Starter repository for learning how to make machine learning models with voice data.
git clone [email protected]:NeuroLexDiagnostics/voice_modeling_starter.git
cd voice_modeling_starter
pip3 install -r requirements.txt
python3 model.py
jimschwoebel@Jims-MBP voice_modeling_starter % python3 model.py
males: 0%| | 0/51 [00:00<?, ?it/s]featurizing 16.WAV
males: 2%|▋ | 1/51 [00:00<00:38, 1.31it/s]featurizing 17.WAV
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males: 18%|██████▌ | 9/51 [00:03<00:15, 2.66it/s]featurizing 39.WAV
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males: 22%|███████▊ | 11/51 [00:03<00:12, 3.20it/s]featurizing 12.WAV
males: 24%|████████▍ | 12/51 [00:04<00:10, 3.63it/s]featurizing 49.WAV
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males: 39%|██████████████ | 20/51 [00:05<00:04, 6.74it/s]featurizing 5.WAV
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males: 45%|████████████████▏ | 23/51 [00:06<00:05, 5.50it/s]featurizing 4.WAV
males: 47%|████████████████▉ | 24/51 [00:06<00:04, 5.89it/s]featurizing 0.WAV
males: 49%|█████████████████▋ | 25/51 [00:06<00:04, 5.99it/s]featurizing 46.WAV
males: 51%|██████████████████▎ | 26/51 [00:06<00:03, 6.34it/s]featurizing 47.WAV
males: 53%|███████████████████ | 27/51 [00:06<00:03, 6.00it/s]featurizing 1.WAV
males: 55%|███████████████████▊ | 28/51 [00:06<00:04, 4.93it/s]featurizing 45.WAV
males: 57%|████████████████████▍ | 29/51 [00:07<00:04, 4.95it/s]featurizing 3.WAV
males: 59%|█████████████████████▏ | 30/51 [00:07<00:07, 2.89it/s]featurizing 2.WAV
males: 61%|█████████████████████▉ | 31/51 [00:08<00:06, 3.18it/s]featurizing 50.WAV
males: 63%|██████████████████████▌ | 32/51 [00:08<00:05, 3.62it/s]featurizing 44.WAV
males: 65%|███████████████████████▎ | 33/51 [00:08<00:04, 3.86it/s]featurizing 23.WAV
males: 67%|████████████████████████ | 34/51 [00:08<00:04, 3.93it/s]featurizing 37.WAV
males: 69%|████████████████████████▋ | 35/51 [00:09<00:04, 3.99it/s]featurizing 36.WAV
males: 71%|█████████████████████████▍ | 36/51 [00:09<00:03, 4.50it/s]featurizing 22.WAV
males: 73%|██████████████████████████ | 37/51 [00:09<00:03, 3.76it/s]featurizing 34.WAV
males: 75%|██████████████████████████▊ | 38/51 [00:09<00:03, 3.33it/s]featurizing 20.WAV
males: 76%|███████████████████████████▌ | 39/51 [00:10<00:03, 3.97it/s]featurizing 21.WAV
males: 78%|████████████████████████████▏ | 40/51 [00:10<00:02, 4.16it/s]featurizing 35.WAV
males: 80%|████████████████████████████▉ | 41/51 [00:10<00:02, 3.93it/s]featurizing 19.WAV
males: 82%|█████████████████████████████▋ | 42/51 [00:10<00:02, 3.74it/s]featurizing 31.WAV
males: 84%|██████████████████████████████▎ | 43/51 [00:11<00:02, 3.59it/s]featurizing 25.WAV
males: 86%|███████████████████████████████ | 44/51 [00:11<00:02, 2.87it/s]featurizing 24.WAV
males: 88%|███████████████████████████████▊ | 45/51 [00:11<00:02, 2.94it/s]featurizing 30.WAV
males: 90%|████████████████████████████████▍ | 46/51 [00:12<00:01, 3.33it/s]featurizing 18.WAV
males: 92%|█████████████████████████████████▏ | 47/51 [00:12<00:01, 3.65it/s]featurizing 26.WAV
males: 94%|█████████████████████████████████▉ | 48/51 [00:12<00:00, 3.56it/s]featurizing 32.WAV
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males: 98%|███████████████████████████████████▎| 50/51 [00:13<00:00, 2.49it/s]featurizing 27.WAV
males: 100%|████████████████████████████████████| 51/51 [00:14<00:00, 2.39it/s]
females: 0%| | 0/52 [00:00<?, ?it/s]featurizing 14_11.WAV
females: 2%|▋ | 1/52 [00:00<00:09, 5.33it/s]featurizing 13.WAV
females: 6%|██ | 3/52 [00:00<00:09, 5.24it/s]featurizing 12.WAV
females: 8%|██▋ | 4/52 [00:00<00:10, 4.76it/s]featurizing 49.WAV
females: 10%|███▎ | 5/52 [00:01<00:09, 5.12it/s]featurizing 48.WAV
females: 12%|████ | 6/52 [00:01<00:10, 4.28it/s]featurizing 12_38.WAV
females: 13%|████▋ | 7/52 [00:01<00:10, 4.21it/s]featurizing 9_14.WAV
females: 15%|█████▍ | 8/52 [00:01<00:10, 4.39it/s]featurizing 7_29.WAV
females: 17%|██████ | 9/52 [00:02<00:11, 3.72it/s]featurizing 9.WAV
females: 19%|██████▌ | 10/52 [00:02<00:09, 4.21it/s]featurizing 8.WAV
females: 21%|███████▏ | 11/52 [00:02<00:11, 3.59it/s]featurizing 15_39.WAV
females: 23%|███████▊ | 12/52 [00:02<00:11, 3.49it/s]featurizing 40.WAV
females: 25%|████████▌ | 13/52 [00:03<00:10, 3.84it/s]featurizing 6.WAV
females: 27%|█████████▏ | 14/52 [00:03<00:09, 3.98it/s]featurizing 2_17.WAV
females: 29%|█████████▊ | 15/52 [00:03<00:08, 4.38it/s]featurizing 7.WAV
females: 31%|██████████▍ | 16/52 [00:03<00:08, 4.49it/s]featurizing 41.WAV
females: 33%|███████████ | 17/52 [00:03<00:07, 4.77it/s]featurizing 5.WAV
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females: 48%|████████████████▎ | 25/52 [00:06<00:06, 4.08it/s]featurizing 0_16.WAV
females: 50%|█████████████████ | 26/52 [00:06<00:07, 3.33it/s]featurizing 45.WAV
females: 52%|█████████████████▋ | 27/52 [00:06<00:06, 3.87it/s]featurizing 3.WAV
females: 54%|██████████████████▎ | 28/52 [00:07<00:10, 2.19it/s]featurizing 2.WAV
females: 56%|██████████████████▉ | 29/52 [00:07<00:09, 2.45it/s]featurizing 50.WAV
females: 58%|███████████████████▌ | 30/52 [00:08<00:07, 2.97it/s]featurizing 44.WAV
females: 60%|████████████████████▎ | 31/52 [00:08<00:06, 3.33it/s]featurizing 23.WAV
females: 62%|████████████████████▉ | 32/52 [00:08<00:04, 4.04it/s]featurizing 37.WAV
females: 63%|█████████████████████▌ | 33/52 [00:08<00:04, 4.36it/s]featurizing 8_28.WAV
females: 65%|██████████████████████▏ | 34/52 [00:09<00:05, 3.28it/s]featurizing 36.WAV
females: 67%|██████████████████████▉ | 35/52 [00:09<00:05, 3.23it/s]featurizing 22.WAV
females: 69%|███████████████████████▌ | 36/52 [00:09<00:04, 3.67it/s]featurizing 34.WAV
females: 71%|████████████████████████▏ | 37/52 [00:09<00:03, 3.95it/s]featurizing 13_10.WAV
females: 73%|████████████████████████▊ | 38/52 [00:09<00:03, 4.33it/s]featurizing 20.WAV
females: 75%|█████████████████████████▌ | 39/52 [00:10<00:02, 4.50it/s]featurizing 21.WAV
females: 77%|██████████████████████████▏ | 40/52 [00:10<00:03, 3.02it/s]featurizing 35.WAV
females: 79%|██████████████████████████▊ | 41/52 [00:10<00:03, 3.23it/s]featurizing 19.WAV
females: 81%|███████████████████████████▍ | 42/52 [00:11<00:02, 3.74it/s]featurizing 31.WAV
females: 83%|████████████████████████████ | 43/52 [00:11<00:02, 4.01it/s]featurizing 25.WAV
females: 85%|████████████████████████████▊ | 44/52 [00:11<00:02, 3.97it/s]featurizing 24.WAV
females: 87%|█████████████████████████████▍ | 45/52 [00:11<00:01, 4.42it/s]featurizing 30.WAV
females: 88%|██████████████████████████████ | 46/52 [00:12<00:01, 4.12it/s]featurizing 18.WAV
females: 90%|██████████████████████████████▋ | 47/52 [00:12<00:01, 4.51it/s]featurizing 26.WAV
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females: 94%|████████████████████████████████ | 49/52 [00:12<00:00, 3.63it/s]featurizing 6_15.WAV
females: 96%|████████████████████████████████▋ | 50/52 [00:13<00:00, 2.24it/s]featurizing 33.WAV
females: 98%|█████████████████████████████████▎| 51/52 [00:14<00:00, 2.42it/s]featurizing 27.WAV
females: 100%|██████████████████████████████████| 52/52 [00:14<00:00, 2.74it/s]
[[-332.7022071842247, 0.0, -332.7022071842247], [-291.38172048142155, 0.0, -291.38172048142155], [-267.16918961311717, 0.0, -267.16918961311717], [-526.2824482679787, 0.0, -526.2824482679787], [-266.3269341708422, 0.0, -266.3269341708422], [-299.97643317634913, 0.0, -299.97643317634913], [-295.15690143932846, 0.0, -295.15690143932846], [-263.66598263786483, 0.0, -263.66598263786483], [-283.7068816218348, 0.0, -283.7068816218348], [-273.31786084286205, 0.0, -273.31786084286205], [-214.64915350621519, 0.0, -214.64915350621519], [-290.6509021611551, 0.0, -290.6509021611551], [-335.69851657407656, 0.0, -335.69851657407656], [-413.89971499531543, 0.0, -413.89971499531543], [-365.2273134615648, 0.0, -365.2273134615648], [-220.11867839017657, 0.0, -220.11867839017657], [-183.7143270628675, 0.0, -183.7143270628675], [-325.8768342571848, 0.0, -325.8768342571848], [-282.3613479051469, 0.0, -282.3613479051469], [-156.2532618270719, 0.0, -156.2532618270719], [-489.22045181011083, 0.0, -489.22045181011083], [-219.55113104238424, 0.0, -219.55113104238424], [-367.20979349176673, 0.0, -367.20979349176673], [-244.64957605866755, 0.0, -244.64957605866755], [-401.1483941602569, 0.0, -401.1483941602569], [-320.12076118211735, 0.0, -320.12076118211735], [-214.38332315821634, 0.0, -214.38332315821634], [-281.0335536258094, 0.0, -281.0335536258094], [-494.2981635994644, 0.0, -494.2981635994644], [-396.2839065904706, 0.0, -396.2839065904706], [-588.7699043249702, 0.0, -588.7699043249702], [-190.16170387868306, 0.0, -190.16170387868306], [-270.4326373712914, 0.0, -270.4326373712914], [-312.86297614709997, 0.0, -312.86297614709997], [-117.71133906923016, 0.0, -117.71133906923016], [-138.35053479012996, 0.0, -138.35053479012996], [-269.8699188949503, 0.0, -269.8699188949503], [-226.07050109682612, 0.0, -226.07050109682612], [-176.09874760274923, 0.0, -176.09874760274923], [-259.12746395055626, 0.0, -259.12746395055626], [-292.2559426911754, 0.0, -292.2559426911754], [-297.58849130606234, 0.0, -297.58849130606234], [-268.9616664385824, 0.0, -268.9616664385824], [-238.60753654840406, 0.0, -238.60753654840406], [-238.6284013649816, 0.0, -238.6284013649816], [-332.6889396412106, 0.0, -332.6889396412106], [-375.7905321992956, 0.0, -375.7905321992956], [-351.57294198564574, 0.0, -351.57294198564574], [-346.09308978802085, 0.0, -346.09308978802085], [-325.8800718391209, 0.0, -325.8800718391209], [-288.1042687922045, 0.0, -288.1042687922045], [-234.03292796556119, 0.0, -234.03292796556119], [-230.80047867801204, 0.0, -230.80047867801204], [-213.6225241940984, 0.0, -213.6225241940984], [-228.73035782973278, 0.0, -228.73035782973278], [-299.5881167578717, 0.0, -299.5881167578717], [-259.7913558582271, 0.0, -259.7913558582271], [-333.9857996456521, 0.0, -333.9857996456521], [-253.80383798961734, 0.0, -253.80383798961734], [-225.44820834507297, 0.0, -225.44820834507297], [-303.12780279155936, 0.0, -303.12780279155936], [-423.0670007057619, 0.0, -423.0670007057619], [-437.033999896658, 0.0, -437.033999896658], [-152.90502626713317, 0.0, -152.90502626713317], [-138.62135363721865, 0.0, -138.62135363721865], [-268.4946949695764, 0.0, -268.4946949695764], [-419.7228995188986, 0.0, -419.7228995188986], [-87.49722265398148, 0.0, -87.49722265398148], [-166.67364016040162, 0.0, -166.67364016040162], [-273.54813707822234, 0.0, -273.54813707822234], [-523.6054908344385, 0.0, -523.6054908344385], [-246.96073404082549, 0.0, -246.96073404082549], [-161.47025349126875, 0.0, -161.47025349126875], [-301.28788169319375, 0.0, -301.28788169319375], [-183.66423064909398, 0.0, -183.66423064909398], [-244.07829833779266, 0.0, -244.07829833779266], [-200.0730887500782, 0.0, -200.0730887500782], [-173.99854137725887, 0.0, -173.99854137725887], [-304.44739289211907, 0.0, -304.44739289211907], [-261.23395694716476, 0.0, -261.23395694716476], [-72.61431757312803, 0.0, -72.61431757312803], [-360.18434957620735, 0.0, -360.18434957620735], [-45.28121750133728, 0.0, -45.28121750133728], [-265.69035269465667, 0.0, -265.69035269465667], [-216.65452039895627, 0.0, -216.65452039895627], [-206.2458815265072, 0.0, -206.2458815265072], [-141.0411153341764, 0.0, -141.0411153341764], [-387.90817962444606, 0.0, -387.90817962444606], [-423.13978604460937, 0.0, -423.13978604460937], [-288.82246824095824, 0.0, -288.82246824095824], [-209.54872687141003, 0.0, -209.54872687141003], [-145.30080216738057, 0.0, -145.30080216738057], [-135.8070491468406, 0.0, -135.8070491468406], [-304.63402545404097, 0.0, -304.63402545404097], [-289.37034626972536, 0.0, -289.37034626972536], [-354.3972059107408, 0.0, -354.3972059107408], [-119.0971832031602, 0.0, -119.0971832031602], [-340.8181795847191, 0.0, -340.8181795847191], [-424.39493684678763, 0.0, -424.39493684678763], [-494.2016650350951, 0.0, -494.2016650350951], [-234.69725507100026, 0.0, -234.69725507100026], [-282.2899000650856, 0.0, -282.2899000650856]]
['males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'males', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females', 'females']
training model
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
FutureWarning)
accuracy-->
0.5769230769230769
saving model
loading model
loaded model accuracy-->
0.5769230769230769
Any feedback on this repository is greatly appreciated.
- Learn more about voice computing with the textbook we wrote Introduction to Voice Computing in Python.
- If you'd like to be mentored by someone on our team, check out the Innovation Fellows Program.
- If you want to talk to me directly, please send me an email @ [email protected].