audio_to_melspec_precessor does not return proper MelSpectrogram
```python
import torchaudio
from nemo.collections.tts.models.base import Vocoder

audio, sample_rate = torchaudio.load(audio_path) # sample_rate = 22050
vocoder = Vocoder.from_pretrained('tts_melgan')

audio_len = torch.tensor(audio.size(1)/22050).view(1,1)
gen_mel, _ = vocoder.audio_to_melspec_precessor(audio.cuda(), audio_len.cuda())
plt.imshow(gen_mel.cpu().detach().numpy().squeeze())
plt.show()
```

![image](https://user-images.githubusercontent.com/21142903/125405170-0cf73980-e3d5-11eb-8b25-ebd6fe04d4e8.png)


This code returns a blank(constant) spectrogram.
How do I get the Mel spectrogram for vocoder training/inference?