A system for an audio waveform processing based on a generative adversarial network generator, comprising: one or more memories storing one or more instructions; one or more processors configured to executed the one or more instructions to implement: a spectral preprocessing module (SpectralUnet) configured to: obtain a mel-spectrogram from an input audio waveform by performing short-time Fourier transform (STFT) operation on the input audio waveform, and obtain an updated mel-spectrogram by at least one or removing noise from the mel-spectrogram or restore high frequency components by applying two-dimensional Unet convolutional blocks to the mel-spectrogram; a fully convolutional neural network module (HiFi-generator) configured to convert the updated mel-spectrogram to a converted audio waveform in a waveform domain; a time domain one-dimensional convolutional Unet neural module (WaveUNet) configured to correct the converted audio waveform in a time domain; a learnable spectral masking module (SpectralMaskNet) configured to: correct the converted audio waveform in a frequency domain to remove artifacts or noise, and an one-dimensional convolutional layer configured to: process the corrected audio waveform corrected in the time domain and corrected in the frequency domain, and output the processed audio waveform in the time domain and in the frequency domain. one or more memories storing one or more instructions; one or more processors configured to executed the one or more instructions to implement: a spectral preprocessing module (SpectralUnet) configured to: obtain a mel-spectrogram from an input audio waveform by performing short-time Fourier transform (STFT) operation on the input audio waveform, and obtain an updated mel-spectrogram by at least one or removing noise from the mel-spectrogram or restore high frequency components by applying two-dimensional Unet convolutional blocks to the mel-spectrogram; a fully convolutional neural network module (HiFi-generator) configured to convert the updated mel-spectrogram to a converted audio waveform in a waveform domain; a time domain one-dimensional convolutional Unet neural module (WaveUNet) configured to correct the converted audio waveform in a time domain; a learnable spectral masking module (SpectralMaskNet) configured to: correct the converted audio waveform in a frequency domain to remove artifacts or noise, and an one-dimensional convolutional layer configured to: process the corrected audio waveform corrected in the time domain and corrected in the frequency domain, and output the processed audio waveform in the time domain and in the frequency domain. obtain a mel-spectrogram from an input audio waveform by performing short-time Fourier transform (STFT) operation on the input audio waveform, and obtain an updated mel-spectrogram by at least one or removing noise from the mel-spectrogram or restore high frequency components by applying two-dimensional Unet convolutional blocks to the mel-spectrogram; a fully convolutional neural network module (HiFi-generator) configured to convert the updated mel-spectrogram to a converted audio waveform in a waveform domain; a time domain one-dimensional convolutional Unet neural module (WaveUNet) configured to correct the converted audio waveform in a time domain; a learnable spectral masking module (SpectralMaskNet) configured to: correct the converted audio waveform in a frequency domain to remove artifacts or noise, and an one-dimensional convolutional layer configured to: process the corrected audio waveform corrected in the time domain and corrected in the frequency domain, and output the processed audio waveform in the time domain and in the frequency domain. correct the converted audio waveform in a frequency domain to remove artifacts or noise, and an one-dimensional convolutional layer configured to: process the corrected audio waveform corrected in the time domain and corrected in the frequency domain, and output the processed audio waveform in the time domain and in the frequency domain. process the corrected audio waveform corrected in the time domain and corrected in the frequency domain, and output the processed audio waveform in the time domain and in the frequency domain.