From 98abc0e5ac1a1da0fe1802d9ffb623802fbf0b2f Mon Sep 17 00:00:00 2001
From: jmwang66 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期四, 29 六月 2023 16:30:39 +0800
Subject: [PATCH] update setup (#686)
---
funasr/datasets/small_datasets/collate_fn.py | 5 -----
1 files changed, 0 insertions(+), 5 deletions(-)
diff --git a/funasr/datasets/small_datasets/collate_fn.py b/funasr/datasets/small_datasets/collate_fn.py
index 573f581..5fd4162 100644
--- a/funasr/datasets/small_datasets/collate_fn.py
+++ b/funasr/datasets/small_datasets/collate_fn.py
@@ -6,8 +6,6 @@
import numpy as np
import torch
-from typeguard import check_argument_types
-from typeguard import check_return_type
from funasr.modules.nets_utils import pad_list
@@ -22,7 +20,6 @@
not_sequence: Collection[str] = (),
max_sample_size=None
):
- assert check_argument_types()
self.float_pad_value = float_pad_value
self.int_pad_value = int_pad_value
self.not_sequence = set(not_sequence)
@@ -53,7 +50,6 @@
) -> Tuple[List[str], Dict[str, torch.Tensor]]:
"""Concatenate ndarray-list to an array and convert to torch.Tensor.
"""
- assert check_argument_types()
uttids = [u for u, _ in data]
data = [d for _, d in data]
@@ -79,7 +75,6 @@
output[key + "_lengths"] = lens
output = (uttids, output)
- assert check_return_type(output)
return output
def crop_to_max_size(feature, target_size):
--
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