From b5d3df75cf6462aa3bf42fd3c86fa2aa7f1c8a15 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 24 十一月 2023 00:54:44 +0800
Subject: [PATCH] setup jamo

---
 funasr/build_utils/build_trainer.py |   14 +++-----------
 1 files changed, 3 insertions(+), 11 deletions(-)

diff --git a/funasr/build_utils/build_trainer.py b/funasr/build_utils/build_trainer.py
index aff99b5..498d05d 100644
--- a/funasr/build_utils/build_trainer.py
+++ b/funasr/build_utils/build_trainer.py
@@ -25,7 +25,6 @@
 import torch
 import torch.nn
 import torch.optim
-from typeguard import check_argument_types
 
 from funasr.iterators.abs_iter_factory import AbsIterFactory
 from funasr.main_funcs.average_nbest_models import average_nbest_models
@@ -118,7 +117,6 @@
 
     def build_options(self, args: argparse.Namespace) -> TrainerOptions:
         """Build options consumed by train(), eval()"""
-        assert check_argument_types()
         return build_dataclass(TrainerOptions, args)
 
     @classmethod
@@ -156,7 +154,6 @@
 
     def run(self) -> None:
         """Perform training. This method performs the main process of training."""
-        assert check_argument_types()
         # NOTE(kamo): Don't check the type more strictly as far trainer_options
         model = self.model
         optimizers = self.optimizers
@@ -249,14 +246,11 @@
         for iepoch in range(start_epoch, trainer_options.max_epoch + 1):
             if iepoch != start_epoch:
                 logging.info(
-                    "{}/{}epoch started. Estimated time to finish: {}".format(
+                    "{}/{}epoch started. Estimated time to finish: {} hours".format(
                         iepoch,
                         trainer_options.max_epoch,
-                        humanfriendly.format_timespan(
-                            (time.perf_counter() - start_time)
-                            / (iepoch - start_epoch)
-                            * (trainer_options.max_epoch - iepoch + 1)
-                        ),
+                        (time.perf_counter() - start_time) / 3600.0 / (iepoch - start_epoch) * (
+                                trainer_options.max_epoch - iepoch + 1),
                     )
                 )
             else:
@@ -522,7 +516,6 @@
             options: TrainerOptions,
             distributed_option: DistributedOption,
     ) -> Tuple[bool, bool]:
-        assert check_argument_types()
 
         grad_noise = options.grad_noise
         accum_grad = options.accum_grad
@@ -758,7 +751,6 @@
             options: TrainerOptions,
             distributed_option: DistributedOption,
     ) -> None:
-        assert check_argument_types()
         ngpu = options.ngpu
         # no_forward_run = options.no_forward_run
         distributed = distributed_option.distributed

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