""" Custom port of librosa trim code, to remove numba dependency. This allows us to use librosa.trim effect without the librosa or numba dependancy. All code below adapted from librosa open source github: """ import numpy as np import torch import torch.nn.functional as F import warnings def amplitude_to_db(S, ref=1.0, amin=1e-5, top_db=80.0): """Convert an amplitude spectrogram to dB-scaled spectrogram. This is equivalent to ``power_to_db(S**2)``, but is provided for convenience. Parameters ---------- S : np.ndarray input amplitude ref : scalar or callable If scalar, the amplitude ``abs(S)`` is scaled relative to ``ref``: ``20 * log10(S / ref)``. Zeros in the output correspond to positions where ``S == ref``. If callable, the reference value is computed as ``ref(S)``. amin : float > 0 [scalar] minimum threshold for ``S`` and ``ref`` top_db : float >= 0 [scalar] threshold the output at ``top_db`` below the peak: ``max(20 * log10(S)) - top_db`` Returns ------- S_db : np.ndarray ``S`` measured in dB See Also -------- power_to_db, db_to_amplitude Notes ----- This function caches at level 30. """ # S = np.asarray(S) S = torch.asarray(S) magnitude = S.abs() if callable(ref): # User supplied a function to calculate reference power ref_value = ref(magnitude) else: ref_value = torch.abs(ref) power = torch.square(magnitude, out=magnitude) return power_to_db(power, ref=ref_value ** 2, amin=amin ** 2, top_db=top_db) def _signal_to_frame_nonsilent( y, frame_length=2048, hop_length=512, top_db=60, ref=torch.max ): """Frame-wise non-silent indicator for audio input. This is a helper function for `trim` and `split`. Parameters ---------- y : np.ndarray, shape=(n,) or (2,n) Audio signal, mono or stereo frame_length : int > 0 The number of samples per frame hop_length : int > 0 The number of samples between frames top_db : number > 0 The threshold (in decibels) below reference to consider as silence ref : callable or float The reference power Returns ------- non_silent : np.ndarray, shape=(m,), dtype=bool Indicator of non-silent frames """ # Convert to mono if y.ndim > 1: y_mono = torch.mean(y, dim=0) else: y_mono = y # Compute the MSE for the signal mse = rms(y=y_mono, frame_length=frame_length, hop_length=hop_length) ** 2 return power_to_db(mse.squeeze(), ref=ref, top_db=None) > -top_db def trim(y, top_db=60, ref=torch.max, frame_length=2048, hop_length=512): """Trim leading and trailing silence from an audio signal. Parameters ---------- y : np.ndarray, shape=(n,) or (2,n) Audio signal, can be mono or stereo top_db : number > 0 The threshold (in decibels) below reference to consider as silence ref : number or callable The reference power. By default, it uses `np.max` and compares to the peak power in the signal. frame_length : int > 0 The number of samples per analysis frame hop_length : int > 0 The number of samples between analysis frames Returns ------- y_trimmed : np.ndarray, shape=(m,) or (2, m) The trimmed signal index : np.ndarray, shape=(2,) the interval of ``y`` corresponding to the non-silent region: ``y_trimmed = y[index[0]:index[1]]`` (for mono) or ``y_trimmed = y[:, index[0]:index[1]]`` (for stereo). Examples -------- >>> # Load some audio >>> y, sr = librosa.load(librosa.ex('choice')) >>> # Trim the beginning and ending silence >>> yt, index = librosa.effects.trim(y) >>> # Print the durations >>> print(librosa.get_duration(y), librosa.get_duration(yt)) 25.025986394557822 25.007891156462584 """ non_silent = _signal_to_frame_nonsilent( y, frame_length=frame_length, hop_length=hop_length, ref=ref, top_db=top_db ) # nonzero = np.flatnonzero(non_silent) nonzero = torch.nonzero(torch.ravel(non_silent)).squeeze()#[0] if nonzero.numel() > 0: # Compute the start and end positions # End position goes one frame past the last non-zero start = int(frames_to_samples(nonzero[0], hop_length)) end = min(y.shape[-1], int(frames_to_samples(nonzero[-1] + 1, hop_length))) else: # The signal only contains zeros start, end = 0, 0 # Build the mono/stereo index full_index = [slice(None)] * y.ndim full_index[-1] = slice(start, end) # print(non_silent) # print(non_silent.shape, nonzero.shape) return y[tuple(full_index)], torch.asarray([start, end]) def rms( y=None, S=None, frame_length=2048, hop_length=512, center=True, pad_mode="reflect" ): """Compute root-mean-square (RMS) value for each frame, either from the audio samples ``y`` or from a spectrogram ``S``. Computing the RMS value from audio samples is faster as it doesn't require a STFT calculation. However, using a spectrogram will give a more accurate representation of energy over time because its frames can be windowed, thus prefer using ``S`` if it's already available. Parameters ---------- y : np.ndarray [shape=(n,)] or None (optional) audio time series. Required if ``S`` is not input. S : np.ndarray [shape=(d, t)] or None (optional) spectrogram magnitude. Required if ``y`` is not input. frame_length : int > 0 [scalar] length of analysis frame (in samples) for energy calculation hop_length : int > 0 [scalar] hop length for STFT. See `librosa.stft` for details. center : bool If `True` and operating on time-domain input (``y``), pad the signal by ``frame_length//2`` on either side. If operating on spectrogram input, this has no effect. pad_mode : str Padding mode for centered analysis. See `numpy.pad` for valid values. Returns ------- rms : np.ndarray [shape=(1, t)] RMS value for each frame Examples -------- >>> y, sr = librosa.load(librosa.ex('trumpet')) >>> librosa.feature.rms(y=y) array([[1.248e-01, 1.259e-01, ..., 1.845e-05, 1.796e-05]], dtype=float32) Or from spectrogram input >>> S, phase = librosa.magphase(librosa.stft(y)) >>> rms = librosa.feature.rms(S=S) >>> import matplotlib.pyplot as plt >>> fig, ax = plt.subplots(nrows=2, sharex=True) >>> times = librosa.times_like(rms) >>> ax[0].semilogy(times, rms[0], label='RMS Energy') >>> ax[0].set(xticks=[]) >>> ax[0].legend() >>> ax[0].label_outer() >>> librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max), ... y_axis='log', x_axis='time', ax=ax[1]) >>> ax[1].set(title='log Power spectrogram') Use a STFT window of constant ones and no frame centering to get consistent results with the RMS computed from the audio samples ``y`` >>> S = librosa.magphase(librosa.stft(y, window=np.ones, center=False))[0] >>> librosa.feature.rms(S=S) >>> plt.show() """ if y is not None: if y.dim() > 1: y = torch.mean(y, dim=0) if center: y = F.pad(y[None, None], (int(frame_length//2), int(frame_length//2)), mode=pad_mode)[0, 0] # y = np.pad(y, int(frame_length // 2), mode=pad_mode) x = frame(y, frame_length=frame_length, hop_length=hop_length) # print(y.shape, x.shape, x) # Calculate power power = torch.mean(x.abs() ** 2, dim=0, keepdim=True) elif S is not None: # Check the frame length if S.shape[0] != frame_length // 2 + 1: raise AssertionError( "Since S.shape[0] is {}, " "frame_length is expected to be {} or {}; " "found {}".format( S.shape[0], S.shape[0] * 2 - 2, S.shape[0] * 2 - 1, frame_length ) ) # power spectrogram x = torch.abs(S) ** 2 # Adjust the DC and sr/2 component x[0] *= 0.5 if frame_length % 2 == 0: x[-1] *= 0.5 # Calculate power power = 2 * torch.sum(x, dim=0, keepdim=True) / frame_length ** 2 else: raise AssertionError("Either `y` or `S` must be input.") return torch.sqrt(power) def frame(x, frame_length, hop_length, axis=-1): """Slice a data array into (overlapping) frames. This implementation uses low-level stride manipulation to avoid making a copy of the data. The resulting frame representation is a new view of the same input data. However, if the input data is not contiguous in memory, a warning will be issued and the output will be a full copy, rather than a view of the input data. For example, a one-dimensional input ``x = [0, 1, 2, 3, 4, 5, 6]`` can be framed with frame length 3 and hop length 2 in two ways. The first (``axis=-1``), results in the array ``x_frames``:: [[0, 2, 4], [1, 3, 5], [2, 4, 6]] where each column ``x_frames[:, i]`` contains a contiguous slice of the input ``x[i * hop_length : i * hop_length + frame_length]``. The second way (``axis=0``) results in the array ``x_frames``:: [[0, 1, 2], [2, 3, 4], [4, 5, 6]] where each row ``x_frames[i]`` contains a contiguous slice of the input. This generalizes to higher dimensional inputs, as shown in the examples below. In general, the framing operation increments by 1 the number of dimensions, adding a new "frame axis" either to the end of the array (``axis=-1``) or the beginning of the array (``axis=0``). Parameters ---------- x : np.ndarray Array to frame frame_length : int > 0 [scalar] Length of the frame hop_length : int > 0 [scalar] Number of steps to advance between frames axis : 0 or -1 The axis along which to frame. If ``axis=-1`` (the default), then ``x`` is framed along its last dimension. ``x`` must be "F-contiguous" in this case. If ``axis=0``, then ``x`` is framed along its first dimension. ``x`` must be "C-contiguous" in this case. Returns ------- x_frames : np.ndarray [shape=(..., frame_length, N_FRAMES) or (N_FRAMES, frame_length, ...)] A framed view of ``x``, for example with ``axis=-1`` (framing on the last dimension):: x_frames[..., j] == x[..., j * hop_length : j * hop_length + frame_length] If ``axis=0`` (framing on the first dimension), then:: x_frames[j] = x[j * hop_length : j * hop_length + frame_length] Raises ------ ParameterError If ``x`` is not an `np.ndarray`. If ``x.shape[axis] < frame_length``, there is not enough data to fill one frame. If ``hop_length < 1``, frames cannot advance. If ``axis`` is not 0 or -1. Framing is only supported along the first or last axis. See Also -------- numpy.asfortranarray : Convert data to F-contiguous representation numpy.ascontiguousarray : Convert data to C-contiguous representation numpy.ndarray.flags : information about the memory layout of a numpy `ndarray`. Examples -------- Extract 2048-sample frames from monophonic signal with a hop of 64 samples per frame >>> y, sr = librosa.load(librosa.ex('trumpet')) >>> frames = librosa.util.frame(y, frame_length=2048, hop_length=64) >>> frames array([[-1.407e-03, -2.604e-02, ..., -1.795e-05, -8.108e-06], [-4.461e-04, -3.721e-02, ..., -1.573e-05, -1.652e-05], ..., [ 7.960e-02, -2.335e-01, ..., -6.815e-06, 1.266e-05], [ 9.568e-02, -1.252e-01, ..., 7.397e-06, -1.921e-05]], dtype=float32) >>> y.shape (117601,) >>> frames.shape (2048, 1806) Or frame along the first axis instead of the last: >>> frames = librosa.util.frame(y, frame_length=2048, hop_length=64, axis=0) >>> frames.shape (1806, 2048) Frame a stereo signal: >>> y, sr = librosa.load(librosa.ex('trumpet', hq=True), mono=False) >>> y.shape (2, 117601) >>> frames = librosa.util.frame(y, frame_length=2048, hop_length=64) (2, 2048, 1806) Carve an STFT into fixed-length patches of 32 frames with 50% overlap >>> y, sr = librosa.load(librosa.ex('trumpet')) >>> S = np.abs(librosa.stft(y)) >>> S.shape (1025, 230) >>> S_patch = librosa.util.frame(S, frame_length=32, hop_length=16) >>> S_patch.shape (1025, 32, 13) >>> # The first patch contains the first 32 frames of S >>> np.allclose(S_patch[:, :, 0], S[:, :32]) True >>> # The second patch contains frames 16 to 16+32=48, and so on >>> np.allclose(S_patch[:, :, 1], S[:, 16:48]) True """ # if not isinstance(x, np.ndarray): # raise AssertionError( # "Input must be of type numpy.ndarray, " "given type(x)={}".format(type(x)) # ) x: torch.Tensor = x if x.shape[axis] < frame_length: raise AssertionError( "Input is too short (n={:d})" " for frame_length={:d}".format(x.shape[axis], frame_length) ) if hop_length < 1: raise AssertionError("Invalid hop_length: {:d}".format(hop_length)) if axis == -1 and not x.is_contiguous(): warnings.warn( "librosa.util.frame called with axis={} " "on a non-contiguous input. This will result in a copy.".format(axis) ) x = x.contiguous() elif axis == 0 and not x.is_contiguous(): warnings.warn( "librosa.util.frame called with axis={} " "on a non-contiguous input. This will result in a copy.".format(axis) ) x = x.contiguous() n_frames = 1 + (x.shape[axis] - frame_length) // hop_length strides = torch.asarray(x.numpy().strides) # print(strides, x) new_stride = torch.prod(strides[strides > 0] // x.itemsize) * x.itemsize if axis == -1: shape = list(x.shape)[:-1] + [frame_length, n_frames] strides = list(strides) + [hop_length * new_stride] elif axis == 0: shape = [n_frames, frame_length] + list(x.shape)[1:] strides = [hop_length * new_stride] + list(strides) else: raise AssertionError("Frame axis={} must be either 0 or -1".format(axis)) return torch.from_numpy(as_strided(x, shape=shape, strides=strides)) # return x.as_strided(size=shape, stride=strides) class DummyArray: """Dummy object that just exists to hang __array_interface__ dictionaries and possibly keep alive a reference to a base array. """ def __init__(self, interface, base=None): self.__array_interface__ = interface self.base = base def as_strided(x, shape=None, strides=None, subok=False, writeable=True): """ Create a view into the array with the given shape and strides. .. warning:: This function has to be used with extreme care, see notes. Parameters ---------- x : ndarray Array to create a new. shape : sequence of int, optional The shape of the new array. Defaults to ``x.shape``. strides : sequence of int, optional The strides of the new array. Defaults to ``x.strides``. subok : bool, optional .. versionadded:: 1.10 If True, subclasses are preserved. writeable : bool, optional .. versionadded:: 1.12 If set to False, the returned array will always be readonly. Otherwise it will be writable if the original array was. It is advisable to set this to False if possible (see Notes). Returns ------- view : ndarray See also -------- broadcast_to : broadcast an array to a given shape. reshape : reshape an array. lib.stride_tricks.sliding_window_view : userfriendly and safe function for the creation of sliding window views. Notes ----- ``as_strided`` creates a view into the array given the exact strides and shape. This means it manipulates the internal data structure of ndarray and, if done incorrectly, the array elements can point to invalid memory and can corrupt results or crash your program. It is advisable to always use the original ``x.strides`` when calculating new strides to avoid reliance on a contiguous memory layout. Furthermore, arrays created with this function often contain self overlapping memory, so that two elements are identical. Vectorized write operations on such arrays will typically be unpredictable. They may even give different results for small, large, or transposed arrays. Since writing to these arrays has to be tested and done with great care, you may want to use ``writeable=False`` to avoid accidental write operations. For these reasons it is advisable to avoid ``as_strided`` when possible. """ # first convert input to array, possibly keeping subclass x = np.array(x, copy=False, subok=subok) interface = dict(x.__array_interface__) if shape is not None: interface['shape'] = tuple(shape) if strides is not None: interface['strides'] = tuple(strides) array = np.asarray(DummyArray(interface, base=x)) # The route via `__interface__` does not preserve structured # dtypes. Since dtype should remain unchanged, we set it explicitly. array.dtype = x.dtype view = _maybe_view_as_subclass(x, array) if view.flags.writeable and not writeable: view.flags.writeable = False return view def _maybe_view_as_subclass(original_array, new_array): if type(original_array) is not type(new_array): # if input was an ndarray subclass and subclasses were OK, # then view the result as that subclass. new_array = new_array.view(type=type(original_array)) # Since we have done something akin to a view from original_array, we # should let the subclass finalize (if it has it implemented, i.e., is # not None). if new_array.__array_finalize__: new_array.__array_finalize__(original_array) return new_array def power_to_db(S, ref=1.0, amin=1e-10, top_db=80.0): """Convert a power spectrogram (amplitude squared) to decibel (dB) units This computes the scaling ``10 * log10(S / ref)`` in a numerically stable way. Parameters ---------- S : np.ndarray input power ref : scalar or callable If scalar, the amplitude ``abs(S)`` is scaled relative to ``ref``:: 10 * log10(S / ref) Zeros in the output correspond to positions where ``S == ref``. If callable, the reference value is computed as ``ref(S)``. amin : float > 0 [scalar] minimum threshold for ``abs(S)`` and ``ref`` top_db : float >= 0 [scalar] threshold the output at ``top_db`` below the peak: ``max(10 * log10(S)) - top_db`` Returns ------- S_db : np.ndarray ``S_db ~= 10 * log10(S) - 10 * log10(ref)`` See Also -------- perceptual_weighting db_to_power amplitude_to_db db_to_amplitude Notes ----- This function caches at level 30. Examples -------- Get a power spectrogram from a waveform ``y`` >>> y, sr = librosa.load(librosa.ex('trumpet')) >>> S = np.abs(librosa.stft(y)) >>> librosa.power_to_db(S**2) array([[-41.809, -41.809, ..., -41.809, -41.809], [-41.809, -41.809, ..., -41.809, -41.809], ..., [-41.809, -41.809, ..., -41.809, -41.809], [-41.809, -41.809, ..., -41.809, -41.809]], dtype=float32) Compute dB relative to peak power >>> librosa.power_to_db(S**2, ref=np.max) array([[-80., -80., ..., -80., -80.], [-80., -80., ..., -80., -80.], ..., [-80., -80., ..., -80., -80.], [-80., -80., ..., -80., -80.]], dtype=float32) Or compare to median power >>> librosa.power_to_db(S**2, ref=np.median) array([[16.578, 16.578, ..., 16.578, 16.578], [16.578, 16.578, ..., 16.578, 16.578], ..., [16.578, 16.578, ..., 16.578, 16.578], [16.578, 16.578, ..., 16.578, 16.578]], dtype=float32) And plot the results >>> import matplotlib.pyplot as plt >>> fig, ax = plt.subplots(nrows=2, sharex=True, sharey=True) >>> imgpow = librosa.display.specshow(S**2, sr=sr, y_axis='log', x_axis='time', ... ax=ax[0]) >>> ax[0].set(title='Power spectrogram') >>> ax[0].label_outer() >>> imgdb = librosa.display.specshow(librosa.power_to_db(S**2, ref=np.max), ... sr=sr, y_axis='log', x_axis='time', ax=ax[1]) >>> ax[1].set(title='Log-Power spectrogram') >>> fig.colorbar(imgpow, ax=ax[0]) >>> fig.colorbar(imgdb, ax=ax[1], format="%+2.0f dB") """ S = torch.asarray(S) if amin <= 0: raise AssertionError("amin must be strictly positive") # if np.issubdtype(S.dtype, np.complexfloating): # warnings.warn( # "power_to_db was called on complex input so phase " # "information will be discarded. To suppress this warning, " # "call power_to_db(np.abs(D)**2) instead." # ) # magnitude = np.abs(S) # else: magnitude = S if callable(ref): # User supplied a function to calculate reference power ref_value = ref(magnitude) else: ref_value = torch.abs(ref) log_spec = 10.0 * torch.log10(torch.maximum(torch.tensor(amin), magnitude)) log_spec -= 10.0 * torch.log10(torch.maximum(torch.tensor(amin), ref_value)) if top_db is not None: if top_db < 0: raise AssertionError("top_db must be non-negative") log_spec = torch.maximum(log_spec, log_spec.max() - top_db) return log_spec def frames_to_samples(frames, hop_length=512, n_fft=None): """Converts frame indices to audio sample indices. Parameters ---------- frames : number or np.ndarray [shape=(n,)] frame index or vector of frame indices hop_length : int > 0 [scalar] number of samples between successive frames n_fft : None or int > 0 [scalar] Optional: length of the FFT window. If given, time conversion will include an offset of ``n_fft // 2`` to counteract windowing effects when using a non-centered STFT. Returns ------- times : number or np.ndarray time (in samples) of each given frame number:: times[i] = frames[i] * hop_length See Also -------- frames_to_time : convert frame indices to time values samples_to_frames : convert sample indices to frame indices Examples -------- >>> y, sr = librosa.load(librosa.ex('choice')) >>> tempo, beats = librosa.beat.beat_track(y, sr=sr) >>> beat_samples = librosa.frames_to_samples(beats) """ offset = 0 if n_fft is not None: offset = int(n_fft // 2) return (torch.asarray(frames) * hop_length + offset).to(torch.int)