Upload vocoder/models/deepmind_version.py with huggingface_hub

vocoder/models/deepmind_version.py

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+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)