def test_transform_get_audio(mix_source_folder):
dataset = nussl.datasets.MixSourceFolder(mix_source_folder)
item = dataset[0]
index = 1
tfm = transforms.GetAudio()
assert isinstance(str(tfm), str)
pytest.raises(TransformException, tfm, {'sources': []})
ga_output = tfm(item)
assert np.allclose(
ga_output['mix_audio'], item['mix'].audio_data)
source_names = sorted(list(item['sources'].keys()))
for i, key in enumerate(source_names):
assert np.allclose(
ga_output['source_audio'][..., i], item['sources'][key].audio_data)
item.pop('sources')
item.pop('source_audio')
ga_output = tfm(item)
assert np.allclose(
ga_output['mix_audio'], item['mix'].audio_data)
def transform(stft_params: nussl.STFTParams,
sample_rate: int,
target_instrument,
only_audio_signal: bool,
mask_type: str = 'msa',
audio_only: bool = False):
"""
Builds transforms that get applied to
training and validation datasets.
Parameters
----------
stft_params : nussl.STFTParams
Parameters of STFT (see: signal).
sample_rate : int
Sample rate of audio signal (see: signal).
target_instrument : str
Which instrument to learn to separate out of
a mixture.
only_audio_signal : bool
Whether to return only the audio signals, no
tensors (useful for eval).
mask_type : str, optional
What type of masking to use. Either phase
sensitive spectrum approx. (psa) or
magnitude spectrum approx (msa), by default
'msa'.
audio_only : bool, optional
Whether or not to only apply GetAudio in
transform (don't compute STFTs).
"""
tfm = []
other_labels = [k for k in LABELS if k != target_instrument]
tfm.append(nussl_tfm.SumSources([other_labels]))
new_labels = [target_instrument] + tfm[-1].group_names
new_labels = sorted(new_labels)
if not only_audio_signal:
if not audio_only:
if mask_type == 'psa':
tfm.append(nussl_tfm.PhaseSensitiveSpectrumApproximation())
elif mask_type == 'msa':
tfm.append(nussl_tfm.MagnitudeSpectrumApproximation())
tfm.append(nussl_tfm.MagnitudeWeights())
tfm.append(nussl_tfm.GetAudio())
target_index = new_labels.index(target_instrument)
tfm.append(nussl_tfm.IndexSources('source_magnitudes', target_index))
tfm.append(nussl_tfm.ToSeparationModel())
return nussl_tfm.Compose(tfm), new_labels
def one_item(scaper_folder):
stft_params = nussl.STFTParams(window_length=512, hop_length=128)
tfms = transforms.Compose([
transforms.PhaseSensitiveSpectrumApproximation(),
transforms.GetAudio(),
transforms.ToSeparationModel()
])
dataset = nussl.datasets.Scaper(scaper_folder,
transform=tfms,
stft_params=stft_params)
i = np.random.randint(len(dataset))
data = dataset[i]
for k in data:
# fake a batch dimension
if torch.is_tensor(data[k]):
data[k] = data[k].unsqueeze(0)
yield data
def test_transform_get_excerpt(musdb_tracks):
track = musdb_tracks[10]
mix, sources = nussl.utils.musdb_track_to_audio_signals(track)
msa = transforms.MagnitudeSpectrumApproximation()
tdl = transforms.ToSeparationModel()
excerpt_lengths = [400, 1000, 2000]
for excerpt_length in excerpt_lengths:
data = {
'mix': mix,
'sources': sources,
'metadata': {'labels': []}
}
exc = transforms.GetExcerpt(excerpt_length=excerpt_length)
assert isinstance(str(exc), str)
com = transforms.Compose([msa, tdl, exc])
data = com(data)
for key in data:
assert torch.is_tensor(data[key])
assert data[key].shape[0] == excerpt_length
assert data[key].shape[1] == mix.stft().shape[0]
assert torch.mean((data['source_magnitudes'].sum(dim=-1) -
data['mix_magnitude']) ** 2).item() < 1e-5
data = {
'mix': mix,
'sources': sources,
'metadata': {'labels': []}
}
exc = transforms.GetExcerpt(excerpt_length=excerpt_length)
assert isinstance(str(exc), str)
com = transforms.Compose([msa, tdl])
data = com(data)
for key in data:
data[key] = data[key].cpu().data.numpy()
data = exc(data)
for key in data:
assert data[key].shape[0] == excerpt_length
assert data[key].shape[1] == mix.stft().shape[0]
assert np.mean((data['source_magnitudes'].sum(axis=-1) -
data['mix_magnitude']) ** 2) < 1e-5
data = {
'mix_magnitude': 'not an array or tensor'
}
pytest.raises(TransformException, exc, data)
excerpt_lengths = [1009, 16000, 612140]
ga = transforms.GetAudio()
for excerpt_length in excerpt_lengths:
data = {
'mix': sum(sources.values()),
'sources': sources,
'metadata': {'labels': []}
}
exc = transforms.GetExcerpt(
excerpt_length=excerpt_length,
tf_keys = ['mix_audio', 'source_audio'],
time_dim=1,
)
com = transforms.Compose([ga, tdl, exc])
data = com(data)
for key in data:
assert torch.is_tensor(data[key])
assert data[key].shape[1] == excerpt_length
assert torch.allclose(
data['source_audio'].sum(dim=-1), data['mix_audio'], atol=1e-3)
