Medix-AI/coqui-tts
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Make hifigan discriminator configurable

This commit is contained in:
Eren G??lge 2022-05-17 13:34:57 +02:00
parent c437db15fd
commit 8e915b70e0
1 changed files with 14 additions and 14 deletions
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28
TTS/tts/layers/vits/discriminator.py
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@ -2,7 +2,7 @@ import torch
from torch import nn
from torch.nn.modules.conv import Conv1d
from TTS.vocoder.models.hifigan_discriminator import DiscriminatorP, MultiPeriodDiscriminator
from TTS.vocoder.models.hifigan_discriminator import DiscriminatorP
class DiscriminatorS(torch.nn.Module):
@ -12,19 +12,19 @@ class DiscriminatorS(torch.nn.Module):
use_spectral_norm (bool): if `True` swith to spectral norm instead of weight norm.
"""
def __init__(self, use_spectral_norm=False):
def __init__(self, use_spectral_norm=False, upsampling_rates=[4, 4, 4, 4]):
super().__init__()
norm_f = nn.utils.spectral_norm if use_spectral_norm else nn.utils.weight_norm
self.convs = nn.ModuleList(
[
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
]
)
self.convs = nn.ModuleList([norm_f(Conv1d(1, 16, 15, 1, padding=7))])
groups = 4
in_channels = 16
out_channels = 64
for rate in upsampling_rates:
self.convs.append(norm_f(Conv1d(in_channels, out_channels, 41, rate, groups=groups, padding=20)))
groups = min(groups * rate, 256)
in_channels = min(in_channels * rate, 1024)
out_channels = min(out_channels * rate, 1024)
self.convs += [norm_f(Conv1d(1024, 1024, 5, 1, padding=2))]
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
def forward(self, x):
@ -58,10 +58,10 @@ class VitsDiscriminator(nn.Module):
use_spectral_norm (bool): if `True` swith to spectral norm instead of weight norm.
"""
def __init__(self, periods=(2, 3, 5, 7, 11), use_spectral_norm=False):
def __init__(self, use_spectral_norm=False, periods=[2, 3, 5, 7, 11], upsampling_rates=[4,4,4,4]):
super().__init__()
self.nets = nn.ModuleList()
self.nets.append(DiscriminatorS(use_spectral_norm=use_spectral_norm))
self.nets.append(DiscriminatorS(use_spectral_norm=use_spectral_norm, upsampling_rates=upsampling_rates))
self.nets.extend([DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods])
def forward(self, x, x_hat=None):