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model.py
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model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Categorical
from tqdm import tqdm
import numpy as np
from preprocess import mulaw_decode
import math
class Encoder(nn.Module):
def __init__(self, in_channels, channels, n_embeddings, z_dim, c_dim):
super(Encoder, self).__init__()
self.conv = nn.Conv1d(in_channels, channels, 4, 2, 1, bias=False)
self.encoder = nn.Sequential(
nn.LayerNorm(channels),
nn.ReLU(True),
nn.Linear(channels, channels, bias=False),
nn.LayerNorm(channels),
nn.ReLU(True),
nn.Linear(channels, channels, bias=False),
nn.LayerNorm(channels),
nn.ReLU(True),
nn.Linear(channels, channels, bias=False),
nn.LayerNorm(channels),
nn.ReLU(True),
nn.Linear(channels, channels, bias=False),
nn.LayerNorm(channels),
nn.ReLU(True),
nn.Linear(channels, z_dim),
)
self.codebook = VQEmbeddingEMA(n_embeddings, z_dim)
self.rnn = nn.LSTM(z_dim, c_dim, batch_first=True)
def encode(self, mel):
z = self.conv(mel)
z = self.encoder(z.transpose(1, 2))
z, indices = self.codebook.encode(z)
c, _ = self.rnn(z)
return z, c, indices
def forward(self, mels):
z = self.conv(mels)
z = self.encoder(z.transpose(1, 2))
z, loss, perplexity = self.codebook(z)
c, _ = self.rnn(z)
return z, c, loss, perplexity
class VQEmbeddingEMA(nn.Module):
def __init__(self, n_embeddings, embedding_dim, commitment_cost=0.25, decay=0.999, epsilon=1e-5):
super(VQEmbeddingEMA, self).__init__()
self.commitment_cost = commitment_cost
self.decay = decay
self.epsilon = epsilon
init_bound = 1 / 512
embedding = torch.Tensor(n_embeddings, embedding_dim)
embedding.uniform_(-init_bound, init_bound)
self.register_buffer("embedding", embedding)
self.register_buffer("ema_count", torch.zeros(n_embeddings))
self.register_buffer("ema_weight", self.embedding.clone())
def encode(self, x):
M, D = self.embedding.size()
x_flat = x.detach().reshape(-1, D)
distances = torch.addmm(torch.sum(self.embedding ** 2, dim=1) +
torch.sum(x_flat ** 2, dim=1, keepdim=True),
x_flat, self.embedding.t(),
alpha=-2.0, beta=1.0)
indices = torch.argmin(distances.float(), dim=-1)
quantized = F.embedding(indices, self.embedding)
quantized = quantized.view_as(x)
return quantized, indices.view(x.size(0), x.size(1))
def forward(self, x):
M, D = self.embedding.size()
x_flat = x.detach().reshape(-1, D)
distances = torch.addmm(torch.sum(self.embedding ** 2, dim=1) +
torch.sum(x_flat ** 2, dim=1, keepdim=True),
x_flat, self.embedding.t(),
alpha=-2.0, beta=1.0)
indices = torch.argmin(distances.float(), dim=-1)
encodings = F.one_hot(indices, M).float()
quantized = F.embedding(indices, self.embedding)
quantized = quantized.view_as(x)
if self.training:
self.ema_count = self.decay * self.ema_count + (1 - self.decay) * torch.sum(encodings, dim=0)
n = torch.sum(self.ema_count)
self.ema_count = (self.ema_count + self.epsilon) / (n + M * self.epsilon) * n
dw = torch.matmul(encodings.t(), x_flat)
self.ema_weight = self.decay * self.ema_weight + (1 - self.decay) * dw
self.embedding = self.ema_weight / self.ema_count.unsqueeze(-1)
e_latent_loss = F.mse_loss(x, quantized.detach())
loss = self.commitment_cost * e_latent_loss
quantized = x + (quantized - x).detach()
avg_probs = torch.mean(encodings, dim=0)
perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10)))
return quantized, loss, perplexity
class CPCLoss(nn.Module):
def __init__(self, n_speakers_per_batch, n_utterances_per_speaker, n_prediction_steps, n_negatives, z_dim, c_dim):
super(CPCLoss, self).__init__()
self.n_speakers_per_batch = n_speakers_per_batch
self.n_utterances_per_speaker = n_utterances_per_speaker
self.n_prediction_steps = n_prediction_steps // 2
self.n_negatives = n_negatives
self.z_dim = z_dim
self.c_dim = c_dim
self.predictors = nn.ModuleList([
nn.Linear(c_dim, z_dim) for _ in range(n_prediction_steps)
])
def forward(self, z, c):
length = z.size(1) - self.n_prediction_steps
z = z.reshape(
self.n_speakers_per_batch,
self.n_utterances_per_speaker,
-1,
self.z_dim
)
c = c[:, :-self.n_prediction_steps, :]
losses, accuracies = list(), list()
for k in range(1, self.n_prediction_steps+1):
z_shift = z[:, :, k:length + k, :]
Wc = self.predictors[k-1](c)
Wc = Wc.view(
self.n_speakers_per_batch,
self.n_utterances_per_speaker,
-1,
self.z_dim
)
batch_index = torch.randint(
0, self.n_utterances_per_speaker,
size=(
self.n_utterances_per_speaker,
self.n_negatives
),
device=z.device
)
batch_index = batch_index.view(
1, self.n_utterances_per_speaker, self.n_negatives, 1
)
seq_index = torch.randint(
1, length,
size=(
self.n_speakers_per_batch,
self.n_utterances_per_speaker,
self.n_negatives,
length
),
device=z.device
)
seq_index += torch.arange(length, device=z.device)
seq_index = torch.remainder(seq_index, length)
speaker_index = torch.arange(self.n_speakers_per_batch, device=z.device)
speaker_index = speaker_index.view(-1, 1, 1, 1)
z_negatives = z_shift[speaker_index, batch_index, seq_index, :]
zs = torch.cat((z_shift.unsqueeze(2), z_negatives), dim=2)
f = torch.sum(zs * Wc.unsqueeze(2) / math.sqrt(self.z_dim), dim=-1)
f = f.view(
self.n_speakers_per_batch * self.n_utterances_per_speaker,
self.n_negatives + 1,
-1
)
labels = torch.zeros(
self.n_speakers_per_batch * self.n_utterances_per_speaker, length,
dtype=torch.long, device=z.device
)
loss = F.cross_entropy(f, labels)
accuracy = f.argmax(dim=1) == labels
accuracy = torch.mean(accuracy.float())
losses.append(loss)
accuracies.append(accuracy.item())
loss = torch.stack(losses).mean()
return loss, accuracies
def get_gru_cell(gru):
gru_cell = nn.GRUCell(gru.input_size, gru.hidden_size)
gru_cell.weight_hh.data = gru.weight_hh_l0.data
gru_cell.weight_ih.data = gru.weight_ih_l0.data
gru_cell.bias_hh.data = gru.bias_hh_l0.data
gru_cell.bias_ih.data = gru.bias_ih_l0.data
return gru_cell
class Vocoder(nn.Module):
def __init__(self, in_channels, n_speakers, speaker_embedding_dim,
conditioning_channels, mu_embedding_dim, rnn_channels,
fc_channels, bits, hop_length):
super(Vocoder, self).__init__()
self.rnn_channels = rnn_channels
self.quantization_channels = 2**bits
self.hop_length = hop_length
self.code_embedding = nn.Embedding(512, 64)
self.speaker_embedding = nn.Embedding(n_speakers, speaker_embedding_dim)
self.rnn1 = nn.GRU(in_channels + speaker_embedding_dim, conditioning_channels,
num_layers=2, batch_first=True, bidirectional=True)
self.mu_embedding = nn.Embedding(self.quantization_channels, mu_embedding_dim)
self.rnn2 = nn.GRU(mu_embedding_dim + 2*conditioning_channels, rnn_channels, batch_first=True)
self.fc1 = nn.Linear(rnn_channels, fc_channels)
self.fc2 = nn.Linear(fc_channels, self.quantization_channels)
def forward(self, x, z, speakers):
z = self.code_embedding(z)
z = F.interpolate(z.transpose(1, 2), scale_factor=2)
z = z.transpose(1, 2)
speakers = self.speaker_embedding(speakers)
speakers = speakers.unsqueeze(1).expand(-1, z.size(1), -1)
z = torch.cat((z, speakers), dim=-1)
z, _ = self.rnn1(z)
z = F.interpolate(z.transpose(1, 2), scale_factor=self.hop_length)
z = z.transpose(1, 2)
x = self.mu_embedding(x)
x, _ = self.rnn2(torch.cat((x, z), dim=2))
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
def generate(self, z, speaker):
output = []
cell = get_gru_cell(self.rnn2)
z = self.code_embedding(z)
z = F.interpolate(z.transpose(1, 2), scale_factor=2)
z = z.transpose(1, 2)
speaker = self.speaker_embedding(speaker)
speaker = speaker.unsqueeze(1).expand(-1, z.size(1), -1)
z = torch.cat((z, speaker), dim=-1)
z, _ = self.rnn1(z)
z = F.interpolate(z.transpose(1, 2), scale_factor=self.hop_length)
z = z.transpose(1, 2)
batch_size, sample_size, _ = z.size()
h = torch.zeros(batch_size, self.rnn_channels, device=z.device)
x = torch.zeros(batch_size, device=z.device).fill_(self.quantization_channels // 2).long()
for m in tqdm(torch.unbind(z, dim=1), leave=False):
x = self.mu_embedding(x)
h = cell(torch.cat((x, m), dim=1), h)
x = F.relu(self.fc1(h))
logits = self.fc2(x)
dist = Categorical(logits=logits)
x = dist.sample()
output.append(2 * x.float().item() / (self.quantization_channels - 1.) - 1.)
output = np.asarray(output, dtype=np.float64)
output = mulaw_decode(output, self.quantization_channels)
return output