python/FunASR-XL.git

			@@ -30,7 +30,7 @@
			from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
			from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank

			import pdb

			if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
			from torch.cuda.amp import autocast
			else:
			@@ -99,6 +99,7 @@
			)
			self.train_decoder = kwargs.get("train_decoder", False)
			self.NO_BIAS = kwargs.get("NO_BIAS", 8377)
			self.predictor_name = kwargs.get("predictor")

			def forward(
			self,
			@@ -116,6 +117,8 @@
			text: (Batch, Length)
			text_lengths: (Batch,)
			"""
			text_lengths = text_lengths.squeeze()
			speech_lengths = speech_lengths.squeeze()
			assert text_lengths.dim() == 1, text_lengths.shape
			# Check that batch_size is unified
			assert (
			@@ -127,7 +130,7 @@

			hotword_pad = kwargs.get("hotword_pad")
			hotword_lengths = kwargs.get("hotword_lengths")
			dha_pad = kwargs.get("dha_pad")
			seaco_label_pad = kwargs.get("seaco_label_pad")

			batch_size = speech.shape[0]
			# for data-parallel
			@@ -147,7 +150,7 @@
			ys_lengths,
			hotword_pad,
			hotword_lengths,
			dha_pad,
			seaco_label_pad,
			)
			if self.train_decoder:
			loss_att, acc_att = self._calc_att_loss(
			@@ -163,12 +166,18 @@

			# force_gatherable: to-device and to-tensor if scalar for DataParallel
			if self.length_normalized_loss:
			batch_size = (text_lengths + self.predictor_bias).sum().type_as(batch_size)
			batch_size = (text_lengths + self.predictor_bias).sum()
			loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
			return loss, stats, weight

			def _merge(self, cif_attended, dec_attended):
			return cif_attended + dec_attended

			def calc_predictor(self, encoder_out, encoder_out_lens):
			encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
			encoder_out.device)
			predictor_outs = self.predictor(encoder_out, None, encoder_out_mask, ignore_id=self.ignore_id)
			return predictor_outs[:4]

			def _calc_seaco_loss(
			self,
			@@ -178,13 +187,12 @@
			ys_lengths: torch.Tensor,
			hotword_pad: torch.Tensor,
			hotword_lengths: torch.Tensor,
			dha_pad: torch.Tensor,
			seaco_label_pad: torch.Tensor,
			):
			# predictor forward
			encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
			encoder_out.device)
			pre_acoustic_embeds, _, _, _ = self.predictor(encoder_out, ys_pad, encoder_out_mask,
			ignore_id=self.ignore_id)
			pre_acoustic_embeds = self.predictor(encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id)[0]
			# decoder forward
			decoder_out, _ = self.decoder(encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_lengths, return_hidden=True)
			selected = self._hotword_representation(hotword_pad,
			@@ -197,7 +205,7 @@
			dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_out, ys_lengths)
			merged = self._merge(cif_attended, dec_attended)
			dha_output = self.hotword_output_layer(merged[:, :-1]) # remove the last token in loss calculation
			loss_att = self.criterion_seaco(dha_output, dha_pad)
			loss_att = self.criterion_seaco(dha_output, seaco_label_pad)
			return loss_att

			def _seaco_decode_with_ASF(self,
			@@ -248,7 +256,7 @@
			def _merge_res(dec_output, dha_output):
			lmbd = torch.Tensor([seaco_weight] * dha_output.shape[0])
			dha_ids = dha_output.max(-1)[-1]# [0]
			dha_mask = (dha_ids == 8377).int().unsqueeze(-1)
			dha_mask = (dha_ids == self.NO_BIAS).int().unsqueeze(-1)
			a = (1 - lmbd) / lmbd
			b = 1 / lmbd
			a, b = a.to(dec_output.device), b.to(dec_output.device)
			@@ -332,23 +340,28 @@
			if isinstance(encoder_out, tuple):
			encoder_out = encoder_out[0]


			# predictor
			predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens)
			pre_acoustic_embeds, pre_token_length, _, _ = predictor_outs[0], predictor_outs[1], \
			predictor_outs[2], predictor_outs[3]
			pre_acoustic_embeds, pre_token_length = predictor_outs[0], predictor_outs[1]
			pre_token_length = pre_token_length.round().long()
			if torch.max(pre_token_length) < 1:
			return []

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

			# decoder_out, _ = decoder_outs[0], decoder_outs[1]
			_, _, us_alphas, us_peaks = self.calc_predictor_timestamp(encoder_out, encoder_out_lens,
			pre_token_length)
			if self.predictor_name == "CifPredictorV3":
			_, _, us_alphas, us_peaks = self.calc_predictor_timestamp(encoder_out,
			encoder_out_lens,
			pre_token_length)
			else:
			us_alphas = None

			results = []
			b, n, d = decoder_out.size()
			for i in range(b):
			@@ -393,29 +406,30 @@
			# Change integer-ids to tokens
			token = tokenizer.ids2tokens(token_int)
			text = tokenizer.tokens2text(token)

			_, timestamp = ts_prediction_lfr6_standard(us_alphas[i][:encoder_out_lens[i] * 3],
			us_peaks[i][:encoder_out_lens[i] * 3],
			copy.copy(token),
			vad_offset=kwargs.get("begin_time", 0))

			text_postprocessed, time_stamp_postprocessed, word_lists = postprocess_utils.sentence_postprocess(
			token, timestamp)

			result_i = {"key": key[i], "text": text_postprocessed,
			"timestamp": time_stamp_postprocessed
			}

			if ibest_writer is not None:
			ibest_writer["token"][key[i]] = " ".join(token)
			ibest_writer["timestamp"][key[i]] = time_stamp_postprocessed
			ibest_writer["text"][key[i]] = text_postprocessed
			if us_alphas is not None:
			_, timestamp = ts_prediction_lfr6_standard(us_alphas[i][:encoder_out_lens[i] * 3],
			us_peaks[i][:encoder_out_lens[i] * 3],
			copy.copy(token),
			vad_offset=kwargs.get("begin_time", 0))
			text_postprocessed, time_stamp_postprocessed, _ = \
			postprocess_utils.sentence_postprocess(token, timestamp)
			result_i = {"key": key[i], "text": text_postprocessed,
			"timestamp": time_stamp_postprocessed}
			if ibest_writer is not None:
			ibest_writer["token"][key[i]] = " ".join(token)
			ibest_writer["timestamp"][key[i]] = time_stamp_postprocessed
			ibest_writer["text"][key[i]] = text_postprocessed
			else:
			text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
			result_i = {"key": key[i], "text": text_postprocessed}
			if ibest_writer is not None:
			ibest_writer["token"][key[i]] = " ".join(token)
			ibest_writer["text"][key[i]] = text_postprocessed
			else:
			result_i = {"key": key[i], "token_int": token_int}
			results.append(result_i)

			return results, meta_data


			def generate_hotwords_list(self, hotword_list_or_file, tokenizer=None, frontend=None):
			def load_seg_dict(seg_dict_file):
			@@ -518,3 +532,13 @@
			hotword_list = None
			return hotword_list

			def export(
			self,
			**kwargs,
			):
			if 'max_seq_len' not in kwargs:
			kwargs['max_seq_len'] = 512
			from .export_meta import export_rebuild_model
			models = export_rebuild_model(model=self, **kwargs)
			return models