diff --git a/TTS/vocoder/layers/parallel_wavegan.py b/TTS/vocoder/layers/parallel_wavegan.py
new file mode 100644
index 00000000..35a56e8d
--- /dev/null
+++ b/TTS/vocoder/layers/parallel_wavegan.py
@@ -0,0 +1,75 @@
+import torch
+from torch.nn import functional as F
+
+
+class ResidualBlock(torch.nn.Module):
+    """Residual block module in WaveNet."""
+
+    def __init__(self,
+                 kernel_size=3,
+                 res_channels=64,
+                 gate_channels=128,
+                 skip_channels=64,
+                 aux_channels=80,
+                 dropout=0.0,
+                 dilation=1,
+                 bias=True,
+                 use_causal_conv=False
+                 ):
+        super(ResidualBlock, self).__init__()
+        self.dropout = dropout
+        # no future time stamps available
+        if use_causal_conv:
+            padding = (kernel_size - 1) * dilation
+        else:
+            assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size."
+            padding = (kernel_size - 1) // 2 * dilation
+        self.use_causal_conv = use_causal_conv
+
+        # dilation conv
+        self.conv = torch.nn.Conv1d(res_channels, gate_channels, kernel_size,
+                           padding=padding, dilation=dilation, bias=bias)
+
+        # local conditioning
+        if aux_channels > 0:
+            self.conv1x1_aux = torch.nn.Conv1d(aux_channels, gate_channels, 1, bias=False)
+        else:
+            self.conv1x1_aux = None
+
+        # conv output is split into two groups
+        gate_out_channels = gate_channels // 2
+        self.conv1x1_out = torch.nn.Conv1d(gate_out_channels, res_channels, 1, bias=bias)
+        self.conv1x1_skip = torch.nn.Conv1d(gate_out_channels, skip_channels, 1, bias=bias)
+
+    def forward(self, x, c):
+        """
+        x: B x D_res x T
+        c: B x D_aux x T
+        """
+        residual = x
+        x = F.dropout(x, p=self.dropout, training=self.training)
+        x = self.conv(x)
+
+        # remove future time steps if use_causal_conv conv
+        x = x[:, :, :residual.size(-1)] if self.use_causal_conv else x
+
+        # split into two part for gated activation
+        splitdim = 1
+        xa, xb = x.split(x.size(splitdim) // 2, dim=splitdim)
+
+        # local conditioning
+        if c is not None:
+            assert self.conv1x1_aux is not None
+            c = self.conv1x1_aux(c)
+            ca, cb = c.split(c.size(splitdim) // 2, dim=splitdim)
+            xa, xb = xa + ca, xb + cb
+
+        x = torch.tanh(xa) * torch.sigmoid(xb)
+
+        # for skip connection
+        s = self.conv1x1_skip(x)
+
+        # for residual connection
+        x = (self.conv1x1_out(x) + residual) * (0.5 ** 2)
+
+        return x, s
diff --git a/TTS/vocoder/layers/upsample.py b/TTS/vocoder/layers/upsample.py
new file mode 100644
index 00000000..1f70c9f6
--- /dev/null
+++ b/TTS/vocoder/layers/upsample.py
@@ -0,0 +1,100 @@
+import numpy as np
+import torch
+from torch.nn import functional as F
+
+
+class Stretch2d(torch.nn.Module):
+    def __init__(self, x_scale, y_scale, mode="nearest"):
+        super(Stretch2d, self).__init__()
+        self.x_scale = x_scale
+        self.y_scale = y_scale
+        self.mode = mode
+
+    def forward(self, x):
+        """
+            x (Tensor): Input tensor (B, C, F, T).
+            Tensor: Interpolated tensor (B, C, F * y_scale, T * x_scale),
+        """
+        return F.interpolate(
+            x, scale_factor=(self.y_scale, self.x_scale), mode=self.mode)
+
+
+class UpsampleNetwork(torch.nn.Module):
+    def __init__(self,
+                 upsample_factors,
+                 nonlinear_activation=None,
+                 nonlinear_activation_params={},
+                 interpolate_mode="nearest",
+                 freq_axis_kernel_size=1,
+                 use_causal_conv=False,
+                 ):
+        super(UpsampleNetwork, self).__init__()
+        self.use_causal_conv = use_causal_conv
+        self.up_layers = torch.nn.ModuleList()
+        for scale in upsample_factors:
+            # interpolation layer
+            stretch = Stretch2d(scale, 1, interpolate_mode)
+            self.up_layers += [stretch]
+
+            # conv layer
+            assert (freq_axis_kernel_size - 1) % 2 == 0, "Not support even number freq axis kernel size."
+            freq_axis_padding = (freq_axis_kernel_size - 1) // 2
+            kernel_size = (freq_axis_kernel_size, scale * 2 + 1)
+            if use_causal_conv:
+                padding = (freq_axis_padding, scale * 2)
+            else:
+                padding = (freq_axis_padding, scale)
+            conv = torch.nn.Conv2d(1, 1, kernel_size=kernel_size, padding=padding, bias=False)
+            self.up_layers += [conv]
+
+            # nonlinear
+            if nonlinear_activation is not None:
+                nonlinear = getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params)
+                self.up_layers += [nonlinear]
+
+    def forward(self, c):
+        """
+            c :  (B, C, T_in).
+            Tensor: (B, C, T_upsample)
+        """
+        c = c.unsqueeze(1)  # (B, 1, C, T)
+        for f in self.up_layers:
+            c = f(c)
+        return c.squeeze(1)  # (B, C, T')
+
+
+class ConvUpsample(torch.nn.Module):
+    def __init__(self,
+                 upsample_factors,
+                 nonlinear_activation=None,
+                 nonlinear_activation_params={},
+                 interpolate_mode="nearest",
+                 freq_axis_kernel_size=1,
+                 aux_channels=80,
+                 aux_context_window=0,
+                 use_causal_conv=False
+                 ):
+        super(ConvUpsample, self).__init__()
+        self.aux_context_window = aux_context_window
+        self.use_causal_conv = use_causal_conv and aux_context_window > 0
+        # To capture wide-context information in conditional features
+        kernel_size = aux_context_window + 1 if use_causal_conv else 2 * aux_context_window + 1
+        # NOTE(kan-bayashi): Here do not use padding because the input is already padded
+        self.conv_in = torch.nn.Conv1d(aux_channels, aux_channels, kernel_size=kernel_size, bias=False)
+        self.upsample = UpsampleNetwork(
+            upsample_factors=upsample_factors,
+            nonlinear_activation=nonlinear_activation,
+            nonlinear_activation_params=nonlinear_activation_params,
+            interpolate_mode=interpolate_mode,
+            freq_axis_kernel_size=freq_axis_kernel_size,
+            use_causal_conv=use_causal_conv,
+        )
+
+    def forward(self, c):
+        """
+        c : (B, C, T_in).
+        Tensor: (B, C, T_upsampled),
+        """
+        c_ = self.conv_in(c)
+        c = c_[:, :, :-self.aux_context_window] if self.use_causal_conv else c_
+        return self.upsample(c)
diff --git a/TTS/vocoder/models/parallel_wavegan_discriminator.py b/TTS/vocoder/models/parallel_wavegan_discriminator.py
new file mode 100644
index 00000000..de03ccdb
--- /dev/null
+++ b/TTS/vocoder/models/parallel_wavegan_discriminator.py
@@ -0,0 +1,192 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from TTS.vocoder.layers.parallel_wavegan import ResidualBlock
+
+
+class ParallelWaveganDiscriminator(nn.Module):
+    """PWGAN discriminator as in https://arxiv.org/abs/1910.11480.
+    It classifies each audio window real/fake and returns a sequence
+    of predictions.
+        It is a stack of convolutional blocks with dilation.
+    """
+
+    def __init__(self,
+                 in_channels=1,
+                 out_channels=1,
+                 kernel_size=3,
+                 num_layers=10,
+                 conv_channels=64,
+                 dilation_factor=1,
+                 nonlinear_activation="LeakyReLU",
+                 nonlinear_activation_params={"negative_slope": 0.2},
+                 bias=True,
+                 ):
+        super(ParallelWaveganDiscriminator, self).__init__()
+        assert (kernel_size - 1) % 2 == 0, " [!] does not support even number kernel size."
+        assert dilation_factor > 0, " [!] dilation factor must be > 0."
+        self.conv_layers = nn.ModuleList()
+        conv_in_channels = in_channels
+        for i in range(num_layers - 1):
+            if i == 0:
+                dilation = 1
+            else:
+                dilation = i if dilation_factor == 1 else dilation_factor ** i
+                conv_in_channels = conv_channels
+            padding = (kernel_size - 1) // 2 * dilation
+            conv_layer = [
+                nn.Conv1d(conv_in_channels, conv_channels,
+                       kernel_size=kernel_size, padding=padding,
+                       dilation=dilation, bias=bias),
+                getattr(nn, nonlinear_activation)(inplace=True, **nonlinear_activation_params)
+            ]
+            self.conv_layers += conv_layer
+        padding = (kernel_size - 1) // 2
+        last_conv_layer = nn.Conv1d(
+            conv_in_channels, out_channels,
+            kernel_size=kernel_size, padding=padding, bias=bias)
+        self.conv_layers += [last_conv_layer]
+        self.apply_weight_norm()
+
+    def forward(self, x):
+        """
+            x : (B, 1, T).
+        Returns:
+            Tensor: (B, 1, T)
+        """
+        for f in self.conv_layers:
+            x = f(x)
+        return x
+
+    def apply_weight_norm(self):
+        def _apply_weight_norm(m):
+            if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.Conv2d):
+                torch.nn.utils.weight_norm(m)
+        self.apply(_apply_weight_norm)
+
+    def remove_weight_norm(self):
+        def _remove_weight_norm(m):
+            try:
+                # print(f"Weight norm is removed from {m}.")
+                nn.utils.remove_weight_norm(m)
+            except ValueError:  # this module didn't have weight norm
+                return
+        self.apply(_remove_weight_norm)
+
+
+class ResidualParallelWaveganDiscriminator(nn.Module):
+    def __init__(self,
+                 in_channels=1,
+                 out_channels=1,
+                 kernel_size=3,
+                 num_layers=30,
+                 stacks=3,
+                 res_channels=64,
+                 gate_channels=128,
+                 skip_channels=64,
+                 dropout=0.0,
+                 bias=True,
+                 nonlinear_activation="LeakyReLU",
+                 nonlinear_activation_params={"negative_slope": 0.2},
+                 ):
+        super(ResidualParallelWaveganDiscriminator, self).__init__()
+        assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size."
+
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.num_layers = num_layers
+        self.stacks = stacks
+        self.kernel_size = kernel_size
+        self.res_factor = math.sqrt(1.0 / num_layers)
+
+        # check the number of num_layers and stacks
+        assert num_layers % stacks == 0
+        layers_per_stack = num_layers // stacks
+
+        # define first convolution
+        self.first_conv = nn.Sequential(
+            nn.Conv1d(in_channels,
+                      res_channels,
+                      kernel_size=1,
+                      padding=0,
+                      dilation=1,
+                      bias=True),
+            getattr(nn, nonlinear_activation)(inplace=True,
+                                              **nonlinear_activation_params),
+        )
+
+        # define residual blocks
+        self.conv_layers = nn.ModuleList()
+        for layer in range(num_layers):
+            dilation = 2 ** (layer % layers_per_stack)
+            conv = ResidualBlock(
+                kernel_size=kernel_size,
+                res_channels=res_channels,
+                gate_channels=gate_channels,
+                skip_channels=skip_channels,
+                aux_channels=-1,
+                dilation=dilation,
+                dropout=dropout,
+                bias=bias,
+                use_causal_conv=False,
+            )
+            self.conv_layers += [conv]
+
+        # define output layers
+        self.last_conv_layers = nn.ModuleList([
+            getattr(nn, nonlinear_activation)(inplace=True,
+                                              **nonlinear_activation_params),
+            nn.Conv1d(skip_channels,
+                      skip_channels,
+                      kernel_size=1,
+                      padding=0,
+                      dilation=1,
+                      bias=True),
+            getattr(nn, nonlinear_activation)(inplace=True,
+                                              **nonlinear_activation_params),
+            nn.Conv1d(skip_channels,
+                      out_channels,
+                      kernel_size=1,
+                      padding=0,
+                      dilation=1,
+                      bias=True),
+        ])
+
+        # apply weight norm
+        self.apply_weight_norm()
+
+    def forward(self, x):
+        """
+        x: (B, 1, T).
+        """
+        x = self.first_conv(x)
+
+        skips = 0
+        for f in self.conv_layers:
+            x, h = f(x, None)
+            skips += h
+        skips *= self.res_factor
+
+        # apply final layers
+        x = skips
+        for f in self.last_conv_layers:
+            x = f(x)
+        return x
+
+    def apply_weight_norm(self):
+        def _apply_weight_norm(m):
+            if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.Conv2d):
+                torch.nn.utils.weight_norm(m)
+        self.apply(_apply_weight_norm)
+
+    def remove_weight_norm(self):
+        def _remove_weight_norm(m):
+            try:
+                print(f"Weight norm is removed from {m}.")
+                nn.utils.remove_weight_norm(m)
+            except ValueError:  # this module didn't have weight norm
+                return
+
+        self.apply(_remove_weight_norm)
diff --git a/TTS/vocoder/models/parallel_wavegan_generator.py b/TTS/vocoder/models/parallel_wavegan_generator.py
new file mode 100644
index 00000000..56316a41
--- /dev/null
+++ b/TTS/vocoder/models/parallel_wavegan_generator.py
@@ -0,0 +1,162 @@
+import math
+import numpy as np
+import torch
+from torch.nn.utils import weight_norm
+
+from TTS.vocoder.layers.parallel_wavegan import ResidualBlock
+from TTS.vocoder.layers.upsample import ConvUpsample
+
+
+class ParallelWaveganGenerator(torch.nn.Module):
+    """PWGAN generator as in https://arxiv.org/pdf/1910.11480.pdf.
+    It is similar to WaveNet with no causal convolution.
+        It is conditioned on an aux feature (spectrogram) to generate
+    an output waveform from an input noise.
+    """
+    def __init__(self,
+                 in_channels=1,
+                 out_channels=1,
+                 kernel_size=3,
+                 num_res_blocks=30,
+                 stacks=3,
+                 res_channels=64,
+                 gate_channels=128,
+                 skip_channels=64,
+                 aux_channels=80,
+                 aux_context_window=2,
+                 dropout=0.0,
+                 bias=True,
+                 use_weight_norm=True,
+                 use_causal_conv=False,
+                 upsample_conditional_features=True,
+                 upsample_net="ConvInUpsampleNetwork",
+                 upsample_factors=[4, 4, 4, 4],
+                 inference_padding=2):
+
+        super(ParallelWaveganGenerator, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.aux_channels = aux_channels
+        self.num_res_blocks = num_res_blocks
+        self.stacks = stacks
+        self.kernel_size = kernel_size
+        self.upsample_factors = upsample_factors
+        self.upsample_scale = np.prod(upsample_factors)
+        self.inference_padding = inference_padding
+
+        # check the number of layers and stacks
+        assert num_res_blocks % stacks == 0
+        layers_per_stack = num_res_blocks // stacks
+
+        # define first convolution
+        self.first_conv = torch.nn.Conv1d(in_channels,
+                                          res_channels,
+                                          kernel_size=1,
+                                          bias=True)
+
+        # define conv + upsampling network
+        self.upsample_net = ConvUpsample(upsample_factors=upsample_factors)
+
+        # define residual blocks
+        self.conv_layers = torch.nn.ModuleList()
+        for layer in range(num_res_blocks):
+            dilation = 2**(layer % layers_per_stack)
+            conv = ResidualBlock(
+                kernel_size=kernel_size,
+                res_channels=res_channels,
+                gate_channels=gate_channels,
+                skip_channels=skip_channels,
+                aux_channels=aux_channels,
+                dilation=dilation,
+                dropout=dropout,
+                bias=bias,
+            )
+            self.conv_layers += [conv]
+
+        # define output layers
+        self.last_conv_layers = torch.nn.ModuleList([
+            torch.nn.ReLU(inplace=True),
+            torch.nn.Conv1d(skip_channels,
+                            skip_channels,
+                            kernel_size=1,
+                            bias=True),
+            torch.nn.ReLU(inplace=True),
+            torch.nn.Conv1d(skip_channels,
+                            out_channels,
+                            kernel_size=1,
+                            bias=True),
+        ])
+
+        # apply weight norm
+        if use_weight_norm:
+            self.apply_weight_norm()
+
+    def forward(self, c):
+        """
+            c: (B, C ,T').
+            o: Output tensor (B, out_channels, T)
+        """
+        # random noise
+        x = torch.randn([c.shape[0], 1, c.shape[2] * self.upsample_scale])
+        x = x.to(self.first_conv.bias.device)
+
+        # perform upsampling
+        if c is not None and self.upsample_net is not None:
+            c = self.upsample_net(c)
+            assert c.shape[-1] == x.shape[
+                -1], f" [!] Upsampling scale does not match the expected output. {c.shape} vs {x.shape}"
+
+        # encode to hidden representation
+        x = self.first_conv(x)
+        skips = 0
+        for f in self.conv_layers:
+            x, h = f(x, c)
+            skips += h
+        skips *= math.sqrt(1.0 / len(self.conv_layers))
+
+        # apply final layers
+        x = skips
+        for f in self.last_conv_layers:
+            x = f(x)
+
+        return x
+
+    def inference(self, c):
+        c = c.to(self.first_conv.weight.device)
+        c = torch.nn.functional.pad(
+            c, (self.inference_padding, self.inference_padding), 'replicate')
+        return self.forward(c)
+
+    def remove_weight_norm(self):
+        def _remove_weight_norm(m):
+            try:
+                # print(f"Weight norm is removed from {m}.")
+                torch.nn.utils.remove_weight_norm(m)
+            except ValueError:  # this module didn't have weight norm
+                return
+
+        self.apply(_remove_weight_norm)
+
+    def apply_weight_norm(self):
+        def _apply_weight_norm(m):
+            if isinstance(m, torch.nn.Conv1d) or isinstance(
+                    m, torch.nn.Conv2d):
+                torch.nn.utils.weight_norm(m)
+                # print(f"Weight norm is applied to {m}.")
+
+        self.apply(_apply_weight_norm)
+
+    @staticmethod
+    def _get_receptive_field_size(layers,
+                                  stacks,
+                                  kernel_size,
+                                  dilation=lambda x: 2**x):
+        assert layers % stacks == 0
+        layers_per_cycle = layers // stacks
+        dilations = [dilation(i % layers_per_cycle) for i in range(layers)]
+        return (kernel_size - 1) * sum(dilations) + 1
+
+    @property
+    def receptive_field_size(self):
+        return self._get_receptive_field_size(self.layers, self.stacks,
+                                              self.kernel_size)
diff --git a/tests/test_vocoder_parallel_wavegan_discriminator.py b/tests/test_vocoder_parallel_wavegan_discriminator.py
new file mode 100644
index 00000000..b496e216
--- /dev/null
+++ b/tests/test_vocoder_parallel_wavegan_discriminator.py
@@ -0,0 +1,41 @@
+import numpy as np
+import torch
+
+from TTS.vocoder.models.parallel_wavegan_discriminator import ParallelWaveganDiscriminator, ResidualParallelWaveganDiscriminator
+
+
+def test_pwgan_disciminator():
+    model = ParallelWaveganDiscriminator(
+        in_channels=1,
+        out_channels=1,
+        kernel_size=3,
+        num_layers=10,
+        conv_channels=64,
+        dilation_factor=1,
+        nonlinear_activation="LeakyReLU",
+        nonlinear_activation_params={"negative_slope": 0.2},
+        bias=True)
+    dummy_x = torch.rand((4, 1, 64 * 256))
+    output = model(dummy_x)
+    assert np.all(output.shape == (4, 1, 64 * 256))
+    model.remove_weight_norm()
+
+
+def test_redisual_pwgan_disciminator():
+    model = ResidualParallelWaveganDiscriminator(
+        in_channels=1,
+        out_channels=1,
+        kernel_size=3,
+        num_layers=30,
+        stacks=3,
+        res_channels=64,
+        gate_channels=128,
+        skip_channels=64,
+        dropout=0.0,
+        bias=True,
+        nonlinear_activation="LeakyReLU",
+        nonlinear_activation_params={"negative_slope": 0.2})
+    dummy_x = torch.rand((4, 1, 64 * 256))
+    output = model(dummy_x)
+    assert np.all(output.shape == (4, 1, 64 * 256))
+    model.remove_weight_norm()
diff --git a/tests/test_vocoder_parallel_wavegan_generator.py b/tests/test_vocoder_parallel_wavegan_generator.py
new file mode 100644
index 00000000..f904ed24
--- /dev/null
+++ b/tests/test_vocoder_parallel_wavegan_generator.py
@@ -0,0 +1,30 @@
+import numpy as np
+import torch
+
+from TTS.vocoder.models.parallel_wavegan_generator import ParallelWaveganGenerator
+
+
+def test_pwgan_generator():
+    model = ParallelWaveganGenerator(
+        in_channels=1,
+        out_channels=1,
+        kernel_size=3,
+        num_res_blocks=30,
+        stacks=3,
+        res_channels=64,
+        gate_channels=128,
+        skip_channels=64,
+        aux_channels=80,
+        aux_context_window=2,
+        dropout=0.0,
+        bias=True,
+        use_weight_norm=True,
+        use_causal_conv=False,
+        upsample_conditional_features=True,
+        upsample_factors=[4, 4, 4, 4])
+    dummy_c = torch.rand((4, 80, 64))
+    output = model(dummy_c)
+    assert np.all(output.shape == (4, 1, 64 * 256))
+    model.remove_weight_norm()
+    output = model.inference(dummy_c)
+    assert np.all(output.shape == (4, 1, (64 + 4) * 256))
diff --git a/tests/test_vocoder_tf_melgan_generator.py b/tests/test_vocoder_tf_melgan_generator.py
new file mode 100644
index 00000000..40a167a2
--- /dev/null
+++ b/tests/test_vocoder_tf_melgan_generator.py
@@ -0,0 +1,12 @@
+import numpy as np
+import tensorflow as tf
+
+from TTS.vocoder.tf.models.melgan_generator import MelganGenerator
+
+def test_melgan_generator():
+    hop_length = 256
+    model = MelganGenerator()
+    dummy_input = tf.random.uniform((4, 80, 64))
+    output = model(dummy_input, training=False)
+    assert np.all(output.shape == (4, 1, 64 * hop_length)), output.shape
+
diff --git a/tests/test_vocoder_tf_pqmf.py b/tests/test_vocoder_tf_pqmf.py
new file mode 100644
index 00000000..75f00d5f
--- /dev/null
+++ b/tests/test_vocoder_tf_pqmf.py
@@ -0,0 +1,29 @@
+import os
+import tensorflow as tf
+
+import soundfile as sf
+from librosa.core import load
+
+from TTS.tests import get_tests_path, get_tests_input_path
+from TTS.vocoder.tf.layers.pqmf import PQMF
+
+
+TESTS_PATH = get_tests_path()
+WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
+
+
+def test_pqmf():
+    w, sr = load(WAV_FILE)
+
+    layer = PQMF(N=4, taps=62, cutoff=0.15, beta=9.0)
+    w, sr = load(WAV_FILE)
+    w2 = tf.convert_to_tensor(w[None, None, :])
+    b2 = layer.analysis(w2)
+    w2_ = layer.synthesis(b2)
+    w2_ = w2.numpy()
+
+    print(w2_.max())
+    print(w2_.min())
+    print(w2_.mean())
+    sf.write('tf_pqmf_output.wav', w2_.flatten(), sr)
+