How to use trainNetwork with transform datastore with multiple outputs?

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Noam
Noam 2023-2-9
评论： Noam 2023-2-9
my code create a audioDataStore, transform it to yamnet features (mel spectrum) and try to train it.
But i get the following error:
Input datastore returned more than one observation per row for network input 1.
The code:
net = yamnet;
DS = audioDatastore(FolderName, ...
'FileExtensions',{'.wav','.mp3'},"IncludeSubfolders",true,'LabelSource','foldernames');
DS.Labels = setcats(DS.Labels,cellstr(net.Layers(86).Classes));
TR = transform(DS,@(audio,info)preProcess(audio,info),"IncludeInfo",true);
options = trainingOptions("adam");
trainNetwork(TR,net.Layers,options)
function [data,info] = preProcess(audio,info)
data{1} = yamnetPreprocess(audio,info.SampleRate);
data{2} = repmat(info.Label,1,size(data{1},4));
end
Yamnet produce multiple observations for each audio file (for instance, for 10 seconds file it will create 96×64×1×19 matrix which are 19 observations. For some reason trainNetwork want only one observation, how do I fix it?

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采纳的回答

jibrahim
jibrahim 2023-2-9
Hi Noam,
Check out this example:
Transfer Learning with Pretrained Audio Networks in Deep Network Designer
The example uses a transformed datastore as well. the transformation function is at the bottom:
function [data,info] = audioPreprocess(audioIn,info)
class = info.Label;
fs = info.SampleRate;
features = yamnetPreprocess(audioIn,fs);
numSpectrograms = size(features,4);
data = cell(numSpectrograms,2);
for index = 1:numSpectrograms
data{index,1} = features(:,:,:,index);
data{index,2} = class;
end
end

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