python/FunASR-XL.git

			@@ -24,7 +24,7 @@
			from packaging.version import parse as V
			from typeguard import check_argument_types
			from typeguard import check_return_type

			from funasr.build_utils.build_model_from_file import build_model_from_file
			from funasr.models.e2e_asr_contextual_paraformer import NeatContextualParaformer
			from funasr.models.e2e_asr_paraformer import BiCifParaformer, ContextualParaformer
			from funasr.models.frontend.wav_frontend import WavFrontend, WavFrontendOnline
			@@ -35,9 +35,7 @@
			from funasr.modules.beam_search.beam_search_transducer import Hypothesis as HypothesisTransducer
			from funasr.modules.scorers.ctc import CTCPrefixScorer
			from funasr.modules.scorers.length_bonus import LengthBonus
			from funasr.tasks.asr import ASRTask
			from funasr.tasks.asr import frontend_choices
			from funasr.tasks.lm import LMTask
			from funasr.build_utils.build_asr_model import frontend_choices
			from funasr.text.build_tokenizer import build_tokenizer
			from funasr.text.token_id_converter import TokenIDConverter
			from funasr.torch_utils.device_funcs import to_device
			@@ -84,7 +82,7 @@

			# 1. Build ASR model
			scorers = {}
			asr_model, asr_train_args = ASRTask.build_model_from_file(
			asr_model, asr_train_args = build_model_from_file(
			asr_train_config, asr_model_file, cmvn_file, device
			)
			frontend = None
			@@ -92,7 +90,6 @@
			if asr_train_args.frontend == 'wav_frontend':
			frontend = WavFrontend(cmvn_file=cmvn_file, **asr_train_args.frontend_conf)
			else:
			from funasr.tasks.asr import frontend_choices
			frontend_class = frontend_choices.get_class(asr_train_args.frontend)
			frontend = frontend_class(**asr_train_args.frontend_conf).eval()

			@@ -112,7 +109,7 @@

			# 2. Build Language model
			if lm_train_config is not None:
			lm, lm_train_args = LMTask.build_model_from_file(
			lm, lm_train_args = build_model_from_file(
			lm_train_config, lm_file, None, device
			)
			scorers["lm"] = lm.lm
			@@ -295,9 +292,8 @@

			# 1. Build ASR model
			scorers = {}
			from funasr.tasks.asr import ASRTaskParaformer as ASRTask
			asr_model, asr_train_args = ASRTask.build_model_from_file(
			asr_train_config, asr_model_file, cmvn_file, device
			asr_model, asr_train_args = build_model_from_file(
			asr_train_config, asr_model_file, cmvn_file, device, mode="paraformer"
			)
			frontend = None
			if asr_train_args.frontend is not None and asr_train_args.frontend_conf is not None:
			@@ -319,8 +315,8 @@

			# 2. Build Language model
			if lm_train_config is not None:
			lm, lm_train_args = LMTask.build_model_from_file(
			lm_train_config, lm_file, device
			lm, lm_train_args = build_model_from_file(
			lm_train_config, lm_file, None, device, task_name="lm"
			)
			scorers["lm"] = lm.lm

			@@ -381,6 +377,7 @@
			self.asr_train_args = asr_train_args
			self.converter = converter
			self.tokenizer = tokenizer
			self.cmvn_file = cmvn_file

			# 6. [Optional] Build hotword list from str, local file or url
			self.hotword_list = None
			@@ -523,6 +520,44 @@
			return results

			def generate_hotwords_list(self, hotword_list_or_file):
			def load_seg_dict(seg_dict_file):
			seg_dict = {}
			assert isinstance(seg_dict_file, str)
			with open(seg_dict_file, "r", encoding="utf8") as f:
			lines = f.readlines()
			for line in lines:
			s = line.strip().split()
			key = s[0]
			value = s[1:]
			seg_dict[key] = " ".join(value)
			return seg_dict

			def seg_tokenize(txt, seg_dict):
			pattern = re.compile(r'^[\u4E00-\u9FA50-9]+$')
			out_txt = ""
			for word in txt:
			word = word.lower()
			if word in seg_dict:
			out_txt += seg_dict[word] + " "
			else:
			if pattern.match(word):
			for char in word:
			if char in seg_dict:
			out_txt += seg_dict[char] + " "
			else:
			out_txt += "<unk>" + " "
			else:
			out_txt += "<unk>" + " "
			return out_txt.strip().split()

			seg_dict = None
			if self.cmvn_file is not None:
			model_dir = os.path.dirname(self.cmvn_file)
			seg_dict_file = os.path.join(model_dir, 'seg_dict')
			if os.path.exists(seg_dict_file):
			seg_dict = load_seg_dict(seg_dict_file)
			else:
			seg_dict = None
			# for None
			if hotword_list_or_file is None:
			hotword_list = None
			@@ -534,8 +569,11 @@
			with codecs.open(hotword_list_or_file, 'r') as fin:
			for line in fin.readlines():
			hw = line.strip()
			hw_list = hw.split()
			if seg_dict is not None:
			hw_list = seg_tokenize(hw_list, seg_dict)
			hotword_str_list.append(hw)
			hotword_list.append(self.converter.tokens2ids([i for i in hw]))
			hotword_list.append(self.converter.tokens2ids(hw_list))
			hotword_list.append([self.asr_model.sos])
			hotword_str_list.append('<s>')
			logging.info("Initialized hotword list from file: {}, hotword list: {}."
			@@ -555,8 +593,11 @@
			with codecs.open(hotword_list_or_file, 'r') as fin:
			for line in fin.readlines():
			hw = line.strip()
			hw_list = hw.split()
			if seg_dict is not None:
			hw_list = seg_tokenize(hw_list, seg_dict)
			hotword_str_list.append(hw)
			hotword_list.append(self.converter.tokens2ids([i for i in hw]))
			hotword_list.append(self.converter.tokens2ids(hw_list))
			hotword_list.append([self.asr_model.sos])
			hotword_str_list.append('<s>')
			logging.info("Initialized hotword list from file: {}, hotword list: {}."
			@@ -568,7 +609,10 @@
			hotword_str_list = []
			for hw in hotword_list_or_file.strip().split():
			hotword_str_list.append(hw)
			hotword_list.append(self.converter.tokens2ids([i for i in hw]))
			hw_list = hw.strip().split()
			if seg_dict is not None:
			hw_list = seg_tokenize(hw_list, seg_dict)
			hotword_list.append(self.converter.tokens2ids(hw_list))
			hotword_list.append([self.asr_model.sos])
			hotword_str_list.append('<s>')
			logging.info("Hotword list: {}.".format(hotword_str_list))
			@@ -616,9 +660,8 @@

			# 1. Build ASR model
			scorers = {}
			from funasr.tasks.asr import ASRTaskParaformer as ASRTask
			asr_model, asr_train_args = ASRTask.build_model_from_file(
			asr_train_config, asr_model_file, cmvn_file, device
			asr_model, asr_train_args = build_model_from_file(
			asr_train_config, asr_model_file, cmvn_file, device, mode="paraformer"
			)
			frontend = None
			if asr_train_args.frontend is not None and asr_train_args.frontend_conf is not None:
			@@ -640,8 +683,8 @@

			# 2. Build Language model
			if lm_train_config is not None:
			lm, lm_train_args = LMTask.build_model_from_file(
			lm_train_config, lm_file, device
			lm, lm_train_args = build_model_from_file(
			lm_train_config, lm_file, None, device, task_name="lm"
			)
			scorers["lm"] = lm.lm

			@@ -873,9 +916,8 @@

			# 1. Build ASR model
			scorers = {}
			from funasr.tasks.asr import ASRTaskUniASR as ASRTask
			asr_model, asr_train_args = ASRTask.build_model_from_file(
			asr_train_config, asr_model_file, cmvn_file, device
			asr_model, asr_train_args = build_model_from_file(
			asr_train_config, asr_model_file, cmvn_file, device, mode="uniasr"
			)
			frontend = None
			if asr_train_args.frontend is not None and asr_train_args.frontend_conf is not None:
			@@ -901,8 +943,8 @@

			# 2. Build Language model
			if lm_train_config is not None:
			lm, lm_train_args = LMTask.build_model_from_file(
			lm_train_config, lm_file, device
			lm, lm_train_args = build_model_from_file(
			lm_train_config, lm_file, device, "lm"
			)
			scorers["lm"] = lm.lm

			@@ -1104,9 +1146,8 @@
			assert check_argument_types()

			# 1. Build ASR model
			from funasr.tasks.asr import ASRTaskMFCCA as ASRTask
			scorers = {}
			asr_model, asr_train_args = ASRTask.build_model_from_file(
			asr_model, asr_train_args = build_model_from_file(
			asr_train_config, asr_model_file, cmvn_file, device
			)

			@@ -1126,8 +1167,8 @@

			# 2. Build Language model
			if lm_train_config is not None:
			lm, lm_train_args = LMTask.build_model_from_file(
			lm_train_config, lm_file, device
			lm, lm_train_args = build_model_from_file(
			lm_train_config, lm_file, None, device, task_name="lm"
			)
			lm.to(device)
			scorers["lm"] = lm.lm
			@@ -1315,8 +1356,7 @@
			super().__init__()

			assert check_argument_types()
			from funasr.tasks.asr import ASRTransducerTask
			asr_model, asr_train_args = ASRTransducerTask.build_model_from_file(
			asr_model, asr_train_args = build_model_from_file(
			asr_train_config, asr_model_file, cmvn_file, device
			)

			@@ -1350,8 +1390,8 @@
			asr_model.to(dtype=getattr(torch, dtype)).eval()

			if lm_train_config is not None:
			lm, lm_train_args = LMTask.build_model_from_file(
			lm_train_config, lm_file, device
			lm, lm_train_args = build_model_from_file(
			lm_train_config, lm_file, None, device, task_name="lm"
			)
			lm_scorer = lm.lm
			else:
			@@ -1638,15 +1678,16 @@
			assert check_argument_types()

			# 1. Build ASR model
			from funasr.tasks.sa_asr import ASRTask
			scorers = {}
			asr_model, asr_train_args = ASRTask.build_model_from_file(
			asr_model, asr_train_args = build_model_from_file(
			asr_train_config, asr_model_file, cmvn_file, device
			)
			frontend = None
			if asr_train_args.frontend is not None and asr_train_args.frontend_conf is not None:
			if asr_train_args.frontend == 'wav_frontend':
			frontend = WavFrontend(cmvn_file=cmvn_file, **asr_train_args.frontend_conf)
			from funasr.tasks.sa_asr import frontend_choices
			if asr_train_args.frontend == 'wav_frontend' or asr_train_args.frontend == "multichannelfrontend":
			frontend_class = frontend_choices.get_class(asr_train_args.frontend)
			frontend = frontend_class(cmvn_file=cmvn_file, **asr_train_args.frontend_conf).eval()
			else:
			frontend_class = frontend_choices.get_class(asr_train_args.frontend)
			frontend = frontend_class(**asr_train_args.frontend_conf).eval()
			@@ -1667,8 +1708,8 @@

			# 2. Build Language model
			if lm_train_config is not None:
			lm, lm_train_args = LMTask.build_model_from_file(
			lm_train_config, lm_file, None, device
			lm, lm_train_args = build_model_from_file(
			lm_train_config, lm_file, None, device, task_name="lm"
			)
			scorers["lm"] = lm.lm