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import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from .wav2vec import Wav2Vec2Model
from .vqvae_modules import VectorQuantizerEMA, ConvNormRelu, Res_CNR_Stack
class AudioEncoder(nn.Module):
def __init__(self, in_dim, num_hiddens, num_residual_layers, num_residual_hiddens):
super(AudioEncoder, self).__init__()
self._num_hiddens = num_hiddens
self._num_residual_layers = num_residual_layers
self._num_residual_hiddens = num_residual_hiddens
self.project = ConvNormRelu(in_dim, self._num_hiddens // 4, leaky=True)
self._enc_1 = Res_CNR_Stack(self._num_hiddens // 4, self._num_residual_layers, leaky=True)
self._down_1 = ConvNormRelu(self._num_hiddens // 4, self._num_hiddens // 2, leaky=True, residual=True,
sample='down')
self._enc_2 = Res_CNR_Stack(self._num_hiddens // 2, self._num_residual_layers, leaky=True)
self._down_2 = ConvNormRelu(self._num_hiddens // 2, self._num_hiddens, leaky=True, residual=True, sample='down')
self._enc_3 = Res_CNR_Stack(self._num_hiddens, self._num_residual_layers, leaky=True)
def forward(self, x, frame_num=0):
h = self.project(x)
h = self._enc_1(h)
h = self._down_1(h)
h = self._enc_2(h)
h = self._down_2(h)
h = self._enc_3(h)
return h
class Wav2VecEncoder(nn.Module):
def __init__(self, num_hiddens, num_residual_layers):
super(Wav2VecEncoder, self).__init__()
self._num_hiddens = num_hiddens
self._num_residual_layers = num_residual_layers
self.audio_encoder = Wav2Vec2Model.from_pretrained(
"facebook/wav2vec2-base-960h") # "vitouphy/wav2vec2-xls-r-300m-phoneme""facebook/wav2vec2-base-960h"
self.audio_encoder.feature_extractor._freeze_parameters()
self.project = ConvNormRelu(768, self._num_hiddens, leaky=True)
self._enc_1 = Res_CNR_Stack(self._num_hiddens, self._num_residual_layers, leaky=True)
self._down_1 = ConvNormRelu(self._num_hiddens, self._num_hiddens, leaky=True, residual=True, sample='down')
self._enc_2 = Res_CNR_Stack(self._num_hiddens, self._num_residual_layers, leaky=True)
self._down_2 = ConvNormRelu(self._num_hiddens, self._num_hiddens, leaky=True, residual=True, sample='down')
self._enc_3 = Res_CNR_Stack(self._num_hiddens, self._num_residual_layers, leaky=True)
def forward(self, x, frame_num):
h = self.audio_encoder(x.squeeze(), frame_num=frame_num).last_hidden_state.transpose(1, 2)
h = self.project(h)
h = self._enc_1(h)
h = self._down_1(h)
h = self._enc_2(h)
h = self._down_2(h)
h = self._enc_3(h)
return h
class Encoder(nn.Module):
def __init__(self, in_dim, embedding_dim, num_hiddens, num_residual_layers, num_residual_hiddens):
super(Encoder, self).__init__()
self._num_hiddens = num_hiddens
self._num_residual_layers = num_residual_layers
self._num_residual_hiddens = num_residual_hiddens
self.project = ConvNormRelu(in_dim, self._num_hiddens // 4, leaky=True)
self._enc_1 = Res_CNR_Stack(self._num_hiddens // 4, self._num_residual_layers, leaky=True)
self._down_1 = ConvNormRelu(self._num_hiddens // 4, self._num_hiddens // 2, leaky=True, residual=True,
sample='down')
self._enc_2 = Res_CNR_Stack(self._num_hiddens // 2, self._num_residual_layers, leaky=True)
self._down_2 = ConvNormRelu(self._num_hiddens // 2, self._num_hiddens, leaky=True, residual=True, sample='down')
self._enc_3 = Res_CNR_Stack(self._num_hiddens, self._num_residual_layers, leaky=True)
self.pre_vq_conv = nn.Conv1d(self._num_hiddens, embedding_dim, 1, 1)
def forward(self, x):
h = self.project(x)
h = self._enc_1(h)
h = self._down_1(h)
h = self._enc_2(h)
h = self._down_2(h)
h = self._enc_3(h)
h = self.pre_vq_conv(h)
return h
class Frame_Enc(nn.Module):
def __init__(self, in_dim, num_hiddens):
super(Frame_Enc, self).__init__()
self.in_dim = in_dim
self.num_hiddens = num_hiddens
# self.enc = transformer_Enc(in_dim, num_hiddens, 2, 8, 256, 256, 256, 256, 0, dropout=0.1, n_position=4)
self.proj = nn.Conv1d(in_dim, num_hiddens, 1, 1)
self.enc = Res_CNR_Stack(num_hiddens, 2, leaky=True)
self.proj_1 = nn.Conv1d(256*4, num_hiddens, 1, 1)
self.proj_2 = nn.Conv1d(256*4, num_hiddens*2, 1, 1)
def forward(self, x):
# x = self.enc(x, None)[0].reshape(x.shape[0], -1, 1)
x = self.enc(self.proj(x)).reshape(x.shape[0], -1, 1)
second_last = self.proj_2(x)
last = self.proj_1(x)
return second_last, last
class Decoder(nn.Module):
def __init__(self, out_dim, embedding_dim, num_hiddens, num_residual_layers, num_residual_hiddens, ae=False):
super(Decoder, self).__init__()
self._num_hiddens = num_hiddens
self._num_residual_layers = num_residual_layers
self._num_residual_hiddens = num_residual_hiddens
self.aft_vq_conv = nn.Conv1d(embedding_dim, self._num_hiddens, 1, 1)
self._dec_1 = Res_CNR_Stack(self._num_hiddens, self._num_residual_layers, leaky=True)
self._up_2 = ConvNormRelu(self._num_hiddens, self._num_hiddens // 2, leaky=True, residual=True, sample='up')
self._dec_2 = Res_CNR_Stack(self._num_hiddens // 2, self._num_residual_layers, leaky=True)
self._up_3 = ConvNormRelu(self._num_hiddens // 2, self._num_hiddens // 4, leaky=True, residual=True,
sample='up')
self._dec_3 = Res_CNR_Stack(self._num_hiddens // 4, self._num_residual_layers, leaky=True)
if ae:
self.frame_enc = Frame_Enc(out_dim, self._num_hiddens // 4)
self.gru_sl = nn.GRU(self._num_hiddens // 2, self._num_hiddens // 2, 1, batch_first=True)
self.gru_l = nn.GRU(self._num_hiddens // 4, self._num_hiddens // 4, 1, batch_first=True)
self.project = nn.Conv1d(self._num_hiddens // 4, out_dim, 1, 1)
def forward(self, h, last_frame=None):
h = self.aft_vq_conv(h)
h = self._dec_1(h)
h = self._up_2(h)
h = self._dec_2(h)
h = self._up_3(h)
h = self._dec_3(h)
recon = self.project(h)
return recon, None
class Pre_VQ(nn.Module):
def __init__(self, num_hiddens, embedding_dim, num_chunks):
super(Pre_VQ, self).__init__()
self.conv = nn.Conv1d(num_hiddens, num_hiddens, 1, 1, 0, groups=num_chunks)
self.bn = nn.GroupNorm(num_chunks, num_hiddens)
self.relu = nn.ReLU()
self.proj = nn.Conv1d(num_hiddens, embedding_dim, 1, 1, 0, groups=num_chunks)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
x = self.proj(x)
return x
class VQVAE(nn.Module):
"""VQ-VAE"""
def __init__(self, in_dim, embedding_dim, num_embeddings,
num_hiddens, num_residual_layers, num_residual_hiddens,
commitment_cost=0.25, decay=0.99, share=False):
super().__init__()
self.in_dim = in_dim
self.embedding_dim = embedding_dim
self.num_embeddings = num_embeddings
self.share_code_vq = share
self.encoder = Encoder(in_dim, embedding_dim, num_hiddens, num_residual_layers, num_residual_hiddens)
self.vq_layer = VectorQuantizerEMA(embedding_dim, num_embeddings, commitment_cost, decay)
self.decoder = Decoder(in_dim, embedding_dim, num_hiddens, num_residual_layers, num_residual_hiddens)
def forward(self, gt_poses, id=None, pre_state=None):
z = self.encoder(gt_poses.transpose(1, 2))
if not self.training:
e, _ = self.vq_layer(z)
x_recon, cur_state = self.decoder(e, pre_state.transpose(1, 2) if pre_state is not None else None)
return e, x_recon
e, e_q_loss = self.vq_layer(z)
gt_recon, cur_state = self.decoder(e, pre_state.transpose(1, 2) if pre_state is not None else None)
return e_q_loss, gt_recon.transpose(1, 2)
def encode(self, gt_poses, id=None):
z = self.encoder(gt_poses.transpose(1, 2))
e, latents = self.vq_layer(z)
return e, latents
def decode(self, b, w, e=None, latents=None, pre_state=None):
if e is not None:
x = self.decoder(e, pre_state.transpose(1, 2) if pre_state is not None else None)
else:
e = self.vq_layer.quantize(latents)
e = e.view(b, w, -1).permute(0, 2, 1).contiguous()
x = self.decoder(e, pre_state.transpose(1, 2) if pre_state is not None else None)
return x
class AE(nn.Module):
"""VQ-VAE"""
def __init__(self, in_dim, embedding_dim, num_embeddings,
num_hiddens, num_residual_layers, num_residual_hiddens):
super().__init__()
self.in_dim = in_dim
self.embedding_dim = embedding_dim
self.num_embeddings = num_embeddings
self.encoder = Encoder(in_dim, embedding_dim, num_hiddens, num_residual_layers, num_residual_hiddens)
self.decoder = Decoder(in_dim, embedding_dim, num_hiddens, num_residual_layers, num_residual_hiddens, True)
def forward(self, gt_poses, id=None, pre_state=None):
z = self.encoder(gt_poses.transpose(1, 2))
if not self.training:
x_recon, cur_state = self.decoder(z, pre_state.transpose(1, 2) if pre_state is not None else None)
return z, x_recon
gt_recon, cur_state = self.decoder(z, pre_state.transpose(1, 2) if pre_state is not None else None)
return gt_recon.transpose(1, 2)
def encode(self, gt_poses, id=None):
z = self.encoder(gt_poses.transpose(1, 2))
return z
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