python/FunASR-XL.git

			@@ -30,9 +30,8 @@
			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

			from funasr import __version__
			from funasr.datasets.dataset import AbsDataset
			@@ -43,17 +42,19 @@
			from funasr.iterators.chunk_iter_factory import ChunkIterFactory
			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.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.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
			@@ -83,6 +84,7 @@

			optim_classes = dict(
			adam=torch.optim.Adam,
			fairseq_adam=FairseqAdam,
			adamw=torch.optim.AdamW,
			sgd=SGD,
			adadelta=torch.optim.Adadelta,
			@@ -149,6 +151,7 @@
			CosineAnnealingLR=torch.optim.lr_scheduler.CosineAnnealingLR,
			noamlr=NoamLR,
			warmuplr=WarmupLR,
			tri_stage=TriStageLR,
			cycliclr=torch.optim.lr_scheduler.CyclicLR,
			onecyclelr=torch.optim.lr_scheduler.OneCycleLR,
			CosineAnnealingWarmRestarts=torch.optim.lr_scheduler.CosineAnnealingWarmRestarts,
			@@ -225,8 +228,8 @@
			>>> 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
			@@ -246,8 +249,8 @@
			>>> 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
			@@ -258,12 +261,12 @@

			@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:
			assert check_argument_types()

			class ArgumentDefaultsRawTextHelpFormatter(
			argparse.RawTextHelpFormatter,
			@@ -440,6 +443,12 @@
			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,
			@@ -458,6 +467,12 @@
			type=int,
			default=sys.maxsize,
			help="The maximum number update step to train",
			)
			parser.add_argument(
			"--batch_interval",
			type=int,
			default=-1,
			help="The batch interval for saving model.",
			)
			group.add_argument(
			"--patience",
			@@ -536,6 +551,12 @@
			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",
			@@ -624,8 +645,8 @@
			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, "
			@@ -634,12 +655,12 @@
			"and exclude_keys excludes keys of model states for the initialization."
			"e.g.\n"
			" # Load all parameters"
			" --init_param some/where/model.pth\n"
			" --init_param some/where/model.pb\n"
			" # Load only decoder parameters"
			" --init_param some/where/model.pth:decoder:decoder\n"
			" --init_param some/where/model.pb:decoder:decoder\n"
			" # Load only decoder parameters excluding decoder.embed"
			" --init_param some/where/model.pth:decoder:decoder:decoder.embed\n"
			" --init_param some/where/model.pth:decoder:decoder:decoder.embed\n",
			" --init_param some/where/model.pb:decoder:decoder:decoder.embed\n"
			" --init_param some/where/model.pb:decoder:decoder:decoder.embed\n",
			)
			group.add_argument(
			"--ignore_init_mismatch",
			@@ -651,7 +672,7 @@
			"--freeze_param",
			type=str,
			default=[],
			nargs="*",
			action="append",
			help="Freeze parameters",
			)

			@@ -935,7 +956,6 @@
			cls.trainer.add_arguments(parser)
			cls.add_task_arguments(parser)

			assert check_return_type(parser)
			return parser

			@classmethod
			@@ -983,7 +1003,6 @@
			return _cls

			# This method is used only for --print_config
			assert check_argument_types()
			parser = cls.get_parser()
			args, _ = parser.parse_known_args()
			config = vars(args)
			@@ -1023,7 +1042,6 @@

			@classmethod
			def check_required_command_args(cls, args: argparse.Namespace):
			assert check_argument_types()
			if hasattr(args, "required"):
			for k in vars(args):
			if "-" in k:
			@@ -1053,7 +1071,6 @@
			inference: bool = False,
			) -> None:
			"""Check if the dataset satisfy the requirement of current Task"""
			assert check_argument_types()
			mes = (
			f"If you intend to use an additional input, modify "
			f'"{cls.__name__}.required_data_names()" or '
			@@ -1080,14 +1097,12 @@

			@classmethod
			def print_config(cls, file=sys.stdout) -> None:
			assert check_argument_types()
			# Shows the config: e.g. python train.py asr --print_config
			config = cls.get_default_config()
			file.write(yaml_no_alias_safe_dump(config, indent=4, sort_keys=False))

			@classmethod
			def main(cls, args: argparse.Namespace = None, cmd: Sequence[str] = None):
			assert check_argument_types()
			print(get_commandline_args(), file=sys.stderr)
			if args is None:
			parser = cls.get_parser()
			@@ -1124,7 +1139,6 @@

			@classmethod
			def main_worker(cls, args: argparse.Namespace):
			assert check_argument_types()

			# 0. Init distributed process
			distributed_option = build_dataclass(DistributedOption, args)
			@@ -1142,14 +1156,15 @@
			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
			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)
			@@ -1158,8 +1173,10 @@

			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")]
			@@ -1188,12 +1205,18 @@
			# logging.basicConfig() is invoked in main_worker() instead of main()
			# because it can be invoked only once in a process.
			# FIXME(kamo): Should we use logging.getLogger()?
			# BUGFIX: Remove previous handlers and reset log level
			for handler in logging.root.handlers[:]:
			logging.root.removeHandler(handler)
			logging.basicConfig(
			level=args.log_level,
			format=f"[{os.uname()[1].split('.')[0]}]"
			f" %(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
			)
			else:
			# BUGFIX: Remove previous handlers and reset log level
			for handler in logging.root.handlers[:]:
			logging.root.removeHandler(handler)
			# Suppress logging if RANK != 0
			logging.basicConfig(
			level="ERROR",
			@@ -1215,9 +1238,9 @@

			# 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),
			@@ -1276,6 +1299,54 @@

			if args.dry_run:
			pass
			elif args.collect_stats:
			# Perform on collect_stats mode. This mode has two roles
			# - Derive the length and dimension of all input data
			# - Accumulate feats, square values, and the length for whitening

			if args.valid_batch_size is None:
			args.valid_batch_size = args.batch_size

			if len(args.train_shape_file) != 0:
			train_key_file = args.train_shape_file[0]
			else:
			train_key_file = None
			if len(args.valid_shape_file) != 0:
			valid_key_file = args.valid_shape_file[0]
			else:
			valid_key_file = None

			collect_stats(
			model=model,
			train_iter=cls.build_streaming_iterator(
			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,
			ngpu=args.ngpu,
			preprocess_fn=cls.build_preprocess_fn(args, train=False),
			collate_fn=cls.build_collate_fn(args, train=False),
			),
			valid_iter=cls.build_streaming_iterator(
			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,
			ngpu=args.ngpu,
			preprocess_fn=cls.build_preprocess_fn(args, train=False),
			collate_fn=cls.build_collate_fn(args, train=False),
			),
			output_dir=output_dir,
			ngpu=args.ngpu,
			log_interval=args.log_interval,
			write_collected_feats=args.write_collected_feats,
			)
			else:
			logging.info("Training args: {}".format(args))
			# 6. Loads pre-trained model
			@@ -1295,15 +1366,10 @@

			# 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,
			seg_dict_file=args.seg_dict_file if hasattr(args,
			"seg_dict_file") else None,
			mode="train")
			valid_iter_factory = ArkDataLoader(args.valid_data_file, args.token_list, args.dataset_conf,
			seg_dict_file=args.seg_dict_file if hasattr(args,
			"seg_dict_file") 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,
			@@ -1480,7 +1546,6 @@
			- 4 epoch with "--num_iters_per_epoch" == 4

			"""
			assert check_argument_types()
			iter_options = cls.build_iter_options(args, distributed_option, mode)

			# Overwrite iter_options if any kwargs is given
			@@ -1513,7 +1578,14 @@
			def build_sequence_iter_factory(
			cls, args: argparse.Namespace, iter_options: IteratorOptions, mode: str
			) -> AbsIterFactory:
			assert check_argument_types()

			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

			dataset = ESPnetDataset(
			iter_options.data_path_and_name_and_type,
			@@ -1521,6 +1593,7 @@
			preprocess=iter_options.preprocess_fn,
			max_cache_size=iter_options.max_cache_size,
			max_cache_fd=iter_options.max_cache_fd,
			dest_sample_rate=dest_sample_rate,
			)
			cls.check_task_requirements(
			dataset, args.allow_variable_data_keys, train=iter_options.train
			@@ -1598,7 +1671,6 @@
			iter_options: IteratorOptions,
			mode: str,
			) -> AbsIterFactory:
			assert check_argument_types()

			dataset = ESPnetDataset(
			iter_options.data_path_and_name_and_type,
			@@ -1703,7 +1775,6 @@
			def build_multiple_iter_factory(
			cls, args: argparse.Namespace, distributed_option: DistributedOption, mode: str
			):
			assert check_argument_types()
			iter_options = cls.build_iter_options(args, distributed_option, mode)
			assert len(iter_options.data_path_and_name_and_type) > 0, len(
			iter_options.data_path_and_name_and_type
			@@ -1792,6 +1863,7 @@
			key_file: str = None,
			batch_size: int = 1,
			fs: dict = None,
			mc: bool = False,
			dtype: str = np.float32,
			num_workers: int = 1,
			allow_variable_data_keys: bool = False,
			@@ -1799,7 +1871,6 @@
			inference: bool = False,
			) -> DataLoader:
			"""Build DataLoader using iterable dataset"""
			assert check_argument_types()
			# For backward compatibility for pytorch DataLoader
			if collate_fn is not None:
			kwargs = dict(collate_fn=collate_fn)
			@@ -1810,6 +1881,7 @@
			data_path_and_name_and_type,
			float_dtype=dtype,
			fs=fs,
			mc=mc,
			preprocess=preprocess_fn,
			key_file=key_file,
			)
			@@ -1837,7 +1909,7 @@
			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.
			@@ -1848,7 +1920,6 @@
			device: Device type, "cpu", "cuda", or "cuda:N".

			"""
			assert check_argument_types()
			if config_file is None:
			assert model_file is not None, (
			"The argument 'model_file' must be provided "
			@@ -1864,9 +1935,9 @@
			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: