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import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from models.tts.naturalspeech2.diffusion import Diffusion
from models.tts.naturalspeech2.diffusion_flow import DiffusionFlow
from models.tts.naturalspeech2.wavenet import WaveNet
from models.tts.naturalspeech2.prior_encoder import PriorEncoder
from modules.naturalpseech2.transformers import TransformerEncoder
from encodec import EncodecModel
from einops import rearrange, repeat
import os
import json
class NaturalSpeech2(nn.Module):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
self.latent_dim = cfg.latent_dim
self.query_emb_num = cfg.query_emb.query_token_num
self.prior_encoder = PriorEncoder(cfg.prior_encoder)
if cfg.diffusion.diffusion_type == "diffusion":
self.diffusion = Diffusion(cfg.diffusion)
elif cfg.diffusion.diffusion_type == "flow":
self.diffusion = DiffusionFlow(cfg.diffusion)
self.prompt_encoder = TransformerEncoder(cfg=cfg.prompt_encoder)
if self.latent_dim != cfg.prompt_encoder.encoder_hidden:
self.prompt_lin = nn.Linear(
self.latent_dim, cfg.prompt_encoder.encoder_hidden
)
self.prompt_lin.weight.data.normal_(0.0, 0.02)
else:
self.prompt_lin = None
self.query_emb = nn.Embedding(self.query_emb_num, cfg.query_emb.hidden_size)
self.query_attn = nn.MultiheadAttention(
cfg.query_emb.hidden_size, cfg.query_emb.head_num, batch_first=True
)
codec_model = EncodecModel.encodec_model_24khz()
codec_model.set_target_bandwidth(12.0)
codec_model.requires_grad_(False)
self.quantizer = codec_model.quantizer
@torch.no_grad()
def code_to_latent(self, code):
latent = self.quantizer.decode(code.transpose(0, 1))
return latent
def latent_to_code(self, latent, nq=16):
residual = latent
all_indices = []
all_dist = []
for i in range(nq):
layer = self.quantizer.vq.layers[i]
x = rearrange(residual, "b d n -> b n d")
x = layer.project_in(x)
shape = x.shape
x = layer._codebook.preprocess(x)
embed = layer._codebook.embed.t()
dist = -(
x.pow(2).sum(1, keepdim=True)
- 2 * x @ embed
+ embed.pow(2).sum(0, keepdim=True)
)
indices = dist.max(dim=-1).indices
indices = layer._codebook.postprocess_emb(indices, shape)
dist = dist.reshape(*shape[:-1], dist.shape[-1])
quantized = layer.decode(indices)
residual = residual - quantized
all_indices.append(indices)
all_dist.append(dist)
out_indices = torch.stack(all_indices)
out_dist = torch.stack(all_dist)
return out_indices, out_dist # (nq, B, T); (nq, B, T, 1024)
@torch.no_grad()
def latent_to_latent(self, latent, nq=16):
codes, _ = self.latent_to_code(latent, nq)
latent = self.quantizer.vq.decode(codes)
return latent
def forward(
self,
code=None,
pitch=None,
duration=None,
phone_id=None,
phone_id_frame=None,
frame_nums=None,
ref_code=None,
ref_frame_nums=None,
phone_mask=None,
mask=None,
ref_mask=None,
):
ref_latent = self.code_to_latent(ref_code)
latent = self.code_to_latent(code)
if self.latent_dim is not None:
ref_latent = self.prompt_lin(ref_latent.transpose(1, 2))
ref_latent = self.prompt_encoder(ref_latent, ref_mask, condition=None)
spk_emb = ref_latent.transpose(1, 2) # (B, d, T')
spk_query_emb = self.query_emb(
torch.arange(self.query_emb_num).to(latent.device)
).repeat(
latent.shape[0], 1, 1
) # (B, query_emb_num, d)
spk_query_emb, _ = self.query_attn(
spk_query_emb,
spk_emb.transpose(1, 2),
spk_emb.transpose(1, 2),
key_padding_mask=~(ref_mask.bool()),
) # (B, query_emb_num, d)
prior_out = self.prior_encoder(
phone_id=phone_id,
duration=duration,
pitch=pitch,
phone_mask=phone_mask,
mask=mask,
ref_emb=spk_emb,
ref_mask=ref_mask,
is_inference=False,
)
prior_condition = prior_out["prior_out"] # (B, T, d)
diff_out = self.diffusion(latent, mask, prior_condition, spk_query_emb)
return diff_out, prior_out
@torch.no_grad()
def inference(
self, ref_code=None, phone_id=None, ref_mask=None, inference_steps=1000
):
ref_latent = self.code_to_latent(ref_code)
if self.latent_dim is not None:
ref_latent = self.prompt_lin(ref_latent.transpose(1, 2))
ref_latent = self.prompt_encoder(ref_latent, ref_mask, condition=None)
spk_emb = ref_latent.transpose(1, 2) # (B, d, T')
spk_query_emb = self.query_emb(
torch.arange(self.query_emb_num).to(ref_latent.device)
).repeat(
ref_latent.shape[0], 1, 1
) # (B, query_emb_num, d)
spk_query_emb, _ = self.query_attn(
spk_query_emb,
spk_emb.transpose(1, 2),
spk_emb.transpose(1, 2),
key_padding_mask=~(ref_mask.bool()),
) # (B, query_emb_num, d)
prior_out = self.prior_encoder(
phone_id=phone_id,
duration=None,
pitch=None,
phone_mask=None,
mask=None,
ref_emb=spk_emb,
ref_mask=ref_mask,
is_inference=True,
)
prior_condition = prior_out["prior_out"] # (B, T, d)
z = torch.randn(
prior_condition.shape[0], self.latent_dim, prior_condition.shape[1]
).to(ref_latent.device) / (1.20)
x0 = self.diffusion.reverse_diffusion(
z, None, prior_condition, inference_steps, spk_query_emb
)
return x0, prior_out
@torch.no_grad()
def reverse_diffusion_from_t(
self,
code=None,
pitch=None,
duration=None,
phone_id=None,
ref_code=None,
phone_mask=None,
mask=None,
ref_mask=None,
n_timesteps=None,
t=None,
):
# o Only for debug
ref_latent = self.code_to_latent(ref_code)
latent = self.code_to_latent(code)
if self.latent_dim is not None:
ref_latent = self.prompt_lin(ref_latent.transpose(1, 2))
ref_latent = self.prompt_encoder(ref_latent, ref_mask, condition=None)
spk_emb = ref_latent.transpose(1, 2) # (B, d, T')
spk_query_emb = self.query_emb(
torch.arange(self.query_emb_num).to(latent.device)
).repeat(
latent.shape[0], 1, 1
) # (B, query_emb_num, d)
spk_query_emb, _ = self.query_attn(
spk_query_emb,
spk_emb.transpose(1, 2),
spk_emb.transpose(1, 2),
key_padding_mask=~(ref_mask.bool()),
) # (B, query_emb_num, d)
prior_out = self.prior_encoder(
phone_id=phone_id,
duration=duration,
pitch=pitch,
phone_mask=phone_mask,
mask=mask,
ref_emb=spk_emb,
ref_mask=ref_mask,
is_inference=False,
)
prior_condition = prior_out["prior_out"] # (B, T, d)
diffusion_step = (
torch.ones(
latent.shape[0],
dtype=latent.dtype,
device=latent.device,
requires_grad=False,
)
* t
)
diffusion_step = torch.clamp(diffusion_step, 1e-5, 1.0 - 1e-5)
xt, _ = self.diffusion.forward_diffusion(
x0=latent, diffusion_step=diffusion_step
)
# print(torch.abs(xt-latent).max(), torch.abs(xt-latent).mean(), torch.abs(xt-latent).std())
x0 = self.diffusion.reverse_diffusion_from_t(
xt, mask, prior_condition, n_timesteps, spk_query_emb, t_start=t
)
return x0, prior_out, xt
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