python/FunASR-XL.git

			@@ -39,9 +39,7 @@
			if x.ndim == 3:
			x = x.reshape(shape_or[0] * shape_or[1], shape_or[2])
			else:
			x = x.reshape(
			shape_or[0] * shape_or[1], shape_or[3], shape_or[2]
			)
			x = x.reshape(shape_or[0] * shape_or[1], shape_or[3], shape_or[2])

			elif not self.skip_transpose:
			x = x.transpose(-1, 1)
			@@ -105,9 +103,7 @@
			x = x.unsqueeze(1)

			if self.padding == "same":
			x = self._manage_padding(
			x, self.kernel_size, self.dilation, self.stride
			)
			x = self._manage_padding(x, self.kernel_size, self.dilation, self.stride)

			elif self.padding == "causal":
			num_pad = (self.kernel_size - 1) * self.dilation
			@@ -117,10 +113,7 @@
			pass

			else:
			raise ValueError(
			"Padding must be 'same', 'valid' or 'causal'. Got "
			+ self.padding
			)
			raise ValueError("Padding must be 'same', 'valid' or 'causal'. Got " + self.padding)

			wx = self.conv(x)

			@@ -133,7 +126,11 @@
			return wx

			def _manage_padding(
			self, x, kernel_size: int, dilation: int, stride: int,
			self,
			x,
			kernel_size: int,
			dilation: int,
			stride: int,
			):
			# Detecting input shape
			L_in = x.shape[-1]
			@@ -147,8 +144,7 @@
			return x

			def _check_input_shape(self, shape):
			"""Checks the input shape and returns the number of input channels.
			"""
			"""Checks the input shape and returns the number of input channels."""

			if len(shape) == 2:
			self.unsqueeze = True
			@@ -158,15 +154,12 @@
			elif len(shape) == 3:
			in_channels = shape[2]
			else:
			raise ValueError(
			"conv1d expects 2d, 3d inputs. Got " + str(len(shape))
			)
			raise ValueError("conv1d expects 2d, 3d inputs. Got " + str(len(shape)))

			# Kernel size must be odd
			if self.kernel_size % 2 == 0:
			raise ValueError(
			"The field kernel size must be an odd number. Got %s."
			% (self.kernel_size)
			"The field kernel size must be an odd number. Got %s." % (self.kernel_size)
			)
			return in_channels

			@@ -200,9 +193,9 @@

			if max_len is None:
			max_len = length.max().long().item() # using arange to generate mask
			mask = torch.arange(
			max_len, device=length.device, dtype=length.dtype
			).expand(len(length), max_len) < length.unsqueeze(1)
			mask = torch.arange(max_len, device=length.device, dtype=length.dtype).expand(
			len(length), max_len
			) < length.unsqueeze(1)

			if dtype is None:
			dtype = length.dtype
			@@ -264,9 +257,7 @@
			torch.Size([8, 120, 64])
			"""

			def __init__(
			self, in_channels, out_channels, scale=8, kernel_size=3, dilation=1
			):
			def __init__(self, in_channels, out_channels, scale=8, kernel_size=3, dilation=1):
			super(Res2NetBlock, self).__init__()
			assert in_channels % scale == 0
			assert out_channels % scale == 0
			@@ -326,13 +317,9 @@
			def __init__(self, in_channels, se_channels, out_channels):
			super(SEBlock, self).__init__()

			self.conv1 = Conv1d(
			in_channels=in_channels, out_channels=se_channels, kernel_size=1
			)
			self.conv1 = Conv1d(in_channels=in_channels, out_channels=se_channels, kernel_size=1)
			self.relu = torch.nn.ReLU(inplace=True)
			self.conv2 = Conv1d(
			in_channels=se_channels, out_channels=out_channels, kernel_size=1
			)
			self.conv2 = Conv1d(in_channels=se_channels, out_channels=out_channels, kernel_size=1)
			self.sigmoid = torch.nn.Sigmoid()

			def forward(self, x, lengths=None):
			@@ -382,9 +369,7 @@
			else:
			self.tdnn = TDNNBlock(channels, attention_channels, 1, 1)
			self.tanh = nn.Tanh()
			self.conv = Conv1d(
			in_channels=attention_channels, out_channels=channels, kernel_size=1
			)
			self.conv = Conv1d(in_channels=attention_channels, out_channels=channels, kernel_size=1)

			def forward(self, x, lengths=None):
			"""Calculates mean and std for a batch (input tensor).
			@@ -398,9 +383,7 @@

			def _compute_statistics(x, m, dim=2, eps=self.eps):
			mean = (m * x).sum(dim)
			std = torch.sqrt(
			(m * (x - mean.unsqueeze(dim)).pow(2)).sum(dim).clamp(eps)
			)
			std = torch.sqrt((m * (x - mean.unsqueeze(dim)).pow(2)).sum(dim).clamp(eps))
			return mean, std

			if lengths is None:
			@@ -638,9 +621,12 @@
			for i in range(num_chunk):
			# B x C
			st, ed = i * self.window_shift, i * self.window_shift + self.window_size
			x = self.asp(x[:, :, st: ed],
			lengths=torch.clamp(lengths - i, 0, self.window_size)
			if lengths is not None else None)
			x = self.asp(
			x[:, :, st:ed],
			lengths=(
			torch.clamp(lengths - i, 0, self.window_size) if lengths is not None else None
			),
			)
			x = self.asp_bn(x)
			x = self.fc(x)
			stat_list.append(x)