update

speech_asr

2023-04-18 1eb85d7d17a12877e36feefcb3bb2f20a2c171f0

update

 funasr/datasets/small_datasets/build_loader.py

@@ -1,13 +1,15 @@
import logging
import os

import torch
from funasr.datasets.small_datasets.dataset import ESPnetDataset
from funasr.datasets.small_datasets.preprocessor import build_preprocess
from funasr.samplers.build_batch_sampler import build_batch_sampler
from funasr.samplers.length_batch_sampler import LengthBatchSampler


def build_dataloader(args, mode="train"):
    preprocess_fn = build_preprocess(args, train=mode=="train")
    dest_sample_rate = args.frontend_conf["fs"] if (args.frontend_conf is not None and "fs" in args.frontend_conf) else 16000
    dest_sample_rate = args.frontend_conf["fs"] if (
                args.frontend_conf is not None and "fs" in args.frontend_conf) else 16000
    if mode == "train":
        data_path_and_name_and_type = args.train_data_path_and_name_and_type
        shape_files = args.train_shape_file
@@ -25,15 +27,22 @@
        utt2category_file = os.path.join(data_path_and_name_and_type[0][0].parent, "utt2category")
    else:
        utt2category_file = None
    batch_sampler = build_batch_sampler(
        type=args.batch_type,
        shape_files=iter_options.shape_files,
        fold_lengths=args.fold_length,
        batch_size=iter_options.batch_size,
        batch_bins=iter_options.batch_bins,
        sort_in_batch=args.sort_in_batch,
        sort_batch=args.sort_batch,

    dataset_conf = args.dataset_conf
    batch_sampler = LengthBatchSampler(
        batch_bins=dataset_conf["batch_size"],
        shape_files=shape_files,
        sort_in_batch=dataset_conf["sort_in_batch"] if hasattr(dataset_conf, "sort_in_batch") else "descending",
        sort_batch=dataset_conf["sort_batch"] if hasattr(dataset_conf, "sort_batch") else "ascending",
        drop_last=False,
        min_batch_size=torch.distributed.get_world_size() if args.distributed else 1,
        utt2category_file=utt2category_file,
        padding=True,
    )

    batches = list(batch_sampler)
    bs_list = [len(batch) for batch in batches]
    logging.info(f"[{mode}] dataset:\n{dataset}")
    logging.info(f"[{mode}] Batch sampler: {batch_sampler}")
    logging.info(
        f"[{mode}] mini-batch sizes summary: N-batch={len(bs_list)}, "
        f"mean={np.mean(bs_list):.1f}, min={np.min(bs_list)}, max={np.max(bs_list)}"
    )