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import os
# from tqdm import tqdm
import torch
import numpy as np
import random
import scipy.io as scio
import src.utils.audio as audio
def crop_pad_audio(wav, audio_length):
if len(wav) > audio_length:
wav = wav[:audio_length]
elif len(wav) < audio_length:
wav = np.pad(wav, [0, audio_length - len(wav)], mode='constant', constant_values=0)
return wav
def parse_audio_length(audio_length, sr, fps):
bit_per_frames = sr / fps
num_frames = int(audio_length / bit_per_frames)
audio_length = int(num_frames * bit_per_frames)
return audio_length, num_frames
def generate_blink_seq(num_frames):
ratio = np.zeros((num_frames,1))
frame_id = 0
while frame_id in range(num_frames):
start = 80
if frame_id+start+9<=num_frames - 1:
ratio[frame_id+start:frame_id+start+9, 0] = [0.5,0.6,0.7,0.9,1, 0.9, 0.7,0.6,0.5]
frame_id = frame_id+start+9
else:
break
return ratio
def generate_blink_seq_randomly(num_frames):
ratio = np.zeros((num_frames,1))
if num_frames<=20:
return ratio
frame_id = 0
while frame_id in range(num_frames):
start = random.choice(range(min(10,num_frames), min(int(num_frames/2), 70)))
if frame_id+start+5<=num_frames - 1:
ratio[frame_id+start:frame_id+start+5, 0] = [0.5, 0.9, 1.0, 0.9, 0.5]
frame_id = frame_id+start+5
else:
break
return ratio
def get_data(first_coeff_path, audio_path, device, ref_eyeblink_coeff_path, still=False, idlemode=False, length_of_audio=False, use_blink=True, fps=25):
syncnet_mel_step_size = 16
pic_name = os.path.splitext(os.path.split(first_coeff_path)[-1])[0]
audio_name = os.path.splitext(os.path.split(audio_path)[-1])[0]
# if idlemode:
# num_frames = int(length_of_audio * 25)
# indiv_mels = np.zeros((num_frames, 80, 16))
# else:
wav = audio.load_wav(audio_path, 16000)
wav_length, num_frames = parse_audio_length(len(wav), 16000, fps)
wav = crop_pad_audio(wav, wav_length)
orig_mel = audio.melspectrogram(wav).T
spec = orig_mel.copy() # nframes 80
indiv_mels = []
for i in range(num_frames):
start_frame_num = i-2
start_idx = int(80. * (start_frame_num / float(fps)))
end_idx = start_idx + syncnet_mel_step_size
seq = list(range(start_idx, end_idx))
seq = [ min(max(item, 0), orig_mel.shape[0]-1) for item in seq ]
m = spec[seq, :]
indiv_mels.append(m.T)
indiv_mels = np.asarray(indiv_mels) # T 80 16
ratio = generate_blink_seq_randomly(num_frames) # T
source_semantics_path = first_coeff_path
source_semantics_dict = scio.loadmat(source_semantics_path)
ref_coeff = source_semantics_dict['coeff_3dmm'][:1,:70] #1 70
ref_coeff = np.repeat(ref_coeff, num_frames, axis=0)
# if ref_eyeblink_coeff_path is not None:
# ratio[:num_frames] = 0
# refeyeblink_coeff_dict = scio.loadmat(ref_eyeblink_coeff_path)
# refeyeblink_coeff = refeyeblink_coeff_dict['coeff_3dmm'][:,:64]
# refeyeblink_num_frames = refeyeblink_coeff.shape[0]
# if refeyeblink_num_frames<num_frames:
# div = num_frames//refeyeblink_num_frames
# re = num_frames%refeyeblink_num_frames
# refeyeblink_coeff_list = [refeyeblink_coeff for i in range(div)]
# refeyeblink_coeff_list.append(refeyeblink_coeff[:re, :64])
# refeyeblink_coeff = np.concatenate(refeyeblink_coeff_list, axis=0)
# print(refeyeblink_coeff.shape[0])
# ref_coeff[:, :64] = refeyeblink_coeff[:num_frames, :64]
indiv_mels = torch.FloatTensor(indiv_mels).unsqueeze(1).unsqueeze(0) # bs T 1 80 16
if use_blink:
ratio = torch.FloatTensor(ratio).unsqueeze(0) # bs T
else:
ratio = torch.FloatTensor(ratio).unsqueeze(0).fill_(0.)
# bs T
ref_coeff = torch.FloatTensor(ref_coeff).unsqueeze(0) # bs 1 70
indiv_mels = indiv_mels.to(device)
ratio = ratio.to(device)
ref_coeff = ref_coeff.to(device)
return {'indiv_mels': indiv_mels,
'ref': ref_coeff,
'num_frames': num_frames,
'ratio_gt': ratio,
'audio_name': audio_name, 'pic_name': pic_name}
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