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norm mel

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This commit is contained in:
James Betker 2022-05-06 00:49:54 -06:00
parent b13d983c24
commit b83b53cf84
1 changed files with 6 additions and 3 deletions
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9
codes/trainer/injectors/audio_injectors.py
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@ -11,10 +11,10 @@ from utils.util import opt_get, load_model_from_config, pad_or_truncate
TACOTRON_MEL_MAX = 2.3143386840820312 TACOTRON_MEL_MAX = 2.3143386840820312
TACOTRON_MEL_MIN = -11.512925148010254 TACOTRON_MEL_MIN = -11.512925148010254
def normalize_tacotron_mel(mel): def normalize_mel(mel):
return 2 * ((mel - TACOTRON_MEL_MIN) / (TACOTRON_MEL_MAX - TACOTRON_MEL_MIN)) - 1 return 2 * ((mel - TACOTRON_MEL_MIN) / (TACOTRON_MEL_MAX - TACOTRON_MEL_MIN)) - 1
def denormalize_tacotron_mel(norm_mel): def denormalize_mel(norm_mel):
return ((norm_mel+1)/2)*(TACOTRON_MEL_MAX-TACOTRON_MEL_MIN)+TACOTRON_MEL_MIN return ((norm_mel+1)/2)*(TACOTRON_MEL_MAX-TACOTRON_MEL_MIN)+TACOTRON_MEL_MIN
class MelSpectrogramInjector(Injector): class MelSpectrogramInjector(Injector):
@ -40,7 +40,7 @@ class MelSpectrogramInjector(Injector):
self.stft = self.stft.to(inp.device) self.stft = self.stft.to(inp.device)
mel = self.stft.mel_spectrogram(inp) mel = self.stft.mel_spectrogram(inp)
if self.do_normalization: if self.do_normalization:
mel = normalize_tacotron_mel(mel) mel = normalize_mel(mel)
return {self.output: mel} return {self.output: mel}
@ -56,6 +56,7 @@ class TorchMelSpectrogramInjector(Injector):
self.mel_fmax = opt_get(opt, ['mel_fmax'], 8000) self.mel_fmax = opt_get(opt, ['mel_fmax'], 8000)
self.sampling_rate = opt_get(opt, ['sampling_rate'], 22050) self.sampling_rate = opt_get(opt, ['sampling_rate'], 22050)
norm = opt_get(opt, ['normalize'], False) norm = opt_get(opt, ['normalize'], False)
self.true_norm = opt_get(opt, ['true_normalization'], False)
self.mel_stft = torchaudio.transforms.MelSpectrogram(n_fft=self.filter_length, hop_length=self.hop_length, self.mel_stft = torchaudio.transforms.MelSpectrogram(n_fft=self.filter_length, hop_length=self.hop_length,
win_length=self.win_length, power=2, normalized=norm, win_length=self.win_length, power=2, normalized=norm,
sample_rate=self.sampling_rate, f_min=self.mel_fmin, sample_rate=self.sampling_rate, f_min=self.mel_fmin,
@ -79,6 +80,8 @@ class TorchMelSpectrogramInjector(Injector):
if self.mel_norms is not None: if self.mel_norms is not None:
self.mel_norms = self.mel_norms.to(mel.device) self.mel_norms = self.mel_norms.to(mel.device)
mel = mel / self.mel_norms.unsqueeze(0).unsqueeze(-1) mel = mel / self.mel_norms.unsqueeze(0).unsqueeze(-1)
if self.true_norm:
mel = normalize_mel(mel)
return {self.output: mel} return {self.output: mel}