Kikirilkov commited on
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Delete TTS/vocoder/models/deepmind_version.py

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