kernel source

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	"""resample-poly: polyphase rational resampling for audio.

	Each filter phase stores only its own support rather than being padded to a
	common width, so an output sample multiplies through its taps and nothing else.
	The filter matches torchaudio's construction, so results agree to floating-point
	rounding and this is a drop-in replacement.

	High-level API:
	Resampler(orig_freq, new_freq, ...) plan holding the polyphase bank
	resample(x, orig_freq, new_freq) one-shot convenience
	"""
	import math
	from typing import Optional

	import torch

	from ._ops import ops


	def sinc_kernel(orig_freq: int, new_freq: int, lowpass_filter_width: int = 6,
	rolloff: float = 0.99, method: str = "sinc_interp_hann",
	beta: Optional[float] = None):
	"""Windowed-sinc polyphase bank, matching torchaudio's construction.

	Returns (kernels [L, W], width), with frequencies already reduced by their
	greatest common divisor.
	"""
	if orig_freq <= 0 or new_freq <= 0:
	raise ValueError("frequencies must be positive")
	if lowpass_filter_width <= 0:
	raise ValueError("lowpass_filter_width must be positive")
	g = math.gcd(int(orig_freq), int(new_freq))
	M = int(orig_freq) // g # input samples per block
	L = int(new_freq) // g # output samples per block

	base_freq = min(M, L) * rolloff
	width = math.ceil(lowpass_filter_width * M / base_freq)

	idx = torch.arange(-width, width + M, dtype=torch.float64) / M
	t = (torch.arange(0, -L, -1, dtype=torch.float64)[:, None] / L) + idx[None, :]
	t = t * base_freq
	t = t.clamp_(-lowpass_filter_width, lowpass_filter_width)

	if method == "sinc_interp_hann":
	window = torch.cos(t * math.pi / lowpass_filter_width / 2) ** 2
	elif method == "sinc_interp_kaiser":
	b = 14.769656459379492 if beta is None else float(beta)
	window = (torch.i0(b * torch.sqrt(
	1 - (t / lowpass_filter_width) ** 2).clamp(min=0)) / torch.i0(
	torch.tensor(b, dtype=torch.float64)))
	else:
	raise ValueError(f"unknown method {method!r}")

	tp = t * math.pi
	k = torch.where(tp == 0, torch.ones_like(tp), torch.sin(tp) / tp)
	k = k * window * (base_freq / M)
	return k.to(torch.float32), width, L, M


	class Resampler:
	"""Cached polyphase plan for a fixed rate pair."""

	def __init__(self, orig_freq: int, new_freq: int,
	lowpass_filter_width: int = 6, rolloff: float = 0.99,
	method: str = "sinc_interp_hann", beta: Optional[float] = None):
	kern, width, L, M = sinc_kernel(orig_freq, new_freq,
	lowpass_filter_width, rolloff,
	method, beta)
	self.orig_freq, self.new_freq = int(orig_freq), int(new_freq)
	self.L, self.M, self.width = L, M, width
	self.kernel = kern

	# Keep only each phase's support. Entries clamped past the filter half
	# width carry a window value of exactly zero, so the support is exact
	# rather than a threshold.
	starts, lens, offs, taps = [], [], [], []
	cursor = 0
	for i in range(L):
	row = kern[i]
	nz = torch.nonzero(row, as_tuple=False).flatten()
	if nz.numel() == 0:
	starts.append(-width); lens.append(0); offs.append(cursor)
	continue
	j0, j1 = int(nz[0]), int(nz[-1]) + 1
	starts.append(j0 - width)
	lens.append(j1 - j0)
	offs.append(cursor)
	taps.append(row[j0:j1].contiguous())
	cursor += j1 - j0
	self.start = torch.tensor(starts, dtype=torch.int32)
	self.len = torch.tensor(lens, dtype=torch.int32)
	self.off = torch.tensor(offs, dtype=torch.int64)
	self.taps = (torch.cat(taps) if taps else
	torch.zeros(0, dtype=torch.float32)).contiguous()

	# The fused path computes one dot product per output and reduces it.
	# That reduction is fixed cost per output, so it pays only when a phase
	# carries enough taps to amortize it. Measured against the conv1d bank,
	# the crossover sits between 12 and 17 taps per output: 16.7 taps wins
	# 1.5-1.8x, 12.1 loses 0.5x even while doing 13x fewer multiplies. Below
	# the threshold the bank formulation is used instead, so this is never
	# slower than the baseline it replaces.
	self.taps_per_output = float(self.taps.numel()) / float(L)
	self.fused = self.taps_per_output >= 16.0

	@property
	def density(self) -> float:
	"""Stored taps over the padded-kernel size the conv1d form would use."""
	return float(self.taps.numel()) / float(self.kernel.numel())

	def _bank(self, x: torch.Tensor) -> torch.Tensor:
	"""Padded-bank formulation, for rate pairs whose phases are too short
	for the fused path to pay for its reduction."""
	B, T = x.shape
	pad = torch.nn.functional.pad(x, (self.width, self.width + self.M))
	r = torch.nn.functional.conv1d(pad[:, None], self.kernel[:, None, :],
	stride=self.M)
	return r.transpose(1, 2).reshape(B, -1)[:, :self.out_len(T)]

	def out_len(self, n: int) -> int:
	return int(math.ceil(self.L * n / self.M))

	def __call__(self, x: torch.Tensor) -> torch.Tensor:
	"""x [T] or [B, T] -> resampled, matching torchaudio's output length."""
	squeeze = x.dim() == 1
	if squeeze:
	x = x.unsqueeze(0)
	if x.dim() != 2:
	raise ValueError("x must be [T] or [B, T]")
	x = x.to(torch.float32).contiguous()
	B, T = x.shape
	if not self.fused:
	y = self._bank(x)
	return y.squeeze(0) if squeeze else y
	out = torch.empty(B, self.out_len(T), dtype=torch.float32)
	ops.rp_resample(out, x, self.taps, self.start, self.len, self.off,
	self.L, self.M)
	return out.squeeze(0) if squeeze else out


	def resample(x: torch.Tensor, orig_freq: int, new_freq: int,
	**kw) -> torch.Tensor:
	"""One-shot resample. Prefer Resampler when converting repeatedly."""
	return Resampler(orig_freq, new_freq, **kw)(x)


	__all__ = ["Resampler", "resample", "sinc_kernel"]