python/FunASR-XL.git

			@@ -30,6 +30,7 @@
			import torch.nn
			import torch.optim
			import yaml
			from funasr.models.base_model import FunASRModel
			from torch.utils.data import DataLoader
			from typeguard import check_argument_types
			from typeguard import check_return_type
			@@ -44,19 +45,18 @@
			from funasr.iterators.multiple_iter_factory import MultipleIterFactory
			from funasr.iterators.sequence_iter_factory import SequenceIterFactory
			from funasr.main_funcs.collect_stats import collect_stats
			from funasr.optimizers.sgd import SGD
			from funasr.optimizers.fairseq_adam import FairseqAdam
			from funasr.optimizers.sgd import SGD
			from funasr.samplers.build_batch_sampler import BATCH_TYPES
			from funasr.samplers.build_batch_sampler import build_batch_sampler
			from funasr.samplers.unsorted_batch_sampler import UnsortedBatchSampler
			from funasr.schedulers.noam_lr import NoamLR
			from funasr.schedulers.warmup_lr import WarmupLR
			from funasr.schedulers.tri_stage_scheduler import TriStageLR
			from funasr.schedulers.warmup_lr import WarmupLR
			from funasr.torch_utils.load_pretrained_model import load_pretrained_model
			from funasr.torch_utils.model_summary import model_summary
			from funasr.torch_utils.pytorch_version import pytorch_cudnn_version
			from funasr.torch_utils.set_all_random_seed import set_all_random_seed
			from funasr.train.abs_espnet_model import AbsESPnetModel
			from funasr.train.class_choices import ClassChoices
			from funasr.train.distributed_utils import DistributedOption
			from funasr.train.trainer import Trainer
			@@ -230,8 +230,8 @@
			>>> cls.check_task_requirements()
			If your model is defined as following,

			>>> from funasr.train.abs_espnet_model import AbsESPnetModel
			>>> class Model(AbsESPnetModel):
			>>> from funasr.models.base_model import FunASRModel
			>>> class Model(FunASRModel):
			... def forward(self, input, output, opt=None): pass

			then "required_data_names" should be as
			@@ -251,8 +251,8 @@
			>>> cls.check_task_requirements()
			If your model is defined as follows,

			>>> from funasr.train.abs_espnet_model import AbsESPnetModel
			>>> class Model(AbsESPnetModel):
			>>> from funasr.models.base_model import FunASRModel
			>>> class Model(FunASRModel):
			... def forward(self, input, output, opt=None): pass

			then "optional_data_names" should be as
			@@ -263,8 +263,9 @@

			@classmethod
			@abstractmethod
			def build_model(cls, args: argparse.Namespace) -> AbsESPnetModel:
			def build_model(cls, args: argparse.Namespace) -> FunASRModel:
			raise NotImplementedError


			@classmethod
			def get_parser(cls) -> config_argparse.ArgumentParser:
			@@ -1172,7 +1173,8 @@
			args.batch_bins = args.batch_bins * args.ngpu

			# filter samples if wav.scp and text are mismatch
			if (args.train_shape_file is None and args.dataset_type == "small") or args.train_data_file is None and args.dataset_type == "large":
			if (
			args.train_shape_file is None and args.dataset_type == "small") or args.train_data_file is None and args.dataset_type == "large":
			if not args.simple_ddp or distributed_option.dist_rank == 0:
			filter_wav_text(args.data_dir, args.train_set)
			filter_wav_text(args.data_dir, args.dev_set)
			@@ -1181,8 +1183,10 @@

			if args.train_shape_file is None and args.dataset_type == "small":
			if not args.simple_ddp or distributed_option.dist_rank == 0:
			calc_shape(args.data_dir, args.train_set, args.frontend_conf, args.speech_length_min, args.speech_length_max)
			calc_shape(args.data_dir, args.dev_set, args.frontend_conf, args.speech_length_min, args.speech_length_max)
			calc_shape(args.data_dir, args.train_set, args.frontend_conf, args.speech_length_min,
			args.speech_length_max)
			calc_shape(args.data_dir, args.dev_set, args.frontend_conf, args.speech_length_min,
			args.speech_length_max)
			if args.simple_ddp:
			dist.barrier()
			args.train_shape_file = [os.path.join(args.data_dir, args.train_set, "speech_shape")]
			@@ -1244,9 +1248,9 @@

			# 2. Build model
			model = cls.build_model(args=args)
			if not isinstance(model, AbsESPnetModel):
			if not isinstance(model, FunASRModel):
			raise RuntimeError(
			f"model must inherit {AbsESPnetModel.__name__}, but got {type(model)}"
			f"model must inherit {FunASRModel.__name__}, but got {type(model)}"
			)
			model = model.to(
			dtype=getattr(torch, args.train_dtype),
			@@ -1374,15 +1378,21 @@
			if args.dataset_type == "large":
			from funasr.datasets.large_datasets.build_dataloader import ArkDataLoader
			train_iter_factory = ArkDataLoader(args.train_data_file, args.token_list, args.dataset_conf,
			frontend_conf=args.frontend_conf if hasattr(args, "frontend_conf") else None,
			seg_dict_file=args.seg_dict_file if hasattr(args, "seg_dict_file") else None,
			punc_dict_file=args.punc_list if hasattr(args, "punc_list") else None,
			frontend_conf=args.frontend_conf if hasattr(args,
			"frontend_conf") else None,
			seg_dict_file=args.seg_dict_file if hasattr(args,
			"seg_dict_file") else None,
			punc_dict_file=args.punc_list if hasattr(args,
			"punc_list") else None,
			bpemodel_file=args.bpemodel if hasattr(args, "bpemodel") else None,
			mode="train")
			valid_iter_factory = ArkDataLoader(args.valid_data_file, args.token_list, args.dataset_conf,
			frontend_conf=args.frontend_conf if hasattr(args, "frontend_conf") else None,
			seg_dict_file=args.seg_dict_file if hasattr(args, "seg_dict_file") else None,
			punc_dict_file=args.punc_list if hasattr(args, "punc_list") else None,
			valid_iter_factory = ArkDataLoader(args.valid_data_file, args.token_list, args.dataset_conf,
			frontend_conf=args.frontend_conf if hasattr(args,
			"frontend_conf") else None,
			seg_dict_file=args.seg_dict_file if hasattr(args,
			"seg_dict_file") else None,
			punc_dict_file=args.punc_list if hasattr(args,
			"punc_list") else None,
			bpemodel_file=args.bpemodel if hasattr(args, "bpemodel") else None,
			mode="eval")
			elif args.dataset_type == "small":
			@@ -1929,7 +1939,7 @@
			model_file: Union[Path, str] = None,
			cmvn_file: Union[Path, str] = None,
			device: str = "cpu",
			) -> Tuple[AbsESPnetModel, argparse.Namespace]:
			) -> Tuple[FunASRModel, argparse.Namespace]:
			"""Build model from the files.

			This method is used for inference or fine-tuning.
			@@ -1956,9 +1966,9 @@
			args["cmvn_file"] = cmvn_file
			args = argparse.Namespace(**args)
			model = cls.build_model(args)
			if not isinstance(model, AbsESPnetModel):
			if not isinstance(model, FunASRModel):
			raise RuntimeError(
			f"model must inherit {AbsESPnetModel.__name__}, but got {type(model)}"
			f"model must inherit {FunASRModel.__name__}, but got {type(model)}"
			)
			model.to(device)
			if model_file is not None: