def test_transform_to_separation_model(musdb_tracks):
track = musdb_tracks[10]
mix, sources = nussl.utils.musdb_track_to_audio_signals(track)
data = {
'mix': mix,
'sources': sources,
'metadata': {'labels': []}
}
msa = transforms.MagnitudeSpectrumApproximation()
tdl = transforms.ToSeparationModel()
assert tdl.__class__.__name__ in str(tdl)
com = transforms.Compose([msa, tdl])
data = com(data)
accepted_keys = ['mix_magnitude', 'source_magnitudes']
rejected_keys = ['mix', 'sources', 'metadata']
for a in accepted_keys:
assert a in data
for r in rejected_keys:
assert r not in data
for key in data:
assert torch.is_tensor(data[key])
assert data[key].shape[0] == mix.stft().shape[1]
assert data[key].shape[1] == mix.stft().shape[0]
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_cache(musdb_tracks):
track = musdb_tracks[10]
mix, sources = nussl.utils.musdb_track_to_audio_signals(track)
data = {
'mix': mix,
'sources': sources,
'metadata': {
'labels': sorted(list(sources.keys()))
},
'index': 0
}
with tempfile.TemporaryDirectory() as tmpdir:
tfm = transforms.Cache(os.path.join(tmpdir, 'cache'),
cache_size=2,
overwrite=True)
_data_a = tfm(data)
_info_a = tfm.info
tfm.overwrite = False
_data_b = tfm({'index': 0})
pytest.raises(TransformException, tfm, {})
pytest.raises(TransformException, tfm, {'index': 1})
for key in _data_a:
assert _data_a[key] == _data_b[key]
com = transforms.Compose([
transforms.MagnitudeSpectrumApproximation(),
transforms.ToSeparationModel(),
transforms.Cache(os.path.join(tmpdir, 'cache'), overwrite=True),
])
_data_a = com(data)
com.transforms[-1].overwrite = False
_data_b = com.transforms[-1]({'index': 0})
for key in _data_a:
if torch.is_tensor(_data_a[key]):
assert torch.allclose(_data_a[key], _data_b[key])
else:
assert _data_a[key] == _data_b[key]
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)
def test_dataset_base_with_caching(benchmark_audio, monkeypatch):
keys = [benchmark_audio[k] for k in benchmark_audio]
def dummy_get(self, folder):
return keys
monkeypatch.setattr(BaseDataset, 'get_items', dummy_get)
monkeypatch.setattr(BaseDataset, 'process_item',
dummy_process_item_by_audio)
with tempfile.TemporaryDirectory() as tmpdir:
tfm = transforms.Cache(os.path.join(tmpdir, 'cache'), overwrite=True)
_dataset = BaseDataset('test', transform=tfm, cache_populated=False)
assert tfm.cache_size == len(_dataset)
_data_a = _dataset[0]
_dataset.cache_populated = True
pytest.raises(transforms.TransformException, _dataset.__getitem__,
1) # haven't written to this yet!
assert len(_dataset.post_cache_transforms.transforms) == 1
_data_b = _dataset[0]
for key in _data_a:
assert _data_a[key] == _data_b[key]
_dataset.cache_populated = False
outputs_a = []
outputs_b = []
for i in range(len(_dataset)):
outputs_a.append(_dataset[i])
_dataset.cache_populated = True
for i in range(len(_dataset)):
outputs_b.append(_dataset[i])
for _data_a, _data_b in zip(outputs_a, outputs_b):
for key in _data_a:
assert _data_a[key] == _data_b[key]
with tempfile.TemporaryDirectory() as tmpdir:
tfm = transforms.Compose([
transforms.MagnitudeSpectrumApproximation(),
transforms.ToSeparationModel(),
transforms.Cache(os.path.join(tmpdir, 'cache'), overwrite=True),
])
_dataset = BaseDataset('test', transform=tfm, cache_populated=False)
assert tfm.transforms[-1].cache_size == len(_dataset)
_data_a = _dataset[0]
_dataset.cache_populated = True
pytest.raises(transforms.TransformException, _dataset.__getitem__,
1) # haven't written to this yet!
assert len(_dataset.post_cache_transforms.transforms) == 1
_data_b = _dataset[0]
for key in _data_a:
if torch.is_tensor(_data_a[key]):
assert torch.allclose(_data_a[key], _data_b[key])
else:
assert _data_a[key] == _data_b[key]
_dataset.cache_populated = False
outputs_a = []
outputs_b = []
for i in range(len(_dataset)):
outputs_a.append(_dataset[i])
_dataset.cache_populated = True
for i in range(len(_dataset)):
outputs_b.append(_dataset[i])
for _data_a, _data_b in zip(outputs_a, outputs_b):
for key in _data_a:
if torch.is_tensor(_data_a[key]):
assert torch.allclose(_data_a[key], _data_b[key])
else:
assert _data_a[key] == _data_b[key]
for L in [100, 400, 1000]:
with tempfile.TemporaryDirectory() as tmpdir:
tfm = transforms.Compose([
transforms.MagnitudeSpectrumApproximation(),
transforms.ToSeparationModel(),
transforms.Cache(os.path.join(tmpdir, 'cache'),
overwrite=True),
transforms.GetExcerpt(L)
])
_dataset = BaseDataset('test',
transform=tfm,
cache_populated=False)
assert tfm.transforms[-2].cache_size == len(_dataset)
assert len(_dataset.post_cache_transforms.transforms) == 2
for i in range(len(_dataset)):
_ = _dataset[i]
_dataset.cache_populated = True
outputs = []
for i in range(len(_dataset)):
outputs.append(_dataset[i])
for _output in outputs:
for key, val in _output.items():
if torch.is_tensor(val):
assert val.shape[0] == L