Refactor: Format Code with Ruff and Update Deprecated G2PW Link (#2255)
* ruff check --fix * ruff format --line-length 120 --target-version py39 * Change the link for G2PW Model * update pytorch version and colab
This commit is contained in:
XXXXRT666
@@ -1,9 +1,7 @@
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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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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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@@ -13,16 +11,18 @@ from module import commons
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from module import modules
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from module import attentions
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from f5_tts.model import DiT
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from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
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from torch.nn import Conv1d, ConvTranspose1d, 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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import contextlib
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import random
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class StochasticDurationPredictor(nn.Module):
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@@ -48,29 +48,21 @@ class StochasticDurationPredictor(nn.Module):
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self.flows = nn.ModuleList()
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self.flows.append(modules.ElementwiseAffine(2))
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for i in range(n_flows):
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self.flows.append(
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modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
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)
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self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
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self.flows.append(modules.Flip())
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self.post_pre = nn.Conv1d(1, filter_channels, 1)
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self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
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self.post_convs = modules.DDSConv(
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filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
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)
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self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
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self.post_flows = nn.ModuleList()
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self.post_flows.append(modules.ElementwiseAffine(2))
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for i in range(4):
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self.post_flows.append(
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modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
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)
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self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
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self.post_flows.append(modules.Flip())
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self.pre = nn.Conv1d(in_channels, filter_channels, 1)
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self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
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self.convs = modules.DDSConv(
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filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
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)
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self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
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if gin_channels != 0:
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self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
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@@ -91,10 +83,7 @@ class StochasticDurationPredictor(nn.Module):
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h_w = self.post_pre(w)
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h_w = self.post_convs(h_w, x_mask)
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h_w = self.post_proj(h_w) * x_mask
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e_q = (
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torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype)
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* x_mask
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)
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e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
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z_q = e_q
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for flow in self.post_flows:
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z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
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@@ -102,13 +91,8 @@ class StochasticDurationPredictor(nn.Module):
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z_u, z1 = torch.split(z_q, [1, 1], 1)
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u = torch.sigmoid(z_u) * x_mask
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z0 = (w - u) * x_mask
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logdet_tot_q += torch.sum(
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(F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2]
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)
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logq = (
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torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q**2)) * x_mask, [1, 2])
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- logdet_tot_q
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)
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logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2])
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logq = torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q**2)) * x_mask, [1, 2]) - logdet_tot_q
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logdet_tot = 0
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z0, logdet = self.log_flow(z0, x_mask)
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@@ -117,18 +101,12 @@ class StochasticDurationPredictor(nn.Module):
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for flow in flows:
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z, logdet = flow(z, x_mask, g=x, reverse=reverse)
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logdet_tot = logdet_tot + logdet
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nll = (
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torch.sum(0.5 * (math.log(2 * math.pi) + (z**2)) * x_mask, [1, 2])
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- logdet_tot
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)
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nll = torch.sum(0.5 * (math.log(2 * math.pi) + (z**2)) * x_mask, [1, 2]) - logdet_tot
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return nll + logq # [b]
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else:
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flows = list(reversed(self.flows))
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flows = flows[:-2] + [flows[-1]] # remove a useless vflow
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z = (
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torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype)
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* noise_scale
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)
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z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
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for flow in flows:
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z = flow(z, x_mask, g=x, reverse=reverse)
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z0, z1 = torch.split(z, [1, 1], 1)
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@@ -137,9 +115,7 @@ class StochasticDurationPredictor(nn.Module):
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class DurationPredictor(nn.Module):
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def __init__(
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self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
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):
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def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
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super().__init__()
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self.in_channels = in_channels
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@@ -149,13 +125,9 @@ class DurationPredictor(nn.Module):
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self.gin_channels = gin_channels
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self.drop = nn.Dropout(p_dropout)
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self.conv_1 = nn.Conv1d(
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in_channels, filter_channels, kernel_size, padding=kernel_size // 2
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)
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self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
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self.norm_1 = modules.LayerNorm(filter_channels)
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self.conv_2 = nn.Conv1d(
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filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
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)
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self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
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self.norm_2 = modules.LayerNorm(filter_channels)
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self.proj = nn.Conv1d(filter_channels, 1, 1)
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@@ -190,7 +162,7 @@ class TextEncoder(nn.Module):
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kernel_size,
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p_dropout,
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latent_channels=192,
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version = "v2",
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version="v2",
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):
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super().__init__()
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self.out_channels = out_channels
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@@ -237,26 +209,22 @@ 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, y_lengths, text, text_lengths, ge, speed=1,test=None):
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y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(
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y.dtype
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)
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def forward(self, y, y_lengths, text, text_lengths, ge, speed=1, test=None):
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y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(y.dtype)
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y = self.ssl_proj(y * y_mask) * y_mask
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y = self.encoder_ssl(y * y_mask, y_mask)
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text_mask = torch.unsqueeze(
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commons.sequence_mask(text_lengths, text.size(1)), 1
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).to(y.dtype)
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text_mask = torch.unsqueeze(commons.sequence_mask(text_lengths, text.size(1)), 1).to(y.dtype)
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if test == 1:
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text[:, :] = 0
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text = self.text_embedding(text).transpose(1, 2)
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text = self.encoder_text(text * text_mask, text_mask)
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y = self.mrte(y, y_mask, text, text_mask, ge)
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y = self.encoder2(y * y_mask, y_mask)
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if(speed!=1):
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y = F.interpolate(y, size=int(y.shape[-1] / speed)+1, mode="linear")
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if speed != 1:
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y = F.interpolate(y, size=int(y.shape[-1] / speed) + 1, mode="linear")
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y_mask = F.interpolate(y_mask, size=y.shape[-1], mode="nearest")
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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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@@ -360,9 +328,7 @@ class PosteriorEncoder(nn.Module):
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def forward(self, x, x_lengths, g=None):
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if g != None:
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g = g.detach()
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x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
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x.dtype
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)
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x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
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x = self.pre(x) * x_mask
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x = self.enc(x, x_mask, g=g)
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stats = self.proj(x) * x_mask
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@@ -372,14 +338,9 @@ class PosteriorEncoder(nn.Module):
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class Encoder(nn.Module):
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def __init__(self,
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in_channels,
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out_channels,
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hidden_channels,
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kernel_size,
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dilation_rate,
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n_layers,
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gin_channels=0):
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def __init__(
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self, in_channels, out_channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0
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):
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super().__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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@@ -394,7 +355,7 @@ class Encoder(nn.Module):
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self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
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def forward(self, x, x_lengths, g=None):
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if(g!=None):
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if g != None:
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g = g.detach()
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x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
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x = self.pre(x) * x_mask
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@@ -402,6 +363,7 @@ class Encoder(nn.Module):
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stats = self.proj(x) * x_mask
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return stats, x_mask
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class WNEncoder(nn.Module):
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def __init__(
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self,
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@@ -434,9 +396,7 @@ class WNEncoder(nn.Module):
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self.norm = modules.LayerNorm(out_channels)
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def forward(self, x, x_lengths, g=None):
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x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
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x.dtype
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)
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x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
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x = self.pre(x) * x_mask
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x = self.enc(x, x_mask, g=g)
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out = self.proj(x) * x_mask
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@@ -459,9 +419,7 @@ class Generator(torch.nn.Module):
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super(Generator, self).__init__()
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self.num_kernels = len(resblock_kernel_sizes)
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self.num_upsamples = len(upsample_rates)
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self.conv_pre = Conv1d(
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initial_channel, upsample_initial_channel, 7, 1, padding=3
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)
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self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
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resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
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self.ups = nn.ModuleList()
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@@ -481,9 +439,7 @@ class Generator(torch.nn.Module):
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self.resblocks = nn.ModuleList()
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for i in range(len(self.ups)):
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ch = upsample_initial_channel // (2 ** (i + 1))
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for j, (k, d) in enumerate(
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zip(resblock_kernel_sizes, resblock_dilation_sizes)
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):
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for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
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self.resblocks.append(resblock(ch, k, d))
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self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
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@@ -636,9 +592,7 @@ class MultiPeriodDiscriminator(torch.nn.Module):
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periods = [2, 3, 5, 7, 11]
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discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
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discs = discs + [
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DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
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]
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discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
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self.discriminators = nn.ModuleList(discs)
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def forward(self, y, y_hat):
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@@ -738,10 +692,7 @@ class Quantizer(torch.nn.Module):
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super(Quantizer, self).__init__()
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assert embed_dim % n_code_groups == 0
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self.quantizer_modules = nn.ModuleList(
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[
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Quantizer_module(n_codes, embed_dim // n_code_groups)
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for _ in range(n_code_groups)
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]
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[Quantizer_module(n_codes, embed_dim // n_code_groups) for _ in range(n_code_groups)]
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)
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self.n_code_groups = n_code_groups
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self.embed_dim = embed_dim
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@@ -759,9 +710,7 @@ class Quantizer(torch.nn.Module):
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z_q.append(_z_q)
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min_indicies.append(_min_indicies) # B * T,
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z_q = torch.cat(z_q, -1).reshape(xin.shape)
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loss = 0.25 * torch.mean((z_q.detach() - xin) ** 2) + torch.mean(
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(z_q - xin.detach()) ** 2
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)
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loss = 0.25 * torch.mean((z_q.detach() - xin) ** 2) + torch.mean((z_q - xin.detach()) ** 2)
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z_q = xin + (z_q - xin).detach()
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z_q = z_q.transpose(1, 2)
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codes = torch.stack(min_indicies, -1).reshape(B, T, self.n_code_groups)
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@@ -801,13 +750,9 @@ class CodePredictor(nn.Module):
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self.p_dropout = p_dropout
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self.vq_proj = nn.Conv1d(ssl_dim, hidden_channels, 1)
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self.ref_enc = modules.MelStyleEncoder(
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ssl_dim, style_vector_dim=hidden_channels
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)
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self.ref_enc = modules.MelStyleEncoder(ssl_dim, style_vector_dim=hidden_channels)
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self.encoder = attentions.Encoder(
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hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
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)
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self.encoder = attentions.Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout)
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self.out_proj = nn.Conv1d(hidden_channels, (n_q - 1) * dims, 1)
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self.n_q = n_q
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@@ -820,9 +765,7 @@ class CodePredictor(nn.Module):
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x = x + g
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x = self.encoder(x * x_mask, x_mask)
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x = self.out_proj(x * x_mask) * x_mask
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logits = x.reshape(x.shape[0], self.n_q - 1, self.dims, x.shape[-1]).transpose(
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2, 3
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)
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logits = x.reshape(x.shape[0], self.n_q - 1, self.dims, x.shape[-1]).transpose(2, 3)
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target = codes[1:].transpose(0, 1)
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if not infer:
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logits = logits.reshape(-1, self.dims)
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@@ -870,8 +813,8 @@ class SynthesizerTrn(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 = "v2",
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**kwargs
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version="v2",
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**kwargs,
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):
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super().__init__()
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self.spec_channels = spec_channels
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@@ -902,7 +845,7 @@ class SynthesizerTrn(nn.Module):
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n_layers,
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kernel_size,
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p_dropout,
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version = version,
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version=version,
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)
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self.dec = Generator(
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inter_channels,
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@@ -923,12 +866,10 @@ class SynthesizerTrn(nn.Module):
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16,
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gin_channels=gin_channels,
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)
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self.flow = ResidualCouplingBlock(
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inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels
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)
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self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
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# self.version=os.environ.get("version","v1")
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if(self.version=="v1"):
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if self.version == "v1":
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self.ref_enc = modules.MelStyleEncoder(spec_channels, style_vector_dim=gin_channels)
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else:
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self.ref_enc = modules.MelStyleEncoder(704, style_vector_dim=gin_channels)
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@@ -945,13 +886,11 @@ class SynthesizerTrn(nn.Module):
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self.freeze_quantizer = freeze_quantizer
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def forward(self, ssl, y, y_lengths, text, text_lengths):
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y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(
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y.dtype
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)
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if(self.version=="v1"):
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y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(y.dtype)
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if self.version == "v1":
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ge = self.ref_enc(y * y_mask, y_mask)
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else:
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ge = self.ref_enc(y[:,:704] * y_mask, y_mask)
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ge = self.ref_enc(y[:, :704] * y_mask, y_mask)
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with autocast(enabled=False):
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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:
|
||||
@@ -959,24 +898,16 @@ class SynthesizerTrn(nn.Module):
|
||||
self.ssl_proj.eval()
|
||||
self.quantizer.eval()
|
||||
ssl = self.ssl_proj(ssl)
|
||||
quantized, codes, commit_loss, quantized_list = self.quantizer(
|
||||
ssl, layers=[0]
|
||||
)
|
||||
quantized, codes, commit_loss, quantized_list = self.quantizer(ssl, layers=[0])
|
||||
|
||||
if self.semantic_frame_rate == "25hz":
|
||||
quantized = F.interpolate(
|
||||
quantized, size=int(quantized.shape[-1] * 2), mode="nearest"
|
||||
)
|
||||
quantized = F.interpolate(quantized, size=int(quantized.shape[-1] * 2), mode="nearest")
|
||||
|
||||
x, m_p, logs_p, y_mask = self.enc_p(
|
||||
quantized, y_lengths, text, text_lengths, ge
|
||||
)
|
||||
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
|
||||
)
|
||||
z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
|
||||
o = self.dec(z_slice, g=ge)
|
||||
return (
|
||||
o,
|
||||
@@ -989,24 +920,18 @@ class SynthesizerTrn(nn.Module):
|
||||
)
|
||||
|
||||
def infer(self, ssl, y, y_lengths, text, text_lengths, test=None, noise_scale=0.5):
|
||||
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(
|
||||
y.dtype
|
||||
)
|
||||
if(self.version=="v1"):
|
||||
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(y.dtype)
|
||||
if self.version == "v1":
|
||||
ge = self.ref_enc(y * y_mask, y_mask)
|
||||
else:
|
||||
ge = self.ref_enc(y[:,:704] * y_mask, y_mask)
|
||||
ge = self.ref_enc(y[:, :704] * y_mask, y_mask)
|
||||
|
||||
ssl = self.ssl_proj(ssl)
|
||||
quantized, codes, commit_loss, _ = self.quantizer(ssl, layers=[0])
|
||||
if self.semantic_frame_rate == "25hz":
|
||||
quantized = F.interpolate(
|
||||
quantized, size=int(quantized.shape[-1] * 2), mode="nearest"
|
||||
)
|
||||
quantized = F.interpolate(quantized, size=int(quantized.shape[-1] * 2), mode="nearest")
|
||||
|
||||
x, m_p, logs_p, y_mask = self.enc_p(
|
||||
quantized, y_lengths, text, text_lengths, ge, test=test
|
||||
)
|
||||
x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge, test=test)
|
||||
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
|
||||
|
||||
z = self.flow(z_p, y_mask, g=ge, reverse=True)
|
||||
@@ -1015,39 +940,34 @@ class SynthesizerTrn(nn.Module):
|
||||
return o, y_mask, (z, z_p, m_p, logs_p)
|
||||
|
||||
@torch.no_grad()
|
||||
def decode(self, codes, text, refer, noise_scale=0.5,speed=1):
|
||||
def decode(self, codes, text, refer, noise_scale=0.5, speed=1):
|
||||
def get_ge(refer):
|
||||
ge = None
|
||||
if refer is not 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)
|
||||
if (self.version == "v1"):
|
||||
refer_mask = torch.unsqueeze(commons.sequence_mask(refer_lengths, refer.size(2)), 1).to(refer.dtype)
|
||||
if self.version == "v1":
|
||||
ge = self.ref_enc(refer * refer_mask, refer_mask)
|
||||
else:
|
||||
ge = self.ref_enc(refer[:, :704] * refer_mask, refer_mask)
|
||||
return ge
|
||||
if(type(refer)==list):
|
||||
ges=[]
|
||||
|
||||
if type(refer) == list:
|
||||
ges = []
|
||||
for _refer in refer:
|
||||
ge=get_ge(_refer)
|
||||
ge = get_ge(_refer)
|
||||
ges.append(ge)
|
||||
ge=torch.stack(ges,0).mean(0)
|
||||
ge = torch.stack(ges, 0).mean(0)
|
||||
else:
|
||||
ge=get_ge(refer)
|
||||
ge = get_ge(refer)
|
||||
|
||||
y_lengths = torch.LongTensor([codes.size(2) * 2]).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, size=int(quantized.shape[-1] * 2), mode="nearest"
|
||||
)
|
||||
x, m_p, logs_p, y_mask = self.enc_p(
|
||||
quantized, y_lengths, text, text_lengths, ge,speed
|
||||
)
|
||||
quantized = F.interpolate(quantized, size=int(quantized.shape[-1] * 2), mode="nearest")
|
||||
x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge, speed)
|
||||
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
|
||||
|
||||
z = self.flow(z_p, y_mask, g=ge, reverse=True)
|
||||
@@ -1059,11 +979,10 @@ class SynthesizerTrn(nn.Module):
|
||||
ssl = self.ssl_proj(x)
|
||||
quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
|
||||
return codes.transpose(0, 1)
|
||||
|
||||
|
||||
class CFM(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,dit
|
||||
):
|
||||
def __init__(self, in_channels, dit):
|
||||
super().__init__()
|
||||
self.sigma_min = 1e-6
|
||||
|
||||
@@ -1077,41 +996,54 @@ class CFM(torch.nn.Module):
|
||||
def inference(self, mu, x_lens, prompt, n_timesteps, temperature=1.0, inference_cfg_rate=0):
|
||||
"""Forward diffusion"""
|
||||
B, T = mu.size(0), mu.size(1)
|
||||
x = torch.randn([B, self.in_channels, T], device=mu.device,dtype=mu.dtype) * temperature
|
||||
x = torch.randn([B, self.in_channels, T], device=mu.device, dtype=mu.dtype) * temperature
|
||||
prompt_len = prompt.size(-1)
|
||||
prompt_x = torch.zeros_like(x,dtype=mu.dtype)
|
||||
prompt_x = torch.zeros_like(x, dtype=mu.dtype)
|
||||
prompt_x[..., :prompt_len] = prompt[..., :prompt_len]
|
||||
x[..., :prompt_len] = 0
|
||||
mu=mu.transpose(2,1)
|
||||
mu = mu.transpose(2, 1)
|
||||
t = 0
|
||||
d = 1 / n_timesteps
|
||||
for j in range(n_timesteps):
|
||||
t_tensor = torch.ones(x.shape[0], device=x.device,dtype=mu.dtype) * t
|
||||
d_tensor = torch.ones(x.shape[0], device=x.device,dtype=mu.dtype) * d
|
||||
t_tensor = torch.ones(x.shape[0], device=x.device, dtype=mu.dtype) * t
|
||||
d_tensor = torch.ones(x.shape[0], device=x.device, dtype=mu.dtype) * d
|
||||
# v_pred = model(x, t_tensor, d_tensor, **extra_args)
|
||||
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)
|
||||
if inference_cfg_rate>1e-5:
|
||||
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)
|
||||
v_pred=v_pred+(v_pred-neg)*inference_cfg_rate
|
||||
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)
|
||||
if inference_cfg_rate > 1e-5:
|
||||
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)
|
||||
v_pred = v_pred + (v_pred - neg) * inference_cfg_rate
|
||||
x = x + d * v_pred
|
||||
t = t + d
|
||||
x[:, :, :prompt_len] = 0
|
||||
return x
|
||||
|
||||
def forward(self, x1, x_lens, prompt_lens, mu, use_grad_ckpt):
|
||||
b, _, t = x1.shape
|
||||
t = torch.rand([b], device=mu.device, dtype=x1.dtype)
|
||||
x0 = torch.randn_like(x1,device=mu.device)
|
||||
x0 = torch.randn_like(x1, device=mu.device)
|
||||
vt = x1 - x0
|
||||
xt = x0 + t[:, None, None] * vt
|
||||
dt = torch.zeros_like(t,device=mu.device)
|
||||
dt = torch.zeros_like(t, device=mu.device)
|
||||
prompt = torch.zeros_like(x1)
|
||||
for i in range(b):
|
||||
prompt[i, :, :prompt_lens[i]] = x1[i, :, :prompt_lens[i]]
|
||||
xt[i, :, :prompt_lens[i]] = 0
|
||||
gailv=0.3# if ttime()>1736250488 else 0.1
|
||||
prompt[i, :, : prompt_lens[i]] = x1[i, :, : prompt_lens[i]]
|
||||
xt[i, :, : prompt_lens[i]] = 0
|
||||
gailv = 0.3 # if ttime()>1736250488 else 0.1
|
||||
if random.random() < gailv:
|
||||
base = torch.randint(2, 8, (t.shape[0],), device=mu.device)
|
||||
d = 1/torch.pow(2, base)
|
||||
d = 1 / torch.pow(2, base)
|
||||
d_input = d.clone()
|
||||
d_input[d_input < 1e-2] = 0
|
||||
# with torch.no_grad():
|
||||
@@ -1119,52 +1051,55 @@ class CFM(torch.nn.Module):
|
||||
# v_pred_1 = self.diffusion(xt, t, d_input, cond=conditioning).detach()
|
||||
x_mid = xt + d[:, None, None] * v_pred_1
|
||||
# v_pred_2 = self.diffusion(x_mid, t+d, d_input, cond=conditioning).detach()
|
||||
v_pred_2 = self.estimator(x_mid, prompt, x_lens, t+d, d_input, mu, use_grad_ckpt).transpose(2, 1).detach()
|
||||
v_pred_2 = self.estimator(x_mid, prompt, x_lens, t + d, d_input, mu, use_grad_ckpt).transpose(2, 1).detach()
|
||||
vt = (v_pred_1 + v_pred_2) / 2
|
||||
vt = vt.detach()
|
||||
dt = 2*d
|
||||
dt = 2 * d
|
||||
|
||||
vt_pred = self.estimator(xt, prompt, x_lens, t,dt, mu, use_grad_ckpt).transpose(2,1)
|
||||
vt_pred = self.estimator(xt, prompt, x_lens, t, dt, mu, use_grad_ckpt).transpose(2, 1)
|
||||
loss = 0
|
||||
for i in range(b):
|
||||
loss += self.criterion(vt_pred[i, :, prompt_lens[i]:x_lens[i]], vt[i, :, prompt_lens[i]:x_lens[i]])
|
||||
loss += self.criterion(vt_pred[i, :, prompt_lens[i] : x_lens[i]], vt[i, :, prompt_lens[i] : x_lens[i]])
|
||||
loss /= b
|
||||
|
||||
return loss
|
||||
|
||||
|
||||
def set_no_grad(net_g):
|
||||
for name, param in net_g.named_parameters():
|
||||
param.requires_grad=False
|
||||
param.requires_grad = False
|
||||
|
||||
|
||||
class SynthesizerTrnV3(nn.Module):
|
||||
"""
|
||||
Synthesizer for Training
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
spec_channels,
|
||||
segment_size,
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
resblock,
|
||||
resblock_kernel_sizes,
|
||||
resblock_dilation_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
upsample_kernel_sizes,
|
||||
n_speakers=0,
|
||||
gin_channels=0,
|
||||
use_sdp=True,
|
||||
semantic_frame_rate=None,
|
||||
freeze_quantizer=None,
|
||||
version="v3",
|
||||
**kwargs):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
spec_channels,
|
||||
segment_size,
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
resblock,
|
||||
resblock_kernel_sizes,
|
||||
resblock_dilation_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
upsample_kernel_sizes,
|
||||
n_speakers=0,
|
||||
gin_channels=0,
|
||||
use_sdp=True,
|
||||
semantic_frame_rate=None,
|
||||
freeze_quantizer=None,
|
||||
version="v3",
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__()
|
||||
self.spec_channels = spec_channels
|
||||
self.inter_channels = inter_channels
|
||||
@@ -1185,132 +1120,133 @@ class SynthesizerTrnV3(nn.Module):
|
||||
self.gin_channels = gin_channels
|
||||
self.version = version
|
||||
|
||||
self.model_dim=512
|
||||
self.model_dim = 512
|
||||
self.use_sdp = use_sdp
|
||||
self.enc_p = TextEncoder(inter_channels,hidden_channels,filter_channels,n_heads,n_layers,kernel_size,p_dropout)
|
||||
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.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"]
|
||||
assert semantic_frame_rate in ["25hz", "50hz"]
|
||||
self.semantic_frame_rate = semantic_frame_rate
|
||||
if semantic_frame_rate == '25hz':
|
||||
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
|
||||
if self.freeze_quantizer==True:
|
||||
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
|
||||
if self.freeze_quantizer == True:
|
||||
set_no_grad(self.ssl_proj)
|
||||
set_no_grad(self.quantizer)
|
||||
set_no_grad(self.enc_p)
|
||||
|
||||
def forward(self, ssl, y, mel,ssl_lengths,y_lengths, text, text_lengths,mel_lengths, use_grad_ckpt):#ssl_lengths no need now
|
||||
def forward(
|
||||
self, ssl, y, mel, ssl_lengths, y_lengths, text, text_lengths, mel_lengths, use_grad_ckpt
|
||||
): # 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[:, :704] * y_mask, y_mask)
|
||||
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.ssl_proj.eval() #
|
||||
self.quantizer.eval()
|
||||
self.enc_p.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
|
||||
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)
|
||||
fea=self.bridge(x)
|
||||
fea = F.interpolate(fea, scale_factor=1.875, mode="nearest")##BCT
|
||||
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.
|
||||
B=ssl.shape[0]
|
||||
prompt_len_max = mel_lengths*2/3
|
||||
fea = self.bridge(x)
|
||||
fea = F.interpolate(fea, scale_factor=1.875, mode="nearest") ##BCT
|
||||
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.
|
||||
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, use_grad_ckpt)
|
||||
minn = min(mel.shape[-1], fea.shape[-1])
|
||||
mel = mel[:, :, :minn]
|
||||
fea = fea[:, :, :minn]
|
||||
cfm_loss = self.cfm(mel, mel_lengths, prompt_len, fea, use_grad_ckpt)
|
||||
return cfm_loss
|
||||
|
||||
@torch.no_grad()
|
||||
def decode_encp(self, codes,text, refer,ge=None,speed=1):
|
||||
def decode_encp(self, codes, text, refer, ge=None, speed=1):
|
||||
# print(2333333,refer.shape)
|
||||
# ge=None
|
||||
if(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)
|
||||
if speed==1:
|
||||
sizee=int(codes.size(2)*2.5*1.5)
|
||||
ge = self.ref_enc(refer[:, :704] * refer_mask, refer_mask)
|
||||
y_lengths = torch.LongTensor([int(codes.size(2) * 2)]).to(codes.device)
|
||||
if speed == 1:
|
||||
sizee = int(codes.size(2) * 2.5 * 1.5)
|
||||
else:
|
||||
sizee=int(codes.size(2)*2.5*1.5/speed)+1
|
||||
sizee = int(codes.size(2) * 2.5 * 1.5 / speed) + 1
|
||||
y_lengths1 = torch.LongTensor([sizee]).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,speed)
|
||||
fea=self.bridge(x)
|
||||
fea = F.interpolate(fea, scale_factor=1.875, mode="nearest")##BCT
|
||||
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, speed)
|
||||
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
|
||||
return fea, ge
|
||||
|
||||
def extract_latent(self, x):
|
||||
ssl = self.ssl_proj(x)
|
||||
ssl = self.ssl_proj(x)
|
||||
quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
|
||||
return codes.transpose(0,1)
|
||||
return codes.transpose(0, 1)
|
||||
|
||||
|
||||
class SynthesizerTrnV3b(nn.Module):
|
||||
"""
|
||||
Synthesizer for Training
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
spec_channels,
|
||||
segment_size,
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
resblock,
|
||||
resblock_kernel_sizes,
|
||||
resblock_dilation_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
upsample_kernel_sizes,
|
||||
n_speakers=0,
|
||||
gin_channels=0,
|
||||
use_sdp=True,
|
||||
semantic_frame_rate=None,
|
||||
freeze_quantizer=None,
|
||||
**kwargs):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
spec_channels,
|
||||
segment_size,
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
resblock,
|
||||
resblock_kernel_sizes,
|
||||
resblock_dilation_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
upsample_kernel_sizes,
|
||||
n_speakers=0,
|
||||
gin_channels=0,
|
||||
use_sdp=True,
|
||||
semantic_frame_rate=None,
|
||||
freeze_quantizer=None,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__()
|
||||
self.spec_channels = spec_channels
|
||||
self.inter_channels = inter_channels
|
||||
@@ -1330,47 +1266,52 @@ class SynthesizerTrnV3b(nn.Module):
|
||||
self.n_speakers = n_speakers
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.model_dim=512
|
||||
self.model_dim = 512
|
||||
self.use_sdp = use_sdp
|
||||
self.enc_p = TextEncoder(inter_channels,hidden_channels,filter_channels,n_heads,n_layers,kernel_size,p_dropout)
|
||||
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.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"]
|
||||
assert semantic_frame_rate in ["25hz", "50hz"]
|
||||
self.semantic_frame_rate = semantic_frame_rate
|
||||
if semantic_frame_rate == '25hz':
|
||||
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
|
||||
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
|
||||
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
|
||||
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[:, :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()
|
||||
@@ -1379,51 +1320,59 @@ class SynthesizerTrnV3b(nn.Module):
|
||||
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
|
||||
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 = 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
|
||||
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):
|
||||
def decode_encp(self, codes, text, refer, ge=None):
|
||||
# print(2333333,refer.shape)
|
||||
# ge=None
|
||||
if(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)
|
||||
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
|
||||
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
|
||||
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
|
||||
return fea, ge
|
||||
|
||||
def extract_latent(self, x):
|
||||
ssl = self.ssl_proj(x)
|
||||
ssl = self.ssl_proj(x)
|
||||
quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
|
||||
return codes.transpose(0,1)
|
||||
return codes.transpose(0, 1)
|
||||
|
||||
Reference in New Issue
Block a user