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Light-SERNet: A lightweight fully convolutional neural network for speech emotion recognition

Python 95.21% Shell 1.57% Jupyter Notebook 3.22%
speech-emotion-recognition lightweight fully-convolutional-networks tflite deep-learning tensorflow2

light-sernet's Issues

InvalidArgumentError: Cannot batch tensors with different shapes in component 0.

Hello! Good job! But I have an error. I want to test the model with my audio files. I have created a folder my_test_3.0s_Segmented in date where the audio is tagged by emotion. Everything goes well, but I always get an error at the moment: list(test_dataset.as_numpy_iterator())
InvalidArgumentError: Cannot batch tensors with different shapes in component 0. First element had shape [103,40,1] and element 1 had shape [92,40,1]. [Op:IteratorGetNext]
This prevents me from testing. I used my code on test data generated while training the model. The code works and I get the result. How can I fix it?

MFCC hop size problem.

"Good job on the paper. However, there seems to be a discrepancy regarding the frame overlaps and hop size between your text and the provided code. In your paper, it's stated that a Hamming window is used to split the audio signal into 64-ms frames with 16-ms overlaps, which are considered as quasi-stationary segments. From this, it would logically follow that the hop size is 48 ms.

However, in the hyperparameters.py file, it's stated "FRAME_STEP = 256". Given a sampling rate (fs) of 16 kHz, this implies a hop size of 16 ms, not 48 ms. Could you please clarify if there's a typographical error in the paper, or if there's a specific reason for this inconsistency?"

function cleaning_directory_filename()

I think the function cleaning_directory_filename() breaks the speaker independence in the paper, i.e., 10-fold cross-validation, causing speaker overlap in the training and test sets. Removing this function, I get an 8% drop in WA. Could you explain my confusion.

cannot run the IEMOCAP dataset on windows

Hello, could you show the data folder architecture so I understand the way you organised the dataset.
I kept getting errors to segment the data.
I extracted the IEMOCAP_full_release in the data folder the renamed it as IEMOCAP, however, I kept getting errors of files not found.

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