https://github.com/muhdhuz/audio2spec

audio2spec-master/README.md

@@ -0,0 +1,49 @@
+## wav2png
+
+Run this script to convert wav files to spectrograms, which are saved as png files.
+Is able to run on a folder structure with class labels:
+      
+      root/dog/0001.wav
+      root/dog/0002.wav
+
+      root/cat/0001.wav
+      root/cat/0002.wav   etc.
+Or otherwise on single files.
+
+Example use:  
+
+**For class folder structure**
+```bash
+python ./wav2png.py folder --rootdir [rootdir]
+```
+**For single files**
+```bash
+ python ./wav2png.py single --filename [filename.wav]
+```
+Scaling is done on the STFT output to be compatible with 8-bit png format. The script searches the dataset for the maximum and minimum values, rounds up and down to the nearest integer respectively then scales to [0,255].
+
+## png2wav
+
+Run to convert individual png spectrograms back to wav. Script assumes (and inverts) similar scaling as in wav2png. Griffin-Lim algortihm is initialized with [SPSI](http://ieeexplore.ieee.org/abstract/document/7251907/). SPSI code originally from [here](https://github.com/lonce/SPSI_Python).
+
+Example use:  
+
+**For single png spectrogram**
+```bash
+python ./png2wav.py [filename.png]
+```
+
+## cqtconv
+
+This script is to transform a linear frequency scaled spectrogram image (like the ones generated by wav2png) to a pseudo constant-Q (CQT) scaled spectrogram. Algorithm is adapted from [Matlab code written by Dan Ellis](http://www.ee.columbia.edu/ln/rosa/matlab/sgram/).
+
+Example use:  
+
+**For linear to cqt**
+```bash
+python ./cqtconv.py --rootdir [rootdir] --outdir [outdir] --conversion spec2cqt
+```
+**For cqt to linear**
+```bash
+python ./cqtconv.py --rootdir [rootdir] --outdir [outdir] --conversion cqt2spec 
+```

audio2spec-master/cqtconv.py

@@ -0,0 +1,219 @@
+#!/usr/bin/env python 
+import numpy as np
+# https://github.com/librosa/librosa
+import librosa
+import librosa.display
+import argparse
+import os
+from PIL import Image
+from PIL import PngImagePlugin
+import json
+import scipy
+
+
+FLAGS = None
+# ------------------------------------------------------
+# get any args provided on the command line
+parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+parser.add_argument('--conversion', type=str, choices=['spec2cqt','cqt2spec'], help='direction of conversion', default='spec2cqt')
+parser.add_argument('--filename', type=str, help='For single mode, enter filename', default=None) 
+parser.add_argument('--rootdir', type=str, help='Roots folder where class folders containing audio files are kept', default='./') 
+parser.add_argument('--outdir', type=str, help='Output directory', default='./output')
+#parser.add_argument('--sr', type=int, help='Samplerate', default=None) 
+parser.add_argument('--fftsize', type=int, help='Size of fft window in samples', default=1024)
+#parser.add_argument('--hopsize', type=int, help='Size of frame hop through sample file', default=256)
+
+FLAGS, unparsed = parser.parse_known_args()
+print('\n FLAGS parsed :  {0}'.format(FLAGS))
+
+#filetypes = ['.wav','.mp3'] not yet implimented. manually change fileExtList in listDirectory
+
+def get_subdirs(a_dir):
+    """ Returns a list of sub directory names in a_dir """ 
+    return [name for name in os.listdir(a_dir)
+            if (os.path.isdir(os.path.join(a_dir, name)) and not (name.startswith('.')))]
+
+
+def listDirectory(directory, fileExtList):                                        
+    """Returns a list of file info objects in directory that extension in the list fileExtList - include the . in your extension string"""
+    fnameList = [os.path.normcase(f)
+                for f in os.listdir(directory)
+                    if (not(f.startswith('.')))]            
+    fileList = [os.path.join(directory, f) 
+               for f in fnameList
+                if os.path.splitext(f)[1] in fileExtList]  
+    return fileList , fnameList
+
+
+def logSpect2PNG(outimg, fname, lwinfo=None) :
+    
+    info = PngImagePlugin.PngInfo()
+    lwinfo = lwinfo or {}
+    lwinfo['fileMin'] = str(np.amin(outimg))
+    lwinfo['fileMax'] = str(np.amax(outimg))
+    info.add_text('meta',json.dumps(lwinfo)) #info required to reverse scaling
+    
+    shift = int(lwinfo['scaleMax']) - int(lwinfo['scaleMin'])
+    SC2 = 255*(outimg-int(lwinfo['scaleMin']))/shift
+    savimg = Image.fromarray(np.flipud(SC2))
+
+    pngimg = savimg.convert('L')  
+    pngimg.save(fname,pnginfo=info)
+    
+
+def PNG2LogSpect(fname,scalemin,scalemax):
+
+    """
+    Read png spectrograms, expand to original scale and return numpy array.
+    If not stored in one of png metadata, the values needed to undo previous scaling are required to be specified.
+    """
+    img = Image.open(fname)
+    #info = PngImagePlugin.PngInfo()
+    
+    try:       
+        img.text = img.text
+        lwinfo = json.loads(img.text['meta'])
+    except:
+        print('PNG2LogSpect: no img.text, using user specified values!')
+        lwinfo = {}
+        lwinfo['scaleMin'] = scalemin #require to pass in
+        lwinfo['scaleMax'] = scalemax
+        #info.add_text('meta',json.dumps(lwinfo))
+   
+    minx, maxx = float(lwinfo['scaleMin']), float(lwinfo['scaleMax'])
+    #minx, maxx = float(lwinfo['oldmin']), float(lwinfo['oldmax'])
+    
+    img = img.convert('L')
+    outimg = np.asarray(img, dtype=np.float32)
+    outimg = (outimg - 0)/(255-0)*(maxx-minx) + minx
+
+    return np.flipud(outimg), lwinfo
+
+
+def logfmap(I, L, H) :
+    """
+    % [M,N] = logfmap(I,L,H)
+    I - number of rows in the original spectrogram
+    L - low bin to preserve
+    H - high bin to preserve
+    
+    %     Return a maxtrix for premultiplying spectrograms to map
+    %     the rows into a log frequency space.
+    %     Output map covers bins L to H of input
+    %     L must be larger than 1, since the lowest bin of the FFT
+    %     (corresponding to 0 Hz) cannot be represented on a 
+    %     log frequency axis.  Including bins close to 1 makes 
+    %     the number of output rows exponentially larger.
+    %     N returns the recovery matrix such that N*M is approximately I
+    %     (for dimensions L to H).
+    %     
+    % Ported from MATLAB code written by Dan Ellis:
+    % 2004-05-21 dpwe@ee.columbia.edu
+    """
+    ratio = (H-1)/H;
+    opr = np.int(np.round(np.log(L/H)/np.log(ratio))) #number of frequency bins in log rep + 1
+    print('opr is ' + str(opr))
+    ibin = L*np.exp(list(range(0,opr)*-np.log(ratio)))  #fractional bin numbers (len(ibin) = opr-1)
+    
+    M = np.zeros((opr,I))
+    eps=np.finfo(float).eps
+    
+    for i in range(0, opr) :
+        # Where do we sample this output bin?
+        # Idea is to make them 1:1 at top, and progressively denser below
+        # i.e. i = max -> bin = topbin, i = max-1 -> bin = topbin-1, 
+        # but general form is bin = A exp (i/B)
+
+        tt = np.multiply(np.pi, (list(range(0,I))-ibin[i]))
+        M[i,:] = np.divide((np.sin(tt)+eps) , (tt+eps));
+
+    # Normalize rows, but only if they are boosted by the operation
+    G = np.ones((I));
+    print ('H is ' + str(H))
+    G[0:H] = np.divide(list(range(0,H)), H)
+    
+    N = np.transpose(np.multiply(M,np.matlib.repmat(G,opr,1)))
+                   
+    return M,N
+
+def spect2CQT(topdir, outdir, fftSize, lowRow=1):
+    """ 
+        Creates psuedo constant-Q spectrograms from linear frequency spectrograms. 
+        Creates class folders in outdir with the same structure found in topdir.
+        
+        Parameters
+            topdir - the dir containing class folders containing png (log magnitude) spectrogram files. 
+            outdir - the top level directory to write psuedo constantQ files to (written in to class subfolders)
+            lowRow is the lowest row in the FFT that you want to include in the psuedo constant Q spectrogram
+    """ 
+    
+    # First lets get the logf map we want
+    LIN_FREQ_BINS = int(fftSize/2+1) #number of bins in original linear frequency mag spectrogram
+    LOW_ROW = lowRow
+    LOG_FREQ_BINS = int(fftSize/2+1) #resample the lgfmapped psuedo consantQ matrix to have this many frequency bins
+    M,N = logfmap(LIN_FREQ_BINS,LOW_ROW,LOG_FREQ_BINS)
+    
+    
+    subdirs = get_subdirs(topdir)
+    count = 0
+    for subdir in subdirs:
+
+        fullpaths, _ = listDirectory(topdir + '/' + subdir, '.png')
+            
+        for idx in range(len(fullpaths)) : 
+            fname = os.path.basename(fullpaths[idx])
+            D, pnginfo = PNG2LogSpect(fullpaths[idx],None,None)  
+            
+            # Here's the beef
+            MD = np.dot(M,D)
+            MD = scipy.signal.resample(MD, LIN_FREQ_BINS) #downsample to something reasonable 
+
+            #save
+            #info={}
+            pnginfo["linFreqBins"] =  LIN_FREQ_BINS
+            pnginfo["lowRow"] = LOW_ROW
+            pnginfo["logFreqBins"] = LOG_FREQ_BINS
+            
+            try:
+                os.stat(outdir + '/' + subdir) # test for existence
+            except:
+                os.makedirs(outdir + '/' + subdir) # create if necessary
+            
+            print(str(count) + ': ' + subdir + '/' + os.path.splitext(fname)[0])
+            logSpect2PNG(MD, outdir+'/'+subdir+'/'+os.path.splitext(fname)[0]+'.png',lwinfo=pnginfo)               
+            
+            count +=1
+    print("COMPLETE")
+    
+def CQT2spec(topdir,outdir):
+    
+    subdirs = get_subdirs(topdir)
+    count = 0
+    for subdir in subdirs:
+
+        fullpaths, _ = listDirectory(topdir + '/' + subdir, '.png')
+            
+        for idx in range(len(fullpaths)) : 
+            fname = os.path.basename(fullpaths[idx])
+            D, pnginfo = PNG2LogSpect(fullpaths[idx],None,None) 
+            M,N = logfmap(pnginfo["linFreqBins"],pnginfo["lowRow"],pnginfo["logFreqBins"])
+            resampledD = scipy.signal.resample(D, M.shape[0]) #upsample
+            
+            # Here's the beef
+            ND = np.dot(N,resampledD)
+            
+            try:
+                os.stat(outdir + '/' + subdir) # test for existence
+            except:
+                os.makedirs(outdir + '/' + subdir) # create if necessary
+            
+            print(str(count) + ': ' + subdir + '/' + os.path.splitext(fname)[0])
+            logSpect2PNG(ND, outdir+'/'+subdir+'/'+os.path.splitext(fname)[0]+'.png',lwinfo=pnginfo)               
+            
+            count +=1
+    print("COMPLETE")
+    
+if FLAGS.conversion == 'spec2cqt':
+    spect2CQT(FLAGS.rootdir,FLAGS.outdir,FLAGS.fftsize,lowRow=10)
+elif FLAGS.conversion == 'cqt2spec':
+    CQT2spec(FLAGS.rootdir,FLAGS.outdir)

audio2spec-master/png2wav.py

@@ -0,0 +1,95 @@
+#!/usr/bin/env python 
+import numpy as np
+# https://github.com/librosa/librosa
+import librosa
+import librosa.display
+import argparse
+import os
+from PIL import Image
+from PIL import PngImagePlugin
+import json
+
+from spsi import spsi
+
+FLAGS = None
+# ------------------------------------------------------
+# get any args provided on the command line
+parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+parser.add_argument('filename', type=str, help='Name of log mag spectrogram. Include extension')
+parser.add_argument('--outdir', type=str, help='Output directory', default='./output')
+parser.add_argument('--scalemax', type=int, help='Value to use as the max when scaling from png [0,255] to original [min,max]', default=None)
+parser.add_argument('--scalemin', type=int, help='Value to use as the min when scaling from png [0,255] to original [min,max]', default=None)
+parser.add_argument('--sr', type=int, help='Samplerate', default=22050) 
+parser.add_argument('--hopsize', type=int, help='Size of frame hop through sample file', default=256) 
+parser.add_argument('--glsteps', type=int, help='Number of Griffin&Lim iterations following SPSI', default=50 ) 
+parser.add_argument('--wavfile', type=str, help='Optional name for output audio file. Unspecified means use the png filename', default=None) 
+
+FLAGS, unparsed = parser.parse_known_args()
+print('\n FLAGS parsed :  {0}'.format(FLAGS))
+
+
+def inv_log(img):
+    img = np.exp(img) - 1.
+    return img
+
+
+def PNG2LogSpect(fname,scalemin,scalemax):
+
+    """
+    Read png spectrograms, expand to original scale and return numpy array.
+    If not stored in one of png metadata, the values needed to undo previous scaling are required to be specified.
+    """
+    img = Image.open(fname)
+    #info = PngImagePlugin.PngInfo()
+    
+    try:       
+        img.text = img.text
+        lwinfo = json.loads(img.text['meta'])
+    except:
+        print('PNG2LogSpect: no img.text, using user specified values!')
+        lwinfo = {}
+        lwinfo['scaleMin'] = scalemin #require to pass in
+        lwinfo['scaleMax'] = scalemax
+        #info.add_text('meta',json.dumps(lwinfo))
+   
+    minx, maxx = float(lwinfo['scaleMin']), float(lwinfo['scaleMax'])
+    #minx, maxx = float(lwinfo['oldmin']), float(lwinfo['oldmax'])
+    
+    img = img.convert('L')
+    outimg = np.asarray(img, dtype=np.float32)
+    outimg = (outimg - 0)/(255-0)*(maxx-minx) + minx
+
+    return np.flipud(outimg), lwinfo
+
+
+D,_ = PNG2LogSpect(FLAGS.filename,FLAGS.scalemin,FLAGS.scalemax)
+Dsize, _  = D.shape
+fftsize = 2*(Dsize-1) #infer fftsize from no. of fft bins i.e. height of image
+
+magD = inv_log(D)
+y_out = spsi(magD, fftsize=fftsize, hop_length=FLAGS.hopsize)
+#print(magD.shape)
+#print(y_out.shape)
+
+if FLAGS.glsteps != 0 : #use spsi result for initial phase 
+    x = librosa.stft(y_out, fftsize, FLAGS.hopsize, center=False)
+    p = np.angle(x)
+    #print(x.shape)
+    for i in range(FLAGS.glsteps):
+        S = magD * np.exp(1j*p)
+        y_out = librosa.istft(S, FLAGS.hopsize, center=True) # Griffin Lim, assumes hann window, librosa only does one iteration?
+        p = np.angle(librosa.stft(y_out, fftsize, FLAGS.hopsize, center=True))
+
+scalefactor = np.amax(np.abs(y_out))
+#print(np.amin(np.abs(y_out)))
+#print(y_out[50:70])
+print('scaling peak sample, ' + str(scalefactor) + ' to 1')
+#y_out/=scalefactor
+
+if FLAGS.wavfile == None:
+    librosa.output.write_wav(os.path.splitext(FLAGS.filename)[0]+'.wav', y_out, FLAGS.sr)
+else:
+    librosa.output.write_wav(FLAGS.wavfile, y_out, FLAGS.sr)
+
+
+

audio2spec-master/spsi.py

@@ -0,0 +1,97 @@
+import numpy as np
+import scipy
+
+
+def spsi(msgram, fftsize, hop_length) :
+    """
+    Takes a 2D spectrogram ([freqs,frames]), the fft length (= window length) and the hope size (both in units of samples).
+    Returns an audio signal.
+    """
+    
+    numBins, numFrames  = msgram.shape
+    y_out=np.zeros(numFrames*hop_length+fftsize-hop_length)
+    #np.zeros(numFrames*hop_length+fftsize-hop_length)
+        
+    m_phase=np.zeros(numBins);      
+    m_win=scipy.signal.hanning(fftsize, sym=True)  # assumption here that hann was used to create the frames of the spectrogram
+    
+    #processes one frame of audio at a time
+    for i in range(numFrames) :
+            m_mag=msgram[:, i] 
+            for j in range(1,numBins-1) : 
+                if(m_mag[j]>m_mag[j-1] and m_mag[j]>m_mag[j+1]) : #if j is a peak
+                    alpha=m_mag[j-1];
+                    beta=m_mag[j];
+                    gamma=m_mag[j+1];
+                    denom=alpha-2*beta+gamma;
+                    
+                    if(denom!=0) :
+                        p=0.5*(alpha-gamma)/denom;
+                    else :
+                        p=0;
+                        
+                    phaseRate=2*np.pi*(j+p)/fftsize;    #adjusted phase rate
+                    m_phase[j]= m_phase[j] + hop_length*phaseRate; #phase accumulator for this peak bin
+                    peakPhase=m_phase[j];
+                    
+                    # If actual peak is to the right of the bin freq
+                    if (p>0) :
+                        # First bin to right has pi shift
+                        bin=j+1;
+                        m_phase[bin]=peakPhase+np.pi;
+                        
+                        # Bins to left have shift of pi
+                        bin=j-1;
+                        while((bin>1) and (m_mag[bin]<m_mag[bin+1])) : # until you reach the trough
+                            m_phase[bin]=peakPhase+np.pi;
+                            bin=bin-1;
+                        
+                        #Bins to the right (beyond the first) have 0 shift
+                        bin=j+2;
+                        while((bin<(numBins)) and (m_mag[bin]<m_mag[bin-1])) :
+                            m_phase[bin]=peakPhase;
+                            bin=bin+1;
+                            
+                    #if actual peak is to the left of the bin frequency
+                    if(p<0) :
+                        # First bin to left has pi shift
+                        bin=j-1;
+                        m_phase[bin]=peakPhase+np.pi;
+
+                        # and bins to the right of me - here I am stuck in the middle with you
+                        bin=j+1;
+                        while((bin<(numBins)) and (m_mag[bin]<m_mag[bin-1])) :
+                            m_phase[bin]=peakPhase+np.pi;
+                            bin=bin+1;
+                        
+                        # and further to the left have zero shift
+                        bin=j-2;
+                        while((bin>1) and (m_mag[bin]<m_mag[bin+1])) : # until trough
+                            m_phase[bin]=peakPhase;
+                            bin=bin-1;
+                            
+                #end ops for peaks
+            #end loop over fft bins with
+
+            magphase=m_mag*np.exp(1j*m_phase)  #reconstruct with new phase (elementwise mult)
+            magphase[0]=0; magphase[numBins-1] = 0 #remove dc and nyquist
+            m_recon=np.concatenate([magphase,np.flip(np.conjugate(magphase[1:numBins-1]), 0)]) 
+            
+            #overlap and add
+            m_recon=np.real(np.fft.ifft(m_recon))*m_win
+            y_out[i*hop_length:i*hop_length+fftsize]+=m_recon
+            
+    return y_out
+
+
+def magspect2audio(msgram, fftsize, hop_length)  :
+    return spsi(msgram, fftsize, hop_length)
+
+def logspect2audio(lsgram, fftsize, hop_length) :
+    return spsi(np.power(10, lsgram/20), fftsize, hop_length)
+
+
+
+
+
+

audio2spec-master/wav2png.py

@@ -0,0 +1,202 @@
+#!/usr/bin/env python 
+import numpy as np
+# https://github.com/librosa/librosa
+import librosa
+import librosa.display
+import argparse
+import os
+from PIL import Image
+from PIL import PngImagePlugin
+import json
+import math
+
+
+FLAGS = None
+# ------------------------------------------------------
+# get any args provided on the command line
+parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+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')
+parser.add_argument('--filename', type=str, help='For single mode, enter filename', default=None) 
+parser.add_argument('--rootdir', type=str, help='Roots folder where class folders containing audio files are kept', default='./') 
+parser.add_argument('--outdir', type=str, help='Output directory', default='./output')
+parser.add_argument('--sr', type=int, help='Samplerate', default=None) 
+parser.add_argument('--fftsize', type=int, help='Size of fft window in samples', default=1024)
+parser.add_argument('--hopsize', type=int, help='Size of frame hop through sample file', default=256)
+parser.add_argument('--dur', type=int, help='Make files this duration in sec. If unspecified keep original duration', default=None)
+
+FLAGS, unparsed = parser.parse_known_args()
+print('\n FLAGS parsed :  {0}'.format(FLAGS))
+
+#filetypes = ['.wav','.mp3'] not yet implimented. manually change fileExtList in listDirectory
+
+def get_subdirs(a_dir):
+    """ Returns a list of sub directory names in a_dir """ 
+    return [name for name in os.listdir(a_dir)
+            if (os.path.isdir(os.path.join(a_dir, name)) and not (name.startswith('.')))]
+
+
+def listDirectory(directory, fileExtList):                                        
+    """Returns a list of file info objects in directory that extension in the list fileExtList - include the . in your extension string"""
+    fnameList = [os.path.normcase(f)
+                for f in os.listdir(directory)
+                    if (not(f.startswith('.')))]            
+    fileList = [os.path.join(directory, f) 
+               for f in fnameList
+                if os.path.splitext(f)[1] in fileExtList]  
+    return fileList , fnameList
+
+
+def wav2stft(fname, srate, fftSize, fftHop, dur) :
+    try:
+        audiodata, samplerate = librosa.load(fname, sr=srate, mono=True, duration=dur)
+        #print(np.amax(np.abs(audiodata)))
+        #print(np.amin(np.abs(audiodata)))
+        #print(audiodata[50:70])
+    except:
+        print('can not read ' + fname)
+        return
+    
+    if srate == None:
+        print('Using native samplerate of ' + str(samplerate))
+    S = np.abs(librosa.stft(audiodata, n_fft=fftSize, hop_length=fftHop, win_length=fftSize,  center=True))
+    #print(audiodata.shape)
+    #print(S.shape)
+    return S
+
+
+def log_scale(x):
+    output = np.log1p(x)
+    return output
+
+
+def findMinMax(img):
+    return np.amin(img),np.amax(img)
+
+
+def logSpect2PNG(outimg, fname, lwinfo=None) :
+    
+    info = PngImagePlugin.PngInfo()
+    lwinfo = lwinfo or {}
+    lwinfo['fileMin'] = str(np.amin(outimg))
+    lwinfo['fileMax'] = str(np.amax(outimg))
+    info.add_text('meta',json.dumps(lwinfo)) #info required to reverse scaling
+    
+    shift = int(lwinfo['scaleMax']) - int(lwinfo['scaleMin'])
+    SC2 = 255*(outimg-int(lwinfo['scaleMin']))/shift
+    savimg = Image.fromarray(np.flipud(SC2))
+
+    pngimg = savimg.convert('L')  
+    pngimg.save(fname,pnginfo=info)
+ 
+ 
+def checkScaling(topdir, outdir, srate, fftSize, fftHop, dur):
+    """ 
+        Returns the max and min values of the log magnitude after STFT in the whole dataset.
+        This is to provide a known and standardized mapping from [min,max] -> [0,255] when saving as a png image.
+        
+        Parameters
+            topdir - the dir containing class folders containing wav files. 
+            outdir - the top level directory to write wave files to (written in to class subfolders)
+            dur - (in seconds) all files will be truncated or zeropadded to have this duration given the srate
+            srate - input files will be resampled to srate as they are read in before being saved as wav files            
+    """ 
+    print("Now determining maximum log magnitude value in dataset for scaling to png...") 
+    subdirs = get_subdirs(topdir)
+    max_mag = 0
+    min_mag = 0
+    for subdir in subdirs:
+        
+        fullpaths, _ = listDirectory(topdir + '/' + subdir, '.wav') 
+        
+        for idx in range(len(fullpaths)) : 
+            fname = os.path.basename(fullpaths[idx])
+            
+            D = wav2stft(fullpaths[idx], srate, fftSize, fftHop, dur)
+            D = log_scale(D)
+            minM,maxM = findMinMax(D)
+            if minM > min_mag:
+                min_mag = minM
+            if maxM > max_mag:
+                max_mag = maxM
+    
+    print("Dataset: min magnitude=",min_mag,"max magnitude=",max_mag)
+    minScale = int(math.floor(min_mag))
+    maxScale = int(math.ceil(max_mag)) 
+    #print("New scale: min magnitude=",minScale,"max magnitude=",maxScale)
+    print("Using [{0},{1}] -> [0,255] for png conversion".format(minScale,maxScale))
+    pnginfo = {}
+    pnginfo['datasetMin'] = str(min_mag)
+    pnginfo['datasetMax'] = str(max_mag)
+    pnginfo['scaleMin'] = str(minScale)
+    pnginfo['scaleMax'] = str(maxScale)
+    return pnginfo
+
+    
+def wav2Spect(topdir, outdir, srate, fftSize, fftHop, dur, pnginfo) :
+    """ 
+        Creates spectrograms for subfolder-labeled wavfiles. 
+        Creates class folders for the spectrogram files in outdir with the same structure found in topdir.
+        
+        Parameters
+            topdir - the dir containing class folders containing wav files. 
+            outdir - the top level directory to write wave files to (written in to class subfolders)
+            dur - (in seconds) all files will be truncated or zeropadded to have this duration given the srate
+            srate - input files will be resampled to srate as they are read in before being saved as wav files            
+    """ 
+    
+    subdirs = get_subdirs(topdir)
+    count = 0
+    for subdir in subdirs:
+        
+        fullpaths, _ = listDirectory(topdir + '/' + subdir, '.wav') 
+        
+        for idx in range(len(fullpaths)) : 
+            fname = os.path.basename(fullpaths[idx])
+            D = wav2stft(fullpaths[idx], srate, fftSize, fftHop, dur)
+            D = log_scale(D)
+            
+            try:
+                os.stat(outdir + '/' + subdir) # test for existence
+            except:
+                os.makedirs(outdir + '/' + subdir) # create if necessary
+            
+            print(str(count) + ': ' + subdir + '/' + os.path.splitext(fname)[0])
+
+            logSpect2PNG(D, outdir+'/'+subdir+'/'+os.path.splitext(fname)[0]+'.png', lwinfo=pnginfo)
+            
+            count +=1
+    print("COMPLETE") 
+    
+    
+def wav2Spect_single(filename, srate, fftSize, fftHop, dur) :
+    """ 
+        Creates spectrograms from single wavfiles. 
+    """    
+    D = wav2stft(filename, srate, fftSize, fftHop, dur)
+    D = log_scale(D)
+    minM,maxM = findMinMax(D)
+    
+    print("Dataset: min magnitude=",minM,"max magnitude=",maxM)
+    minScale = int(math.floor(minM))
+    maxScale = int(math.ceil(maxM)) 
+    print("Using [{0},{1}] -> [0,255] for png conversion".format(minScale,maxScale))
+    pnginfo = {}
+    pnginfo['datasetMin'] = str(minM)
+    pnginfo['datasetMax'] = str(maxM)
+    pnginfo['scaleMin'] = str(minScale)
+    pnginfo['scaleMax'] = str(maxScale)
+        
+    print(str(0) + ': ' + os.path.splitext(filename)[0])
+    logSpect2PNG(D, os.path.splitext(filename)[0] +'.png',pnginfo)
+
+    print("COMPLETE") 
+    
+
+if FLAGS.mode == 'folder':
+    pnginfo = checkScaling(FLAGS.rootdir,FLAGS.outdir,FLAGS.sr,FLAGS.fftsize,FLAGS.hopsize,FLAGS.dur)
+    wav2Spect(FLAGS.rootdir,FLAGS.outdir,FLAGS.sr,FLAGS.fftsize,FLAGS.hopsize,FLAGS.dur,pnginfo)
+elif FLAGS.mode == 'single':
+    wav2Spect_single(FLAGS.filename,FLAGS.sr,FLAGS.fftsize,FLAGS.hopsize,FLAGS.dur)
+else:
+    raise ValueError("Not an acceptable mode! Choices: folder | single. folder mode for wav files in directory structure, single mode for single files")
+