def test_AmplitudeToDB(self):
waveform, sample_rate = torchaudio.load(self.test_filepath)
mag_to_db_transform = transforms.AmplitudeToDB('magnitude', 80.)
power_to_db_transform = transforms.AmplitudeToDB('power', 80.)
mag_to_db_torch = mag_to_db_transform(torch.abs(waveform))
power_to_db_torch = power_to_db_transform(torch.pow(waveform, 2))
self.assertTrue(torch.allclose(mag_to_db_torch, power_to_db_torch))
def test_batch_AmplitudeToDB(self):
spec = torch.rand((6, 201))
# Single then transform then batch
expected = transforms.AmplitudeToDB()(spec).repeat(3, 1, 1)
# Batch then transform
computed = transforms.AmplitudeToDB()(spec.repeat(3, 1, 1))
self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))
self.assertTrue(torch.allclose(computed, expected))
def test_AmplitudeToDB(self):
filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav')
waveform = common_utils.load_wav(filepath)[0]
mag_to_db_transform = transforms.AmplitudeToDB('magnitude', 80.)
power_to_db_transform = transforms.AmplitudeToDB('power', 80.)
mag_to_db_torch = mag_to_db_transform(torch.abs(waveform))
power_to_db_torch = power_to_db_transform(torch.pow(waveform, 2))
self.assertEqual(mag_to_db_torch, power_to_db_torch)
def get_train_transforms(
config: object,
transforms_set: TformsSet = TformsSet.Audtorch) -> object:
if config.use_mels:
if transforms_set == TformsSet.TorchAudio:
trans = tforms_vision.Compose([
tforms_torch.Resample(orig_freq=44100,
new_freq=config.resampling_rate),
tforms_torch.MelSpectrogram(sample_rate=config.resampling_rate,
n_fft=config.n_fft,
win_length=config.hop_length,
hop_length=config.hop_length,
f_min=float(config.fmin),
f_max=float(config.fmax),
pad=0,
n_mels=config.n_mels),
tforms_torch.AmplitudeToDB(stype='power', top_db=80),
#tforms_aud.RandomCrop(config.max_length_frames), # Raises "Can't call numpy() on Variable that requires grad. Use var.detach().numpy() instead."
])
elif transforms_set == TformsSet.MySet: # this works
trans = tforms_aud.Compose([
tforms_torch.Resample(orig_freq=44100,
new_freq=config.resampling_rate),
tforms_mine.Spectrogram(config),
tforms_aud.RandomCrop(config.max_length_frames)
])
else:
if transforms_set == TformsSet.TorchAudio: # this works
trans = tforms_aud.Compose([
tforms_torch.Resample(orig_freq=44100,
new_freq=config.resampling_rate),
tforms_torch.Spectrogram(n_fft=config.n_fft,
win_length=config.hop_length,
hop_length=config.hop_length,
pad=0,
power=2,
normalized=True),
tforms_torch.AmplitudeToDB(stype='power', top_db=80),
tforms_aud.RandomCrop(config.max_length_frames)
])
elif transforms_set == TformsSet.MySet: # this works
trans = tforms_aud.Compose([
tforms_torch.Resample(orig_freq=44100,
new_freq=config.resampling_rate),
tforms_mine.Spectrogram(config),
tforms_aud.RandomCrop(config.max_length_frames)
])
return trans
def tfm_spectro(ad=None,
sig=None,
sr=16000,
to_db_scale=False,
n_fft=1024,
ws=None,
hop=None,
f_min=0.0,
f_max=-80,
pad=0,
n_mels=128):
# We must reshape signal for torchaudio to generate the spectrogram.
mel = transforms.MelSpectrogram(sample_rate=ad.sr,
n_mels=n_mels,
n_fft=n_fft,
hop_length=hop,
f_min=f_min,
f_max=f_max,
pad=pad)(sig.reshape(1, -1))
mel = mel.permute(0, 2,
1) # swap dimension, mostly to look sane to a human.
if to_db_scale:
mel = transforms.AmplitudeToDB(stype='magnitude', top_db=f_max)(mel)
return mel
def __init__(self, d, src_path, batch_size, device):
super(VAE, self).__init__()
'''
=========== ARGUMENTS ===========
> d - dimensionality of latent space
> src_path - path to source samples
> batch_size - number of training examples in single batch
> device - CPU or GPU in use
=================================
'''
self.enc = ResNetBigger(d=d)
self.dec = nn.Sequential(nn.Linear(d, 40), nn.ReLU(),
nn.Linear(40, 50), nn.Sigmoid())
# For computing spectrogram then normalizing
self.spectrogram = T.Spectrogram(
n_fft=2048,
win_length=None,
hop_length=512,
power=2,
)
self.amp_to_db = T.AmplitudeToDB(stype='power')
self.src = torch.from_numpy(np.load(src_path))
self.src = self.src.unsqueeze(0).repeat(batch_size, 1, 1)
self.src = self.src.to(device)
self.d = d
self.device = device
def make_mel_spectrogram(self, sig_t, framerate):
# Get tensor (128 x num_samples), where 128
# is the default number of mel bands. Can
# change in call to MelSpectrogram:
mel_spec_t = transforms.MelSpectrogram(
sample_rate=framerate,
n_fft=self.n_fft,
win_length=self.win_length,
hop_length=self.hop_length
)(sig_t)
# Turn energy values to db of max energy
# in the spectrogram:
mel_spec_db_t = transforms.AmplitudeToDB()(mel_spec_t)
(num_mel_bands, _num_timebins) = mel_spec_t.shape
# Number of columns in the spectrogram:
num_time_label_choices = DSPUtils.compute_timeticks(framerate,
mel_spec_db_t
)
# Enumeration of the mel bands to use as y-axis labels:
freq_labels = np.array(range(num_mel_bands))
return(freq_labels, num_time_label_choices, mel_spec_db_t)
def test_amplitude_to_db(self):
sample_rate = 8000
transform = T.AmplitudeToDB()
waveform = get_whitenoise(sample_rate=sample_rate,
duration=0.05,
n_channels=2)
self.assert_grad(transform, [waveform])
def test_power_to_db(self):
spectrogram = get_spectrogram(get_whitenoise(), n_fft=400,
power=2).to(self.device, self.dtype)
result = T.AmplitudeToDB('power', 80.).to(self.device,
self.dtype)(spectrogram)[0]
expected = librosa.core.spectrum.power_to_db(
spectrogram[0].cpu().numpy())
self.assertEqual(result, torch.from_numpy(expected))
def __init__(self, classes_num):
super(ResNet54, self).__init__()
window = 'hann'
center = True
pad_mode = 'reflect'
ref = 1.0
amin = 1e-10
top_db = None
self.mel_spectrogram = nn.Sequential(
AT.MelSpectrogram(sample_rate=16000,
n_fft=512,
win_length=400,
hop_length=160,
n_mels=80,
f_max=8000), AT.AmplitudeToDB())
melkwargs = {
"n_fft": 512,
"hop_length": 160,
"win_length": 400,
"n_mels": 80,
"f_max": 8000
}
self.mfcc = AT.MFCC(sample_rate=16000, n_mfcc=40, melkwargs=melkwargs)
# Spectrogram extractor
# self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size,
# win_length=window_size, window=window, center=center, pad_mode=pad_mode,
# freeze_parameters=True)
# # Logmel feature extractor
# self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size,
# n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db,
# freeze_parameters=True)
# # Spec augmenter
# self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2,
# freq_drop_width=8, freq_stripes_num=2)
self.bn0 = nn.BatchNorm2d(64)
self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
# self.conv_block2 = ConvBlock(in_channels=64, out_channels=64)
self.resnet = _ResNet(block=_ResnetBottleneck,
layers=[3, 4, 6, 3],
zero_init_residual=True)
self.conv_block_after1 = ConvBlock(in_channels=2048, out_channels=2048)
self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
# self.fc1 = nn.Linear(2048, 2048)
self.fc_audioset = nn.Linear(2048, classes_num, bias=True)
self.init_weights()
def spectrogram(aud, n_mels=64, n_fft=1024, hop_len=None):
sig, sr = aud
top_db = 80
spec = transforms.MelSpectrogram(sr,
n_fft=n_fft,
hop_length=hop_len,
n_mels=n_mels)(sig)
spec = transforms.AmplitudeToDB(top_db=top_db)(spec)
return spec
def test_mel2(self):
top_db = 80.
s2db = transforms.AmplitudeToDB('power', top_db)
waveform = self.waveform.clone() # (1, 16000)
waveform_scaled = self.scale(waveform) # (1, 16000)
mel_transform = transforms.MelSpectrogram()
# check defaults
spectrogram_torch = s2db(
mel_transform(waveform_scaled)) # (1, 128, 321)
self.assertTrue(spectrogram_torch.dim() == 3)
self.assertTrue(
spectrogram_torch.ge(spectrogram_torch.max() - top_db).all())
self.assertEqual(spectrogram_torch.size(1), mel_transform.n_mels)
# check correctness of filterbank conversion matrix
self.assertTrue(mel_transform.mel_scale.fb.sum(1).le(1.).all())
self.assertTrue(mel_transform.mel_scale.fb.sum(1).ge(0.).all())
# check options
kwargs = {
'window_fn': torch.hamming_window,
'pad': 10,
'win_length': 500,
'hop_length': 125,
'n_fft': 800,
'n_mels': 50
}
mel_transform2 = transforms.MelSpectrogram(**kwargs)
spectrogram2_torch = s2db(
mel_transform2(waveform_scaled)) # (1, 50, 513)
self.assertTrue(spectrogram2_torch.dim() == 3)
self.assertTrue(
spectrogram_torch.ge(spectrogram_torch.max() - top_db).all())
self.assertEqual(spectrogram2_torch.size(1), mel_transform2.n_mels)
self.assertTrue(mel_transform2.mel_scale.fb.sum(1).le(1.).all())
self.assertTrue(mel_transform2.mel_scale.fb.sum(1).ge(0.).all())
# check on multi-channel audio
filepath = common_utils.get_asset_path(
'steam-train-whistle-daniel_simon.wav')
x_stereo = common_utils.load_wav(filepath)[0] # (2, 278756), 44100
spectrogram_stereo = s2db(mel_transform(x_stereo)) # (2, 128, 1394)
self.assertTrue(spectrogram_stereo.dim() == 3)
self.assertTrue(spectrogram_stereo.size(0) == 2)
self.assertTrue(
spectrogram_torch.ge(spectrogram_torch.max() - top_db).all())
self.assertEqual(spectrogram_stereo.size(1), mel_transform.n_mels)
# check filterbank matrix creation
fb_matrix_transform = transforms.MelScale(n_mels=100,
sample_rate=16000,
f_min=0.,
f_max=None,
n_stft=400)
self.assertTrue(fb_matrix_transform.fb.sum(1).le(1.).all())
self.assertTrue(fb_matrix_transform.fb.sum(1).ge(0.).all())
self.assertEqual(fb_matrix_transform.fb.size(), (400, 100))
def get_mel_spectogram(file_path, n_fft, win_length, hop_length,
n_mels) -> torch.Tensor:
x, sr = torchaudio.load(file_path,
normalization=lambda x: torch.abs(x).max())
mel_spectrogram = nn.Sequential(
AT.MelSpectrogram(sample_rate=sr,
n_fft=n_fft,
win_length=win_length,
hop_length=hop_length,
n_mels=n_mels), AT.AmplitudeToDB())
return mel_spectrogram(x)
def get_lmfs(name: str, dsp: DSP) -> torch.Tensor:
"""Return the log mel frequency spectrogram."""
map_path = f'{RAW_PATH}/{name}'
mono_sig, fs = load(glob(f'{map_path}/*.mp3')[0], sr=dsp.fs, res_type='kaiser_fast')
mono_sig = torch.from_numpy(mono_sig)
norm_sig = normalize(mono_sig)
mfs = transforms.MelSpectrogram(sample_rate=fs, n_fft=dsp.W,
f_min=dsp.f_min, f_max=dsp.f_max,
n_mels=dsp.bands, hop_length=dsp.stride,
window_fn=torch.hamming_window)(norm_sig)
lmfs = transforms.AmplitudeToDB()(mfs).unsqueeze(0).half().detach()
return lmfs
def __init__(self,
wav_paths,
script_paths,
bos_id=1307,
eos_id=1308,
is_train=True):
self.wav_paths = wav_paths
self.script_paths = script_paths
self.bos_id, self.eos_id = bos_id, eos_id
self.is_train = is_train
self.melspec = transforms.MelSpectrogram(sample_rate=SAMPLE_RATE,
n_fft=N_FFT,
n_mels=128)
self.todb = transforms.AmplitudeToDB(stype="magnitude", top_db=80)
def spectrogram_to_db(cls, spect_magnitude):
'''
Takes a numpy spectrogram of magnitudes.
Returns a numpy spectrogram containing
dB scaled power.
@param spect_magnitude:
@type spect_magnitude:
'''
transformer = transforms.AmplitudeToDB('power')
spect_tensor = torch.Tensor(spect_magnitude)
spect_dB_tensor = transformer.forward(spect_tensor)
spect_dB = spect_dB_tensor.numpy()
return spect_dB
def __init__(self, d, src_path, batch_size, device, dropout_rate=0.5, encoder = 'ResNet'):
super(VAE, self).__init__()
'''
=========== ARGUMENTS ===========
> d - dimensionality of latent space
> src_path - path to source samples
> batch_size - number of training examples in single batch
> device - CPU or GPU in use
=================================
'''
if encoder == 'ResNet':
self.enc = ResNetBigger(d=d, dropout_rate=dropout_rate)
elif encoder == 'CNN':
self.enc = CNN(d=d, batch_size=batch_size, dropout_rate=dropout_rate)
elif encoder == 'linear':
self.enc == nn.Sequential(
nn.Linear(1025, 400),
nn.ReLU(),
nn.Linear(400, 100),
nn.ReLU(),
nn.Linear(100, 50),
nn.ReLU(),
nn.Linear(50, d)
)
self.dec = nn.Sequential(
nn.Linear(d, 40),
nn.ReLU(),
nn.Linear(40, 50),
nn.Softmax()
)
self.softmax = nn.Softmax(dim=0)
# For computing spectrogram then normalizing
self.spectrogram = T.Spectrogram(
n_fft=2048,
win_length=None,
hop_length=512,
power=2,
)
self.amp_to_db = T.AmplitudeToDB(stype='power')
self.src = torch.from_numpy(np.load(src_path))
self.src = self.src.unsqueeze(0).repeat(batch_size, 1, 1)
self.src = self.src.to(device)
self.d = d
self.global_step = 0
self.epoch = 0
def tfm_spectro(sig, sr=32000, to_db_scale=True, n_fft=2048,
ws=None, hop=512, f_min=20.0, f_max=160000, pad=0, n_mels=128):
"""
img_size: 224
melspectrogram_parameters:
n_mels: 128
fmin: 20
fmax: 16000
"""
# We must reshape signal for torchaudio to generate the spectrogram.
mel = transforms.MelSpectrogram(sample_rate=sr, n_mels=n_mels, n_fft=n_fft, hop_length=hop,
f_min=f_min, f_max=f_max, pad=pad)(sig.reshape(1, -1))
mel = mel.permute(0,2,1) # swap dimension, mostly to look sane to a human.
if to_db_scale: mel = transforms.AmplitudeToDB(stype='magnitude', top_db=f_max)(mel)
return mel
def spectro_gram(aud, spectro_type='mel', n_mels=64, n_fft=1024, hop_len=None):
sig,sr = aud
f_min, f_max, ws, top_db, pad = 0.0, None, None, 80, 0
# spec has shape [channel, n_mels, time], where channel is mono, stereo etc
if (spectro_type == 'mel'):
spec = transforms.MelSpectrogram(sr, n_fft, ws, hop_len, f_min, f_max, pad, n_mels)(sig)
elif (spectro_type == 'mfcc'):
pass
else:
spec = transforms.Spectrogram(n_fft, ws, hop_len, pad, normalize=False)(sig)
# Convert to decibels
spec = transforms.AmplitudeToDB(top_db=top_db)(spec)
return (spec)
def __call__(self, data):
signal = data["signal"]
sr = data['sample_rate']
self.n_fft = int(np.ceil(0.025 * sr))
self.win_length = int(np.ceil(0.025 * sr))
self.hop_length = int(np.ceil(0.01 * sr))
spec = nn.Sequential(
T.Spectrogram(n_fft=self.n_fft,
win_length=self.win_length,
hop_length=self.hop_length), T.AmplitudeToDB())
data['Spectrogram'] = spec(signal)
data['input'] = spec(signal)
return data
def extract_features(x, sr):
step = 0.01
fft_time = 0.05
n_mels = 128
n_mfcc = 40
n_fft = int(fft_time * sr)
hop_length = int(step * sr)
spec = AT.MelSpectrogram(sample_rate=sr,
n_fft=n_fft,
hop_length=hop_length,
n_mels=n_mels,
f_max=8000)(x)[0]
intensity = spec.mean(dim=0).log()
spec = AT.AmplitudeToDB()(spec)
mfcc = AT.MFCC(
sample_rate=sr,
n_mfcc=n_mfcc,
melkwargs={
"n_fft": n_fft,
"hop_length": hop_length,
"n_mels": n_mels,
"f_max": 8000,
},
)(x)[0]
mfcc = (mfcc - mfcc.mean(dim=1, keepdim=True)) / mfcc.std(dim=1,
keepdim=True)
pitch_feature = AF.compute_kaldi_pitch(
x,
sample_rate=sr,
frame_length=fft_time * 1000,
frame_shift=step * 1000,
snip_edges=True,
min_f0=70,
max_f0=350,
penalty_factor=0.01,
)
pitch = pitch_feature[0]
return {
"Waveform": x[0],
"MelSpectrogram": spec,
"MFCC": mfcc,
"Pitch": pitch,
"Intensity": intensity,
}
def get_time_frequency_transform(config):
"""
Returns a nn.Sequential block to do a time-frequency transform, and crop to the desired size.
The spectrogram has shape: [batch, channels, freq_bins, frames]
:param config:
:return:
"""
if config.use_mels:
transformer = nn.Sequential(
tforms_torch.MelSpectrogram(sample_rate=config.new_fs,
n_fft=config.n_fft,
win_length=config.win_length,
hop_length=config.hop_length,
f_min=float(config.fmin),
f_max=float(config.fmax),
pad=0,
n_mels=config.n_mels),
#utils.make_module(tforms_mine.RandomCrop)(config.max_length_frames),
tforms_mine.RandomCrop(
(1, config.n_mels if config.use_mels else config.n_fft // 2 +
1, config.max_length_frames),
value=0),
tforms_torch.AmplitudeToDB(stype='power', top_db=80),
tforms_mine.ReScaleSpec([-1, 1]),
)
else:
transformer = nn.Sequential(
tforms_torch.Spectrogram(n_fft=config.n_fft,
win_length=config.win_length,
hop_length=config.hop_length,
pad=0,
power=2,
normalized=True),
tforms_mine.RandomCrop(
(1, config.n_mels if config.use_mels else config.n_fft // 2 +
1, config.max_length_frames),
value=0),
tforms_mine.AmplitudeToDB(stype='power', top_db=80),
#utils.make_module(tforms_mine.RandomCrop)(config.max_length_frames),
tforms_mine.ReScaleSpec([-1, 1]),
)
return transformer
def __getitem__(self, index):
audio, sr = load(self.file_paths[index])
audio = torch.mean(audio, dim=0, keepdim=True)
if self.sr != sr:
audio = transforms.Resample(sr, self.sr)(audio)
mel_spectrogram = transforms.MelSpectrogram(sample_rate=self.sr,
n_fft=self.n_fft,
win_length=self.win_length,
hop_length=self.hop_length,
n_mels=self.n_mels,
f_max=self.sr / 2)(audio)
if self.log_mel:
offset = 1e-6
mel_spectrogram = torch.log(mel_spectrogram + offset)
else:
mel_spectrogram = transforms.AmplitudeToDB(
stype="power", top_db=80)(mel_spectrogram)
if self.augment:
audio = transforms.FrequencyMasking(freq_mask_param=20)(audio)
audio = transforms.TimeMasking(time_mask_param=10)(audio)
label = self.labels[index]
return mel_spectrogram, label
def Mel_spectroize(train_feature_path, train_label_path):
x_data = sorted(glob(train_feature_path))
x_data = data_loader(x_data)
y_data = pd.read_csv(train_label_path, index_col=0)
y_data = y_data.values
mel_spectrogram = nn.Sequential(
AT.MelSpectrogram(sample_rate=16000,
n_fft=512,
win_length=400,
hop_length=160,
n_mels=80),
AT.AmplitudeToDB()
)
mel0 = mel_spectrogram(torch.tensor(x_data[0])).view(1, 1, 80, 101)
mel1 = mel_spectrogram(torch.tensor(x_data[1])).view(1, 1, 80, 101)
mel = torch.cat((mel0, mel1), 0)
for i in range(2, 100000):
if i % 100 == 0:
print("Mel spectrogram progress: {}%".format(i/100000*100))
mel_temp = mel_spectrogram(torch.tensor(x_data[i])).view(1, 1, 80, 101)
mel = torch.cat((mel, mel_temp), 0)
return mel, y_data
def get_train_transforms(config: object,
set: TformsSet = TformsSet.Audtorch) -> object:
if config.use_mels:
if set == TformsSet.TorchAudio:
trans = transforms.Compose([
tforms2.Crop((441000, 441000 + 441000)),
tforms.MelSpectrogram(sample_rate=config.resampling_rate,
n_fft=config.n_fft,
win_length=config.hop_length,
hop_length=config.hop_length,
f_min=float(config.fmin),
f_max=float(config.fmax),
pad=0,
n_mels=config.n_mels),
tforms.AmplitudeToDB(stype='power', top_db=80),
# transforms.ToPILImage(),
# transforms.RandomCrop((96, 256), pad_if_needed=True,
# padding_mode='reflect'),
# transforms.ToTensor(),
])
elif set == TformsSet.Audtorch: ## no real mel spectrogram in audtorch
trans = tforms2.Compose([
myTforms.ToNumpy(),
tforms2.Crop((441000, 441000 + 441000)),
# tforms2.Normalize(),
tforms2.Spectrogram(
window_size=config.hop_length,
hop_size=config.hop_length,
fft_size=config.n_fft,
),
tforms2.Log(),
myTforms.ToTensor(),
tforms.AmplitudeToDB(stype='magnitude', top_db=80)
])
elif set == TformsSet.MySet:
trans = tforms2.Compose([
tforms2.Crop((441000, 441000 + 441000)),
myTforms.Spectrogram(config)
])
else:
if set == TformsSet.TorchAudio:
trans = transforms.Compose([
tforms2.Crop((441000, 441000 + 441000)),
tforms.Spectrogram(n_fft=config.n_fft,
win_length=config.hop_length,
hop_length=config.hop_length,
pad=0,
power=2,
normalized=True),
tforms.AmplitudeToDB(stype='power', top_db=80),
# tforms.MelSpectrogram(sample_rate=config.resampling_rate,
# n_fft=config.n_fft,
# win_length=config.hop_length,
# hop_length=config.hop_length,
# f_min=float(config.fmin),
# f_max=float(config.fmax),
# pad=0,
# n_mels=config.n_mels),
#transforms.ToPILImage(),
#transforms.RandomCrop((96, 256), pad_if_needed=True,
# padding_mode='reflect'),
#transforms.ToTensor(),
])
elif set == TformsSet.Audtorch:
trans = tforms2.Compose([
myTforms.ToNumpy(),
tforms2.Crop((441000, 441000 + 441000)),
#tforms2.Normalize(),
tforms2.Spectrogram(
window_size=config.hop_length,
hop_size=config.hop_length,
fft_size=config.n_fft,
),
myTforms.ToTensor(),
tforms.AmplitudeToDB(stype='magnitude', top_db=80)
])
elif set == TformsSet.MySet:
trans = tforms2.Compose([
tforms2.Crop((441000, 441000 + 441000)),
myTforms.Spectrogram(config)
])
return trans
def get_spectrogram(amplitude: Tensor, top_db: float = 80) -> Tensor:
device = amplitude.device
amplitude_to_db = _transf.AmplitudeToDB(top_db=top_db).to(device)
return amplitude_to_db(amplitude.pow(2))
def test_AmplitudeToDB(self):
spec = torch.rand((6, 201))
self._assert_consistency(T.AmplitudeToDB(), spec)
default=None,
help='Optimizer path')
parser.add_argument('--x',
type=int,
default=1,
help='Number of AE layers to use')
parser.add_argument('--batch_size', type=int, default=64, help='Batch size')
parser.add_argument('--learn_rate',
type=float,
default=0.001,
help='Learning rate')
args = parser.parse_args()
transform = tvt.Compose([
transforms.AmplitudeToDB(stype='power',
top_db=None), # it won't square the input
tvt.Normalize(mean=[-38.39992], std=[13.462255])
])
class MyIterableDataset(torch.utils.data.Dataset):
def __init__(self, audios, transform):
super(MyIterableDataset).__init__()
self.all_audios = audios # [os.path.join(path, f) for f in os.listdir(path)]
self.start = 0
self.end = len(self.all_audios)
self.transform = transform
def __getitem__(self, index):
output, _ = torchaudio.load(self.all_audios[index], normalization=True)
drop_last=True,
**params)
test_path = r'X:\DS Training Data\samples\test.npy'
test_data = Dataset(test_path)
test_loader = torch.utils.data.DataLoader(test_data,
drop_last=True,
**params)
spectrogram = T.Spectrogram(
n_fft=2048,
win_length=None,
hop_length=512,
power=2,
)
amp_to_db = T.AmplitudeToDB(stype='power')
src_path = test_path = r'X:\DS Training Data\samples\src.npy'
model = VAE(d=32,
src_path=src_path,
batch_size=params['batch_size'],
device=device,
dropout_rate=0.25,
encoder='CNN').cuda()
print(model)
criterion = loss_function
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
def compareTforms(config):
'''
Here I compare different transfromations sets for spectrograms, using (torchaudio, audtorch, and my own custom
spectrogram using librosa. This codes is applied to a sample audio file from the librispeech dataset.
This code was done mostly to post as an issue in github. As a minimal working example.
'''
config.use_mels = False
config.win_length = 400
config.hop_length = 400
config.n_fft = 2048
config.resampling_rate = 16000
augment1 = tforms2.Compose([
myTforms.ToTensor(),
tforms.Spectrogram(
n_fft=2048,
win_length=400, # 400 samples @ 16k = 25 ms,
hop_length=400,
pad=0,
power=2,
normalized=False),
tforms.AmplitudeToDB(stype='power', top_db=80)
])
augment2 = tforms2.Compose([
tforms2.Spectrogram(
window_size=400, # 400 samples @ 16k = 25 ms
hop_size=400,
fft_size=2048),
myTforms.ToTensor(),
tforms.AmplitudeToDB(stype='magnitude', top_db=80)
])
augment3 = tforms2.Compose([myTforms.Spectrogram(config)])
data1 = dsets.LibriSpeech(
root='/m/cs/work/falconr1/datasets/librespeech/LibriSpeech',
sets='dev-clean',
download=False,
transform=augment1)
data2 = dsets.LibriSpeech(
root='/m/cs/work/falconr1/datasets/librespeech/LibriSpeech',
sets='dev-clean',
download=False,
transform=augment2)
data3 = dsets.LibriSpeech(
root='/m/cs/work/falconr1/datasets/librespeech/LibriSpeech',
sets='dev-clean',
download=False,
transform=augment3)
plt.figure(figsize=(16, 8))
titles = ['torchaudio', 'audtorch', 'myset']
for i, data in enumerate([data1, data2, data3]):
spec, label = data[0]
if isinstance(spec, torch.Tensor):
spec = spec.numpy()
plt.subplot(1, 3, i + 1)
plt.imshow(spec.squeeze(),
interpolation='nearest',
cmap='inferno',
origin='lower',
aspect='auto')
plt.colorbar()
plt.title(titles[i])
plt.savefig(os.path.join('./results', 'Test_Output_compare_specs.png'))
plt.show()
