python/FunASR-XL.git

			@@ -212,88 +212,63 @@
			nfilter=50,
			seaco_weight=1.0):
			# decoder forward
			pdb.set_trace()

			decoder_out, decoder_hidden, _ = self.decoder(encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, return_hidden=True, return_both=True)
			pdb.set_trace()

			decoder_pred = torch.log_softmax(decoder_out, dim=-1)
			if hw_list is not None:
			pdb.set_trace()
			hw_lengths = [len(i) for i in hw_list]
			hw_list_ = [torch.Tensor(i).long() for i in hw_list]
			hw_list_pad = pad_list(hw_list_, 0).to(encoder_out.device)
			pdb.set_trace()
			selected = self._hotword_representation(hw_list_pad, torch.Tensor(hw_lengths).int().to(encoder_out.device))
			pdb.set_trace()

			contextual_info = selected.squeeze(0).repeat(encoder_out.shape[0], 1, 1).to(encoder_out.device)
			pdb.set_trace()
			num_hot_word = contextual_info.shape[1]
			_contextual_length = torch.Tensor([num_hot_word]).int().repeat(encoder_out.shape[0]).to(encoder_out.device)
			pdb.set_trace()

			# ASF Core
			if nfilter > 0 and nfilter < num_hot_word:
			for dec in self.seaco_decoder.decoders:
			dec.reserve_attn = True
			pdb.set_trace()

			# cif_attended, _ = self.decoder2(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens)
			dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens)
			# cif_filter = torch.topk(self.decoder2.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1], min(nfilter, num_hot_word-1))[1].tolist()
			pdb.set_trace()

			hotword_scores = self.seaco_decoder.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1]
			# hotword_scores /= torch.sqrt(torch.tensor(hw_lengths)[:-1].float()).to(hotword_scores.device)
			pdb.set_trace()
			dec_filter = torch.topk(hotword_scores, min(nfilter, num_hot_word-1))[1].tolist()
			pdb.set_trace()
			add_filter = dec_filter
			pdb.set_trace()
			add_filter.append(len(hw_list_pad)-1)
			# filter hotword embedding
			pdb.set_trace()
			selected = selected[add_filter]
			# again
			pdb.set_trace()
			contextual_info = selected.squeeze(0).repeat(encoder_out.shape[0], 1, 1).to(encoder_out.device)
			pdb.set_trace()
			num_hot_word = contextual_info.shape[1]
			_contextual_length = torch.Tensor([num_hot_word]).int().repeat(encoder_out.shape[0]).to(encoder_out.device)
			pdb.set_trace()
			for dec in self.seaco_decoder.decoders:
			dec.attn_mat = []
			dec.reserve_attn = False
			pdb.set_trace()
			# SeACo Core
			cif_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens)
			pdb.set_trace()
			dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens)
			pdb.set_trace()
			merged = self._merge(cif_attended, dec_attended)
			pdb.set_trace()

			dha_output = self.hotword_output_layer(merged) # remove the last token in loss calculation
			pdb.set_trace()
			dha_pred = torch.log_softmax(dha_output, dim=-1)
			pdb.set_trace()
			def _merge_res(dec_output, dha_output):
			pdb.set_trace()
			lmbd = torch.Tensor([seaco_weight] * dha_output.shape[0])
			pdb.set_trace()
			dha_ids = dha_output.max(-1)[-1]# [0]
			pdb.set_trace()
			dha_mask = (dha_ids == 8377).int().unsqueeze(-1)
			pdb.set_trace()
			a = (1 - lmbd) / lmbd
			b = 1 / lmbd
			pdb.set_trace()
			a, b = a.to(dec_output.device), b.to(dec_output.device)
			pdb.set_trace()
			dha_mask = (dha_mask + a.reshape(-1, 1, 1)) / b.reshape(-1, 1, 1)
			# logits = dec_output * dha_mask + dha_output[:,:,:-1] * (1-dha_mask)
			pdb.set_trace()
			logits = dec_output * dha_mask + dha_output[:,:,:] * (1-dha_mask)
			return logits

			merged_pred = _merge_res(decoder_pred, dha_pred)
			pdb.set_trace()
			# import pdb; pdb.set_trace()
			return merged_pred
			else:
			return decoder_pred
			@@ -347,7 +322,6 @@
			logging.info("enable beam_search")
			self.init_beam_search(**kwargs)
			self.nbest = kwargs.get("nbest", 1)
			pdb.set_trace()
			meta_data = {}

			# extract fbank feats
			@@ -355,7 +329,6 @@
			audio_sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000))
			time2 = time.perf_counter()
			meta_data["load_data"] = f"{time2 - time1:0.3f}"
			pdb.set_trace()
			speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
			frontend=frontend)
			time3 = time.perf_counter()
			@@ -366,18 +339,15 @@
			speech = speech.to(device=kwargs["device"])
			speech_lengths = speech_lengths.to(device=kwargs["device"])

			pdb.set_trace()
			# hotword
			self.hotword_list = self.generate_hotwords_list(kwargs.get("hotword", None), tokenizer=tokenizer, frontend=frontend)

			pdb.set_trace()
			# Encoder
			encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
			if isinstance(encoder_out, tuple):
			encoder_out = encoder_out[0]


			pdb.set_trace()
			# predictor
			predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens)
			pre_acoustic_embeds, pre_token_length, _, _ = predictor_outs[0], predictor_outs[1], \
			@@ -386,16 +356,14 @@
			if torch.max(pre_token_length) < 1:
			return []

			pdb.set_trace()
			decoder_out = self._seaco_decode_with_ASF(encoder_out, encoder_out_lens,
			pre_acoustic_embeds,
			pre_token_length,
			hw_list=self.hotword_list)
			pdb.set_trace()

			# decoder_out, _ = decoder_outs[0], decoder_outs[1]
			_, _, us_alphas, us_peaks = self.calc_predictor_timestamp(encoder_out, encoder_out_lens,
			pre_token_length)
			pdb.set_trace()
			results = []
			b, n, d = decoder_out.size()
			for i in range(b):