python/FunASR-XL.git

			@@ -3,6 +3,7 @@
			import logging
			import sys
			import time
			import json
			from pathlib import Path
			from typing import Optional
			from typing import Sequence
			@@ -100,10 +101,13 @@
			# logging.info("asr_train_args: {}".format(asr_train_args))
			asr_model.to(dtype=getattr(torch, dtype)).eval()

			ctc = CTCPrefixScorer(ctc=asr_model.ctc, eos=asr_model.eos)
			if asr_model.ctc != None:
			ctc = CTCPrefixScorer(ctc=asr_model.ctc, eos=asr_model.eos)
			scorers.update(
			ctc=ctc
			)
			token_list = asr_model.token_list
			scorers.update(
			ctc=ctc,
			length_bonus=LengthBonus(len(token_list)),
			)

			@@ -171,7 +175,7 @@
			self.converter = converter
			self.tokenizer = tokenizer
			is_use_lm = lm_weight != 0.0 and lm_file is not None
			if ctc_weight == 0.0 and not is_use_lm:
			if (ctc_weight == 0.0 or asr_model.ctc == None) and not is_use_lm:
			beam_search = None
			self.beam_search = beam_search
			logging.info(f"Beam_search: {self.beam_search}")
			@@ -364,201 +368,6 @@
			return fbanks, segments


			# def inference(
			# maxlenratio: float,
			# minlenratio: float,
			# batch_size: int,
			# beam_size: int,
			# ngpu: int,
			# ctc_weight: float,
			# lm_weight: float,
			# penalty: float,
			# log_level: Union[int, str],
			# data_path_and_name_and_type,
			# asr_train_config: Optional[str],
			# asr_model_file: Optional[str],
			# cmvn_file: Optional[str] = None,
			# raw_inputs: Union[np.ndarray, torch.Tensor] = None,
			# lm_train_config: Optional[str] = None,
			# lm_file: Optional[str] = None,
			# token_type: Optional[str] = None,
			# key_file: Optional[str] = None,
			# word_lm_train_config: Optional[str] = None,
			# bpemodel: Optional[str] = None,
			# allow_variable_data_keys: bool = False,
			# streaming: bool = False,
			# output_dir: Optional[str] = None,
			# dtype: str = "float32",
			# seed: int = 0,
			# ngram_weight: float = 0.9,
			# nbest: int = 1,
			# num_workers: int = 1,
			# vad_infer_config: Optional[str] = None,
			# vad_model_file: Optional[str] = None,
			# vad_cmvn_file: Optional[str] = None,
			# time_stamp_writer: bool = False,
			# punc_infer_config: Optional[str] = None,
			# punc_model_file: Optional[str] = None,
			# **kwargs,
			# ):
			# assert check_argument_types()
			#
			# if word_lm_train_config is not None:
			# raise NotImplementedError("Word LM is not implemented")
			# if ngpu > 1:
			# raise NotImplementedError("only single GPU decoding is supported")
			#
			# logging.basicConfig(
			# level=log_level,
			# format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
			# )
			#
			# if ngpu >= 1 and torch.cuda.is_available():
			# device = "cuda"
			# else:
			# device = "cpu"
			#
			# # 1. Set random-seed
			# set_all_random_seed(seed)
			#
			# # 2. Build speech2vadsegment
			# speech2vadsegment_kwargs = dict(
			# vad_infer_config=vad_infer_config,
			# vad_model_file=vad_model_file,
			# vad_cmvn_file=vad_cmvn_file,
			# device=device,
			# dtype=dtype,
			# )
			# # logging.info("speech2vadsegment_kwargs: {}".format(speech2vadsegment_kwargs))
			# speech2vadsegment = Speech2VadSegment(**speech2vadsegment_kwargs)
			#
			# # 3. Build speech2text
			# speech2text_kwargs = dict(
			# asr_train_config=asr_train_config,
			# asr_model_file=asr_model_file,
			# cmvn_file=cmvn_file,
			# lm_train_config=lm_train_config,
			# lm_file=lm_file,
			# token_type=token_type,
			# bpemodel=bpemodel,
			# device=device,
			# maxlenratio=maxlenratio,
			# minlenratio=minlenratio,
			# dtype=dtype,
			# beam_size=beam_size,
			# ctc_weight=ctc_weight,
			# lm_weight=lm_weight,
			# ngram_weight=ngram_weight,
			# penalty=penalty,
			# nbest=nbest,
			# frontend_conf=frontend_conf,
			# )
			# speech2text = Speech2Text(**speech2text_kwargs)
			#
			# text2punc = Text2Punc(punc_infer_config, punc_model_file, device=device, dtype=dtype)
			#
			# # 3. Build data-iterator
			# loader = ASRTask.build_streaming_iterator(
			# data_path_and_name_and_type,
			# dtype=dtype,
			# batch_size=1,
			# key_file=key_file,
			# num_workers=num_workers,
			# preprocess_fn=VADTask.build_preprocess_fn(speech2vadsegment.vad_infer_args, False),
			# collate_fn=VADTask.build_collate_fn(speech2vadsegment.vad_infer_args, False),
			# allow_variable_data_keys=allow_variable_data_keys,
			# inference=True,
			# )
			#
			# forward_time_total = 0.0
			# length_total = 0.0
			# finish_count = 0
			# file_count = 1
			# # 7 .Start for-loop
			# asr_result_list = []
			# if output_dir is not None:
			# writer = DatadirWriter(output_dir)
			# else:
			# writer = None
			#
			# for keys, batch in loader:
			# assert isinstance(batch, dict), type(batch)
			# assert all(isinstance(s, str) for s in keys), keys
			# _bs = len(next(iter(batch.values())))
			# assert len(keys) == _bs, f"{len(keys)} != {_bs}"
			# # batch = {k: v for k, v in batch.items() if not k.endswith("_lengths")}
			#
			# logging.info("decoding, utt_id: {}".format(keys))
			# # N-best list of (text, token, token_int, hyp_object)
			# time_beg = time.time()
			# vad_results = speech2vadsegment(**batch)
			# time_end = time.time()
			# fbanks, vadsegments = vad_results[0], vad_results[1]
			# for i, segments in enumerate(vadsegments):
			# result_segments = [["", [], [], ]]
			# for j, segment_idx in enumerate(segments):
			# bed_idx, end_idx = int(segment_idx[0]/10), int(segment_idx[1]/10)
			# segment = fbanks[:, bed_idx:end_idx, :].to(device)
			# speech_lengths = torch.Tensor([end_idx-bed_idx]).int().to(device)
			# batch = {"speech": segment, "speech_lengths": speech_lengths, "begin_time": vadsegments[i][j][0], "end_time": vadsegments[i][j][1]}
			# results = speech2text(**batch)
			# if len(results) < 1:
			# hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
			# results = [[" ", ["<space>"], [2], 10, 6]] * nbest
			# time_end = time.time()
			# forward_time = time_end - time_beg
			# lfr_factor = results[0][-1]
			# length = results[0][-2]
			# forward_time_total += forward_time
			# length_total += length
			# logging.info(
			# "decoding, feature length: {}, forward_time: {:.4f}, rtf: {:.4f}".
			# format(length, forward_time, 100 * forward_time / (length*lfr_factor)))
			# result_cur = [results[0][:-2]]
			# if j == 0:
			# result_segments = result_cur
			# else:
			# result_segments = [[result_segments[0][i] + result_cur[0][i] for i in range(len(result_cur[0]))]]
			#
			# key = keys[0]
			# result = result_segments[0]
			# text, token, token_int, time_stamp = result
			#
			# # Create a directory: outdir/{n}best_recog
			# if writer is not None:
			# ibest_writer = writer[f"1best_recog"]
			#
			# # Write the result to each file
			# ibest_writer["token"][key] = " ".join(token)
			# ibest_writer["token_int"][key] = " ".join(map(str, token_int))
			#
			# if text is not None:
			# postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
			# if len(postprocessed_result) == 3:
			# text_postprocessed, time_stamp_postprocessed, word_lists = postprocessed_result[0], postprocessed_result[1], postprocessed_result[2]
			# text_postprocessed_punc, punc_id_list = text2punc(word_lists, 20)
			# text_postprocessed_punc_time_stamp = "predictions: {} time_stamp: {}".format(text_postprocessed_punc, time_stamp_postprocessed)
			# else:
			# text_postprocessed = postprocessed_result
			# time_stamp_postprocessed = None
			# word_lists = None
			# text_postprocessed_punc_time_stamp = None
			# punc_id_list = None
			#
			# item = {'key': key, 'value': text_postprocessed_punc_time_stamp, 'text': text_postprocessed, 'time_stamp': time_stamp_postprocessed, 'punc': punc_id_list}
			# asr_result_list.append(item)
			# finish_count += 1
			# # asr_utils.print_progress(finish_count / file_count)
			# if writer is not None:
			# ibest_writer["text"][key] = text_postprocessed
			# if time_stamp_writer and time_stamp_postprocessed is not None:
			# ibest_writer["time_stamp"][key] = " ".join(["-".join(map(str, ts)) for ts in time_stamp_postprocessed])
			#
			# logging.info("decoding, utt: {}, predictions: {}, time_stamp: {}".format(key, text_postprocessed_punc, time_stamp_postprocessed))
			#
			# logging.info("decoding, feature length total: {}, forward_time total: {:.4f}, rtf avg: {:.4f}".
			# format(length_total, forward_time_total, 100 * forward_time_total / (length_total*lfr_factor)))
			# return asr_result_list

			def inference(
			maxlenratio: float,
			@@ -666,9 +475,10 @@
			vad_infer_config: Optional[str] = None,
			vad_model_file: Optional[str] = None,
			vad_cmvn_file: Optional[str] = None,
			time_stamp_writer: bool = False,
			time_stamp_writer: bool = True,
			punc_infer_config: Optional[str] = None,
			punc_model_file: Optional[str] = None,
			outputs_dict: Optional[bool] = True,
			**kwargs,
			):
			assert check_argument_types()
			@@ -725,6 +535,11 @@
			speech2text = Speech2Text(**speech2text_kwargs)

			text2punc = Text2Punc(punc_infer_config, punc_model_file, device=device, dtype=dtype)

			if output_dir is not None:
			writer = DatadirWriter(output_dir)
			ibest_writer = writer[f"1best_recog"]
			ibest_writer["token_list"][""] = " ".join(speech2text.asr_train_args.token_list)

			def _forward(data_path_and_name_and_type,
			raw_inputs: Union[np.ndarray, torch.Tensor] = None,
			@@ -751,11 +566,15 @@
			length_total = 0.0
			finish_count = 0
			file_count = 1
			lfr_factor = 6
			# 7 .Start for-loop
			asr_result_list = []
			output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
			if output_path is not None:
			writer = DatadirWriter(output_path)
			ibest_writer = writer[f"1best_recog"]
			# ibest_writer["punc_dict"][""] = " ".join(punc_infer_config.punc_list)
			# ibest_writer["token_list"][""] = " ".join(asr_train_config.token_list)
			else:
			writer = None

			@@ -783,7 +602,7 @@
			results = speech2text(**batch)
			if len(results) < 1:
			hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
			results = [[" ", ["<space>"], [2], 10, 6]] * nbest
			results = [[" ", ["<space>"], [2], 0, 1, 6]] * nbest
			time_end = time.time()
			forward_time = time_end - time_beg
			lfr_factor = results[0][-1]
			@@ -801,15 +620,15 @@

			key = keys[0]
			result = result_segments[0]
			text, token, token_int, time_stamp = result
			text, token, token_int = result[0], result[1], result[2]
			time_stamp = None if len(result) < 4 else result[3]

			# Create a directory: outdir/{n}best_recog
			if writer is not None:
			ibest_writer = writer[f"1best_recog"]

			# Write the result to each file
			ibest_writer["token"][key] = " ".join(token)
			# ibest_writer["token_int"][key] = " ".join(map(str, token_int))
			ibest_writer["token_int"][key] = " ".join(map(str, token_int))
			ibest_writer["vad"][key] = "{}".format(vadsegments)

			if text is not None:
			postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
			@@ -817,32 +636,45 @@
			text_postprocessed, time_stamp_postprocessed, word_lists = postprocessed_result[0], \
			postprocessed_result[1], \
			postprocessed_result[2]
			text_postprocessed_punc, punc_id_list = text2punc(word_lists, 20)
			text_postprocessed_punc_time_stamp = "predictions: {} time_stamp: {}".format(
			text_postprocessed_punc, time_stamp_postprocessed)
			if len(word_lists) > 0:
			text_postprocessed_punc, punc_id_list = text2punc(word_lists, 20)
			text_postprocessed_punc_time_stamp = json.dumps({"predictions": text_postprocessed_punc,
			"time_stamp": time_stamp_postprocessed},
			ensure_ascii=False)
			else:
			text_postprocessed_punc = ""
			punc_id_list = []
			text_postprocessed_punc_time_stamp = ""

			else:
			text_postprocessed = postprocessed_result
			time_stamp_postprocessed = None
			word_lists = None
			text_postprocessed_punc_time_stamp = None
			punc_id_list = None

			text_postprocessed = ""
			time_stamp_postprocessed = ""
			word_lists = ""
			text_postprocessed_punc_time_stamp = ""
			punc_id_list = ""
			text_postprocessed_punc = ""

			item = {'key': key, 'value': text_postprocessed_punc_time_stamp, 'text': text_postprocessed,
			'time_stamp': time_stamp_postprocessed, 'punc': punc_id_list}
			'time_stamp': time_stamp_postprocessed, 'punc': punc_id_list, 'token': token}
			if outputs_dict:
			item = {'text_punc': text_postprocessed_punc, 'text': text_postprocessed,
			'punc_id': punc_id_list, 'token': token, 'time_stamp': time_stamp_postprocessed}
			item = {'key': key, 'value': item}
			asr_result_list.append(item)
			finish_count += 1
			# asr_utils.print_progress(finish_count / file_count)
			if writer is not None:
			ibest_writer["text"][key] = text_postprocessed
			if time_stamp_writer and time_stamp_postprocessed is not None:
			ibest_writer["time_stamp"][key] = " ".join(
			["-".join(map(str, ts)) for ts in time_stamp_postprocessed])
			ibest_writer["punc_id"][key] = "{}".format(punc_id_list)
			ibest_writer["text_with_punc"][key] = text_postprocessed_punc_time_stamp
			if time_stamp_postprocessed is not None:
			ibest_writer["time_stamp"][key] = "{}".format(time_stamp_postprocessed)

			logging.info("decoding, utt: {}, predictions: {}, time_stamp: {}".format(key, text_postprocessed_punc,
			time_stamp_postprocessed))

			logging.info("decoding, feature length total: {}, forward_time total: {:.4f}, rtf avg: {:.4f}".
			format(length_total, forward_time_total, 100 * forward_time_total / (length_total * lfr_factor)))
			format(length_total, forward_time_total, 100 * forward_time_total / (length_total * lfr_factor+1e-6)))
			return asr_result_list
			return _forward

			@@ -869,7 +701,6 @@
			punc_list[i] = "？"
			elif punc_list[i] == "。":
			period = i

			preprocessor = CommonPreprocessor(
			train=False,
			token_type="word",
			@@ -887,7 +718,8 @@
			cache_sent = []
			mini_sentences = split_to_mini_sentence(words, split_size)
			new_mini_sentence = ""
			new_mini_sentence_punc = ""
			new_mini_sentence_punc = []
			cache_pop_trigger_limit = 200
			for mini_sentence_i in range(len(mini_sentences)):
			mini_sentence = mini_sentences[mini_sentence_i]
			mini_sentence = cache_sent + mini_sentence
			@@ -904,24 +736,31 @@
			if indices.size()[0] != 1:
			punctuations = torch.squeeze(indices)
			assert punctuations.size()[0] == len(mini_sentence)


			# Search for the last Period/QuestionMark as cache
			if mini_sentence_i < len(mini_sentences) - 1:
			sentenceEnd = -1
			last_comma_index = -1
			for i in range(len(punctuations) - 2, 1, -1):
			if punc_list[punctuations[i]] == "。" or punc_list[punctuations[i]] == "？":
			sentenceEnd = i
			break

			if last_comma_index < 0 and punc_list[punctuations[i]] == "，":
			last_comma_index = i

			if sentenceEnd < 0 and len(mini_sentence) > cache_pop_trigger_limit and last_comma_index >= 0:
			# The sentence it too long, cut off at a comma.
			sentenceEnd = last_comma_index
			punctuations[sentenceEnd] = period
			cache_sent = mini_sentence[sentenceEnd + 1:]
			mini_sentence = mini_sentence[0:sentenceEnd + 1]
			punctuations = punctuations[0:sentenceEnd + 1]


			# if len(punctuations) == 0:
			# continue


			punctuations_np = punctuations.cpu().numpy()
			new_mini_sentence_punc += "".join([str(x) for x in punctuations_np])
			new_mini_sentence_punc += [int(x) for x in punctuations_np]
			words_with_punc = []
			for i in range(len(mini_sentence)):
			if i > 0:
			@@ -931,9 +770,8 @@
			if punc_list[punctuations[i]] != "_":
			words_with_punc.append(punc_list[punctuations[i]])
			new_mini_sentence += "".join(words_with_punc)


			return new_mini_sentence, new_mini_sentence_punc

			return _forward

			def get_parser():