python/FunASR-XL.git

			@@ -43,14 +43,13 @@
			Paper2: Achieving timestamp prediction while recognizing with non-autoregressive end-to-end ASR model
			https://arxiv.org/abs/2301.12343
			"""


			def __init__(
			self,
			*args,
			**kwargs,
			):
			super().__init__(args, *kwargs)


			def _calc_pre2_loss(
			self,
			@@ -59,19 +58,21 @@
			ys_pad: torch.Tensor,
			ys_pad_lens: torch.Tensor,
			):
			encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
			encoder_out.device)
			encoder_out_mask = (
			~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]
			).to(encoder_out.device)
			if self.predictor_bias == 1:
			_, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
			ys_pad_lens = ys_pad_lens + self.predictor_bias
			_, _, _, _, pre_token_length2 = self.predictor(encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id)

			_, _, _, _, pre_token_length2 = self.predictor(
			encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id
			)

			# loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)
			loss_pre2 = self.criterion_pre(ys_pad_lens.type_as(pre_token_length2), pre_token_length2)


			return loss_pre2



			def _calc_att_loss(
			self,
			encoder_out: torch.Tensor,
			@@ -79,29 +80,29 @@
			ys_pad: torch.Tensor,
			ys_pad_lens: torch.Tensor,
			):
			encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
			encoder_out.device)
			encoder_out_mask = (
			~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]
			).to(encoder_out.device)
			if self.predictor_bias == 1:
			_, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
			ys_pad_lens = ys_pad_lens + self.predictor_bias
			pre_acoustic_embeds, pre_token_length, _, pre_peak_index, _ = self.predictor(encoder_out, ys_pad,
			encoder_out_mask,
			ignore_id=self.ignore_id)

			pre_acoustic_embeds, pre_token_length, _, pre_peak_index, _ = self.predictor(
			encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id
			)

			# 0. sampler
			decoder_out_1st = None
			if self.sampling_ratio > 0.0:
			sematic_embeds, decoder_out_1st = self.sampler(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens,
			pre_acoustic_embeds)
			sematic_embeds, decoder_out_1st = self.sampler(
			encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds
			)
			else:
			sematic_embeds = pre_acoustic_embeds


			# 1. Forward decoder
			decoder_outs = self.decoder(
			encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens
			)
			decoder_outs = self.decoder(encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens)
			decoder_out, _ = decoder_outs[0], decoder_outs[1]


			if decoder_out_1st is None:
			decoder_out_1st = decoder_out
			# 2. Compute attention loss
			@@ -112,36 +113,34 @@
			ignore_label=self.ignore_id,
			)
			loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)


			# Compute cer/wer using attention-decoder
			if self.training or self.error_calculator is None:
			cer_att, wer_att = None, None
			else:
			ys_hat = decoder_out_1st.argmax(dim=-1)
			cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())


			return loss_att, acc_att, cer_att, wer_att, loss_pre


			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)
			pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index, pre_token_length2 = self.predictor(encoder_out,
			None,
			encoder_out_mask,
			ignore_id=self.ignore_id)
			encoder_out_mask = (
			~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]
			).to(encoder_out.device)
			pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index, pre_token_length2 = (
			self.predictor(encoder_out, None, encoder_out_mask, ignore_id=self.ignore_id)
			)
			return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index


			def calc_predictor_timestamp(self, encoder_out, encoder_out_lens, token_num):
			encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
			encoder_out.device)
			ds_alphas, ds_cif_peak, us_alphas, us_peaks = self.predictor.get_upsample_timestamp(encoder_out,
			encoder_out_mask,
			token_num)
			encoder_out_mask = (
			~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]
			).to(encoder_out.device)
			ds_alphas, ds_cif_peak, us_alphas, us_peaks = self.predictor.get_upsample_timestamp(
			encoder_out, encoder_out_mask, token_num
			)
			return ds_alphas, ds_cif_peak, us_alphas, us_peaks



			def forward(
			self,
			speech: torch.Tensor,
			@@ -161,44 +160,48 @@
			text_lengths = text_lengths[:, 0]
			if len(speech_lengths.size()) > 1:
			speech_lengths = speech_lengths[:, 0]


			batch_size = speech.shape[0]


			# Encoder
			encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)


			loss_ctc, cer_ctc = None, None
			loss_pre = None
			stats = dict()


			# decoder: CTC branch
			if self.ctc_weight != 0.0:
			loss_ctc, cer_ctc = self._calc_ctc_loss(
			encoder_out, encoder_out_lens, text, text_lengths
			)


			# Collect CTC branch stats
			stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
			stats["cer_ctc"] = cer_ctc


			# decoder: Attention decoder branch
			loss_att, acc_att, cer_att, wer_att, loss_pre = self._calc_att_loss(
			encoder_out, encoder_out_lens, text, text_lengths
			)

			loss_pre2 = self._calc_pre2_loss(
			encoder_out, encoder_out_lens, text, text_lengths
			)


			loss_pre2 = self._calc_pre2_loss(encoder_out, encoder_out_lens, text, text_lengths)

			# 3. CTC-Att loss definition
			if self.ctc_weight == 0.0:
			loss = loss_att + loss_pre * self.predictor_weight + loss_pre2 * self.predictor_weight * 0.5
			loss = (
			loss_att
			+ loss_pre * self.predictor_weight
			+ loss_pre2 * self.predictor_weight * 0.5
			)
			else:
			loss = self.ctc_weight * loss_ctc + (
			1 - self.ctc_weight) * loss_att + loss_pre * self.predictor_weight + loss_pre2 * self.predictor_weight * 0.5

			loss = (
			self.ctc_weight * loss_ctc
			+ (1 - self.ctc_weight) * loss_att
			+ loss_pre * self.predictor_weight
			+ loss_pre2 * self.predictor_weight * 0.5
			)

			# Collect Attn branch stats
			stats["loss_att"] = loss_att.detach() if loss_att is not None else None
			stats["acc"] = acc_att
			@@ -206,34 +209,36 @@
			stats["wer"] = wer_att
			stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None
			stats["loss_pre2"] = loss_pre2.detach().cpu()


			stats["loss"] = torch.clone(loss.detach())


			# force_gatherable: to-device and to-tensor if scalar for DataParallel
			if self.length_normalized_loss:
			batch_size = int((text_lengths + self.predictor_bias).sum())


			loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
			return loss, stats, weight

			def inference(
			self,
			data_in,
			data_lengths=None,
			key: list = None,
			tokenizer=None,
			frontend=None,
			**kwargs,
			):

			def inference(self,
			data_in,
			data_lengths=None,
			key: list = None,
			tokenizer=None,
			frontend=None,
			**kwargs,
			):

			# init beamsearch
			is_use_ctc = kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None
			is_use_lm = kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
			is_use_lm = (
			kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
			)
			if self.beam_search is None and (is_use_lm or is_use_ctc):
			logging.info("enable beam_search")
			self.init_beam_search(**kwargs)
			self.nbest = kwargs.get("nbest", 1)


			meta_data = {}
			# if isinstance(data_in, torch.Tensor): # fbank
			# speech, speech_lengths = data_in, data_lengths
			@@ -244,59 +249,70 @@
			# else:
			# extract fbank feats
			time1 = time.perf_counter()
			audio_sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000))
			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}"
			speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
			frontend=frontend)
			speech, speech_lengths = extract_fbank(
			audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=frontend
			)
			time3 = time.perf_counter()
			meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
			meta_data["batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000

			meta_data["batch_data_time"] = (
			speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
			)

			speech = speech.to(device=kwargs["device"])
			speech_lengths = speech_lengths.to(device=kwargs["device"])


			# Encoder
			encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
			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, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], \
			predictor_outs[2], predictor_outs[3]
			pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = (
			predictor_outs[0],
			predictor_outs[1],
			predictor_outs[2],
			predictor_outs[3],
			)
			pre_token_length = pre_token_length.round().long()
			if torch.max(pre_token_length) < 1:
			return []
			decoder_outs = self.cal_decoder_with_predictor(encoder_out, encoder_out_lens, pre_acoustic_embeds,
			pre_token_length)
			decoder_outs = self.cal_decoder_with_predictor(
			encoder_out, encoder_out_lens, pre_acoustic_embeds, pre_token_length
			)
			decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]


			# BiCifParaformer, test no bias cif2
			_, _, us_alphas, us_peaks = self.calc_predictor_timestamp(encoder_out, encoder_out_lens,
			pre_token_length)

			_, _, us_alphas, us_peaks = self.calc_predictor_timestamp(
			encoder_out, encoder_out_lens, pre_token_length
			)

			results = []
			b, n, d = decoder_out.size()
			for i in range(b):
			x = encoder_out[i, :encoder_out_lens[i], :]
			am_scores = decoder_out[i, :pre_token_length[i], :]
			x = encoder_out[i, : encoder_out_lens[i], :]
			am_scores = decoder_out[i, : pre_token_length[i], :]
			if self.beam_search is not None:
			nbest_hyps = self.beam_search(
			x=x, am_scores=am_scores, maxlenratio=kwargs.get("maxlenratio", 0.0),
			minlenratio=kwargs.get("minlenratio", 0.0)
			x=x,
			am_scores=am_scores,
			maxlenratio=kwargs.get("maxlenratio", 0.0),
			minlenratio=kwargs.get("minlenratio", 0.0),
			)


			nbest_hyps = nbest_hyps[: self.nbest]
			else:


			yseq = am_scores.argmax(dim=-1)
			score = am_scores.max(dim=-1)[0]
			score = torch.sum(score, dim=-1)
			# pad with mask tokens to ensure compatibility with sos/eos tokens
			yseq = torch.tensor(
			[self.sos] + yseq.tolist() + [self.eos], device=yseq.device
			)
			yseq = torch.tensor([self.sos] + yseq.tolist() + [self.eos], device=yseq.device)
			nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
			for nbest_idx, hyp in enumerate(nbest_hyps):
			ibest_writer = None
			@@ -304,34 +320,43 @@
			if not hasattr(self, "writer"):
			self.writer = DatadirWriter(kwargs.get("output_dir"))
			ibest_writer = self.writer[f"{nbest_idx+1}best_recog"]


			# remove sos/eos and get results
			last_pos = -1
			if isinstance(hyp.yseq, list):
			token_int = hyp.yseq[1:last_pos]
			else:
			token_int = hyp.yseq[1:last_pos].tolist()


			# remove blank symbol id, which is assumed to be 0
			token_int = list(filter(lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int))

			token_int = list(
			filter(
			lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int
			)
			)

			if tokenizer is not None:
			# 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,
			}

			_, 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["text"][key[i]] = text
			@@ -340,89 +365,13 @@
			else:
			result_i = {"key": key[i], "token_int": token_int}
			results.append(result_i)


			return results, meta_data

			def export(
			self,
			max_seq_len=512,
			**kwargs,
			):
			self.device = kwargs.get("device")
			is_onnx = kwargs.get("type", "onnx") == "onnx"
			encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
			self.encoder = encoder_class(self.encoder, onnx=is_onnx)

			predictor_class = tables.predictor_classes.get(kwargs["predictor"] + "Export")
			self.predictor = predictor_class(self.predictor, onnx=is_onnx)

			decoder_class = tables.decoder_classes.get(kwargs["decoder"] + "Export")
			self.decoder = decoder_class(self.decoder, onnx=is_onnx)

			from funasr.utils.torch_function import sequence_mask
			def export(self, **kwargs):
			from .export_meta import export_rebuild_model

			self.make_pad_mask = sequence_mask(max_seq_len, flip=False)


			self.forward = self.export_forward

			return self

			def export_forward(
			self,
			speech: torch.Tensor,
			speech_lengths: torch.Tensor,
			):
			# a. To device
			batch = {"speech": speech, "speech_lengths": speech_lengths}
			batch = to_device(batch, device=self.device)

			enc, enc_len = self.encoder(**batch)
			mask = self.make_pad_mask(enc_len)[:, None, :]
			pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(enc, mask)
			pre_token_length = pre_token_length.round().type(torch.int32)

			decoder_out, _ = self.decoder(enc, enc_len, pre_acoustic_embeds, pre_token_length)
			decoder_out = torch.log_softmax(decoder_out, dim=-1)

			# get predicted timestamps
			us_alphas, us_cif_peak = self.predictor.get_upsample_timestmap(enc, mask, pre_token_length)

			return decoder_out, pre_token_length, us_alphas, us_cif_peak

			def export_dummy_inputs(self):
			speech = torch.randn(2, 30, 560)
			speech_lengths = torch.tensor([6, 30], dtype=torch.int32)
			return (speech, speech_lengths)

			def export_input_names(self):
			return ['speech', 'speech_lengths']

			def export_output_names(self):
			return ['logits', 'token_num', 'us_alphas', 'us_cif_peak']

			def export_dynamic_axes(self):
			return {
			'speech': {
			0: 'batch_size',
			1: 'feats_length'
			},
			'speech_lengths': {
			0: 'batch_size',
			},
			'logits': {
			0: 'batch_size',
			1: 'logits_length'
			},
			'us_alphas': {
			0: 'batch_size',
			1: 'alphas_length'
			},
			'us_cif_peak': {
			0: 'batch_size',
			1: 'alphas_length'
			},
			}

			def export_name(self, ):
			return "model.onnx"
			if "max_seq_len" not in kwargs:
			kwargs["max_seq_len"] = 512
			models = export_rebuild_model(model=self, **kwargs)
			return models