Merge pull request #3183 from coqui-ai/dev

v0.20.3
coqui-ai · Nov 10, 2023 · f477322 · f477322
2 parents ab57c36 + 6f1cba2
commit f477322
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Showing 27 changed files with 158 additions and 158 deletions.
diff --git a/TTS/VERSION b/TTS/VERSION
@@ -1 +1 @@
-0.20.2
+0.20.3
diff --git a/TTS/tts/datasets/formatters.py b/TTS/tts/datasets/formatters.py
@@ -280,7 +280,7 @@ def css10(root_path, meta_file, **kwargs):  # pylint: disable=unused-argument
             cols = line.split("|")
             wav_file = os.path.join(root_path, cols[0])
             text = cols[1]
-            items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name})
+            items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
     return items
 
 
@@ -294,7 +294,7 @@ def nancy(root_path, meta_file, **kwargs):  # pylint: disable=unused-argument
             utt_id = line.split()[1]
             text = line[line.find('"') + 1 : line.rfind('"') - 1]
             wav_file = os.path.join(root_path, "wavn", utt_id + ".wav")
-            items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name})
+            items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
     return items
 
 

diff --git a/TTS/tts/layers/delightful_tts/conv_layers.py b/TTS/tts/layers/delightful_tts/conv_layers.py
@@ -3,6 +3,7 @@
 import torch
 import torch.nn as nn  # pylint: disable=consider-using-from-import
 import torch.nn.functional as F
+from torch.nn.utils import parametrize
 
 from TTS.tts.layers.delightful_tts.kernel_predictor import KernelPredictor
 
@@ -73,7 +74,7 @@ def __init__(
         )
         nn.init.xavier_uniform_(self.conv.weight, gain=nn.init.calculate_gain(w_init_gain))
         if self.use_weight_norm:
-            self.conv = nn.utils.weight_norm(self.conv)
+            self.conv = nn.utils.parametrizations.weight_norm(self.conv)
 
     def forward(self, signal, mask=None):
         conv_signal = self.conv(signal)
@@ -113,7 +114,7 @@ def __init__(
                 dilation=1,
                 w_init_gain="relu",
             )
-            conv_layer = nn.utils.weight_norm(conv_layer.conv, name="weight")
+            conv_layer = nn.utils.parametrizations.weight_norm(conv_layer.conv, name="weight")
             convolutions.append(conv_layer)
 
         self.convolutions = nn.ModuleList(convolutions)
@@ -567,7 +568,7 @@ def __init__(  # pylint: disable=dangerous-default-value
 
         self.convt_pre = nn.Sequential(
             nn.LeakyReLU(lReLU_slope),
-            nn.utils.weight_norm(
+            nn.utils.parametrizations.weight_norm(
                 nn.ConvTranspose1d(
                     in_channels,
                     in_channels,
@@ -584,7 +585,7 @@ def __init__(  # pylint: disable=dangerous-default-value
             self.conv_blocks.append(
                 nn.Sequential(
                     nn.LeakyReLU(lReLU_slope),
-                    nn.utils.weight_norm(
+                    nn.utils.parametrizations.weight_norm(
                         nn.Conv1d(
                             in_channels,
                             in_channels,
@@ -665,6 +666,6 @@ def location_variable_convolution(self, x, kernel, bias, dilation=1, hop_size=25
 
     def remove_weight_norm(self):
         self.kernel_predictor.remove_weight_norm()
-        nn.utils.remove_weight_norm(self.convt_pre[1])
+        parametrize.remove_parametrizations(self.convt_pre[1], "weight")
         for block in self.conv_blocks:
-            nn.utils.remove_weight_norm(block[1])
+            parametrize.remove_parametrizations(block[1], "weight")
diff --git a/TTS/tts/layers/delightful_tts/kernel_predictor.py b/TTS/tts/layers/delightful_tts/kernel_predictor.py
@@ -1,4 +1,5 @@
 import torch.nn as nn  # pylint: disable=consider-using-from-import
+from torch.nn.utils import parametrize
 
 
 class KernelPredictor(nn.Module):
@@ -36,7 +37,9 @@ def __init__(  # pylint: disable=dangerous-default-value
         kpnet_bias_channels = conv_out_channels * conv_layers  # l_b
 
         self.input_conv = nn.Sequential(
-            nn.utils.weight_norm(nn.Conv1d(cond_channels, kpnet_hidden_channels, 5, padding=2, bias=True)),
+            nn.utils.parametrizations.weight_norm(
+                nn.Conv1d(cond_channels, kpnet_hidden_channels, 5, padding=2, bias=True)
+            ),
             getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
         )
 
@@ -46,7 +49,7 @@ def __init__(  # pylint: disable=dangerous-default-value
             self.residual_convs.append(
                 nn.Sequential(
                     nn.Dropout(kpnet_dropout),
-                    nn.utils.weight_norm(
+                    nn.utils.parametrizations.weight_norm(
                         nn.Conv1d(
                             kpnet_hidden_channels,
                             kpnet_hidden_channels,
@@ -56,7 +59,7 @@ def __init__(  # pylint: disable=dangerous-default-value
                         )
                     ),
                     getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
-                    nn.utils.weight_norm(
+                    nn.utils.parametrizations.weight_norm(
                         nn.Conv1d(
                             kpnet_hidden_channels,
                             kpnet_hidden_channels,
@@ -68,7 +71,7 @@ def __init__(  # pylint: disable=dangerous-default-value
                     getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
                 )
             )
-        self.kernel_conv = nn.utils.weight_norm(
+        self.kernel_conv = nn.utils.parametrizations.weight_norm(
             nn.Conv1d(
                 kpnet_hidden_channels,
                 kpnet_kernel_channels,
@@ -77,7 +80,7 @@ def __init__(  # pylint: disable=dangerous-default-value
                 bias=True,
             )
         )
-        self.bias_conv = nn.utils.weight_norm(
+        self.bias_conv = nn.utils.parametrizations.weight_norm(
             nn.Conv1d(
                 kpnet_hidden_channels,
                 kpnet_bias_channels,
@@ -117,9 +120,9 @@ def forward(self, c):
         return kernels, bias
 
     def remove_weight_norm(self):
-        nn.utils.remove_weight_norm(self.input_conv[0])
-        nn.utils.remove_weight_norm(self.kernel_conv)
-        nn.utils.remove_weight_norm(self.bias_conv)
+        parametrize.remove_parametrizations(self.input_conv[0], "weight")
+        parametrize.remove_parametrizations(self.kernel_conv, "weight")
+        parametrize.remove_parametrizations(self.bias_conv, "weight")
         for block in self.residual_convs:
-            nn.utils.remove_weight_norm(block[1])
-            nn.utils.remove_weight_norm(block[3])
+            parametrize.remove_parametrizations(block[1], "weight")
+            parametrize.remove_parametrizations(block[3], "weight")
diff --git a/TTS/tts/layers/generic/wavenet.py b/TTS/tts/layers/generic/wavenet.py
@@ -1,5 +1,6 @@
 import torch
 from torch import nn
+from torch.nn.utils import parametrize
 
 
 @torch.jit.script
@@ -62,7 +63,7 @@ def __init__(
         # init conditioning layer
         if c_in_channels > 0:
             cond_layer = torch.nn.Conv1d(c_in_channels, 2 * hidden_channels * num_layers, 1)
-            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
+            self.cond_layer = torch.nn.utils.parametrizations.weight_norm(cond_layer, name="weight")
         # intermediate layers
         for i in range(num_layers):
             dilation = dilation_rate**i
@@ -75,7 +76,7 @@ def __init__(
                 in_layer = torch.nn.Conv1d(
                     hidden_channels, 2 * hidden_channels, kernel_size, dilation=dilation, padding=padding
                 )
-            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
+            in_layer = torch.nn.utils.parametrizations.weight_norm(in_layer, name="weight")
             self.in_layers.append(in_layer)
 
             if i < num_layers - 1:
@@ -84,7 +85,7 @@ def __init__(
                 res_skip_channels = hidden_channels
 
             res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
+            res_skip_layer = torch.nn.utils.parametrizations.weight_norm(res_skip_layer, name="weight")
             self.res_skip_layers.append(res_skip_layer)
         # setup weight norm
         if not weight_norm:
@@ -115,11 +116,11 @@ def forward(self, x, x_mask=None, g=None, **kwargs):  # pylint: disable=unused-a
 
     def remove_weight_norm(self):
         if self.c_in_channels != 0:
-            torch.nn.utils.remove_weight_norm(self.cond_layer)
+            parametrize.remove_parametrizations(self.cond_layer, "weight")
         for l in self.in_layers:
-            torch.nn.utils.remove_weight_norm(l)
+            parametrize.remove_parametrizations(l, "weight")
         for l in self.res_skip_layers:
-            torch.nn.utils.remove_weight_norm(l)
+            parametrize.remove_parametrizations(l, "weight")
 
 
 class WNBlocks(nn.Module):

diff --git a/TTS/tts/layers/glow_tts/glow.py b/TTS/tts/layers/glow_tts/glow.py
@@ -186,7 +186,7 @@ def __init__(
         self.sigmoid_scale = sigmoid_scale
         # input layer
         start = torch.nn.Conv1d(in_channels // 2, hidden_channels, 1)
-        start = torch.nn.utils.weight_norm(start)
+        start = torch.nn.utils.parametrizations.weight_norm(start)
         self.start = start
         # output layer
         # Initializing last layer to 0 makes the affine coupling layers

diff --git a/TTS/tts/layers/tortoise/vocoder.py b/TTS/tts/layers/tortoise/vocoder.py
@@ -1,11 +1,11 @@
-import json
 from dataclasses import dataclass
 from enum import Enum
 from typing import Callable, Optional
 
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+import torch.nn.utils.parametrize as parametrize
 
 MAX_WAV_VALUE = 32768.0
 
@@ -44,7 +44,9 @@ def __init__(
         kpnet_bias_channels = conv_out_channels * conv_layers  # l_b
 
         self.input_conv = nn.Sequential(
-            nn.utils.weight_norm(nn.Conv1d(cond_channels, kpnet_hidden_channels, 5, padding=2, bias=True)),
+            nn.utils.parametrizations.weight_norm(
+                nn.Conv1d(cond_channels, kpnet_hidden_channels, 5, padding=2, bias=True)
+            ),
             getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
         )
 
@@ -54,7 +56,7 @@ def __init__(
             self.residual_convs.append(
                 nn.Sequential(
                     nn.Dropout(kpnet_dropout),
-                    nn.utils.weight_norm(
+                    nn.utils.parametrizations.weight_norm(
                         nn.Conv1d(
                             kpnet_hidden_channels,
                             kpnet_hidden_channels,
@@ -64,7 +66,7 @@ def __init__(
                         )
                     ),
                     getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
-                    nn.utils.weight_norm(
+                    nn.utils.parametrizations.weight_norm(
                         nn.Conv1d(
                             kpnet_hidden_channels,
                             kpnet_hidden_channels,
@@ -76,7 +78,7 @@ def __init__(
                     getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
                 )
             )
-        self.kernel_conv = nn.utils.weight_norm(
+        self.kernel_conv = nn.utils.parametrizations.weight_norm(
             nn.Conv1d(
                 kpnet_hidden_channels,
                 kpnet_kernel_channels,
@@ -85,7 +87,7 @@ def __init__(
                 bias=True,
             )
         )
-        self.bias_conv = nn.utils.weight_norm(
+        self.bias_conv = nn.utils.parametrizations.weight_norm(
             nn.Conv1d(
                 kpnet_hidden_channels,
                 kpnet_bias_channels,
@@ -125,12 +127,12 @@ def forward(self, c):
         return kernels, bias
 
     def remove_weight_norm(self):
-        nn.utils.remove_weight_norm(self.input_conv[0])
-        nn.utils.remove_weight_norm(self.kernel_conv)
-        nn.utils.remove_weight_norm(self.bias_conv)
+        parametrize.remove_parametrizations(self.input_conv[0], "weight")
+        parametrize.remove_parametrizations(self.kernel_conv, "weight")
+        parametrize.remove_parametrizations(self.bias_conv)
         for block in self.residual_convs:
-            nn.utils.remove_weight_norm(block[1])
-            nn.utils.remove_weight_norm(block[3])
+            parametrize.remove_parametrizations(block[1], "weight")
+            parametrize.remove_parametrizations(block[3], "weight")
 
 
 class LVCBlock(torch.nn.Module):
@@ -169,7 +171,7 @@ def __init__(
 
         self.convt_pre = nn.Sequential(
             nn.LeakyReLU(lReLU_slope),
-            nn.utils.weight_norm(
+            nn.utils.parametrizations.weight_norm(
                 nn.ConvTranspose1d(
                     in_channels,
                     in_channels,
@@ -186,7 +188,7 @@ def __init__(
             self.conv_blocks.append(
                 nn.Sequential(
                     nn.LeakyReLU(lReLU_slope),
-                    nn.utils.weight_norm(
+                    nn.utils.parametrizations.weight_norm(
                         nn.Conv1d(
                             in_channels,
                             in_channels,
@@ -267,9 +269,9 @@ def location_variable_convolution(self, x, kernel, bias, dilation=1, hop_size=25
 
     def remove_weight_norm(self):
         self.kernel_predictor.remove_weight_norm()
-        nn.utils.remove_weight_norm(self.convt_pre[1])
+        parametrize.remove_parametrizations(self.convt_pre[1], "weight")
         for block in self.conv_blocks:
-            nn.utils.remove_weight_norm(block[1])
+            parametrize.remove_parametrizations(block[1], "weight")
 
 
 class UnivNetGenerator(nn.Module):
@@ -314,11 +316,13 @@ def __init__(
                 )
             )
 
-        self.conv_pre = nn.utils.weight_norm(nn.Conv1d(noise_dim, channel_size, 7, padding=3, padding_mode="reflect"))
+        self.conv_pre = nn.utils.parametrizations.weight_norm(
+            nn.Conv1d(noise_dim, channel_size, 7, padding=3, padding_mode="reflect")
+        )
 
         self.conv_post = nn.Sequential(
             nn.LeakyReLU(lReLU_slope),
-            nn.utils.weight_norm(nn.Conv1d(channel_size, 1, 7, padding=3, padding_mode="reflect")),
+            nn.utils.parametrizations.weight_norm(nn.Conv1d(channel_size, 1, 7, padding=3, padding_mode="reflect")),
             nn.Tanh(),
         )
 
@@ -346,11 +350,11 @@ def eval(self, inference=False):
             self.remove_weight_norm()
 
     def remove_weight_norm(self):
-        nn.utils.remove_weight_norm(self.conv_pre)
+        parametrize.remove_parametrizations(self.conv_pre, "weight")
 
         for layer in self.conv_post:
             if len(layer.state_dict()) != 0:
-                nn.utils.remove_weight_norm(layer)
+                parametrize.remove_parametrizations(layer, "weight")
 
         for res_block in self.res_stack:
             res_block.remove_weight_norm()

diff --git a/TTS/tts/layers/vits/discriminator.py b/TTS/tts/layers/vits/discriminator.py
@@ -14,7 +14,7 @@ class DiscriminatorS(torch.nn.Module):
 
     def __init__(self, use_spectral_norm=False):
         super().__init__()
-        norm_f = nn.utils.spectral_norm if use_spectral_norm else nn.utils.weight_norm
+        norm_f = nn.utils.spectral_norm if use_spectral_norm else nn.utils.parametrizations.weight_norm
         self.convs = nn.ModuleList(
             [
                 norm_f(Conv1d(1, 16, 15, 1, padding=7)),