python/FunASR-XL.git

			@@ -30,7 +30,7 @@
			import torch.nn
			import torch.optim
			import yaml
			from funasr.train.abs_espnet_model import AbsESPnetModel
			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
			@@ -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:
			@@ -445,6 +446,12 @@
			help='Perform on "collect stats" mode',
			)
			group.add_argument(
			"--mc",
			type=bool,
			default=False,
			help="MultiChannel input",
			)
			group.add_argument(
			"--write_collected_feats",
			type=str2bool,
			default=False,
			@@ -467,7 +474,7 @@
			parser.add_argument(
			"--batch_interval",
			type=int,
			default=10000,
			default=-1,
			help="The batch interval for saving model.",
			)
			group.add_argument(
			@@ -547,6 +554,12 @@
			type=int,
			default=1,
			help="The number of gradient accumulation",
			)
			group.add_argument(
			"--bias_grad_times",
			type=float,
			default=1.0,
			help="To scale the gradient of contextual related params",
			)
			group.add_argument(
			"--no_forward_run",
			@@ -635,8 +648,8 @@
			group.add_argument(
			"--init_param",
			type=str,
			action="append",
			default=[],
			nargs="*",
			help="Specify the file path used for initialization of parameters. "
			"The format is '<file_path>:<src_key>:<dst_key>:<exclude_keys>', "
			"where file_path is the model file path, "
			@@ -662,7 +675,7 @@
			"--freeze_param",
			type=str,
			default=[],
			nargs="*",
			action="append",
			help="Freeze parameters",
			)

			@@ -1153,10 +1166,10 @@
			elif args.distributed and args.simple_ddp:
			distributed_option.init_torch_distributed_pai(args)
			args.ngpu = dist.get_world_size()
			if args.dataset_type == "small":
			if args.dataset_type == "small" and args.ngpu > 0:
			if args.batch_size is not None:
			args.batch_size = args.batch_size * args.ngpu
			if args.batch_bins is not None:
			if args.batch_bins is not None and args.ngpu > 0:
			args.batch_bins = args.batch_bins * args.ngpu

			# filter samples if wav.scp and text are mismatch
			@@ -1235,9 +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),
			@@ -1319,6 +1332,7 @@
			data_path_and_name_and_type=args.train_data_path_and_name_and_type,
			key_file=train_key_file,
			batch_size=args.batch_size,
			mc=args.mc,
			dtype=args.train_dtype,
			num_workers=args.num_workers,
			allow_variable_data_keys=args.allow_variable_data_keys,
			@@ -1330,6 +1344,7 @@
			data_path_and_name_and_type=args.valid_data_path_and_name_and_type,
			key_file=valid_key_file,
			batch_size=args.valid_batch_size,
			mc=args.mc,
			dtype=args.train_dtype,
			num_workers=args.num_workers,
			allow_variable_data_keys=args.allow_variable_data_keys,
			@@ -1361,25 +1376,10 @@

			# 7. Build iterator factories
			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,
			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,
			bpemodel_file=args.bpemodel if hasattr(args, "bpemodel") else None,
			mode="eval")
			from funasr.datasets.large_datasets.build_dataloader import LargeDataLoader
			train_iter_factory = LargeDataLoader(args, mode="train")
			valid_iter_factory = LargeDataLoader(args, mode="eval")

			elif args.dataset_type == "small":
			train_iter_factory = cls.build_iter_factory(
			args=args,
			@@ -1591,8 +1591,11 @@
			) -> AbsIterFactory:
			assert check_argument_types()

			if args.frontend_conf is not None and "fs" in args.frontend_conf:
			dest_sample_rate = args.frontend_conf["fs"]
			if hasattr(args, "frontend_conf"):
			if args.frontend_conf is not None and "fs" in args.frontend_conf:
			dest_sample_rate = args.frontend_conf["fs"]
			else:
			dest_sample_rate = 16000
			else:
			dest_sample_rate = 16000

			@@ -1921,7 +1924,7 @@
			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.
			@@ -1948,9 +1951,9 @@
			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: