def create_spectro(self, item:AudioItem):
if self.config.mfcc:
mel = MFCC(sample_rate=item.sr, n_mfcc=self.config.sg_cfg.n_mfcc, melkwargs=self.config.sg_cfg.mel_args())(item.sig)
else:
if self.config.sg_cfg.custom_spectro != None:
mel = self.config.sg_cfg.custom_spectro(item.sig)
else:
if self.config.sg_cfg.n_mels > 0:
c = self.config.sg_cfg
mel = librosa.feature.melspectrogram(y=np.array(item.sig[0,:]), sr=item.sr, fmax=c.f_max, fmin=c.f_min, **(self.config.sg_cfg.mel_args()))
mel = torch.from_numpy(mel)
mel.unsqueeze_(0)
else:
mel = Spectrogram(**(self.config.sg_cfg.spectro_args()))(item.sig)
if self.config.sg_cfg.to_db_scale:
mel = AmplitudeToDB(top_db=self.config.sg_cfg.top_db)(mel)
mel = mel.detach()
if self.config.standardize:
mel = standardize(mel)
if self.config.delta:
mel = torch.cat([torch.stack([m,torchdelta(m),torchdelta(m, order=2)]) for m in mel])
return mel
def __init__(self, sample_rate, n_fft, top_db, max_perc):
super().__init__()
self.time_stretch = TimeStretch(hop_length=None, n_freq=n_fft // 2 + 1)
self.stft = Spectrogram(n_fft=n_fft, power=None)
self.com_norm = ComplexNorm(power=2.)
self.fm = FrequencyMasking(100)
self.tm = TimeMasking(100)
self.mel_specgram = MelSpectrogram(sample_rate,
n_fft=n_fft,
f_max=8000)
self.AtoDB = AmplitudeToDB(top_db=top_db)
self.max_perc = max_perc
self.sample_rate = sample_rate
self.resamples = [
Resample(sample_rate, sample_rate * 0.6),
Resample(sample_rate, sample_rate * 0.7),
Resample(sample_rate, sample_rate * 0.8),
Resample(sample_rate, sample_rate * 0.9),
Resample(sample_rate, sample_rate * 1),
Resample(sample_rate, sample_rate * 1.1),
Resample(sample_rate, sample_rate * 1.2),
Resample(sample_rate, sample_rate * 1.3),
Resample(sample_rate, sample_rate * 1.4)
]
sr=16000 #sampling rate
min_level_db=-100 #reference values to normalize data
ref_level_db=20
shape=24 #length of time axis of split specrograms to feed to generator
vec_len=128 #length of vector generated by siamese vector
bs = 128 #batch size
delta = 2. #constant for siamese loss
tag='HAP' #the tag for the training
"""#helper functions"""
torch.set_default_tensor_type('torch.cuda.FloatTensor')
#MEL-SPECTRUM
print("finally start...")
specobj = Spectrogram(n_fft=6*hop, win_length=6*hop, hop_length=hop, pad=0, power=2, normalized=True)
specfunc = specobj.forward
melobj = MelScale(n_mels=hop, sample_rate=sr, f_min=0.)
melfunc = melobj.forward
def melspecfunc(waveform):
specgram = specfunc(waveform)
mel_specgram = melfunc(specgram)
return mel_specgram
def spectral_convergence(input, target):
return 20 * ((input - target).norm().log10() - target.norm().log10())
def GRAD(spec, transform_fn, samples=None, init_x0=None, maxiter=1000, tol=1e-6, verbose=1, evaiter=10, lr=0.003):
spec = torch.Tensor(spec)
def __init__(self, nfft):
self.spectro = Spectrogram(nfft, normalized=True, power=2)
def ex_waveform_spectro():
dataset = load_dataset("train",
_DEFAULT_COMMONVOICE_ROOT,
_DEFAULT_COMMONVOICE_VERSION)
# Take one of the waveforms
idx = 10
waveform, rate, dictionary = dataset[idx]
n_begin = rate # 1 s.
n_end = 3*rate # 2 s.
waveform = waveform[:, n_begin:n_end] # B, T
nfft = int(_DEFAULT_WIN_LENGTH * 1e-3 * _DEFAULT_RATE)
# nmels = _DEFAULT_NUM_MELS
nstep = int(_DEFAULT_WIN_STEP * 1e-3 * _DEFAULT_RATE)
trans_spectro = nn.Sequential(
Spectrogram(n_fft=nfft,
hop_length=nstep),
AmplitudeToDB()
)
spectro = trans_spectro(waveform) # B, n_mels, T
trans_mel_spectro = WaveformProcessor(rate=rate,
win_length=_DEFAULT_WIN_LENGTH*1e-3,
win_step=_DEFAULT_WIN_STEP*1e-3,
nmels=_DEFAULT_NUM_MELS,
augment=False,
spectro_normalization=None)
mel_spectro = trans_mel_spectro(waveform.transpose(0, 1)) # T, B, n_mels
plot_spectro(mel_spectro[:, 0, :], [],
_DEFAULT_WIN_STEP*1e-3,
CharMap())
fig, axes = plt.subplots(nrows=1,ncols=3, figsize=(15, 3))
ax = axes[0]
ax.plot( [i/rate for i in range(n_begin, n_end)], waveform[0])
ax.set_xlabel('Time (s.)')
ax.set_ylabel('Amplitude')
ax.set_title('Waveform')
ax = axes[1]
im = ax.imshow(spectro[0],
extent=[n_begin/rate, n_end/rate,
0, spectro.shape[1]],
aspect='auto',
cmap='magma',
origin='lower')
ax.set_ylabel('Frequency bins')
ax.set_xlabel('TIme (s.)')
ax.set_title("Spectrogram (dB)")
fig.colorbar(im, ax=ax)
ax = axes[2]
im = ax.imshow(mel_spectro[:, 0, :].T,
extent=[n_begin/rate, n_end/rate,
0, mel_spectro.shape[0]],
aspect='auto',
cmap='magma',
origin='lower')
ax.set_ylabel('Mel scales')
ax.set_xlabel('TIme (s.)')
ax.set_title("Mel-Spectrogram (dB)")
fig.colorbar(im, ax=ax)
plt.tight_layout()
plt.savefig("waveform_to_spectro.png")
plt.show()
def __init__(self, train_loader, test_loader, valid_loader, general_args,
trainer_args):
super(GanTrainer, self).__init__(train_loader, test_loader,
valid_loader, general_args)
# Paths
self.loadpath = trainer_args.loadpath
self.savepath = trainer_args.savepath
# Load the auto-encoder
self.use_autoencoder = False
if trainer_args.autoencoder_path and os.path.exists(
trainer_args.autoencoder_path):
self.use_autoencoder = True
self.autoencoder = AutoEncoder(general_args=general_args).to(
self.device)
self.load_pretrained_autoencoder(trainer_args.autoencoder_path)
self.autoencoder.eval()
# Load the generator
self.generator = Generator(general_args=general_args).to(self.device)
if trainer_args.generator_path and os.path.exists(
trainer_args.generator_path):
self.load_pretrained_generator(trainer_args.generator_path)
self.discriminator = Discriminator(general_args=general_args).to(
self.device)
# Optimizers and schedulers
self.generator_optimizer = torch.optim.Adam(
params=self.generator.parameters(), lr=trainer_args.generator_lr)
self.discriminator_optimizer = torch.optim.Adam(
params=self.discriminator.parameters(),
lr=trainer_args.discriminator_lr)
self.generator_scheduler = lr_scheduler.StepLR(
optimizer=self.generator_optimizer,
step_size=trainer_args.generator_scheduler_step,
gamma=trainer_args.generator_scheduler_gamma)
self.discriminator_scheduler = lr_scheduler.StepLR(
optimizer=self.discriminator_optimizer,
step_size=trainer_args.discriminator_scheduler_step,
gamma=trainer_args.discriminator_scheduler_gamma)
# Load saved states
if os.path.exists(self.loadpath):
self.load()
# Loss function and stored losses
self.adversarial_criterion = nn.BCEWithLogitsLoss()
self.generator_time_criterion = nn.MSELoss()
self.generator_frequency_criterion = nn.MSELoss()
self.generator_autoencoder_criterion = nn.MSELoss()
# Define labels
self.real_label = 1
self.generated_label = 0
# Loss scaling factors
self.lambda_adv = trainer_args.lambda_adversarial
self.lambda_freq = trainer_args.lambda_freq
self.lambda_autoencoder = trainer_args.lambda_autoencoder
# Spectrogram converter
self.spectrogram = Spectrogram(normalized=True).to(self.device)
# Boolean indicating if the model needs to be saved
self.need_saving = True
# Boolean if the generator receives the feedback from the discriminator
self.use_adversarial = trainer_args.use_adversarial
def __init__(self, train_loader, test_loader, valid_loader, general_args):
# Device
self.device = ('cuda' if torch.cuda.is_available() else 'cpu')
# Data generators
self.train_loader = train_loader
self.valid_loader = valid_loader
self.test_loader = test_loader
# Iterators to cycle over the datasets
self.train_loader_iter = cycle(iter(self.train_loader))
self.valid_loader_iter = cycle(iter(self.valid_loader))
self.test_loader_iter = cycle(iter(self.test_loader))
# Epoch counter
self.epoch = 0
# Stored losses
self.train_losses = {
'time_l2': [],
'freq_l2': [],
'autoencoder_l2': [],
'generator_adversarial': [],
'discriminator_adversarial': {
'real': [],
'fake': []
}
}
self.test_losses = {
'time_l2': [],
'freq_l2': [],
'autoencoder_l2': [],
'generator_adversarial': [],
'discriminator_adversarial': {
'real': [],
'fake': []
}
}
self.valid_losses = {
'time_l2': [],
'freq_l2': [],
'autoencoder_l2': [],
'generator_adversarial': [],
'discriminator_adversarial': {
'real': [],
'fake': []
}
}
# Time to frequency converter
self.spectrogram = Spectrogram(normalized=True,
n_fft=512,
hop_length=128).to(self.device)
self.amplitude_to_db = AmplitudeToDB()
# Boolean indicting if auto-encoder or generator
self.is_autoencoder = False
# Boolean indicating if the model needs to be saved
self.need_saving = True
# Set the pseudo-epochs
self.train_batches_per_epoch = general_args.train_batches_per_epoch
self.test_batches_per_epoch = general_args.test_batches_per_epoch
self.valid_batches_per_epoch = general_args.valid_batches_per_epoch
parser.add_argument('-dir', '--dataset-dir', type=str)
parser.add_argument('-e', '--epoch', type=int, default=50)
parser.add_argument('-d', '--device', type=str, default='cuda:0')
args = parser.parse_args()
sr = args.sample_rate
n_fft = int(30e-3 * sr) # 48
hop_length = int(10e-3 * sr) # 16
dataset_dir = args.dataset_dir
batch_size = args.batch_size
lr = args.learning_rate
epoch = args.epoch
device = args.device
pad = Pad(size)
spec = Spectrogram(n_fft=n_fft, hop_length=hop_length)
melscale = MelScaleDelta(order=delta_order,
n_mels=n_mels,
sample_rate=sr,
f_min=f_min,
f_max=f_max,
dct_type='slaney')
rescale = Rescale()
transform = torchvision.transforms.Compose([pad, spec, melscale, rescale])
print(label_cnt)
train_dataset = SPEECHCOMMANDS(label_dict,
dataset_dir,
silence_cnt=2300,
#ORIGINAL CODE FROM https://github.com/yoyololicon/spectrogram-inversion
import torch
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm
from functools import partial
import math
import heapq
from torchaudio.transforms import MelScale, Spectrogram
torch.set_default_tensor_type('torch.cuda.FloatTensor')
specobj = Spectrogram(n_fft=4 * hop,
win_length=4 * hop,
hop_length=hop,
pad=0,
power=2,
normalized=False)
specfunc = specobj.forward
melobj = MelScale(n_mels=hop, sample_rate=sr, f_min=0.)
melfunc = melobj.forward
def melspecfunc(waveform):
specgram = specfunc(waveform)
mel_specgram = melfunc(specgram)
return mel_specgram
def spectral_convergence(input, target):
return 20 * ((input - target).norm().log10() - target.norm().log10())
dataloader = DataLoader(dataset, batch_size=16, drop_last=False, shuffle=True)
noisy_batch, clean_batch = next(iter(dataloader))
# enable eval mode
model.zero_grad()
model.eval()
model.freeze()
# disable gradients to save memory
torch.set_grad_enabled(False)
n_fft = (model.n_frequency_bins - 1) * 2
x_waveform = noisy_batch
transform = Spectrogram(n_fft=n_fft, power=None)
x_stft = transform(x_waveform)
y_stft = transform(clean_batch)
x_ms = x_stft.pow(2).sum(-1).sqrt()
y_ms = y_stft.pow(2).sum(-1).sqrt()
y_ms_hat = model(x_ms)
y_stft_hat = torch.stack([y_ms_hat * torch.cos(angle(x_stft)),
y_ms_hat * torch.sin(angle(x_stft))], dim=-1)
window = torch.hann_window(n_fft)
y_waveform_hat = istft(y_stft_hat, n_fft=n_fft, hop_length=n_fft // 2, win_length=n_fft, window=window, length=x_waveform.shape[-1])
for i, waveform in enumerate(y_waveform_hat.numpy()):
sf.write('denoised' + str(i) + '.wav', waveform, 16000)
def __init__(self, sr: int, sg_cfg: SpectrogramConfig):
self.sg_cfg = sg_cfg
self.spec = Spectrogram(**sg_cfg.spec_args)
self.to_mel = MelScale(sample_rate=sr, **sg_cfg.mel_args)
self.mfcc = MFCC(sample_rate=sr, **sg_cfg.mfcc_args)
self.to_db = AmplitudeToDB(top_db=sg_cfg.top_db)
max_iter=2 * 2048).to(device)
griffin_lim = GriffinLim(n_fft=1024, hop_length=256).to(device)
writer = tensorboard.SummaryWriter(log_dir=f'logs/test')
dataset = Dataset('../DATASETS/LJSpeech-1.1/metadata.csv',
'../DATASETS/LJSpeech-1.1')
dataloader = DataLoader(dataset,
collate_fn=dataset.collocate,
batch_size=batch_size,
shuffle=False,
num_workers=0,
drop_last=True)
resample = Resample(orig_freq=22050, new_freq=sample_rate)
spectogram = Spectrogram(n_fft=1024, hop_length=256).to(device)
to_mel = MelScale(n_mels=80, sample_rate=sample_rate,
n_stft=1024 // 2 + 1).to(device)
with open('../DATASETS/LJSpeech-1.1/metadata.csv', encoding='utf8') as file:
data = [line.strip().split('|') for line in file]
path, text = data[0][0], data[0][1]
path = f'../DATASETS/LJSpeech-1.1/wavs/{path}.wav'
data, sr = torchaudio.load(path)
data = resample(data)
data = data.to(device)
data = spectogram(data.squeeze(0))
mel_norm = (
(data.unsqueeze(0) - data.mean()) / data.std()).clamp(-1, 1) * .5 + .5
writer.add_image(f'spec/origin', mel_norm, 0)
