fix: handle None scheduler_conf in trainer, add scheduler to VAD finetune

- Fix TypeError when scheduler_conf is not provided (NoneType unpacking)
- Add explicit scheduler and scheduler_conf to VAD finetune.sh
- Fixes user report in #2961

Author: LauraGPT

examples/industrial_data_pretraining/fsmn_vad_streaming/finetune.sh

@@ -61,4 +61,6 @@ torchrun \
 ++train_conf.avg_nbest_model=3 \
 ++train_conf.use_deepspeed=false \
 ++optim_conf.lr=0.00005 \
+++scheduler="warmuplr" \
+++scheduler_conf.warmup_steps=1000 \
 ++output_dir="${output_dir}" &> ${log_file}

funasr/train_utils/trainer_ds.py

@@ -1018,7 +1018,7 @@ def warp_optim_scheduler(self, model, **kwargs):
         scheduler = kwargs.get("scheduler", "warmuplr")
         assert scheduler in scheduler_classes
         scheduler_class = scheduler_classes.get(scheduler)
-        scheduler = scheduler_class(optim, **kwargs.get("scheduler_conf"))
+        scheduler = scheduler_class(optim, **(kwargs.get("scheduler_conf") or {}))
 
         if self.use_deepspeed:
             import deepspeed
@@ -1047,7 +1047,7 @@ def scheduler(opt):
                             Args:
                                 opt: TODO.
                             """
-                        return scheduler_class(opt, **kwargs.get("scheduler_conf"))
+                        return scheduler_class(opt, **(kwargs.get("scheduler_conf") or {}))
 
             model, optimizer, _, scheduler = deepspeed.initialize(
                 args=args,