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https://github.com/RVC-Boss/GPT-SoVITS.git
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onnx export
onnx export
This commit is contained in:
Ναρουσέ·μ·γιουμεμί·Χινακάννα
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parent
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commit
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@ -1,24 +1,28 @@
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import warnings
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warnings.filterwarnings("ignore")
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import copy
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import math
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from typing import Optional
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import os
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import pdb
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import torch
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from torch import nn
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from torch.nn import functional as F
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from module import commons
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from module import modules
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from module import attentions_onnx as attentions
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from f5_tts.model import DiT
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from module import attentions
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#from f5_tts.model.backbones.dit import DiT
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from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
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from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
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from module.commons import init_weights, get_padding
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from module.mrte_model import MRTE
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from module.quantize import ResidualVectorQuantizer
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# from text import symbols
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from text import symbols as symbols_v1
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from text import symbols2 as symbols_v2
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from torch.cuda.amp import autocast
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import contextlib,random
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class StochasticDurationPredictor(nn.Module):
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@ -220,7 +224,7 @@ class TextEncoder(nn.Module):
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symbols = symbols_v2.symbols
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self.text_embedding = nn.Embedding(len(symbols), hidden_channels)
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self.mrte = attentions.MRTE()
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self.mrte = MRTE()
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self.encoder2 = attentions.Encoder(
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hidden_channels,
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@ -233,7 +237,7 @@ class TextEncoder(nn.Module):
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self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
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def forward(self, y, text, ge, speed=1):
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def forward(self, y, text, ge, speed=1,test=None):
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y_mask = torch.ones_like(y[:1,:1,:])
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y = self.ssl_proj(y * y_mask) * y_mask
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@ -254,6 +258,25 @@ class TextEncoder(nn.Module):
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m, logs = torch.split(stats, self.out_channels, dim=1)
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return y, m, logs, y_mask
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def extract_latent(self, x):
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x = self.ssl_proj(x)
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quantized, codes, commit_loss, quantized_list = self.quantizer(x)
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return codes.transpose(0, 1)
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def decode_latent(self, codes, y_mask, refer, refer_mask, ge):
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quantized = self.quantizer.decode(codes)
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y = self.vq_proj(quantized) * y_mask
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y = self.encoder_ssl(y * y_mask, y_mask)
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y = self.mrte(y, y_mask, refer, refer_mask, ge)
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y = self.encoder2(y * y_mask, y_mask)
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stats = self.proj(y) * y_mask
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m, logs = torch.split(stats, self.out_channels, dim=1)
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return y, m, logs, y_mask, quantized
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class ResidualCouplingBlock(nn.Module):
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def __init__(
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@ -465,7 +488,7 @@ class Generator(torch.nn.Module):
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if gin_channels != 0:
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self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
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def forward(self, x, g:Optional[torch.Tensor]=None):
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def forward(self, x, g=None):
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x = self.conv_pre(x)
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if g is not None:
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x = x + self.cond(g)
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@ -923,7 +946,7 @@ class SynthesizerTrn(nn.Module):
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# self.enc_p.encoder_text.requires_grad_(False)
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# self.enc_p.mrte.requires_grad_(False)
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def forward(self, codes, text, refer,noise_scale=0.5, speed=1):
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def forward(self, codes, text, refer, noise_scale=0.5):
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refer_mask = torch.ones_like(refer[:1,:1,:])
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if (self.version == "v1"):
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ge = self.ref_enc(refer * refer_mask, refer_mask)
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@ -935,79 +958,98 @@ class SynthesizerTrn(nn.Module):
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dquantized = torch.cat([quantized, quantized]).permute(1, 2, 0)
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quantized = dquantized.contiguous().view(1, self.ssl_dim, -1)
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x, m_p, logs_p, y_mask = self.enc_p(
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quantized, text, ge, speed
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_, m_p, logs_p, y_mask = self.enc_p(
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quantized, text, ge
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)
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z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
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z = self.flow(z_p, y_mask, g=ge, reverse=True)
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o = self.dec((z * y_mask)[:, :, :], g=ge)
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return o
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def extract_latent(self, x):
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ssl = self.ssl_proj(x)
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quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
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_, codes, _, _ = self.quantizer(ssl)
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return codes.transpose(0, 1)
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class CFM(torch.nn.Module):
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def __init__(
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self,
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in_channels,dit
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):
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super().__init__()
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# self.sigma_min = 1e-6
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self.sigma_min = 1e-6
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self.estimator = dit
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self.in_channels = in_channels
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# self.criterion = torch.nn.MSELoss()
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self.criterion = torch.nn.MSELoss()
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def forward(self, mu:torch.Tensor, x_lens:torch.LongTensor, prompt:torch.Tensor, n_timesteps:torch.LongTensor, temperature:float=1.0):
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@torch.inference_mode()
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def inference(self, mu, x_lens, prompt, n_timesteps, temperature=1.0, inference_cfg_rate=0):
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"""Forward diffusion"""
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B, T = mu.size(0), mu.size(1)
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x = torch.randn([B, self.in_channels, T], device=mu.device,dtype=mu.dtype)
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ntimesteps = int(n_timesteps)
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x = torch.randn([B, self.in_channels, T], device=mu.device,dtype=mu.dtype) * temperature
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prompt_len = prompt.size(-1)
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prompt_x = torch.zeros_like(x,dtype=mu.dtype)
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prompt_x[..., :prompt_len] = prompt[..., :prompt_len]
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x[..., :prompt_len] = 0.0
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x[..., :prompt_len] = 0
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mu=mu.transpose(2,1)
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t = torch.tensor(0.0,dtype=x.dtype,device=x.device)
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d = torch.tensor(1.0/ntimesteps,dtype=x.dtype,device=x.device)
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d_tensor = torch.ones(x.shape[0], device=x.device,dtype=mu.dtype) * d
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for j in range(ntimesteps):
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t = 0
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d = 1 / n_timesteps
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for j in range(n_timesteps):
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t_tensor = torch.ones(x.shape[0], device=x.device,dtype=mu.dtype) * t
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# d_tensor = torch.ones(x.shape[0], device=x.device,dtype=mu.dtype) * d
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d_tensor = torch.ones(x.shape[0], device=x.device,dtype=mu.dtype) * d
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# v_pred = model(x, t_tensor, d_tensor, **extra_args)
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v_pred = self.estimator(x, prompt_x, x_lens, t_tensor,d_tensor, mu).transpose(2, 1)
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# if inference_cfg_rate>1e-5:
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# neg = self.estimator(x, prompt_x, x_lens, t_tensor, d_tensor, mu, use_grad_ckpt=False, drop_audio_cond=True, drop_text=True).transpose(2, 1)
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# v_pred=v_pred+(v_pred-neg)*inference_cfg_rate
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v_pred = self.estimator(x, prompt_x, x_lens, t_tensor,d_tensor, mu, use_grad_ckpt=False,drop_audio_cond=False,drop_text=False).transpose(2, 1)
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if inference_cfg_rate>1e-5:
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neg = self.estimator(x, prompt_x, x_lens, t_tensor, d_tensor, mu, use_grad_ckpt=False, drop_audio_cond=True, drop_text=True).transpose(2, 1)
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v_pred=v_pred+(v_pred-neg)*inference_cfg_rate
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x = x + d * v_pred
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t = t + d
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x[:, :, :prompt_len] = 0.0
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x[:, :, :prompt_len] = 0
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return x
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def forward(self, x1, x_lens, prompt_lens, mu, use_grad_ckpt):
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b, _, t = x1.shape
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t = torch.rand([b], device=mu.device, dtype=x1.dtype)
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x0 = torch.randn_like(x1,device=mu.device)
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vt = x1 - x0
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xt = x0 + t[:, None, None] * vt
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dt = torch.zeros_like(t,device=mu.device)
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prompt = torch.zeros_like(x1)
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for i in range(b):
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prompt[i, :, :prompt_lens[i]] = x1[i, :, :prompt_lens[i]]
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xt[i, :, :prompt_lens[i]] = 0
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gailv=0.3# if ttime()>1736250488 else 0.1
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if random.random() < gailv:
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base = torch.randint(2, 8, (t.shape[0],), device=mu.device)
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d = 1/torch.pow(2, base)
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d_input = d.clone()
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d_input[d_input < 1e-2] = 0
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# with torch.no_grad():
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v_pred_1 = self.estimator(xt, prompt, x_lens, t, d_input, mu, use_grad_ckpt).transpose(2, 1).detach()
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# v_pred_1 = self.diffusion(xt, t, d_input, cond=conditioning).detach()
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x_mid = xt + d[:, None, None] * v_pred_1
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# v_pred_2 = self.diffusion(x_mid, t+d, d_input, cond=conditioning).detach()
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v_pred_2 = self.estimator(x_mid, prompt, x_lens, t+d, d_input, mu, use_grad_ckpt).transpose(2, 1).detach()
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vt = (v_pred_1 + v_pred_2) / 2
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vt = vt.detach()
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dt = 2*d
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vt_pred = self.estimator(xt, prompt, x_lens, t,dt, mu, use_grad_ckpt).transpose(2,1)
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loss = 0
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for i in range(b):
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loss += self.criterion(vt_pred[i, :, prompt_lens[i]:x_lens[i]], vt[i, :, prompt_lens[i]:x_lens[i]])
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loss /= b
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return loss
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def set_no_grad(net_g):
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for name, param in net_g.named_parameters():
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param.requires_grad=False
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@torch.jit.script_if_tracing
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def compile_codes_length(codes):
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y_lengths1 = torch.LongTensor([codes.size(2)]).to(codes.device)
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return y_lengths1 * 2.5 * 1.5
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@torch.jit.script_if_tracing
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def compile_ref_length(refer):
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refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
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return refer_lengths
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class SynthesizerTrnV3(nn.Module):
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"""
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@ -1035,7 +1077,6 @@ class SynthesizerTrnV3(nn.Module):
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use_sdp=True,
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semantic_frame_rate=None,
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freeze_quantizer=None,
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version="v3",
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**kwargs):
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super().__init__()
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@ -1056,7 +1097,6 @@ class SynthesizerTrnV3(nn.Module):
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self.segment_size = segment_size
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self.n_speakers = n_speakers
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self.gin_channels = gin_channels
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self.version = version
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self.model_dim=512
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self.use_sdp = use_sdp
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@ -1083,7 +1123,7 @@ class SynthesizerTrnV3(nn.Module):
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n_q=1,
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bins=1024
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)
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freeze_quantizer
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self.freeze_quantizer=freeze_quantizer
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inter_channels2=512
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self.bridge=nn.Sequential(
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nn.Conv1d(inter_channels, inter_channels2, 1, stride=1),
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@ -1092,32 +1132,213 @@ class SynthesizerTrnV3(nn.Module):
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self.wns1=Encoder(inter_channels2, inter_channels2, inter_channels2, 5, 1, 8,gin_channels=gin_channels)
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self.linear_mel=nn.Conv1d(inter_channels2,100,1,stride=1)
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self.cfm = CFM(100,DiT(**dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=inter_channels2, conv_layers=4)),)#text_dim is condition feature dim
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if freeze_quantizer==True:
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if self.freeze_quantizer==True:
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set_no_grad(self.ssl_proj)
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set_no_grad(self.quantizer)
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set_no_grad(self.enc_p)
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def create_ge(self, refer):
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refer_lengths = compile_ref_length(refer)
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def forward(self, ssl, y, mel,ssl_lengths,y_lengths, text, text_lengths,mel_lengths, use_grad_ckpt):#ssl_lengths no need now
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with autocast(enabled=False):
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y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(y.dtype)
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ge = self.ref_enc(y[:,:704] * y_mask, y_mask)
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maybe_no_grad = torch.no_grad() if self.freeze_quantizer else contextlib.nullcontext()
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with maybe_no_grad:
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if self.freeze_quantizer:
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self.ssl_proj.eval()#
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self.quantizer.eval()
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self.enc_p.eval()
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ssl = self.ssl_proj(ssl)
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quantized, codes, commit_loss, quantized_list = self.quantizer(
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ssl, layers=[0]
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)
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quantized = F.interpolate(quantized, scale_factor=2, mode="nearest")##BCT
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x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge)
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fea=self.bridge(x)
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fea = F.interpolate(fea, scale_factor=1.875, mode="nearest")##BCT
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fea, y_mask_ = self.wns1(fea, mel_lengths, ge)##If the 1-minute fine-tuning works fine, no need to manually adjust the learning rate.
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B=ssl.shape[0]
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prompt_len_max = mel_lengths*2/3
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prompt_len = (torch.rand([B], device=fea.device) * prompt_len_max).floor().to(dtype=torch.long)
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minn=min(mel.shape[-1],fea.shape[-1])
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mel=mel[:,:,:minn]
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fea=fea[:,:,:minn]
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cfm_loss= self.cfm(mel, mel_lengths, prompt_len, fea, use_grad_ckpt)
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return cfm_loss
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@torch.no_grad()
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def decode_encp(self, codes,text, refer,ge=None,speed=1):
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# print(2333333,refer.shape)
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# ge=None
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if(ge==None):
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refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
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refer_mask = torch.unsqueeze(commons.sequence_mask(refer_lengths, refer.size(2)), 1).to(refer.dtype)
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ge = self.ref_enc(refer[:,:704] * refer_mask, refer_mask)
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return ge
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def forward(self, codes, text,ge,speed=1):
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y_lengths1=compile_codes_length(codes)
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y_lengths = torch.LongTensor([int(codes.size(2)*2)]).to(codes.device)
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if speed==1:
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sizee=int(codes.size(2)*2.5*1.5)
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else:
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sizee=int(codes.size(2)*2.5*1.5/speed)+1
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y_lengths1 = torch.LongTensor([sizee]).to(codes.device)
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text_lengths = torch.LongTensor([text.size(-1)]).to(text.device)
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quantized = self.quantizer.decode(codes)
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if self.semantic_frame_rate == '25hz':
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quantized = F.interpolate(quantized, scale_factor=2, mode="nearest")##BCT
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x, m_p, logs_p, y_mask = self.enc_p(quantized, text, ge,speed)
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x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge,speed)
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fea=self.bridge(x)
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fea = F.interpolate(fea, scale_factor=1.875, mode="nearest")##BCT
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####more wn paramter to learn mel
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fea, y_mask_ = self.wns1(fea, y_lengths1, ge)
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return fea
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return fea,ge
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def extract_latent(self, x):
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ssl = self.ssl_proj(x)
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quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
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return codes.transpose(0,1)
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class SynthesizerTrnV3b(nn.Module):
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"""
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Synthesizer for Training
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"""
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def __init__(self,
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spec_channels,
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segment_size,
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inter_channels,
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
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p_dropout,
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resblock,
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resblock_kernel_sizes,
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resblock_dilation_sizes,
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upsample_rates,
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upsample_initial_channel,
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upsample_kernel_sizes,
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n_speakers=0,
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gin_channels=0,
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use_sdp=True,
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semantic_frame_rate=None,
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freeze_quantizer=None,
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**kwargs):
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super().__init__()
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self.spec_channels = spec_channels
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self.inter_channels = inter_channels
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self.hidden_channels = hidden_channels
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self.filter_channels = filter_channels
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self.n_heads = n_heads
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self.n_layers = n_layers
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.resblock = resblock
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self.resblock_kernel_sizes = resblock_kernel_sizes
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self.resblock_dilation_sizes = resblock_dilation_sizes
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self.upsample_rates = upsample_rates
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self.upsample_initial_channel = upsample_initial_channel
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self.upsample_kernel_sizes = upsample_kernel_sizes
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self.segment_size = segment_size
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self.n_speakers = n_speakers
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self.gin_channels = gin_channels
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self.model_dim=512
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self.use_sdp = use_sdp
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self.enc_p = TextEncoder(inter_channels,hidden_channels,filter_channels,n_heads,n_layers,kernel_size,p_dropout)
|
||||
# self.ref_enc = modules.MelStyleEncoder(spec_channels, style_vector_dim=gin_channels)###Rollback
|
||||
self.ref_enc = modules.MelStyleEncoder(704, style_vector_dim=gin_channels)###Rollback
|
||||
self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
|
||||
upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
|
||||
self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16,
|
||||
gin_channels=gin_channels)
|
||||
self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
|
||||
|
||||
|
||||
ssl_dim = 768
|
||||
assert semantic_frame_rate in ['25hz', "50hz"]
|
||||
self.semantic_frame_rate = semantic_frame_rate
|
||||
if semantic_frame_rate == '25hz':
|
||||
self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 2, stride=2)
|
||||
else:
|
||||
self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 1, stride=1)
|
||||
|
||||
self.quantizer = ResidualVectorQuantizer(
|
||||
dimension=ssl_dim,
|
||||
n_q=1,
|
||||
bins=1024
|
||||
)
|
||||
self.freeze_quantizer=freeze_quantizer
|
||||
|
||||
inter_channels2=512
|
||||
self.bridge=nn.Sequential(
|
||||
nn.Conv1d(inter_channels, inter_channels2, 1, stride=1),
|
||||
nn.LeakyReLU()
|
||||
)
|
||||
self.wns1=Encoder(inter_channels2, inter_channels2, inter_channels2, 5, 1, 8,gin_channels=gin_channels)
|
||||
self.linear_mel=nn.Conv1d(inter_channels2,100,1,stride=1)
|
||||
self.cfm = CFM(100,DiT(**dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=inter_channels2, conv_layers=4)),)#text_dim is condition feature dim
|
||||
|
||||
|
||||
def forward(self, ssl, y, mel,ssl_lengths,y_lengths, text, text_lengths,mel_lengths):#ssl_lengths no need now
|
||||
with autocast(enabled=False):
|
||||
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(y.dtype)
|
||||
ge = self.ref_enc(y[:,:704] * y_mask, y_mask)
|
||||
# ge = self.ref_enc(y * y_mask, y_mask)#change back, new spec setting is whole 24k
|
||||
# ge=None
|
||||
maybe_no_grad = torch.no_grad() if self.freeze_quantizer else contextlib.nullcontext()
|
||||
with maybe_no_grad:
|
||||
if self.freeze_quantizer:
|
||||
self.ssl_proj.eval()
|
||||
self.quantizer.eval()
|
||||
ssl = self.ssl_proj(ssl)
|
||||
quantized, codes, commit_loss, quantized_list = self.quantizer(
|
||||
ssl, layers=[0]
|
||||
)
|
||||
quantized = F.interpolate(quantized, scale_factor=2, mode="nearest")##BCT
|
||||
x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge)
|
||||
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=ge)
|
||||
z_p = self.flow(z, y_mask, g=ge)
|
||||
z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
|
||||
o = self.dec(z_slice, g=ge)
|
||||
fea=self.bridge(x)
|
||||
fea = F.interpolate(fea, scale_factor=1.875, mode="nearest")##BCT
|
||||
fea, y_mask_ = self.wns1(fea, mel_lengths, ge)
|
||||
learned_mel = self.linear_mel(fea)
|
||||
B=ssl.shape[0]
|
||||
prompt_len_max = mel_lengths*2/3
|
||||
prompt_len = (torch.rand([B], device=fea.device) * prompt_len_max).floor().to(dtype=torch.long)#
|
||||
minn=min(mel.shape[-1],fea.shape[-1])
|
||||
mel=mel[:,:,:minn]
|
||||
fea=fea[:,:,:minn]
|
||||
cfm_loss= self.cfm(mel, mel_lengths, prompt_len, fea)#fea==cond,y_lengths==target_mel_lengths#ge not need
|
||||
return commit_loss,cfm_loss,F.mse_loss(learned_mel, mel),o, ids_slice, y_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q), quantized
|
||||
|
||||
@torch.no_grad()
|
||||
def decode_encp(self, codes,text, refer,ge=None):
|
||||
# print(2333333,refer.shape)
|
||||
# ge=None
|
||||
if(ge==None):
|
||||
refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
|
||||
refer_mask = torch.unsqueeze(commons.sequence_mask(refer_lengths, refer.size(2)), 1).to(refer.dtype)
|
||||
ge = self.ref_enc(refer[:,:704] * refer_mask, refer_mask)
|
||||
y_lengths = torch.LongTensor([int(codes.size(2)*2)]).to(codes.device)
|
||||
y_lengths1 = torch.LongTensor([int(codes.size(2)*2.5*1.5)]).to(codes.device)
|
||||
text_lengths = torch.LongTensor([text.size(-1)]).to(text.device)
|
||||
|
||||
quantized = self.quantizer.decode(codes)
|
||||
if self.semantic_frame_rate == '25hz':
|
||||
quantized = F.interpolate(quantized, scale_factor=2, mode="nearest")##BCT
|
||||
x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge)
|
||||
fea=self.bridge(x)
|
||||
fea = F.interpolate(fea, scale_factor=1.875, mode="nearest")##BCT
|
||||
####more wn paramter to learn mel
|
||||
fea, y_mask_ = self.wns1(fea, y_lengths1, ge)
|
||||
return fea,ge
|
||||
|
||||
def extract_latent(self, x):
|
||||
ssl = self.ssl_proj(x)
|
||||
quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
|
||||
return codes.transpose(0,1)
|
||||
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user