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游雁

2023-03-13 fc08b62d05723cdc1ce021bb8ba044ca014fb1f7

 funasr/tasks/abs_task.py

@@ -71,7 +71,7 @@
from funasr.utils.types import str2triple_str
from funasr.utils.types import str_or_int
from funasr.utils.types import str_or_none
from funasr.utils.wav_utils import calc_shape, generate_data_list
from funasr.utils.wav_utils import calc_shape, generate_data_list, filter_wav_text
from funasr.utils.yaml_no_alias_safe_dump import yaml_no_alias_safe_dump

try:
@@ -1153,6 +1153,14 @@
                if args.batch_bins is not None:
                    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 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)
            if args.simple_ddp:
                dist.barrier()

        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)
@@ -1340,12 +1348,16 @@
            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,
                                                   mode="train")
                valid_iter_factory = ArkDataLoader(args.valid_data_file, args.token_list, args.dataset_conf,
                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,
                                                   mode="eval")
            elif args.dataset_type == "small":
                train_iter_factory = cls.build_iter_factory(
@@ -1564,6 +1576,7 @@
            preprocess=iter_options.preprocess_fn,
            max_cache_size=iter_options.max_cache_size,
            max_cache_fd=iter_options.max_cache_fd,
            dest_sample_rate=args.frontend_conf["fs"],
        )
        cls.check_task_requirements(
            dataset, args.allow_variable_data_keys, train=iter_options.train
@@ -1835,6 +1848,7 @@
            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,
@@ -1853,6 +1867,7 @@
            data_path_and_name_and_type,
            float_dtype=dtype,
            fs=fs,
            mc=mc,
            preprocess=preprocess_fn,
            key_file=key_file,
        )