Update app.py

app.py

@@ -20,10 +20,8 @@ import torch
 #import torch_xla.core.xla_model as xm
 from pyannote.audio import Pipeline
 from pyannote.core import Annotation, Segment, Timeline
-from df.enhance import enhance, init_df
 import datetime as dt
 
-enableDenoise = False
 earlyCleanup = True
 
 # [None,Low,Medium,High,Debug]
@@ -68,27 +66,13 @@ def save_data(
     scheduler.append(data)
 
 def processFile(filePath):
-    global attenLimDb
     global gainWindow
     global minimumGain
     global maximumGain
     print("Loading file")
     waveformList, sampleRate = su.splitIntoTimeSegments(filePath,600)
     print("File loaded")
-    enhancedWaveformList = []
-    if (enableDenoise):
-        print("Denoising")
-    for w in waveformList:
-        if (enableDenoise):
-            newW = enhance(dfModel,dfState,w,atten_lim_db=attenLimDB).detach().cpu()
-            enhancedWaveformList.append(newW)
-        else:
-            enhancedWaveformList.append(w)
-    if (enableDenoise):
-        print("Audio denoised")
-    waveformEnhanced = su.combineWaveforms(enhancedWaveformList)    
-    if (earlyCleanup):
-        del enhancedWaveformList
+    waveformEnhanced = su.combineWaveforms(waveformList)    
     print("Equalizing Audio")
     waveform_gain_adjusted = su.equalizeVolume()(waveformEnhanced,sampleRate,gainWindow,minimumGain,maximumGain)
     if (earlyCleanup):
@@ -287,7 +271,6 @@ secondDifference = 5
 gainWindow = 4
 minimumGain = -45
 maximumGain = -5
-attenLimDB = 3
 
 isGPU = False
 
@@ -304,10 +287,6 @@ except RuntimeError as e:
     print(f"Using {device} instead.")
 #device = xm.xla_device()
 
-if (enableDenoise):
-    # Instantiate and prepare model for training.
-    dfModel, dfState, _ = init_df(model_base_dir="DeepFilterNet3")
-    dfModel.to(device)#torch.device("cuda"))
 pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1")
 pipeline.to(device)#torch.device("cuda"))