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import torch.nn as nn
import torch.optim as optim
import pytorch_lightning.core.module as pl
import matplotlib.pyplot as plt
from module import commons
from transformer.embedding import SinePositionalEmbedding
from transformer.transformer import LayerNorm, TransformerEncoder, TransformerEncoderLayer
plt.switch_backend('agg')
from module.models import SynthesizerTrn
from module.modules import MelStyleEncoder
def init_vits_s2(config_path, cpt_path):
import utils
hps = utils.get_hparams_from_file(config_path)
vits_s2 = SynthesizerTrn(
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
**hps.model)
ckpt = torch.load(cpt_path, map_location='cpu')['model']
vits_s2.load_state_dict(ckpt)
vits_s2.eval()
return vits_s2
import math
import torch.nn.functional as F
from tqdm import tqdm
class SinusoidalPosEmb(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
device = x.device
half_dim = self.dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
emb = x[:, None] * emb[None, :]
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
return emb
class DiT(nn.Module):
def __init__(self, io_channels, cond_channels):
super().__init__()
self.in_proj = nn.Linear(io_channels+cond_channels, 512)
norm_first = False
self.position = SinePositionalEmbedding(
512, dropout=0.1, scale=False, alpha=True)
self.encoder = TransformerEncoder(
TransformerEncoderLayer(
d_model=512,
nhead=16,
dim_feedforward=2048,
dropout=0.1,
batch_first=True,
norm_first=False, ),
num_layers=12,
norm=LayerNorm(512) if norm_first else None, )
self.proj = nn.Linear(512, io_channels)
self.diffusion_embedding = SinusoidalPosEmb(512)
def forward(self, x, t, cond, mask=None):
if mask is None:
mask = torch.ones(x.shape[0], x.shape[-1], device=x.device).long()
mask_bool = ~mask.bool()
diffusion_step = self.diffusion_embedding(t).unsqueeze(1)
x = torch.cat([x, cond], dim=1).transpose(1, 2)
x = self.in_proj(x)
x = torch.cat([diffusion_step, x], dim=1)
mask_bool = torch.cat([torch.zeros([mask_bool.shape[0], 1], device=mask_bool.device).bool(), mask_bool], dim=1)
x = self.position(x)
x, _ = self.encoder(
(x, None), src_key_padding_mask=mask_bool)
x = x[:, 1:, :]
x = self.proj(x).transpose(1, 2)
return x
class ReFlow(nn.Module):
def __init__(self,
io_channels,
cond_channels,
):
super().__init__()
self.model = DiT(io_channels, cond_channels)
self.io_channels = io_channels
self.rng = torch.quasirandom.SobolEngine(1, scramble=True)
def forward(self, reals, cond, mask):
# reals : [B, 128, T]
# cond : [B, 192, T]
# mask : [B, 1, T]
t = self.rng.draw(reals.shape[0])[:, 0].to(reals.device)
alphas, sigmas = 1 - t, t
alphas = alphas[:, None, None]
sigmas = sigmas[:, None, None]
noise = torch.randn_like(reals)
noised_inputs = reals * alphas + noise * sigmas
targets = noise - reals
output = self.model(noised_inputs, t, cond, mask)
# mse loss with mask
loss = F.mse_loss(output, targets, reduction='none')
loss = loss * mask.unsqueeze(1)
return loss.mean()
def sample(self, cond, uncond, steps, guidance, sigma_max=1):
x = torch.randn(cond.shape[0], self.io_channels, cond.shape[2]).to(cond.device)
t = torch.linspace(sigma_max, 0, steps + 1)
for t_curr, t_prev in tqdm(zip(t[:-1], t[1:])):
t_curr_tensor = t_curr * torch.ones(
(x.shape[0],), dtype=x.dtype, device=x.device
)
dt = t_prev - t_curr
v_cond = self.model(x, t_curr_tensor, cond)
v_uncond = self.model(x, t_curr_tensor, uncond)
v = guidance * v_cond + (1 - guidance) * v_uncond
x = x + dt * v
return x
class NARPredictor(nn.Module):
def __init__(self, latent_dim=16):
super().__init__()
self.latent_dim = latent_dim
self.bert_proj = nn.Linear(1024, 192)
self.x_emb = nn.Linear(192, 192)
self.ref_enc = MelStyleEncoder(latent_dim, style_vector_dim=192)
self.null_emb = nn.Parameter(torch.randn(192))
self.reflow = ReFlow(latent_dim, 192)
def forward(self, x, bert, spk_emb, diffusion_input, mask):
g = self.ref_enc(diffusion_input, mask.float().unsqueeze(1)).transpose(1, 2)
bert = self.bert_proj(bert.transpose(1, 2))
x = self.x_emb(x.transpose(1,2))
cond = (x + bert + g).transpose(1, 2) # [B, 192, T]
# dropout prob 0.1
for b in range(cond.shape[0]):
if torch.rand(1) < 0.1:
cond[b, :, :] = self.null_emb.unsqueeze(1).expand(-1, cond.shape[2])
loss = self.reflow(diffusion_input, cond, mask)
return loss
@torch.inference_mode()
@torch.no_grad()
def infer(self, x, bert, prompt, step, guidance):
g = self.ref_enc(prompt).transpose(1, 2)
bert = self.bert_proj(bert.transpose(1, 2))
x = self.x_emb(x.transpose(1,2))
cond = (x + bert + g).transpose(1, 2)
uncond = self.null_emb.unsqueeze(1).expand(-1, cond.shape[2]).unsqueeze(0)
fakes = self.reflow.sample(cond,uncond, step, guidance)
return fakes
class Wav2TTS(pl.LightningModule):
def __init__(self, hp, vits_s2=None):
super().__init__()
self.model = NARPredictor(latent_dim=4)
self.lr = hp.lr
if vits_s2 is None:
vits_s2 = init_vits_s2('configs/vits.json', 'logs/vits/G_38100.pth')
self.s2 = [vits_s2]
self.inited = False
def configure_optimizers(self):
print('train with lr ', self.lr)
optimizer = optim.AdamW([*self.parameters()], lr=self.lr, betas=(0.9, 0.95), weight_decay=0.1)
return optimizer
def training_step(self, batch, batch_idx):
loss = self.step(batch)
self.log("train/loss", loss, on_step=True, prog_bar=True)
# self.log("train/quantize_loss", quantize_loss, on_step=True, prog_bar=True)
return loss
def validation_step(self, batch, batch_idx):
loss = self.step(batch)
self.log("val/loss", loss, on_step=True, prog_bar=True)
def step(self, batch):
# speaker_embedding = self.spk_proj(batch['spk_emb'])
# speaker_embedding, commitment_loss, codebook_loss, indices, z_e = self.spk_vq(speaker_embedding.unsqueeze(-1))
# quantize_loss = codebook_loss + 0.25*commitment_loss
# speaker_embedding = speaker_embedding.squeeze(-1)
# speaker_embedding = self.spk_emb(batch['spk_emb'])
# Deal with phone segments
phone = batch['phone']
lengths = batch['length']
bert_feature = batch['bert_feature']
speaker_embedding = batch['spk_emb']
latent = batch['latent']
mask = commons.sequence_mask(lengths, latent.size(-1)).long() # [1,1,1,1,0...]
if not self.inited:
self.s2 = [self.s2[0].to(phone.device)]
self.inited = True
s2 = self.s2[0]
phone_features, _ = s2.enc_p(phone, mask.unsqueeze(1))
loss = self.model(phone_features, bert_feature, speaker_embedding, latent, mask)
return loss
@torch.inference_mode()
@torch.no_grad()
def inference(self, phonemes, bert_feature, prompt, n_steps=32, guidance=4):
# speaker_embedding = self.spk_proj(spk_emb)
# speaker_embedding, commitment_loss, codebook_loss, indices, z_e = self.spk_vq(speaker_embedding.unsqueeze(-1))
# speaker_embedding = speaker_embedding.squeeze(-1)
# print('quantized spk code:', indices)
mask = torch.ones(phonemes.shape[0],1, phonemes.shape[1]).to(phonemes.device).float()
s2 = self.s2[0]
phone_features, _ = s2.enc_p(phonemes, mask)
output = self.model.infer(phone_features, bert_feature, prompt, n_steps, guidance)
return output |