merge fix

cli/train.py

@@ -1,3 +1,4 @@
+from genericpath import isfile
 import os
 from types import MethodType
 import hydra
@@ -7,7 +8,7 @@ from torch.optim.lr_scheduler import ReduceLROnPlateau
 from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
 from pytorch_lightning.loggers import MLFlowLogger
 os.environ["HYDRA_FULL_ERROR"] = "1"
-JOB_ID = os.environ.get("SLURM_JOBID")
+JOB_ID = os.environ.get("SLURM_JOBID","0")
 
 @hydra.main(config_path="train_config",config_name="config")
 def main(config: DictConfig):
@@ -55,8 +56,10 @@ def main(config: DictConfig):
 
     trainer = instantiate(config.trainer,logger=logger,callbacks=callbacks)
     trainer.fit(model)
-    if os.path.exists("./model/"):
-        logger.experiment.log_artifact(logger.run_id,f"./model/.*")
+
+    saved_location = os.path.join(trainer.default_root_dir,"model",f"model_{JOB_ID}.ckpt")
+    if os.path.isfile(saved_location):
+        logger.experiment.log_artifact(logger.run_id,saved_location)
 
 
 

cli/train_config/config.yaml

@@ -1,5 +1,5 @@
 defaults:
-  - model : Demucs
+  - model : WaveUnet
   - dataset : Vctk
   - optimizer : Adam
   - hyperparameters : default

cli/train_config/dataset/Vctk.yaml

@@ -3,12 +3,12 @@ name : vctk
 root_dir : /scratch/c.sistc3/DS_10283_2791
 duration : 1.0
 sampling_rate: 16000
-batch_size: 8
+batch_size: 64
 
 files:
-  train_clean : clean_trainset_56spk_wav
+  train_clean : clean_trainset_28spk_wav
   test_clean : clean_testset_wav
-  train_noisy : noisy_trainset_56spk_wav
+  train_noisy : noisy_trainset_28spk_wav
   test_noisy : noisy_testset_wav
 
 

cli/train_config/hyperparameters/default.yaml

@@ -1,7 +1,6 @@
 loss : mse
 metric : mae
 lr : 0.0001
-num_epochs : 100
 ReduceLr_patience : 5
 ReduceLr_factor : 0.1
 min_lr : 0.000001

cli/train_config/trainer/default.yaml

@@ -22,8 +22,8 @@ limit_predict_batches: 1.0
 limit_test_batches: 1.0
 limit_train_batches: 1.0
 limit_val_batches: 1.0
-log_every_n_steps: 10
-max_epochs: 100
+log_every_n_steps: 1
+max_epochs: 10
 max_steps: null
 max_time: null
 min_epochs: 1

enhancer/inference.py

@@ -27,6 +27,8 @@ class Inference:
 
         if isinstance(audio, (np.ndarray, torch.Tensor)):
             assert sr is not None, "Invalid sampling rate!"
+            if len(audio.shape) == 1:
+                audio = audio.reshape(1,-1)
 
         if isinstance(audio, str):
             audio = Path(audio)
@@ -103,6 +105,8 @@ class Inference:
         window = get_window(window=window, Nx=data.shape[-1])
         window = torch.from_numpy(window).to(data.device)
         data *= window
+        step_size = window_size//2 if step_size is None else step_size
+
 
         data = data.permute(1, 2, 0)
         data = F.fold(
@@ -129,6 +133,9 @@ class Inference:
 
         if isinstance(filename, str):
             filename = Path(filename)
+
+        parent, name = filename.parent, "cleaned_"+filename.name
+        filename = parent/Path(name)
         if filename.is_file():
             raise FileExistsError(f"file {filename} already exists")
         else:

enhancer/models/__init__.py

@@ -1,2 +1,3 @@
 from enhancer.models.demucs import Demucs
 from enhancer.models.waveunet import WaveUnet
+from enhancer.models.model import Model

enhancer/models/model.py

@@ -191,8 +191,8 @@ class Model(pl.LightningModule):
             map_location = torch.device(DEFAULT_DEVICE)
 
         loaded_checkpoint = pl_load(model_path_pl,map_location)
-        module_name = loaded_checkpoint["architecture"]["module"]
-        class_name =  loaded_checkpoint["architecture"]["class"]
+        module_name = loaded_checkpoint["enhancer"]["architecture"]["module"]
+        class_name =  loaded_checkpoint["enhancer"]["architecture"]["class"]
         module = import_module(module_name)
         Klass = getattr(module, class_name)
 
@@ -216,7 +216,8 @@ class Model(pl.LightningModule):
         batch_predictions = []
         self.eval().to(self.device)
 
-        for batch_id in range(batch.shape[0],batch_size):
+        with torch.no_grad():
+            for batch_id in range(0,batch.shape[0],batch_size):
                 batch_data = batch[batch_id:batch_id+batch_size,:,:].to(self.device)
                 prediction = self(batch_data)
                 batch_predictions.append(prediction)
@@ -232,9 +233,9 @@ class Model(pl.LightningModule):
         duration:Optional[int]=None,
         step_size:Optional[int]=None,):
 
-        model_sampling_rate = self.model.hprams("sampling_rate")
+        model_sampling_rate = self.hparams["sampling_rate"]
         if duration is None:
-            duration = self.model.hparams("duration")
+            duration = self.hparams["duration"]
         waveform = Inference.read_input(audio,sampling_rate,model_sampling_rate)
         waveform.to(self.device)
         window_size = round(duration * model_sampling_rate)
@@ -246,8 +247,9 @@ class Model(pl.LightningModule):
             Inference.write_output(waveform,audio,model_sampling_rate)
 
         else:
+            waveform = Inference.prepare_output(waveform, model_sampling_rate,
+                                audio, sampling_rate)    
             return waveform
-    
     @property
     def valid_monitor(self):