Merge branch 'main' into funasr1.0

funasr/models/fsmn_vad_streaming/model.py

@@ -255,7 +255,6 @@ def __init__(self,
 		self.waveform = None
 		self.last_drop_frames = 0
 
-
 @tables.register("model_classes", "FsmnVADStreaming")
 class FsmnVADStreaming(nn.Module):
 	"""
@@ -500,8 +499,9 @@ def forward(self, feats: torch.Tensor, waveform: torch.tensor, cache: dict = {},
 		#     # reset class variables and clear the dict for the next query
 		#     self.AllResetDetection()
 		return segments
-
-	def init_cache(self, cache: dict = {}):
+
+	def init_cache(self, cache: dict = {}, **kwargs):
+
 		cache["frontend"] = {}
 		cache["prev_samples"] = torch.empty(0)
 		cache["encoder"] = {}
@@ -528,9 +528,9 @@ def inference(self,
 	              cache: dict = {},
 	              **kwargs,
 	              ):
-		# cache = kwargs.get("cache", {})
+
 		if len(cache) == 0:
-			self.init_cache(cache)
+			self.init_cache(cache, **kwargs)
 
 		meta_data = {}
 		chunk_size = kwargs.get("chunk_size", 60000) # 50ms

funasr/train_utils/trainer.py

@@ -147,6 +147,7 @@ def run(self):
         for epoch in range(self.start_epoch, self.max_epoch + 1):
 
             self._train_epoch(epoch)
+
 
             if self.use_ddp or self.use_fsdp:
                 dist.barrier()
@@ -156,6 +157,7 @@ def run(self):
             if self.use_ddp or self.use_fsdp:
                 dist.barrier()
 
+
             if self.rank == 0:
                 self._save_checkpoint(epoch)
 
@@ -170,7 +172,7 @@ def run(self):
 
         if self.use_ddp or self.use_fsdp:
             dist.barrier()
-            
+
         if self.writer:
             self.writer.close()