Feature.process_signal() does not fail for unsupported signal types

[hagenw]

When you use an ONNX machine learning model, it usually expects float32 as input:

>>> model = audinterface.Process(process_func=onnx)
>>> sampling_rate = 16000
>>> signal = np.random.randn(1, 16000)
>>> model.process_signal(signal, sampling_rate)
...
InvalidArgument: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Unexpected input data type. Actual: (tensor(double)) , expected: (tensor(float))
>>> model.process_signal(signal.astype('float32'), sampling_rate)
start   end            
0 days  0 days 00:00:01    [[0.5469817]]
dtype: object

If we use audinterface.Feature instead it works with the double signal without any issue:

>>> model = audinterface.Feature(['feature'], process_func=onnx)
>>> model.process_signal(signal, sampling_rate)
                         feature
start  end                      
0 days 0 days 00:00:01  0.546982
>>> model.process_signal(signal.astype('float32'), sampling_rate)
                         feature
start  end                      
0 days 0 days 00:00:01  0.546982

I would propose that both should behave the same way. So we need audinterface.Feature to raise an error or change audinterface.Process to not raise the error.

[frankenjoe]

I tried to replicate, but for me it fails in both cases.

Sorry, my bad. Can replicate :)

[frankenjoe]

Here is the answer: In Feature we always set channels, which means we always call audresample.remix()in preprocess_signal() and that is changing the dtype to float32. So it is basically a bug in audresample.remix().

[frankenjoe]

I created audeering/audresample#15