mirror of
https://github.com/babysor/MockingBird.git
synced 2024-03-22 13:11:31 +08:00
170 lines
6.9 KiB
Python
170 lines
6.9 KiB
Python
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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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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self.hidden_size = hidden_size
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self.split_size = hidden_size // 2
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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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# 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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# 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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# 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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# display num params
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self.num_params()
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def forward(self, prev_y, prev_hidden, current_coarse) :
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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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# 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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# 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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# 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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# 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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# Split the hidden state
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hidden_coarse, hidden_fine = torch.split(hidden, self.split_size, dim=1)
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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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return out_coarse, out_fine, hidden
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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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# Lists for the two output seqs
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c_outputs, f_outputs = [], []
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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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# We'll meed a hidden state
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hidden = self.init_hidden()
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# Need a clock for display
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start = time.time()
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# Loop for generation
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for i in range(seq_len) :
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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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# 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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# 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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# 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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# 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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# 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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# 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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# 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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# 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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# Put the hidden state back together
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hidden = torch.cat([hidden_coarse, hidden_fine], dim=1)
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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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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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return output, coarse, fine
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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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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) |