Finished flattening script

flatten_RMS.py

@@ -8,16 +8,52 @@ from pysndfile import sndio
 from pathtype import PathType
 import csv
 import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
 
 def flattenRMS(AudioFile, AnnotationFile):
     with open(AnnotationFile, 'r') as f:
         csvData = pd.read_csv(f)
     data, fs, encStr, fmtStr = sndio.read(AudioFile, return_format=True)
     csvData['start'] *= fs
+    csvData['start'] = csvData['start'].astype(int)
     csvData['stop'] *= fs
+    csvData['stop'] = csvData['stop'].astype(int)
+
+    zerox = np.where(np.diff(np.sign(data)))[0]
     # get silent sections
-    csvData.loc[csvData['name'] == '#']
-    pdb.set_trace()
+    silences = csvData.loc[csvData['name'] == '#']
+    audio = csvData.loc[csvData['name'] != '#']
+
+    # Find nearest zero-crossing to start and stop times of silences
+    nearestZerox = zerox[np.abs(zerox - csvData['start'][:, np.newaxis]).argmin(axis=1)]
+    csvData['start'] = nearestZerox
+    nearestZerox = zerox[np.abs(zerox - csvData['stop'][:, np.newaxis]).argmin(axis=1)]
+    csvData['stop'] = nearestZerox
+
+    csvData['rms'] = np.nan
+    for ind, chunk in csvData.iterrows():
+        if not chunk['name'] == '#':
+            rms = np.sqrt(np.mean(data[chunk['start']:chunk['stop']]**2))
+            csvData.iloc[ind, csvData.columns.get_loc('rms')] = rms
+    avgRMS = csvData['rms'][csvData['rms'].notnull()].mean()
+
+    silentData = np.zeros(int(0.3*fs))
+    out = np.array([])
+    for ind, chunk in csvData.iterrows():
+        if chunk['name'] == '#':
+            out = np.append(out, silentData)
+        else:
+            rmsCorFactor = avgRMS / chunk['rms']
+
+            out = np.append(out, data[chunk['start']:chunk['stop']])#*rmsCorFactor)
+            print(np.sqrt(np.mean((data[chunk['start']:chunk['stop']]*rmsCorFactor)**2)))
+
+    sndio.write('./out.wav', out, rate=fs, format=fmtStr, enc=encStr)
+
+
+
+    #silences['start'] = np.abs(zerox - silences['start'])).min()
 
 
     #for line in lines[1:]: