class RondomStretchMelSpectrogram(nn.Module):
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.mel_specgram = MelSpectrogram(sample_rate,
n_fft=n_fft,
f_max=8000)
self.AtoDB = AmplitudeToDB(top_db=top_db)
self.dist = Uniform(1. - max_perc, 1 + max_perc)
def forward(self, x, train):
x = self.stft(x)
if train:
x = self.time_stretch(x, self.dist.sample().item())
x = self.com_norm(x)
x = self.mel_specgram.mel_scale(x)
x = self.AtoDB(x)
size = torch.tensor(x.size())
if size[3] > 157:
x = x[:, :, :, 0:157]
else:
x = torch.cat([
x,
torch.cuda.FloatTensor(size[0], size[1], size[2],
157 - size[3]).fill_(0)
],
dim=3)
return x
class RondomStretchMelSpectrogram(nn.Module):
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)
]
def forward(self, x, train):
x = random.choice(self.resamples)(x)
x = self.stft(x)
if train:
dist = Uniform(1. - self.max_perc, 1 + self.max_perc)
x = self.time_stretch(x, dist.sample().item())
x = self.com_norm(x)
x = self.fm(x, 0)
x = self.tm(x, 0)
else:
x = self.com_norm(x)
x = self.mel_specgram.mel_scale(x)
x = self.AtoDB(x)
size = torch.tensor(x.size())
if size[3] > 157:
x = x[:, :, :, 0:157]
else:
x = torch.cat([
x,
torch.cuda.FloatTensor(size[0], size[1], size[2],
157 - size[3]).fill_(0)
],
dim=3)
return x
class MelspectrogramStretch(object):
def __init__(self):
sample_rate = 44100
num_mels = 128
fft_length = 2048
hop_length = fft_length // 2
self.stft = Spectrogram(n_fft=fft_length,
win_length=fft_length,
hop_length=None,
pad=0,
power=None,
normalized=False)
self.mst = MelSpectrogram(sample_rate=sample_rate,
n_fft=fft_length,
hop_length=hop_length,
n_mels=num_mels)
# Normalization (pot spec processing)
self.complex_norm = ComplexNorm(power=2.)
def forward(self, data):
tsf = AudioTransforms()
sig_t, sr, _ = tsf.apply(data, None)
length = torch.tensor(sig_t.size(0))
sr = torch.tensor(sr)
data = [d.unsqueeze(0).to("cpu") for d in [sig_t, length, sr]]
# x-> (batch, time, channel)
x, lengths, _ = data # unpacking seqs, lengths and srs
# x-> (batch, channel, time)
xt = x.float().transpose(1, 2)
# xt -> (batch, channel, freq, time)
x = self.stft(xt)
# x -> (fft_length//2+1,bins,channel)
#print(x.shape) #torch.Size([1, 1, 1025, 173, 2])
x = self.complex_norm(x)
#print(x.shape) #torch.Size([1, 1, 1025, 173])
x = self.mst.mel_scale(x)
#print(x.shape) #torch.Size([1, 1, 128, 173])
# Normalize melspectrogram
# Independent mean, std per batch
non_batch_inds = [1, 2, 3]
mean = x.mean(non_batch_inds, keepdim=True)
std = x.std(non_batch_inds, keepdim=True)
x = (x - mean) / std
x = x.to('cpu').detach().numpy().copy()
lengths = [x.shape[3]]
return x, lengths