Merge branch 'pwgan' into dev

vocoder/configs/parallel_wavegan_config.json

@@ -0,0 +1,143 @@
+{
+    "run_name": "pwgan",
+    "run_description": "parallel-wavegan training",
+
+    // AUDIO PARAMETERS
+    "audio":{
+        "fft_size": 1024,         // number of stft frequency levels. Size of the linear spectogram frame.
+        "win_length": 1024,      // stft window length in ms.
+        "hop_length": 256,       // stft window hop-lengh in ms.
+        "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
+        "frame_shift_ms": null,  // stft window hop-lengh in ms. If null, 'hop_length' is used.
+
+        // Audio processing parameters
+        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
+        "preemphasis": 0.0,     // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
+        "ref_level_db": 0,     // reference level db, theoretically 20db is the sound of air.
+
+        // Silence trimming
+        "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
+        "trim_db": 60,          // threshold for timming silence. Set this according to your dataset.
+
+        // MelSpectrogram parameters
+        "num_mels": 80,         // size of the mel spec frame.
+        "mel_fmin": 50.0,        // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
+        "mel_fmax": 7600.0,     // maximum freq level for mel-spec. Tune for dataset!!
+        "spec_gain": 1.0,         // scaler value appplied after log transform of spectrogram.
+
+        // Normalization parameters
+        "signal_norm": true,    // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
+        "min_level_db": -100,   // lower bound for normalization
+        "symmetric_norm": true, // move normalization to range [-1, 1]
+        "max_norm": 4.0,        // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
+        "clip_norm": true,      // clip normalized values into the range.
+        "stats_path": "/home/erogol/Data/LJSpeech-1.1/scale_stats.npy"    // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
+    },
+
+    // DISTRIBUTED TRAINING
+    // "distributed":{
+    //     "backend": "nccl",
+    //     "url": "tcp:\/\/localhost:54321"
+    // },
+
+    // MODEL PARAMETERS
+    "use_pqmf": true,
+
+    // LOSS PARAMETERS
+    "use_stft_loss": true,
+    "use_subband_stft_loss": false,  // USE ONLY WITH MULTIBAND MODELS
+    "use_mse_gan_loss": true,
+    "use_hinge_gan_loss": false,
+    "use_feat_match_loss": false,  // use only with melgan discriminators
+
+    // loss weights
+    "stft_loss_weight": 0.5,
+    "subband_stft_loss_weight": 0.5,
+    "mse_G_loss_weight": 2.5,
+    "hinge_G_loss_weight": 2.5,
+    "feat_match_loss_weight": 25,
+
+    // multiscale stft loss parameters
+    "stft_loss_params": {
+        "n_ffts": [1024, 2048, 512],
+        "hop_lengths": [120, 240, 50],
+        "win_lengths": [600, 1200, 240]
+    },
+
+    // subband multiscale stft loss parameters
+    "subband_stft_loss_params":{
+        "n_ffts": [384, 683, 171],
+        "hop_lengths": [30, 60, 10],
+        "win_lengths": [150, 300, 60]
+    },
+
+    "target_loss": "avg_G_loss",  // loss value to pick the best model to save after each epoch
+
+    // DISCRIMINATOR
+    "discriminator_model": "parallel_wavegan_discriminator",
+    "discriminator_model_params":{
+        "num_layers": 10
+    },
+    "steps_to_start_discriminator": 200000,      // steps required to start GAN trainining.1
+
+    // GENERATOR
+    "generator_model": "parallel_wavegan_generator",
+    "generator_model_params": {
+        "upsample_factors":[4, 4, 4, 4],
+        "stacks": 3,
+        "num_res_blocks": 30
+    },
+
+    // DATASET
+    "data_path": "/home/erogol/Data/LJSpeech-1.1/wavs/",
+    "feature_path": null,
+    "seq_len": 25600,
+    "pad_short": 2000,
+    "conv_pad": 0,
+    "use_noise_augment": false,
+    "use_cache": true,
+
+    "reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
+
+    // TRAINING
+    "batch_size": 6,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
+
+    // VALIDATION
+    "run_eval": true,
+    "test_delay_epochs": 10,  //Until attention is aligned, testing only wastes computation time.
+    "test_sentences_file": null,  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
+
+    // OPTIMIZER
+    "epochs": 10000,                // total number of epochs to train.
+    "wd": 0.0,                // Weight decay weight.
+    "gen_clip_grad": -1,      // Generator gradient clipping threshold. Apply gradient clipping if > 0
+    "disc_clip_grad": -1,     // Discriminator gradient clipping threshold.
+    "lr_scheduler_gen": "MultiStepLR",   // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
+    "lr_scheduler_gen_params": {
+        "gamma": 0.5,
+        "milestones": [100000, 200000, 300000, 400000, 500000, 600000]
+    },
+    "lr_scheduler_disc": "MultiStepLR",   // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
+    "lr_scheduler_disc_params": {
+        "gamma": 0.5,
+        "milestones": [100000, 200000, 300000, 400000, 500000, 600000]
+    },
+    "lr_gen": 1e-4,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
+    "lr_disc": 1e-4,
+
+    // TENSORBOARD and LOGGING
+    "print_step": 25,       // Number of steps to log traning on console.
+    "print_eval": false,     // If True, it prints loss values for each step in eval run.
+    "save_step": 25000,      // Number of training steps expected to plot training stats on TB and save model checkpoints.
+    "checkpoint": true,     // If true, it saves checkpoints per "save_step"
+    "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
+
+    // DATA LOADING
+    "num_loader_workers": 4,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
+    "num_val_loader_workers": 4,    // number of evaluation data loader processes.
+    "eval_split_size": 10,
+
+    // PATHS
+    "output_path": "/home/erogol/Models/LJSpeech/"
+}
+

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

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)

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)

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)

vocoder/tests/test_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()

vocoder/tests/test_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))

vocoder/tests/test_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
+

vocoder/tests/test_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)
+

vocoder/train.py

@@ -124,6 +124,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
             y_hat_vis = y_hat
             y_G_sub = model_G.pqmf_analysis(y_G)
 
+        scores_fake, feats_fake, feats_real = None, None, None
         if global_step > c.steps_to_start_discriminator:
 
             # run D with or without cond. features
@@ -146,8 +147,6 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
                     _, feats_real = D_out_real
             else:
                 scores_fake = D_out_fake
-        else:
-            scores_fake, feats_fake, feats_real = None, None, None
 
         # compute losses
         loss_G_dict = criterion_G(y_hat, y_G, scores_fake, feats_fake,
@@ -328,6 +327,7 @@ def evaluate(model_G, criterion_G, model_D, criterion_D, ap, global_step, epoch)
             y_G_sub = model_G.pqmf_analysis(y_G)
 
 
+        scores_fake, feats_fake, feats_real = None, None, None
         if global_step > c.steps_to_start_discriminator:
 
             if len(signature(model_D.forward).parameters) == 2:
@@ -349,8 +349,7 @@ def evaluate(model_G, criterion_G, model_D, criterion_D, ap, global_step, epoch)
                     _, feats_real = D_out_real
             else:
                 scores_fake = D_out_fake
-        else:
-            scores_fake, feats_fake, feats_real = None, None, None
+                feats_fake, feats_real = None, None
 
         # compute losses
         loss_G_dict = criterion_G(y_hat, y_G, scores_fake, feats_fake,

vocoder/utils/generic_utils.py

@@ -67,14 +67,34 @@ def setup_generator(c):
             upsample_factors=c.generator_model_params['upsample_factors'],
             res_kernel=3,
             num_res_blocks=c.generator_model_params['num_res_blocks'])
+    if c.generator_model in 'parallel_wavegan_generator':
+        model = MyModel(
+           in_channels=1,
+            out_channels=1,
+            kernel_size=3,
+            num_res_blocks=c.generator_model_params['num_res_blocks'],
+            stacks=c.generator_model_params['stacks'],
+            res_channels=64,
+            gate_channels=128,
+            skip_channels=64,
+            aux_channels=c.audio['num_mels'],
+            aux_context_window=c['conv_pad'],
+            dropout=0.0,
+            bias=True,
+            use_weight_norm=True,
+            upsample_conditional_features=True,
+            upsample_factors=c.generator_model_params['upsample_factors'])
     return model
 
 
 def setup_discriminator(c):
     print(" > Discriminator Model: {}".format(c.discriminator_model))
-    MyModel = importlib.import_module('TTS.vocoder.models.' +
-                                      c.discriminator_model.lower())
-    MyModel = getattr(MyModel, to_camel(c.discriminator_model))
+    if 'parallel_wavegan' in c.discriminator_model:
+        MyModel = importlib.import_module('TTS.vocoder.models.parallel_wavegan_discriminator')
+    else:
+        MyModel = importlib.import_module('TTS.vocoder.models.' +
+                                        c.discriminator_model.lower())
+    MyModel = getattr(MyModel, to_camel(c.discriminator_model.lower()))
     if c.discriminator_model in 'random_window_discriminator':
         model = MyModel(
             cond_channels=c.audio['num_mels'],
@@ -95,6 +115,33 @@ def setup_discriminator(c):
             max_channels=c.discriminator_model_params['max_channels'],
             downsample_factors=c.
             discriminator_model_params['downsample_factors'])
+    if c.discriminator_model == 'residual_parallel_wavegan_discriminator':
+        model = MyModel(
+            in_channels=1,
+            out_channels=1,
+            kernel_size=3,
+            num_layers=c.discriminator_model_params['num_layers'],
+            stacks=c.discriminator_model_params['stacks'],
+            res_channels=64,
+            gate_channels=128,
+            skip_channels=64,
+            dropout=0.0,
+            bias=True,
+            nonlinear_activation="LeakyReLU",
+            nonlinear_activation_params={"negative_slope": 0.2},
+        )
+    if c.discriminator_model == 'parallel_wavegan_discriminator':
+        model = MyModel(
+            in_channels=1,
+            out_channels=1,
+            kernel_size=3,
+            num_layers=c.discriminator_model_params['num_layers'],
+            conv_channels=64,
+            dilation_factor=1,
+            nonlinear_activation="LeakyReLU",
+            nonlinear_activation_params={"negative_slope": 0.2},
+            bias=True
+        )
     return model