def test_pmsqe_pit(n_src, sample_rate):
# Define supported STFT
if sample_rate == 16000:
stft = Encoder(STFTFB(kernel_size=512, n_filters=512, stride=256))
else:
stft = Encoder(STFTFB(kernel_size=256, n_filters=256, stride=128))
# Usage by itself
ref, est = torch.randn(2, n_src, 16000), torch.randn(2, n_src, 16000)
ref_spec = transforms.mag(stft(ref))
est_spec = transforms.mag(stft(est))
loss_func = PITLossWrapper(SingleSrcPMSQE(sample_rate=sample_rate), pit_from="pw_pt")
# Assert forward ok.
loss_func(est_spec, ref_spec)
def make_model_and_optimizer(conf):
""" Function to define the model and optimizer for a config dictionary.
Args:
conf: Dictionary containing the output of hierachical argparse.
Returns:
model, optimizer.
The main goal of this function is to make reloading for resuming
and evaluation very simple.
"""
# Define building blocks for local model
stft = Encoder(STFTFB(**conf['filterbank']))
# Because we concatenate (re, im, mag) as input and compute a complex mask.
if conf['main_args']['is_complex']:
inp_size = int(stft.n_feats_out * 3 / 2)
output_size = stft.n_feats_out
else:
inp_size = output_size = int(stft.n_feats_out / 2)
# Add these fields to the mask model dict
conf['masknet'].update(dict(input_size=inp_size, output_size=output_size))
masker = SimpleModel(**conf['masknet'])
# Make the complete model
model = Model(stft, masker, is_complex=conf['main_args']['is_complex'])
# Define optimizer of this model
optimizer = make_optimizer(model.parameters(), **conf['optim'])
return model, optimizer
def test_pinv_of(fb_class):
fb = fb_class(n_filters=500, kernel_size=16, stride=8)
encoder = Encoder(fb)
# Pseudo inverse can be taken from an Encoder/Decoder class or Filterbank.
decoder_e = Decoder.pinv_of(encoder)
decoder_f = Decoder.pinv_of(fb)
assert_allclose(decoder_e.filters, decoder_f.filters)
# Check filter computing
inp = torch.randn(1, 1, 32000)
_ = decoder_e(encoder(inp))
decoder = Decoder(fb)
# Pseudo inverse can be taken from an Encoder/Decoder class or Filterbank.
encoder_e = Encoder.pinv_of(decoder)
encoder_f = Encoder.pinv_of(fb)
assert_allclose(encoder_e.filters, encoder_f.filters)
def test_stft_def(fb_config):
""" Check consistency between two calls."""
fb = STFTFB(**fb_config)
enc = Encoder(fb)
dec = Decoder(fb)
enc2, dec2 = make_enc_dec('stft', **fb_config)
testing.assert_allclose(enc.filterbank.filters, enc2.filterbank.filters)
testing.assert_allclose(dec.filterbank.filters, dec2.filterbank.filters)
def test_pmsqe(sample_rate):
# Define supported STFT
if sample_rate == 16000:
stft = Encoder(STFTFB(kernel_size=512, n_filters=512, stride=256))
else:
stft = Encoder(STFTFB(kernel_size=256, n_filters=256, stride=128))
# Usage by itself
ref, est = torch.randn(2, 1, 16000), torch.randn(2, 1, 16000)
ref_spec = transforms.mag(stft(ref))
est_spec = transforms.mag(stft(est))
loss_func = SingleSrcPMSQE(sample_rate=sample_rate)
loss_value = loss_func(est_spec, ref_spec)
# Assert output has shape (batch,)
assert loss_value.shape[0] == ref.shape[0]
# Assert support for transposed inputs.
tr_loss_value = loss_func(est_spec.transpose(1, 2), ref_spec.transpose(1, 2))
assert_allclose(loss_value, tr_loss_value)
def test_fb_def_and_forward(fb_class, fb_config):
""" Test filterbank defintion and encoder/decoder forward."""
# Definition
enc = Encoder(fb_class(**fb_config))
dec = Decoder(fb_class(**fb_config))
# Forward
inp = torch.randn(1, 1, 32000)
tf_out = enc(inp)
out = dec(tf_out)
# 4d forward + unit test
tf_out_4d = tf_out.repeat(1, 2, 1, 1)
out_4d = dec(tf_out_4d)
assert_allclose(out, out_4d[:, 0])
# Get config tests
dec_config = dec.get_config()
enc_config = enc.get_config()
# N feats out test
assert tf_out.shape[1] == enc.filterbank.n_feats_out
def test_griffinlim(fb_config, feed_istft, feed_angle):
stft = Encoder(STFTFB(**fb_config))
istft = None if not feed_istft else Decoder(STFTFB(**fb_config))
wav = torch.randn(2, 1, 8000)
spec = stft(wav)
tf_mask = torch.sigmoid(torch.randn_like(spec))
masked_spec = spec * tf_mask
mag = transforms.take_mag(masked_spec, -2)
angles = None if not feed_angle else transforms.angle(masked_spec, -2)
griffin_lim(mag, stft, angles=angles, istft_dec=istft, n_iter=3)
def __init__(self, fb_conf, mask_conf):
super().__init__()
self.n_src = mask_conf['n_src']
self.n_filters = fb_conf['n_filters']
# Create TasNet encoders and decoders (could use nn.Conv1D as well)
self.encoder_sig = Encoder(FreeFB(**fb_conf))
self.encoder_relu = Encoder(FreeFB(**fb_conf))
self.decoder = Decoder(FreeFB(**fb_conf))
self.bn_layer = GlobLN(fb_conf['n_filters'])
# Create TasNet masker
self.masker = nn.Sequential(
SingleRNN('lstm',
fb_conf['n_filters'],
hidden_size=mask_conf['n_units'],
n_layers=mask_conf['n_layers'],
bidirectional=True,
dropout=mask_conf['dropout']),
nn.Linear(2 * mask_conf['n_units'], self.n_src * self.n_filters),
nn.Sigmoid())
def test_fb_def_and_forward_all_dims(fb_class, fb_config):
""" Test encoder/decoder on other shapes than 3D"""
# Definition
enc = Encoder(fb_class(**fb_config))
dec = Decoder(fb_class(**fb_config))
# 3D Forward with one channel
inp = torch.randn(3, 1, 32000)
tf_out = enc(inp)
assert tf_out.shape[:2] == (3, enc.filterbank.n_feats_out)
out = dec(tf_out)
assert out.shape[:-1] == inp.shape[:-1] # Time axis can differ
def test_melgram_encoder(n_filters, n_mels, ndim):
n_mels = n_mels if n_mels is not None else n_filters // 2 + 1
melgram_fb = MelGramFB(n_filters=n_filters,
kernel_size=n_filters,
n_mels=n_mels)
enc = Encoder(melgram_fb)
tensor_shape = tuple([random.randint(2, 3)
for _ in range(ndim - 1)]) + (4000, )
wav = torch.randn(tensor_shape)
mel_spec = enc(wav)
assert wav.shape[:-1] == mel_spec.shape[:-2]
assert mel_spec.shape[-2] == n_mels
conf = melgram_fb.get_config()
def test_fb_forward_multichannel(fb_class, fb_config, ndim):
""" Test encoder/decoder in multichannel setting"""
# Definition
enc = Encoder(fb_class(**fb_config))
dec = Decoder(fb_class(**fb_config))
# 3D Forward with several channels
tensor_shape = tuple([random.randint(2, 4)
for _ in range(ndim)]) + (4000, )
inp = torch.randn(tensor_shape)
tf_out = enc(inp)
assert tf_out.shape[:ndim + 1] == (tensor_shape[:-1] +
(enc.filterbank.n_feats_out, ))
out = dec(tf_out)
assert out.shape[:-1] == inp.shape[:-1] # Time axis can differ
def test_perfect_resyn_window(fb_config, analysis_window_name):
""" Unit test perfect reconstruction """
kernel_size = fb_config["kernel_size"]
window = get_window(analysis_window_name, kernel_size)
enc = Encoder(STFTFB(**fb_config, window=window))
# Compute window for perfect resynthesis
synthesis_window = perfect_synthesis_window(enc.filterbank.window,
enc.stride)
dec = Decoder(STFTFB(**fb_config, window=synthesis_window))
inp_wav = torch.ones(1, 1, 32000)
out_wav = dec(enc(inp_wav))[:, :, kernel_size:-kernel_size]
inp_test = inp_wav[:, :, kernel_size:-kernel_size]
testing.assert_allclose(inp_test, out_wav)
def test_misi(fb_config, feed_istft, feed_angle):
stft = Encoder(STFTFB(**fb_config))
istft = None if not feed_istft else Decoder(STFTFB(**fb_config))
n_src = 3
# Create mixture
wav = torch.randn(2, 1, 8000)
spec = stft(wav).unsqueeze(1)
# Create n_src masks on mixture spec and apply them
shape = list(spec.shape)
shape[1] *= n_src
tf_mask = torch.sigmoid(torch.randn(*shape))
masked_specs = spec * tf_mask
# Separate mag and angle.
mag = transforms.take_mag(masked_specs, -2)
angles = None if not feed_angle else transforms.angle(masked_specs, -2)
est_wavs = misi(wav, mag, stft, angles=angles, istft_dec=istft, n_iter=2)
# We actually don't know the last dim because ISTFT(STFT()) cuts the end
assert est_wavs.shape[:-1] == (2, n_src)
def __init__(self,
n_filters=None,
windows_size=None,
hops_size=None,
alpha=1.0):
super().__init__()
if windows_size is None:
windows_size = [2048, 1024, 512, 256, 128, 64, 32]
if n_filters is None:
n_filters = [2048, 1024, 512, 256, 128, 64, 32]
if hops_size is None:
hops_size = [1024, 512, 256, 128, 64, 32, 16]
self.windows_size = windows_size
self.n_filters = n_filters
self.hops_size = hops_size
self.alpha = alpha
self.encoders = nn.ModuleList(
Encoder(STFTFB(n_filters[i], windows_size[i], hops_size[i]))
for i in range(len(self.n_filters)))
def test_fb_def_and_forward_lowdim(fb_class, fb_config):
""" Test filterbank definition and encoder/decoder forward."""
# Definition
enc = Encoder(fb_class(**fb_config))
dec = Decoder(fb_class(**fb_config))
# Forward
inp = torch.randn(1, 1, 16000)
tf_out = enc(inp)
# Assert for 2D inputs
with pytest.warns(UserWarning):
# STFT(2D) gives 3D and iSTFT(3D) gives 3D. UserWarning about that.
assert_allclose(enc(inp), enc(inp[0]))
# Assert for 1D inputs
assert_allclose(enc(inp)[0], enc(inp[0, 0]))
out = dec(tf_out)
# Assert for 4D inputs
tf_out_4d = tf_out.repeat(1, 2, 1, 1)
out_4d = dec(tf_out_4d)
assert_allclose(out, out_4d[:, 0])
# Asser for 2D inputs
assert_allclose(out[0, 0], dec(tf_out[0]))
assert tf_out.shape[1] == enc.filterbank.n_feats_out