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+./Files/Tracks/2012/Feb/TRK13-2-12.WAV
+./Files/Tracks/2012/Feb/TRK14-2-12.WAV
+./Files/Tracks/2012/Feb/TRK15a-2-12.WAV
+./Files/Tracks/2012/Feb/TRK15b-2-12.WAV
+./Files/Tracks/2012/Feb/TRK17-2-12.WAV
+./Files/Tracks/2012/Feb/TRK18-2-12.WAV
+./Files/Tracks/2012/Feb/TRK19-2-12.WAV
+./Files/Tracks/2012/Feb/TRK23-2-12.WAV
+./Files/Tracks/2012/Feb/TRK24-2-12.WAV
+./Files/Tracks/2012/Feb/TRK25-2-12.WAV
+./Files/Tracks/2012/Feb/TRK27-2-12.WAV
+./Files/Tracks/2012/Feb/TRK28-2-12.WAV
+./Files/Tracks/2012/Feb/TRK29-2-12.WAV
+./Files/Tracks/2012/Feb/TRK30-2-12.WAV
+./Files/Tracks/2012/Feb/TRK31-2-12.WAV
+./Files/Tracks/2012/Feb/TRK34a-2-12.WAV
+./Files/Tracks/2012/Feb/TRK34b-2-12.WAV
+./Files/Tracks/2012/Feb/TRK35-2-12.WAV
+./Files/Tracks/2012/Feb/TRK37-2-12.WAV
+./Files/Tracks/2012/Feb/TRK38-2-12.WAV
+./Files/Tracks/2012/Feb/TRK39-2-12.WAV
+./Files/Tracks/2012/Feb/TRK40-2-12.WAV
+./Files/Tracks/2012/Jan/TRK08-12.WAV
+./Files/Tracks/2012/Jan/TRK10-12.WAV
+./Files/Tracks/2012/Jan/TRK11-12.WAV
+./Files/Tracks/2012/Jan/TRK12-12.WAV
+./Files/Tracks/2012/Jan/TRK13-12.WAV
+./Files/Tracks/2012/Jan/TRK14-12.WAV
+./Files/Tracks/2012/Jan/TRK15a-12.WAV
+./Files/Tracks/2012/Jan/TRK17-12.WAV
+./Files/Tracks/2012/Jan/TRK18-12.WAV
+./Files/Tracks/2012/Jan/TRK19-12.WAV
+./Files/Tracks/2012/Jan/TRK20-12.WAV
+./Files/Tracks/2012/Jan/TRK21-12.WAV
+./Files/Tracks/2012/Jan/TRK22-12.WAV
+./Files/Tracks/2012/Jan/TRK23-12.WAV
+./Files/Tracks/2012/Jan/TRK24-12.WAV
+./Files/Tracks/2012/Jan/TRK25-12.WAV
+./Files/Tracks/2012/Jan/TRK26-12.WAV
+./Files/Tracks/2012/Jan/TRK27-12.WAV
+./Files/Tracks/2012/Mar/TRK18-3-12.WAV
+./Files/Tracks/2012/Mar/TRKa37-3-12.WAV
+./Files/Tracks/2012/Mar/TRKa41-3-12.WAV
+./Files/Tracks/2012/Mar/TRKa42-3-12.WAV
+./Files/Tracks/2012/Mar/TRKa43-3-12.WAV
+./Files/Tracks/2012/Mar/TRKa44-3-12.WAV
+./Files/Tracks/2012/Mar/TRKa45-3-12.WAV
+./Files/Tracks/2012/Mar/TRKa46-3-12.WAV
+./Files/Tracks/2012/Mar/TRKa47-3-12.WAV
+./Files/Tracks/2012/Mar/TRKa48-3-12.WAV
+./Files/Tracks/2012/Mar/TRKa49-3-12.WAV
+./Files/Tracks/2012/Mar/TRKa51-3-12.WAV
+./Files/Tracks/2012/Mar/TRKa52-3-12.WAV
+./Files/Tracks/2012/Mar/TRKa53-3-12.WAV
+./Files/Tracks/2012/Mar/TRKa54-3-12.WAV
+./Files/Tracks/2012/Mar/TRKb06-3-12.WAV
+./Files/Tracks/2012/Mar/TRKb07-3-12.WAV
+./Files/Tracks/2012/Mar/TRKb09-3-12.WAV
+./Files/Tracks/2012/Mar/TRKb10-3-12.WAV
+./Files/Tracks/2012/Mar/TRKb11-3-12.WAV
+./Files/Tracks/2012/Mar/TRKb12-3-12.WAV
+./Files/Tracks/2012/Mar/TRKb13-3-12.WAV
+./Files/Tracks/2012/Mar/TRKb16-3-12.WAV
+./Files/Tracks/2012/Mar/TRKb17-3-12.WAV
+./Files/Tracks/2013/Jan/SMMJAN13TRK1.WAV
+./Files/Tracks/2013/Jan/SMMJAN13TRK10.WAV
+./Files/Tracks/2013/Jan/SMMJAN13TRK6.WAV
+./Files/Tracks/2013/Jan/SMMJAN13TRK8.WAV
+./Files/Tracks/2013/Jan/SMMJAN13TRK9.WAV
+<tbody><tr role=

specie_list.txt → good_species.txt

 bengalese_finch1
 bengalese_finch2
+globi
 california_thrashers
 cassin_vireo
 black-headed_grosbeaks
-orcas
 humpback
+humpback2
+zebra_finch
+orcas

humpback2/extract_annot.py

+import pandas as pd
+import os
+
+out = []
+for fn in os.listdir('annots/'):
+    a = open('annots/'+fn).read().split('<label ')[1:]
+    for l in a:
+        out.append({'fn':fn[:-3]+'aif', 'pos':l.split('"')[1], 'label':(l.split('"')[-2]).replace(' ','')})
+
+df = pd.DataFrame().from_dict(out)
+df.label = df.label.replace({'c':'C'})
+grp = df.groupby('label').count()
+df.drop(df[df.label.isin(grp[grp.fn<50].index)].index, inplace=True)
+print(df.label.value_counts())
+df.to_csv('humpback2.csv', index=False)

print_annot.py

@@ -14,14 +14,14 @@ args = parser.parse_args()
 meta = models.meta[args.specie]
 df = pd.read_csv(f'{args.specie}/{args.specie}.csv')
 frontend = models.frontend[args.frontend](meta['sr'], meta['nfft'], meta['sampleDur'], args.nMel)
+os.system(f'rm -R {args.specie}/annot_pngs/*')
 for label, grp in df.groupby('label'):
-    # if os.path.isdir(f'{args.specie}/annot_pngs/{label}'):
-        # continue
     os.system(f'mkdir -p "{args.specie}/annot_pngs/{label}"')
     loader = torch.utils.data.DataLoader(u.Dataset(grp.sample(min(len(grp), 100)), args.specie+'/audio/', meta['sr'], meta['sampleDur']),\
                                          batch_size=1, num_workers=4, pin_memory=True)
     for x, idx in tqdm(loader, desc=args.specie + ' ' + label, leave=False):
         x = frontend(x).squeeze().detach()
+        assert not torch.isnan(x).any(), "Found a NaN in spectrogram... :/"
         plt.figure()
         plt.imshow(x, origin='lower', aspect='auto')
         plt.savefig(f'{args.specie}/annot_pngs/{label}/{idx.item()}')

test_AE.py

@@ -30,9 +30,9 @@ if os.path.isfile(f'{args.specie}/encodings_{modelname[:-4]}npy'):
 else:
     gpu = torch.device('cuda')
     frontend = models.frontend[args.frontend](meta['sr'], meta['nfft'], meta['sampleDur'], args.nMel)
-    encoder = models.__dict__[args.encoder](args.bottleneck)
-    decoder = models.sparrow_decoder_old(args.bottleneck, (4, 4) if args.nMel == 128 else (2, 4))
-#    decoder = models.sparrow_decoder(args.bottleneck, (2, 2) if args.nMel == 128 else (1, 2))
+    encoder = models.__dict__[args.encoder](args.bottleneck, (4, 4) if args.nMel == 128 else (2, 4))
+#    decoder = models.sparrow_decoder_old(args.bottleneck, (4, 4) if args.nMel == 128 else (2, 4))
+    decoder = models.sparrow_decoder(args.bottleneck, (4, 4) if args.nMel == 128 else (2, 4))
     model = torch.nn.Sequential(frontend, encoder, decoder).to(gpu)
     model.load_state_dict(torch.load(f'{args.specie}/{modelname}'))
     model.eval()

utils.py

@@ -21,18 +21,14 @@ class Dataset(data.Dataset):
 
     def __getitem__(self, idx):
         row = self.df.iloc[idx]
-        # try:
         info = sf.info(self.audiopath+row.fn)
         dur, fs = info.duration, info.samplerate
         start = int(np.clip(row.pos - self.sampleDur/2, 0, max(0, dur - self.sampleDur)) * fs)
         sig, fs = sf.read(self.audiopath+row.fn, start=start, stop=start + int(self.sampleDur*fs))
-        # except:
-        #     print(f'failed with {row.name} {row.fn}')
-        #     return None
         if sig.ndim == 2:
             sig = sig[:,0]
         if len(sig) < self.sampleDur * fs:
-            sig = np.pad(sig, int(self.sampleDur * fs - len(sig))//2+1, mode='reflect')[:int(self.sampleDur * fs)]
+            sig = np.concatenate([sig, np.zeros(int(self.sampleDur * fs) - len(sig))])
         if fs != self.sr:
             sig = resample(sig, int(len(sig)/fs*self.sr))
         if np.std(sig) == 0: