_ret = []
+
+
+# def ocr_frame(frame):
+#     img_path = os.path.join(frame_path, str(frame)+'.jpg')
+#     now_ocr_result = imageocr.work(img_path)
+#     ocr_ret.append({'frame': frame, 'result': now_ocr_result})
+
+
+# def get_frame(x):
+#     return x['frame']
+
+
+# def OCR(path):
+#     imgH = imghash()
+#     cropimgH = imghash()
+#     # 返回结果
+#     ret = []
+
+#     # 当前帧
+#     now_frame = -15
+#     # 上一次执行ocr的帧
+#     last_frame = 0
+#     # 上一次的ocr结果拼接字符串
+#     last_ocr_result_string = "ocr at first"
+#     # 限
+#     k = 8
+#     # 历史最高k点
+#     kmax = 20
+#     # 匹配标识
+#     marchflag = False
+
+#     while True:
+#         now_frame += 15
+
+#         img_path = os.path.join(frame_path, str(now_frame)+'.jpg')
+
+#         if not os.path.exists(img_path):
+#             print('[OCR all] done', now_frame)
+#             break
+
+#         # 相似度高，无需ocr
+#         if not imgH.work(Image.open(img_path), k):
+#             print("continue " + str(now_frame))
+#             continue
+
+#         print('[OCR working] ocr at frame', now_frame)
+
+#         # 进行ocr
+#         now_ocr_result = imageocr.work(img_path, cropimgH)
+
+#         # 将识别结果添加
+#         ret.append({'frame': now_frame, 'result': now_ocr_result})
+#         # print('[OCR done] ocr at frame', now_frame)
+#     return ret
+
+
+# 处理视频
+def video_work(w2t, model_vggish, video_file_path, output_path, audio_path='./FakeVD/code_test/preprocessed_feature/1.wav'):
+    video_file_name = os.path.basename(video_file_path)
+    video_id = os.path.splitext(video_file_name)[0]
+    
+    st = time.time()
+    print('[video_work] working')
+    # v2f = V2frame()
+
+    ##################
+    # w2t = wav2text()
+
+    # 分离wav
+    print('[extract audio]', video_file_path, audio_path)
+    extract_audio(model_vggish, video_file_path, feature_path="./FakeVD/code_test/preprocessed_feature/1.pkl", audio_file_path=audio_path)
+    # 分离帧
+    # print('[extract frame]', path, audio_path)
+    # v2f.work(path, frame_path)
+
+    # 进行wav处理
+    print('[wav2text] working')
+    wav_result = w2t.work(audio_path)
+    #print('[wav_result]', wav_result)
+
+    # 进行ocr处理
+    # ocr_result = OCR(frame_path)
+    # print('ocr_result', ocr_result)
+
+    # cap = cv2.VideoCapture(path)
+    # fps = cap.get(cv2.CAP_PROP_FPS)
+
+    print("time:     " + str(time.time() - st))
+
+    result = {"video_id": video_id, 'text': wav_result[0]['text']} # , 'ocr_result': ocr_result , "fps": fps
+
+    with open(output_path, 'w') as f:
+        f.write(json.dumps(result, ensure_ascii=False))
+
+
+# if __name__ == '__main__':
+    # file = sys.argv[1]
+    # r = open(file, "r").readlines()
+    # for name in r:
+    #     name = name.strip()
+    #     data_path = os.path.join(datasave_path, name[:-4]+'.json')
+    #     if os.path.exists(data_path):
+    #         # print("continue " + name)
+    #         continue
+    #     if name == '':
+    #         continue
+    #     path = os.path.join(video_path, name)
+
+    #     result = video_work(path, name[:-4])
+
+    #     with open(data_path, 'w') as f:
+    #         f.write(json.dumps(result, ensure_ascii=False))

FakeVD/code_test/Text_Feature_Extractor/test.py

@@ -0,0 +1,10 @@
+from funasr import AutoModel
+# paraformer-zh is a multi-functional asr model
+# use vad, punc, spk or not as you need
+model = AutoModel(model="paraformer-zh",  vad_model="fsmn-vad", punc_model="ct-punc", 
+                  # spk_model="cam++"
+                  )
+res = model.generate(input="/mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/dataset/1.wav", 
+            batch_size_s=300, 
+            hotword='魔搭')
+print(res)

FakeVD/code_test/Text_Feature_Extractor/v0.txt

@@ -0,0 +1,16 @@
+ckpt: /home/group2024-gen6/.cache/modelscope/hub/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/model.pt
+ckpt: /home/group2024-gen6/.cache/modelscope/hub/iic/speech_fsmn_vad_zh-cn-16k-common-pytorch/model.pt
+ckpt: /home/group2024-gen6/.cache/modelscope/hub/iic/punc_ct-transformer_cn-en-common-vocab471067-large/model.pt
+[video_work] working
+[enc_init] working
+[Errno 20] Not a directory: '/mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/dataset/1.wav'
+[Errno 2] No such file or directory: ''
+[extract audio] /mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/code_test/Text_Feature/videos/douyin_6700861687563570439.mp4 /mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/dataset/1.wav
+MoviePy - Writing audio in /mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/dataset/1.wav
+MoviePy - Done.
+Audio features have been saved to ./1.pkl
+[wav2text] working
+[wav_result] [{'key': '1', 'text': '故宫每到墙顶都不会有鸟落下。因为在修建故宫时，古人考虑到墙顶屋檐边上，如果有鸟停下，多多少少会有鸟粪留下会污染空气，也影响了红墙黄瓦的美观。所以就创造了一种叫鹰不落的墙顶。它是根据鸟类的纸距计算出墙体的距离。整个房檐瓦片上都有坡度，这样使鸟抓不住，而且也不适合鸟类的站立，久而久之，鸟类就不会在此停留。咱们不得不佩服古人那无穷无尽的智慧，还有那环保意识。', 'timestamp': [[860, 1040], [1040, 1280], [1400, 1580], [1580, 1800], [1800, 2040], [2060, 2300], [2300, 2500], [2500, 2640], [2640, 2820], [2820, 2980], [2980, 3180], [3180, 3360], [3360, 3600], [3880, 4080], [4080, 4320], [4320, 4560], [4560, 4800], [4800, 5040], [5060, 5220], [5220, 5400], [5400, 5640], [5760, 5940], [5940, 6160], [6160, 6360], [6360, 6540], [6540, 6780], [6960, 7200], [7260, 7500], [7600, 7800], [7800, 8000], [8000, 8220], [8220, 8460], [8679, 8880], [8880, 9060], [9060, 9240], [9240, 9480], [9500, 9660], [9660, 9900], [10180, 10380], [10380, 10600], [10600, 10800], [10800, 11040], [11060, 11220], [11220, 11420], [11420, 11560], [11560, 11760], [11760, 11980], [11980, 12220], [12480, 12720], [12740, 12880], [12880, 13080], [13080, 13300], [13300, 13540], [13760, 14000], [14000, 14200], [14200, 14440], [14440, 14660], [14660, 14900], [14920, 15160], [15340, 15580], [15580, 15820], [15820, 16020], [16020, 16180], [16180, 16420], [16820, 17020], [17020, 17260], [17280, 17520], [17540, 17780], [17800, 18040], [18040, 18240], [18240, 18400], [18400, 18640], [18660, 18880], [18880, 19120], [19120, 19300], [19300, 19540], [19540, 19680], [19680, 19900], [19900, 20140], [20340, 20500], [20500, 20700], [20700, 20860], [20860, 21100], [21140, 21380], [21380, 21560], [21560, 21700], [21700, 21920], [21920, 22160], [22320, 22520], [22520, 22760], [22760, 23000], [23040, 23280], [23300, 23540], [23540, 23680], [23680, 23900], [23900, 24140], [24380, 24560], [24560, 24800], [24840, 25080], [25100, 25340], [25580, 25820], [25820, 26060], [26120, 26360], [26440, 26600], [26600, 26820], [26820, 27020], [27020, 27260], [27560, 27740], [27740, 27920], [27920, 28100], [28100, 28340], [28460, 28640], [28640, 28820], [28820, 29060], [29180, 29360], [29360, 29600], [29700, 29920], [29920, 30160], [30180, 30380], [30380, 30620], [30760, 30980], [30980, 31140], [31140, 31300], [31300, 31440], [31440, 31680], [32020, 32220], [32220, 32420], [32420, 32640], [32640, 32880], [33060, 33300], [33300, 33540], [33580, 33820], [33820, 34020], [34020, 34260], [34380, 34560], [34560, 34800], [34840, 35080], [35100, 35340], [35480, 35580], [35580, 35820], [35840, 36000], [36000, 36240], [36240, 36440], [36440, 36660], [36660, 36900], [37060, 37200], [37200, 37380], [37380, 37620], [37700, 37920], [37920, 38160], [38200, 38400], [38400, 38580], [38580, 38720], [38720, 38940], [38940, 39180], [39260, 39420], [39420, 39660], [39660, 39880], [39880, 40120], [40200, 40280], [40280, 40520], [40600, 41005]]}]
+[Errno 20] Not a directory: '/mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/dataset/1.wav'
+[Errno 2] No such file or directory: ''
+time:     6.538952589035034

FakeVD/code_test/Text_Feature_Extractor/videos-0.txt

@@ -0,0 +1 @@
+douyin_6700861687563570439.mp4

FakeVD/code_test/Text_Feature_Extractor/wav2text.py

@@ -0,0 +1,18 @@
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+# ./FakeVD/Models/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/
+# damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch
+class wav2text:
+    def __init__(self) -> None:
+        self.model = pipeline('auto-speech-recognition',
+                    './FakeVD/Models/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/', device="cpu")
+
+    def work(self, wav_path):
+        return self.model(wav_path,)
+    
+
+if __name__ == "__main__":
+    model = wav2text()
+    result = model.work('./FakeVD/dataset/videos_1/douyin_6671891732524829965.mp4')
+    print(result)

FakeVD/code_test/VGG19_Feature_Extractor/outputs/douyin_6571001202379590925.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:425f2e4b59dad3bff21fa7bb4089ac4775beefbb0d9e33c722af1845fbfd6e92
+size 245922

FakeVD/code_test/VGG19_Feature_Extractor/test.py

@@ -0,0 +1,11 @@
+# cPickle是python2系列用的，3系列已经不用了，直接用pickle就好了
+import pickle
+
+# 重点是rb和r的区别，rb是打开2进制文件，文本文件用r
+# f = open('/mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/code/VGG19/outputs/video_features.pkl','rb')
+f = open('/mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/dataset/ptvgg19_frames/6571001202379590925.pkl','rb')
+data = pickle.load(f)
+ 
+print(data)
+print(len(data))
+print(data.shape)

FakeVD/code_test/VGG19_Feature_Extractor/vgg19_feature.py

@@ -0,0 +1,108 @@
+import cv2
+import torch
+import torchvision.models as models
+import torchvision.transforms as transforms
+import numpy as np
+import pickle
+from PIL import Image
+import os
+
+
+def load_model_vgg19():
+
+    # 加载预训练的 VGG19 模型
+    vgg19 = models.vgg19(pretrained=True)
+
+
+    # structure = torch.nn.Sequential(*list(vgg19.children())[:])
+    # # 查看整体结构
+    # print(structure) 
+    # # 查看模型各部分名称  ['features', 'avgpool', 'classifier']
+    # print('模型各部分名称', vgg19._modules.keys())
+    # # 原始feature
+    # features = torch.nn.Sequential(*list(vgg19.children())[0])
+    # # 原始classifier
+    # classifier = torch.nn.Sequential(*list(vgg19.children())[-1])
+
+    new_classifier = torch.nn.Sequential(*list(vgg19.children())[-1][:4])
+
+    vgg19.classifier = new_classifier
+    vgg19.eval()
+
+    return vgg19
+
+
+# 定义图像预处理步骤
+transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+# 处理视频，提取帧并特征，并保存为 .pkl 文件
+def extract_frames_and_features(model_vgg19, video_path):
+    # 打开视频文件
+    cap = cv2.VideoCapture(video_path)
+    feature_list = []
+    frame_count = 0
+    buffer = []
+
+    count = 0
+    while True:
+        # 读取视频帧
+        ret, frame = cap.read()
+        if not ret:
+            break
+        # 将 BGR 格式的帧转换为 RGB 格式
+        frame_count += 1
+        if frame_count % 16 == 0:
+            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            # 将帧转换为 PIL.Image
+            pil_frame = Image.fromarray(frame_rgb)
+            # 将帧进行预处理
+            input_tensor = transform(pil_frame)
+            # 添加一个批次维度
+            input_batch = input_tensor.unsqueeze(0)
+            # 使用 VGG19 特征提取器提取特征
+            with torch.no_grad():
+                output = model_vgg19(input_batch).data[0]
+                feature_list.append(output.numpy()) 
+    # 关闭视频文件
+    cap.release()
+    feature_matrix = np.stack(feature_list)
+    print(feature_matrix.shape)
+    return feature_matrix
+
+# # 视频文件路径
+# video_path = "/mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/code/C3D_Feature_Extractor/raw_video/douyin_6571001202379590925.mp4"
+# # 提取视频帧并特征
+# video_features = extract_frames_and_features(video_path)
+# # 将特征列表保存为 .pkl 文件
+# # 确保输出目录存在，如果不存在则创建
+# output_dir = 'outputs'
+# if not os.path.exists(output_dir):
+#     os.makedirs(output_dir)
+# output_file_path = os.path.join(output_dir, 'video_features.pkl')
+# with open(output_file_path, 'wb') as f:
+#     pickle.dump(video_features, f)
+# print("Video features saved to", output_file_path)
+
+def process_video(model_vgg19, video_name, input_folder, output_folder):
+    # 确保输出目录存在，如果不存在则创建
+    if not os.path.exists(output_folder):
+        os.makedirs(output_folder)
+    video_path = os.path.join(input_folder, video_name)
+    video_features = extract_frames_and_features(model_vgg19, video_path)
+    output_file_path = os.path.join(output_folder, video_name.split('.')[0] + '.pkl')
+    with open(output_file_path, 'wb') as f:
+        pickle.dump(video_features, f)
+    print("Video features saved to", output_file_path)
+
+if __name__ == "__main__":
+    input_folder = './FakeVD/code/C3D_Feature_Extractor/raw_video'
+    output_folder = './FakeVD/code/VGG19/outputs'
+    video_name = 'douyin_6571001202379590925.mp4'
+
+    model_vgg19 = load_model_vgg19()
+    process_video(model_vgg19, video_name, input_folder, output_folder)

FakeVD/code_test/VGGish_Feature_Extractor/mel_features.py

@@ -0,0 +1,223 @@
+# Copyright 2017 The TensorFlow Authors All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Defines routines to compute mel spectrogram features from audio waveform."""
+
+import numpy as np
+
+
+def frame(data, window_length, hop_length):
+  """Convert array into a sequence of successive possibly overlapping frames.
+
+  An n-dimensional array of shape (num_samples, ...) is converted into an
+  (n+1)-D array of shape (num_frames, window_length, ...), where each frame
+  starts hop_length points after the preceding one.
+
+  This is accomplished using stride_tricks, so the original data is not
+  copied.  However, there is no zero-padding, so any incomplete frames at the
+  end are not included.
+
+  Args:
+    data: np.array of dimension N >= 1.
+    window_length: Number of samples in each frame.
+    hop_length: Advance (in samples) between each window.
+
+  Returns:
+    (N+1)-D np.array with as many rows as there are complete frames that can be
+    extracted.
+  """
+  num_samples = data.shape[0]
+  num_frames = 1 + int(np.floor((num_samples - window_length) / hop_length))
+  shape = (num_frames, window_length) + data.shape[1:]
+  strides = (data.strides[0] * hop_length,) + data.strides
+  return np.lib.stride_tricks.as_strided(data, shape=shape, strides=strides)
+
+
+def periodic_hann(window_length):
+  """Calculate a "periodic" Hann window.
+
+  The classic Hann window is defined as a raised cosine that starts and
+  ends on zero, and where every value appears twice, except the middle
+  point for an odd-length window.  Matlab calls this a "symmetric" window
+  and np.hanning() returns it.  However, for Fourier analysis, this
+  actually represents just over one cycle of a period N-1 cosine, and
+  thus is not compactly expressed on a length-N Fourier basis.  Instead,
+  it's better to use a raised cosine that ends just before the final
+  zero value - i.e. a complete cycle of a period-N cosine.  Matlab
+  calls this a "periodic" window. This routine calculates it.
+
+  Args:
+    window_length: The number of points in the returned window.
+
+  Returns:
+    A 1D np.array containing the periodic hann window.
+  """
+  return 0.5 - (0.5 * np.cos(2 * np.pi / window_length *
+                             np.arange(window_length)))
+
+
+def stft_magnitude(signal, fft_length,
+                   hop_length=None,
+                   window_length=None):
+  """Calculate the short-time Fourier transform magnitude.
+
+  Args:
+    signal: 1D np.array of the input time-domain signal.
+    fft_length: Size of the FFT to apply.
+    hop_length: Advance (in samples) between each frame passed to FFT.
+    window_length: Length of each block of samples to pass to FFT.
+
+  Returns:
+    2D np.array where each row contains the magnitudes of the fft_length/2+1
+    unique values of the FFT for the corresponding frame of input samples.
+  """
+  frames = frame(signal, window_length, hop_length)
+  # Apply frame window to each frame. We use a periodic Hann (cosine of period
+  # window_length) instead of the symmetric Hann of np.hanning (period
+  # window_length-1).
+  window = periodic_hann(window_length)
+  windowed_frames = frames * window
+  return np.abs(np.fft.rfft(windowed_frames, int(fft_length)))
+
+
+# Mel spectrum constants and functions.
+_MEL_BREAK_FREQUENCY_HERTZ = 700.0
+_MEL_HIGH_FREQUENCY_Q = 1127.0
+
+
+def hertz_to_mel(frequencies_hertz):
+  """Convert frequencies to mel scale using HTK formula.
+
+  Args:
+    frequencies_hertz: Scalar or np.array of frequencies in hertz.
+
+  Returns:
+    Object of same size as frequencies_hertz containing corresponding values
+    on the mel scale.
+  """
+  return _MEL_HIGH_FREQUENCY_Q * np.log(
+      1.0 + (frequencies_hertz / _MEL_BREAK_FREQUENCY_HERTZ))
+
+
+def spectrogram_to_mel_matrix(num_mel_bins=20,
+                              num_spectrogram_bins=129,
+                              audio_sample_rate=8000,
+                              lower_edge_hertz=125.0,
+                              upper_edge_hertz=3800.0):
+  """Return a matrix that can post-multiply spectrogram rows to make mel.
+
+  Returns a np.array matrix A that can be used to post-multiply a matrix S of
+  spectrogram values (STFT magnitudes) arranged as frames x bins to generate a
+  "mel spectrogram" M of frames x num_mel_bins.  M = S A.
+
+  The classic HTK algorithm exploits the complementarity of adjacent mel bands
+  to multiply each FFT bin by only one mel weight, then add it, with positive
+  and negative signs, to the two adjacent mel bands to which that bin
+  contributes.  Here, by expressing this operation as a matrix multiply, we go
+  from num_fft multiplies per frame (plus around 2*num_fft adds) to around
+  num_fft^2 multiplies and adds.  However, because these are all presumably
+  accomplished in a single call to np.dot(), it's not clear which approach is
+  faster in Python.  The matrix multiplication has the attraction of being more
+  general and flexible, and much easier to read.
+
+  Args:
+    num_mel_bins: How many bands in the resulting mel spectrum.  This is
+      the number of columns in the output matrix.
+    num_spectrogram_bins: How many bins there are in the source spectrogram
+      data, which is understood to be fft_size/2 + 1, i.e. the spectrogram
+      only contains the nonredundant FFT bins.
+    audio_sample_rate: Samples per second of the audio at the input to the
+      spectrogram. We need this to figure out the actual frequencies for
+      each spectrogram bin, which dictates how they are mapped into mel.
+    lower_edge_hertz: Lower bound on the frequencies to be included in the mel
+      spectrum.  This corresponds to the lower edge of the lowest triangular
+      band.
+    upper_edge_hertz: The desired top edge of the highest frequency band.
+
+  Returns:
+    An np.array with shape (num_spectrogram_bins, num_mel_bins).
+
+  Raises:
+    ValueError: if frequency edges are incorrectly ordered or out of range.
+  """
+  nyquist_hertz = audio_sample_rate / 2.
+  if lower_edge_hertz < 0.0:
+    raise ValueError("lower_edge_hertz %.1f must be >= 0" % lower_edge_hertz)
+  if lower_edge_hertz >= upper_edge_hertz:
+    raise ValueError("lower_edge_hertz %.1f >= upper_edge_hertz %.1f" %
+                     (lower_edge_hertz, upper_edge_hertz))
+  if upper_edge_hertz > nyquist_hertz:
+    raise ValueError("upper_edge_hertz %.1f is greater than Nyquist %.1f" %
+                     (upper_edge_hertz, nyquist_hertz))
+  spectrogram_bins_hertz = np.linspace(0.0, nyquist_hertz, num_spectrogram_bins)
+  spectrogram_bins_mel = hertz_to_mel(spectrogram_bins_hertz)
+  # The i'th mel band (starting from i=1) has center frequency
+  # band_edges_mel[i], lower edge band_edges_mel[i-1], and higher edge
+  # band_edges_mel[i+1].  Thus, we need num_mel_bins + 2 values in
+  # the band_edges_mel arrays.
+  band_edges_mel = np.linspace(hertz_to_mel(lower_edge_hertz),
+                               hertz_to_mel(upper_edge_hertz), num_mel_bins + 2)
+  # Matrix to post-multiply feature arrays whose rows are num_spectrogram_bins
+  # of spectrogram values.
+  mel_weights_matrix = np.empty((num_spectrogram_bins, num_mel_bins))
+  for i in range(num_mel_bins):
+    lower_edge_mel, center_mel, upper_edge_mel = band_edges_mel[i:i + 3]
+    # Calculate lower and upper slopes for every spectrogram bin.
+    # Line segments are linear in the *mel* domain, not hertz.
+    lower_slope = ((spectrogram_bins_mel - lower_edge_mel) /
+                   (center_mel - lower_edge_mel))
+    upper_slope = ((upper_edge_mel - spectrogram_bins_mel) /
+                   (upper_edge_mel - center_mel))
+    # .. then intersect them with each other and zero.
+    mel_weights_matrix[:, i] = np.maximum(0.0, np.minimum(lower_slope,
+                                                          upper_slope))
+  # HTK excludes the spectrogram DC bin; make sure it always gets a zero
+  # coefficient.
+  mel_weights_matrix[0, :] = 0.0
+  return mel_weights_matrix
+
+
+def log_mel_spectrogram(data,
+                        audio_sample_rate=8000,
+                        log_offset=0.0,
+                        window_length_secs=0.025,
+                        hop_length_secs=0.010,
+                        **kwargs):
+  """Convert waveform to a log magnitude mel-frequency spectrogram.
+
+  Args:
+    data: 1D np.array of waveform data.
+    audio_sample_rate: The sampling rate of data.
+    log_offset: Add this to values when taking log to avoid -Infs.
+    window_length_secs: Duration of each window to analyze.
+    hop_length_secs: Advance between successive analysis windows.
+    **kwargs: Additional arguments to pass to spectrogram_to_mel_matrix.
+
+  Returns:
+    2D np.array of (num_frames, num_mel_bins) consisting of log mel filterbank
+    magnitudes for successive frames.
+  """
+  window_length_samples = int(round(audio_sample_rate * window_length_secs))
+  hop_length_samples = int(round(audio_sample_rate * hop_length_secs))
+  fft_length = 2 ** int(np.ceil(np.log(window_length_samples) / np.log(2.0)))
+  spectrogram = stft_magnitude(
+      data,
+      fft_length=fft_length,
+      hop_length=hop_length_samples,
+      window_length=window_length_samples)
+  mel_spectrogram = np.dot(spectrogram, spectrogram_to_mel_matrix(
+      num_spectrogram_bins=spectrogram.shape[1],
+      audio_sample_rate=audio_sample_rate, **kwargs))
+  return np.log(mel_spectrogram + log_offset)

FakeVD/code_test/VGGish_Feature_Extractor/my_vggish.py

@@ -0,0 +1,72 @@
+# # 使用moviepy从视频中分离音频
+
+# from moviepy.editor import VideoFileClip
+
+# # 定义输入视频文件的路径
+# video_file_path = '/mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/dataset/douyin_6571001202379590925.mp4'
+
+# # 定义输出音频文件的路径
+# audio_file_path = '/mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/dataset/douyin_6571001202379590925.mp3'
+
+# # 加载视频文件
+# video_clip = VideoFileClip(video_file_path)
+
+# # 提取音频
+# audio_clip = video_clip.audio
+
+# # 将音频保存为文件
+# audio_clip.write_audiofile(audio_file_path)
+
+# # 关闭VideoClip对象以释放资源
+# video_clip.close()
+# audio_clip.close()
+
+import os
+import torch
+import numpy as np
+from moviepy.editor import VideoFileClip
+import pickle
+
+from vggish_modified import VGGish as VGGish_modified  # 修改后的VGGish代码
+
+# 定义输入视频文件夹和输出特征文件路径
+video_file_path = '/mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/dataset/videos/douyin_6571001202379590925.mp4'
+feature_path = '/mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/code/VGGish_Feature_Extractor/my_dict_vid_audioconvfea.pkl'
+
+# 加载模型
+urls = {
+    'vggish': 'https://github.com/harritaylor/torchvggish/'
+              'releases/download/v0.1/vggish-10086976.pth',
+    'pca': 'https://github.com/harritaylor/torchvggish/'
+           'releases/download/v0.1/vggish_pca_params-970ea276.pth'
+}
+vggish_model = VGGish_modified(urls, pretrained=True)
+
+# 初始化保存特征的字典
+features_dict = {}
+
+audio_file_path = '/mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/dataset/1.wav'
+
+
+# 提取视频文件名（不包括扩展名）作为视频ID
+video_file_name = os.path.basename(video_file_path)
+video_id = os.path.splitext(video_file_name)[0]
+# video_id = video_file_name.split('_')[1].split('.')[0]
+
+# 从视频中提取音频
+video = VideoFileClip(video_file_path)
+video.audio.write_audiofile(audio_file_path)
+
+# 提取特征
+features = vggish_model(audio_file_path)
+
+
+# 保存特征到字典中
+features_dict[video_id] = features.cpu().detach().numpy()
+
+
+# 保存特征字典到文件
+with open(feature_path, 'wb') as f:
+    pickle.dump(features_dict, f)
+
+print(f"Audio features have been saved to {feature_path}")

FakeVD/code_test/VGGish_Feature_Extractor/my_vggish_folder.py

@@ -0,0 +1,51 @@
+import os
+import torch
+import numpy as np
+from moviepy.editor import VideoFileClip
+import pickle
+
+from vggish_modified import VGGish as VGGish_modified  # 修改后的VGGish代码
+
+# 定义输入视频文件夹和输出特征文件路径
+video_folder_path = '/mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/dataset/videos'
+feature_path = '/mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/code/VGGish_Feature_Extractor/my_dict_vid_audioconvfea.pkl'
+
+# 加载模型
+urls = {
+    'vggish': 'https://github.com/harritaylor/torchvggish/releases/download/v0.1/vggish-10086976.pth',
+    'pca': 'https://github.com/harritaylor/torchvggish/releases/download/v0.1/vggish_pca_params-970ea276.pth'
+}
+vggish_model = VGGish_modified(urls, pretrained=True)
+
+# 初始化保存特征的字典
+features_dict = {}
+
+audio_file_path = '/mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/dataset/1.wav'
+
+# 遍历视频文件夹中的所有视频文件
+for video_file_name in os.listdir(video_folder_path):
+    video_file_path = os.path.join(video_folder_path, video_file_name)
+    
+    # 提取视频文件名（不包括扩展名）作为视频ID
+    video_id = os.path.splitext(video_file_name)[0]
+    # video_id = video_file_name.split('_')[1].split('.')[0]
+
+    try:
+        # 从视频中提取音频
+        video = VideoFileClip(video_file_path)
+        video.audio.write_audiofile(audio_file_path)
+
+        # 提取特征
+        features = vggish_model(audio_file_path)
+
+        # 保存特征到字典中
+        features_dict[video_id] = features.cpu().detach().numpy()
+
+    except Exception as e:
+        print(f"Error processing {video_file_name}: {e}")
+
+# 保存特征字典到文件
+with open(feature_path, 'wb') as f:
+    pickle.dump(features_dict, f)
+
+print(f"Audio features have been saved to {feature_path}")

FakeVD/code_test/VGGish_Feature_Extractor/my_vggish_folder_fun.py

@@ -0,0 +1,57 @@
+import os
+import torch
+import numpy as np
+from moviepy.editor import VideoFileClip
+import pickle
+
+from .vggish_modified import VGGish as VGGish_modified  # 修改后的VGGish代码
+
+def vggish_audio(video_folder_path, feature_path):
+    # 加载模型
+    urls = {
+        'vggish': 'https://github.com/harritaylor/torchvggish/releases/download/v0.1/vggish-10086976.pth',
+        'pca': 'https://github.com/harritaylor/torchvggish/releases/download/v0.1/vggish_pca_params-970ea276.pth'
+    }
+    vggish_model = VGGish_modified(urls, pretrained=True)
+
+    # 初始化保存特征的字典
+    features_dict = {}
+
+    audio_file_path = '/mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/dataset/1.wav'
+
+    # 遍历视频文件夹中的所有视频文件
+    for video_file_name in os.listdir(video_folder_path):
+        video_file_path = os.path.join(video_folder_path, video_file_name)
+        
+        # 提取视频文件名（不包括扩展名）作为视频ID
+        video_id = os.path.splitext(video_file_name)[0]
+        # video_id = video_file_name.split('_')[1].split('.')[0]
+
+        try:
+            # 从视频中提取音频
+            video = VideoFileClip(video_file_path)
+            video.audio.write_audiofile(audio_file_path)
+
+            # 提取特征
+            features = vggish_model(audio_file_path)
+
+            # 保存特征到字典中
+            features_dict[video_id] = features.cpu().detach().numpy()
+
+        except Exception as e:
+            print(f"Error processing {video_file_name}: {e}")
+
+    # 保存特征字典到文件
+    with open(feature_path, 'wb') as f:
+        pickle.dump(features_dict, f)
+
+    print(f"Audio features have been saved to {feature_path}")
+
+
+if __name__ == "__main__":
+
+    # 定义输入视频文件夹和输出特征文件路径
+    video_folder_path = '/mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/dataset/videos_1'
+    feature_path = '/mnt/data10t/dazuoye/GROUP2024-GEN6/FakeSV/code/VGGish_Feature_Extractor/my_dict_vid_audioconvfea.pkl'
+    
+    vggish_audio(video_folder_path, feature_path)

FakeVD/code_test/VGGish_Feature_Extractor/my_vggish_fun.py

@@ -0,0 +1,57 @@
+import os
+import torch
+import numpy as np
+from moviepy.editor import VideoFileClip
+import pickle
+
+from .vggish_modified import VGGish as VGGish_modified  # 修改后的VGGish代码
+
+def load_model_vggish():
+    # 加载模型
+    urls = {
+        'vggish': 'https://github.com/harritaylor/torchvggish/'
+                'releases/download/v0.1/vggish-10086976.pth',
+        'pca': 'https://github.com/harritaylor/torchvggish/'
+            'releases/download/v0.1/vggish_pca_params-970ea276.pth'
+    }
+    model_vggish = VGGish_modified(urls, pretrained=True)
+    return model_vggish
+
+def vggish_audio(model_vggish, video_file_path, feature_path, audio_file_path='./FakeVD/code_test/preprocessed_feature/1.wav'):
+
+    # 初始化保存特征的字典
+    features_dict = {}
+
+    # 提取视频文件名（不包括扩展名）作为视频ID
+    video_file_name = os.path.basename(video_file_path)
+    video_id = os.path.splitext(video_file_name)[0]
+    # video_id = video_file_name.split('_')[1].split('.')[0]
+
+    # 从视频中提取音频
+    video = VideoFileClip(video_file_path)
+    video.audio.write_audiofile(audio_file_path)
+
+    # 提取特征
+    features = model_vggish(audio_file_path)
+
+
+    # 保存特征到字典中
+    features_dict[video_id] = features.cpu().detach().numpy()
+
+
+    # 保存特征字典到文件
+    with open(feature_path, 'wb') as f:
+        pickle.dump(features_dict, f)
+
+    print(f"Audio features have been saved to {feature_path}")
+
+
+if __name__ == "__main__":
+
+    # 定义输入视频文件夹和输出特征文件路径
+    video_file_path = './FakeVD/dataset/videos_1/douyin_6559701594739313923.mp4'
+    feature_path = './FakeVD/code/VGGish_Feature_Extractor/my_dict_vid_audioconvfea.pkl'
+    
+    # 加载模型
+    model_vggish = load_model_vggish()
+    vggish_audio(model_vggish, video_file_path, feature_path)

FakeVD/code_test/VGGish_Feature_Extractor/vggish.py

@@ -0,0 +1,189 @@
+import numpy as np
+import torch
+import torch.nn as nn
+from torch import hub
+
+from . import vggish_input, vggish_params
+
+
+class VGG(nn.Module):
+    def __init__(self, features):
+        super(VGG, self).__init__()
+        self.features = features
+        self.embeddings = nn.Sequential(
+            nn.Linear(512 * 4 * 6, 4096),
+            nn.ReLU(True),
+            nn.Linear(4096, 4096),
+            nn.ReLU(True),
+            nn.Linear(4096, 128),
+            nn.ReLU(True))
+
+    def forward(self, x):
+        x = self.features(x)
+
+        # Transpose the output from features to
+        # remain compatible with vggish embeddings
+        x = torch.transpose(x, 1, 3)
+        x = torch.transpose(x, 1, 2)
+        x = x.contiguous()
+        x = x.view(x.size(0), -1)
+
+        return self.embeddings(x)
+
+
+class Postprocessor(nn.Module):
+    """Post-processes VGGish embeddings. Returns a torch.Tensor instead of a
+    numpy array in order to preserve the gradient.
+
+    "The initial release of AudioSet included 128-D VGGish embeddings for each
+    segment of AudioSet. These released embeddings were produced by applying
+    a PCA transformation (technically, a whitening transform is included as well)
+    and 8-bit quantization to the raw embedding output from VGGish, in order to
+    stay compatible with the YouTube-8M project which provides visual embeddings
+    in the same format for a large set of YouTube videos. This class implements
+    the same PCA (with whitening) and quantization transformations."
+    """
+
+    def __init__(self):
+        """Constructs a postprocessor."""
+        super(Postprocessor, self).__init__()
+        # Create empty matrix, for user's state_dict to load
+        self.pca_eigen_vectors = torch.empty(
+            (vggish_params.EMBEDDING_SIZE, vggish_params.EMBEDDING_SIZE,),
+            dtype=torch.float,
+        )
+        self.pca_means = torch.empty(
+            (vggish_params.EMBEDDING_SIZE, 1), dtype=torch.float
+        )
+
+        self.pca_eigen_vectors = nn.Parameter(self.pca_eigen_vectors, requires_grad=False)
+        self.pca_means = nn.Parameter(self.pca_means, requires_grad=False)
+
+    def postprocess(self, embeddings_batch):
+        """Applies tensor postprocessing to a batch of embeddings.
+
+        Args:
+          embeddings_batch: An tensor of shape [batch_size, embedding_size]
+            containing output from the embedding layer of VGGish.
+
+        Returns:
+          A tensor of the same shape as the input, containing the PCA-transformed,
+          quantized, and clipped version of the input.
+        """
+        assert len(embeddings_batch.shape) == 2, "Expected 2-d batch, got %r" % (
+            embeddings_batch.shape,
+        )
+        assert (
+            embeddings_batch.shape[1] == vggish_params.EMBEDDING_SIZE
+        ), "Bad batch shape: %r" % (embeddings_batch.shape,)
+
+        # Apply PCA.
+        # - Embeddings come in as [batch_size, embedding_size].
+        # - Transpose to [embedding_size, batch_size].
+        # - Subtract pca_means column vector from each column.
+        # - Premultiply by PCA matrix of shape [output_dims, input_dims]
+        #   where both are are equal to embedding_size in our case.
+        # - Transpose result back to [batch_size, embedding_size].
+        pca_applied = torch.mm(self.pca_eigen_vectors, (embeddings_batch.t() - self.pca_means)).t()
+
+        # Quantize by:
+        # - clipping to [min, max] range
+        clipped_embeddings = torch.clamp(
+            pca_applied, vggish_params.QUANTIZE_MIN_VAL, vggish_params.QUANTIZE_MAX_VAL
+        )
+        # - convert to 8-bit in range [0.0, 255.0]
+        quantized_embeddings = torch.round(
+            (clipped_embeddings - vggish_params.QUANTIZE_MIN_VAL)
+            * (
+                255.0
+                / (vggish_params.QUANTIZE_MAX_VAL - vggish_params.QUANTIZE_MIN_VAL)
+            )
+        )
+        return torch.squeeze(quantized_embeddings)
+
+    def forward(self, x):
+        return self.postprocess(x)
+
+
+def make_layers():
+    layers = []
+    in_channels = 1
+    for v in [64, "M", 128, "M", 256, 256, "M", 512, 512, "M"]:
+        if v == "M":
+            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
+        else:
+            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
+            layers += [conv2d, nn.ReLU(inplace=True)]
+            in_channels = v
+    return nn.Sequential(*layers)
+
+
+def _vgg():
+    return VGG(make_layers())
+
+
+# def _spectrogram():
+#     config = dict(
+#         sr=16000,
+#         n_fft=400,
+#         n_mels=64,
+#         hop_length=160,
+#         window="hann",
+#         center=False,
+#         pad_mode="reflect",
+#         htk=True,
+#         fmin=125,
+#         fmax=7500,
+#         output_format='Magnitude',
+#         #             device=device,
+#     )
+#     return Spectrogram.MelSpectrogram(**config)
+
+
+class VGGish(VGG):
+    def __init__(self, urls, device=None, pretrained=True, preprocess=True, postprocess=True, progress=True):
+        super().__init__(make_layers())
+        if pretrained:
+            state_dict = hub.load_state_dict_from_url(urls['vggish'], progress=progress)
+            super().load_state_dict(state_dict)
+
+        if device is None:
+            device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        self.device = device
+        self.preprocess = preprocess
+        self.postprocess = postprocess
+        if self.postprocess:
+            self.pproc = Postprocessor()
+            if pretrained:
+                state_dict = hub.load_state_dict_from_url(urls['pca'], progress=progress)
+                # TODO: Convert the state_dict to torch
+                state_dict[vggish_params.PCA_EIGEN_VECTORS_NAME] = torch.as_tensor(
+                    state_dict[vggish_params.PCA_EIGEN_VECTORS_NAME], dtype=torch.float
+                )
+                state_dict[vggish_params.PCA_MEANS_NAME] = torch.as_tensor(
+                    state_dict[vggish_params.PCA_MEANS_NAME].reshape(-1, 1), dtype=torch.float
+                )
+
+                self.pproc.load_state_dict(state_dict)
+        self.to(self.device)
+
+    def forward(self, x, fs=None):
+        if self.preprocess:
+            x = self._preprocess(x, fs)
+        x = x.to(self.device)
+        x = VGG.forward(self, x)
+        if self.postprocess:
+            x = self._postprocess(x)
+        return x
+
+    def _preprocess(self, x, fs):
+        if isinstance(x, np.ndarray):
+            x = vggish_input.waveform_to_examples(x, fs)
+        elif isinstance(x, str):
+            x = vggish_input.wavfile_to_examples(x)
+        else:
+            raise AttributeError
+        return x
+
+    def _postprocess(self, x):
+        return self.pproc(x)

FakeVD/code_test/VGGish_Feature_Extractor/vggish_input.py

@@ -0,0 +1,98 @@
+# Copyright 2017 The TensorFlow Authors All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Compute input examples for VGGish from audio waveform."""
+
+# Modification: Return torch tensors rather than numpy arrays
+import torch
+
+import numpy as np
+import resampy
+
+from . import mel_features
+from . import vggish_params
+
+import soundfile as sf
+
+
+def waveform_to_examples(data, sample_rate, return_tensor=True):
+    """Converts audio waveform into an array of examples for VGGish.
+
+  Args:
+    data: np.array of either one dimension (mono) or two dimensions
+      (multi-channel, with the outer dimension representing channels).
+      Each sample is generally expected to lie in the range [-1.0, +1.0],
+      although this is not required.
+    sample_rate: Sample rate of data.
+    return_tensor: Return data as a Pytorch tensor ready for VGGish
+
+  Returns:
+    3-D np.array of shape [num_examples, num_frames, num_bands] which represents
+    a sequence of examples, each of which contains a patch of log mel
+    spectrogram, covering num_frames frames of audio and num_bands mel frequency
+    bands, where the frame length is vggish_params.STFT_HOP_LENGTH_SECONDS.
+
+  """
+    # Convert to mono.
+    if len(data.shape) > 1:
+        data = np.mean(data, axis=1)
+    # Resample to the rate assumed by VGGish.
+    if sample_rate != vggish_params.SAMPLE_RATE:
+        data = resampy.resample(data, sample_rate, vggish_params.SAMPLE_RATE)
+
+    # Compute log mel spectrogram features.
+    log_mel = mel_features.log_mel_spectrogram(
+        data,
+        audio_sample_rate=vggish_params.SAMPLE_RATE,
+        log_offset=vggish_params.LOG_OFFSET,
+        window_length_secs=vggish_params.STFT_WINDOW_LENGTH_SECONDS,
+        hop_length_secs=vggish_params.STFT_HOP_LENGTH_SECONDS,
+        num_mel_bins=vggish_params.NUM_MEL_BINS,
+        lower_edge_hertz=vggish_params.MEL_MIN_HZ,
+        upper_edge_hertz=vggish_params.MEL_MAX_HZ)
+
+    # Frame features into examples.
+    features_sample_rate = 1.0 / vggish_params.STFT_HOP_LENGTH_SECONDS
+    example_window_length = int(round(
+        vggish_params.EXAMPLE_WINDOW_SECONDS * features_sample_rate))
+    example_hop_length = int(round(
+        vggish_params.EXAMPLE_HOP_SECONDS * features_sample_rate))
+    log_mel_examples = mel_features.frame(
+        log_mel,
+        window_length=example_window_length,
+        hop_length=example_hop_length)
+
+    if return_tensor:
+        log_mel_examples = torch.tensor(
+            log_mel_examples, requires_grad=True)[:, None, :, :].float()
+
+    return log_mel_examples
+
+
+def wavfile_to_examples(wav_file, return_tensor=True):
+    """Convenience wrapper around waveform_to_examples() for a common WAV format.
+
+  Args:
+    wav_file: String path to a file, or a file-like object. The file
+    is assumed to contain WAV audio data with signed 16-bit PCM samples.
+    torch: Return data as a Pytorch tensor ready for VGGish
+
+  Returns:
+    See waveform_to_examples.
+  """
+    wav_data, sr = sf.read(wav_file, dtype='int16')
+    assert wav_data.dtype == np.int16, 'Bad sample type: %r' % wav_data.dtype
+    samples = wav_data / 32768.0  # Convert to [-1.0, +1.0]
+    return waveform_to_examples(samples, sr, return_tensor)

FakeVD/code_test/VGGish_Feature_Extractor/vggish_modified.py

@@ -0,0 +1,200 @@
+
+# This file is about how to transform an audio to features. (You can use open-source tools such as moviepy to split the audio from video. )
+# The original file "vggish.py" and the pre-trained model weight are from https://github.com/harritaylor/torchvggish. 
+# We save the audio convolutional features as dict_vid_audioconvfea.pkl in the preprocessing, and merge the fc layers (L20-26 self.embedding) into SV-FEND to train.
+# Therefore, in this modified file, we comment out "self.embeddings" (L39) and "postprocess"(L183-184) to obtain the output features of the convolutional layers.     
+# 本文件介绍了如何将音频转换为特征。（你可以使用诸如moviepy之类的开源工具从视频中分离音频。）
+# 原始文件“vggish.py”和预训练模型权重来自https://github.com/harritaylor/torchvggish。
+# 我们在预处理过程中将音频卷积特征保存为dict_vid_audioconvfea.pkl，并将全连接层（第20-26行的self.embedding）合并到SV-FEND中进行训练。
+# 因此，在这个修改后的文件中，我们注释掉了“self.embeddings”（第39行）和“postprocess”（第183-184行）以获取卷积层的输出特征。
+
+import numpy as np
+import torch
+import torch.nn as nn
+from torch import hub
+
+from . import vggish_input, vggish_params
+
+
+class VGG(nn.Module):
+    def __init__(self, features):
+        super(VGG, self).__init__()
+        self.features = features
+        self.embeddings = nn.Sequential(
+            nn.Linear(512 * 4 * 6, 4096),
+            nn.ReLU(True),
+            nn.Linear(4096, 4096),
+            nn.ReLU(True),
+            nn.Linear(4096, 128),
+            nn.ReLU(True))
+
+    def forward(self, x):
+        x = self.features(x)
+
+        # Transpose the output from features to
+        # remain compatible with vggish embeddings
+        x = torch.transpose(x, 1, 3)
+        x = torch.transpose(x, 1, 2)
+        x = x.contiguous()
+        x = x.view(x.size(0), -1)
+
+        return x
+        # return self.embeddings(x)
+
+
+class Postprocessor(nn.Module):
+    """Post-processes VGGish embeddings. Returns a torch.Tensor instead of a
+    numpy array in order to preserve the gradient.
+
+    "The initial release of AudioSet included 128-D VGGish embeddings for each
+    segment of AudioSet. These released embeddings were produced by applying
+    a PCA transformation (technically, a whitening transform is included as well)
+    and 8-bit quantization to the raw embedding output from VGGish, in order to
+    stay compatible with the YouTube-8M project which provides visual embeddings
+    in the same format for a large set of YouTube videos. This class implements
+    the same PCA (with whitening) and quantization transformations."
+    """
+
+    def __init__(self):
+        """Constructs a postprocessor."""
+        super(Postprocessor, self).__init__()
+        # Create empty matrix, for user's state_dict to load
+        self.pca_eigen_vectors = torch.empty(
+            (vggish_params.EMBEDDING_SIZE, vggish_params.EMBEDDING_SIZE,),
+            dtype=torch.float,
+        )
+        self.pca_means = torch.empty(
+            (vggish_params.EMBEDDING_SIZE, 1), dtype=torch.float
+        )
+
+        self.pca_eigen_vectors = nn.Parameter(self.pca_eigen_vectors, requires_grad=False)
+        self.pca_means = nn.Parameter(self.pca_means, requires_grad=False)
+
+    def postprocess(self, embeddings_batch):
+        """Applies tensor postprocessing to a batch of embeddings.
+
+        Args:
+          embeddings_batch: An tensor of shape [batch_size, embedding_size]
+            containing output from the embedding layer of VGGish.
+
+        Returns:
+          A tensor of the same shape as the input, containing the PCA-transformed,
+          quantized, and clipped version of the input.
+        """
+        assert len(embeddings_batch.shape) == 2, "Expected 2-d batch, got %r" % (
+            embeddings_batch.shape,
+        )
+        assert (
+            embeddings_batch.shape[1] == vggish_params.EMBEDDING_SIZE
+        ), "Bad batch shape: %r" % (embeddings_batch.shape,)
+
+        # Apply PCA.
+        # - Embeddings come in as [batch_size, embedding_size].
+        # - Transpose to [embedding_size, batch_size].
+        # - Subtract pca_means column vector from each column.
+        # - Premultiply by PCA matrix of shape [output_dims, input_dims]
+        #   where both are are equal to embedding_size in our case.
+        # - Transpose result back to [batch_size, embedding_size].
+        pca_applied = torch.mm(self.pca_eigen_vectors, (embeddings_batch.t() - self.pca_means)).t()
+
+        # Quantize by:
+        # - clipping to [min, max] range
+        clipped_embeddings = torch.clamp(
+            pca_applied, vggish_params.QUANTIZE_MIN_VAL, vggish_params.QUANTIZE_MAX_VAL
+        )
+        # - convert to 8-bit in range [0.0, 255.0]
+        quantized_embeddings = torch.round(
+            (clipped_embeddings - vggish_params.QUANTIZE_MIN_VAL)
+            * (
+                255.0
+                / (vggish_params.QUANTIZE_MAX_VAL - vggish_params.QUANTIZE_MIN_VAL)
+            )
+        )
+        return torch.squeeze(quantized_embeddings)
+
+    def forward(self, x):
+        return self.postprocess(x)
+
+
+def make_layers():
+    layers = []
+    in_channels = 1
+    for v in [64, "M", 128, "M", 256, 256, "M", 512, 512, "M"]:
+        if v == "M":
+            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
+        else:
+            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
+            layers += [conv2d, nn.ReLU(inplace=True)]
+            in_channels = v
+    return nn.Sequential(*layers)
+
+
+def _vgg():
+    return VGG(make_layers())
+
+
+# def _spectrogram():
+#     config = dict(
+#         sr=16000,
+#         n_fft=400,
+#         n_mels=64,
+#         hop_length=160,
+#         window="hann",
+#         center=False,
+#         pad_mode="reflect",
+#         htk=True,
+#         fmin=125,
+#         fmax=7500,
+#         output_format='Magnitude',
+#         #             device=device,
+#     )
+#     return Spectrogram.MelSpectrogram(**config)
+
+
+class VGGish(VGG):
+    def __init__(self, urls, device=None, pretrained=True, preprocess=True, postprocess=True, progress=True):
+        super().__init__(make_layers())
+        if pretrained:
+            state_dict = hub.load_state_dict_from_url(urls['vggish'], progress=progress)
+            super().load_state_dict(state_dict)
+
+        if device is None:
+            device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        self.device = device
+        self.preprocess = preprocess
+        self.postprocess = postprocess
+        if self.postprocess:
+            self.pproc = Postprocessor()
+            if pretrained:
+                state_dict = hub.load_state_dict_from_url(urls['pca'], progress=progress)
+                # TODO: Convert the state_dict to torch
+                state_dict[vggish_params.PCA_EIGEN_VECTORS_NAME] = torch.as_tensor(
+                    state_dict[vggish_params.PCA_EIGEN_VECTORS_NAME], dtype=torch.float
+                )
+                state_dict[vggish_params.PCA_MEANS_NAME] = torch.as_tensor(
+                    state_dict[vggish_params.PCA_MEANS_NAME].reshape(-1, 1), dtype=torch.float
+                )
+
+                self.pproc.load_state_dict(state_dict)
+        self.to(self.device)
+
+    def forward(self, x, fs=None):
+        if self.preprocess:
+            x = self._preprocess(x, fs)
+        x = x.to(self.device)
+        x = VGG.forward(self, x)
+        # if self.postprocess:
+        #     x = self._postprocess(x)
+        return x
+
+    def _preprocess(self, x, fs):
+        if isinstance(x, np.ndarray):
+            x = vggish_input.waveform_to_examples(x, fs)
+        elif isinstance(x, str):
+            x = vggish_input.wavfile_to_examples(x)
+        else:
+            raise AttributeError
+        return x
+
+    def _postprocess(self, x):
+        return self.pproc(x)

FakeVD/code_test/VGGish_Feature_Extractor/vggish_params.py

@@ -0,0 +1,53 @@
+# Copyright 2017 The TensorFlow Authors All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Global parameters for the VGGish model.
+
+See vggish_slim.py for more information.
+"""
+
+# Architectural constants.
+NUM_FRAMES = 96  # Frames in input mel-spectrogram patch.
+NUM_BANDS = 64  # Frequency bands in input mel-spectrogram patch.
+EMBEDDING_SIZE = 128  # Size of embedding layer.
+
+# Hyperparameters used in feature and example generation.
+SAMPLE_RATE = 16000
+STFT_WINDOW_LENGTH_SECONDS = 0.025
+STFT_HOP_LENGTH_SECONDS = 0.010
+NUM_MEL_BINS = NUM_BANDS
+MEL_MIN_HZ = 125
+MEL_MAX_HZ = 7500
+LOG_OFFSET = 0.01  # Offset used for stabilized log of input mel-spectrogram.
+EXAMPLE_WINDOW_SECONDS = 0.96  # Each example contains 96 10ms frames
+EXAMPLE_HOP_SECONDS = 0.96  # with zero overlap.
+
+# Parameters used for embedding postprocessing.
+PCA_EIGEN_VECTORS_NAME = 'pca_eigen_vectors'
+PCA_MEANS_NAME = 'pca_means'
+QUANTIZE_MIN_VAL = -2.0
+QUANTIZE_MAX_VAL = +2.0
+
+# Hyperparameters used in training.
+INIT_STDDEV = 0.01  # Standard deviation used to initialize weights.
+LEARNING_RATE = 1e-4  # Learning rate for the Adam optimizer.
+ADAM_EPSILON = 1e-8  # Epsilon for the Adam optimizer.
+
+# Names of ops, tensors, and features.
+INPUT_OP_NAME = 'vggish/input_features'
+INPUT_TENSOR_NAME = INPUT_OP_NAME + ':0'
+OUTPUT_OP_NAME = 'vggish/embedding'
+OUTPUT_TENSOR_NAME = OUTPUT_OP_NAME + ':0'
+AUDIO_EMBEDDING_FEATURE_NAME = 'audio_embedding'

FakeVD/code_test/torchvggish/hubconf.py

@@ -0,0 +1,15 @@
+dependencies = ['torch', 'numpy', 'resampy', 'soundfile']
+
+from torchvggish.vggish import VGGish
+
+model_urls = {
+    'vggish': 'https://github.com/harritaylor/torchvggish/'
+              'releases/download/v0.1/vggish-10086976.pth',
+    'pca': 'https://github.com/harritaylor/torchvggish/'
+           'releases/download/v0.1/vggish_pca_params-970ea276.pth'
+}
+
+
+def vggish(**kwargs):
+    model = VGGish(urls=model_urls, **kwargs)
+    return model

FakeVD/code_test/torchvggish/torchvggish/mel_features.py

@@ -0,0 +1,223 @@
+# Copyright 2017 The TensorFlow Authors All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Defines routines to compute mel spectrogram features from audio waveform."""
+
+import numpy as np
+
+
+def frame(data, window_length, hop_length):
+  """Convert array into a sequence of successive possibly overlapping frames.
+
+  An n-dimensional array of shape (num_samples, ...) is converted into an
+  (n+1)-D array of shape (num_frames, window_length, ...), where each frame
+  starts hop_length points after the preceding one.
+
+  This is accomplished using stride_tricks, so the original data is not
+  copied.  However, there is no zero-padding, so any incomplete frames at the
+  end are not included.
+
+  Args:
+    data: np.array of dimension N >= 1.
+    window_length: Number of samples in each frame.
+    hop_length: Advance (in samples) between each window.
+
+  Returns:
+    (N+1)-D np.array with as many rows as there are complete frames that can be
+    extracted.
+  """
+  num_samples = data.shape[0]
+  num_frames = 1 + int(np.floor((num_samples - window_length) / hop_length))
+  shape = (num_frames, window_length) + data.shape[1:]
+  strides = (data.strides[0] * hop_length,) + data.strides
+  return np.lib.stride_tricks.as_strided(data, shape=shape, strides=strides)
+
+
+def periodic_hann(window_length):
+  """Calculate a "periodic" Hann window.
+
+  The classic Hann window is defined as a raised cosine that starts and
+  ends on zero, and where every value appears twice, except the middle
+  point for an odd-length window.  Matlab calls this a "symmetric" window
+  and np.hanning() returns it.  However, for Fourier analysis, this
+  actually represents just over one cycle of a period N-1 cosine, and
+  thus is not compactly expressed on a length-N Fourier basis.  Instead,
+  it's better to use a raised cosine that ends just before the final
+  zero value - i.e. a complete cycle of a period-N cosine.  Matlab
+  calls this a "periodic" window. This routine calculates it.
+
+  Args:
+    window_length: The number of points in the returned window.
+
+  Returns:
+    A 1D np.array containing the periodic hann window.
+  """
+  return 0.5 - (0.5 * np.cos(2 * np.pi / window_length *
+                             np.arange(window_length)))
+
+
+def stft_magnitude(signal, fft_length,
+                   hop_length=None,
+                   window_length=None):
+  """Calculate the short-time Fourier transform magnitude.
+
+  Args:
+    signal: 1D np.array of the input time-domain signal.
+    fft_length: Size of the FFT to apply.
+    hop_length: Advance (in samples) between each frame passed to FFT.
+    window_length: Length of each block of samples to pass to FFT.
+
+  Returns:
+    2D np.array where each row contains the magnitudes of the fft_length/2+1
+    unique values of the FFT for the corresponding frame of input samples.
+  """
+  frames = frame(signal, window_length, hop_length)
+  # Apply frame window to each frame. We use a periodic Hann (cosine of period
+  # window_length) instead of the symmetric Hann of np.hanning (period
+  # window_length-1).
+  window = periodic_hann(window_length)
+  windowed_frames = frames * window
+  return np.abs(np.fft.rfft(windowed_frames, int(fft_length)))
+
+
+# Mel spectrum constants and functions.
+_MEL_BREAK_FREQUENCY_HERTZ = 700.0
+_MEL_HIGH_FREQUENCY_Q = 1127.0
+
+
+def hertz_to_mel(frequencies_hertz):
+  """Convert frequencies to mel scale using HTK formula.
+
+  Args:
+    frequencies_hertz: Scalar or np.array of frequencies in hertz.
+
+  Returns:
+    Object of same size as frequencies_hertz containing corresponding values
+    on the mel scale.
+  """
+  return _MEL_HIGH_FREQUENCY_Q * np.log(
+      1.0 + (frequencies_hertz / _MEL_BREAK_FREQUENCY_HERTZ))
+
+
+def spectrogram_to_mel_matrix(num_mel_bins=20,
+                              num_spectrogram_bins=129,
+                              audio_sample_rate=8000,
+                              lower_edge_hertz=125.0,
+                              upper_edge_hertz=3800.0):
+  """Return a matrix that can post-multiply spectrogram rows to make mel.
+
+  Returns a np.array matrix A that can be used to post-multiply a matrix S of
+  spectrogram values (STFT magnitudes) arranged as frames x bins to generate a
+  "mel spectrogram" M of frames x num_mel_bins.  M = S A.
+
+  The classic HTK algorithm exploits the complementarity of adjacent mel bands
+  to multiply each FFT bin by only one mel weight, then add it, with positive
+  and negative signs, to the two adjacent mel bands to which that bin
+  contributes.  Here, by expressing this operation as a matrix multiply, we go
+  from num_fft multiplies per frame (plus around 2*num_fft adds) to around
+  num_fft^2 multiplies and adds.  However, because these are all presumably
+  accomplished in a single call to np.dot(), it's not clear which approach is
+  faster in Python.  The matrix multiplication has the attraction of being more
+  general and flexible, and much easier to read.
+
+  Args:
+    num_mel_bins: How many bands in the resulting mel spectrum.  This is
+      the number of columns in the output matrix.
+    num_spectrogram_bins: How many bins there are in the source spectrogram
+      data, which is understood to be fft_size/2 + 1, i.e. the spectrogram
+      only contains the nonredundant FFT bins.
+    audio_sample_rate: Samples per second of the audio at the input to the
+      spectrogram. We need this to figure out the actual frequencies for
+      each spectrogram bin, which dictates how they are mapped into mel.
+    lower_edge_hertz: Lower bound on the frequencies to be included in the mel
+      spectrum.  This corresponds to the lower edge of the lowest triangular
+      band.
+    upper_edge_hertz: The desired top edge of the highest frequency band.
+
+  Returns:
+    An np.array with shape (num_spectrogram_bins, num_mel_bins).
+
+  Raises:
+    ValueError: if frequency edges are incorrectly ordered or out of range.
+  """
+  nyquist_hertz = audio_sample_rate / 2.
+  if lower_edge_hertz < 0.0:
+    raise ValueError("lower_edge_hertz %.1f must be >= 0" % lower_edge_hertz)
+  if lower_edge_hertz >= upper_edge_hertz:
+    raise ValueError("lower_edge_hertz %.1f >= upper_edge_hertz %.1f" %
+                     (lower_edge_hertz, upper_edge_hertz))
+  if upper_edge_hertz > nyquist_hertz:
+    raise ValueError("upper_edge_hertz %.1f is greater than Nyquist %.1f" %
+                     (upper_edge_hertz, nyquist_hertz))
+  spectrogram_bins_hertz = np.linspace(0.0, nyquist_hertz, num_spectrogram_bins)
+  spectrogram_bins_mel = hertz_to_mel(spectrogram_bins_hertz)
+  # The i'th mel band (starting from i=1) has center frequency
+  # band_edges_mel[i], lower edge band_edges_mel[i-1], and higher edge
+  # band_edges_mel[i+1].  Thus, we need num_mel_bins + 2 values in
+  # the band_edges_mel arrays.
+  band_edges_mel = np.linspace(hertz_to_mel(lower_edge_hertz),
+                               hertz_to_mel(upper_edge_hertz), num_mel_bins + 2)
+  # Matrix to post-multiply feature arrays whose rows are num_spectrogram_bins
+  # of spectrogram values.
+  mel_weights_matrix = np.empty((num_spectrogram_bins, num_mel_bins))
+  for i in range(num_mel_bins):
+    lower_edge_mel, center_mel, upper_edge_mel = band_edges_mel[i:i + 3]
+    # Calculate lower and upper slopes for every spectrogram bin.
+    # Line segments are linear in the *mel* domain, not hertz.
+    lower_slope = ((spectrogram_bins_mel - lower_edge_mel) /
+                   (center_mel - lower_edge_mel))
+    upper_slope = ((upper_edge_mel - spectrogram_bins_mel) /
+                   (upper_edge_mel - center_mel))
+    # .. then intersect them with each other and zero.
+    mel_weights_matrix[:, i] = np.maximum(0.0, np.minimum(lower_slope,
+                                                          upper_slope))
+  # HTK excludes the spectrogram DC bin; make sure it always gets a zero
+  # coefficient.
+  mel_weights_matrix[0, :] = 0.0
+  return mel_weights_matrix
+
+
+def log_mel_spectrogram(data,
+                        audio_sample_rate=8000,
+                        log_offset=0.0,
+                        window_length_secs=0.025,
+                        hop_length_secs=0.010,
+                        **kwargs):
+  """Convert waveform to a log magnitude mel-frequency spectrogram.
+
+  Args:
+    data: 1D np.array of waveform data.
+    audio_sample_rate: The sampling rate of data.
+    log_offset: Add this to values when taking log to avoid -Infs.
+    window_length_secs: Duration of each window to analyze.
+    hop_length_secs: Advance between successive analysis windows.
+    **kwargs: Additional arguments to pass to spectrogram_to_mel_matrix.
+
+  Returns:
+    2D np.array of (num_frames, num_mel_bins) consisting of log mel filterbank
+    magnitudes for successive frames.
+  """
+  window_length_samples = int(round(audio_sample_rate * window_length_secs))
+  hop_length_samples = int(round(audio_sample_rate * hop_length_secs))
+  fft_length = 2 ** int(np.ceil(np.log(window_length_samples) / np.log(2.0)))
+  spectrogram = stft_magnitude(
+      data,
+      fft_length=fft_length,
+      hop_length=hop_length_samples,
+      window_length=window_length_samples)
+  mel_spectrogram = np.dot(spectrogram, spectrogram_to_mel_matrix(
+      num_spectrogram_bins=spectrogram.shape[1],
+      audio_sample_rate=audio_sample_rate, **kwargs))
+  return np.log(mel_spectrogram + log_offset)

FakeVD/code_test/torchvggish/torchvggish/vggish.py

@@ -0,0 +1,189 @@
+import numpy as np
+import torch
+import torch.nn as nn
+from torch import hub
+
+from . import vggish_input, vggish_params
+
+
+class VGG(nn.Module):
+    def __init__(self, features):
+        super(VGG, self).__init__()
+        self.features = features
+        self.embeddings = nn.Sequential(
+            nn.Linear(512 * 4 * 6, 4096),
+            nn.ReLU(True),
+            nn.Linear(4096, 4096),
+            nn.ReLU(True),
+            nn.Linear(4096, 128),
+            nn.ReLU(True))
+
+    def forward(self, x):
+        x = self.features(x)
+
+        # Transpose the output from features to
+        # remain compatible with vggish embeddings
+        x = torch.transpose(x, 1, 3)
+        x = torch.transpose(x, 1, 2)
+        x = x.contiguous()
+        x = x.view(x.size(0), -1)
+
+        return self.embeddings(x)
+
+
+class Postprocessor(nn.Module):
+    """Post-processes VGGish embeddings. Returns a torch.Tensor instead of a
+    numpy array in order to preserve the gradient.
+
+    "The initial release of AudioSet included 128-D VGGish embeddings for each
+    segment of AudioSet. These released embeddings were produced by applying
+    a PCA transformation (technically, a whitening transform is included as well)
+    and 8-bit quantization to the raw embedding output from VGGish, in order to
+    stay compatible with the YouTube-8M project which provides visual embeddings
+    in the same format for a large set of YouTube videos. This class implements
+    the same PCA (with whitening) and quantization transformations."
+    """
+
+    def __init__(self):
+        """Constructs a postprocessor."""
+        super(Postprocessor, self).__init__()
+        # Create empty matrix, for user's state_dict to load
+        self.pca_eigen_vectors = torch.empty(
+            (vggish_params.EMBEDDING_SIZE, vggish_params.EMBEDDING_SIZE,),
+            dtype=torch.float,
+        )
+        self.pca_means = torch.empty(
+            (vggish_params.EMBEDDING_SIZE, 1), dtype=torch.float
+        )
+
+        self.pca_eigen_vectors = nn.Parameter(self.pca_eigen_vectors, requires_grad=False)
+        self.pca_means = nn.Parameter(self.pca_means, requires_grad=False)
+
+    def postprocess(self, embeddings_batch):
+        """Applies tensor postprocessing to a batch of embeddings.
+
+        Args:
+          embeddings_batch: An tensor of shape [batch_size, embedding_size]
+            containing output from the embedding layer of VGGish.
+
+        Returns:
+          A tensor of the same shape as the input, containing the PCA-transformed,
+          quantized, and clipped version of the input.
+        """
+        assert len(embeddings_batch.shape) == 2, "Expected 2-d batch, got %r" % (
+            embeddings_batch.shape,
+        )
+        assert (
+            embeddings_batch.shape[1] == vggish_params.EMBEDDING_SIZE
+        ), "Bad batch shape: %r" % (embeddings_batch.shape,)
+
+        # Apply PCA.
+        # - Embeddings come in as [batch_size, embedding_size].
+        # - Transpose to [embedding_size, batch_size].
+        # - Subtract pca_means column vector from each column.
+        # - Premultiply by PCA matrix of shape [output_dims, input_dims]
+        #   where both are are equal to embedding_size in our case.
+        # - Transpose result back to [batch_size, embedding_size].
+        pca_applied = torch.mm(self.pca_eigen_vectors, (embeddings_batch.t() - self.pca_means)).t()
+
+        # Quantize by:
+        # - clipping to [min, max] range
+        clipped_embeddings = torch.clamp(
+            pca_applied, vggish_params.QUANTIZE_MIN_VAL, vggish_params.QUANTIZE_MAX_VAL
+        )
+        # - convert to 8-bit in range [0.0, 255.0]
+        quantized_embeddings = torch.round(
+            (clipped_embeddings - vggish_params.QUANTIZE_MIN_VAL)
+            * (
+                255.0
+                / (vggish_params.QUANTIZE_MAX_VAL - vggish_params.QUANTIZE_MIN_VAL)
+            )
+        )
+        return torch.squeeze(quantized_embeddings)
+
+    def forward(self, x):
+        return self.postprocess(x)
+
+
+def make_layers():
+    layers = []
+    in_channels = 1
+    for v in [64, "M", 128, "M", 256, 256, "M", 512, 512, "M"]:
+        if v == "M":
+            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
+        else:
+            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
+            layers += [conv2d, nn.ReLU(inplace=True)]
+            in_channels = v
+    return nn.Sequential(*layers)
+
+
+def _vgg():
+    return VGG(make_layers())
+
+
+# def _spectrogram():
+#     config = dict(
+#         sr=16000,
+#         n_fft=400,
+#         n_mels=64,
+#         hop_length=160,
+#         window="hann",
+#         center=False,
+#         pad_mode="reflect",
+#         htk=True,
+#         fmin=125,
+#         fmax=7500,
+#         output_format='Magnitude',
+#         #             device=device,
+#     )
+#     return Spectrogram.MelSpectrogram(**config)
+
+
+class VGGish(VGG):
+    def __init__(self, urls, device=None, pretrained=True, preprocess=True, postprocess=True, progress=True):
+        super().__init__(make_layers())
+        if pretrained:
+            state_dict = hub.load_state_dict_from_url(urls['vggish'], progress=progress)
+            super().load_state_dict(state_dict)
+
+        if device is None:
+            device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        self.device = device
+        self.preprocess = preprocess
+        self.postprocess = postprocess
+        if self.postprocess:
+            self.pproc = Postprocessor()
+            if pretrained:
+                state_dict = hub.load_state_dict_from_url(urls['pca'], progress=progress)
+                # TODO: Convert the state_dict to torch
+                state_dict[vggish_params.PCA_EIGEN_VECTORS_NAME] = torch.as_tensor(
+                    state_dict[vggish_params.PCA_EIGEN_VECTORS_NAME], dtype=torch.float
+                )
+                state_dict[vggish_params.PCA_MEANS_NAME] = torch.as_tensor(
+                    state_dict[vggish_params.PCA_MEANS_NAME].reshape(-1, 1), dtype=torch.float
+                )
+
+                self.pproc.load_state_dict(state_dict)
+        self.to(self.device)
+
+    def forward(self, x, fs=None):
+        if self.preprocess:
+            x = self._preprocess(x, fs)
+        x = x.to(self.device)
+        x = VGG.forward(self, x)
+        if self.postprocess:
+            x = self._postprocess(x)
+        return x
+
+    def _preprocess(self, x, fs):
+        if isinstance(x, np.ndarray):
+            x = vggish_input.waveform_to_examples(x, fs)
+        elif isinstance(x, str):
+            x = vggish_input.wavfile_to_examples(x)
+        else:
+            raise AttributeError
+        return x
+
+    def _postprocess(self, x):
+        return self.pproc(x)

FakeVD/code_test/torchvggish/torchvggish/vggish_input.py

@@ -0,0 +1,98 @@
+# Copyright 2017 The TensorFlow Authors All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Compute input examples for VGGish from audio waveform."""
+
+# Modification: Return torch tensors rather than numpy arrays
+import torch
+
+import numpy as np
+import resampy
+
+from . import mel_features
+from . import vggish_params
+
+import soundfile as sf
+
+
+def waveform_to_examples(data, sample_rate, return_tensor=True):
+    """Converts audio waveform into an array of examples for VGGish.
+
+  Args:
+    data: np.array of either one dimension (mono) or two dimensions
+      (multi-channel, with the outer dimension representing channels).
+      Each sample is generally expected to lie in the range [-1.0, +1.0],
+      although this is not required.
+    sample_rate: Sample rate of data.
+    return_tensor: Return data as a Pytorch tensor ready for VGGish
+
+  Returns:
+    3-D np.array of shape [num_examples, num_frames, num_bands] which represents
+    a sequence of examples, each of which contains a patch of log mel
+    spectrogram, covering num_frames frames of audio and num_bands mel frequency
+    bands, where the frame length is vggish_params.STFT_HOP_LENGTH_SECONDS.
+
+  """
+    # Convert to mono.
+    if len(data.shape) > 1:
+        data = np.mean(data, axis=1)
+    # Resample to the rate assumed by VGGish.
+    if sample_rate != vggish_params.SAMPLE_RATE:
+        data = resampy.resample(data, sample_rate, vggish_params.SAMPLE_RATE)
+
+    # Compute log mel spectrogram features.
+    log_mel = mel_features.log_mel_spectrogram(
+        data,
+        audio_sample_rate=vggish_params.SAMPLE_RATE,
+        log_offset=vggish_params.LOG_OFFSET,
+        window_length_secs=vggish_params.STFT_WINDOW_LENGTH_SECONDS,
+        hop_length_secs=vggish_params.STFT_HOP_LENGTH_SECONDS,
+        num_mel_bins=vggish_params.NUM_MEL_BINS,
+        lower_edge_hertz=vggish_params.MEL_MIN_HZ,
+        upper_edge_hertz=vggish_params.MEL_MAX_HZ)
+
+    # Frame features into examples.
+    features_sample_rate = 1.0 / vggish_params.STFT_HOP_LENGTH_SECONDS
+    example_window_length = int(round(
+        vggish_params.EXAMPLE_WINDOW_SECONDS * features_sample_rate))
+    example_hop_length = int(round(
+        vggish_params.EXAMPLE_HOP_SECONDS * features_sample_rate))
+    log_mel_examples = mel_features.frame(
+        log_mel,
+        window_length=example_window_length,
+        hop_length=example_hop_length)
+
+    if return_tensor:
+        log_mel_examples = torch.tensor(
+            log_mel_examples, requires_grad=True)[:, None, :, :].float()
+
+    return log_mel_examples
+
+
+def wavfile_to_examples(wav_file, return_tensor=True):
+    """Convenience wrapper around waveform_to_examples() for a common WAV format.
+
+  Args:
+    wav_file: String path to a file, or a file-like object. The file
+    is assumed to contain WAV audio data with signed 16-bit PCM samples.
+    torch: Return data as a Pytorch tensor ready for VGGish
+
+  Returns:
+    See waveform_to_examples.
+  """
+    wav_data, sr = sf.read(wav_file, dtype='int16')
+    assert wav_data.dtype == np.int16, 'Bad sample type: %r' % wav_data.dtype
+    samples = wav_data / 32768.0  # Convert to [-1.0, +1.0]
+    return waveform_to_examples(samples, sr, return_tensor)

FakeVD/code_test/torchvggish/torchvggish/vggish_params.py

@@ -0,0 +1,53 @@
+# Copyright 2017 The TensorFlow Authors All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Global parameters for the VGGish model.
+
+See vggish_slim.py for more information.
+"""
+
+# Architectural constants.
+NUM_FRAMES = 96  # Frames in input mel-spectrogram patch.
+NUM_BANDS = 64  # Frequency bands in input mel-spectrogram patch.
+EMBEDDING_SIZE = 128  # Size of embedding layer.
+
+# Hyperparameters used in feature and example generation.
+SAMPLE_RATE = 16000
+STFT_WINDOW_LENGTH_SECONDS = 0.025
+STFT_HOP_LENGTH_SECONDS = 0.010
+NUM_MEL_BINS = NUM_BANDS
+MEL_MIN_HZ = 125
+MEL_MAX_HZ = 7500
+LOG_OFFSET = 0.01  # Offset used for stabilized log of input mel-spectrogram.
+EXAMPLE_WINDOW_SECONDS = 0.96  # Each example contains 96 10ms frames
+EXAMPLE_HOP_SECONDS = 0.96  # with zero overlap.
+
+# Parameters used for embedding postprocessing.
+PCA_EIGEN_VECTORS_NAME = 'pca_eigen_vectors'
+PCA_MEANS_NAME = 'pca_means'
+QUANTIZE_MIN_VAL = -2.0
+QUANTIZE_MAX_VAL = +2.0
+
+# Hyperparameters used in training.
+INIT_STDDEV = 0.01  # Standard deviation used to initialize weights.
+LEARNING_RATE = 1e-4  # Learning rate for the Adam optimizer.
+ADAM_EPSILON = 1e-8  # Epsilon for the Adam optimizer.
+
+# Names of ops, tensors, and features.
+INPUT_OP_NAME = 'vggish/input_features'
+INPUT_TENSOR_NAME = INPUT_OP_NAME + ':0'
+OUTPUT_OP_NAME = 'vggish/embedding'
+OUTPUT_TENSOR_NAME = OUTPUT_OP_NAME + ':0'
+AUDIO_EMBEDDING_FEATURE_NAME = 'audio_embedding'

FakeVD/code_test/utils/dataloader.py

@@ -0,0 +1,593 @@
+
+
+import os
+import pickle
+
+import h5py
+import jieba
+import jieba.analyse as analyse
+import numpy as np
+import pandas as pd
+import torch
+from scipy.spatial import distance
+from sklearn import preprocessing
+from sklearn.decomposition import LatentDirichletAllocation
+from sklearn.feature_extraction.text import TfidfVectorizer
+from torch.utils.data import Dataset
+from transformers import BertTokenizer
+import math
+
+import json
+
+def str2num(str_x):
+    if isinstance(str_x, float):
+        return str_x
+    elif str_x.isdigit():
+        return int(str_x)
+    elif 'w' in str_x:
+        return float(str_x[:-1])*10000
+    elif '亿' in str_x:
+        return float(str_x[:-1])*100000000
+    else:
+        print ("error")
+        print (str_x)
+        
+
+class SVFENDDataset(Dataset):
+
+    def __init__(self, data, data_paths):
+        
+        self.data = data
+
+        self.text = []
+        with open(data_paths['ASR_text'], 'r', encoding='utf-8') as file:
+            for line in file:
+                self.text.append(json.loads(line))
+
+        # self.text = pd.read_json(data_paths['ASR_text'], lines=True)
+
+        with open(data_paths['VGGish_audio'], "rb") as fr:
+            self.dict_vid_convfea = pickle.load(fr)
+
+        self.framefeapath = data_paths['VGG19_frame']
+
+        self.c3dfeapath = data_paths['C3D_video']
+
+        self.tokenizer = BertTokenizer.from_pretrained('./FakeVD/Models/bert-base-chinese/')
+
+        
+    def __len__(self):
+        # return self.data.shape[0]
+        return len(self.data)
+    
+    def __getitem__(self, idx):
+
+        vid = self.data[idx]
+
+        # text
+        # 遍历列表，查找匹配的 video_id
+        for itm in self.text:
+            if itm['video_id'] == vid:
+                text = itm['text']
+                break
+        title_tokens = self.tokenizer(text, max_length=512, padding='max_length', truncation=True)
+        title_inputid = torch.LongTensor(title_tokens['input_ids'])
+        title_mask = torch.LongTensor(title_tokens['attention_mask'])
+
+        # comments
+
+        
+        # audio
+        audioframes = self.dict_vid_convfea[vid]
+        audioframes = torch.FloatTensor(audioframes)
+        
+        # frames
+        frames=pickle.load(open(os.path.join(self.framefeapath,vid+'.pkl'),'rb'))
+        frames=torch.FloatTensor(frames)
+        
+        # video
+        c3d = h5py.File(self.c3dfeapath+vid+".hdf5", "r")[vid]['c3d_features']
+        c3d = torch.FloatTensor(c3d)
+
+        # # user
+
+
+        return {
+            'label': torch.tensor(-1),
+            'title_inputid': title_inputid,
+            'title_mask': title_mask,
+            'audioframes': audioframes,
+            'frames':frames,
+            'c3d': c3d,
+            # 'comments_inputid': comments_inputid,
+            # 'comments_mask': comments_mask,
+            # 'comments_like': comments_like,
+            # 'intro_inputid': intro_inputid,
+            # 'intro_mask': intro_mask,
+        }
+
+
+def split_word(df):
+    title = df['description'].values
+    comments = df['comments'].apply(lambda x:' '.join(x)).values
+    text = np.concatenate([title, comments],axis=0)
+    analyse.set_stop_words('./data/stopwords.txt')
+    all_word = [analyse.extract_tags(txt) for txt in text.tolist()]
+    corpus = [' '.join(word) for word in all_word]
+    return corpus
+
+
+class FANVMDataset_train(Dataset):
+
+    def __init__(self, path_vid_train):
+        self.data_complete = pd.read_json('./data/data.json',orient='records',dtype=False,lines=True)
+
+        self.framefeapath='./data/ptvgg19_frames/'
+        self.thumbframefeapath='./data/ptvgg19_frame_thumb/'
+
+        self.vid_train = []
+        with open('./data/vids/'+path_vid_train, "r") as fr:
+            for line in fr.readlines():
+                self.vid_train.append(line.strip())
+        self.data_train = self.data_complete[self.data_complete.video_id.isin(self.vid_train)]  
+        self.data_train['video_id'] = self.data_train['video_id'].astype('category')
+        self.data_train['video_id'].cat.set_categories(self.vid_train, inplace=True)
+        self.data_train.sort_values('video_id', ascending=True, inplace=True)    
+        self.data_train.reset_index(inplace=True)    
+
+        self.tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')
+
+        corpus = split_word(self.data_train)
+        tfidf = TfidfVectorizer().fit_transform(corpus)
+        lda = LatentDirichletAllocation(n_components=15,random_state=2022)
+        docres = lda.fit_transform(tfidf)
+        self.topic_title = []
+        s = []
+        for idx in range(self.data_train.shape[0]):
+            theta_title = docres[idx]
+            self.topic_title.append(theta_title)
+            theta_comments = docres[idx+self.data_train.shape[0]]
+            s.append(distance.jensenshannon(theta_title, theta_comments) ** 2)
+        min_max_scaler = preprocessing.MinMaxScaler()
+        s_minMax = min_max_scaler.fit_transform(np.array(s).reshape(-1, 1))
+        self.s_minMax = s_minMax.reshape(s_minMax.shape[0])
+
+    def __len__(self):
+        return self.data_train.shape[0]
+     
+    def __getitem__(self, idx):
+        item = self.data_train.iloc[idx]
+        vid = item['video_id']
+
+        label = 1 if item['annotation']=='假' else 0
+        label = torch.tensor(label)
+        
+        title_tokens = self.tokenizer(item['description'], max_length=512, padding='max_length', truncation=True)
+        title_inputid = torch.LongTensor(title_tokens['input_ids'])
+        title_mask = torch.LongTensor(title_tokens['attention_mask'])
+
+        comments_inputid = []
+        comments_mask = []
+        for comment in item['comments']:
+            comment_tokens = self.tokenizer(comment, max_length=250, padding='max_length', truncation=True)
+            comments_inputid.append(comment_tokens['input_ids'])
+            comments_mask.append(comment_tokens['attention_mask'])
+        comments_inputid = torch.LongTensor(comments_inputid) 
+        comments_mask = torch.LongTensor(comments_mask)
+        
+        comments_like = []
+        for num in item['comments_like']:
+            num_like = num.split(" ")[0] 
+            comments_like.append(str2num(num_like))
+        comments_like = torch.tensor(comments_like)
+        
+        frames=pickle.load(open(os.path.join(self.framefeapath,vid+'.pkl'),'rb'))
+        frames=torch.FloatTensor(frames)
+
+        frame_thmub = pickle.load(open(os.path.join(self.thumbframefeapath,vid+'.pkl'),'rb'))
+        frame_thmub = torch.FloatTensor(frame_thmub)
+
+        s = self.s_minMax[idx]
+        s = torch.tensor(s)
+
+        topic_title = self.topic_title[idx]
+        topic_title = torch.FloatTensor(topic_title)
+        
+
+        return {
+            'label': label,
+            'title_inputid': title_inputid,
+            'title_mask': title_mask,
+            'comments_inputid': comments_inputid,
+            'comments_mask': comments_mask,
+            'comments_like': comments_like,
+            'frames':frames,
+            'frame_thmub':frame_thmub,
+            's':s,
+            'label_event':topic_title,
+        }
+
+
+class FANVMDataset_test(Dataset):
+
+    def __init__(self, path_vid_train, path_vid_test):
+        self.data_complete = pd.read_json('./data/data.json',orient='records',dtype=False,lines=True)
+
+        self.framefeapath='./data/ptvgg19_frames/'
+        self.thumbframefeapath='./data/ptvgg19_frame_thumb/'
+
+        self.vid_train = []
+        with open('./data/vids/'+path_vid_train, "r") as fr:
+            for line in fr.readlines():
+                self.vid_train.append(line.strip())
+        self.data_train = self.data_complete[self.data_complete.video_id.isin(self.vid_train)]  
+        self.data_train['video_id'] = self.data_train['video_id'].astype('category')
+        self.data_train['video_id'].cat.set_categories(self.vid_train, inplace=True)
+        self.data_train.sort_values('video_id', ascending=True, inplace=True)    
+        self.data_train.reset_index(inplace=True)  
+
+        self.vid_test = []
+        with open('./data/vids/'+path_vid_test, "r") as fr:
+            for line in fr.readlines():
+                self.vid_test.append(line.strip())        
+        self.data_test = self.data_complete[self.data_complete.video_id.isin(self.vid_test)]  
+        self.data_test['video_id'] = self.data_test['video_id'].astype('category')
+        self.data_test['video_id'].cat.set_categories(self.vid_test, inplace=True)
+        self.data_test.sort_values('video_id', ascending=True, inplace=True)    
+        self.data_test.reset_index(inplace=True)  
+
+        self.tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')
+
+        # Use data_train to train
+        corpus_train = split_word(self.data_train)
+        tfidf = TfidfVectorizer()
+        tfidf_matrix_train = tfidf.fit_transform(corpus_train)
+        lda = LatentDirichletAllocation(n_components=15,random_state=2022)
+        lda.fit(tfidf_matrix_train)
+
+        # apply on data_test
+        corpus_test = split_word(self.data_test)
+        tfidf_matrix_test = tfidf.transform(corpus_test)
+        docres = lda.transform(tfidf_matrix_test)
+
+        s = []
+        self.topic_title = []
+        for idx in range(self.data_test.shape[0]):
+            theta_title = docres[idx]
+            self.topic_title.append(theta_title)
+            theta_comments = docres[idx+self.data_test.shape[0]]
+            s.append(distance.jensenshannon(theta_title, theta_comments) ** 2)
+        min_max_scaler = preprocessing.MinMaxScaler()
+        s_minMax = min_max_scaler.fit_transform(np.array(s).reshape(-1, 1))
+        self.s_minMax = s_minMax.reshape(s_minMax.shape[0])
+
+    def __len__(self):
+        return self.data_test.shape[0]
+     
+    def __getitem__(self, idx):
+        item = self.data_test.iloc[idx]
+        vid = item['video_id']
+
+        label = 1 if item['annotation']=='假' else 0
+        label = torch.tensor(label)
+        
+        title_tokens = self.tokenizer(item['description'], max_length=512, padding='max_length', truncation=True)
+        title_inputid = torch.LongTensor(title_tokens['input_ids'])
+        title_mask = torch.LongTensor(title_tokens['attention_mask'])
+
+        comments_inputid = []
+        comments_mask = []
+        for comment in item['comments']:
+            comment_tokens = self.tokenizer(comment, max_length=250, padding='max_length', truncation=True)
+            comments_inputid.append(comment_tokens['input_ids'])
+            comments_mask.append(comment_tokens['attention_mask'])
+        comments_inputid = torch.LongTensor(comments_inputid) 
+        comments_mask = torch.LongTensor(comments_mask)
+        
+        comments_like = []
+        for num in item['comments_like']:
+            num_like = num.split(" ")[0] 
+            comments_like.append(str2num(num_like))
+        comments_like = torch.tensor(comments_like)
+        
+        frames=pickle.load(open(os.path.join(self.framefeapath,vid+'.pkl'),'rb'))
+        frames=torch.FloatTensor(frames)
+
+        frame_thmub = pickle.load(open(os.path.join(self.thumbframefeapath,vid+'.pkl'),'rb'))
+        frame_thmub = torch.FloatTensor(frame_thmub)
+
+        s = self.s_minMax[idx]
+        s = torch.tensor(s)
+
+        topic_title = self.topic_title[idx]
+        topic_title = torch.FloatTensor(topic_title)
+        
+
+        return {
+            'label': label,
+            'title_inputid': title_inputid,
+            'title_mask': title_mask,
+            'comments_inputid': comments_inputid,
+            'comments_mask': comments_mask,
+            'comments_like': comments_like,
+            'frames':frames,
+            'frame_thmub':frame_thmub,
+            's':s,
+            'label_event':topic_title,
+        }
+
+
+class TikTecDataset(Dataset):
+
+    def __init__(self, path_vid):
+        self.data_complete = pd.read_json('./data/data.json',orient='records',dtype=False,lines=True)
+
+        self.vid = []
+        with open(f'./data/vids/{path_vid}', "r") as fr:
+            for line in fr.readlines():
+                self.vid.append(line.strip())
+        self.data = self.data_complete[self.data_complete['video_id'].isin(self.vid)]
+
+    def __len__(self):
+        return self.data.shape[0]
+
+    def __getitem__(self, idx):
+        item = self.data.iloc[idx]
+        vid = item['video_id']
+
+        label = 1 if item['label']=='假' else 0
+        label = torch.tensor(label)
+
+        max_K = 200  # max num of frames
+        max_N = 500  # max num of ASR words
+
+        # get caption feature
+        with open('./data/caption_w2v_pad%s.pkl' % vid, 'rb') as f:
+            caption_feature = pickle.load(f)  # (num_frame, 100, 300)
+        if max_K / caption_feature.shape[0] >= 2:
+            times = math.floor(max_K / caption_feature.shape[0])
+            caption_feature = caption_feature.repeat_interleave(times, dim=0)
+        elif caption_feature.shape[0] > max_K:
+            times = math.ceil(caption_feature.shape[0] / max_K)
+            caption_feature = caption_feature[::times][:max_K]
+        actual_K = caption_feature.shape[0]
+        caption_feature = torch.cat([caption_feature, torch.zeros((max_K - caption_feature.shape[0], 100, 300))], dim=0)
+
+        # get visual feature
+        with open( './data/vgg19_result%s.pkl' % vid, 'rb') as f:
+            visual_feature = pickle.load(f)  # (num_frame, 45, 1000)
+        if max_K / visual_feature.shape[0] >= 2:
+            times = math.floor(max_K / visual_feature.shape[0])
+            visual_feature = visual_feature.repeat_interleave(times, dim=0)
+        elif visual_feature.shape[0] > max_K:
+            times = math.ceil(visual_feature.shape[0] / max_K)
+            visual_feature = visual_feature[::times][:max_K]
+        visual_feature = torch.cat([visual_feature, torch.zeros((max_K - visual_feature.shape[0], 45, 1000))], dim=0)
+
+        # get ASR feature
+        with open('./data/asr_w2v+mfcc%s.pkl' % vid, 'rb') as f:
+            asr_feature = pickle.load(f)  # (num_word, 300+650)
+        asr_feature = asr_feature[:max_N]
+        actual_N = asr_feature.shape[0]
+        asr_feature = torch.cat([asr_feature, torch.zeros((max_N - asr_feature.shape[0], 300+650))], dim=0)
+
+        # get frames mask & ASR words mask
+        mask_K = torch.zeros(max_K, dtype=torch.int)
+        mask_K[:actual_K] = 1
+        mask_N = torch.zeros(max_N, dtype=torch.int)
+        mask_N[:actual_N] = 1
+        if actual_N == 0:
+            mask_N[:] = 1
+
+        return {
+            'label': label,
+            'caption_feature': caption_feature,
+            'visual_feature': visual_feature,
+            'asr_feature': asr_feature,
+            'mask_K': mask_K,
+            'mask_N': mask_N,
+        }
+
+
+class C3DDataset(Dataset):
+
+    def __init__(self, path_vid):
+        self.data_complete = pd.read_json('./data/data.json',orient='records',dtype=False,lines=True)
+
+        self.vid = []
+        with open('./data/vids/'+path_vid, "r") as fr:
+            for line in fr.readlines():
+                self.vid.append(line.strip())
+        self.data = self.data_complete[self.data_complete.video_id.isin(self.vid)]  
+        self.data['video_id'] = self.data['video_id'].astype('category')
+        self.data['video_id'].cat.set_categories(self.vid, inplace=True)
+        self.data.sort_values('video_id', ascending=True, inplace=True)    
+        self.data.reset_index(inplace=True) 
+
+        self.c3dfeapath='./data/c3d/'
+
+    def __len__(self):
+        return self.data.shape[0]
+     
+    def __getitem__(self, idx):
+        item = self.data.iloc[idx]
+        vid = item['video_id']
+
+        label = 1 if item['annotation']=='假' else 0
+        label = torch.tensor(label)
+
+        c3d = h5py.File(self.c3dfeapath+vid+".hdf5", "r")[vid]['c3d_features']
+        c3d = torch.FloatTensor(c3d)
+
+        return {
+            'label': label,
+            'c3d': c3d,
+        }
+
+
+class VGGDataset(Dataset):
+
+    def __init__(self, path_vid):
+        self.data_complete = pd.read_json('./data/data.json',orient='records',dtype=False,lines=True)
+
+        self.vid = []
+        with open('./data/vids/'+path_vid, "r") as fr:
+            for line in fr.readlines():
+                self.vid.append(line.strip())
+        self.data = self.data_complete[self.data_complete.video_id.isin(self.vid)]  
+        self.data['video_id'] = self.data['video_id'].astype('category')
+        self.data['video_id'].cat.set_categories(self.vid, inplace=True)
+        self.data.sort_values('video_id', ascending=True, inplace=True)    
+        self.data.reset_index(inplace=True) 
+   
+        self.framefeapath='./data/ptvgg19_frames/'
+
+        
+    def __len__(self):
+        return self.data.shape[0]
+     
+    def __getitem__(self, idx):
+        item = self.data.iloc[idx]
+        vid = item['video_id']
+
+        label = 1 if item['annotation']=='假' else 0
+        label = torch.tensor(label)
+
+        frames=pickle.load(open(os.path.join(self.framefeapath,vid+'.pkl'),'rb'))
+        frames=torch.FloatTensor(frames)
+
+        return {
+            'label': label,
+            'frames': frames,
+        }
+
+
+class BboxDataset(Dataset):
+    def __init__(self, path_vid):
+        self.data_complete = pd.read_json('./data/data_5500_revised.json',orient='records',dtype=False,lines=True)
+        
+        self.vid = []
+        with open('./data/vids/'+path_vid, "r") as fr:
+            for line in fr.readlines():
+                self.vid.append(line.strip())
+        self.data = self.data_complete[self.data_complete.video_id.isin(self.vid)]  
+        self.data['video_id'] = self.data['video_id'].astype('category')
+        self.data['video_id'].cat.set_categories(self.vid, inplace=True)
+        self.data.sort_values('video_id', ascending=True, inplace=True)    
+        self.data.reset_index(inplace=True)  
+
+        self.bboxfeapath = './data/bbox_vgg19/'
+
+    def __len__(self):
+        return self.data.shape[0]
+
+    def __getitem__(self, idx):
+        item = self.data.iloc[idx]
+        vid = item['video_id']
+
+        label = 1 if item['annotation']=='假' else 0
+        label = torch.tensor(label)
+
+        bbox_vgg = pickle.load(open(os.path.join(self.bboxfeapath,vid+'.pkl'),'rb'))
+        bbox_vgg = torch.FloatTensor(bbox_vgg)
+
+        return {
+            'label': label,
+            'bbox_vgg': bbox_vgg
+        }
+
+
+class Title_W2V_Dataset(Dataset):
+    def __init__(self, path_vid, wv_from_text):
+        self.data_complete = pd.read_json('./data/data.json',orient='records',dtype=False,lines=True)
+
+        self.vid = []
+        with open('./data/vids/'+path_vid, "r") as fr:
+            for line in fr.readlines():
+                self.vid.append(line.strip())
+        self.data = self.data_complete[self.data_complete.video_id.isin(self.vid)]  
+        self.data['video_id'] = self.data['video_id'].astype('category')
+        self.data['video_id'].cat.set_categories(self.vid, inplace=True)
+        self.data.sort_values('video_id', ascending=True, inplace=True)    
+        self.data.reset_index(inplace=True)     
+
+        self.wv_from_text = wv_from_text
+
+    def __len__(self):
+        return self.data.shape[0]
+
+    def __getitem__(self, idx):
+        item = self.data.iloc[idx]
+
+        label = 1 if item['annotation']=='假' else 0
+        label = torch.tensor(label)
+
+        text = item['description']+' '+item['ocr']
+        title_w2v = []
+        for word in jieba.cut(text, cut_all=False):
+            if self.wv_from_text.__contains__(word):
+                try:
+                    title_w2v.append(self.wv_from_text[word])
+                except:
+                    continue
+
+        title_w2v = torch.FloatTensor(title_w2v)
+        
+        return {
+            'label': label,
+            'title_w2v': title_w2v,
+        }
+
+
+class CommentsDataset(Dataset):
+
+    def __init__(self, path_vid):
+        self.data_complete = pd.read_json('./data/data.json',orient='records',dtype=False,lines=True)
+
+        self.vid = []
+        with open('./data/vids/'+path_vid, "r") as fr:
+            for line in fr.readlines():
+                self.vid.append(line.strip())
+        self.data = self.data_complete[self.data_complete.video_id.isin(self.vid)]  
+        self.data['video_id'] = self.data['video_id'].astype('category')
+        self.data['video_id'].cat.set_categories(self.vid, inplace=True)
+        self.data.sort_values('video_id', ascending=True, inplace=True)    
+        self.data.reset_index(inplace=True)       
+
+        self.tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')
+
+        hascomments = self.data['comments'].apply(lambda x:len(x)>0)
+        self.data = self.data[hascomments]
+        print (self.data.shape)
+        
+    def __len__(self):
+        return self.data.shape[0]
+     
+    def __getitem__(self, idx):
+        item = self.data.iloc[idx]
+        vid = item['video_id']
+
+        label = 1 if item['annotation']=='假' else 0
+        label = torch.tensor(label)
+
+        comments_inputid = []
+        comments_mask = []
+        for comment in item['comments']:
+            comment_tokens = self.tokenizer(comment, max_length=250, padding='max_length', truncation=True)
+            comments_inputid.append(comment_tokens['input_ids'])
+            comments_mask.append(comment_tokens['attention_mask'])
+        comments_inputid = torch.LongTensor(comments_inputid)
+        comments_mask = torch.LongTensor(comments_mask)
+        
+        comments_like = []
+        for num in item['comments_like']:
+            num_like = num.split(" ")[0] 
+            comments_like.append(str2num(num_like))
+        comments_like = torch.tensor(comments_like)
+
+        return {
+            'label': label,
+            'comments_inputid': comments_inputid,
+            'comments_mask': comments_mask,
+            'comments_like': comments_like,
+        }

FakeVD/code_test/utils/metrics.py

@@ -0,0 +1,26 @@
+
+import numpy as np
+from sklearn.metrics import (accuracy_score, classification_report,
+                             confusion_matrix, f1_score, precision_score,
+                             recall_score, roc_auc_score)
+
+
+def get_confusionmatrix_fnd(preds, labels):
+    # label_predicted = np.argmax(preds, axis=1)
+    label_predicted = preds
+    print (accuracy_score(labels, label_predicted))
+    print(classification_report(labels, label_predicted, labels=[0.0, 1.0], target_names=['real', 'fake'],digits=4))
+    print (confusion_matrix(labels, label_predicted, labels=[0,1]))
+
+
+def metrics(y_true, y_pred):
+
+    metrics = {}
+    metrics['auc'] = roc_auc_score(y_true, y_pred, average='macro')
+    y_pred = np.around(np.array(y_pred)).astype(int)
+    metrics['f1'] = f1_score(y_true, y_pred, average='macro')
+    metrics['recall'] = recall_score(y_true, y_pred, average='macro')
+    metrics['precision'] = precision_score(y_true, y_pred, average='macro')
+    metrics['acc'] = accuracy_score(y_true, y_pred)
+
+    return metrics

FakeVD/code_test/utils/vggish_modified.py

@@ -0,0 +1,200 @@
+
+# This file is about how to transform an audio to features. (You can use open-source tools such as moviepy to split the audio from video. )
+# The original file "vggish.py" and the pre-trained model weight are from https://github.com/harritaylor/torchvggish. 
+# We save the audio convolutional features as dict_vid_audioconvfea.pkl in the preprocessing, and merge the fc layers (L20-26 self.embedding) into SV-FEND to train.
+# Therefore, in this modified file, we comment out "self.embeddings" (L39) and "postprocess"(L183-184) to obtain the output features of the convolutional layers.     
+# 本文件介绍了如何将音频转换为特征。（你可以使用诸如moviepy之类的开源工具从视频中分离音频。）
+# 原始文件“vggish.py”和预训练模型权重来自https://github.com/harritaylor/torchvggish。
+# 我们在预处理过程中将音频卷积特征保存为dict_vid_audioconvfea.pkl，并将全连接层（第20-26行的self.embedding）合并到SV-FEND中进行训练。
+# 因此，在这个修改后的文件中，我们注释掉了“self.embeddings”（第39行）和“postprocess”（第183-184行）以获取卷积层的输出特征。
+
+import numpy as np
+import torch
+import torch.nn as nn
+from torch import hub
+
+from . import vggish_input, vggish_params
+
+
+class VGG(nn.Module):
+    def __init__(self, features):
+        super(VGG, self).__init__()
+        self.features = features
+        self.embeddings = nn.Sequential(
+            nn.Linear(512 * 4 * 6, 4096),
+            nn.ReLU(True),
+            nn.Linear(4096, 4096),
+            nn.ReLU(True),
+            nn.Linear(4096, 128),
+            nn.ReLU(True))
+
+    def forward(self, x):
+        x = self.features(x)
+
+        # Transpose the output from features to
+        # remain compatible with vggish embeddings
+        x = torch.transpose(x, 1, 3)
+        x = torch.transpose(x, 1, 2)
+        x = x.contiguous()
+        x = x.view(x.size(0), -1)
+
+        return x
+        # return self.embeddings(x)
+
+
+class Postprocessor(nn.Module):
+    """Post-processes VGGish embeddings. Returns a torch.Tensor instead of a
+    numpy array in order to preserve the gradient.
+
+    "The initial release of AudioSet included 128-D VGGish embeddings for each
+    segment of AudioSet. These released embeddings were produced by applying
+    a PCA transformation (technically, a whitening transform is included as well)
+    and 8-bit quantization to the raw embedding output from VGGish, in order to
+    stay compatible with the YouTube-8M project which provides visual embeddings
+    in the same format for a large set of YouTube videos. This class implements
+    the same PCA (with whitening) and quantization transformations."
+    """
+
+    def __init__(self):
+        """Constructs a postprocessor."""
+        super(Postprocessor, self).__init__()
+        # Create empty matrix, for user's state_dict to load
+        self.pca_eigen_vectors = torch.empty(
+            (vggish_params.EMBEDDING_SIZE, vggish_params.EMBEDDING_SIZE,),
+            dtype=torch.float,
+        )
+        self.pca_means = torch.empty(
+            (vggish_params.EMBEDDING_SIZE, 1), dtype=torch.float
+        )
+
+        self.pca_eigen_vectors = nn.Parameter(self.pca_eigen_vectors, requires_grad=False)
+        self.pca_means = nn.Parameter(self.pca_means, requires_grad=False)
+
+    def postprocess(self, embeddings_batch):
+        """Applies tensor postprocessing to a batch of embeddings.
+
+        Args:
+          embeddings_batch: An tensor of shape [batch_size, embedding_size]
+            containing output from the embedding layer of VGGish.
+
+        Returns:
+          A tensor of the same shape as the input, containing the PCA-transformed,
+          quantized, and clipped version of the input.
+        """
+        assert len(embeddings_batch.shape) == 2, "Expected 2-d batch, got %r" % (
+            embeddings_batch.shape,
+        )
+        assert (
+            embeddings_batch.shape[1] == vggish_params.EMBEDDING_SIZE
+        ), "Bad batch shape: %r" % (embeddings_batch.shape,)
+
+        # Apply PCA.
+        # - Embeddings come in as [batch_size, embedding_size].
+        # - Transpose to [embedding_size, batch_size].
+        # - Subtract pca_means column vector from each column.
+        # - Premultiply by PCA matrix of shape [output_dims, input_dims]
+        #   where both are are equal to embedding_size in our case.
+        # - Transpose result back to [batch_size, embedding_size].
+        pca_applied = torch.mm(self.pca_eigen_vectors, (embeddings_batch.t() - self.pca_means)).t()
+
+        # Quantize by:
+        # - clipping to [min, max] range
+        clipped_embeddings = torch.clamp(
+            pca_applied, vggish_params.QUANTIZE_MIN_VAL, vggish_params.QUANTIZE_MAX_VAL
+        )
+        # - convert to 8-bit in range [0.0, 255.0]
+        quantized_embeddings = torch.round(
+            (clipped_embeddings - vggish_params.QUANTIZE_MIN_VAL)
+            * (
+                255.0
+                / (vggish_params.QUANTIZE_MAX_VAL - vggish_params.QUANTIZE_MIN_VAL)
+            )
+        )
+        return torch.squeeze(quantized_embeddings)
+
+    def forward(self, x):
+        return self.postprocess(x)
+
+
+def make_layers():
+    layers = []
+    in_channels = 1
+    for v in [64, "M", 128, "M", 256, 256, "M", 512, 512, "M"]:
+        if v == "M":
+            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
+        else:
+            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
+            layers += [conv2d, nn.ReLU(inplace=True)]
+            in_channels = v
+    return nn.Sequential(*layers)
+
+
+def _vgg():
+    return VGG(make_layers())
+
+
+# def _spectrogram():
+#     config = dict(
+#         sr=16000,
+#         n_fft=400,
+#         n_mels=64,
+#         hop_length=160,
+#         window="hann",
+#         center=False,
+#         pad_mode="reflect",
+#         htk=True,
+#         fmin=125,
+#         fmax=7500,
+#         output_format='Magnitude',
+#         #             device=device,
+#     )
+#     return Spectrogram.MelSpectrogram(**config)
+
+
+class VGGish(VGG):
+    def __init__(self, urls, device=None, pretrained=True, preprocess=True, postprocess=True, progress=True):
+        super().__init__(make_layers())
+        if pretrained:
+            state_dict = hub.load_state_dict_from_url(urls['vggish'], progress=progress)
+            super().load_state_dict(state_dict)
+
+        if device is None:
+            device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        self.device = device
+        self.preprocess = preprocess
+        self.postprocess = postprocess
+        if self.postprocess:
+            self.pproc = Postprocessor()
+            if pretrained:
+                state_dict = hub.load_state_dict_from_url(urls['pca'], progress=progress)
+                # TODO: Convert the state_dict to torch
+                state_dict[vggish_params.PCA_EIGEN_VECTORS_NAME] = torch.as_tensor(
+                    state_dict[vggish_params.PCA_EIGEN_VECTORS_NAME], dtype=torch.float
+                )
+                state_dict[vggish_params.PCA_MEANS_NAME] = torch.as_tensor(
+                    state_dict[vggish_params.PCA_MEANS_NAME].reshape(-1, 1), dtype=torch.float
+                )
+
+                self.pproc.load_state_dict(state_dict)
+        self.to(self.device)
+
+    def forward(self, x, fs=None):
+        if self.preprocess:
+            x = self._preprocess(x, fs)
+        x = x.to(self.device)
+        x = VGG.forward(self, x)
+        # if self.postprocess:
+        #     x = self._postprocess(x)
+        return x
+
+    def _preprocess(self, x, fs):
+        if isinstance(x, np.ndarray):
+            x = vggish_input.waveform_to_examples(x, fs)
+        elif isinstance(x, str):
+            x = vggish_input.wavfile_to_examples(x)
+        else:
+            raise AttributeError
+        return x
+
+    def _postprocess(self, x):
+        return self.pproc(x)

FakeVD/dataset/data-split/event/vid_fold_1.txt

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