Delete TTS/vocoder/models/deepmind_version.py

TTS/vocoder/models/deepmind_version.py

@@ -1,170 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from utils.display import *
-from utils.dsp import *
-
-
-class WaveRNN(nn.Module) :
-    def __init__(self, hidden_size=896, quantisation=256) :
-        super(WaveRNN, self).__init__()
-        
-        self.hidden_size = hidden_size
-        self.split_size = hidden_size // 2
-        
-        # The main matmul
-        self.R = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
-        
-        # Output fc layers
-        self.O1 = nn.Linear(self.split_size, self.split_size)
-        self.O2 = nn.Linear(self.split_size, quantisation)
-        self.O3 = nn.Linear(self.split_size, self.split_size)
-        self.O4 = nn.Linear(self.split_size, quantisation)
-        
-        # Input fc layers
-        self.I_coarse = nn.Linear(2, 3 * self.split_size, bias=False)
-        self.I_fine = nn.Linear(3, 3 * self.split_size, bias=False)
-
-        # biases for the gates
-        self.bias_u = nn.Parameter(torch.zeros(self.hidden_size))
-        self.bias_r = nn.Parameter(torch.zeros(self.hidden_size))
-        self.bias_e = nn.Parameter(torch.zeros(self.hidden_size))
-        
-        # display num params
-        self.num_params()
-
-        
-    def forward(self, prev_y, prev_hidden, current_coarse) :
-        
-        # Main matmul - the projection is split 3 ways
-        R_hidden = self.R(prev_hidden)
-        R_u, R_r, R_e, = torch.split(R_hidden, self.hidden_size, dim=1)
-        
-        # Project the prev input 
-        coarse_input_proj = self.I_coarse(prev_y)
-        I_coarse_u, I_coarse_r, I_coarse_e = \
-            torch.split(coarse_input_proj, self.split_size, dim=1)
-        
-        # Project the prev input and current coarse sample
-        fine_input = torch.cat([prev_y, current_coarse], dim=1)
-        fine_input_proj = self.I_fine(fine_input)
-        I_fine_u, I_fine_r, I_fine_e = \
-            torch.split(fine_input_proj, self.split_size, dim=1)
-        
-        # concatenate for the gates
-        I_u = torch.cat([I_coarse_u, I_fine_u], dim=1)
-        I_r = torch.cat([I_coarse_r, I_fine_r], dim=1)
-        I_e = torch.cat([I_coarse_e, I_fine_e], dim=1)
-        
-        # Compute all gates for coarse and fine 
-        u = F.sigmoid(R_u + I_u + self.bias_u)
-        r = F.sigmoid(R_r + I_r + self.bias_r)
-        e = F.tanh(r * R_e + I_e + self.bias_e)
-        hidden = u * prev_hidden + (1. - u) * e
-        
-        # Split the hidden state
-        hidden_coarse, hidden_fine = torch.split(hidden, self.split_size, dim=1)
-        
-        # Compute outputs 
-        out_coarse = self.O2(F.relu(self.O1(hidden_coarse)))
-        out_fine = self.O4(F.relu(self.O3(hidden_fine)))
-
-        return out_coarse, out_fine, hidden
-    
-        
-    def generate(self, seq_len):
-        with torch.no_grad():
-            # First split up the biases for the gates 
-            b_coarse_u, b_fine_u = torch.split(self.bias_u, self.split_size)
-            b_coarse_r, b_fine_r = torch.split(self.bias_r, self.split_size)
-            b_coarse_e, b_fine_e = torch.split(self.bias_e, self.split_size)
-
-            # Lists for the two output seqs
-            c_outputs, f_outputs = [], []
-
-            # Some initial inputs
-            out_coarse = torch.LongTensor([0]).cuda()
-            out_fine = torch.LongTensor([0]).cuda()
-
-            # We'll meed a hidden state
-            hidden = self.init_hidden()
-
-            # Need a clock for display
-            start = time.time()
-
-            # Loop for generation
-            for i in range(seq_len) :
-
-                # Split into two hidden states
-                hidden_coarse, hidden_fine = \
-                    torch.split(hidden, self.split_size, dim=1)
-
-                # Scale and concat previous predictions
-                out_coarse = out_coarse.unsqueeze(0).float() / 127.5 - 1.
-                out_fine = out_fine.unsqueeze(0).float() / 127.5 - 1.
-                prev_outputs = torch.cat([out_coarse, out_fine], dim=1)
-
-                # Project input 
-                coarse_input_proj = self.I_coarse(prev_outputs)
-                I_coarse_u, I_coarse_r, I_coarse_e = \
-                    torch.split(coarse_input_proj, self.split_size, dim=1)
-
-                # Project hidden state and split 6 ways
-                R_hidden = self.R(hidden)
-                R_coarse_u , R_fine_u, \
-                R_coarse_r, R_fine_r, \
-                R_coarse_e, R_fine_e = torch.split(R_hidden, self.split_size, dim=1)
-
-                # Compute the coarse gates
-                u = F.sigmoid(R_coarse_u + I_coarse_u + b_coarse_u)
-                r = F.sigmoid(R_coarse_r + I_coarse_r + b_coarse_r)
-                e = F.tanh(r * R_coarse_e + I_coarse_e + b_coarse_e)
-                hidden_coarse = u * hidden_coarse + (1. - u) * e
-
-                # Compute the coarse output
-                out_coarse = self.O2(F.relu(self.O1(hidden_coarse)))
-                posterior = F.softmax(out_coarse, dim=1)
-                distrib = torch.distributions.Categorical(posterior)
-                out_coarse = distrib.sample()
-                c_outputs.append(out_coarse)
-
-                # Project the [prev outputs and predicted coarse sample]
-                coarse_pred = out_coarse.float() / 127.5 - 1.
-                fine_input = torch.cat([prev_outputs, coarse_pred.unsqueeze(0)], dim=1)
-                fine_input_proj = self.I_fine(fine_input)
-                I_fine_u, I_fine_r, I_fine_e = \
-                    torch.split(fine_input_proj, self.split_size, dim=1)
-
-                # Compute the fine gates
-                u = F.sigmoid(R_fine_u + I_fine_u + b_fine_u)
-                r = F.sigmoid(R_fine_r + I_fine_r + b_fine_r)
-                e = F.tanh(r * R_fine_e + I_fine_e + b_fine_e)
-                hidden_fine = u * hidden_fine + (1. - u) * e
-
-                # Compute the fine output
-                out_fine = self.O4(F.relu(self.O3(hidden_fine)))
-                posterior = F.softmax(out_fine, dim=1)
-                distrib = torch.distributions.Categorical(posterior)
-                out_fine = distrib.sample()
-                f_outputs.append(out_fine)
-
-                # Put the hidden state back together
-                hidden = torch.cat([hidden_coarse, hidden_fine], dim=1)
-
-                # Display progress
-                speed = (i + 1) / (time.time() - start)
-                stream('Gen: %i/%i -- Speed: %i',  (i + 1, seq_len, speed))
-
-            coarse = torch.stack(c_outputs).squeeze(1).cpu().data.numpy()
-            fine = torch.stack(f_outputs).squeeze(1).cpu().data.numpy()        
-            output = combine_signal(coarse, fine)
-        
-        return output, coarse, fine
-
-    def init_hidden(self, batch_size=1) :
-        return torch.zeros(batch_size, self.hidden_size).cuda()
-    
-    def num_params(self) :
-        parameters = filter(lambda p: p.requires_grad, self.parameters())
-        parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000
-        print('Trainable Parameters: %.3f million' % parameters)