added FWGAN weight dumping code

5 files changed, 805 insertions, 0 deletions

diff --git a/dnn/torch/fwgan/dump_model_weights.py b/dnn/torch/fwgan/dump_model_weights.py
new file mode 100644
index 00000000..f4e38c15
--- /dev/null
+++ b/dnn/torch/fwgan/dump_model_weights.py
@@ -0,0 +1,89 @@
+import os
+import sys
+import argparse
+
+import torch
+from torch import nn
+
+
+sys.path.append(os.path.join(os.path.split(__file__)[0], '../weight-exchange'))
+import wexchange.torch
+
+from models import model_dict
+
+unquantized = [
+    'feat_in_conv1.conv',
+    'bfcc_with_corr_upsampler.fc',
+    'cont_net.0',
+    'fwc6.cont_fc.0',
+    'fwc6.fc.0',
+    'fwc6.fc.1.gate',
+    'fwc7.cont_fc.0',
+    'fwc7.fc.0',
+    'fwc7.fc.1.gate'
+]
+
+description=f"""
+This is an unsafe dumping script for FWGAN models. It assumes that all weights are included in Linear, Conv1d or GRU layer
+and will fail to export any other weights.
+
+Furthermore, the quanitze option relies on the following explicit list of layers to be excluded:
+{unquantized}.
+
+Modify this script manually if adjustments are needed.
+"""
+
+parser = argparse.ArgumentParser(description=description)
+parser.add_argument('model', choices=['fwgan400', 'fwgan500'], help='model name')
+parser.add_argument('weightfile', type=str, help='weight file path')
+parser.add_argument('export_folder', type=str)
+parser.add_argument('--export-filename', type=str, default='fwgan_data', help='filename for source and header file (.c and .h will be added), defaults to fwgan_data')
+parser.add_argument('--struct-name', type=str, default='FWGAN', help='name for C struct, defaults to FWGAN')
+parser.add_argument('--quantize', action='store_true', help='apply quantization')
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+
+    model = model_dict[args.model]()
+
+    print(f"loading weights from {args.weightfile}...")
+    saved_gen= torch.load(args.weightfile, map_location='cpu')
+    model.load_state_dict(saved_gen)
+    def _remove_weight_norm(m):
+        try:
+            torch.nn.utils.remove_weight_norm(m)
+        except ValueError:  # this module didn't have weight norm
+            return
+    model.apply(_remove_weight_norm)
+
+
+    print("dumping model...")
+    quantize_model=args.quantize
+
+    output_folder = args.export_folder
+    os.makedirs(output_folder, exist_ok=True)
+
+    writer = wexchange.c_export.c_writer.CWriter(os.path.join(output_folder, args.export_filename), model_struct_name=args.struct_name)
+
+    for name, module in model.named_modules():
+
+        if quantize_model:
+            quantize=name not in unquantized
+            scale = None if quantize else 1/128
+        else:
+            quantize=False
+            scale=1/128
+
+        if isinstance(module, nn.Linear):
+            print(f"dumping linear layer {name}...")
+            wexchange.torch.dump_torch_dense_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale)
+
+        if isinstance(module, nn.Conv1d):
+            print(f"dumping conv1d layer {name}...")
+            wexchange.torch.dump_torch_conv1d_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale)
+
+        if isinstance(module, nn.GRU):
+            print(f"dumping GRU layer {name}...")
+            wexchange.torch.dump_torch_gru_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale, recurrent_scale=scale)
+
+    writer.close()
diff --git a/dnn/torch/fwgan/inference.py b/dnn/torch/fwgan/inference.py
new file mode 100644
index 00000000..c06b68b1
--- /dev/null
+++ b/dnn/torch/fwgan/inference.py
@@ -0,0 +1,141 @@
+import os
+import time
+import torch
+import numpy as np
+from scipy import signal as si
+from scipy.io import wavfile
+import argparse
+
+from models import model_dict
+
+parser = argparse.ArgumentParser()
+parser.add_argument('model', choices=['fwgan400', 'fwgan500'], help='model name')
+parser.add_argument('weightfile', type=str, help='weight file')
+parser.add_argument('input', type=str, help='input: feature file or folder with feature files')
+parser.add_argument('output', type=str, help='output: wav file name or folder name, depending on input')
+
+
+########################### Signal Processing Layers ###########################
+
+def preemphasis(x, coef= -0.85):
+
+    return si.lfilter(np.array([1.0, coef]), np.array([1.0]), x).astype('float32')
+
+def deemphasis(x, coef= -0.85):
+
+    return si.lfilter(np.array([1.0]), np.array([1.0, coef]), x).astype('float32')
+
+gamma = 0.92
+weighting_vector = np.array([gamma**i for i in range(16,0,-1)])
+
+
+def lpc_synthesis_one_frame(frame, filt, buffer, weighting_vector=np.ones(16)):
+
+    out = np.zeros_like(frame)
+
+    filt = np.flip(filt)
+
+    inp = frame[:]
+
+
+    for i in range(0, inp.shape[0]):
+
+        s = inp[i] - np.dot(buffer*weighting_vector, filt)
+
+        buffer[0] = s
+
+        buffer = np.roll(buffer, -1)
+
+        out[i] = s
+
+    return out
+
+def inverse_perceptual_weighting (pw_signal, filters, weighting_vector):
+
+    #inverse perceptual weighting= H_preemph / W(z/gamma)
+
+    pw_signal = preemphasis(pw_signal)
+
+    signal = np.zeros_like(pw_signal)
+    buffer = np.zeros(16)
+    num_frames = pw_signal.shape[0] //160
+    assert num_frames == filters.shape[0]
+
+    for frame_idx in range(0, num_frames):
+
+        in_frame = pw_signal[frame_idx*160: (frame_idx+1)*160][:]
+        out_sig_frame = lpc_synthesis_one_frame(in_frame, filters[frame_idx, :], buffer, weighting_vector)
+        signal[frame_idx*160: (frame_idx+1)*160] = out_sig_frame[:]
+        buffer[:] = out_sig_frame[-16:]
+
+    return signal
+
+
+def process_item(generator, feature_filename, output_filename, verbose=False):
+
+    feat = np.memmap(feature_filename, dtype='float32', mode='r')
+
+    num_feat_frames = len(feat) // 36
+    feat = np.reshape(feat, (num_feat_frames, 36))
+
+    bfcc = np.copy(feat[:, :18])
+    corr = np.copy(feat[:, 19:20]) + 0.5
+    bfcc_with_corr =  torch.from_numpy(np.hstack((bfcc, corr))).type(torch.FloatTensor).unsqueeze(0)#.to(device)
+
+    period = torch.from_numpy((0.1 + 50 * np.copy(feat[:, 18:19]) + 100)\
+                            .astype('int32')).type(torch.long).view(1,-1)#.to(device)
+
+    lpc_filters = np.copy(feat[:, -16:])
+
+    start_time = time.time()
+    x1 = generator(period, bfcc_with_corr, torch.zeros(1,320)) #this means the vocoder runs in complete synthesis mode with zero history audio frames
+    end_time = time.time()
+    total_time = end_time - start_time
+    x1 = x1.squeeze(1).squeeze(0).detach().cpu().numpy()
+    gen_seconds = len(x1)/16000
+    out = deemphasis(inverse_perceptual_weighting(x1, lpc_filters, weighting_vector))
+    if verbose:
+        print(f"Took {total_time:.3f}s to generate {len(x1)}  samples ({gen_seconds}s) -> {gen_seconds/total_time:.2f}x real time")
+
+    out = np.clip(np.round(2**15 * out), -2**15, 2**15 -1).astype(np.int16)
+    wavfile.write(output_filename, 16000, out)
+
+
+########################### The inference loop over folder containing lpcnet feature files #################################
+if __name__ == "__main__":
+
+    args = parser.parse_args()
+
+    generator = model_dict[args.model]()
+
+
+    #Load the FWGAN500Hz Checkpoint
+    saved_gen= torch.load(args.weightfile, map_location='cpu')
+    generator.load_state_dict(saved_gen)
+
+    #this is just to remove the weight_norm from the model layers as it's no longer needed
+    def _remove_weight_norm(m):
+        try:
+            torch.nn.utils.remove_weight_norm(m)
+        except ValueError:  # this module didn't have weight norm
+            return
+    generator.apply(_remove_weight_norm)
+
+    #enable inference mode
+    generator = generator.eval()
+
+    print('Successfully loaded the generator model ... start generation:')
+
+    if os.path.isdir(args.input):
+
+        os.makedirs(args.output, exist_ok=True)
+
+        for fn in os.listdir(args.input):
+            print(f"processing input {fn}...")
+            feature_filename = os.path.join(args.input, fn)
+            output_filename = os.path.join(args.output, os.path.splitext(fn)[0] + f"_{args.model}.wav")
+            process_item(generator, feature_filename, output_filename)
+    else:
+        process_item(generator, args.input, args.output)
+
+    print("Finished!")
\ No newline at end of file
diff --git a/dnn/torch/fwgan/models/__init__.py b/dnn/torch/fwgan/models/__init__.py
new file mode 100644
index 00000000..d52a6eb0
--- /dev/null
+++ b/dnn/torch/fwgan/models/__init__.py
@@ -0,0 +1,7 @@
+from .fwgan400 import FWGAN400ContLarge
+from .fwgan500 import FWGAN500Cont
+
+model_dict = {
+    'fwgan400': FWGAN400ContLarge,
+    'fwgan500': FWGAN500Cont
+}
\ No newline at end of file
diff --git a/dnn/torch/fwgan/models/fwgan400.py b/dnn/torch/fwgan/models/fwgan400.py
new file mode 100644
index 00000000..84d9849e
--- /dev/null
+++ b/dnn/torch/fwgan/models/fwgan400.py
@@ -0,0 +1,308 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.utils import weight_norm
+import numpy as np
+
+which_norm = weight_norm
+
+#################### Definition of basic model components ####################
+
+#Convolutional layer with 1 frame look-ahead (used for feature PreCondNet)
+class ConvLookahead(nn.Module):
+    def __init__(self, in_ch, out_ch, kernel_size, dilation=1, groups=1, bias= False):
+        super(ConvLookahead, self).__init__()
+        torch.manual_seed(5)
+
+        self.padding_left = (kernel_size - 2) * dilation
+        self.padding_right = 1 * dilation
+
+        self.conv = which_norm(nn.Conv1d(in_ch,out_ch,kernel_size,dilation=dilation, groups=groups, bias= bias))
+
+        self.init_weights()
+
+    def init_weights(self):
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding):
+                nn.init.orthogonal_(m.weight.data)
+
+    def forward(self, x):
+
+        x = F.pad(x,(self.padding_left, self.padding_right))
+        conv_out = self.conv(x)
+        return conv_out
+
+#(modified) GLU Activation layer definition
+class GLU(nn.Module):
+    def __init__(self, feat_size):
+        super(GLU, self).__init__()
+
+        torch.manual_seed(5)
+
+        self.gate = which_norm(nn.Linear(feat_size, feat_size, bias=False))
+
+        self.init_weights()
+
+    def init_weights(self):
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d)\
+            or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding):
+                nn.init.orthogonal_(m.weight.data)
+
+    def forward(self, x):
+
+        out = torch.tanh(x) * torch.sigmoid(self.gate(x))
+
+        return out
+
+#GRU layer definition
+class ContForwardGRU(nn.Module):
+    def __init__(self, input_size, hidden_size, num_layers=1):
+        super(ContForwardGRU, self).__init__()
+
+        torch.manual_seed(5)
+
+        self.hidden_size = hidden_size
+
+        self.cont_fc = nn.Sequential(which_norm(nn.Linear(64, self.hidden_size, bias=False)),
+                        nn.Tanh())
+
+        self.gru = nn.GRU(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True,\
+                          bias=False)
+
+        self.nl = GLU(self.hidden_size)
+
+        self.init_weights()
+
+    def init_weights(self):
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding):
+                nn.init.orthogonal_(m.weight.data)
+
+    def forward(self, x, x0):
+
+        self.gru.flatten_parameters()
+
+        h0 = self.cont_fc(x0).unsqueeze(0)
+
+        output, h0 = self.gru(x, h0)
+
+        return self.nl(output)
+
+# Framewise convolution layer definition
+class ContFramewiseConv(torch.nn.Module):
+
+    def __init__(self, frame_len, out_dim, frame_kernel_size=3, act='glu', causal=True):
+
+        super(ContFramewiseConv, self).__init__()
+        torch.manual_seed(5)
+
+        self.frame_kernel_size = frame_kernel_size
+        self.frame_len = frame_len
+
+        if (causal == True) or (self.frame_kernel_size == 2):
+
+            self.required_pad_left = (self.frame_kernel_size - 1) * self.frame_len
+            self.required_pad_right = 0
+
+            self.cont_fc = nn.Sequential(which_norm(nn.Linear(64, self.required_pad_left, bias=False)),
+                                    nn.Tanh()
+                                   )
+
+        else:
+
+            self.required_pad_left = (self.frame_kernel_size - 1)//2 * self.frame_len
+            self.required_pad_right = (self.frame_kernel_size - 1)//2 * self.frame_len
+
+        self.fc_input_dim = self.frame_kernel_size * self.frame_len
+        self.fc_out_dim = out_dim
+
+        if act=='glu':
+            self.fc = nn.Sequential(which_norm(nn.Linear(self.fc_input_dim, self.fc_out_dim, bias=False)),
+                                    GLU(self.fc_out_dim)
+                                   )
+        if act=='tanh':
+            self.fc = nn.Sequential(which_norm(nn.Linear(self.fc_input_dim, self.fc_out_dim, bias=False)),
+                                    nn.Tanh()
+                                   )
+
+        self.init_weights()
+
+
+    def init_weights(self):
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or\
+            isinstance(m, nn.Embedding):
+                nn.init.orthogonal_(m.weight.data)
+
+    def forward(self, x, x0):
+
+        if self.frame_kernel_size == 1:
+            return self.fc(x)
+
+        x_flat = x.reshape(x.size(0),1,-1)
+        pad = self.cont_fc(x0).view(x0.size(0),1,-1)
+        x_flat_padded = torch.cat((pad, x_flat), dim=-1).unsqueeze(2)
+
+        x_flat_padded_unfolded = F.unfold(x_flat_padded,\
+                    kernel_size= (1,self.fc_input_dim), stride=self.frame_len).permute(0,2,1).contiguous()
+
+        out = self.fc(x_flat_padded_unfolded)
+        return out
+
+# A fully-connected based upsampling layer definition
+class UpsampleFC(nn.Module):
+    def __init__(self, in_ch, out_ch, upsample_factor):
+        super(UpsampleFC, self).__init__()
+        torch.manual_seed(5)
+
+        self.in_ch = in_ch
+        self.out_ch = out_ch
+        self.upsample_factor = upsample_factor
+        self.fc = nn.Linear(in_ch, out_ch * upsample_factor, bias=False)
+        self.nl = nn.Tanh()
+
+        self.init_weights()
+
+    def init_weights(self):
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or\
+            isinstance(m, nn.Linear) or isinstance(m, nn.Embedding):
+                nn.init.orthogonal_(m.weight.data)
+
+    def forward(self, x):
+
+        batch_size = x.size(0)
+        x = x.permute(0, 2, 1)
+        x = self.nl(self.fc(x))
+        x = x.reshape((batch_size, -1, self.out_ch))
+        x = x.permute(0, 2, 1)
+        return x
+
+########################### The complete model definition #################################
+
+class FWGAN400ContLarge(nn.Module):
+    def __init__(self):
+        super().__init__()
+        torch.manual_seed(5)
+
+        self.bfcc_with_corr_upsampler = UpsampleFC(19,80,4)
+
+        self.feat_in_conv1 = ConvLookahead(160,256,kernel_size=5)
+        self.feat_in_nl1 = GLU(256)
+
+        self.cont_net = nn.Sequential(which_norm(nn.Linear(321, 160, bias=False)),
+                                      nn.Tanh(),
+                                      which_norm(nn.Linear(160, 160, bias=False)),
+                                      nn.Tanh(),
+                                      which_norm(nn.Linear(160, 80, bias=False)),
+                                      nn.Tanh(),
+                                      which_norm(nn.Linear(80, 80, bias=False)),
+                                      nn.Tanh(),
+                                      which_norm(nn.Linear(80, 64, bias=False)),
+                                      nn.Tanh(),
+                                      which_norm(nn.Linear(64, 64, bias=False)),
+                                      nn.Tanh())
+
+        self.rnn = ContForwardGRU(256,256)
+
+        self.fwc1 = ContFramewiseConv(256, 256)
+        self.fwc2 = ContFramewiseConv(256, 128)
+        self.fwc3 = ContFramewiseConv(128, 128)
+        self.fwc4 = ContFramewiseConv(128, 64)
+        self.fwc5 = ContFramewiseConv(64, 64)
+        self.fwc6 = ContFramewiseConv(64, 40)
+        self.fwc7 = ContFramewiseConv(40, 40)
+
+        self.init_weights()
+        self.count_parameters()
+
+    def init_weights(self):
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or\
+            isinstance(m, nn.Embedding):
+                nn.init.orthogonal_(m.weight.data)
+
+    def count_parameters(self):
+        num_params =  sum(p.numel() for p in self.parameters() if p.requires_grad)
+        print(f"Total number of {self.__class__.__name__} network parameters = {num_params}\n")
+
+    def create_phase_signals(self, periods):
+
+        batch_size = periods.size(0)
+        progression = torch.arange(1, 160 + 1, dtype=periods.dtype, device=periods.device).view((1, -1))
+        progression = torch.repeat_interleave(progression, batch_size, 0)
+
+        phase0 = torch.zeros(batch_size, dtype=periods.dtype, device=periods.device).unsqueeze(-1)
+        chunks = []
+        for sframe in range(periods.size(1)):
+            f = (2.0 * torch.pi / periods[:, sframe]).unsqueeze(-1)
+
+            chunk_sin = torch.sin(f  * progression + phase0)
+            chunk_sin = chunk_sin.reshape(chunk_sin.size(0),-1,40)
+
+            chunk_cos = torch.cos(f  * progression + phase0)
+            chunk_cos = chunk_cos.reshape(chunk_cos.size(0),-1,40)
+
+            chunk = torch.cat((chunk_sin, chunk_cos), dim = -1)
+
+            phase0 = phase0 + 160 * f
+
+            chunks.append(chunk)
+
+        phase_signals = torch.cat(chunks, dim=1)
+
+        return phase_signals
+
+
+    def gain_multiply(self, x, c0):
+
+        gain = 10**(0.5*c0/np.sqrt(18.0))
+        gain = torch.repeat_interleave(gain, 160, dim=-1)
+        gain  = gain.reshape(gain.size(0),1,-1).squeeze(1)
+
+        return x * gain
+
+    def forward(self, pitch_period, bfcc_with_corr, x0):
+
+        norm_x0 = torch.norm(x0,2, dim=-1, keepdim=True)
+        x0 = x0 / torch.sqrt((1e-8) +  norm_x0**2)
+        x0 = torch.cat((torch.log(norm_x0 + 1e-7), x0), dim=-1)
+
+        p_embed = self.create_phase_signals(pitch_period).permute(0, 2, 1).contiguous()
+
+        envelope = self.bfcc_with_corr_upsampler(bfcc_with_corr.permute(0,2,1).contiguous())
+
+        feat_in = torch.cat((p_embed , envelope), dim=1)
+
+        wav_latent1 = self.feat_in_nl1(self.feat_in_conv1(feat_in).permute(0,2,1).contiguous())
+
+        cont_latent = self.cont_net(x0)
+
+        rnn_out = self.rnn(wav_latent1, cont_latent)
+
+        fwc1_out = self.fwc1(rnn_out, cont_latent)
+
+        fwc2_out = self.fwc2(fwc1_out, cont_latent)
+
+        fwc3_out = self.fwc3(fwc2_out, cont_latent)
+
+        fwc4_out = self.fwc4(fwc3_out, cont_latent)
+
+        fwc5_out = self.fwc5(fwc4_out, cont_latent)
+
+        fwc6_out = self.fwc6(fwc5_out, cont_latent)
+
+        fwc7_out = self.fwc7(fwc6_out, cont_latent)
+
+        waveform = fwc7_out.reshape(fwc7_out.size(0),1,-1).squeeze(1)
+
+        waveform = self.gain_multiply(waveform,bfcc_with_corr[:,:,:1])
+
+        return  waveform
\ No newline at end of file
diff --git a/dnn/torch/fwgan/models/fwgan500.py b/dnn/torch/fwgan/models/fwgan500.py
new file mode 100644
index 00000000..2c6dea5f
--- /dev/null
+++ b/dnn/torch/fwgan/models/fwgan500.py
@@ -0,0 +1,260 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.utils import weight_norm
+import numpy as np
+
+
+which_norm = weight_norm
+
+#################### Definition of basic model components ####################
+
+#Convolutional layer with 1 frame look-ahead (used for feature PreCondNet)
+class ConvLookahead(nn.Module):
+    def __init__(self, in_ch, out_ch, kernel_size, dilation=1, groups=1, bias= False):
+        super(ConvLookahead, self).__init__()
+        torch.manual_seed(5)
+
+        self.padding_left = (kernel_size - 2) * dilation
+        self.padding_right = 1 * dilation
+
+        self.conv = which_norm(nn.Conv1d(in_ch,out_ch,kernel_size,dilation=dilation, groups=groups, bias= bias))
+
+        self.init_weights()
+
+    def init_weights(self):
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding):
+                nn.init.orthogonal_(m.weight.data)
+
+    def forward(self, x):
+
+        x = F.pad(x,(self.padding_left, self.padding_right))
+        conv_out = self.conv(x)
+        return conv_out
+
+#(modified) GLU Activation layer definition
+class GLU(nn.Module):
+    def __init__(self, feat_size):
+        super(GLU, self).__init__()
+
+        torch.manual_seed(5)
+
+        self.gate = which_norm(nn.Linear(feat_size, feat_size, bias=False))
+
+        self.init_weights()
+
+    def init_weights(self):
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d)\
+            or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding):
+                nn.init.orthogonal_(m.weight.data)
+
+    def forward(self, x):
+
+        out = torch.tanh(x) * torch.sigmoid(self.gate(x))
+
+        return out
+
+#GRU layer definition
+class ContForwardGRU(nn.Module):
+    def __init__(self, input_size, hidden_size, num_layers=1):
+        super(ContForwardGRU, self).__init__()
+
+        torch.manual_seed(5)
+
+        self.hidden_size = hidden_size
+
+        #This is to initialize the layer with history audio samples for continuation.
+        self.cont_fc = nn.Sequential(which_norm(nn.Linear(320, self.hidden_size, bias=False)),
+                        nn.Tanh())
+
+        self.gru = nn.GRU(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True,\
+                          bias=False)
+
+        self.nl = GLU(self.hidden_size)
+
+        self.init_weights()
+
+    def init_weights(self):
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding):
+                nn.init.orthogonal_(m.weight.data)
+
+    def forward(self, x, x0):
+
+        self.gru.flatten_parameters()
+
+        h0 = self.cont_fc(x0).unsqueeze(0)
+
+        output, h0 = self.gru(x, h0)
+
+        return self.nl(output)
+
+# Framewise convolution layer definition
+class ContFramewiseConv(torch.nn.Module):
+
+    def __init__(self, frame_len, out_dim, frame_kernel_size=3, act='glu', causal=True):
+
+        super(ContFramewiseConv, self).__init__()
+        torch.manual_seed(5)
+
+        self.frame_kernel_size = frame_kernel_size
+        self.frame_len = frame_len
+
+        if (causal == True) or (self.frame_kernel_size == 2):
+
+            self.required_pad_left = (self.frame_kernel_size - 1) * self.frame_len
+            self.required_pad_right = 0
+
+            #This is to initialize the layer with history audio samples for continuation.
+            self.cont_fc = nn.Sequential(which_norm(nn.Linear(320, self.required_pad_left, bias=False)),
+                                    nn.Tanh()
+                                   )
+
+        else:
+            #This means non-causal frame-wise convolution. We don't use it at the moment
+            self.required_pad_left = (self.frame_kernel_size - 1)//2 * self.frame_len
+            self.required_pad_right = (self.frame_kernel_size - 1)//2 * self.frame_len
+
+        self.fc_input_dim = self.frame_kernel_size * self.frame_len
+        self.fc_out_dim = out_dim
+
+        if act=='glu':
+            self.fc = nn.Sequential(which_norm(nn.Linear(self.fc_input_dim, self.fc_out_dim, bias=False)),
+                                    GLU(self.fc_out_dim)
+                                   )
+        if act=='tanh':
+            self.fc = nn.Sequential(which_norm(nn.Linear(self.fc_input_dim, self.fc_out_dim, bias=False)),
+                                    nn.Tanh()
+                                   )
+
+        self.init_weights()
+
+
+    def init_weights(self):
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or\
+            isinstance(m, nn.Embedding):
+                nn.init.orthogonal_(m.weight.data)
+
+    def forward(self, x, x0):
+
+        if self.frame_kernel_size == 1:
+            return self.fc(x)
+
+        x_flat = x.reshape(x.size(0),1,-1)
+        pad = self.cont_fc(x0).view(x0.size(0),1,-1)
+        x_flat_padded = torch.cat((pad, x_flat), dim=-1).unsqueeze(2)
+
+        x_flat_padded_unfolded = F.unfold(x_flat_padded,\
+                    kernel_size= (1,self.fc_input_dim), stride=self.frame_len).permute(0,2,1).contiguous()
+
+        out = self.fc(x_flat_padded_unfolded)
+        return out
+
+########################### The complete model definition #################################
+
+class FWGAN500Cont(nn.Module):
+    def __init__(self):
+        super().__init__()
+        torch.manual_seed(5)
+
+        #PrecondNet:
+        self.bfcc_with_corr_upsampler = nn.Sequential(nn.ConvTranspose1d(19,64,kernel_size=5,stride=5,padding=0,\
+                                                      bias=False),
+                                                      nn.Tanh())
+
+        self.feat_in_conv = ConvLookahead(128,256,kernel_size=5)
+        self.feat_in_nl = GLU(256)
+
+        #GRU:
+        self.rnn = ContForwardGRU(256,256)
+
+        #Frame-wise convolution stack:
+        self.fwc1 = ContFramewiseConv(256, 256)
+        self.fwc2 = ContFramewiseConv(256, 128)
+        self.fwc3 = ContFramewiseConv(128, 128)
+        self.fwc4 = ContFramewiseConv(128, 64)
+        self.fwc5 = ContFramewiseConv(64, 64)
+        self.fwc6 = ContFramewiseConv(64, 32)
+        self.fwc7 = ContFramewiseConv(32, 32, act='tanh')
+
+        self.init_weights()
+        self.count_parameters()
+
+    def init_weights(self):
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or\
+            isinstance(m, nn.Embedding):
+                nn.init.orthogonal_(m.weight.data)
+
+    def count_parameters(self):
+        num_params =  sum(p.numel() for p in self.parameters() if p.requires_grad)
+        print(f"Total number of {self.__class__.__name__} network parameters = {num_params}\n")
+
+    def create_phase_signals(self, periods):
+
+        batch_size = periods.size(0)
+        progression = torch.arange(1, 160 + 1, dtype=periods.dtype, device=periods.device).view((1, -1))
+        progression = torch.repeat_interleave(progression, batch_size, 0)
+
+        phase0 = torch.zeros(batch_size, dtype=periods.dtype, device=periods.device).unsqueeze(-1)
+        chunks = []
+        for sframe in range(periods.size(1)):
+            f = (2.0 * torch.pi / periods[:, sframe]).unsqueeze(-1)
+
+            chunk_sin = torch.sin(f  * progression + phase0)
+            chunk_sin = chunk_sin.reshape(chunk_sin.size(0),-1,32)
+
+            chunk_cos = torch.cos(f  * progression + phase0)
+            chunk_cos = chunk_cos.reshape(chunk_cos.size(0),-1,32)
+
+            chunk = torch.cat((chunk_sin, chunk_cos), dim = -1)
+
+            phase0 = phase0 + 160 * f
+
+            chunks.append(chunk)
+
+        phase_signals = torch.cat(chunks, dim=1)
+
+        return phase_signals
+
+
+    def gain_multiply(self, x, c0):
+
+        gain = 10**(0.5*c0/np.sqrt(18.0))
+        gain = torch.repeat_interleave(gain, 160, dim=-1)
+        gain  = gain.reshape(gain.size(0),1,-1).squeeze(1)
+
+        return x * gain
+
+    def forward(self, pitch_period, bfcc_with_corr, x0):
+
+        #This should create a latent representation of shape [Batch_dim, 500 frames, 256 elemets per frame]
+        p_embed = self.create_phase_signals(pitch_period).permute(0, 2, 1).contiguous()
+        envelope = self.bfcc_with_corr_upsampler(bfcc_with_corr.permute(0,2,1).contiguous())
+        feat_in = torch.cat((p_embed , envelope), dim=1)
+        wav_latent = self.feat_in_nl(self.feat_in_conv(feat_in).permute(0,2,1).contiguous())
+
+        #Generation with continuation using history samples x0 starts from here:
+
+        rnn_out = self.rnn(wav_latent, x0)
+
+        fwc1_out = self.fwc1(rnn_out, x0)
+        fwc2_out = self.fwc2(fwc1_out, x0)
+        fwc3_out = self.fwc3(fwc2_out, x0)
+        fwc4_out = self.fwc4(fwc3_out, x0)
+        fwc5_out = self.fwc5(fwc4_out, x0)
+        fwc6_out = self.fwc6(fwc5_out, x0)
+        fwc7_out = self.fwc7(fwc6_out, x0)
+
+        waveform_unscaled = fwc7_out.reshape(fwc7_out.size(0),1,-1).squeeze(1)
+        waveform = self.gain_multiply(waveform_unscaled,bfcc_with_corr[:,:,:1])
+
+        return  waveform