Audio processing by using pytorch 1D convolution network. By doing so, spectrograms can be generated from audio on-the-fly during neural network training.
The name of nnAudio comes from torch.nn, since most of the codes are built from torch.nn.
Numpy 1.14.5
Scipy 1.2.0
PyTorch 1.1.0
Python >= 3.6
All the required codes and examples are inside the jupyter-notebook. The audio processing layer can be integrated as part of the neural network as shown below.
pip install nnAudio
To use nnAudio, you need to import the Spectrogram method first
from nnAudio import Spectrogram
Then do the following
sr, song = wavfile.read('./Bach.wav') # Loading your audio
x = song.mean(1) # Converting Stereo to Mono
x = torch.tensor(x, dtype=torch.float) # casting the array into a PyTorch Tensor
spec_layer = Spectrogram.STFT(n_fft=2048, freq_bins=None, hop_length=512,
window='hann', freq_scale='linear', center=True, pad_mode='reflect',
fmin=50,fmax=11025, sr=sr) # Initializing the model
spec = spec_layer(x) # Feed-forward your waveform to get the spectrogram
One application for nnAudio is on-the-fly spectrogram generation when integrating it inside your neural network
class Model(torch.nn.Module):
def __init__(self, avg=.9998):
super(Model, self).__init__()
# Getting Mel Spectrogram on the fly
+ self.spec_layer = Spectrogram.STFT(sr=44100, n_fft=n_fft, freq_bins=freq_bins, fmin=50, fmax=6000, freq_scale='log', pad_mode='constant', center=True)
self.n_bins = freq_bins
# Creating CNN Layers
self.CNN_freq_kernel_size=(128,1)
self.CNN_freq_kernel_stride=(2,1)
k_out = 128
k2_out = 256
self.CNN_freq = nn.Conv2d(1,k_out,
kernel_size=self.CNN_freq_kernel_size,stride=self.CNN_freq_kernel_stride)
self.CNN_time = nn.Conv2d(k_out,k2_out,
kernel_size=(1,regions),stride=(1,1))
self.region_v = 1 + (self.n_bins-self.CNN_freq_kernel_size[0])//self.CNN_freq_kernel_stride[0]
self.linear = torch.nn.Linear(k2_out*self.region_v, m, bias=False)
def forward(self,x):
+ z = self.spec_layer(x)
z = torch.log(z+epsilon)
z2 = torch.relu(self.CNN_freq(z.unsqueeze(1)))
z3 = torch.relu(self.CNN_time(z2))
y = self.linear(torch.relu(torch.flatten(z3,1)))
return torch.sigmoid(y)If GPU is avaliable in your computer, you should put the following command at the beginning of your script to ensure nnAudio is run in GPU. By default, PyTorch runs in CPU, so as nnAudio.
if torch.cuda.is_available():
device = "cuda:0"
torch.set_default_tensor_type('torch.cuda.FloatTensor')
else:
device = "cpu"
The spectrogram outputs from nnAudio are nearly identical to the implmentation of librosa. The only difference is CQT, where we normalized the CQT kernel with L1 norm and then CQT output is normalized with the CQT kernel length. I am unable to explain the normalization used by librosa.
To use nnAudio, you need to define the neural network layer. After that, you can pass a batch of waveform to that layer to obtain the spectrograms. The input shape should be (batch, len_audio).
import Spectrogram
CQT_layer = Spectrogram.CQT2019(sr=44100, n_bins=84*2, bins_per_octave=24, fmin=55) # Defining the neural network
spec = CQT_layer(x) # x is the audio clips with shape=(batch, len_audio)
The speed test is conducted using DGX Station with the following specs
CPU: Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz
GPU: Tesla v100 32gb
RAM: 256 GB RDIMM DDR4
During the test, only 1 single GPU is used, and the same test is conducted when
(a) the DGX is idel
(b) the DGX has ongoing jobs
