import librosa from pysndfx import AudioEffectsChain import numpy as np import math import python_speech_features import scipy as sp from scipy import signal from os import listdir from os.path import isfile, join import argparse from tqdm import tqdm import soundfile def read_file(file_name): # sample_file = file_name # sample_directory = '00_samples/' # sample_path = sample_directory + sample_file # generating audio time series and a sampling rate (int) y, sr = librosa.load(file_name) return y, sr def reduce_noise_power(y, sr): cent = librosa.feature.spectral_centroid(y=y, sr=sr) threshold_h = round(np.median(cent))*1.5 threshold_l = round(np.median(cent))*0.1 less_noise = AudioEffectsChain().lowshelf(gain=-30.0, frequency=threshold_l, slope=0.8).highshelf(gain=-12.0, frequency=threshold_h, slope=0.5)#.limiter(gain=6.0) y_clean = less_noise(y) return y_clean def reduce_noise_centroid_s(y, sr): cent = librosa.feature.spectral_centroid(y=y, sr=sr) threshold_h = np.max(cent) threshold_l = np.min(cent) less_noise = AudioEffectsChain().lowshelf(gain=-12.0, frequency=threshold_l, slope=0.5).highshelf(gain=-12.0, frequency=threshold_h, slope=0.5).limiter(gain=6.0) y_cleaned = less_noise(y) return y_cleaned def reduce_noise_centroid_mb(y, sr): cent = librosa.feature.spectral_centroid(y=y, sr=sr) threshold_h = np.max(cent) threshold_l = np.min(cent) less_noise = AudioEffectsChain().lowshelf(gain=-30.0, frequency=threshold_l, slope=0.5).highshelf(gain=-30.0, frequency=threshold_h, slope=0.5).limiter(gain=10.0) # less_noise = AudioEffectsChain().lowpass(frequency=threshold_h).highpass(frequency=threshold_l) y_cleaned = less_noise(y) cent_cleaned = librosa.feature.spectral_centroid(y=y_cleaned, sr=sr) columns, rows = cent_cleaned.shape boost_h = math.floor(rows/3*2) boost_l = math.floor(rows/6) boost = math.floor(rows/3) # boost_bass = AudioEffectsChain().lowshelf(gain=20.0, frequency=boost, slope=0.8) boost_bass = AudioEffectsChain().lowshelf(gain=16.0, frequency=boost_h, slope=0.5)#.lowshelf(gain=-20.0, frequency=boost_l, slope=0.8) y_clean_boosted = boost_bass(y_cleaned) return y_clean_boosted def reduce_noise_mfcc_down(y, sr): hop_length = 512 ## librosa # mfcc = librosa.feature.mfcc(y=y, sr=sr, hop_length=hop_length, n_mfcc=13) # librosa.mel_to_hz(mfcc) ## mfcc mfcc = python_speech_features.base.mfcc(y) mfcc = python_speech_features.base.logfbank(y) mfcc = python_speech_features.base.lifter(mfcc) sum_of_squares = [] index = -1 for r in mfcc: sum_of_squares.append(0) index = index + 1 for n in r: sum_of_squares[index] = sum_of_squares[index] + n**2 strongest_frame = sum_of_squares.index(max(sum_of_squares)) hz = python_speech_features.base.mel2hz(mfcc[strongest_frame]) max_hz = max(hz) min_hz = min(hz) speech_booster = AudioEffectsChain().highshelf(frequency=min_hz*(-1)*1.2, gain=-12.0, slope=0.6).limiter(gain=8.0) y_speach_boosted = speech_booster(y) return (y_speach_boosted) def reduce_noise_mfcc_up(y, sr): hop_length = 512 ## librosa # mfcc = librosa.feature.mfcc(y=y, sr=sr, hop_length=hop_length, n_mfcc=13) # librosa.mel_to_hz(mfcc) ## mfcc mfcc = python_speech_features.base.mfcc(y) mfcc = python_speech_features.base.logfbank(y) mfcc = python_speech_features.base.lifter(mfcc) sum_of_squares = [] index = -1 for r in mfcc: sum_of_squares.append(0) index = index + 1 for n in r: sum_of_squares[index] = sum_of_squares[index] + n**2 strongest_frame = sum_of_squares.index(max(sum_of_squares)) hz = python_speech_features.base.mel2hz(mfcc[strongest_frame]) max_hz = max(hz) min_hz = min(hz) speech_booster = AudioEffectsChain().lowshelf(frequency=min_hz*(-1), gain=12.0, slope=0.5)#.highshelf(frequency=min_hz*(-1)*1.2, gain=-12.0, slope=0.5)#.limiter(gain=8.0) y_speach_boosted = speech_booster(y) return (y_speach_boosted) def reduce_noise_median(y, sr): y = sp.signal.medfilt(y,3) return (y) def trim_silence(y): y_trimmed, index = librosa.effects.trim(y, top_db=20, frame_length=2, hop_length=500) trimmed_length = librosa.get_duration(y) - librosa.get_duration(y_trimmed) return y_trimmed, trimmed_length def enhance(y): apply_audio_effects = AudioEffectsChain().lowshelf(gain=10.0, frequency=260, slope=0.1).reverb(reverberance=25, hf_damping=5, room_scale=5, stereo_depth=50, pre_delay=20, wet_gain=0, wet_only=False)#.normalize() y_enhanced = apply_audio_effects(y) return y_enhanced def output_file(destination ,filename, y, sr, ext=""): destination = destination + filename.split("/")[-1][:-4] + ext + '.wav' # librosa.output.write_wav(destination, y, sr) soundfile.write(destination, y, sr, subtype='PCM_16') if __name__ == "__main__": samples = ['01_counting.m4a','02_wind_and_cars.m4a','03_truck.m4a','04_voices.m4a','05_ambeint.m4a','06_office.m4a'] ap = argparse.ArgumentParser() ap.add_argument("-b", "--audio_path", default="../audio_clips", help="downsample or upsample") args = vars(ap.parse_args()) samples = [join(args["audio_path"], f) for f in listdir(args["audio_path"]) if isfile(join(args["audio_path"], f)) and f.endswith(".wav")] for s in tqdm(samples): # reading a file filename = s y, sr = read_file(filename) # reducing noise using db power y_reduced_power = reduce_noise_power(y, sr) y_reduced_centroid_s = reduce_noise_centroid_s(y, sr) y_reduced_centroid_mb = reduce_noise_centroid_mb(y, sr) y_reduced_mfcc_up = reduce_noise_mfcc_up(y, sr) y_reduced_mfcc_down = reduce_noise_mfcc_down(y, sr) y_reduced_median = reduce_noise_median(y, sr) # trimming silences y_reduced_power, time_trimmed = trim_silence(y_reduced_power) # print (time_trimmed) y_reduced_centroid_s, time_trimmed = trim_silence(y_reduced_centroid_s) # print (time_trimmed) y_reduced_power, time_trimmed = trim_silence(y_reduced_power) # print (time_trimmed) y_reduced_centroid_mb, time_trimmed = trim_silence(y_reduced_centroid_mb) # print (time_trimmed) y_reduced_mfcc_up, time_trimmed = trim_silence(y_reduced_mfcc_up) # print (time_trimmed) y_reduced_mfcc_down, time_trimmed = trim_silence(y_reduced_mfcc_down) # print (time_trimmed) y_reduced_median, time_trimmed = trim_silence(y_reduced_median) # generating output file [1] output_file('../pwr/' ,filename, y_reduced_power, sr, '_pwr') output_file('../ctr_s/' ,filename, y_reduced_centroid_s, sr, '_ctr_s') output_file('../ctr_mb/' ,filename, y_reduced_centroid_mb, sr, '_ctr_mb') output_file('../mfcc_up/' ,filename, y_reduced_mfcc_up, sr, '_mfcc_up') output_file('../mfcc_down/' ,filename, y_reduced_mfcc_down, sr, '_mfcc_down') output_file('../median/' ,filename, y_reduced_median, sr, '_median') output_file('../org/' ,filename, y, sr, '_org')