Funasr1.0 (#1275)

* funasr1.0 funetine

* funasr1.0 pbar

* update with main (#1260)

* Update websocket_protocol_zh.md

* update

---------

Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com>
Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com>

* update with main (#1264)

* Funasr1.0 (#1261)

* funasr1.0 funetine

* funasr1.0 pbar

* update with main (#1260)

* Update websocket_protocol_zh.md

* update

---------

Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com>
Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com>

---------

Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com>
Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com>

* bug fix

---------

Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com>
Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com>

* funasr1.0 sanm scama

* funasr1.0 infer_after_finetune

* funasr1.0 fsmn-vad bug fix

* funasr1.0 fsmn-vad bug fix

* funasr1.0 fsmn-vad bug fix

* funasr1.0 finetune

* funasr1.0 finetune

* funasr1.0 finetune

---------

Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com>
Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com>

examples/industrial_data_pretraining/paraformer/finetune.sh

@@ -11,9 +11,9 @@ python funasr/bin/train.py \
 +model_revision="v2.0.2" \
 +train_data_set_list="/Users/zhifu/funasr_github/test_local/aishell2_dev_ios/asr_task_debug_len_10.jsonl" \
 +valid_data_set_list="/Users/zhifu/funasr_github/test_local/aishell2_dev_ios/asr_task_debug_len_10.jsonl" \
-++dataset_conf.batch_size=2 \
+++dataset_conf.batch_size=64 \
 ++dataset_conf.batch_type="example" \
 ++train_conf.max_epoch=2 \
+++dataset_conf.num_workers=4 \
 +output_dir="outputs/debug/ckpt/funasr2/exp2" \
-+device="cpu" \
 +debug="true"

funasr/auto/auto_model.py

@@ -132,7 +132,7 @@ class AutoModel:
         self.punc_kwargs = punc_kwargs
         self.spk_model = spk_model
         self.spk_kwargs = spk_kwargs
-        self.model_path = kwargs["model_path"]
+        self.model_path = kwargs.get("model_path", "./")
   
         
     def build_model(self, **kwargs):

funasr/bin/train.py

@@ -40,7 +40,7 @@ def main_hydra(kwargs: DictConfig):
 
 
 def main(**kwargs):
-    
+    print(kwargs)
     # set random seed
     tables.print()
     set_all_random_seed(kwargs.get("seed", 0))

funasr/datasets/audio_datasets/samplers.py

@@ -28,7 +28,7 @@ class BatchSampler(torch.utils.data.BatchSampler):
         self.shuffle = shuffle and is_training
     
     def __len__(self):
-        return self.total_samples
+        return (self.total_samples-1) // self.batch_size + 1
     
     def set_epoch(self, epoch):
         np.random.seed(epoch)

funasr/models/fsmn_vad_streaming/model.py

@@ -255,7 +255,6 @@ class Stats(object):
 		self.waveform = None
 		self.last_drop_frames = 0
 
-
 @tables.register("model_classes", "FsmnVADStreaming")
 class FsmnVADStreaming(nn.Module):
 	"""
@@ -500,7 +499,6 @@ class FsmnVADStreaming(nn.Module):
 		#     # reset class variables and clear the dict for the next query
 		#     self.AllResetDetection()
 		return segments
-	
 
 	def init_cache(self, cache: dict = {}, **kwargs):
     

funasr/train_utils/trainer.py

@@ -147,9 +147,17 @@ class Trainer:
         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()
+                
             self._validate_epoch(epoch)
-            
+
+            if self.use_ddp or self.use_fsdp:
+                dist.barrier()
+                
+
             if self.rank == 0:
                 self._save_checkpoint(epoch)
             
@@ -164,7 +172,9 @@ class Trainer:
             
         if self.use_ddp or self.use_fsdp:
             dist.barrier()
-        self.writer.close()
+      
+        if self.writer:
+            self.writer.close()
         
     
     def _train_epoch(self, epoch):
@@ -230,6 +240,8 @@ class Trainer:
                         continue
                 
                 # Execute an optimization step (update model parameters)
+                if self.use_ddp or self.use_fsdp:
+                    dist.barrier()
                 self.optim.step()
                 self.scheduler.step()
                 # Clear gradients for the next accumulation stage
@@ -244,7 +256,7 @@ class Trainer:
             pbar.update(1)
             if self.local_rank == 0:
                 description = (
-                    f"Epoch: {epoch}/{self.max_epoch}, "
+                    f"Train epoch: {epoch}/{self.max_epoch}, "
                     f"step {batch_idx}/{len(self.dataloader_train)}, "
                     f"{speed_stats}, "
                     f"(loss: {loss.detach().cpu().item():.3f}), "
@@ -306,7 +318,7 @@ class Trainer:
                 pbar.update(1)
                 if self.local_rank == 0:
                     description = (
-                        f"validation: \nEpoch: {epoch}/{self.max_epoch}, "
+                        f"validation epoch: {epoch}/{self.max_epoch}, "
                         f"step {batch_idx}/{len(self.dataloader_train)}, "
                         f"{speed_stats}, "
                         f"(loss: {loss.detach().cpu().item():.3f}), "