Removed processFile()

utils.py

@@ -34,55 +34,6 @@ TRANSPARENT_BG = dict(
     paper_bgcolor="rgba(0,0,0,0)",
 )
 
-# ---------------------------------------------------------------------------
-# Audio processing
-# ---------------------------------------------------------------------------
-
-def processFile(filePath, pipeline, enableDenoise, earlyCleanup,
-                gainWindow, minimumGain, maximumGain,
-                dfModel=None, dfState=None, attenLimDB=3):
-    """Load, optionally denoise, equalize, and diarize an audio file.
-
-    Returns (annotations, totalTimeInSeconds, waveform_tensor, sample_rate).
-    """
-    print("Loading file")
-    waveformList, sampleRate = su.splitIntoTimeSegments(filePath, 600)
-    print("File loaded")
-
-    enhancedWaveformList = []
-    if enableDenoise:
-        print("Denoising")
-    for w in waveformList:
-        if enableDenoise:
-            from df import enhance
-            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
-
-    print("Equalizing Audio")
-    waveform_gain_adjusted = su.equalizeVolume()(
-        waveformEnhanced, sampleRate, gainWindow, minimumGain, maximumGain
-    )
-    if earlyCleanup:
-        del waveformEnhanced
-    print("Audio Equalized")
-
-    print("Detecting speakers")
-    diarization_output = pipeline({"waveform": waveform_gain_adjusted, "sample_rate": sampleRate})
-    annotations = diarization_output.speaker_diarization
-    print("Speakers Detected")
-
-    totalTimeInSeconds = int(waveform_gain_adjusted.shape[-1] / sampleRate)
-    return annotations, totalTimeInSeconds, waveform_gain_adjusted, sampleRate
-
-
 # ---------------------------------------------------------------------------
 # Speaker sample helpers
 # ---------------------------------------------------------------------------