def __init__(
self,
n_fft=1024,
hop_length=256,
win_length=1024,
n_bins=84,
sampling_rate=22050,
):
super().__init__()
##############################################
# FFT Parameters #
##############################################
window = torch.hann_window(win_length).float()
cqt_basis, lengths = librosa_cqt_fn(sampling_rate,
n_bins=n_bins,
filter_scale=0.5)
cqt_basis = cqt_basis.astype(dtype=np.float32)
cqt_basis = torch.from_numpy(cqt_basis).float()
self.register_buffer("cqt_basis", cqt_basis)
self.register_buffer("window", window)
self.n_fft = n_fft
self.n_bins = n_bins
self.hop_length = hop_length
self.win_length = win_length
self.sampling_rate = sampling_rate
self.spec_layer = Spectrogram.CQT1992v2(sr=sampling_rate,
n_bins=84,
hop_length=hop_length,
output_format='Magnitude',
pad_mode='constant',
device='cuda:0',
verbose=False,
trainable=False)
def main():
args = parse_args()
save_type = args.save_type
spec_layer = Spectrogram.CQT1992v2(sr=22050, n_bins=84, hop_length=256, pad_mode='constant', device='cuda:0', verbose=False, trainable=False, output_format='Magnitude')
transformedSet = AudioDataset('input_audio.txt', 22050 * 4, sampling_rate=22050, augment=False)
transformedLoader = DataLoader(transformedSet, batch_size=1)
transformedVoc = []
f = open('input_audio.txt', 'r')
lines = f.readlines()
lines = list(map(lambda s: s.strip(), lines)) #remove newline character
lines = [track.replace('.wav', '') for track in lines] #remove .wav
print(lines)
if len(lines) != len(transformedLoader):
print('Differences in wavs found and whats in input_audio.txt')
return
for i, x_t in enumerate(transformedLoader):
x_t = x_t.cuda()
s_t = spec_layer(x_t).detach()
s_t = torch.log(torch.clamp(s_t, min=1e-5))
transformedVoc.append(s_t.cuda())
if (save_type == 'torch'):
print('Saving WAVs as torch pt')
for x in range(0, len(transformedVoc)):
torch.save(transformedVoc[x], lines[x] + '.pt')
if (save_type == 'png'):
print('Saving WAVs as image via matplotlib')
for x in range(0, len(transformedVoc)):
save_spec_images(transformedVoc[x], lines[x])
def __init__(self):
super(Model, self).__init__()
f_kernal = 128//network_factor
self.STFT_layer = Spectrogram.CQT1992v2(sr=44100, fmin=27.5, n_bins=n_bins, bins_per_octave=bins_per_octave, pad_mode='constant', hop_length=HOP_LENGTH, center=True, device=device)
self.freq_cnn1 = torch.nn.Conv2d(1,4, (f_kernal,3), stride=(8,1), padding=1)
self.freq_cnn2 = torch.nn.Conv2d(4,8, (f_kernal,3), stride=(8,1), padding=1)
shape = self.shape_inference(f_kernal)
self.bilstm = torch.nn.LSTM(shape*8, shape*8, batch_first=True, bidirectional=True)
self.pitch_classifier = torch.nn.Linear(shape*8*2, 88)
def main():
args = parse_args()
file_name = args.input_file
save_path = args.save_path
spec_layer = Spectrogram.CQT1992v2(sr=22050,
n_bins=84,
hop_length=256,
pad_mode='constant',
device='cuda:0',
verbose=False,
trainable=False,
output_format='Magnitude')
transformedSet = AudioConversionDataset(file_name,
22050 * 4,
sampling_rate=22050,
augment=False)
transformedLoader = DataLoader(transformedSet, batch_size=1)
f = open(file_name, 'r')
lines = f.readlines()
lines = list(map(lambda s: s.strip(), lines)) #remove newline character
lines = [track.replace('.wav', '') for track in lines] #remove .wav
lines = [track.split("/")[-1] for track in lines]
if len(lines) != len(transformedLoader):
print('Differences in wavs found and whats in input_audio.txt')
return
for i, x in tqdm(enumerate(transformedLoader),
ascii=True,
desc='Making spectrogram representations'):
x_t = x[0]
fname = os.path.basename(x[1][0]).replace('.wav', '')
x_t = x_t.cuda()
s_t = spec_layer(x_t).detach()
s_t = torch.log(torch.clamp(s_t, min=1e-5))
if args.save_type == 'pt':
torch.save(s_t.cuda(), save_path + fname + '.pt')
else:
save_image(s_t.cuda(), save_path + fname + '.png', normalize=True)
min_value = torch.min(s_t.cuda()).item()
max_value = torch.max(s_t.cuda()).item()
normalisation_dict[fname] = {"min": min_value, "max": max_value}
with open(save_path + 'normalisation_values.json', 'w') as outfile:
json.dump(normalisation_dict, outfile, indent=4)
def __init__(
self,
hop_length=256,
n_bins=84,
sampling_rate=22050,
):
super().__init__()
##############################################
# FFT Parameters #
##############################################
self.n_bins = n_bins
self.hop_length = hop_length
self.sampling_rate = sampling_rate
self.spec_layer = Spectrogram.CQT1992v2(sr=sampling_rate,
n_bins=84,
hop_length=hop_length,
output_format='Magnitude',
pad_mode='constant',
device='cuda:0',
verbose=False,
trainable=False)
for n_mels in n_mels_ls:
if n_mels < n_fft:
layer = Spectrogram.MelSpectrogram(n_fft=n_fft,
n_mels=n_mels,
hop_length=512,
verbose=False,
device=device)
start = time.time()
for i in tqdm.tqdm(dataset):
i = i.to(device)
layer(i)
result[f'Mel-{n_fft}-n_bins{n_mels}'] = time.time() - start
else:
continue
# CQT
for r in range(1, 11):
layer = Spectrogram.CQT1992v2(sr=44100,
n_bins=84 * r,
bins_per_octave=12 * r,
hop_length=512,
verbose=False,
device=device)
start = time.time()
for i in tqdm.tqdm(dataset):
i = i.to(device)
layer(i)
result[f'CQT-r={r}'] = time.time() - start
pickle.dump(result, open(Path(__file__).parent / './Pytorch_result', 'wb'))
elif args.device == "GPU":
device = "cuda:0"
print("using GPU")
elif args.device == "librosa":
print("using librosa")
print(Path(__file__).parent / './y_list.npy')
y_list = np.load(Path(__file__).parent / './y_list.npy')
if args.device in ["CPU", "GPU"]:
y_torch = torch.tensor(y_list, device=device).float()
spec_layer = Spectrogram.CQT1992v2(sr=44100,
n_bins=84,
bins_per_octave=24,
fmin=55,
device=device)
timing = []
for e in range(20):
t_start = time.time()
spec = spec_layer(y_torch[:1000])
spec = spec_layer(y_torch[1000:])
time_used = time.time() - t_start
# print(time_used)
timing.append(time_used)
print("mean = ", np.mean(timing))
print("std = ", np.std(timing))
data = pd.DataFrame(timing, columns=['t_avg'])
