Eval/classifier is too small, and synthesised speech unclear. about multilingual_text_to_speech

[mudong0419]

Some other parameters are:
sample_rate = 16000 # sample rate of source .wavs, used while computing spectrograms, MFCCs, etc.
num_fft = 1024 # number of frequency bins used during computation of spectrograms
num_mels = 80 # number of mel bins used during computation of mel spectrograms
num_mfcc = 13 # number of MFCCs, used just for MCD computation (during training)
stft_window_ms = 50 # size in ms of the Hann window of short-time Fourier transform, used during spectrogram computation
stft_shift_ms = 12.5 # shift of the window (or better said gap between windows) in ms
griffin_lim_iters = 60 # used if vocoding using Griffin-Lim algorithm (synthesize.py), greater value does not make much sense
griffin_lim_power = 1.5 # power applied to spectrograms before using GL
normalize_spectrogram = True # if True, spectrograms are normalized before passing into the model, a per-channel normalization is used
# statistics (mean and variance) are computed from dataset at the start of training
use_preemphasis = True # if True, a preemphasis is applied to raw waveform before using them (spectrogram computation)
preemphasis = 0.97 # amount of preemphasis, used if use_preemphasis is True

[stale]

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.