Merge branch 'main' of https://github.com/alibaba-damo-academy/FunASR into ...

雾聪

2023-08-09 574404ce9a77aae0c41bc841613132544490673f

 funasr/utils/prepare_data.py

@@ -195,11 +195,35 @@


def prepare_data(args, distributed_option):
    distributed = distributed_option.distributed
    data_names = args.dataset_conf.get("data_names", "speech,text").split(",")
    data_types = args.dataset_conf.get("data_types", "sound,text").split(",")
    file_names = args.data_file_names.split(",")
    batch_type = args.dataset_conf["batch_conf"]["batch_type"]
    print("data_names: {}, data_types: {}, file_names: {}".format(data_names, data_types, file_names))
    assert len(data_names) == len(data_types) == len(file_names)
    if args.dataset_type == "small":
        args.train_shape_file = [os.path.join(args.data_dir, args.train_set, "{}_shape".format(data_names[0]))]
        args.valid_shape_file = [os.path.join(args.data_dir, args.valid_set, "{}_shape".format(data_names[0]))]
        args.train_data_path_and_name_and_type, args.valid_data_path_and_name_and_type = [], []
        for file_name, data_name, data_type in zip(file_names, data_names, data_types):
            args.train_data_path_and_name_and_type.append(
                ["{}/{}/{}".format(args.data_dir, args.train_set, file_name), data_name, data_type])
            args.valid_data_path_and_name_and_type.append(
                ["{}/{}/{}".format(args.data_dir, args.valid_set, file_name), data_name, data_type])
        if os.path.exists(args.train_shape_file[0]):
            assert os.path.exists(args.valid_shape_file[0])
            print('shape file for small dataset already exists.')
            return
    else:
        concat_data_name = "_".join(data_names)
        args.train_data_file = os.path.join(args.data_dir, args.train_set, "{}_data.list".format(concat_data_name))
        args.valid_data_file = os.path.join(args.data_dir, args.valid_set, "{}_data.list".format(concat_data_name))
        if os.path.exists(args.train_data_file):
            assert os.path.exists(args.valid_data_file)
            print('data list for large dataset already exists.')
            return

    distributed = distributed_option.distributed
    if not distributed or distributed_option.dist_rank == 0:
        if hasattr(args, "filter_input") and args.filter_input:
            filter_wav_text(args.data_dir, args.train_set)
@@ -213,20 +237,5 @@
            generate_data_list(args, args.data_dir, args.train_set)
            generate_data_list(args, args.data_dir, args.valid_set)

    print("data_names: {}, data_types: {}, file_names: {}".format(data_names, data_types, file_names))
    assert len(data_names) == len(data_types) == len(file_names)
    if args.dataset_type == "small":
        args.train_shape_file = [os.path.join(args.data_dir, args.train_set, "{}_shape".format(data_names[0]))]
        args.valid_shape_file = [os.path.join(args.data_dir, args.valid_set, "{}_shape".format(data_names[0]))]
        args.train_data_path_and_name_and_type, args.valid_data_path_and_name_and_type = [], []
        for file_name, data_name, data_type in zip(file_names, data_names, data_types):
            args.train_data_path_and_name_and_type.append(
                ["{}/{}/{}".format(args.data_dir, args.train_set, file_name), data_name, data_type])
            args.valid_data_path_and_name_and_type.append(
                ["{}/{}/{}".format(args.data_dir, args.valid_set, file_name), data_name, data_type])
    else:
        concat_data_name = "_".join(data_names)
        args.train_data_file = os.path.join(args.data_dir, args.train_set, "{}_data.list".format(concat_data_name))
        args.valid_data_file = os.path.join(args.data_dir, args.valid_set, "{}_data.list".format(concat_data_name))
    if distributed:
        dist.barrier()