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import torch
import torchaudio
import random
import itertools
import numpy as np
from tools.mix import mix
from PIL import Image
import cv2
from moviepy.editor import VideoFileClip
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize, InterpolationMode, RandomResizedCrop
def normalize_wav(waveform):
waveform = waveform - torch.mean(waveform)
waveform = waveform / (torch.max(torch.abs(waveform)) + 1e-8)
return waveform * 0.5
def sinusoidal_positional_embedding(token_sequence_size, token_embedding_dim, n=10000.0):
if token_embedding_dim % 2 != 0:
raise ValueError("Sinusoidal positional embedding cannot apply to odd token embedding dim (got dim={:d})".format(token_embedding_dim))
T = token_sequence_size
d = token_embedding_dim #d_model=head_num*d_k, not d_q, d_k, d_v
positions = torch.arange(0, T).unsqueeze_(1)
embeddings = torch.zeros(T, d)
denominators = torch.pow(n, 2*torch.arange(0, d//2)/d) # 10000^(2i/d_model), i is the index of embedding
embeddings[:, 0::2] = torch.sin(positions/denominators) # sin(pos/10000^(2i/d_model))
embeddings[:, 1::2] = torch.cos(positions/denominators) # cos(pos/10000^(2i/d_model))
return embeddings
def pad_wav(waveform, segment_length):
waveform_length = len(waveform)
if segment_length is None or waveform_length == segment_length:
return waveform
elif waveform_length > segment_length:
return waveform[:segment_length]
else:
pad_wav = torch.zeros(segment_length - waveform_length).to(waveform.device)
waveform = torch.cat([waveform, pad_wav])
return waveform
def _pad_spec(fbank, target_length=1000):
batch, n_frames, channels = fbank.shape
p = target_length - n_frames
if p > 0:
pad = torch.zeros(batch, p, channels).to(fbank.device)
fbank = torch.cat([fbank, pad], 1)
elif p < 0:
fbank = fbank[:, :target_length, :]
if channels % 2 != 0:
fbank = fbank[:, :, :-1]
return fbank
def read_wav_file(filename, segment_length, tgt_sr=48000):
waveform, sr = torchaudio.load(filename) # Faster!!!
if sr != tgt_sr:
waveform = torchaudio.functional.resample(waveform, orig_freq=sr, new_freq=tgt_sr)[0]
else:
waveform = waveform.squeeze()
try:
waveform = normalize_wav(waveform)
except:
print ("Exception normalizing:", filename)
waveform = torch.ones(tgt_sr * 10)
waveform = pad_wav(waveform, segment_length).unsqueeze(0)
waveform = waveform / torch.max(torch.abs(waveform))
waveform = 0.5 * waveform
return waveform
def get_mel_from_wav(audio, _stft):
audio1 = torch.nan_to_num(torch.clip(audio, -1, 1))
audio2 = torch.autograd.Variable(audio1, requires_grad=False)
melspec, log_magnitudes_stft, energy = _stft.mel_spectrogram(audio2)
return melspec, log_magnitudes_stft, energy
def wav_to_fbank(paths, target_length=1000, sample_rate=16000, fn_STFT=None):
assert fn_STFT is not None
if sample_rate == 16000:
hop_size = 160
elif sample_rate == 24000:
hop_size = 240
elif sample_rate == 32000:
hop_size = 320
elif sample_rate == 48000:
hop_size = 480
else:
raise ValueError(f"sample_rate wrong.")
#print("target_length", target_length, hop_size)
#print("target_length", target_length, sample_rate, fn_STFT)
#for name, param in fn_STFT.named_parameters():
# print(name, param.data)
waveform = torch.cat([read_wav_file(path, target_length * hop_size, tgt_sr=sample_rate) for path in paths], 0) # hop size is 160
#print("waveform", waveform.size())
#np.set_printoptions(threshold=np.inf)
#print("waveform", waveform)
#f_out = open(paths[0].split("/")[-1]+".scp",'w')
fbank, log_magnitudes_stft, energy = get_mel_from_wav(waveform, fn_STFT)
#print("fbank", fbank)
fbank = fbank.transpose(1, 2)
log_magnitudes_stft = log_magnitudes_stft.transpose(1, 2)
fbank, log_magnitudes_stft = _pad_spec(fbank, target_length), _pad_spec(
log_magnitudes_stft, target_length
)
#f_out.write(paths[0]+ "\n" + str(waveform.cpu().numpy())+"\n")
#f_out.write("audio1"+ "\n" + str(audio1.cpu().numpy())+"\n")
#f_out.write("audio2"+ "\n" + str(audio2.cpu().numpy())+"\n")
#f_out.write("fbank" + "\n" + str(fbank.cpu().numpy())+"\n")
#print(fbank2)
return fbank, log_magnitudes_stft, waveform
def get_wav_from_video(video_path, segment_length, tgt_sr=48000):
video = VideoFileClip(video_path)
audio = video.audio
sr = audio.fps
audio_data = audio.to_soundarray() # 441882 * 2 ειι
waveform = torch.mean(torch.tensor(audio_data, dtype=torch.float), dim=1).unsqueeze(0) # εζειι
if sr != tgt_sr:
waveform = torchaudio.functional.resample(waveform, orig_freq=sr, new_freq=tgt_sr)[0]
else:
waveform = waveform.squeeze()
try:
waveform = normalize_wav(waveform)
except:
print ("Exception normalizing:", video_path)
waveform = torch.ones(tgt_sr * 10)
waveform = pad_wav(waveform, segment_length).unsqueeze(0)
waveform = waveform / torch.max(torch.abs(waveform))
waveform = 0.5 * waveform
return waveform
def get_wavs_from_videos(video_paths, segment_length, tgt_sr=48000):
wavs = []
for video_path in video_paths:
waveform = get_wav_from_video(video_path, segment_length, tgt_sr)
wavs.append(waveform)
wavs = torch.cat(wavs, 0)
return wavs
def wav_in_video_to_fbank(input, target_length=1000, sample_rate=16000, fn_STFT=None, waveform=False):
assert fn_STFT is not None
if sample_rate == 16000:
hop_size = 160
elif sample_rate == 24000:
hop_size = 240
elif sample_rate == 32000:
hop_size = 320
elif sample_rate == 48000:
hop_size = 480
else:
raise ValueError(f"sample_rate wrong.")
if not waveform:
paths = input
waveform = get_wavs_from_videos(paths, target_length * hop_size, tgt_sr=sample_rate) # hop size is 160
else:
waveform = input
fbank, log_magnitudes_stft, energy = get_mel_from_wav(waveform, fn_STFT)
fbank = fbank.transpose(1, 2)
log_magnitudes_stft = log_magnitudes_stft.transpose(1, 2)
fbank, log_magnitudes_stft = _pad_spec(fbank, target_length), _pad_spec(
log_magnitudes_stft, target_length
)
return fbank, log_magnitudes_stft, waveform
def uncapitalize(s):
if s:
return s[:1].lower() + s[1:]
else:
return ""
def mix_wavs_and_captions(path1, path2, caption1, caption2, target_length=1000, sample_rate=16000):
if sample_rate == 16000:
hop_size = 160
elif sample_rate == 24000:
hop_size = 240
elif sample_rate == 32000:
hop_size = 320
elif sample_rate == 48000:
hop_size = 480
else:
raise ValueError(f"sample_rate wrong.")
sound1 = read_wav_file(path1, target_length * hop_size)[0].numpy()
#print("sound1", target_length, sound1.size)
sound2 = read_wav_file(path2, target_length * hop_size)[0].numpy()
mixed_sound = mix(sound1, sound2, 0.5, sample_rate).reshape(1, -1)
#print("mixed_sound", mixed_sound.size)
mixed_caption = "{} and {}".format(caption1, uncapitalize(caption2))
return mixed_sound, mixed_caption
def augment(paths, texts, num_items=4, target_length=1000, sample_rate=16000):
mixed_sounds, mixed_captions = [], []
combinations = list(itertools.combinations(list(range(len(texts))), 2))
random.shuffle(combinations)
if len(combinations) < num_items:
selected_combinations = combinations
else:
selected_combinations = combinations[:num_items]
for (i, j) in selected_combinations:
new_sound, new_caption = mix_wavs_and_captions(paths[i], paths[j], texts[i], texts[j], target_length, sample_rate)
mixed_sounds.append(new_sound)
mixed_captions.append(new_caption)
waveform = torch.tensor(np.concatenate(mixed_sounds, 0))
waveform = waveform / torch.max(torch.abs(waveform))
waveform = 0.5 * waveform
return waveform, mixed_captions
def augment_wav_to_fbank(paths, texts, num_items=4, target_length=1000, sample_rate=16000, fn_STFT=None):
assert fn_STFT is not None
waveform, captions = augment(paths, texts, target_length = target_length, sample_rate=sample_rate)
fbank, log_magnitudes_stft, energy = get_mel_from_wav(waveform, fn_STFT)
fbank = fbank.transpose(1, 2)
log_magnitudes_stft = log_magnitudes_stft.transpose(1, 2)
fbank, log_magnitudes_stft = _pad_spec(fbank, target_length), _pad_spec(
log_magnitudes_stft, target_length
)
return fbank, log_magnitudes_stft, waveform, captions
def load_image(impaths, crop_size=384):
imgs = []
RGB_mean = [0.485, 0.456, 0.406]
RGB_std = [0.229, 0.224, 0.225]
image_resize_and_crop = Compose([RandomResizedCrop(crop_size), ToTensor()])
image_normalize = Normalize(mean=RGB_mean, std=RGB_std)
for impath in impaths:
img = Image.open(impath).convert('RGB')
img = image_resize_and_crop(img)
img = image_normalize(img)
imgs.append(img)
imgs = torch.stack(imgs)
return imgs
def load_video(video_path, frame_rate=1.0, size=224):
def preprocess(size, n_px):
return Compose([
Resize(size, interpolation=InterpolationMode.BICUBIC),
CenterCrop(size),
lambda image: image.convert("RGB"),
ToTensor(),
# Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
])(n_px)
videos = []
# for video_path in video_paths:
# cap = cv2.VideoCapture(video_path)
cap = cv2.VideoCapture(video_path, cv2.CAP_FFMPEG)
frameCount = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
if fps < 1:
images = np.zeros([3, size, size], dtype=np.float32)
print("ERROR: problem reading video file: ", video_path)
else:
total_duration = (frameCount + fps - 1) // fps
start_sec, end_sec = 0, total_duration
interval = fps / frame_rate
frames_idx = np.floor(np.arange(start_sec*fps, end_sec*fps, interval))
ret = True
images = np.zeros([len(frames_idx), 3, size, size], dtype=np.float32)
for i, idx in enumerate(frames_idx):
cap.set(cv2.CAP_PROP_POS_FRAMES , idx)
ret, frame = cap.read()
if not ret: break
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
last_frame = i
images[i,:,:,:] = preprocess(size, Image.fromarray(frame).convert("RGB"))
images = images[:last_frame+1]
cap.release()
return torch.tensor(images) |