python/FunASR-XL.git

			@@ -52,17 +52,39 @@
			self.linear = nn.Linear(configuration.hidden_size, self.llm_dim)
			self.norm = nn.LayerNorm(self.llm_dim, eps=1e-5)

			def forward(self, x, atts):
			query = self.query.expand(x.shape[0], -1, -1)
			self.second_per_frame = 0.333333
			self.second_stride = 0.333333

			def split_frames(self, speech_embeds):
			B, T, C = speech_embeds.shape
			kernel = round(T * self.second_per_frame / 30.0)
			stride = round(T * self.second_stride / 30.0)
			kernel = (1, kernel)
			stride = (1, stride)
			speech_embeds_tr = speech_embeds.transpose(1, 2).unsqueeze(2)
			speech_embeds_overlap = torch.nn.functional.unfold(speech_embeds_tr, kernel_size=kernel, dilation=1, padding=0, stride=stride)
			_, _, L = speech_embeds_overlap.shape
			speech_embeds_overlap = speech_embeds_overlap.view(B, -1, kernel[1], L)
			speech_embeds_overlap = torch.permute(speech_embeds_overlap, [0, 3, 2, 1])
			speech_embeds = speech_embeds_overlap.reshape(-1, kernel[1], C)
			speech_atts = torch.ones(speech_embeds.size()[:-1], dtype=torch.long, device=speech_embeds.device)
			return speech_embeds, speech_atts

			def forward(self, x):
			B, T, C = x.size()
			encoder_out_feat, attention_mask = self.split_frames(x)
			query = self.query.expand(encoder_out_feat.shape[0], -1, -1)


			query_output = self.qformer(
			query_embeds=query,
			encoder_hidden_states=x,
			encoder_attention_mask=atts,
			encoder_hidden_states=encoder_out_feat,
			encoder_attention_mask=attention_mask,
			return_dict=True,
			)

			query_proj = self.norm(self.linear(query_output.last_hidden_state))
			query_proj = query_proj.view(B, -1, query_proj.size(2)).contiguous()

			return query_proj