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# DEEPFAKE AUDIO - vocoder/models/deepmind_version.py (DeepMind Architecture)
# ==================================================================================================
#
# 📝 DESCRIPTION
# This module implements the DeepMind-inspired WaveRNN architecture. It
# features a dual-layer GRU structure for high-fidelity audio generation,
# using coarse and fine signal decomposition to manage the high dynamic
# range of speech waveforms.
#
# 👤 AUTHORS
# - Amey Thakur (https://github.com/Amey-Thakur)
# - Mega Satish (https://github.com/msatmod)
#
# 🤝🏻 CREDITS
# Original Real-Time Voice Cloning methodology by CorentinJ
# Repository: https://github.com/CorentinJ/Real-Time-Voice-Cloning
# DeepMind Research references for WaveRNN architecture
#
# 🔗 PROJECT LINKS
# Repository: https://github.com/Amey-Thakur/DEEPFAKE-AUDIO
# Video Demo: https://youtu.be/i3wnBcbHDbs
# Research: https://github.com/Amey-Thakur/DEEPFAKE-AUDIO/blob/main/DEEPFAKE-AUDIO.ipynb
#
# 📜 LICENSE
# Released under the MIT License
# Release Date: 2021-02-06
# ==================================================================================================
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):
"""
Neural Waveform Generator (DeepMind Variant):
Implements the core recurrent logic for autoregressive speech synthesis.
"""
def __init__(self, hidden_size=896, quantisation=256):
super(WaveRNN, self).__init__()
self.hidden_size = hidden_size
self.split_size = hidden_size // 2
# Recurrent projection matrix
self.R = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
# Dual-output heads for signal resolution
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)
# Neural feature ingestion
self.I_coarse = nn.Linear(2, 3 * self.split_size, bias=False)
self.I_fine = nn.Linear(3, 3 * self.split_size, bias=False)
# Learnable gating biases
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))
self.num_params()
def forward(self, prev_y, prev_hidden, current_coarse):
"""Neural Forward Pass: Computes the next hidden state and signal probabilities."""
R_hidden = self.R(prev_hidden)
R_u, R_r, R_e, = torch.split(R_hidden, self.hidden_size, dim=1)
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)
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)
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)
# Gating Logic (Update, Reset, Exit)
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
hidden_coarse, hidden_fine = torch.split(hidden, self.split_size, dim=1)
# Categorical distribution parameters
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):
"""Autoregressive Generation: Synthesizes audio sample-by-sample."""
with torch.no_grad():
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)
c_outputs, f_outputs = [], []
out_coarse = torch.LongTensor([0]).cuda()
out_fine = torch.LongTensor([0]).cuda()
hidden = self.init_hidden()
start = time.time()
for i in range(seq_len):
hidden_coarse, hidden_fine = \
torch.split(hidden, self.split_size, dim=1)
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)
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)
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)
# Coarse Sampling Phase
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
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)
# Fine Sampling Phase
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)
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
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)
hidden = torch.cat([hidden_coarse, hidden_fine], dim=1)
speed = (i + 1) / (time.time() - start)
stream('Neural Inference: %i/%i -- Speed: %i samples/s', (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):
"""Latent Memory Initialization: Resets GRU hidden states."""
return torch.zeros(batch_size, self.hidden_size).cuda()
def num_params(self):
"""Architectural Audit: Logs the total number of trainable model parameters."""
parameters = filter(lambda p: p.requires_grad, self.parameters())
parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000
print('Audit: Trainable Parameters: %.3f million' % parameters)
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