feat: Audio Comparison - Signal Framing Completed

BE-Pronunciation/Audio Comparison.py

@@ -51,11 +51,13 @@ np.allclose(y_filt, np.concatenate([y_filt_1, y_filt_2]))
 
 # Framing and windowing of voice signals
 
-# In[21]:
+# In[6]:
 
 
+import numpy as np
+
 
- def framing(sig, fs=16000, win_len=0.025, win_hop=0.01):
+def framing(sig, fs=16000, win_len=0.025, win_hop=0.01):
     """
     transform a signal into a series of overlapping frames.
 
@@ -72,78 +74,38 @@ np.allclose(y_filt, np.concatenate([y_filt_1, y_filt_2]))
         array of frames.
         frame length.
     """
-
-
-# 
-#     # pre-emphasis
-#     if pre_emph:
-#         sig = pre_emphasis(sig=sig, pre_emph_coeff=0.97)
-#  
-#     # -> framing
-#     frames, frame_length = framing(sig=sig,
-#                                    fs=fs,
-#                                    win_len=win_len,
-#                                    win_hop=win_hop)
-#  
-#     # -> windowing
-#     windows = windowing(frames=frames,
-#                         frame_len=frame_length,
-#                         win_type=win_type)
-#  
-
-# In[25]:
-
-
-# compute frame length and frame step (convert from seconds to samples)
-
-
-frame_length = win_len * fs
-frame_step = win_hop * fs
-signal_length = len(sig)
-frames_overlap = frame_length - frame_step
+    # compute frame length and frame step (convert from seconds to samples)
+    frame_length = win_len * fs
+    frame_step = win_hop * fs
+    signal_length = len(sig)
+    frames_overlap = frame_length - frame_step
 
     # Make sure that we have at least 1 frame+
-
-num_frames = np.abs(signal_length - frames_overlap) // np.abs(frame_length - frames_overlap)
-rest_samples = np.abs(signal_length - frames_overlap) % np.abs(frame_length - frames_overlap)
+    num_frames = np.abs(signal_length - frames_overlap) // np.abs(frame_length - frames_overlap)
+    rest_samples = np.abs(signal_length - frames_overlap) % np.abs(frame_length - frames_overlap)
 
     # Pad Signal to make sure that all frames have equal number of samples
     # without truncating any samples from the original signal
-        
-if rest_samples != 0:
+    if rest_samples != 0:
         pad_signal_length = int(frame_step - rest_samples)
         z = np.zeros((pad_signal_length))
         pad_signal = np.append(sig, z)
         num_frames += 1
-else:
+    else:
         pad_signal = sig
 
     # make sure to use integers as indices
-        
-frame_length = int(frame_length)
-frame_step = int(frame_step)
-num_frames = int(num_frames)
+    frame_length = int(frame_length)
+    frame_step = int(frame_step)
+    num_frames = int(num_frames)
 
     # compute indices
-    
-idx1 = np.tile(np.arange(0, frame_length), (num_frames, 1))
-idx2 = np.tile(np.arange(0, num_frames * frame_step, frame_step),
+    idx1 = np.tile(np.arange(0, frame_length), (num_frames, 1))
+    idx2 = np.tile(np.arange(0, num_frames * frame_step, frame_step),
                    (frame_length, 1)).T
-indices = idx1 + idx2
-frames = pad_signal[indices.astype(np.int32, copy=False)]
-return frames
-
-
-# In[ ]:
-
-
-
-
-
-# In[ ]:
-
-
-
+    indices = idx1 + idx2
+    frames = pad_signal[indices.astype(np.int32, copy=False)]
+    return frames
 
 
 # In[ ]: