Audio processing by using pytorch 1D convolution network. By doing so, spectrograms can be generated from audio on-the-fly during neural network training. Kapre and torch-stft have a similar concept in which they also use 1D convolution from keras to do the waveforms to spectrogram conversions.
Other GPU audio processing tools are torchaudio and tf.signal. But they are not using the neural network approach, and hence the Fourier basis can not be trained.
The name of nnAudio comes from torch.nn, since most of the codes are built from torch.nn.
https://kinwaicheuk.github.io/nnAudio/index.html
nnAudio Abstract for Late-Breaking Demo
Numpy 1.14.5
Scipy 1.2.0
PyTorch 1.1.0
Python >= 3.6
librosa = 0.7.0 (Theortically nnAudio depends on librosa. But we only need to use a single function mel from librosa.filters. To save users troubles from installing librosa for this single function, I just copy the chunks of functions corresponding to mel in my code so that nnAudio runs without the need to install librosa)
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. The demo on colab is also avaliable.
pip install nnAudio
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).
from nnAudio import Spectrogram
from scipy.io import wavfile
import torch
sr, song = wavfile.read('./Bach.wav') # Loading your audio
x = song.mean(1) # Converting Stereo to Mono
x = torch.tensor(x).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):
super(Model, self).__init__()
# Getting Mel Spectrogram on the fly
+ self.spec_layer = Spectrogram.STFT(n_fft=2048, freq_bins=None, hop_length=512, window='hann', freq_scale='no', center=True, pad_mode='reflect', fmin=50,fmax=6000, sr=22050, trainable=False, output_format='Magnitude', device='cuda:0')
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)Fourier basis in STFT() can be set trainable by using trainable=True argument. Fourier basis in MelSpectrogram() can be set trainable by using trainable_STFT=True, and Mel filter banks can be set trainable using trainable_mel=False argument.
The follow demonstrations are avaliable on Google colab.
The figure below shows the STFT basis before and after training.
The figure below shows how is the STFT output affected by the changes in STFT basis. Notice the subtle signal in the background for the trained STFT.
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 tensors are created in CPU, if you want to use nnAudio in GPU, make sure to transfer all your PyTorch tensors to GPU
if torch.cuda.is_available():
device = "cuda:0"
else:
device = "cpu"Then you can initialize nnAudio by choosing either CPU or GPU with the device argument. The default setting for nnAudio is device='cuda:0'
spec_layer = Spectrogram.STFT(device=device)Currently there are 4 models to generate various types of spectrograms.
from nnAudio import Spectrogram
Spectrogram.STFT(n_fft=2048, freq_bins=None, hop_length=512, window='hann', freq_scale='no', center=True, pad_mode='reflect', fmin=50,fmax=6000, sr=22050, trainable=False)freq_scale: 'no', 'linear', or 'log'. This options controls the spacing of frequency among Fourier basis. When chosing 'no', the STFT output is same as the librosa output. fmin and fmax will have no effect under this option. When chosing 'linear' or 'log', the frequency scale will be in linear scale or logarithmic scale with the
Spectrogram.MelSpectrogram(sr=22050, n_fft=2048, n_mels=128, hop_length=512, window='hann', center=True, pad_mode='reflect', htk=False, fmin=0.0, fmax=None, norm=1, trainable_mel=False, trainable_STFT=False)Spectrogram.CQT(sr=22050, hop_length=512, fmin=220, fmax=None, n_bins=84, bins_per_octave=12, norm=1, window='hann', center=True, pad_mode='reflect')The spectrogram outputs from nnAudio are nearly identical to the implmentation of librosa. Four different input singals, linear sine sweep, logarithmic sine sweep, impluse, and piano chromatic scale, are used to test the nnAudio output. The figures below shows the result.
The result for CQT1992 is smoother than CQT2010 and librosa. Since librosa and CQT2010 are using the same algorithm (downsampling approach as mentioned in this paper), you can see similar artifacts as a result of downsampling. The default CQT in nnAudio is the 1992 version, with slighltly modifications to make it faster than the original CQT proposed in 1992.
However, all both versions of CQT are avaliable for users to choose. To explicitly choose which CQT to use, you can call nnAudio with the following code.
It is the default algorithm used in Spectrogram.CQT.
Spectrogram.CQT1992v2(sr=22050, hop_length=512, fmin=220, fmax=None, n_bins=84, bins_per_octave=12, norm=1, window='hann', center=True, pad_mode='reflect')Spectrogram.CQT2010v2(sr=22050, hop_length=512, fmin=220, fmax=None, n_bins=84, bins_per_octave=12, norm=1, window='hann', center=True, pad_mode='reflect')
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
Currently there is no paper avaliable for nnAudio, and we are trying to write one. But our work is submitted to ISMIR2019 as a late-breaking demo
Cheuk, K.W., Agres, K., & Herremans, D. (2019). nnAudio: A Pytorch Audio Processing Tool Using 1D Convolution Neural Networks. ISMIR-LBD.
Kin Wai Cheuk, Kat Agres, Dorien Herremans. “nnAudio: A Pytorch Audio Processing Tool Using 1D Convolution Neural Networks.” ISMIR-LBD (2019).
@inproceedings{kinwai2019LBD, title={nnAudio: A Pytorch Audio Processing Tool Using 1D Convolution Neural Networks}, author={Kin Wai Cheuk, Kat Agres, Dorien Herremans}, booktitle={ISMIR-LBD}, year={2019}
}