python/FunASR-XL.git

			@@ -44,7 +44,7 @@
			att_dropout_rate: float = 0.0,
			ffn_dropout_rate: float = 0.0,
			dropout_rate: float = 0.0,
			subsampling_factor: int =4,
			subsampling_factor: int = 4,
			time_reduction_factor: int = 1,
			kernel: int = 3,
			**kwargs,
			@@ -54,7 +54,7 @@

			self.embed = RWKVConvInput(
			input_size,
			[output_size//4, output_size//2, output_size],
			[output_size // 4, output_size // 2, output_size],
			subsampling_factor,
			conv_kernel_size=kernel,
			output_size=output_size,
			@@ -64,7 +64,7 @@

			linear_size = output_size * 4 if linear_size is None else linear_size
			attention_size = output_size if attention_size is None else attention_size


			self.rwkv_blocks = torch.nn.ModuleList(
			[
			RWKV(
			@@ -122,12 +122,12 @@
			x, _ = block(x)
			else:
			x = self.rwkv_infer(x)


			x = self.final_norm(x)

			if self.time_reduction_factor > 1:
			x = x[:,::self.time_reduction_factor,:]
			olens = torch.floor_divide(olens-1, self.time_reduction_factor) + 1
			x = x[:, :: self.time_reduction_factor, :]
			olens = torch.floor_divide(olens - 1, self.time_reduction_factor) + 1

			return x, olens, None

			@@ -135,9 +135,7 @@

			batch_size = xs_pad.shape[0]

			hidden_sizes = [
			self._output_size for i in range(5)
			]
			hidden_sizes = [self._output_size for i in range(5)]

			state = [
			torch.zeros(
			@@ -152,7 +150,7 @@

			xs_out = []
			for t in range(xs_pad.shape[1]):
			x_t = xs_pad[:,t,:]
			x_t = xs_pad[:, t, :]
			for idx, block in enumerate(self.rwkv_blocks):
			x_t, state = block(x_t, state=state)
			xs_out.append(x_t)