update

嘉渊

2023-04-25 7436acc5ddca0ebb7458a0c4c483079346e10715

 funasr/utils/prepare_data.py

@@ -36,10 +36,8 @@
                f_text.write(sample_name + " " + text_dict[sample_name] + "\n")
            else:
                filter_count += 1
    logging.info(
        "{}/{} samples in {} are filtered because of the mismatch between wav.scp and text".format(len(wav_lines),
                                                                                                   filter_count,
                                                                                                   dataset))
    logging.info("{}/{} samples in {} are filtered because of the mismatch between wav.scp and text".
                 format(filter_count, len(wav_lines), dataset))


def wav2num_frame(wav_path, frontend_conf):
@@ -157,30 +155,34 @@


def prepare_data(args, distributed_option):
    if args.dataset_type == "small" and args.train_data_path_and_name_and_type is not None:
        return
    if args.dataset_type == "large" and args.train_data_file is not None:
        return
    distributed = distributed_option.distributed
    if not hasattr(args, "train_set"):
        args.train_set = "train"
    if not hasattr(args, "dev_set"):
        args.dev_set = "validation"
    if not distributed or distributed_option.dist_rank == 0:
        filter_wav_text(args.data_dir, args.train_set)
        filter_wav_text(args.data_dir, args.dev_set)
        filter_wav_text(args.data_dir, args.valid_set)

        if args.dataset_type == "small" and args.train_shape_file is None:
            calc_shape(args, args.train_set)
            calc_shape(args, args.dev_set)
            calc_shape(args, args.valid_set)

        if args.dataset_type == "large" and args.train_data_file is None:
            generate_data_list(args.data_dir, args.train_set)
            generate_data_list(args.data_dir, args.dev_set)
            generate_data_list(args.data_dir, args.valid_set)

    args.train_shape_file = [os.path.join(args.data_dir, args.train_set, "speech_shape")]
    args.valid_shape_file = [os.path.join(args.data_dir, args.dev_set, "speech_shape")]
    args.train_data_file = os.path.join(args.data_dir, args.train_set, "data.list")
    args.valid_data_file = os.path.join(args.data_dir, args.dev_set, "data.list")
    if args.dataset_type == "small":
        args.train_shape_file = [os.path.join(args.data_dir, args.train_set, "speech_shape")]
        args.valid_shape_file = [os.path.join(args.data_dir, args.valid_set, "speech_shape")]
        data_names = args.dataset_conf.get("data_names", "speech,text").split(",")
        data_types = args.dataset_conf.get("data_types", "sound,text").split(",")
        args.train_data_path_and_name_and_type = [
            ["{}/{}/wav.scp".format(args.data_dir, args.train_set), data_names[0], data_types[0]],
            ["{}/{}/text".format(args.data_dir, args.train_set), data_names[1], data_types[1]]
        ]
        args.valid_data_path_and_name_and_type = [
            ["{}/{}/wav.scp".format(args.data_dir, args.valid_set), data_names[0], data_types[0]],
            ["{}/{}/text".format(args.data_dir, args.valid_set), data_names[1], data_types[1]]
        ]
    else:
        args.train_data_file = os.path.join(args.data_dir, args.train_set, "data.list")
        args.valid_data_file = os.path.join(args.data_dir, args.valid_set, "data.list")
    if distributed:
        dist.barrier()