preprocessing_mel question

[Kerry0123]

hi，I have doubt about the preprocessing_mel function. I use the following preprocessing method. The generated audio file is muted.

def melspectrogram(wav, hparams):
D = _stft(preemphasis(wav, hparams.preemphasis, hparams.preemphasize), hparams)
S = _amp_to_db(_linear_to_mel(np.abs(D), hparams), hparams) - hparams.ref_level_db

np.dot(mel_basis, S)

if hparams.signal_normalization:
	return _normalize(S, hparams)
return S

def _stft(y, hparams):
if hparams.use_lws: False
return _lws_processor(hparams).stft(y).T
else:
return librosa.stft(y=y, n_fft=hparams.n_fft, hop_length=get_hop_size(hparams), win_length=hparams.win_size)
librosa.stft(y, n_fft=num_fft, hop_length=hop_length, win_length=win_length)
def _linear_to_mel(spectogram, hparams):
global _mel_basis
if _mel_basis is None:
_mel_basis = _build_mel_basis(hparams)
return np.dot(_mel_basis, spectogram)

def _amp_to_db(x, hparams):
min_level = np.exp(hparams.min_level_db / 20 * np.log(10))
return 20 * np.log10(np.maximum(min_level, x))

np.exp(-100 / 20 * np.log(10))

min_level = 10**(-100 / 20)
return 20 * np.log10(np.maximum(min_level, x))

def _normalize(S, hparams):
if hparams.allow_clipping_in_normalization: （True）
if hparams.symmetric_mels: （True）
return np.clip((2 * hparams.max_abs_value) * ((S - hparams.min_level_db) / (-hparams.min_level_db)) - hparams.max_abs_value,
-hparams.max_abs_value, hparams.max_abs_value)
else:
return np.clip(hparams.max_abs_value * ((S - hparams.min_level_db) / (-hparams.min_level_db)), 0, hparams.max_abs_value)

The main difference is “S = _amp_to_db(_linear_to_mel(np.abs(D), hparams), hparams) - hparams.ref_level_db” and _normalize，
hparams.ref_level_db =20, hparams.max_abs_value = 4;
data is [-4, 4], your preprocessing data is[0, 1]; the data range has a great influence on the model? I don't understand，I am asking for your help. thank you.

[Kerry0123]

I am asking for your help. thank you.

[bshall]

Hi @Kerry0123,

Did you retrain the model with your preprocessing steps or did you feed your spectrograms directly to the pretrained model?

[Kerry0123]

I retrain the model with my preprocessing steps. The loss of epoch 1 is 0.66. Loss will drop to 0. I am asking for your help. thank you.

[bshall]

@Kerry0123, something weird is going on because that loss is very low. What dataset are you using? The ZeroSpeech one? Also, could you share an example spectrogram so I can check if anything is odd?

[Kerry0123]

The dataset is BZNSYP(Chinese dataset)，To align the output of the synthesizer with the input of the vocoder，I use the preprocessing of the tacotron2 synthesizer. Its github link: https://github.com/cnlinxi/style-token_tacotron2.
python preprocess.py --dataset=biaobei --base_dir=/tmp-data/data/ --output=/nfs/volume-340-1/tts_data_preprocess/training_data_biaobe.
Is it convenient to tell me your email address? I send you mel file. I am asking for your help. thank you.

[bshall]

Sure, you can send it to benjamin.l.van.niekerk@gmail.com

Just to check, you kept all the other preprocessing the same e.g. mu-law encoding and all the padding stuff here?