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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# This code is modified from https://github.com/svc-develop-team/so-vits-svc/blob/4.1-Stable/models.py
import copy
import torch
from torch import nn
from torch.nn import functional as F
from utils.util import *
from modules.transformer.attentions import Encoder
from models.tts.vits.vits import ResidualCouplingBlock, PosteriorEncoder
from models.vocoders.gan.generator.bigvgan import BigVGAN
from models.vocoders.gan.generator.hifigan import HiFiGAN
from models.vocoders.gan.generator.nsfhifigan import NSFHiFiGAN
from models.vocoders.gan.generator.melgan import MelGAN
from models.vocoders.gan.generator.apnet import APNet
from modules.encoder.condition_encoder import ConditionEncoder
def slice_pitch_segments(x, ids_str, segment_size=4):
ret = torch.zeros_like(x[:, :segment_size])
for i in range(x.size(0)):
idx_str = ids_str[i]
idx_end = idx_str + segment_size
ret[i] = x[i, idx_str:idx_end]
return ret
def rand_slice_segments_with_pitch(x, pitch, x_lengths=None, segment_size=4):
b, d, t = x.size()
if x_lengths is None:
x_lengths = t
ids_str_max = x_lengths - segment_size + 1
ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
ret = slice_segments(x, ids_str, segment_size)
ret_pitch = slice_pitch_segments(pitch, ids_str, segment_size)
return ret, ret_pitch, ids_str
class ContentEncoder(nn.Module):
def __init__(
self,
out_channels,
hidden_channels,
kernel_size,
n_layers,
gin_channels=0,
filter_channels=None,
n_heads=None,
p_dropout=None,
):
super().__init__()
self.out_channels = out_channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.n_layers = n_layers
self.gin_channels = gin_channels
self.f0_emb = nn.Embedding(256, hidden_channels)
self.enc_ = Encoder(
hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
)
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
# condition_encoder ver.
def forward(self, x, x_mask, noice_scale=1):
x = self.enc_(x * x_mask, x_mask)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
z = (m + torch.randn_like(m) * torch.exp(logs) * noice_scale) * x_mask
return z, m, logs, x_mask
class SynthesizerTrn(nn.Module):
"""
Synthesizer for Training
"""
def __init__(self, spec_channels, segment_size, cfg):
super().__init__()
self.spec_channels = spec_channels
self.segment_size = segment_size
self.cfg = cfg
self.inter_channels = cfg.model.vits.inter_channels
self.hidden_channels = cfg.model.vits.hidden_channels
self.filter_channels = cfg.model.vits.filter_channels
self.n_heads = cfg.model.vits.n_heads
self.n_layers = cfg.model.vits.n_layers
self.kernel_size = cfg.model.vits.kernel_size
self.p_dropout = cfg.model.vits.p_dropout
self.n_flow_layer = cfg.model.vits.n_flow_layer
self.gin_channels = cfg.model.vits.gin_channels
self.n_speakers = cfg.model.vits.n_speakers
# f0
self.n_bins = cfg.preprocess.pitch_bin
self.f0_min = cfg.preprocess.f0_min
self.f0_max = cfg.preprocess.f0_max
# TODO: sort out the config
self.cfg.model.condition_encoder.f0_min = self.cfg.preprocess.f0_min
self.cfg.model.condition_encoder.f0_max = self.cfg.preprocess.f0_max
self.condition_encoder = ConditionEncoder(self.cfg.model.condition_encoder)
self.emb_g = nn.Embedding(self.n_speakers, self.gin_channels)
self.enc_p = ContentEncoder(
self.inter_channels,
self.hidden_channels,
filter_channels=self.filter_channels,
n_heads=self.n_heads,
n_layers=self.n_layers,
kernel_size=self.kernel_size,
p_dropout=self.p_dropout,
)
assert cfg.model.generator in [
"bigvgan",
"hifigan",
"melgan",
"nsfhifigan",
"apnet",
]
self.dec_name = cfg.model.generator
temp_cfg = copy.deepcopy(cfg)
temp_cfg.preprocess.n_mel = self.inter_channels
if cfg.model.generator == "bigvgan":
temp_cfg.model.bigvgan = cfg.model.generator_config.bigvgan
self.dec = BigVGAN(temp_cfg)
elif cfg.model.generator == "hifigan":
temp_cfg.model.hifigan = cfg.model.generator_config.hifigan
self.dec = HiFiGAN(temp_cfg)
elif cfg.model.generator == "melgan":
temp_cfg.model.melgan = cfg.model.generator_config.melgan
self.dec = MelGAN(temp_cfg)
elif cfg.model.generator == "nsfhifigan":
temp_cfg.model.nsfhifigan = cfg.model.generator_config.nsfhifigan
self.dec = NSFHiFiGAN(temp_cfg) # TODO: nsf need f0
elif cfg.model.generator == "apnet":
temp_cfg.model.apnet = cfg.model.generator_config.apnet
self.dec = APNet(temp_cfg)
self.enc_q = PosteriorEncoder(
self.spec_channels,
self.inter_channels,
self.hidden_channels,
5,
1,
16,
gin_channels=self.gin_channels,
)
self.flow = ResidualCouplingBlock(
self.inter_channels,
self.hidden_channels,
5,
1,
self.n_flow_layer,
gin_channels=self.gin_channels,
)
def forward(self, data):
"""VitsSVC forward function.
Args:
data (dict): condition data & audio data, including:
B: batch size, T: target length
{
"spk_id": [B, singer_table_size]
"target_len": [B]
"mask": [B, T, 1]
"mel": [B, T, n_mel]
"linear": [B, T, n_fft // 2 + 1]
"frame_pitch": [B, T]
"frame_uv": [B, T]
"audio": [B, audio_len]
"audio_len": [B]
"contentvec_feat": [B, T, contentvec_dim]
"whisper_feat": [B, T, whisper_dim]
...
}
"""
# TODO: elegantly handle the dimensions
spec = data["linear"].transpose(1, 2)
g = data["spk_id"]
g = self.emb_g(g).transpose(1, 2)
c_lengths = data["target_len"]
spec_lengths = data["target_len"]
f0 = data["frame_pitch"]
# condition_encoder ver.
x = self.condition_encoder(data).transpose(1, 2)
x_mask = torch.unsqueeze(sequence_mask(c_lengths, f0.size(1)), 1).to(x.dtype)
# prior encoder
z_ptemp, m_p, logs_p, _ = self.enc_p(x, x_mask)
# posterior encoder
z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g)
# flow
z_p = self.flow(z, spec_mask, g=g)
z_slice, pitch_slice, ids_slice = rand_slice_segments_with_pitch(
z, f0, spec_lengths, self.segment_size
)
if self.dec_name == "nsfhifigan":
o = self.dec(z_slice, f0=f0.float())
elif self.dec_name == "apnet":
_, _, _, _, o = self.dec(z_slice)
else:
o = self.dec(z_slice)
outputs = {
"y_hat": o,
"ids_slice": ids_slice,
"x_mask": x_mask,
"z_mask": data["mask"].transpose(1, 2),
"z": z,
"z_p": z_p,
"m_p": m_p,
"logs_p": logs_p,
"m_q": m_q,
"logs_q": logs_q,
}
return outputs
@torch.no_grad()
def infer(self, data, noise_scale=0.35, seed=52468):
# c, f0, uv, g
f0 = data["frame_pitch"]
g = data["spk_id"]
if f0.device == torch.device("cuda"):
torch.cuda.manual_seed_all(seed)
else:
torch.manual_seed(seed)
c_lengths = (torch.ones(f0.size(0)) * f0.size(-1)).to(f0.device)
if g.dim() == 1:
g = g.unsqueeze(0)
g = self.emb_g(g).transpose(1, 2)
# condition_encoder ver.
x = self.condition_encoder(data).transpose(1, 2)
x_mask = torch.unsqueeze(sequence_mask(c_lengths, f0.size(1)), 1).to(x.dtype)
z_p, m_p, logs_p, c_mask = self.enc_p(x, x_mask, noice_scale=noise_scale)
z = self.flow(z_p, c_mask, g=g, reverse=True)
if self.dec_name == "nsfhifigan":
o = self.dec(z * c_mask, f0=f0.float())
elif self.dec_name == "apnet":
_, _, _, _, o = self.dec(z * c_mask)
else:
o = self.dec(z * c_mask)
return o, f0
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