import argparse import torch import os import sys import numpy as np import soundfile as sf import torch.utils.data as data import librosa import cv2 as cv import tqdm from tools import audioread, audiowrite, cal_SISNR, load_model # import fast_bss_eval import pdb import math import torch.nn.functional as F import scipy.io.wavfile as wavfile sys.path.append('/mnt/users/hccl.local/wwu/SEMO-621/data_preparation/') from Visual_perturb import * class dataset_wwu_multispk(data.Dataset): def __init__(self,partition ): self.if_fixed =False self.max_length=6 self.sampling_rate = 16000 self.four2six_length = False self.minibatch =[] self.audio_direc = '/mnt/users/hccl.local/wwu/Voxceleb2/muse/audio_clean/' self.text_direc = '/home/export/base/sc100138/sc100138/online1/wenxuan_tse_data/audio_clean_text/' self.visual_direc = '/mnt/users/hccl.local/wwu/Voxceleb2/origin/video/' self.mixture_direc = '/mnt/Corpus-Upload/Voxceleb2/origin/mixture_3spk/' self.partition = partition self.mix_lst_path = '/mnt/users/hccl.local/wwu/mamba_may20/mixture_data_list_3mix.csv' self.C=3 self.batch_size = 3 self.normMean = 0.4161 self.normStd = 0.1688 self.fps = 25 # import pdb;pdb.set_trace() mix_lst=open(self.mix_lst_path).read().splitlines() mix_lst=list(filter(lambda x: x.split(',')[0]==self.partition, mix_lst))[:] # import pdb;pdb.set_trace() assert (self.batch_size%self.C) == 0, "input batch_size should be multiples of mixture speakers" self.batch_size = int(self.batch_size/self.C ) #sorted by mix_audio duration length!!! # import pdb;pdb.set_trace() # sorted_mix_lst = sorted(mix_lst, key=lambda data: float(data.split(',')[-1]), reverse=True)[:6] sorted_mix_lst = sorted(mix_lst, key=lambda data: float(data.split(',')[-1]), reverse=True) # import pdb;pdb.set_trace() start = 0 while True: end = min(len(sorted_mix_lst), start + self.batch_size) self.minibatch.append(sorted_mix_lst[start:end]) if end == len(sorted_mix_lst): break start = end def __len__(self): return len(self.minibatch) def _audio_norm(self,audio): return np.divide(audio, np.max(np.abs(audio))) def __getitem__(self, index): # import pdb;pdb.set_trace() batch_lst = self.minibatch[index] # import pdb;pdb.set_trace() min_length = self.max_length for _ in range(len(batch_lst)): if float(batch_lst[_].split(",")[-1]) < min_length: min_length = float(batch_lst[_].split(",")[-1]) # import pdb;pdb.set_trace() visuals=[] audios = [] texts = [] file_base_path = [] mixtures = [] # import pdb;pdb.set_trace() for line in batch_lst: mixture_path=self.mixture_direc+self.partition+'/'+ line.replace(',','_').replace('/','_')+'.wav' # import pdb;pdb.set_trace() line=line.split(',') for c in range(self.C): # each line has 2 sample, batch =2, return same mixaudio, so return mix audio twice!!!!!! _, mixture_speech = wavfile.read(mixture_path) mix_audio_data = self._audio_norm(mixture_speech[:int(min_length * self.sampling_rate)]) # mix_log_feat = self.compute_logfilter(mix_audio_data) # mixtures.append(mix_log_feat) mixtures.append(mix_audio_data) clean_speech_path=self.audio_direc+line[c*4+1]+'/'+line[c*4+2]+'/'+line[c*4+3]+'.wav' file_base_path.append(line[c*4+1]+'/'+line[c*4+2]+'/'+line[c*4+3]) # import pdb;pdb.set_trace() _, clean_speech = wavfile.read(clean_speech_path) # print(len(clean_speech),clean_speech_path) audio_data = self._audio_norm(clean_speech[:int(min_length * self.sampling_rate)]) # text_path = self.text_direc+line[c*4+1]+'/'+line[c*4+2]+'/'+line[c*4+3]+'.txt' # with open(text_path, 'r', encoding='utf-8') as file: # text = file.readlines()[0].split('\n')[0] text = '' # print(text) # import pdb;pdb.set_trace() # #print(audio_data) # log_feat = self.compute_logfilter(audio_data) audios.append(audio_data) texts.append(text) # audios.append([]) if self.partition != 'test': visual_path = ( self.visual_direc + 'train' + "/" + line[2 + c * 4] + "/" + line[3 + c * 4] + ".mp4" ) else: visual_path = ( self.visual_direc + line[1 + c * 4] + "/" + line[2 + c * 4] + "/" + line[3 + c * 4] + ".mp4" ) # import pdb;pdb.set_trace() captureObj = cv.VideoCapture(visual_path) # import pdb;pdb.set_trace() roiSequence = [] clean_roiSequence = [] roiSize = 112 start = 0 while captureObj.isOpened(): ret, frame = captureObj.read() if ret == True: grayed = cv.cvtColor(frame, cv.COLOR_BGR2GRAY) grayed = grayed / 255 grayed = cv.resize(grayed, (roiSize * 2, roiSize * 2)) roi = grayed[ int(roiSize - (roiSize / 2)) : int( roiSize + (roiSize / 2) ), int(roiSize - (roiSize / 2)) : int( roiSize + (roiSize / 2) ), ] clean_roiSequence.append(roi) else: break captureObj.release() # import pdb;pdb.set_trace() clean_visual = np.asarray(clean_roiSequence) clean_visual = clean_visual[0 : int(min_length * self.fps), ...] clean_visual = (clean_visual - self.normMean) / self.normStd if clean_visual.shape[0] < int(min_length * self.fps): clean_visual = np.pad( clean_visual, ( ( 0, int(min_length * self.fps) - clean_visual.shape[0], ), (0, 0), (0, 0), ), mode="edge", ) visuals.append(clean_visual) assert np.asarray(mixtures).shape == np.asarray(audios).shape # import pdb;pdb.set_trace() return np.asarray(mixtures),np.asarray(audios),np.asarray(visuals), texts,file_base_path def main(args): # speaker id assignment mix_lst = open(args.mix_lst_path).read().splitlines() train_lst = list(filter(lambda x: x.split(",")[0] == "test", mix_lst)) IDs = 0 speaker_dict = {} for line in train_lst: for i in range(2): ID = line.split(",")[i * 4 + 2] if ID not in speaker_dict: speaker_dict[ID] = IDs IDs += 1 args.speaker_dict = speaker_dict args.speakers = len(speaker_dict) datasets = dataset_wwu_multispk('test')