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import numpy as np |
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import librosa |
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import librosa.display |
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import argparse |
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import os |
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from PIL import Image |
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from PIL import PngImagePlugin |
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import json |
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import math |
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FLAGS = None |
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parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) |
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parser.add_argument('mode', type=str, help='Choices: folder | single. folder mode for wav files in directory structure, single mode for single files', default='folder') |
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parser.add_argument('--filename', type=str, help='For single mode, enter filename', default=None) |
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parser.add_argument('--rootdir', type=str, help='Roots folder where class folders containing audio files are kept', default='./') |
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parser.add_argument('--outdir', type=str, help='Output directory', default='./output') |
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parser.add_argument('--sr', type=int, help='Samplerate', default=None) |
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parser.add_argument('--fftsize', type=int, help='Size of fft window in samples', default=1024) |
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parser.add_argument('--hopsize', type=int, help='Size of frame hop through sample file', default=256) |
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parser.add_argument('--dur', type=int, help='Make files this duration in sec. If unspecified keep original duration', default=None) |
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FLAGS, unparsed = parser.parse_known_args() |
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print('\n FLAGS parsed : {0}'.format(FLAGS)) |
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def get_subdirs(a_dir): |
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""" Returns a list of sub directory names in a_dir """ |
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return [name for name in os.listdir(a_dir) |
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if (os.path.isdir(os.path.join(a_dir, name)) and not (name.startswith('.')))] |
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def listDirectory(directory, fileExtList): |
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"""Returns a list of file info objects in directory that extension in the list fileExtList - include the . in your extension string""" |
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fnameList = [os.path.normcase(f) |
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for f in os.listdir(directory) |
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if (not(f.startswith('.')))] |
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fileList = [os.path.join(directory, f) |
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for f in fnameList |
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if os.path.splitext(f)[1] in fileExtList] |
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return fileList , fnameList |
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def wav2stft(fname, srate, fftSize, fftHop, dur) : |
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try: |
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audiodata, samplerate = librosa.load(fname, sr=srate, mono=True, duration=dur) |
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except: |
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print('can not read ' + fname) |
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return |
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if srate == None: |
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print('Using native samplerate of ' + str(samplerate)) |
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S = np.abs(librosa.stft(audiodata, n_fft=fftSize, hop_length=fftHop, win_length=fftSize, center=True)) |
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return S |
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def log_scale(x): |
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output = np.log1p(x) |
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return output |
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def findMinMax(img): |
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return np.amin(img),np.amax(img) |
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def logSpect2PNG(outimg, fname, lwinfo=None) : |
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info = PngImagePlugin.PngInfo() |
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lwinfo = lwinfo or {} |
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lwinfo['fileMin'] = str(np.amin(outimg)) |
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lwinfo['fileMax'] = str(np.amax(outimg)) |
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info.add_text('meta',json.dumps(lwinfo)) |
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shift = int(lwinfo['scaleMax']) - int(lwinfo['scaleMin']) |
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SC2 = 255*(outimg-int(lwinfo['scaleMin']))/shift |
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savimg = Image.fromarray(np.flipud(SC2)) |
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pngimg = savimg.convert('L') |
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pngimg.save(fname,pnginfo=info) |
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def checkScaling(topdir, outdir, srate, fftSize, fftHop, dur): |
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""" |
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Returns the max and min values of the log magnitude after STFT in the whole dataset. |
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This is to provide a known and standardized mapping from [min,max] -> [0,255] when saving as a png image. |
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Parameters |
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topdir - the dir containing class folders containing wav files. |
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outdir - the top level directory to write wave files to (written in to class subfolders) |
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dur - (in seconds) all files will be truncated or zeropadded to have this duration given the srate |
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srate - input files will be resampled to srate as they are read in before being saved as wav files |
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""" |
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print("Now determining maximum log magnitude value in dataset for scaling to png...") |
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subdirs = get_subdirs(topdir) |
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max_mag = 0 |
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min_mag = 0 |
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for subdir in subdirs: |
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fullpaths, _ = listDirectory(topdir + '/' + subdir, '.wav') |
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for idx in range(len(fullpaths)) : |
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fname = os.path.basename(fullpaths[idx]) |
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D = wav2stft(fullpaths[idx], srate, fftSize, fftHop, dur) |
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D = log_scale(D) |
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minM,maxM = findMinMax(D) |
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if minM > min_mag: |
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min_mag = minM |
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if maxM > max_mag: |
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max_mag = maxM |
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print("Dataset: min magnitude=",min_mag,"max magnitude=",max_mag) |
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minScale = int(math.floor(min_mag)) |
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maxScale = int(math.ceil(max_mag)) |
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print("Using [{0},{1}] -> [0,255] for png conversion".format(minScale,maxScale)) |
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pnginfo = {} |
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pnginfo['datasetMin'] = str(min_mag) |
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pnginfo['datasetMax'] = str(max_mag) |
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pnginfo['scaleMin'] = str(minScale) |
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pnginfo['scaleMax'] = str(maxScale) |
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return pnginfo |
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def wav2Spect(topdir, outdir, srate, fftSize, fftHop, dur, pnginfo) : |
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""" |
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Creates spectrograms for subfolder-labeled wavfiles. |
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Creates class folders for the spectrogram files in outdir with the same structure found in topdir. |
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Parameters |
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topdir - the dir containing class folders containing wav files. |
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outdir - the top level directory to write wave files to (written in to class subfolders) |
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dur - (in seconds) all files will be truncated or zeropadded to have this duration given the srate |
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srate - input files will be resampled to srate as they are read in before being saved as wav files |
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""" |
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subdirs = get_subdirs(topdir) |
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count = 0 |
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for subdir in subdirs: |
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fullpaths, _ = listDirectory(topdir + '/' + subdir, '.wav') |
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for idx in range(len(fullpaths)) : |
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fname = os.path.basename(fullpaths[idx]) |
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D = wav2stft(fullpaths[idx], srate, fftSize, fftHop, dur) |
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D = log_scale(D) |
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try: |
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os.stat(outdir + '/' + subdir) |
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except: |
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os.makedirs(outdir + '/' + subdir) |
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print(str(count) + ': ' + subdir + '/' + os.path.splitext(fname)[0]) |
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logSpect2PNG(D, outdir+'/'+subdir+'/'+os.path.splitext(fname)[0]+'.png', lwinfo=pnginfo) |
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count +=1 |
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print("COMPLETE") |
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def wav2Spect_single(filename, srate, fftSize, fftHop, dur) : |
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""" |
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Creates spectrograms from single wavfiles. |
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""" |
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D = wav2stft(filename, srate, fftSize, fftHop, dur) |
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D = log_scale(D) |
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minM,maxM = findMinMax(D) |
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print("Dataset: min magnitude=",minM,"max magnitude=",maxM) |
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minScale = int(math.floor(minM)) |
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maxScale = int(math.ceil(maxM)) |
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print("Using [{0},{1}] -> [0,255] for png conversion".format(minScale,maxScale)) |
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pnginfo = {} |
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pnginfo['datasetMin'] = str(minM) |
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pnginfo['datasetMax'] = str(maxM) |
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pnginfo['scaleMin'] = str(minScale) |
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pnginfo['scaleMax'] = str(maxScale) |
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print(str(0) + ': ' + os.path.splitext(filename)[0]) |
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logSpect2PNG(D, os.path.splitext(filename)[0] +'.png',pnginfo) |
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print("COMPLETE") |
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if FLAGS.mode == 'folder': |
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pnginfo = checkScaling(FLAGS.rootdir,FLAGS.outdir,FLAGS.sr,FLAGS.fftsize,FLAGS.hopsize,FLAGS.dur) |
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wav2Spect(FLAGS.rootdir,FLAGS.outdir,FLAGS.sr,FLAGS.fftsize,FLAGS.hopsize,FLAGS.dur,pnginfo) |
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elif FLAGS.mode == 'single': |
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wav2Spect_single(FLAGS.filename,FLAGS.sr,FLAGS.fftsize,FLAGS.hopsize,FLAGS.dur) |
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else: |
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raise ValueError("Not an acceptable mode! Choices: folder | single. folder mode for wav files in directory structure, single mode for single files") |
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