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)