Advancements

sound_process.py

@@ -11,7 +11,6 @@ import heapq
 import scipy
 import os
 import random
-from datetime import timedelta
 from pathlib import Path
 from time import sleep
 
@@ -126,8 +125,15 @@ def filter_n_percent(song_name, offset, length, threshold, reduce, show):
     return song_data
 
 def filter_n_percent_serial(song_name, offset, n_iter, step, threshold):
-    # threshold is in ]0, 100]
-    # filter data associated with song_name to keep only the highest threshold% values
+    """
+    song_name : string
+    offset : int
+    n_iter : int (number of turns)
+    step : int (length of each small segment)
+    threshold : int (is in ]0, 100])
+
+    filter data associated with song_name to keep only the highest threshold% values
+    """
 
     subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(offset+step*n_iter), "-i", song_name, "crop.wav"]) 
 
@@ -273,7 +279,22 @@ def get_freq(song_name, offset, step, songlen, data, display=False):
     return frequencies
 
 
-def void_freq(song_name, offset, songlen, increment, minfreq, maxfreq, upperthr, ampthr, ampfreq, ampval, leniency, write, output_file="trimmed.wav"):
+def void_freq(song_name, offset, songlen, increment, minfreq, maxfreq, upperthr, ampthr, ampfreq, ampval, leniency, write, linear, output_file="trimmed.wav"):
+    """
+    song_name : string
+    offset : int 
+    songlen : int (length of the part that will be filtered, starting from offset)
+    increment : float (technical parameter)
+    minfreq and maxfreq : every frequency in [minfreq, maxfreq] will be voided
+    upperthr : every frequency above upperthr will be voided
+    ampthr : every frequency with amplitude under MAX/ampthr (aka amplitudes under (100/ampthr)% of the max will be voided 
+    ampfreq, leniency (if linear is false), linear : technical parameters
+    ampval : int 
+        - if linear is false, then this willbe the maximum amplification possible
+        - if linear is true, this is the multiplier (Amp <- Amp * (ampval * frequency + leniency))
+    write : bool (should be set to True)
+    output_file : technical
+    """
     fft_list = []
     times = []
     current_time = offset
@@ -306,9 +327,14 @@ def void_freq(song_name, offset, songlen, increment, minfreq, maxfreq, upperthr,
     
     print("Finding global max...")
     
+    if(linear == False):
         for i in range(len(fft_list)):
             for j in range(len(fft_list[i])):
                 fft_list[i][j] *= (1 + ampval/max(1, np.abs(pfreq[j] - ampfreq)))  
+    else:
+        for i in range(len(fft_list)):
+            for j in range(len(fft_list[i])):
+                fft_list[i][j] *= (ampval*pfreq[j] + leniency)
 
     print("Trimming...")
 
@@ -369,40 +395,52 @@ def test_sample(timelist):
         print("F")
         sleep(timelist[i]-timelist[i-1])
 
-#Offset = 74.582
-#BPM = 178
-#Length = 48*60/BPM-0.01
-
-#Offset = 0
-#BPM = 180
-#Length = 48*60/BPM-0.01
-
-#Offset = 7
-#BPM = 140
-#Length = 32*60/BPM-0.01
-
-def convert_tuple(datares, freq) -> tuple[timedelta, int, float]:
+def convert_tuple(datares, freq):
     """
     Takes datares and converts it to a list of tuples (amplitude, time in ms)
     """
-    return [(timedelta(milliseconds=i), datares[i], freq[i]) for i in range(len(datares)) if datares[i] > 0]
+    return [(i, datares[i], freq[i]) for i in range(len(datares)) if datares[i] > 0]
 
+def get_songlen(filename):
+    """
+    retrieves the length of the song in seconds 
+    """
+    sample_rate, global_data = wavfile.read(filename)
 
+    return (len(global_data)/sample_rate)
+
+def process_song(filename, bpm, offset=0, div_len_factor=1, n_iter_2=-1, threshold=0.5, divisor=4):
+    """
+    filename : string (name of the song)
+    offset : int [+] (song mapping will start from this time in seconds, default is 0)
+    bpm : int [+]
+    div_len_factor : float [+] (the length multiplier of each segment, default is 1)
+    n_iter : int [+*] (the number of iterations, default is -1 (maps the whole music))
+    threshold : int [0, 100] (used by the filter function to only keep the largest threshold% of timing points, default is 0.5)
+    divisor : int [+] (beat divisor used to snap the notes, default is 4)
+    """
+
+    div_len = div_len_factor*60/bpm-0.01
+
+    n_iter = n_iter_2
+    if(n_iter == -1):
+        song_len = get_songlen(filename)
+        n_iter = int(song_len/div_len)-1
 
-def process_song(filename, offset, bpm, div_len_factor=60, n_iter=48, threshold=0.5, divisor=4):
-    div_len = div_len_factor/bpm-0.01
     filtered_name = f"{filename}_trimmed.wav"
-    void_freq(filename, offset, offset+div_len*(n_iter+1)+0.01, 4*60/bpm, minfreq=0, maxfreq=330, upperthr=5000, ampthr=60, ampfreq = 1200, ampval = 7.27, leniency = 0.005, write=True, output_file=filtered_name)
+
+    void_freq(filename, offset, offset+div_len*(n_iter+1)+0.01, 4*60/bpm, minfreq=0, maxfreq=330, upperthr=5000, ampthr=60, ampfreq = 1200, ampval = 5.0, leniency = 0.005, write=True, linear=False, output_file=filtered_name)
+    #void_freq(filename, offset, offset+div_len*(n_iter+1)+0.01, 4*60/bpm, minfreq=0, maxfreq=330, upperthr=2500, ampthr=60, ampfreq = 1200, ampval = 1/2000, leniency = 0.0, write=True, linear=True, output_file=filtered_name)
     datares = filter_n_percent_serial(filtered_name, offset, n_iter, div_len, threshold) 
     datares = snap(datares, 44100, bpm, 4, True)
     frequencies = get_freq(filtered_name, offset, div_len, div_len*n_iter, datares, True)
-    Path(f"{filename}_trimmed.wav").unlink()
+    #Path(f"{filename}_trimmed.wav").unlink()
     return convert_tuple(datares, frequencies)
 
 
 def main():
-    data = process_song("tetris_4.wav", 0, 160)
-    print(data)
+    data = process_song("tetris_4.wav", 160, n_iter_2 = 32)
+    #print(data)
     print("Program finished with return 0")
 
 if __name__ == "__main__":