train finetune

examples/aishell/conformer/run.sh

@@ -105,7 +105,8 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
   echo "stage 4: ASR Training"
 
   mkdir -p ${exp_dir}/exp/${model_dir}
-  log_file="${exp_dir}/exp/${model_dir}/train.log.txt"
+  current_time=$(date "+%Y-%m-%d_%H-%M")
+  log_file="${exp_dir}/exp/${model_dir}/train.log.txt.${current_time}"
   echo "log_file: ${log_file}"
 
   gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')

funasr/datasets/audio_datasets/preprocessor.py

@@ -26,10 +26,11 @@ class SpeechPreprocessSpeedPerturb(nn.Module):
 			return waveform
 		speed = random.choice(self.speed_perturb)
 		if speed != 1.0:
-			with torch.no_grad():
-				waveform, _ = torchaudio.sox_effects.apply_effects_tensor(
-					torch.tensor(waveform).view(1, -1), fs, [['speed', str(speed)], ['rate', str(fs)]])
-				waveform = waveform.view(-1)
+			if not isinstance(waveform, torch.Tensor):
+				waveform = torch.tensor(waveform)
+			waveform, _ = torchaudio.sox_effects.apply_effects_tensor(
+				waveform.view(1, -1), fs, [['speed', str(speed)], ['rate', str(fs)]])
+			waveform = waveform.view(-1)
 			
 		return waveform
 

funasr/train_utils/trainer.py

@@ -70,6 +70,7 @@ class Trainer:
         self.avg_nbest_model = kwargs.get("avg_nbest_model", 5)
         self.kwargs = kwargs
         self.log_interval = kwargs.get("log_interval", 50)
+        self.batch_total = 0
         
     
         try:
@@ -196,7 +197,9 @@ class Trainer:
         self.optim.zero_grad()
         speed_stats = {}
         time5 = time.perf_counter()
+        
         for batch_idx, batch in enumerate(self.dataloader_train):
+            self.batch_total += 1
             time1 = time.perf_counter()
             speed_stats["data_load"] = f"{time1-time5:0.3f}"
 
@@ -205,25 +208,10 @@ class Trainer:
             my_context = self.model.no_sync if batch_idx % accum_grad != 0 else nullcontext
             with my_context():
                 time2 = time.perf_counter()
-                # print("before, GPU, memory: {:.3f} GB, "
-                #       "{:.3f} GB, "
-                #       "{:.3f} GB, "
-                #       "{:.3f} GB".format(torch.cuda.memory_allocated()/1024/1024/1024,
-                #                      torch.cuda.max_memory_allocated()/1024/1024/1024,
-                #                      torch.cuda.memory_reserved()/1024/1024/1024,
-                #                      torch.cuda.max_memory_reserved()/1024/1024/1024,
-                #                      ))
 
                 retval = self.model(**batch)
                 torch.cuda.empty_cache()
-                # print("after, GPU, memory: {:.3f} GB, "
-                #       "{:.3f} GB, "
-                #       "{:.3f} GB, "
-                #       "{:.3f} GB".format(torch.cuda.memory_allocated()/1024/1024/1024,
-                #                      torch.cuda.max_memory_allocated()/1024/1024/1024,
-                #                      torch.cuda.memory_reserved()/1024/1024/1024,
-                #                      torch.cuda.max_memory_reserved()/1024/1024/1024,
-                #                      ))
+
                 time3 = time.perf_counter()
                 speed_stats["forward_time"] = f"{time3 - time2:0.3f}"
                 loss, stats, weight = retval
@@ -275,7 +263,7 @@ class Trainer:
 
 
             
-            if batch_idx % self.log_interval == 0 or batch_idx == len(self.dataloader_train) - 1:
+            if (batch_idx+1) % self.log_interval == 0 or (batch_idx+1) == len(self.dataloader_train):
                 pbar.update(self.log_interval)
                 gpu_info = "GPU, memory: {:.3f} GB, " \
                            "{:.3f} GB, "\
@@ -287,22 +275,22 @@ class Trainer:
                                              )
                 description = (
                     f"rank: {self.local_rank}, "
-                    f"Train epoch: {epoch}/{self.max_epoch}, "
-                    f"step {batch_idx}/{len(self.dataloader_train)}, "
-                    f"{speed_stats}, "
+                    f"epoch: {epoch}/{self.max_epoch}, "
+                    f"step: {batch_idx}/{len(self.dataloader_train)}, total: {self.batch_total}, "
                     f"(loss: {loss.detach().cpu().item():.3f}), "
                     f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}"
+                    f"{speed_stats}, "
                     f"{gpu_info}"
                 )
                 pbar.set_description(description)
                 if self.writer:
-                    self.writer.add_scalar(f'rank{self.local_rank}, Loss/train', loss.item(),
+                    self.writer.add_scalar(f'rank{self.local_rank}_Loss/train', loss.item(),
                                            epoch*len(self.dataloader_train) + batch_idx)
                     for key, var in stats.items():
-                        self.writer.add_scalar(f'rank{self.local_rank}, {key}/train', var.item(),
+                        self.writer.add_scalar(f'rank{self.local_rank}_{key}/train', var.item(),
                                                epoch * len(self.dataloader_train) + batch_idx)
                     for key, var in speed_stats.items():
-                        self.writer.add_scalar(f'rank{self.local_rank}, {key}/train', eval(var),
+                        self.writer.add_scalar(f'rank{self.local_rank}_{key}/train', eval(var),
                                                epoch * len(self.dataloader_train) + batch_idx)
                     
             # if batch_idx == 2:
@@ -348,24 +336,23 @@ class Trainer:
                 time4 = time.perf_counter()
 
                 
-                if batch_idx % self.log_interval == 0 or batch_idx == len(self.dataloader_train) - 1:
+                if (batch_idx+1) % self.log_interval == 0 or (batch_idx+1) == len(self.dataloader_val):
                     pbar.update(self.log_interval)
                     description = (
                         f"rank: {self.local_rank}, "
                         f"validation epoch: {epoch}/{self.max_epoch}, "
-                        f"step {batch_idx}/{len(self.dataloader_train)}, "
-                        f"{speed_stats}, "
+                        f"step: {batch_idx}/{len(self.dataloader_val)}, "
                         f"(loss: {loss.detach().cpu().item():.3f}), "
                         f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}"
-                        f"rank: {self.local_rank}"
+                        f"{speed_stats}, "
                     )
                     pbar.set_description(description)
                     if self.writer:
-                        self.writer.add_scalar(f"rank{self.local_rank}, Loss/val", loss.item(),
-                                               epoch*len(self.dataloader_train) + batch_idx)
+                        self.writer.add_scalar(f"rank{self.local_rank}_Loss/val", loss.item(),
+                                               epoch*len(self.dataloader_val) + batch_idx)
                         for key, var in stats.items():
-                            self.writer.add_scalar(f'rank{self.local_rank}, {key}/val', var.item(),
-                                                   epoch * len(self.dataloader_train) + batch_idx)
+                            self.writer.add_scalar(f'rank{self.local_rank}_{key}/val', var.item(),
+                                                   epoch * len(self.dataloader_val) + batch_idx)
                         for key, var in speed_stats.items():
-                            self.writer.add_scalar(f'rank{self.local_rank}, {key}/val', eval(var),
-                                                   epoch * len(self.dataloader_train) + batch_idx)
+                            self.writer.add_scalar(f'rank{self.local_rank}_{key}/val', eval(var),
+                                                   epoch * len(self.dataloader_val) + batch_idx)