Add vits

control/cli/ppg2mel_train.py

@@ -2,7 +2,7 @@ import sys
 import torch
 import argparse
 import numpy as np
-from utils.load_yaml import HpsYaml
+from utils.hparams import HpsYaml
 from models.ppg2mel.train.train_linglf02mel_seq2seq_oneshotvc import Solver
 
 # For reproducibility, comment these may speed up training

control/cli/train_ppg2mel.py

@@ -2,7 +2,7 @@ import sys
 import torch
 import argparse
 import numpy as np
-from utils.load_yaml import HpsYaml
+from utils.hparams import HpsYaml
 from models.ppg2mel.train.train_linglf02mel_seq2seq_oneshotvc import Solver
 
 # For reproducibility, comment these may speed up training

control/mkgui/train_vc.py

@@ -4,7 +4,7 @@ from pathlib import Path
 from enum import Enum
 from typing import Any, Tuple
 import numpy as np
-from utils.load_yaml import HpsYaml
+from utils.hparams import HpsYaml
 from utils.util import AttrDict
 import torch
 

models/ppg2mel/__init__.py

@@ -15,7 +15,7 @@ from .rnn_decoder_mol import Decoder
 from .utils.cnn_postnet import Postnet
 from .utils.vc_utils import get_mask_from_lengths
 
-from utils.load_yaml import HpsYaml
+from utils.hparams import HpsYaml
 
 class MelDecoderMOLv2(AbsMelDecoder):
     """Use an encoder to preprocess ppg."""

models/ppg2mel/train.py

@@ -2,7 +2,7 @@ import sys
 import torch
 import argparse
 import numpy as np
-from utils.load_yaml import HpsYaml
+from utils.hparams import HpsYaml
 from models.ppg2mel.train.train_linglf02mel_seq2seq_oneshotvc import Solver
 
 # For reproducibility, comment these may speed up training

models/ppg2mel/train/solver.py

@@ -8,7 +8,6 @@ from torch.utils.tensorboard import SummaryWriter
 
 from .option import default_hparas
 from utils.util import human_format, Timer
-from utils.load_yaml import HpsYaml
 
 
 class BaseSolver():

models/synthesizer/hparams.py

@@ -1,36 +1,4 @@
-import ast
+from utils.hparams import HParams
-import pprint
-import json
-
-class HParams(object):
-    def __init__(self, **kwargs): self.__dict__.update(kwargs)
-    def __setitem__(self, key, value): setattr(self, key, value)
-    def __getitem__(self, key): return getattr(self, key)
-    def __repr__(self): return pprint.pformat(self.__dict__)
-
-    def parse(self, string):
-        # Overrides hparams from a comma-separated string of name=value pairs
-        if len(string) > 0:
-            overrides = [s.split("=") for s in string.split(",")]
-            keys, values = zip(*overrides)
-            keys = list(map(str.strip, keys))
-            values = list(map(str.strip, values))
-            for k in keys:
-                self.__dict__[k] = ast.literal_eval(values[keys.index(k)])
-        return self
-
-    def loadJson(self, dict):
-        print("\Loading the json with %s\n", dict)
-        for k in dict.keys():
-            if k not in ["tts_schedule", "tts_finetune_layers"]: 
-                self.__dict__[k] = dict[k]
-        return self
-
-    def dumpJson(self, fp):
-        print("\Saving the json with %s\n", fp)
-        with fp.open("w", encoding="utf-8") as f:
-            json.dump(self.__dict__, f)
-        return self
 
 hparams = HParams(
         ### Signal Processing (used in both synthesizer and vocoder)
@@ -104,7 +72,7 @@ hparams = HParams(
         ### SV2TTS
         speaker_embedding_size = 256,               # Dimension for the speaker embedding
         silence_min_duration_split = 0.4,           # Duration in seconds of a silence for an utterance to be split
-        utterance_min_duration = 1.6,               # Duration in seconds below which utterances are discarded
+        utterance_min_duration = 0.5,               # Duration in seconds below which utterances are discarded
         use_gst = True,                             # Whether to use global style token    
         use_ser_for_gst = True,                     # Whether to use speaker embedding referenced for global style token  
         )

models/synthesizer/inference.py

@@ -10,7 +10,6 @@ from typing import Union, List
 import numpy as np
 import librosa
 from utils import logmmse
-import json
 from pypinyin import lazy_pinyin, Style
 
 class Synthesizer:
@@ -48,8 +47,7 @@ class Synthesizer:
         # Try to scan config file
         model_config_fpaths = list(self.model_fpath.parent.rglob("*.json"))
         if len(model_config_fpaths)>0 and model_config_fpaths[0].exists():
-            with model_config_fpaths[0].open("r", encoding="utf-8") as f:
+            hparams.loadJson(model_config_fpaths[0])
-                hparams.loadJson(json.load(f))
         """
         Instantiates and loads the model given the weights file that was passed in the constructor.
         """

models/synthesizer/models/base.py

@@ -48,7 +48,11 @@ class Base(nn.Module):
     def load(self, path, device, optimizer=None):
         # Use device of model params as location for loaded state
         checkpoint = torch.load(str(path), map_location=device)
-        self.load_state_dict(checkpoint["model_state"], strict=False)
+        if "model_state" in checkpoint:
+            state = checkpoint["model_state"]
+        else:
+            state = checkpoint["model"]
+        self.load_state_dict(state, strict=False)
 
         if "optimizer_state" in checkpoint and optimizer is not None:
             optimizer.load_state_dict(checkpoint["optimizer_state"])

models/synthesizer/models/sublayer/common/transforms.py

@@ -0,0 +1,193 @@
+import torch
+from torch.nn import functional as F
+
+import numpy as np
+
+
+DEFAULT_MIN_BIN_WIDTH = 1e-3
+DEFAULT_MIN_BIN_HEIGHT = 1e-3
+DEFAULT_MIN_DERIVATIVE = 1e-3
+
+
+def piecewise_rational_quadratic_transform(inputs, 
+                                           unnormalized_widths,
+                                           unnormalized_heights,
+                                           unnormalized_derivatives,
+                                           inverse=False,
+                                           tails=None, 
+                                           tail_bound=1.,
+                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
+
+    if tails is None:
+        spline_fn = rational_quadratic_spline
+        spline_kwargs = {}
+    else:
+        spline_fn = unconstrained_rational_quadratic_spline
+        spline_kwargs = {
+            'tails': tails,
+            'tail_bound': tail_bound
+        }
+
+    outputs, logabsdet = spline_fn(
+            inputs=inputs,
+            unnormalized_widths=unnormalized_widths,
+            unnormalized_heights=unnormalized_heights,
+            unnormalized_derivatives=unnormalized_derivatives,
+            inverse=inverse,
+            min_bin_width=min_bin_width,
+            min_bin_height=min_bin_height,
+            min_derivative=min_derivative,
+            **spline_kwargs
+    )
+    return outputs, logabsdet
+
+
+def searchsorted(bin_locations, inputs, eps=1e-6):
+    bin_locations[..., -1] += eps
+    return torch.sum(
+        inputs[..., None] >= bin_locations,
+        dim=-1
+    ) - 1
+
+
+def unconstrained_rational_quadratic_spline(inputs,
+                                            unnormalized_widths,
+                                            unnormalized_heights,
+                                            unnormalized_derivatives,
+                                            inverse=False,
+                                            tails='linear',
+                                            tail_bound=1.,
+                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
+    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
+    outside_interval_mask = ~inside_interval_mask
+
+    outputs = torch.zeros_like(inputs)
+    logabsdet = torch.zeros_like(inputs)
+
+    if tails == 'linear':
+        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
+        constant = np.log(np.exp(1 - min_derivative) - 1)
+        unnormalized_derivatives[..., 0] = constant
+        unnormalized_derivatives[..., -1] = constant
+
+        outputs[outside_interval_mask] = inputs[outside_interval_mask]
+        logabsdet[outside_interval_mask] = 0
+    else:
+        raise RuntimeError('{} tails are not implemented.'.format(tails))
+
+    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
+        inputs=inputs[inside_interval_mask],
+        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
+        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
+        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
+        inverse=inverse,
+        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
+        min_bin_width=min_bin_width,
+        min_bin_height=min_bin_height,
+        min_derivative=min_derivative
+    )
+
+    return outputs, logabsdet
+
+def rational_quadratic_spline(inputs,
+                              unnormalized_widths,
+                              unnormalized_heights,
+                              unnormalized_derivatives,
+                              inverse=False,
+                              left=0., right=1., bottom=0., top=1.,
+                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+                              min_derivative=DEFAULT_MIN_DERIVATIVE):
+    if torch.min(inputs) < left or torch.max(inputs) > right:
+        raise ValueError('Input to a transform is not within its domain')
+
+    num_bins = unnormalized_widths.shape[-1]
+
+    if min_bin_width * num_bins > 1.0:
+        raise ValueError('Minimal bin width too large for the number of bins')
+    if min_bin_height * num_bins > 1.0:
+        raise ValueError('Minimal bin height too large for the number of bins')
+
+    widths = F.softmax(unnormalized_widths, dim=-1)
+    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
+    cumwidths = torch.cumsum(widths, dim=-1)
+    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
+    cumwidths = (right - left) * cumwidths + left
+    cumwidths[..., 0] = left
+    cumwidths[..., -1] = right
+    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
+
+    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
+
+    heights = F.softmax(unnormalized_heights, dim=-1)
+    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
+    cumheights = torch.cumsum(heights, dim=-1)
+    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
+    cumheights = (top - bottom) * cumheights + bottom
+    cumheights[..., 0] = bottom
+    cumheights[..., -1] = top
+    heights = cumheights[..., 1:] - cumheights[..., :-1]
+
+    if inverse:
+        bin_idx = searchsorted(cumheights, inputs)[..., None]
+    else:
+        bin_idx = searchsorted(cumwidths, inputs)[..., None]
+
+    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
+    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
+
+    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
+    delta = heights / widths
+    input_delta = delta.gather(-1, bin_idx)[..., 0]
+
+    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
+    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
+
+    input_heights = heights.gather(-1, bin_idx)[..., 0]
+
+    if inverse:
+        a = (((inputs - input_cumheights) * (input_derivatives
+                                             + input_derivatives_plus_one
+                                             - 2 * input_delta)
+              + input_heights * (input_delta - input_derivatives)))
+        b = (input_heights * input_derivatives
+             - (inputs - input_cumheights) * (input_derivatives
+                                              + input_derivatives_plus_one
+                                              - 2 * input_delta))
+        c = - input_delta * (inputs - input_cumheights)
+
+        discriminant = b.pow(2) - 4 * a * c
+        assert (discriminant >= 0).all()
+
+        root = (2 * c) / (-b - torch.sqrt(discriminant))
+        outputs = root * input_bin_widths + input_cumwidths
+
+        theta_one_minus_theta = root * (1 - root)
+        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
+                                     * theta_one_minus_theta)
+        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
+                                                     + 2 * input_delta * theta_one_minus_theta
+                                                     + input_derivatives * (1 - root).pow(2))
+        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
+
+        return outputs, -logabsdet
+    else:
+        theta = (inputs - input_cumwidths) / input_bin_widths
+        theta_one_minus_theta = theta * (1 - theta)
+
+        numerator = input_heights * (input_delta * theta.pow(2)
+                                     + input_derivatives * theta_one_minus_theta)
+        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
+                                     * theta_one_minus_theta)
+        outputs = input_cumheights + numerator / denominator
+
+        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
+                                                     + 2 * input_delta * theta_one_minus_theta
+                                                     + input_derivatives * (1 - theta).pow(2))
+        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
+
+        return outputs, logabsdet

models/synthesizer/models/sublayer/vits_modules.py

@@ -0,0 +1,675 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from torch.nn import Conv1d
+from torch.nn.utils import weight_norm, remove_weight_norm
+from utils.util import init_weights, get_padding, convert_pad_shape, convert_pad_shape, subsequent_mask, fused_add_tanh_sigmoid_multiply
+from .common.transforms import piecewise_rational_quadratic_transform
+
+LRELU_SLOPE = 0.1
+
+class LayerNorm(nn.Module):
+  def __init__(self, channels, eps=1e-5):
+    super().__init__()
+    self.channels = channels
+    self.eps = eps
+
+    self.gamma = nn.Parameter(torch.ones(channels))
+    self.beta = nn.Parameter(torch.zeros(channels))
+
+  def forward(self, x):
+    x = x.transpose(1, -1)
+    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+    return x.transpose(1, -1)
+
+ 
+class ConvReluNorm(nn.Module):
+  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
+    super().__init__()
+    self.in_channels = in_channels
+    self.hidden_channels = hidden_channels
+    self.out_channels = out_channels
+    self.kernel_size = kernel_size
+    self.n_layers = n_layers
+    self.p_dropout = p_dropout
+    assert n_layers > 1, "Number of layers should be larger than 0."
+
+    self.conv_layers = nn.ModuleList()
+    self.norm_layers = nn.ModuleList()
+    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
+    self.norm_layers.append(LayerNorm(hidden_channels))
+    self.relu_drop = nn.Sequential(
+        nn.ReLU(),
+        nn.Dropout(p_dropout))
+    for _ in range(n_layers-1):
+      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
+      self.norm_layers.append(LayerNorm(hidden_channels))
+    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
+    self.proj.weight.data.zero_()
+    self.proj.bias.data.zero_()
+
+  def forward(self, x, x_mask):
+    x_org = x
+    for i in range(self.n_layers):
+      x = self.conv_layers[i](x * x_mask)
+      x = self.norm_layers[i](x)
+      x = self.relu_drop(x)
+    x = x_org + self.proj(x)
+    return x * x_mask
+
+
+class DDSConv(nn.Module):
+  """
+  Dilated and Depth-Separable Convolution
+  """
+  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
+    super().__init__()
+    self.channels = channels
+    self.kernel_size = kernel_size
+    self.n_layers = n_layers
+    self.p_dropout = p_dropout
+
+    self.drop = nn.Dropout(p_dropout)
+    self.convs_sep = nn.ModuleList()
+    self.convs_1x1 = nn.ModuleList()
+    self.norms_1 = nn.ModuleList()
+    self.norms_2 = nn.ModuleList()
+    for i in range(n_layers):
+      dilation = kernel_size ** i
+      padding = (kernel_size * dilation - dilation) // 2
+      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
+          groups=channels, dilation=dilation, padding=padding
+      ))
+      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
+      self.norms_1.append(LayerNorm(channels))
+      self.norms_2.append(LayerNorm(channels))
+
+  def forward(self, x, x_mask, g=None):
+    if g is not None:
+      x = x + g
+    for i in range(self.n_layers):
+      y = self.convs_sep[i](x * x_mask)
+      y = self.norms_1[i](y)
+      y = F.gelu(y)
+      y = self.convs_1x1[i](y)
+      y = self.norms_2[i](y)
+      y = F.gelu(y)
+      y = self.drop(y)
+      x = x + y
+    return x * x_mask
+
+
+class WN(torch.nn.Module):
+  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
+    super(WN, self).__init__()
+    assert(kernel_size % 2 == 1)
+    self.hidden_channels =hidden_channels
+    self.kernel_size = kernel_size,
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.gin_channels = gin_channels
+    self.p_dropout = p_dropout
+
+    self.in_layers = torch.nn.ModuleList()
+    self.res_skip_layers = torch.nn.ModuleList()
+    self.drop = nn.Dropout(p_dropout)
+
+    if gin_channels != 0:
+      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
+      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
+
+    for i in range(n_layers):
+      dilation = dilation_rate ** i
+      padding = int((kernel_size * dilation - dilation) / 2)
+      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')
+      self.in_layers.append(in_layer)
+
+      # last one is not necessary
+      if i < n_layers - 1:
+        res_skip_channels = 2 * hidden_channels
+      else:
+        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')
+      self.res_skip_layers.append(res_skip_layer)
+
+  def forward(self, x, x_mask, g=None, **kwargs):
+    output = torch.zeros_like(x)
+    n_channels_tensor = torch.IntTensor([self.hidden_channels])
+
+    if g is not None:
+      g = self.cond_layer(g)
+
+    for i in range(self.n_layers):
+      x_in = self.in_layers[i](x)
+      if g is not None:
+        cond_offset = i * 2 * self.hidden_channels
+        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
+      else:
+        g_l = torch.zeros_like(x_in)
+
+      acts = fused_add_tanh_sigmoid_multiply(
+          x_in,
+          g_l,
+          n_channels_tensor)
+      acts = self.drop(acts)
+
+      res_skip_acts = self.res_skip_layers[i](acts)
+      if i < self.n_layers - 1:
+        res_acts = res_skip_acts[:,:self.hidden_channels,:]
+        x = (x + res_acts) * x_mask
+        output = output + res_skip_acts[:,self.hidden_channels:,:]
+      else:
+        output = output + res_skip_acts
+    return output * x_mask
+
+  def remove_weight_norm(self):
+    if self.gin_channels != 0:
+      torch.nn.utils.remove_weight_norm(self.cond_layer)
+    for l in self.in_layers:
+      torch.nn.utils.remove_weight_norm(l)
+    for l in self.res_skip_layers:
+     torch.nn.utils.remove_weight_norm(l)
+
+
+class ResBlock1(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super(ResBlock1, self).__init__()
+        self.convs1 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+                               padding=get_padding(kernel_size, dilation[0]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+                               padding=get_padding(kernel_size, dilation[1]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
+                               padding=get_padding(kernel_size, dilation[2])))
+        ])
+        self.convs1.apply(init_weights)
+
+        self.convs2 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1)))
+        ])
+        self.convs2.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c2(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs1:
+            remove_weight_norm(l)
+        for l in self.convs2:
+            remove_weight_norm(l)
+
+
+class ResBlock2(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
+        super(ResBlock2, self).__init__()
+        self.convs = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+                               padding=get_padding(kernel_size, dilation[0]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+                               padding=get_padding(kernel_size, dilation[1])))
+        ])
+        self.convs.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c in self.convs:
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs:
+            remove_weight_norm(l)
+
+
+class Log(nn.Module):
+  def forward(self, x, x_mask, reverse=False, **kwargs):
+    if not reverse:
+      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
+      logdet = torch.sum(-y, [1, 2])
+      return y, logdet
+    else:
+      x = torch.exp(x) * x_mask
+      return x
+    
+
+class Flip(nn.Module):
+  def forward(self, x, *args, reverse=False, **kwargs):
+    x = torch.flip(x, [1])
+    if not reverse:
+      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
+      return x, logdet
+    else:
+      return x
+
+
+class ElementwiseAffine(nn.Module):
+  def __init__(self, channels):
+    super().__init__()
+    self.channels = channels
+    self.m = nn.Parameter(torch.zeros(channels,1))
+    self.logs = nn.Parameter(torch.zeros(channels,1))
+
+  def forward(self, x, x_mask, reverse=False, **kwargs):
+    if not reverse:
+      y = self.m + torch.exp(self.logs) * x
+      y = y * x_mask
+      logdet = torch.sum(self.logs * x_mask, [1,2])
+      return y, logdet
+    else:
+      x = (x - self.m) * torch.exp(-self.logs) * x_mask
+      return x
+
+
+class ResidualCouplingLayer(nn.Module):
+  def __init__(self,
+      channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      p_dropout=0,
+      gin_channels=0,
+      mean_only=False):
+    assert channels % 2 == 0, "channels should be divisible by 2"
+    super().__init__()
+    self.channels = channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.half_channels = channels // 2
+    self.mean_only = mean_only
+
+    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
+    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
+    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
+    self.post.weight.data.zero_()
+    self.post.bias.data.zero_()
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
+    h = self.pre(x0) * x_mask
+    h = self.enc(h, x_mask, g=g)
+    stats = self.post(h) * x_mask
+    if not self.mean_only:
+      m, logs = torch.split(stats, [self.half_channels]*2, 1)
+    else:
+      m = stats
+      logs = torch.zeros_like(m)
+
+    if not reverse:
+      x1 = m + x1 * torch.exp(logs) * x_mask
+      x = torch.cat([x0, x1], 1)
+      logdet = torch.sum(logs, [1,2])
+      return x, logdet
+    else:
+      x1 = (x1 - m) * torch.exp(-logs) * x_mask
+      x = torch.cat([x0, x1], 1)
+      return x
+
+
+class ConvFlow(nn.Module):
+  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
+    super().__init__()
+    self.in_channels = in_channels
+    self.filter_channels = filter_channels
+    self.kernel_size = kernel_size
+    self.n_layers = n_layers
+    self.num_bins = num_bins
+    self.tail_bound = tail_bound
+    self.half_channels = in_channels // 2
+
+    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
+    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
+    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
+    self.proj.weight.data.zero_()
+    self.proj.bias.data.zero_()
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
+    h = self.pre(x0)
+    h = self.convs(h, x_mask, g=g)
+    h = self.proj(h) * x_mask
+
+    b, c, t = x0.shape
+    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
+
+    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
+    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
+    unnormalized_derivatives = h[..., 2 * self.num_bins:]
+
+    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
+        unnormalized_widths,
+        unnormalized_heights,
+        unnormalized_derivatives,
+        inverse=reverse,
+        tails='linear',
+        tail_bound=self.tail_bound
+    )
+
+    x = torch.cat([x0, x1], 1) * x_mask
+    logdet = torch.sum(logabsdet * x_mask, [1,2])
+    if not reverse:
+        return x, logdet
+    else:
+        return x
+
+class Encoder(nn.Module):
+  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
+    super().__init__()
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.window_size = window_size
+
+    self.drop = nn.Dropout(p_dropout)
+    self.attn_layers = nn.ModuleList()
+    self.norm_layers_1 = nn.ModuleList()
+    self.ffn_layers = nn.ModuleList()
+    self.norm_layers_2 = nn.ModuleList()
+    for i in range(self.n_layers):
+      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
+      self.norm_layers_1.append(LayerNorm(hidden_channels))
+      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
+      self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+  def forward(self, x, x_mask):
+    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+    x = x * x_mask
+    for i in range(self.n_layers):
+      y = self.attn_layers[i](x, x, attn_mask)
+      y = self.drop(y)
+      x = self.norm_layers_1[i](x + y)
+
+      y = self.ffn_layers[i](x, x_mask)
+      y = self.drop(y)
+      x = self.norm_layers_2[i](x + y)
+    x = x * x_mask
+    return x
+
+
+class Decoder(nn.Module):
+  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
+    super().__init__()
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.proximal_bias = proximal_bias
+    self.proximal_init = proximal_init
+
+    self.drop = nn.Dropout(p_dropout)
+    self.self_attn_layers = nn.ModuleList()
+    self.norm_layers_0 = nn.ModuleList()
+    self.encdec_attn_layers = nn.ModuleList()
+    self.norm_layers_1 = nn.ModuleList()
+    self.ffn_layers = nn.ModuleList()
+    self.norm_layers_2 = nn.ModuleList()
+    for i in range(self.n_layers):
+      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
+      self.norm_layers_0.append(LayerNorm(hidden_channels))
+      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
+      self.norm_layers_1.append(LayerNorm(hidden_channels))
+      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
+      self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+  def forward(self, x, x_mask, h, h_mask):
+    """
+    x: decoder input
+    h: encoder output
+    """
+    self_attn_mask = subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
+    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+    x = x * x_mask
+    for i in range(self.n_layers):
+      y = self.self_attn_layers[i](x, x, self_attn_mask)
+      y = self.drop(y)
+      x = self.norm_layers_0[i](x + y)
+
+      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
+      y = self.drop(y)
+      x = self.norm_layers_1[i](x + y)
+      
+      y = self.ffn_layers[i](x, x_mask)
+      y = self.drop(y)
+      x = self.norm_layers_2[i](x + y)
+    x = x * x_mask
+    return x
+
+
+class MultiHeadAttention(nn.Module):
+  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
+    super().__init__()
+    assert channels % n_heads == 0
+
+    self.channels = channels
+    self.out_channels = out_channels
+    self.n_heads = n_heads
+    self.p_dropout = p_dropout
+    self.window_size = window_size
+    self.heads_share = heads_share
+    self.block_length = block_length
+    self.proximal_bias = proximal_bias
+    self.proximal_init = proximal_init
+    self.attn = None
+
+    self.k_channels = channels // n_heads
+    self.conv_q = nn.Conv1d(channels, channels, 1)
+    self.conv_k = nn.Conv1d(channels, channels, 1)
+    self.conv_v = nn.Conv1d(channels, channels, 1)
+    self.conv_o = nn.Conv1d(channels, out_channels, 1)
+    self.drop = nn.Dropout(p_dropout)
+
+    if window_size is not None:
+      n_heads_rel = 1 if heads_share else n_heads
+      rel_stddev = self.k_channels**-0.5
+      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
+      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
+
+    nn.init.xavier_uniform_(self.conv_q.weight)
+    nn.init.xavier_uniform_(self.conv_k.weight)
+    nn.init.xavier_uniform_(self.conv_v.weight)
+    if proximal_init:
+      with torch.no_grad():
+        self.conv_k.weight.copy_(self.conv_q.weight)
+        self.conv_k.bias.copy_(self.conv_q.bias)
+      
+  def forward(self, x, c, attn_mask=None):
+    q = self.conv_q(x)
+    k = self.conv_k(c)
+    v = self.conv_v(c)
+    
+    x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+    x = self.conv_o(x)
+    return x
+
+  def attention(self, query, key, value, mask=None):
+    # reshape [b, d, t] -> [b, n_h, t, d_k]
+    b, d, t_s, t_t = (*key.size(), query.size(2))
+    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+
+    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
+    if self.window_size is not None:
+      assert t_s == t_t, "Relative attention is only available for self-attention."
+      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
+      scores_local = self._relative_position_to_absolute_position(rel_logits)
+      scores = scores + scores_local
+    if self.proximal_bias:
+      assert t_s == t_t, "Proximal bias is only available for self-attention."
+      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
+    if mask is not None:
+      scores = scores.masked_fill(mask == 0, -1e4)
+      if self.block_length is not None:
+        assert t_s == t_t, "Local attention is only available for self-attention."
+        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
+        scores = scores.masked_fill(block_mask == 0, -1e4)
+    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
+    p_attn = self.drop(p_attn)
+    output = torch.matmul(p_attn, value)
+    if self.window_size is not None:
+      relative_weights = self._absolute_position_to_relative_position(p_attn)
+      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
+      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
+    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
+    return output, p_attn
+
+  def _matmul_with_relative_values(self, x, y):
+    """
+    x: [b, h, l, m]
+    y: [h or 1, m, d]
+    ret: [b, h, l, d]
+    """
+    ret = torch.matmul(x, y.unsqueeze(0))
+    return ret
+
+  def _matmul_with_relative_keys(self, x, y):
+    """
+    x: [b, h, l, d]
+    y: [h or 1, m, d]
+    ret: [b, h, l, m]
+    """
+    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
+    return ret
+
+  def _get_relative_embeddings(self, relative_embeddings, length):
+    max_relative_position = 2 * self.window_size + 1
+    # Pad first before slice to avoid using cond ops.
+    pad_length = max(length - (self.window_size + 1), 0)
+    slice_start_position = max((self.window_size + 1) - length, 0)
+    slice_end_position = slice_start_position + 2 * length - 1
+    if pad_length > 0:
+      padded_relative_embeddings = F.pad(
+          relative_embeddings,
+          convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
+    else:
+      padded_relative_embeddings = relative_embeddings
+    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
+    return used_relative_embeddings
+
+  def _relative_position_to_absolute_position(self, x):
+    """
+    x: [b, h, l, 2*l-1]
+    ret: [b, h, l, l]
+    """
+    batch, heads, length, _ = x.size()
+    # Concat columns of pad to shift from relative to absolute indexing.
+    x = F.pad(x, convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
+
+    # Concat extra elements so to add up to shape (len+1, 2*len-1).
+    x_flat = x.view([batch, heads, length * 2 * length])
+    x_flat = F.pad(x_flat, convert_pad_shape([[0,0],[0,0],[0,length-1]]))
+
+    # Reshape and slice out the padded elements.
+    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
+    return x_final
+
+  def _absolute_position_to_relative_position(self, x):
+    """
+    x: [b, h, l, l]
+    ret: [b, h, l, 2*l-1]
+    """
+    batch, heads, length, _ = x.size()
+    # padd along column
+    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
+    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
+    # add 0's in the beginning that will skew the elements after reshape
+    x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
+    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
+    return x_final
+
+  def _attention_bias_proximal(self, length):
+    """Bias for self-attention to encourage attention to close positions.
+    Args:
+      length: an integer scalar.
+    Returns:
+      a Tensor with shape [1, 1, length, length]
+    """
+    r = torch.arange(length, dtype=torch.float32)
+    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
+    super().__init__()
+    self.in_channels = in_channels
+    self.out_channels = out_channels
+    self.filter_channels = filter_channels
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.activation = activation
+    self.causal = causal
+
+    if causal:
+      self.padding = self._causal_padding
+    else:
+      self.padding = self._same_padding
+
+    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+    self.drop = nn.Dropout(p_dropout)
+
+  def forward(self, x, x_mask):
+    x = self.conv_1(self.padding(x * x_mask))
+    if self.activation == "gelu":
+      x = x * torch.sigmoid(1.702 * x)
+    else:
+      x = torch.relu(x)
+    x = self.drop(x)
+    x = self.conv_2(self.padding(x * x_mask))
+    return x * x_mask
+  
+  def _causal_padding(self, x):
+    if self.kernel_size == 1:
+      return x
+    pad_l = self.kernel_size - 1
+    pad_r = 0
+    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+    x = F.pad(x, convert_pad_shape(padding))
+    return x
+
+  def _same_padding(self, x):
+    if self.kernel_size == 1:
+      return x
+    pad_l = (self.kernel_size - 1) // 2
+    pad_r = self.kernel_size // 2
+    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+    x = F.pad(x, convert_pad_shape(padding))
+    return x

models/synthesizer/models/vits.py

@@ -0,0 +1,524 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from .sublayer.vits_modules import *
+import monotonic_align
+
+from .base import Base
+from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+from utils.util import init_weights, get_padding, sequence_mask, rand_slice_segments, generate_path
+
+
+class StochasticDurationPredictor(nn.Module):
+  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
+    super().__init__()
+    filter_channels = in_channels # it needs to be removed from future version.
+    self.in_channels = in_channels
+    self.filter_channels = filter_channels
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.n_flows = n_flows
+    self.gin_channels = gin_channels
+
+    self.log_flow = Log()
+    self.flows = nn.ModuleList()
+    self.flows.append(ElementwiseAffine(2))
+    for i in range(n_flows):
+      self.flows.append(ConvFlow(2, filter_channels, kernel_size, n_layers=3))
+      self.flows.append(Flip())
+
+    self.post_pre = nn.Conv1d(1, filter_channels, 1)
+    self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
+    self.post_convs = DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
+    self.post_flows = nn.ModuleList()
+    self.post_flows.append(ElementwiseAffine(2))
+    for i in range(4):
+      self.post_flows.append(ConvFlow(2, filter_channels, kernel_size, n_layers=3))
+      self.post_flows.append(Flip())
+
+    self.pre = nn.Conv1d(in_channels, filter_channels, 1)
+    self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
+    self.convs = DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
+    if gin_channels != 0:
+      self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
+
+  def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
+    x = torch.detach(x)
+    x = self.pre(x)
+    if g is not None:
+      g = torch.detach(g)
+      x = x + self.cond(g)
+    x = self.convs(x, x_mask)
+    x = self.proj(x) * x_mask
+
+    if not reverse:
+      flows = self.flows
+      assert w is not None
+
+      logdet_tot_q = 0 
+      h_w = self.post_pre(w)
+      h_w = self.post_convs(h_w, x_mask)
+      h_w = self.post_proj(h_w) * x_mask
+      e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
+      z_q = e_q
+      for flow in self.post_flows:
+        z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
+        logdet_tot_q += logdet_q
+      z_u, z1 = torch.split(z_q, [1, 1], 1) 
+      u = torch.sigmoid(z_u) * x_mask
+      z0 = (w - u) * x_mask
+      logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
+      logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
+
+      logdet_tot = 0
+      z0, logdet = self.log_flow(z0, x_mask)
+      logdet_tot += logdet
+      z = torch.cat([z0, z1], 1)
+      for flow in flows:
+        z, logdet = flow(z, x_mask, g=x, reverse=reverse)
+        logdet_tot = logdet_tot + logdet
+      nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
+      return nll + logq # [b]
+    else:
+      flows = list(reversed(self.flows))
+      flows = flows[:-2] + [flows[-1]] # remove a useless vflow
+      z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
+      for flow in flows:
+        z = flow(z, x_mask, g=x, reverse=reverse)
+      z0, z1 = torch.split(z, [1, 1], 1)
+      logw = z0
+      return logw
+
+
+class DurationPredictor(nn.Module):
+  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
+    super().__init__()
+
+    self.in_channels = in_channels
+    self.filter_channels = filter_channels
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.gin_channels = gin_channels
+
+    self.drop = nn.Dropout(p_dropout)
+    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
+    self.norm_1 = LayerNorm(filter_channels)
+    self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
+    self.norm_2 = LayerNorm(filter_channels)
+    self.proj = nn.Conv1d(filter_channels, 1, 1)
+
+    if gin_channels != 0:
+      self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+  def forward(self, x, x_mask, g=None):
+    x = torch.detach(x)
+    if g is not None:
+      g = torch.detach(g)
+      x = x + self.cond(g)
+    x = self.conv_1(x * x_mask)
+    x = torch.relu(x)
+    x = self.norm_1(x)
+    x = self.drop(x)
+    x = self.conv_2(x * x_mask)
+    x = torch.relu(x)
+    x = self.norm_2(x)
+    x = self.drop(x)
+    x = self.proj(x * x_mask)
+    return x * x_mask
+
+
+class TextEncoder(nn.Module):
+  def __init__(self,
+      n_vocab,
+      out_channels,
+      hidden_channels,
+      filter_channels,
+      n_heads,
+      n_layers,
+      kernel_size,
+      p_dropout):
+    super().__init__()
+    self.n_vocab = n_vocab
+    self.out_channels = out_channels
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+
+    self.emb = nn.Embedding(n_vocab, hidden_channels)
+    self.emo_proj = nn.Linear(1024, hidden_channels)
+
+    nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
+
+    self.encoder = Encoder(
+      hidden_channels,
+      filter_channels,
+      n_heads,
+      n_layers,
+      kernel_size,
+      p_dropout)
+    self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+  def forward(self, x, x_lengths, emo):
+    x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
+    x = x + self.emo_proj(emo.unsqueeze(1))
+    x = torch.transpose(x, 1, -1) # [b, h, t]
+    x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+
+    x = self.encoder(x * x_mask, x_mask)
+    stats = self.proj(x) * x_mask
+
+    m, logs = torch.split(stats, self.out_channels, dim=1)
+    return x, m, logs, x_mask
+
+
+class ResidualCouplingBlock(nn.Module):
+  def __init__(self,
+      channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      n_flows=4,
+      gin_channels=0):
+    super().__init__()
+    self.channels = channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.n_flows = n_flows
+    self.gin_channels = gin_channels
+
+    self.flows = nn.ModuleList()
+    for i in range(n_flows):
+      self.flows.append(ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
+      self.flows.append(Flip())
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    if not reverse:
+      for flow in self.flows:
+        x, _ = flow(x, x_mask, g=g, reverse=reverse)
+    else:
+      for flow in reversed(self.flows):
+        x = flow(x, x_mask, g=g, reverse=reverse)
+    return x
+
+
+class PosteriorEncoder(nn.Module):
+  def __init__(self,
+      in_channels,
+      out_channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      gin_channels=0):
+    super().__init__()
+    self.in_channels = in_channels
+    self.out_channels = out_channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.gin_channels = gin_channels
+
+    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
+    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+  def forward(self, x, x_lengths, g=None):
+    x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+    x = self.pre(x) * x_mask
+    x = self.enc(x, x_mask, g=g)
+    stats = self.proj(x) * x_mask
+    m, logs = torch.split(stats, self.out_channels, dim=1)
+    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+    return z, m, logs, x_mask
+
+
+class Generator(torch.nn.Module):
+    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
+        resblock = ResBlock1 if resblock == '1' else ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(weight_norm(
+                ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
+                                k, u, padding=(k-u)//2)))
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel//(2**(i+1))
+            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+          x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i*self.num_kernels+j](x)
+                else:
+                    xs += self.resblocks[i*self.num_kernels+j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print('Removing weight norm...')
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
+        ])
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0: # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_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.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2,3,5,7,11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+class Vits(Base):
+  """
+  Synthesizer of Vits
+  """
+
+  def __init__(self, 
+    n_vocab,
+    spec_channels,
+    segment_size,
+    inter_channels,
+    hidden_channels,
+    filter_channels,
+    n_heads,
+    n_layers,
+    kernel_size,
+    p_dropout,
+    resblock, 
+    resblock_kernel_sizes, 
+    resblock_dilation_sizes, 
+    upsample_rates, 
+    upsample_initial_channel, 
+    upsample_kernel_sizes,
+    stop_threshold,
+    n_speakers=0,
+    gin_channels=0,
+    use_sdp=True,
+    **kwargs):
+
+    super().__init__(stop_threshold)
+    self.n_vocab = n_vocab
+    self.spec_channels = spec_channels
+    self.inter_channels = inter_channels
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.resblock = resblock
+    self.resblock_kernel_sizes = resblock_kernel_sizes
+    self.resblock_dilation_sizes = resblock_dilation_sizes
+    self.upsample_rates = upsample_rates
+    self.upsample_initial_channel = upsample_initial_channel
+    self.upsample_kernel_sizes = upsample_kernel_sizes
+    self.segment_size = segment_size
+    self.n_speakers = n_speakers
+    self.gin_channels = gin_channels
+
+    self.use_sdp = use_sdp
+
+    self.enc_p = TextEncoder(n_vocab,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout)
+    self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
+    self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
+    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
+
+    if use_sdp:
+      self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
+    else:
+      self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
+
+    if n_speakers > 1:
+      self.emb_g = nn.Embedding(n_speakers, gin_channels)
+
+  def forward(self, x, x_lengths, y, y_lengths, sid=None, emo=None):
+
+    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, emo)
+    if self.n_speakers > 0:
+      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
+    else:
+      g = None
+
+    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
+    z_p = self.flow(z, y_mask, g=g)
+
+    with torch.no_grad():
+      # negative cross-entropy
+      s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
+      neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
+      neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
+      neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
+      neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
+      neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
+
+      attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+      attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
+
+    w = attn.sum(2)
+    if self.use_sdp:
+      l_length = self.dp(x, x_mask, w, g=g)
+      l_length = l_length / torch.sum(x_mask)
+    else:
+      logw_ = torch.log(w + 1e-6) * x_mask
+      logw = self.dp(x, x_mask, g=g)
+      l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging 
+
+    # expand prior
+    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
+    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
+
+    z_slice, ids_slice = rand_slice_segments(z, y_lengths, self.segment_size)
+    o = self.dec(z_slice, g=g)
+    return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
+
+  def infer(self, x, x_lengths, sid=None, emo=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
+    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths,emo)
+    if self.n_speakers > 0:
+      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
+    else:
+      g = None
+
+    if self.use_sdp:
+      logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
+    else:
+      logw = self.dp(x, x_mask, g=g)
+    w = torch.exp(logw) * x_mask * length_scale
+    w_ceil = torch.ceil(w)
+    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
+    y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
+    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+    attn = generate_path(w_ceil, attn_mask)
+
+    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
+    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
+
+    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+    z = self.flow(z_p, y_mask, g=g, reverse=True)
+    o = self.dec((z * y_mask)[:,:,:max_len], g=g)
+    return o, attn, y_mask, (z, z_p, m_p, logs_p)
+

models/synthesizer/models/wav2emo.py

@@ -0,0 +1,50 @@
+import torch
+import torch.nn as nn
+from transformers.models.wav2vec2.modeling_wav2vec2 import (
+    Wav2Vec2Model,
+    Wav2Vec2PreTrainedModel,
+)
+
+
+class RegressionHead(nn.Module):
+    r"""Classification head."""
+
+    def __init__(self, config):
+        super().__init__()
+
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.final_dropout)
+        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
+
+    def forward(self, features, **kwargs):
+        x = features
+        x = self.dropout(x)
+        x = self.dense(x)
+        x = torch.tanh(x)
+        x = self.dropout(x)
+        x = self.out_proj(x)
+
+        return x
+
+
+class EmotionExtractorModel(Wav2Vec2PreTrainedModel):
+    r"""Speech emotion classifier."""
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.config = config
+        self.wav2vec2 = Wav2Vec2Model(config)
+        self.classifier = RegressionHead(config)
+        self.init_weights()
+
+    def forward(
+            self,
+            input_values,
+    ):
+        outputs = self.wav2vec2(input_values)
+        hidden_states = outputs[0]
+        hidden_states = torch.mean(hidden_states, dim=1)
+        logits = self.classifier(hidden_states)
+
+        return hidden_states, logits

models/synthesizer/preprocess.py

@@ -6,37 +6,42 @@ from pathlib import Path
 from tqdm import tqdm
 import numpy as np
 from models.encoder import inference as encoder
-from models.synthesizer.preprocess_speaker import preprocess_speaker_general
+from models.synthesizer.preprocess_audio import preprocess_general
 from models.synthesizer.preprocess_transcript import preprocess_transcript_aishell3, preprocess_transcript_magicdata
 
 data_info = {
     "aidatatang_200zh": {
         "subfolders": ["corpus/train"],
         "trans_filepath": "transcript/aidatatang_200_zh_transcript.txt",
-        "speak_func": preprocess_speaker_general
+        "speak_func": preprocess_general
+    },
+    "aidatatang_200zh_s": {
+        "subfolders": ["corpus/train"],
+        "trans_filepath": "transcript/aidatatang_200_zh_transcript.txt",
+        "speak_func": preprocess_general
     },
     "magicdata": {
         "subfolders": ["train"],
         "trans_filepath": "train/TRANS.txt",
-        "speak_func": preprocess_speaker_general,
+        "speak_func": preprocess_general,
         "transcript_func": preprocess_transcript_magicdata,
     },
     "aishell3":{
         "subfolders": ["train/wav"],
         "trans_filepath": "train/content.txt",
-        "speak_func": preprocess_speaker_general,
+        "speak_func": preprocess_general,
         "transcript_func": preprocess_transcript_aishell3,
     },
     "data_aishell":{
         "subfolders": ["wav/train"],
         "trans_filepath": "transcript/aishell_transcript_v0.8.txt",
-        "speak_func": preprocess_speaker_general
+        "speak_func": preprocess_general
     }
 }
 
 def preprocess_dataset(datasets_root: Path, out_dir: Path, n_processes: int,
-                           skip_existing: bool, hparams, no_alignments: bool,
+                           skip_existing: bool, hparams, no_alignments: bool, 
-                           dataset: str):
+                           dataset: str, emotion_extract = False):
     dataset_info = data_info[dataset]
     # Gather the input directories
     dataset_root = datasets_root.joinpath(dataset)
@@ -47,6 +52,8 @@ def preprocess_dataset(datasets_root: Path, out_dir: Path, n_processes: int,
     # Create the output directories for each output file type
     out_dir.joinpath("mels").mkdir(exist_ok=True)
     out_dir.joinpath("audio").mkdir(exist_ok=True)
+    if emotion_extract:
+        out_dir.joinpath("emo").mkdir(exist_ok=True)
     
     # Create a metadata file
     metadata_fpath = out_dir.joinpath("train.txt")
@@ -68,12 +75,15 @@ def preprocess_dataset(datasets_root: Path, out_dir: Path, n_processes: int,
                 dict_info[v[0]] = " ".join(v[1:])
 
     speaker_dirs = list(chain.from_iterable(input_dir.glob("*") for input_dir in input_dirs))
+    
     func = partial(dataset_info["speak_func"], out_dir=out_dir, skip_existing=skip_existing, 
-                   hparams=hparams, dict_info=dict_info, no_alignments=no_alignments)
+                   hparams=hparams, dict_info=dict_info, no_alignments=no_alignments, emotion_extract=emotion_extract)
     job = Pool(n_processes).imap(func, speaker_dirs)
+    
     for speaker_metadata in tqdm(job, dataset, len(speaker_dirs), unit="speakers"):
-        for metadatum in speaker_metadata:
+        if speaker_metadata is not None:
-            metadata_file.write("|".join(str(x) for x in metadatum) + "\n")
+            for metadatum in speaker_metadata:
+                metadata_file.write("|".join(str(x) for x in metadatum) + "\n")
     metadata_file.close()
 
     # Verify the contents of the metadata file

models/synthesizer/preprocess_audio.py

@@ -9,6 +9,38 @@ from pypinyin import Style
 from pypinyin.contrib.neutral_tone import NeutralToneWith5Mixin
 from pypinyin.converter import DefaultConverter
 from pypinyin.core import Pinyin
+import torch
+from transformers import Wav2Vec2Processor
+from .models.wav2emo import EmotionExtractorModel
+
+SAMPLE_RATE = 16000
+
+# load model from hub 
+device = 'cuda' if torch.cuda.is_available() else "cpu"
+model_name = 'audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim'
+processor = Wav2Vec2Processor.from_pretrained(model_name)
+model = EmotionExtractorModel.from_pretrained(model_name).to(device)
+embs = []
+wavnames = []
+
+def extract_emo(
+    x: np.ndarray,
+    sampling_rate: int,
+    embeddings: bool = False,
+) -> np.ndarray:
+    r"""Predict emotions or extract embeddings from raw audio signal."""
+    y = processor(x, sampling_rate=sampling_rate)
+    y = y['input_values'][0]
+    y = torch.from_numpy(y).to(device)
+
+    # run through model
+    with torch.no_grad():
+        y = model(y)[0 if embeddings else 1]
+
+    # convert to numpy
+    y = y.detach().cpu().numpy()
+
+    return y
 
 class PinyinConverter(NeutralToneWith5Mixin, DefaultConverter):
     pass
@@ -16,8 +48,10 @@ class PinyinConverter(NeutralToneWith5Mixin, DefaultConverter):
 pinyin = Pinyin(PinyinConverter()).pinyin
 
 
+
+
 def _process_utterance(wav: np.ndarray, text: str, out_dir: Path, basename: str, 
-                      skip_existing: bool, hparams):
+                      skip_existing: bool, hparams, emotion_extract: bool):
     ## FOR REFERENCE:
     # For you not to lose your head if you ever wish to change things here or implement your own
     # synthesizer.
@@ -29,12 +63,13 @@ def _process_utterance(wav: np.ndarray, text: str, out_dir: Path, basename: str,
     # - Librosa pads the waveform before computing the mel spectrogram. Here, the waveform is saved
     #   without extra padding. This means that you won't have an exact relation between the length
     #   of the wav and of the mel spectrogram. See the vocoder data loader.
-    
+        
-    
     # Skip existing utterances if needed
     mel_fpath = out_dir.joinpath("mels", "mel-%s.npy" % basename)
     wav_fpath = out_dir.joinpath("audio", "audio-%s.npy" % basename)
-    if skip_existing and mel_fpath.exists() and wav_fpath.exists():
+    emo_fpath = out_dir.joinpath("emo", "emo-%s.npy" % basename)
+    skip_emo_extract = not emotion_extract or (skip_existing and emo_fpath.exists())
+    if skip_existing and mel_fpath.exists() and wav_fpath.exists() and skip_emo_extract:
         return None
 
     # Trim silence
@@ -52,11 +87,14 @@ def _process_utterance(wav: np.ndarray, text: str, out_dir: Path, basename: str,
     # Skip utterances that are too long
     if mel_frames > hparams.max_mel_frames and hparams.clip_mels_length:
         return None
-    
     # Write the spectrogram, embed and audio to disk
     np.save(mel_fpath, mel_spectrogram.T, allow_pickle=False)
     np.save(wav_fpath, wav, allow_pickle=False)
-    
+
+    if not skip_emo_extract:
+        emo = extract_emo(np.expand_dims(wav, 0), hparams.sample_rate, True)
+        np.save(emo_fpath, emo, allow_pickle=False)
+
     # Return a tuple describing this training example
     return wav_fpath.name, mel_fpath.name, "embed-%s.npy" % basename, len(wav), mel_frames, text
  
@@ -80,7 +118,7 @@ def _split_on_silences(wav_fpath, words, hparams):
 
     return wav, res
 
-def preprocess_speaker_general(speaker_dir, out_dir: Path, skip_existing: bool, hparams, dict_info, no_alignments: bool):
+def preprocess_general(speaker_dir, out_dir: Path, skip_existing: bool, hparams, dict_info, no_alignments: bool, emotion_extract: bool):
     metadata = []
     extensions = ["*.wav", "*.flac", "*.mp3"]
     for extension in extensions:
@@ -88,12 +126,12 @@ def preprocess_speaker_general(speaker_dir, out_dir: Path, skip_existing: bool,
         # Iterate over each wav
         for wav_fpath in wav_fpath_list:
             words = dict_info.get(wav_fpath.name.split(".")[0])
-            words = dict_info.get(wav_fpath.name) if not words else words # try with wav 
+            words = dict_info.get(wav_fpath.name) if not words else words # try with extension 
             if not words:
                 print("no wordS")
                 continue
             sub_basename = "%s_%02d" % (wav_fpath.name, 0)
             wav, text = _split_on_silences(wav_fpath, words, hparams)
             metadata.append(_process_utterance(wav, text, out_dir, sub_basename, 
-                                                skip_existing, hparams))
+                                                skip_existing, hparams, emotion_extract))
     return [m for m in metadata if m is not None]

models/synthesizer/synthesize.py

@@ -2,7 +2,6 @@ import torch
 from torch.utils.data import DataLoader
 from models.synthesizer.synthesizer_dataset import SynthesizerDataset, collate_synthesizer
 from models.synthesizer.models.tacotron import Tacotron
-from models.synthesizer.utils.text import text_to_sequence
 from models.synthesizer.utils.symbols import symbols
 import numpy as np
 from pathlib import Path

models/synthesizer/train.py

@@ -78,8 +78,7 @@ def train(run_id: str, syn_dir: str, models_dir: str, save_every: int,
                 # Try to scan config file
         model_config_fpaths = list(weights_fpath.parent.rglob("*.json"))
         if len(model_config_fpaths)>0 and model_config_fpaths[0].exists():
-            with model_config_fpaths[0].open("r", encoding="utf-8") as f:
+            hparams.loadJson(model_config_fpaths[0])
-                hparams.loadJson(json.load(f))
         else:  # save a config
             hparams.dumpJson(weights_fpath.parent.joinpath(run_id).with_suffix(".json"))
 

models/synthesizer/train_vits.py

@@ -0,0 +1,389 @@
+import os
+from loguru import logger
+import torch
+import glob
+from torch.nn import functional as F
+from torch.utils.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.cuda.amp import autocast, GradScaler
+from utils.audio_utils import mel_spectrogram, spec_to_mel
+from utils.loss import feature_loss, generator_loss, discriminator_loss, kl_loss
+from utils.util import slice_segments, clip_grad_value_
+from models.synthesizer.vits_dataset import (
+    VitsDataset,
+    VitsDatasetCollate,
+    DistributedBucketSampler
+)
+from models.synthesizer.models.vits import (
+    Vits,
+    MultiPeriodDiscriminator,
+)
+from models.synthesizer.utils.symbols import symbols
+from models.synthesizer.utils.plot import plot_spectrogram_to_numpy, plot_alignment_to_numpy
+from pathlib import Path
+from utils.hparams import HParams
+import torch.multiprocessing as mp
+import argparse
+
+# torch.backends.cudnn.benchmark = True
+global_step = 0
+
+
+def new_train():
+    """Assume Single Node Multi GPUs Training Only"""
+    assert torch.cuda.is_available(), "CPU training is not allowed."
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--syn_dir", type=str, default="../audiodata/SV2TTS/synthesizer", help= \
+        "Path to the synthesizer directory that contains the ground truth mel spectrograms, "
+        "the wavs, the emos and the embeds.")
+    parser.add_argument("-m", "--model_dir", type=str, default="data/ckpt/synthesizer/vits", help=\
+        "Path to the output directory that will contain the saved model weights and the logs.")
+    parser.add_argument('--ckptG', type=str, required=False,
+                      help='original VITS G checkpoint path')
+    parser.add_argument('--ckptD', type=str, required=False,
+                      help='original VITS D checkpoint path')
+    args, _ = parser.parse_known_args()
+
+    datasets_root = Path(args.syn_dir)
+    hparams= HParams(
+        model_dir = args.model_dir,
+    )
+    hparams.loadJson(Path(hparams.model_dir).joinpath("config.json"))
+    hparams.data["training_files"] = str(datasets_root.joinpath("train.txt"))
+    hparams.data["validation_files"] = str(datasets_root.joinpath("train.txt"))
+    hparams.data["datasets_root"] = str(datasets_root)
+    hparams.ckptG = args.ckptG
+    hparams.ckptD = args.ckptD
+    n_gpus = torch.cuda.device_count()
+    # for spawn
+    os.environ['MASTER_ADDR'] = 'localhost'
+    os.environ['MASTER_PORT'] = '8899'
+    # mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hparams))
+    run(0, 1, hparams)
+
+
+def load_checkpoint(checkpoint_path, model, optimizer=None, is_old=False):
+  assert os.path.isfile(checkpoint_path)
+  checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
+  iteration = checkpoint_dict['iteration']
+  learning_rate = checkpoint_dict['learning_rate']
+  if optimizer is not None:
+    if not is_old:
+      optimizer.load_state_dict(checkpoint_dict['optimizer'])
+    else:
+      new_opt_dict = optimizer.state_dict()
+      new_opt_dict_params = new_opt_dict['param_groups'][0]['params']
+      new_opt_dict['param_groups'] = checkpoint_dict['optimizer']['param_groups']
+      new_opt_dict['param_groups'][0]['params'] = new_opt_dict_params
+      optimizer.load_state_dict(new_opt_dict)
+  saved_state_dict = checkpoint_dict['model']
+  if hasattr(model, 'module'):
+    state_dict = model.module.state_dict()
+  else:
+    state_dict = model.state_dict()
+  new_state_dict= {}
+  for k, v in state_dict.items():
+    try:
+      new_state_dict[k] = saved_state_dict[k]
+    except:
+      logger.info("%s is not in the checkpoint" % k)
+      new_state_dict[k] = v
+  if hasattr(model, 'module'):
+    model.module.load_state_dict(new_state_dict, strict=False)
+  else:
+    model.load_state_dict(new_state_dict, strict=False)
+  logger.info("Loaded checkpoint '{}' (iteration {})" .format(
+    checkpoint_path, iteration))
+  return model, optimizer, learning_rate, iteration
+
+
+def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
+  logger.info("Saving model and optimizer state at iteration {} to {}".format(
+    iteration, checkpoint_path))
+  if hasattr(model, 'module'):
+    state_dict = model.module.state_dict()
+  else:
+    state_dict = model.state_dict()
+  torch.save({'model': state_dict,
+              'iteration': iteration,
+              'optimizer': optimizer.state_dict(),
+              'learning_rate': learning_rate}, checkpoint_path)
+
+def latest_checkpoint_path(dir_path, regex="G_*.pth"):
+  f_list = glob.glob(os.path.join(dir_path, regex))
+  f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
+  x = f_list[-1]
+  print(x)
+  return x
+
+def run(rank, n_gpus, hps):
+    global global_step
+    if rank == 0:
+        logger.info(hps)
+        writer = SummaryWriter(log_dir=hps.model_dir)
+        writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
+
+    dist.init_process_group(backend='gloo', init_method='env://', world_size=n_gpus, rank=rank)
+    torch.manual_seed(hps.train.seed)
+    torch.cuda.set_device(rank)
+    train_dataset = VitsDataset(hps.data.training_files, hps.data)
+    train_sampler = DistributedBucketSampler(
+        train_dataset,
+        hps.train.batch_size,
+        [32, 300, 400, 500, 600, 700, 800, 900, 1000],
+        num_replicas=n_gpus,
+        rank=rank,
+        shuffle=True)
+    collate_fn = VitsDatasetCollate()
+    train_loader = DataLoader(train_dataset, num_workers=8, shuffle=False, pin_memory=True,
+                              collate_fn=collate_fn, batch_sampler=train_sampler)
+    if rank == 0:
+        eval_dataset = VitsDataset(hps.data.validation_files, hps.data)
+        eval_loader = DataLoader(eval_dataset, num_workers=8, shuffle=False,
+                                 batch_size=hps.train.batch_size, pin_memory=True,
+                                 drop_last=False, collate_fn=collate_fn)
+
+    net_g = Vits(
+        len(symbols),
+        hps.data.filter_length // 2 + 1,
+        hps.train.segment_size // hps.data.hop_length,
+        n_speakers=hps.data.n_speakers,
+        **hps.model).cuda(rank)
+    net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
+    optim_g = torch.optim.AdamW(
+        net_g.parameters(),
+        hps.train.learning_rate,
+        betas=hps.train.betas,
+        eps=hps.train.eps)
+    optim_d = torch.optim.AdamW(
+        net_d.parameters(),
+        hps.train.learning_rate,
+        betas=hps.train.betas,
+        eps=hps.train.eps)
+    net_g = DDP(net_g, device_ids=[rank])
+    net_d = DDP(net_d, device_ids=[rank])
+    ckptG = hps.ckptG
+    ckptD = hps.ckptD
+    try:
+        if ckptG is not None:
+            _, _, _, epoch_str = load_checkpoint(ckptG, net_g, optim_g, is_old=True)
+            print("加载原版VITS模型G记录点成功")
+        else:
+            _, _, _, epoch_str = load_checkpoint(latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,
+                                                   optim_g)
+        if ckptD is not None:
+            _, _, _, epoch_str = load_checkpoint(ckptG, net_g, optim_g, is_old=True)
+            print("加载原版VITS模型D记录点成功")
+        else:
+            _, _, _, epoch_str = load_checkpoint(latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,
+                                                   optim_d)
+        global_step = (epoch_str - 1) * len(train_loader)
+    except:
+        epoch_str = 1
+        global_step = 0
+    if ckptG is not None or ckptD is not None:
+        epoch_str = 1
+        global_step = 0
+    scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
+    scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
+
+    scaler = GradScaler(enabled=hps.train.fp16_run)
+
+    for epoch in range(epoch_str, hps.train.epochs + 1):
+        if rank == 0:
+            train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler,
+                               [train_loader, eval_loader], logger, [writer, writer_eval])
+        else:
+            train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler,
+                               [train_loader, None], None, None)
+        scheduler_g.step()
+        scheduler_d.step()
+
+
+def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
+    net_g, net_d = nets
+    optim_g, optim_d = optims
+    scheduler_g, scheduler_d = schedulers
+    train_loader, eval_loader = loaders
+    if writers is not None:
+        writer, writer_eval = writers
+    train_loader.batch_sampler.set_epoch(epoch)
+    global global_step
+
+    net_g.train()
+    net_d.train()
+    for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, emo) in enumerate(train_loader):
+        logger.info(f'====> Step: 1 {batch_idx}')
+        x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)
+        spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True)
+        y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)
+        speakers = speakers.cuda(rank, non_blocking=True)
+        emo = emo.cuda(rank, non_blocking=True)
+
+        with autocast(enabled=hps.train.fp16_run):
+            y_hat, l_length, attn, ids_slice, x_mask, z_mask, \
+            (z, z_p, m_p, logs_p, m_q, logs_q) = net_g(x, x_lengths, spec, spec_lengths, speakers, emo)
+
+            mel = spec_to_mel(
+                spec,
+                hps.data.filter_length,
+                hps.data.n_mel_channels,
+                hps.data.sampling_rate,
+                hps.data.mel_fmin,
+                hps.data.mel_fmax)
+            y_mel = slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
+            y_hat_mel = mel_spectrogram(
+                y_hat.squeeze(1),
+                hps.data.filter_length,
+                hps.data.n_mel_channels,
+                hps.data.sampling_rate,
+                hps.data.hop_length,
+                hps.data.win_length,
+                hps.data.mel_fmin,
+                hps.data.mel_fmax
+            )
+
+            y = slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size)  # slice
+
+            # Discriminator
+            y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
+            with autocast(enabled=False):
+                loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
+                loss_disc_all = loss_disc
+        optim_d.zero_grad()
+        scaler.scale(loss_disc_all).backward()
+        scaler.unscale_(optim_d)
+        grad_norm_d = clip_grad_value_(net_d.parameters(), None)
+        scaler.step(optim_d)
+        logger.info(f'====> Step: 2 {batch_idx}')
+
+        with autocast(enabled=hps.train.fp16_run):
+            # Generator
+            y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
+            with autocast(enabled=False):
+                loss_dur = torch.sum(l_length.float())
+                loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
+                loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
+
+                loss_fm = feature_loss(fmap_r, fmap_g)
+                loss_gen, losses_gen = generator_loss(y_d_hat_g)
+                loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl
+        optim_g.zero_grad()
+        scaler.scale(loss_gen_all.float()).backward()
+        scaler.unscale_(optim_g)
+        grad_norm_g = clip_grad_value_(net_g.parameters(), None)
+        scaler.step(optim_g)
+        scaler.update()
+        # logger.info(f'====> Step: 3 {batch_idx}')
+        if rank == 0:
+            if global_step % hps.train.log_interval == 0:
+                lr = optim_g.param_groups[0]['lr']
+                losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl]
+                logger.info('Train Epoch: {} [{:.0f}%]'.format(
+                    epoch,
+                    100. * batch_idx / len(train_loader)))
+                logger.info([x.item() for x in losses] + [global_step, lr])
+
+                scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr,
+                               "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
+                scalar_dict.update(
+                    {"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl})
+
+                scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
+                scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
+                scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
+                image_dict = {
+                    "slice/mel_org": plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
+                    "slice/mel_gen": plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()),
+                    "all/mel": plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
+                    "all/attn": plot_alignment_to_numpy(attn[0, 0].data.cpu().numpy())
+                }
+                summarize(
+                    writer=writer,
+                    global_step=global_step,
+                    images=image_dict,
+                    scalars=scalar_dict)
+
+            if global_step % hps.train.eval_interval == 0:
+                evaluate(hps, net_g, eval_loader, writer_eval)
+                save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch,
+                                      os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
+                save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch,
+                                      os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
+        global_step += 1
+
+    if rank == 0:
+        logger.info('====> Epoch: {}'.format(epoch))
+
+
+def evaluate(hps, generator, eval_loader, writer_eval):
+    generator.eval()
+    with torch.no_grad():
+        for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, emo) in enumerate(eval_loader):
+            x, x_lengths = x.cuda(0), x_lengths.cuda(0)
+            spec, spec_lengths = spec.cuda(0), spec_lengths.cuda(0)
+            y, y_lengths = y.cuda(0), y_lengths.cuda(0)
+            speakers = speakers.cuda(0)
+            emo = emo.cuda(0)
+            # remove else
+            x = x[:1]
+            x_lengths = x_lengths[:1]
+            spec = spec[:1]
+            spec_lengths = spec_lengths[:1]
+            y = y[:1]
+            y_lengths = y_lengths[:1]
+            speakers = speakers[:1]
+            emo = emo[:1]
+            break
+        y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, speakers, emo, max_len=1000)
+        y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length
+
+        mel = spec_to_mel(
+            spec,
+            hps.data.filter_length,
+            hps.data.n_mel_channels,
+            hps.data.sampling_rate,
+            hps.data.mel_fmin,
+            hps.data.mel_fmax)
+        y_hat_mel = mel_spectrogram(
+            y_hat.squeeze(1).float(),
+            hps.data.filter_length,
+            hps.data.n_mel_channels,
+            hps.data.sampling_rate,
+            hps.data.hop_length,
+            hps.data.win_length,
+            hps.data.mel_fmin,
+            hps.data.mel_fmax
+        )
+    image_dict = {
+        "gen/mel": plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
+    }
+    audio_dict = {
+        "gen/audio": y_hat[0, :, :y_hat_lengths[0]]
+    }
+    if global_step == 0:
+        image_dict.update({"gt/mel": plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
+        audio_dict.update({"gt/audio": y[0, :, :y_lengths[0]]})
+
+    summarize(
+        writer=writer_eval,
+        global_step=global_step,
+        images=image_dict,
+        audios=audio_dict,
+        audio_sampling_rate=hps.data.sampling_rate
+    )
+    generator.train()
+
+def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
+    for k, v in scalars.items():
+        writer.add_scalar(k, v, global_step)
+    for k, v in histograms.items():
+        writer.add_histogram(k, v, global_step)
+    for k, v in images.items():
+        writer.add_image(k, v, global_step, dataformats='HWC')
+    for k, v in audios.items():
+        writer.add_audio(k, v, global_step, audio_sampling_rate)
+

models/synthesizer/utils/plot.py

@@ -3,6 +3,7 @@ matplotlib.use("Agg")
 import matplotlib.pyplot as plt
 import numpy as np
 
+MATPLOTLIB_FLAG = False
 
 def split_title_line(title_text, max_words=5):
 	"""
@@ -112,4 +113,55 @@ def plot_spectrogram_and_trace(pred_spectrogram, path, title=None, split_title=F
 	plt.tight_layout()
 	plt.savefig(path, format="png")
 	sw.add_figure("spectrogram", fig, step)
-	plt.close()
+	plt.close()
+
+
+def plot_spectrogram_to_numpy(spectrogram):
+  global MATPLOTLIB_FLAG
+  if not MATPLOTLIB_FLAG:
+    import matplotlib
+    matplotlib.use("Agg")
+    MATPLOTLIB_FLAG = True
+  import matplotlib.pylab as plt
+  import numpy as np
+  
+  fig, ax = plt.subplots(figsize=(10,2))
+  im = ax.imshow(spectrogram, aspect="auto", origin="lower",
+                  interpolation='none')
+  plt.colorbar(im, ax=ax)
+  plt.xlabel("Frames")
+  plt.ylabel("Channels")
+  plt.tight_layout()
+
+  fig.canvas.draw()
+  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
+  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+  plt.close()
+  return data
+
+
+def plot_alignment_to_numpy(alignment, info=None):
+  global MATPLOTLIB_FLAG
+  if not MATPLOTLIB_FLAG:
+    import matplotlib
+    matplotlib.use("Agg")
+    MATPLOTLIB_FLAG = True
+  import matplotlib.pylab as plt
+  import numpy as np
+
+  fig, ax = plt.subplots(figsize=(6, 4))
+  im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
+                  interpolation='none')
+  fig.colorbar(im, ax=ax)
+  xlabel = 'Decoder timestep'
+  if info is not None:
+      xlabel += '\n\n' + info
+  plt.xlabel(xlabel)
+  plt.ylabel('Encoder timestep')
+  plt.tight_layout()
+
+  fig.canvas.draw()
+  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
+  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+  plt.close()
+  return data

models/synthesizer/vits_dataset.py

@@ -0,0 +1,280 @@
+import os
+import random
+import numpy as np
+import torch
+import torch.utils.data
+
+from utils.audio_utils import spectrogram, load_wav
+from utils.util import intersperse
+from models.synthesizer.utils.text import text_to_sequence
+
+
+"""Multi speaker version"""
+class VitsDataset(torch.utils.data.Dataset):
+    """
+        1) loads audio, speaker_id, text pairs
+        2) normalizes text and converts them to sequences of integers
+        3) computes spectrograms from audio files.
+    """
+    def __init__(self, audio_file_path, hparams):
+        with open(audio_file_path, encoding='utf-8') as f:
+            self.audio_metadata = [line.strip().split('|') for line in f]
+        self.text_cleaners = hparams.text_cleaners
+        self.max_wav_value = hparams.max_wav_value
+        self.sampling_rate = hparams.sampling_rate
+        self.filter_length  = hparams.filter_length
+        self.hop_length     = hparams.hop_length
+        self.win_length     = hparams.win_length
+        self.sampling_rate  = hparams.sampling_rate
+
+        self.cleaned_text = getattr(hparams, "cleaned_text", False)
+
+        self.add_blank = hparams.add_blank
+        self.datasets_root = hparams.datasets_root
+
+        self.min_text_len = getattr(hparams, "min_text_len", 1)
+        self.max_text_len = getattr(hparams, "max_text_len", 190)
+
+        random.seed(1234)
+        random.shuffle(self.audio_metadata)
+        self._filter()
+
+    def _filter(self):
+        """
+        Filter text & store spec lengths
+        """
+        # Store spectrogram lengths for Bucketing
+        # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
+        # spec_length = wav_length // hop_length
+
+        audio_metadata_new = []
+        lengths = []
+        
+        # for audiopath, sid, text in self.audio_metadata:
+        sid = 0
+        spk_to_sid = {}
+        for wav_fpath, mel_fpath, embed_path, wav_length, mel_frames, text in self.audio_metadata:
+            if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
+                # TODO: for magic data only
+                speaker_name = wav_fpath.split("_")[1]
+                if speaker_name not in spk_to_sid:
+                    sid += 1
+                    spk_to_sid[speaker_name] = sid
+                
+                audio_metadata_new.append([wav_fpath, mel_fpath, embed_path, wav_length, mel_frames, text, spk_to_sid[speaker_name]])
+                lengths.append(os.path.getsize(f'{self.datasets_root}{os.sep}audio{os.sep}{wav_fpath}') // (2 * self.hop_length))
+        print("found sid:%d", sid)
+        self.audio_metadata = audio_metadata_new
+        self.lengths = lengths
+
+    def get_audio_text_speaker_pair(self, audio_metadata):
+        # separate filename, speaker_id and text
+        wav_fpath, text, sid = audio_metadata[0], audio_metadata[5], audio_metadata[6]
+        text = self.get_text(text)
+        
+        spec, wav = self.get_audio(f'{self.datasets_root}{os.sep}audio{os.sep}{wav_fpath}')
+        sid = self.get_sid(sid)
+        emo = torch.FloatTensor(np.load(f'{self.datasets_root}{os.sep}emo{os.sep}{wav_fpath.replace("audio", "emo")}'))
+        return (text, spec, wav, sid, emo)
+
+    def get_audio(self, filename):
+        # audio, sampling_rate = load_wav(filename)
+
+        # if sampling_rate != self.sampling_rate:
+        #     raise ValueError("{} {} SR doesn't match target {} SR".format(
+        #         sampling_rate, self.sampling_rate))
+        # audio = torch.load(filename)
+        audio = torch.FloatTensor(np.load(filename).astype(np.float32)) 
+        audio = audio.unsqueeze(0)
+        # audio_norm = audio / self.max_wav_value
+        # audio_norm = audio_norm.unsqueeze(0)
+        # spec_filename = filename.replace(".wav", ".spec.pt")
+        # if os.path.exists(spec_filename):
+        #     spec = torch.load(spec_filename)
+        # else:
+        # spec = spectrogram(audio, self.filter_length,
+        #     self.sampling_rate, self.hop_length, self.win_length,
+        #     center=False)
+        # spec = torch.squeeze(spec, 0)
+        # torch.save(spec, spec_filename)
+        spec = spectrogram(audio, self.filter_length, self.hop_length, self.win_length,
+            center=False)
+        spec = torch.squeeze(spec, 0)
+        return spec, audio
+
+    def get_text(self, text):
+        if self.cleaned_text:
+            text_norm = text_to_sequence(text, self.text_cleaners)
+        if self.add_blank:
+            text_norm = intersperse(text_norm, 0)
+        text_norm = torch.LongTensor(text_norm)
+        return text_norm
+
+    def get_sid(self, sid):
+        sid = torch.LongTensor([int(sid)])
+        return sid
+
+    def __getitem__(self, index):
+        return self.get_audio_text_speaker_pair(self.audio_metadata[index])
+
+    def __len__(self):
+        return len(self.audio_metadata)
+
+
+class VitsDatasetCollate():
+    """ Zero-pads model inputs and targets
+    """
+    def __init__(self, return_ids=False):
+        self.return_ids = return_ids
+
+    def __call__(self, batch):
+        """Collate's training batch from normalized text, audio and speaker identities
+        PARAMS
+        ------
+        batch: [text_normalized, spec_normalized, wav_normalized, sid]
+        """
+        # Right zero-pad all one-hot text sequences to max input length
+        _, ids_sorted_decreasing = torch.sort(
+            torch.LongTensor([x[1].size(1) for x in batch]),
+            dim=0, descending=True)
+
+        max_text_len = max([len(x[0]) for x in batch])
+        max_spec_len = max([x[1].size(1) for x in batch])
+        max_wav_len = max([x[2].size(1) for x in batch])
+
+        text_lengths = torch.LongTensor(len(batch))
+        spec_lengths = torch.LongTensor(len(batch))
+        wav_lengths = torch.LongTensor(len(batch))
+        sid = torch.LongTensor(len(batch))
+
+        text_padded = torch.LongTensor(len(batch), max_text_len)
+        spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
+        wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
+        emo = torch.FloatTensor(len(batch), 1024)
+
+        text_padded.zero_()
+        spec_padded.zero_()
+        wav_padded.zero_()
+        emo.zero_()
+
+        for i in range(len(ids_sorted_decreasing)):
+            row = batch[ids_sorted_decreasing[i]]
+
+            text = row[0]
+            text_padded[i, :text.size(0)] = text
+            text_lengths[i] = text.size(0)
+
+            spec = row[1]
+            spec_padded[i, :, :spec.size(1)] = spec
+            spec_lengths[i] = spec.size(1)
+
+            wav = row[2]
+            wav_padded[i, :, :wav.size(1)] = wav
+            wav_lengths[i] = wav.size(1)
+
+            sid[i] = row[3]
+
+            emo[i, :] = row[4]
+
+        if self.return_ids:
+            return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, ids_sorted_decreasing
+        return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, emo
+
+
+class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
+    """
+    Maintain similar input lengths in a batch.
+    Length groups are specified by boundaries.
+    Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
+  
+    It removes samples which are not included in the boundaries.
+    Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
+    """
+    def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
+        super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
+        self.lengths = dataset.lengths
+        self.batch_size = batch_size
+        self.boundaries = boundaries
+  
+        self.buckets, self.num_samples_per_bucket = self._create_buckets()
+        self.total_size = sum(self.num_samples_per_bucket)
+        self.num_samples = self.total_size // self.num_replicas
+  
+    def _create_buckets(self):
+        buckets = [[] for _ in range(len(self.boundaries) - 1)]
+        for i in range(len(self.lengths)):
+            length = self.lengths[i]
+            idx_bucket = self._bisect(length)
+            if idx_bucket != -1:
+                buckets[idx_bucket].append(i)
+  
+        for i in range(len(buckets) - 1, 0, -1):
+            if len(buckets[i]) == 0:
+                buckets.pop(i)
+                self.boundaries.pop(i+1)
+  
+        num_samples_per_bucket = []
+        for i in range(len(buckets)):
+            len_bucket = len(buckets[i])
+            total_batch_size = self.num_replicas * self.batch_size
+            rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
+            num_samples_per_bucket.append(len_bucket + rem)
+        return buckets, num_samples_per_bucket
+  
+    def __iter__(self):
+      # deterministically shuffle based on epoch
+      g = torch.Generator()
+      g.manual_seed(self.epoch)
+  
+      indices = []
+      if self.shuffle:
+          for bucket in self.buckets:
+              indices.append(torch.randperm(len(bucket), generator=g).tolist())
+      else:
+          for bucket in self.buckets:
+              indices.append(list(range(len(bucket))))
+  
+      batches = []
+      for i in range(len(self.buckets)):
+          bucket = self.buckets[i]
+          len_bucket = len(bucket)
+          ids_bucket = indices[i]
+          num_samples_bucket = self.num_samples_per_bucket[i]
+  
+          # add extra samples to make it evenly divisible
+          rem = num_samples_bucket - len_bucket
+          ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
+  
+          # subsample
+          ids_bucket = ids_bucket[self.rank::self.num_replicas]
+  
+          # batching
+          for j in range(len(ids_bucket) // self.batch_size):
+              batch = [bucket[idx] for idx in ids_bucket[j*self.batch_size:(j+1)*self.batch_size]]
+              batches.append(batch)
+  
+      if self.shuffle:
+          batch_ids = torch.randperm(len(batches), generator=g).tolist()
+          batches = [batches[i] for i in batch_ids]
+      self.batches = batches
+  
+      assert len(self.batches) * self.batch_size == self.num_samples
+      return iter(self.batches)
+  
+    def _bisect(self, x, lo=0, hi=None):
+      if hi is None:
+          hi = len(self.boundaries) - 1
+  
+      if hi > lo:
+          mid = (hi + lo) // 2
+          if self.boundaries[mid] < x and x <= self.boundaries[mid+1]:
+              return mid
+          elif x <= self.boundaries[mid]:
+              return self._bisect(x, lo, mid)
+          else:
+              return self._bisect(x, mid + 1, hi)
+      else:
+          return -1
+
+    def __len__(self):
+        return self.num_samples // self.batch_size

models/vocoder/fregan/meldataset.py

@@ -6,7 +6,7 @@ import torch.utils.data
 import numpy as np
 from librosa.util import normalize
 from scipy.io.wavfile import read
-from librosa.filters import mel as librosa_mel_fn
+from utils.audio_utils import mel_spectrogram
 
 MAX_WAV_VALUE = 32768.0
 
@@ -16,62 +16,6 @@ def load_wav(full_path):
     return data, sampling_rate
 
 
-def dynamic_range_compression(x, C=1, clip_val=1e-5):
-    return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
-
-
-def dynamic_range_decompression(x, C=1):
-    return np.exp(x) / C
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global mel_basis, hann_window
-    if fmax not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
-        hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
-
-    spec = torch.matmul(mel_basis[str(fmax)+'_'+str(y.device)], spec)
-    spec = spectral_normalize_torch(spec)
-
-    return spec
-
-
 def get_dataset_filelist(a):
     #with open(a.input_training_file, 'r', encoding='utf-8') as fi:
     #    training_files = [os.path.join(a.input_wavs_dir, x.split('|')[0] + '.wav')

models/vocoder/fregan/train.py

@@ -13,7 +13,7 @@ from torch.nn.parallel import DistributedDataParallel
 from models.vocoder.fregan.meldataset import MelDataset, mel_spectrogram, get_dataset_filelist
 from models.vocoder.fregan.generator import FreGAN
 from models.vocoder.fregan.discriminator import ResWiseMultiPeriodDiscriminator, ResWiseMultiScaleDiscriminator
-from models.vocoder.fregan.loss import feature_loss, generator_loss, discriminator_loss
+from utils.loss import feature_loss, generator_loss, discriminator_loss
 from models.vocoder.fregan.utils import plot_spectrogram, scan_checkpoint, load_checkpoint, save_checkpoint
 
 

models/vocoder/hifigan/meldataset.py

@@ -6,7 +6,7 @@ import torch.utils.data
 import numpy as np
 from librosa.util import normalize
 from scipy.io.wavfile import read
-from librosa.filters import mel as librosa_mel_fn
+from utils.audio_utils import mel_spectrogram
 
 MAX_WAV_VALUE = 32768.0
 
@@ -32,46 +32,6 @@ def dynamic_range_decompression_torch(x, C=1):
     return torch.exp(x) / C
 
 
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global mel_basis, hann_window
-    if fmax not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
-        hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
-
-    spec = torch.matmul(mel_basis[str(fmax)+'_'+str(y.device)], spec)
-    spec = spectral_normalize_torch(spec)
-
-    return spec
-
-
 def get_dataset_filelist(a):
     # with open(a.input_training_file, 'r', encoding='utf-8') as fi:
     #     training_files = [os.path.join(a.input_wavs_dir, x.split('|')[0] + '.wav')

models/vocoder/hifigan/models.py

@@ -283,38 +283,3 @@ class MultiScaleDiscriminator(torch.nn.Module):
             fmap_gs.append(fmap_g)
 
         return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-def feature_loss(fmap_r, fmap_g):
-    loss = 0
-    for dr, dg in zip(fmap_r, fmap_g):
-        for rl, gl in zip(dr, dg):
-            loss += torch.mean(torch.abs(rl - gl))
-
-    return loss*2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-    loss = 0
-    r_losses = []
-    g_losses = []
-    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-        r_loss = torch.mean((1-dr)**2)
-        g_loss = torch.mean(dg**2)
-        loss += (r_loss + g_loss)
-        r_losses.append(r_loss.item())
-        g_losses.append(g_loss.item())
-
-    return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-    loss = 0
-    gen_losses = []
-    for dg in disc_outputs:
-        l = torch.mean((1-dg)**2)
-        gen_losses.append(l)
-        loss += l
-
-    return loss, gen_losses
-

models/vocoder/hifigan/train.py

@@ -13,8 +13,9 @@ import torch.multiprocessing as mp
 from torch.distributed import init_process_group
 from torch.nn.parallel import DistributedDataParallel
 from models.vocoder.hifigan.meldataset import MelDataset, mel_spectrogram, get_dataset_filelist
-from models.vocoder.hifigan.models import Generator, MultiPeriodDiscriminator, MultiScaleDiscriminator, feature_loss, generator_loss,\
+from models.vocoder.hifigan.models import Generator, MultiPeriodDiscriminator, MultiScaleDiscriminator
-    discriminator_loss
+from utils.loss import feature_loss, generator_loss, discriminator_loss
+
 from models.vocoder.hifigan.utils import plot_spectrogram, scan_checkpoint, load_checkpoint, save_checkpoint
 
 torch.backends.cudnn.benchmark = True

pre.py

@@ -1,16 +1,11 @@
-from models.synthesizer.preprocess import create_embeddings
+from models.synthesizer.preprocess import create_embeddings, preprocess_dataset
-from utils.argutils import print_args
-from pathlib import Path
-import argparse
-
-from models.synthesizer.preprocess import preprocess_dataset
 from models.synthesizer.hparams import hparams
-from utils.argutils import print_args
 from pathlib import Path
 import argparse
 
 recognized_datasets = [
     "aidatatang_200zh",
+    "aidatatang_200zh_s",
     "magicdata",
     "aishell3",
     "data_aishell"
@@ -48,6 +43,8 @@ if __name__ == "__main__":
     parser.add_argument("-ne", "--n_processes_embed", type=int, default=1, help=\
         "Number of processes in parallel.An encoder is created for each, so you may need to lower "
         "this value on GPUs with low memory. Set it to 1 if CUDA is unhappy")
+    parser.add_argument("-ee","--emotion_extract", action="store_true", help=\
+        "Preprocess audio to extract emotional numpy (for emotional vits).")
     args = parser.parse_args()
 
     # Process the arguments
@@ -74,4 +71,5 @@ if __name__ == "__main__":
     del args.n_processes_embed
     preprocess_dataset(**vars(args))
     
-    create_embeddings(synthesizer_root=args.out_dir, n_processes=n_processes_embed, encoder_model_fpath=encoder_model_fpath)    
+    create_embeddings(synthesizer_root=args.out_dir, n_processes=n_processes_embed, encoder_model_fpath=encoder_model_fpath)
+    

requirements.txt

@@ -20,7 +20,7 @@ flask_wtf
 flask_cors==3.0.10
 gevent==21.8.0
 flask_restx
-tensorboard
+tensorboard==1.15
 streamlit==1.8.0
 PyYAML==5.4.1
 torch_complex

run.py

@@ -2,14 +2,13 @@ import time
 import os
 import argparse
 import torch
-import numpy as np
 import glob
 from pathlib import Path
 from tqdm import tqdm
 from models.ppg_extractor import load_model
 import librosa
 import soundfile as sf
-from utils.load_yaml import HpsYaml
+from utils.hparams import HpsYaml
 
 from models.encoder.audio import preprocess_wav
 from models.encoder import inference as speacker_encoder

train.py

@@ -1,9 +1,4 @@
-import sys
-import torch
 import argparse
-import numpy as np
-from utils.load_yaml import HpsYaml
-from models.ppg2mel.train.train_linglf02mel_seq2seq_oneshotvc import Solver
 
 def main():
     # Arguments
@@ -17,6 +12,9 @@ def main():
     if paras.type == "synth":
         from control.cli.synthesizer_train import new_train
         new_train()
+    if paras.type == "vits":
+        from models.synthesizer.train_vits import new_train
+        new_train()
 
 if __name__ == "__main__":
     main()   

utils/audio_utils.py

@@ -1,4 +1,4 @@
-
+import numpy as np
 import torch
 import torch.utils.data
 from scipy.io.wavfile import read
@@ -6,21 +6,50 @@ from librosa.filters import mel as librosa_mel_fn
 
 MAX_WAV_VALUE = 32768.0
 
+mel_basis = {}
+hann_window = {}
 
 def load_wav(full_path):
     sampling_rate, data = read(full_path)
     return data, sampling_rate
 
-def _dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
+def load_wav_to_torch(full_path):
-    return torch.log(torch.clamp(x, min=clip_val) * C)
+  sampling_rate, data = read(full_path)
+  return torch.FloatTensor(data.astype(np.float32)), sampling_rate
 
 
-def _spectral_normalize_torch(magnitudes):
+def spectrogram(y, n_fft, hop_size, win_size, center=False):
-    output = _dynamic_range_compression_torch(magnitudes)
+    if torch.min(y) < -1.:
-    return output
+        print('min value is ', torch.min(y))
+    if torch.max(y) > 1.:
+        print('max value is ', torch.max(y))
+
+    global hann_window
+    dtype_device = str(y.dtype) + '_' + str(y.device)
+    wnsize_dtype_device = str(win_size) + '_' + dtype_device
+    if wnsize_dtype_device not in hann_window:
+        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
+
+    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
+    y = y.squeeze(1)
+
+    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
+                      center=center, pad_mode='reflect', normalized=False, onesided=True)
+
+    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+    return spec
+
+def spec_to_mel(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
+    global mel_basis
+    dtype_device = str(spec.dtype) + '_' + str(spec.device)
+    fmax_dtype_device = str(fmax) + '_' + dtype_device
+    if fmax_dtype_device not in mel_basis:
+        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
+        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
+    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
+    spec = _spectral_normalize_torch(spec)
+    return spec
 
-mel_basis = {}
-hann_window = {}
 
 def mel_spectrogram(
     y, 
@@ -39,18 +68,27 @@ def mel_spectrogram(
     if torch.max(y) > 1.:
         print('max value is ', torch.max(y))
 
+    # global mel_basis, hann_window
+    # if fmax not in mel_basis:
+    #     mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
+    #     mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
+    #     hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device)
     global mel_basis, hann_window
-    if fmax not in mel_basis:
+    dtype_device = str(y.dtype) + '_' + str(y.device)
+    fmax_dtype_device = str(fmax) + '_' + dtype_device
+    wnsize_dtype_device = str(win_size) + '_' + dtype_device
+    if fmax_dtype_device not in mel_basis:
         mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
+        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
-        hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device)
+    if wnsize_dtype_device not in hann_window:
+        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
 
     y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
     y = y.squeeze(1)
 
     spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)],
                       center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-    spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
+    spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-6))
     mel_spec = torch.matmul(mel_basis[str(fmax)+'_'+str(y.device)], spec)
     mel_spec = _spectral_normalize_torch(mel_spec)
     if output_energy:
@@ -58,3 +96,12 @@ def mel_spectrogram(
         return mel_spec, energy
     else:
         return mel_spec
+
+
+def _dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
+    return torch.log(torch.clamp(x, min=clip_val) * C)
+
+
+def _spectral_normalize_torch(magnitudes):
+    output = _dynamic_range_compression_torch(magnitudes)
+    return output

utils/hparams.py

@@ -0,0 +1,110 @@
+import yaml
+import json
+import ast
+
+def load_hparams_json(filename):
+    with open(filename, "r") as f:
+        data = f.read()
+    config = json.loads(data)
+
+    hparams = HParams(**config)
+    return hparams
+
+
+def load_hparams_yaml(filename):
+    stream = open(filename, 'r')
+    docs = yaml.safe_load_all(stream)
+    hparams_dict = dict()
+    for doc in docs:
+        for k, v in doc.items():
+            hparams_dict[k] = v
+    return hparams_dict
+
+def merge_dict(user, default):
+    if isinstance(user, dict) and isinstance(default, dict):
+        for k, v in default.items():
+            if k not in user:
+                user[k] = v
+            else:
+                user[k] = merge_dict(user[k], v)
+    return user
+
+class Dotdict(dict):
+    """
+    a dictionary that supports dot notation 
+    as well as dictionary access notation 
+    usage: d = DotDict() or d = DotDict({'val1':'first'})
+    set attributes: d.val2 = 'second' or d['val2'] = 'second'
+    get attributes: d.val2 or d['val2']
+    """
+    __getattr__ = dict.__getitem__
+    __setattr__ = dict.__setitem__
+    __delattr__ = dict.__delitem__
+
+    def __init__(self, dct=None):
+        dct = dict() if not dct else dct
+        for key, value in dct.items():
+            if hasattr(value, 'keys'):
+                value = Dotdict(value)
+            self[key] = value
+
+class HpsYaml(Dotdict):
+    def __init__(self, yaml_file):
+        super(Dotdict, self).__init__()
+        hps = load_hparams_yaml(yaml_file)
+        hp_dict = Dotdict(hps)
+        for k, v in hp_dict.items():
+            setattr(self, k, v)
+
+    __getattr__ = Dotdict.__getitem__
+    __setattr__ = Dotdict.__setitem__
+    __delattr__ = Dotdict.__delitem__
+    
+class HParams():
+    def __init__(self, **kwargs): 
+        for k, v in kwargs.items():
+            if type(v) == dict:
+                v = HParams(**v)
+            self[k] = v
+    def keys(self):
+        return self.__dict__.keys()
+    def __setitem__(self, key, value): setattr(self, key, value)
+    def __getitem__(self, key): return getattr(self, key)
+    def keys(self):  return self.__dict__.keys()
+    def items(self): return self.__dict__.items()
+    def values(self): return self.__dict__.values()
+    def __contains__(self, key): return key in self.__dict__
+    def __repr__(self):
+        return self.__dict__.__repr__()
+
+    def parse(self, string):
+        # Overrides hparams from a comma-separated string of name=value pairs
+        if len(string) > 0:
+            overrides = [s.split("=") for s in string.split(",")]
+            keys, values = zip(*overrides)
+            keys = list(map(str.strip, keys))
+            values = list(map(str.strip, values))
+            for k in keys:
+                self.__dict__[k] = ast.literal_eval(values[keys.index(k)])
+        return self
+
+    def loadJson(self, fpath):
+        with fpath.open("r", encoding="utf-8") as f:
+            print("\Loading the json with %s\n", fpath)
+            data = json.load(f)
+            for k in data.keys():
+                if k not in ["tts_schedule", "tts_finetune_layers"]: 
+                    v = data[k]
+                    if type(v) == dict:
+                        v = HParams(**v)
+                    self.__dict__[k] = v
+        return self
+
+    def dumpJson(self, fp):
+        print("\Saving the json with %s\n", fp)
+        with fp.open("w", encoding="utf-8") as f:
+            json.dump(self.__dict__, f)
+        return self
+
+
+

utils/load_yaml.py

@@ -1,58 +0,0 @@
-import yaml
-
-
-def load_hparams(filename):
-    stream = open(filename, 'r')
-    docs = yaml.safe_load_all(stream)
-    hparams_dict = dict()
-    for doc in docs:
-        for k, v in doc.items():
-            hparams_dict[k] = v
-    return hparams_dict
-
-def merge_dict(user, default):
-    if isinstance(user, dict) and isinstance(default, dict):
-        for k, v in default.items():
-            if k not in user:
-                user[k] = v
-            else:
-                user[k] = merge_dict(user[k], v)
-    return user
-
-class Dotdict(dict):
-    """
-    a dictionary that supports dot notation 
-    as well as dictionary access notation 
-    usage: d = DotDict() or d = DotDict({'val1':'first'})
-    set attributes: d.val2 = 'second' or d['val2'] = 'second'
-    get attributes: d.val2 or d['val2']
-    """
-    __getattr__ = dict.__getitem__
-    __setattr__ = dict.__setitem__
-    __delattr__ = dict.__delitem__
-
-    def __init__(self, dct=None):
-        dct = dict() if not dct else dct
-        for key, value in dct.items():
-            if hasattr(value, 'keys'):
-                value = Dotdict(value)
-            self[key] = value
-
-class HpsYaml(Dotdict):
-    def __init__(self, yaml_file):
-        super(Dotdict, self).__init__()
-        hps = load_hparams(yaml_file)
-        hp_dict = Dotdict(hps)
-        for k, v in hp_dict.items():
-            setattr(self, k, v)
-
-    __getattr__ = Dotdict.__getitem__
-    __setattr__ = Dotdict.__setitem__
-    __delattr__ = Dotdict.__delitem__
-    
-
-
-
-
-
-

utils/loss.py

@@ -32,4 +32,22 @@ def generator_loss(disc_outputs):
         gen_losses.append(l)
         loss += l
 
-    return loss, gen_losses
+    return loss, gen_losses
+
+
+def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
+  """
+  z_p, logs_q: [b, h, t_t]
+  m_p, logs_p: [b, h, t_t]
+  """
+  z_p = z_p.float()
+  logs_q = logs_q.float()
+  m_p = m_p.float()
+  logs_p = logs_p.float()
+  z_mask = z_mask.float()
+
+  kl = logs_p - logs_q - 0.5
+  kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
+  kl = torch.sum(kl * z_mask)
+  l = kl / torch.sum(z_mask)
+  return l

utils/util.py

@@ -125,4 +125,22 @@ def subsequent_mask(length):
 def intersperse(lst, item):
   result = [item] * (len(lst) * 2 + 1)
   result[1::2] = lst
-  return result
+  return result
+
+
+def clip_grad_value_(parameters, clip_value, norm_type=2):
+  if isinstance(parameters, torch.Tensor):
+    parameters = [parameters]
+  parameters = list(filter(lambda p: p.grad is not None, parameters))
+  norm_type = float(norm_type)
+  if clip_value is not None:
+    clip_value = float(clip_value)
+
+  total_norm = 0
+  for p in parameters:
+    param_norm = p.grad.data.norm(norm_type)
+    total_norm += param_norm.item() ** norm_type
+    if clip_value is not None:
+      p.grad.data.clamp_(min=-clip_value, max=clip_value)
+  total_norm = total_norm ** (1. / norm_type)
+  return total_norm