VocGAN | Fidelity Real-time Vocoder | Speech library

Check e.g. this part of the MelGAN code: https://github.com/descriptinc/melgan-neurips/blob/master/mel2wav/modules.py#L26

Specifically, the Audio2Mel module simply uses standard methods to create log-magnitude mel spectrograms like this:

• Compute the STFT by applying the Fourier transform to windows of the input audio,
• Take the magnitude of the resulting complex spectrogram,
• Multiply the magnitude spectrogram by a mel filter matrix. Note that they actually get this matrix from librosa!
• Take the logarithm of the resulting mel spectrogram.

Your confusion might stem from the fact that, usually, authors of Deep Learning papers only mean their mel-to-audio "decoder" when they talk about "vocoders" -- the audio-to-mel part is always more or less the same. I say this might be confusing since, to my understanding, the classical meaning of the term "vocoder" includes both an encoder and a decoder.

Unfortunately, these methods will not always work exactly in the same manner as there are e.g. different methods to create the mel filter matrix, different padding conventions etc.

For example, librosa.stft has a center argument that will pad the audio before applying the STFT, while tensorflow.signal.stft does not have this (it would require manual padding beforehand).

An example for the different methods to create mel filters would be the htk argument in librosa.filters.mel, which switches between the "HTK" method and "Slaney". Again taking Tensorflow as an example, tf.signal.linear_to_mel_weight_matrix does not support this argument and always uses the HTK method. Unfortunately, I am not familiar with torchaudio, so I don't know if you need to be careful there, as well.

Finally, there are of course many parameters such as the STFT window size, hop length, the frequencies covered by the mel filters etc, and changing these relative to what a reference implementation used may impact your results. Since different code repositories likely use slightly different parameters, I suppose the answer to your question "will every method do the operation(to create a mel spectrogram) in the same manner?" is "not really". At the end of the day, you will have to settle for one set of parameters either way...

The direction Mel -> Audio is hard. Not even Mel -> ("normal") spectrogram is well-defined since the conversion to mel spectrum is lossy and cannot be inverted. Finally, converting a spectrogram to audio is difficult since the phase needs to be estimated. You may be familiar with methods like Griffin-Lim (again, librosa has it so you can try it out). These produce noisy, low-quality audio. So the research focuses on improving this process using powerful models.

On the other hand, Audio -> Mel is simple, well-defined and fast. There is no need to define "custom encoders".

Now, a whole different question is whether mel spectrograms are a "good" encoding. Using methods like variational autoencoders, you could perhaps find better (e.g. more compact, less lossy) audio encodings. These would include custom encoders and decoders and you would not get away with standard librosa functions...