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import torch
from torch.utils.data import DataLoader, Dataset
import torchaudio
import torchvision.transforms as tvt
from denoising_diffusion_pytorch.classifier_free_guidance import Unet, GaussianDiffusion
import glob
import torch.nn as nn
import time, math
from PIL import Image
from diffusers import Mel
import sys
import torchaudio
import librosa
import matplotlib.pyplot as plt
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
args = sys.argv[1:]
class Audio(Dataset):
def __init__(self, folder):
# resample = tat.Resample(48000)
self.waveforms = []
self.labels = []
print("Loading files...")
for file in glob.iglob(folder + '/**/*.wav', recursive=True): # recurse through files
self.labels.append(int(file.split('/')[-1][0])) # get label from file name
waveform, _ = torchaudio.load(file)
# waveform, _ = librosa.load(file, sr=None) # load text
self.waveforms.append(waveform)
def __len__(self):
return len(self.waveforms)
def __getitem__(self, index):
return self.waveforms[index], self.labels[index]
image_size = 256
if len(args) >= 1:
image_size = int(args[0])
MEL = Mel(x_res=image_size, y_res=image_size)
img_to_tensor = tvt.PILToTensor()
def collate(batch):
spectros = []
labels = []
for waveform, label in batch:
MEL.load_audio(raw_audio=waveform[0])
for slice in range(MEL.get_number_of_slices()):
spectro = MEL.audio_slice_to_image(slice)
spectro = img_to_tensor(spectro) / 255.0
# print(spectro.shape)
# plt.imshow(spectro[0])
# plt.show()
# input("continue")
spectros.append(spectro)
labels.append(label)
spectros = torch.stack(spectros)
labels = torch.tensor(labels)
# one_hot = nn.functional.one_hot(labels, num_classes=10) # one hot vectors for conditional generation
return spectros.to(device), labels.to(device)
def initialize(scheduler = None, batch_size=32):
model = Unet(
dim = 64,
num_classes=10,
dim_mults=(1, 2, 4, 8),
channels=1
)
diffusion = GaussianDiffusion(
model,
image_size=image_size,
timesteps=1000,
loss_type = 'l2',
objective='pred_x0',
# channels=1,
)
diffusion.to(device)
optim = torch.optim.AdamW(model.parameters(), lr=1e-4, eps=1e-8)
if scheduler:
scheduler = torch.optim.lr_scheduler.CyclicLR(optim, base_lr=1e-5, max_lr=1e-3, mode="exp_range", cycle_momentum=False)
return diffusion, optim, scheduler
def timeSince(since):
now = time.time()
s = now - since
m = math.floor(s / 60)
s -= m * 60
return '%dm %ds' % (m, s)
start = time.time()
def train(model, optim, train_dl, batch_size=32, epochs=5, scheduler = None):
size = len(train_dl.dataset)
model.train()
losses = []
for e in range(epochs):
batch_loss, batch_counts = 0, 0
for step, batch in enumerate(train_dl):
model.zero_grad()
batch_counts += 1
spectros, labels = batch
loss = model(spectros, classes=labels)
batch_loss += loss.item()
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), 1)
optim.step()
if scheduler is not None:
scheduler.step()
if (step % 100 == 0 and step != 0) or (step == len(train_dl) - 1):
to_print = f"{e + 1:^7} | {step:^7} | {batch_loss / batch_counts:^12.6f} | {timeSince(start)} | {step*batch_size:>5d}/{size:>5d}"
print(to_print)
losses.append(batch_loss)
batch_loss, batch_counts = 0, 0
labels = torch.randint(0,9,(8, )).to(device)
print(labels)
samples = model.sample(labels)
for i, sample in enumerate(samples):
im = Image.fromarray(sample[0].cpu().numpy() * 255).convert('L')
audio = torch.tensor([MEL.image_to_audio(im)])
torchaudio.save(f"audio/sample{e}_{i}_{labels[i]}.wav", audio, 48000)
im.save(f"images/sample{e}_{i}_{labels[i]}.jpg")
return losses
if __name__ == "__main__":
num_epochs = 10
if len(args) >= 2:
num_epochs = int(args[1])
batch_size = 32
if len(args) >= 3:
batch_size = int(args[2])
print(image_size, num_epochs, batch_size)
model, optim, scheduler = initialize(scheduler=True, batch_size=batch_size)
train_data = Audio("AudioMNIST/data")
print("Done Loading")
train_dl = DataLoader(train_data, batch_size, True, collate_fn=collate)
train(model, optim, train_dl, batch_size, num_epochs, scheduler)
torch.save(model.state_dict(), "diffusion_condition_model.pt") |