python/FunASR-XL.git

			@@ -4,6 +4,7 @@
			# MIT License (https://opensource.org/licenses/MIT)

			import time
			import copy
			import torch
			import logging
			from torch.cuda.amp import autocast
			@@ -13,13 +14,15 @@
			from funasr.models.ctc.ctc import CTC
			from funasr.utils import postprocess_utils
			from funasr.metrics.compute_acc import th_accuracy
			from funasr.train_utils.device_funcs import to_device
			from funasr.utils.datadir_writer import DatadirWriter
			from funasr.models.paraformer.search import Hypothesis
			from funasr.models.paraformer.cif_predictor import mae_loss
			from funasr.train_utils.device_funcs import force_gatherable
			from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
			from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
			from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
			from funasr.models.transformer.utils.nets_utils import make_pad_mask
			from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
			from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank


			@@ -30,7 +33,7 @@
			Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
			https://arxiv.org/abs/2206.08317
			"""


			def __init__(
			self,
			specaug: Optional[str] = None,
			@@ -90,17 +93,15 @@
			**decoder_conf,
			)
			if ctc_weight > 0.0:


			if ctc_conf is None:
			ctc_conf = {}

			ctc = CTC(
			odim=vocab_size, encoder_output_size=encoder_output_size, **ctc_conf
			)

			ctc = CTC(odim=vocab_size, encoder_output_size=encoder_output_size, **ctc_conf)
			if predictor is not None:
			predictor_class = tables.predictor_classes.get(predictor)
			predictor = predictor_class(**predictor_conf)


			# note that eos is the same as sos (equivalent ID)
			self.blank_id = blank_id
			self.sos = sos if sos is not None else vocab_size - 1
			@@ -130,7 +131,7 @@
			self.decoder = None
			else:
			self.decoder = decoder


			self.criterion_att = LabelSmoothingLoss(
			size=vocab_size,
			padding_idx=ignore_id,
			@@ -154,17 +155,16 @@
			self.predictor_bias = predictor_bias
			self.sampling_ratio = sampling_ratio
			self.criterion_pre = mae_loss(normalize_length=length_normalized_loss)
			# self.step_cur = 0
			#

			self.share_embedding = share_embedding
			if self.share_embedding:
			self.decoder.embed = None


			self.use_1st_decoder_loss = use_1st_decoder_loss
			self.length_normalized_loss = length_normalized_loss
			self.beam_search = None
			self.error_calculator = None


			def forward(
			self,
			speech: torch.Tensor,
			@@ -180,47 +180,45 @@
			text: (Batch, Length)
			text_lengths: (Batch,)
			"""
			# import pdb;
			# pdb.set_trace()
			if len(text_lengths.size()) > 1:
			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, pre_loss_att = self._calc_att_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
			else:
			loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att + loss_pre * self.predictor_weight


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

			# Collect Attn branch stats
			stats["loss_att"] = loss_att.detach() if loss_att is not None else None
			stats["pre_loss_att"] = pre_loss_att.detach() if pre_loss_att is not None else None
			@@ -228,18 +226,21 @@
			stats["cer"] = cer_att
			stats["wer"] = wer_att
			stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None


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

			stats["batch_size"] = batch_size

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


			def encode(
			self, speech: torch.Tensor, speech_lengths: torch.Tensor, **kwargs,
			self,
			speech: torch.Tensor,
			speech_lengths: torch.Tensor,
			**kwargs,
			) -> Tuple[torch.Tensor, torch.Tensor]:
			"""Encoder. Note that this method is used by asr_inference.py
			Args:
			@@ -252,11 +253,10 @@
			# Data augmentation
			if self.specaug is not None and self.training:
			speech, speech_lengths = self.specaug(speech, speech_lengths)


			# Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
			if self.normalize is not None:
			speech, speech_lengths = self.normalize(speech, speech_lengths)


			# Forward encoder
			encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths)
			@@ -264,21 +264,22 @@
			encoder_out = encoder_out[0]

			return encoder_out, encoder_out_lens


			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 = 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 cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens):

			decoder_outs = self.decoder(
			encoder_out, encoder_out_lens, sematic_embeds, ys_pad_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 = 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 cal_decoder_with_predictor(
			self, 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_out = torch.log_softmax(decoder_out, dim=-1)
			return decoder_out, ys_pad_lens
			@@ -290,30 +291,31 @@
			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
			pre_loss_att = 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
			@@ -324,19 +326,21 @@
			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, pre_loss_att


			def sampler(self, encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds):

			tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(ys_pad.device)

			tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(
			ys_pad.device
			)
			ys_pad_masked = ys_pad * tgt_mask[:, :, 0]
			if self.share_embedding:
			ys_pad_embed = self.decoder.output_layer.weight[ys_pad_masked]
			@@ -354,19 +358,24 @@
			input_mask = torch.ones_like(nonpad_positions)
			bsz, seq_len = ys_pad.size()
			for li in range(bsz):
			target_num = (((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio).long()
			target_num = (
			((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio
			).long()
			if target_num > 0:
			input_mask[li].scatter_(dim=0,
			index=torch.randperm(seq_lens[li])[:target_num].to(input_mask.device),
			value=0)
			input_mask[li].scatter_(
			dim=0,
			index=torch.randperm(seq_lens[li])[:target_num].to(input_mask.device),
			value=0,
			)
			input_mask = input_mask.eq(1)
			input_mask = input_mask.masked_fill(~nonpad_positions, False)
			input_mask_expand_dim = input_mask.unsqueeze(2).to(pre_acoustic_embeds.device)

			sematic_embeds = pre_acoustic_embeds.masked_fill(~input_mask_expand_dim, 0) + ys_pad_embed.masked_fill(
			input_mask_expand_dim, 0)

			sematic_embeds = pre_acoustic_embeds.masked_fill(
			~input_mask_expand_dim, 0
			) + ys_pad_embed.masked_fill(input_mask_expand_dim, 0)
			return sematic_embeds * tgt_mask, decoder_out * tgt_mask


			def _calc_ctc_loss(
			self,
			encoder_out: torch.Tensor,
			@@ -376,7 +385,7 @@
			):
			# Calc CTC loss
			loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)


			# Calc CER using CTC
			cer_ctc = None
			if not self.training and self.error_calculator is not None:
			@@ -384,33 +393,30 @@
			cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True)
			return loss_ctc, cer_ctc


			def init_beam_search(self,
			**kwargs,
			):
			def init_beam_search(
			self,
			**kwargs,
			):
			from funasr.models.paraformer.search import BeamSearchPara
			from funasr.models.transformer.scorers.ctc import CTCPrefixScorer
			from funasr.models.transformer.scorers.length_bonus import LengthBonus


			# 1. Build ASR model
			scorers = {}


			if self.ctc != None:
			ctc = CTCPrefixScorer(ctc=self.ctc, eos=self.eos)
			scorers.update(
			ctc=ctc
			)
			scorers.update(ctc=ctc)
			token_list = kwargs.get("token_list")
			scorers.update(
			length_bonus=LengthBonus(len(token_list)),
			)


			# 3. Build ngram model
			# ngram is not supported now
			ngram = None
			scorers["ngram"] = ngram


			weights = dict(
			decoder=1.0 - kwargs.get("decoding_ctc_weight"),
			ctc=kwargs.get("decoding_ctc_weight", 0.0),
			@@ -433,72 +439,102 @@
			# if isinstance(scorer, torch.nn.Module):
			# scorer.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
			self.beam_search = beam_search

			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
			)
			pred_timestamp = kwargs.get("pred_timestamp", False)
			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
			if (
			isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank"
			): # fbank
			speech, speech_lengths = data_in, data_lengths
			if len(speech.shape) < 3:
			speech = speech[None, :, :]
			if speech_lengths is None:
			if speech_lengths is not None:
			speech_lengths = speech_lengths.squeeze(-1)
			else:
			speech_lengths = speech.shape[1]
			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), data_type=kwargs.get("data_type", "sound"), tokenizer=tokenizer)
			audio_sample_list = load_audio_text_image_video(
			data_in,
			fs=frontend.fs,
			audio_fs=kwargs.get("fs", 16000),
			data_type=kwargs.get("data_type", "sound"),
			tokenizer=tokenizer,
			)
			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
			if kwargs.get("fp16", False):
			speech = speech.half()
			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]


			results = []
			b, n, d = decoder_out.size()
			if isinstance(key[0], (list, tuple)):
			key = key[0]
			if len(key) < b:
			key = key * b
			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:

			@@ -506,35 +542,49 @@
			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
			if ibest_writer is None and kwargs.get("output_dir") is not None:
			writer = DatadirWriter(kwargs.get("output_dir"))
			ibest_writer = writer[f"{nbest_idx+1}best_recog"]
			if kwargs.get("output_dir") is not None:
			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)
			text_postprocessed = tokenizer.tokens2text(token)

			text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)

			result_i = {"key": key[i], "text": text_postprocessed}
			if pred_timestamp:
			timestamp_str, timestamp = ts_prediction_lfr6_standard(
			pre_peak_index[i],
			alphas[i],
			copy.copy(token),
			vad_offset=kwargs.get("begin_time", 0),
			upsample_rate=1,
			)
			if not hasattr(tokenizer, "bpemodel"):
			text_postprocessed, time_stamp_postprocessed, _ = postprocess_utils.sentence_postprocess(token, timestamp)
			result_i = {"key": key[i], "text": text_postprocessed, "timestamp": time_stamp_postprocessed,}
			else:
			if not hasattr(tokenizer, "bpemodel"):
			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
			@@ -542,6 +592,13 @@
			else:
			result_i = {"key": key[i], "token_int": token_int}
			results.append(result_i)


			return results, meta_data

			def export(self, **kwargs):
			from .export_meta import export_rebuild_model

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