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@ -127,7 +127,8 @@ class BaseTransform:
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# masked_batch = {k: v[mask] for k, v in flatten(batch).items()}
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# masked_batch = {k: v[mask] for k, v in flatten(batch).items()}
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masked_batch = {}
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masked_batch = {}
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for k, v in flatten(batch).items():
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for k, v in flatten(batch).items():
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if 0 == mask.dim() and 0 == v.dim():
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# `v` may be `Tensor` or `AudioSignal`
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if 0 == len(v.shape) and 0 == mask.dim():
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if mask: # 0d 的 True
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if mask: # 0d 的 True
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masked_batch[k] = v[None]
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masked_batch[k] = v[None]
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else:
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else:
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@ -998,64 +999,63 @@ class VolumeNorm(BaseTransform):
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return signal.normalize(db)
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return signal.normalize(db)
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# class GlobalVolumeNorm(BaseTransform):
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class GlobalVolumeNorm(BaseTransform):
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# """Similar to :py:func:`audiotools.data.transforms.VolumeNorm`, this
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"""Similar to :py:func:`audiotools.data.transforms.VolumeNorm`, this
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# transform also normalizes the volume of a signal, but it uses
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transform also normalizes the volume of a signal, but it uses
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# the volume of the entire audio file the loaded excerpt comes from,
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the volume of the entire audio file the loaded excerpt comes from,
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# rather than the volume of just the excerpt. The volume of the
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rather than the volume of just the excerpt. The volume of the
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# entire audio file is expected in ``signal.metadata["loudness"]``.
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entire audio file is expected in ``signal.metadata["loudness"]``.
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# If loading audio from a CSV generated by :py:func:`audiotools.data.preprocess.create_csv`
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If loading audio from a CSV generated by :py:func:`audiotools.data.preprocess.create_csv`
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# with ``loudness = True``, like the following:
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with ``loudness = True``, like the following:
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# .. csv-table::
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.. csv-table::
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# :header: path,loudness
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:header: path,loudness
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# daps/produced/f1_script1_produced.wav,-16.299999237060547
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daps/produced/f1_script1_produced.wav,-16.299999237060547
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# daps/produced/f1_script2_produced.wav,-16.600000381469727
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daps/produced/f1_script2_produced.wav,-16.600000381469727
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# daps/produced/f1_script3_produced.wav,-17.299999237060547
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daps/produced/f1_script3_produced.wav,-17.299999237060547
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# daps/produced/f1_script4_produced.wav,-16.100000381469727
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daps/produced/f1_script4_produced.wav,-16.100000381469727
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# daps/produced/f1_script5_produced.wav,-16.700000762939453
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daps/produced/f1_script5_produced.wav,-16.700000762939453
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# daps/produced/f3_script1_produced.wav,-16.5
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daps/produced/f3_script1_produced.wav,-16.5
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# The ``AudioLoader`` will automatically load the loudness column into
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The ``AudioLoader`` will automatically load the loudness column into
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# the metadata of the signal.
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the metadata of the signal.
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# Uses :py:func:`audiotools.core.effects.EffectMixin.volume_change`.
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Uses :py:func:`audiotools.core.effects.EffectMixin.volume_change`.
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# Parameters
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Parameters
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# ----------
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----------
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# db : tuple, optional
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db : tuple, optional
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# dB to normalize signal to, by default ("const", -24)
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dB to normalize signal to, by default ("const", -24)
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# name : str, optional
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name : str, optional
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# Name of this transform, used to identify it in the dictionary
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Name of this transform, used to identify it in the dictionary
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# produced by ``self.instantiate``, by default None
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produced by ``self.instantiate``, by default None
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# prob : float, optional
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prob : float, optional
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# Probability of applying this transform, by default 1.0
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Probability of applying this transform, by default 1.0
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# """
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"""
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# def __init__(
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def __init__(
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# self,
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self,
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# db: tuple = ("const", -24),
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db: tuple=("const", -24),
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# name: str = None,
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name: str=None,
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# prob: float = 1.0,
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prob: float=1.0, ):
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# ):
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super().__init__(name=name, prob=prob)
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# super().__init__(name=name, prob=prob)
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# self.db = db
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self.db = db
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# def _instantiate(self, state: RandomState, signal: AudioSignal):
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def _instantiate(self, state: RandomState, signal: AudioSignal):
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# if "loudness" not in signal.metadata:
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if "loudness" not in signal.metadata:
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# db_change = 0.0
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db_change = 0.0
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# elif float(signal.metadata["loudness"]) == float("-inf"):
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elif float(signal.metadata["loudness"]) == float("-inf"):
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# db_change = 0.0
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db_change = 0.0
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# else:
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else:
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# db = util.sample_from_dist(self.db, state)
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db = util.sample_from_dist(self.db, state)
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# db_change = db - float(signal.metadata["loudness"])
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db_change = db - float(signal.metadata["loudness"])
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# return {"db": db_change}
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return {"db": db_change}
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# def _transform(self, signal, db):
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def _transform(self, signal, db):
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# return signal.volume_change(db)
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return signal.volume_change(db)
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class Silence(BaseTransform):
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class Silence(BaseTransform):
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@ -1266,94 +1266,95 @@ class HighPass(BaseTransform):
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# def _transform(self, signal, corruption):
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# def _transform(self, signal, corruption):
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# return signal.shift_phase(shift=corruption)
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# return signal.shift_phase(shift=corruption)
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# class FrequencyMask(SpectralTransform):
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# """Masks a band of frequencies at a center frequency
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# from the audio.
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# Uses :py:func:`audiotools.core.dsp.DSPMixin.mask_frequencies`.
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class FrequencyMask(SpectralTransform):
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"""Masks a band of frequencies at a center frequency
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from the audio.
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# Parameters
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Uses :py:func:`audiotools.core.dsp.DSPMixin.mask_frequencies`.
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# ----------
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# f_center : tuple, optional
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# Center frequency between 0.0 and 1.0 (Nyquist), by default ("uniform", 0.0, 1.0)
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# f_width : tuple, optional
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# Width of zero'd out band, by default ("const", 0.1)
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# name : str, optional
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# Name of this transform, used to identify it in the dictionary
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# produced by ``self.instantiate``, by default None
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# prob : float, optional
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# Probability of applying this transform, by default 1.0
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# """
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# def __init__(
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Parameters
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# self,
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----------
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# f_center: tuple = ("uniform", 0.0, 1.0),
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f_center : tuple, optional
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# f_width: tuple = ("const", 0.1),
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Center frequency between 0.0 and 1.0 (Nyquist), by default ("uniform", 0.0, 1.0)
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# name: str = None,
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f_width : tuple, optional
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# prob: float = 1,
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Width of zero'd out band, by default ("const", 0.1)
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# ):
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name : str, optional
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|
# super().__init__(name=name, prob=prob)
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|
Name of this transform, used to identify it in the dictionary
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# self.f_center = f_center
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|
produced by ``self.instantiate``, by default None
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# self.f_width = f_width
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prob : float, optional
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|
Probability of applying this transform, by default 1.0
|
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"""
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|
|
# def _instantiate(self, state: RandomState, signal: AudioSignal):
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|
def __init__(
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|
# f_center = util.sample_from_dist(self.f_center, state)
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self,
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|
# f_width = util.sample_from_dist(self.f_width, state)
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|
f_center: tuple=("uniform", 0.0, 1.0),
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f_width: tuple=("const", 0.1),
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name: str=None,
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prob: float=1, ):
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super().__init__(name=name, prob=prob)
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self.f_center = f_center
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self.f_width = f_width
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# fmin = max(f_center - (f_width / 2), 0.0)
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|
def _instantiate(self, state: RandomState, signal: AudioSignal):
|
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|
|
# fmax = min(f_center + (f_width / 2), 1.0)
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|
f_center = util.sample_from_dist(self.f_center, state)
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f_width = util.sample_from_dist(self.f_width, state)
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# fmin_hz = (signal.sample_rate / 2) * fmin
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fmin = max(f_center - (f_width / 2), 0.0)
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|
# fmax_hz = (signal.sample_rate / 2) * fmax
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|
fmax = min(f_center + (f_width / 2), 1.0)
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# return {"fmin_hz": fmin_hz, "fmax_hz": fmax_hz}
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|
fmin_hz = (signal.sample_rate / 2) * fmin
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|
fmax_hz = (signal.sample_rate / 2) * fmax
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# def _transform(self, signal, fmin_hz: float, fmax_hz: float):
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return {"fmin_hz": fmin_hz, "fmax_hz": fmax_hz}
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# return signal.mask_frequencies(fmin_hz=fmin_hz, fmax_hz=fmax_hz)
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# class TimeMask(SpectralTransform):
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def _transform(self, signal, fmin_hz: float, fmax_hz: float):
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|
# """Masks out contiguous time-steps from signal.
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|
return signal.mask_frequencies(fmin_hz=fmin_hz, fmax_hz=fmax_hz)
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# Uses :py:func:`audiotools.core.dsp.DSPMixin.mask_timesteps`.
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# Parameters
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|
class TimeMask(SpectralTransform):
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|
# ----------
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|
"""Masks out contiguous time-steps from signal.
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|
# t_center : tuple, optional
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|
# Center time in terms of 0.0 and 1.0 (duration of signal),
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# by default ("uniform", 0.0, 1.0)
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# t_width : tuple, optional
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# Width of dropped out portion, by default ("const", 0.025)
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|
# name : str, optional
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# Name of this transform, used to identify it in the dictionary
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# produced by ``self.instantiate``, by default None
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# prob : float, optional
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# Probability of applying this transform, by default 1.0
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# """
|
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|
# def __init__(
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|
Uses :py:func:`audiotools.core.dsp.DSPMixin.mask_timesteps`.
|
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|
# self,
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# t_center: tuple = ("uniform", 0.0, 1.0),
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# t_width: tuple = ("const", 0.025),
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# name: str = None,
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# prob: float = 1,
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# ):
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# super().__init__(name=name, prob=prob)
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# self.t_center = t_center
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# self.t_width = t_width
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|
# def _instantiate(self, state: RandomState, signal: AudioSignal):
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|
|
Parameters
|
|
|
|
# t_center = util.sample_from_dist(self.t_center, state)
|
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|
|
----------
|
|
|
|
# t_width = util.sample_from_dist(self.t_width, state)
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|
|
t_center : tuple, optional
|
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|
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|
Center time in terms of 0.0 and 1.0 (duration of signal),
|
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|
by default ("uniform", 0.0, 1.0)
|
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|
|
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|
t_width : tuple, optional
|
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|
|
|
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|
Width of dropped out portion, by default ("const", 0.025)
|
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|
|
|
|
|
|
name : str, optional
|
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|
|
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|
|
Name of this transform, used to identify it in the dictionary
|
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|
|
|
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|
|
produced by ``self.instantiate``, by default None
|
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|
|
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|
|
prob : float, optional
|
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|
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|
|
|
Probability of applying this transform, by default 1.0
|
|
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|
|
|
|
|
"""
|
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|
|
def __init__(
|
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|
|
self,
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|
|
|
|
|
|
t_center: tuple=("uniform", 0.0, 1.0),
|
|
|
|
|
|
|
|
t_width: tuple=("const", 0.025),
|
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|
|
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|
|
name: str=None,
|
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|
|
|
|
|
|
prob: float=1, ):
|
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|
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|
|
|
|
super().__init__(name=name, prob=prob)
|
|
|
|
|
|
|
|
self.t_center = t_center
|
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|
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|
|
|
|
self.t_width = t_width
|
|
|
|
|
|
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|
|
|
|
# tmin = max(t_center - (t_width / 2), 0.0)
|
|
|
|
def _instantiate(self, state: RandomState, signal: AudioSignal):
|
|
|
|
# tmax = min(t_center + (t_width / 2), 1.0)
|
|
|
|
t_center = util.sample_from_dist(self.t_center, state)
|
|
|
|
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|
|
|
t_width = util.sample_from_dist(self.t_width, state)
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|
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# tmin_s = signal.signal_duration * tmin
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tmin = max(t_center - (t_width / 2), 0.0)
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# tmax_s = signal.signal_duration * tmax
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tmax = min(t_center + (t_width / 2), 1.0)
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# return {"tmin_s": tmin_s, "tmax_s": tmax_s}
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tmin_s = signal.signal_duration * tmin
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tmax_s = signal.signal_duration * tmax
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return {"tmin_s": tmin_s, "tmax_s": tmax_s}
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def _transform(self, signal, tmin_s: float, tmax_s: float):
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return signal.mask_timesteps(tmin_s=tmin_s, tmax_s=tmax_s)
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# def _transform(self, signal, tmin_s: float, tmax_s: float):
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# return signal.mask_timesteps(tmin_s=tmin_s, tmax_s=tmax_s)
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# class MaskLowMagnitudes(SpectralTransform):
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# class MaskLowMagnitudes(SpectralTransform):
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# """Masks low magnitude regions out of signal.
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# """Masks low magnitude regions out of signal.
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@ -1387,55 +1388,55 @@ class HighPass(BaseTransform):
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# def _transform(self, signal, db_cutoff: float):
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# def _transform(self, signal, db_cutoff: float):
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# return signal.mask_low_magnitudes(db_cutoff)
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# return signal.mask_low_magnitudes(db_cutoff)
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# class Smoothing(BaseTransform):
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# """Convolves the signal with a smoothing window.
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# Uses :py:func:`audiotools.core.effects.EffectMixin.convolve`.
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class Smoothing(BaseTransform):
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"""Convolves the signal with a smoothing window.
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# Parameters
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Uses :py:func:`audiotools.core.effects.EffectMixin.convolve`.
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# ----------
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# window_type : tuple, optional
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# Type of window to use, by default ("const", "average")
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# window_length : tuple, optional
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# Length of smoothing window, by
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# default ("choice", [8, 16, 32, 64, 128, 256, 512])
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# name : str, optional
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# Name of this transform, used to identify it in the dictionary
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# produced by ``self.instantiate``, by default None
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# prob : float, optional
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# Probability of applying this transform, by default 1.0
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# """
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# def __init__(
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Parameters
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# self,
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----------
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# window_type: tuple = ("const", "average"),
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window_type : tuple, optional
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# window_length: tuple = ("choice", [8, 16, 32, 64, 128, 256, 512]),
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Type of window to use, by default ("const", "average")
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# name: str = None,
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window_length : tuple, optional
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# prob: float = 1,
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Length of smoothing window, by
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# ):
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default ("choice", [8, 16, 32, 64, 128, 256, 512])
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# super().__init__(name=name, prob=prob)
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name : str, optional
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# self.window_type = window_type
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Name of this transform, used to identify it in the dictionary
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# self.window_length = window_length
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produced by ``self.instantiate``, by default None
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prob : float, optional
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Probability of applying this transform, by default 1.0
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"""
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# def _instantiate(self, state: RandomState, signal: AudioSignal = None):
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def __init__(
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# window_type = util.sample_from_dist(self.window_type, state)
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self,
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# window_length = util.sample_from_dist(self.window_length, state)
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window_type: tuple=("const", "average"),
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# window = signal.get_window(
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window_length: tuple=("choice", [8, 16, 32, 64, 128, 256, 512]),
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# window_type=window_type, window_length=window_length, device="cpu"
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name: str=None,
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# )
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prob: float=1, ):
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# return {"window": AudioSignal(window, signal.sample_rate)}
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super().__init__(name=name, prob=prob)
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self.window_type = window_type
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self.window_length = window_length
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def _instantiate(self, state: RandomState, signal: AudioSignal=None):
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window_type = util.sample_from_dist(self.window_type, state)
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window_length = util.sample_from_dist(self.window_length, state)
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window = signal.get_window(
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window_type=window_type, window_length=window_length, device="cpu")
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return {"window": AudioSignal(window, signal.sample_rate)}
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# def _transform(self, signal, window):
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def _transform(self, signal, window):
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# sscale = signal.audio_data.abs().max(dim=-1, keepdim=True).values
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sscale = signal.audio_data.abs().max(axis=-1, keepdim=True)
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# sscale[sscale == 0.0] = 1.0
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sscale[sscale == 0.0] = 1.0
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# out = signal.convolve(window)
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out = signal.convolve(window)
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# oscale = out.audio_data.abs().max(dim=-1, keepdim=True).values
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oscale = out.audio_data.abs().max(axis=-1, keepdim=True)
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# oscale[oscale == 0.0] = 1.0
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oscale[oscale == 0.0] = 1.0
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out = out * (sscale / oscale)
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return out
|
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|
# out = out * (sscale / oscale)
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|
# return out
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|
# class TimeNoise(TimeMask):
|
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|
|
# class TimeNoise(TimeMask):
|
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|
|
# """Similar to :py:func:`audiotools.data.transforms.TimeMask`, but
|
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|
|
# """Similar to :py:func:`audiotools.data.transforms.TimeMask`, but
|
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|
|
@ -1478,45 +1479,51 @@ class HighPass(BaseTransform):
|
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|
|
# signal.phase = phase
|
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|
|
# signal.phase = phase
|
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|
|
# return signal
|
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|
# return signal
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|
|
# class FrequencyNoise(FrequencyMask):
|
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|
|
# """Similar to :py:func:`audiotools.data.transforms.FrequencyMask`, but
|
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|
|
# replaces with noise instead of zeros.
|
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|
|
# Parameters
|
|
|
|
class FrequencyNoise(FrequencyMask):
|
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|
|
# ----------
|
|
|
|
"""Similar to :py:func:`audiotools.data.transforms.FrequencyMask`, but
|
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|
|
# f_center : tuple, optional
|
|
|
|
replaces with noise instead of zeros.
|
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|
|
# Center frequency between 0.0 and 1.0 (Nyquist), by default ("uniform", 0.0, 1.0)
|
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|
|
# f_width : tuple, optional
|
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|
|
# Width of zero'd out band, by default ("const", 0.1)
|
|
|
|
|
|
|
|
# name : str, optional
|
|
|
|
|
|
|
|
# Name of this transform, used to identify it in the dictionary
|
|
|
|
|
|
|
|
# produced by ``self.instantiate``, by default None
|
|
|
|
|
|
|
|
# prob : float, optional
|
|
|
|
|
|
|
|
# Probability of applying this transform, by default 1.0
|
|
|
|
|
|
|
|
# """
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# def __init__(
|
|
|
|
Parameters
|
|
|
|
# self,
|
|
|
|
----------
|
|
|
|
# f_center: tuple = ("uniform", 0.0, 1.0),
|
|
|
|
f_center : tuple, optional
|
|
|
|
# f_width: tuple = ("const", 0.1),
|
|
|
|
Center frequency between 0.0 and 1.0 (Nyquist), by default ("uniform", 0.0, 1.0)
|
|
|
|
# name: str = None,
|
|
|
|
f_width : tuple, optional
|
|
|
|
# prob: float = 1,
|
|
|
|
Width of zero'd out band, by default ("const", 0.1)
|
|
|
|
# ):
|
|
|
|
name : str, optional
|
|
|
|
# super().__init__(f_center=f_center, f_width=f_width, name=name, prob=prob)
|
|
|
|
Name of this transform, used to identify it in the dictionary
|
|
|
|
|
|
|
|
produced by ``self.instantiate``, by default None
|
|
|
|
|
|
|
|
prob : float, optional
|
|
|
|
|
|
|
|
Probability of applying this transform, by default 1.0
|
|
|
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
|
|
# def _transform(self, signal, fmin_hz: float, fmax_hz: float):
|
|
|
|
def __init__(
|
|
|
|
# signal = signal.mask_frequencies(fmin_hz=fmin_hz, fmax_hz=fmax_hz)
|
|
|
|
self,
|
|
|
|
# mag, phase = signal.magnitude, signal.phase
|
|
|
|
f_center: tuple=("uniform", 0.0, 1.0),
|
|
|
|
|
|
|
|
f_width: tuple=("const", 0.1),
|
|
|
|
|
|
|
|
name: str=None,
|
|
|
|
|
|
|
|
prob: float=1, ):
|
|
|
|
|
|
|
|
super().__init__(
|
|
|
|
|
|
|
|
f_center=f_center, f_width=f_width, name=name, prob=prob)
|
|
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|
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|
|
|
|
|
|
# mag_r, phase_r = torch.randn_like(mag), torch.randn_like(phase)
|
|
|
|
def _transform(self, signal, fmin_hz: float, fmax_hz: float):
|
|
|
|
# mask = (mag == 0.0) * (phase == 0.0)
|
|
|
|
signal = signal.mask_frequencies(fmin_hz=fmin_hz, fmax_hz=fmax_hz)
|
|
|
|
|
|
|
|
mag, phase = signal.magnitude, signal.phase
|
|
|
|
|
|
|
|
|
|
|
|
# mag[mask] = mag_r[mask]
|
|
|
|
mag_r, phase_r = paddle.randn(
|
|
|
|
# phase[mask] = phase_r[mask]
|
|
|
|
shape=mag.shape, dtype=mag.dtype), paddle.randn(
|
|
|
|
|
|
|
|
shape=phase.shape, dtype=phase.dtype)
|
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|
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|
|
mask = (mag == 0.0) * (phase == 0.0)
|
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|
|
# mag[mask] = mag_r[mask]
|
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|
|
|
|
|
# phase[mask] = phase_r[mask]
|
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|
|
|
|
|
|
mag = paddle.where(mask, mag_r, mag)
|
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|
|
phase = paddle.where(mask, phase_r, phase)
|
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|
|
signal.magnitude = mag
|
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|
|
signal.phase = phase
|
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|
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|
|
return signal
|
|
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|
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|
|
|
|
|
|
|
# signal.magnitude = mag
|
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|
|
# signal.phase = phase
|
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|
|
|
|
# return signal
|
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|
# class SpectralDenoising(Equalizer):
|
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|
|
# class SpectralDenoising(Equalizer):
|
|
|
|
# """Applies denoising algorithm detailed in
|
|
|
|
# """Applies denoising algorithm detailed in
|
|
|
|
|
drryanhuang
9 months ago