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diff --git a/AIMeiSheng/docker_demo/Dockerfile b/AIMeiSheng/docker_demo/Dockerfile
index 1f2806d..5e2bd37 100644
--- a/AIMeiSheng/docker_demo/Dockerfile
+++ b/AIMeiSheng/docker_demo/Dockerfile
@@ -1,28 +1,28 @@
# 系统版本 CUDA Version 11.8.0
# NAME="CentOS Linux" VERSION="7 (Core)"
# FROM starmaker.tencentcloudcr.com/starmaker/av/av:1.1
# 基础镜像, python3.9,cuda118,centos7，外加ffmpeg
#FROM starmaker.tencentcloudcr.com/starmaker/av/av_base:1.0
FROM registry.ushow.media/av/av_base:1.0
#FROM av_base_test:1.0
RUN source /etc/profile && sed -i 's|mirrorlist=|#mirrorlist=|g' /etc/yum.repos.d/CentOS-Base.repo && sed -i 's|#baseurl=http://mirror.centos.org|baseurl=http://vault.centos.org|g' /etc/yum.repos.d/CentOS-Base.repo && yum clean all && yum install -y unzip && yum install -y libsndfile && yum install -y libsamplerate libsamplerate-devel
RUN source /etc/profile && pip3 install librosa==0.9.1 && pip3 install gradio && pip3 install torch==2.1.2 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
RUN source /etc/profile && pip3 install urllib3==1.26.15 && pip3 install coscmd && coscmd config -a AKIDoQmshFWXGitnQmrfCTYNwEExPaU6RVHm -s F9n9E2ZonWy93f04qMaYFfogHadPt62h -b log-sg-1256122840 -r ap-singapore
RUN source /etc/profile && pip3 install asteroid-filterbanks
RUN source /etc/profile && pip3 install praat-parselmouth==0.4.3
RUN source /etc/profile && pip3 install pyworld
RUN source /etc/profile && pip3 install faiss-cpu
RUN source /etc/profile && pip3 install torchcrepe
RUN source /etc/profile && pip3 install thop
RUN source /etc/profile && pip3 install ffmpeg-python
RUN source /etc/profile && pip3 install fairseq
RUN source /etc/profile && pip3 install redis==4.5.0
-RUN #source /etc/profile && pip3 install numpy=1.26.4
+RUN source /etc/profile && pip3 install numpy=1.26.4
COPY ./ /data/code/
WORKDIR /data/code
-#CMD ["/bin/bash", "-c", "source /etc/profile; export PYTHONPATH=/data/code; cd /data/code/AIMeiSheng/docker_demo; python3 offline_server.py"]
-CMD ["/bin/bash", "-c", "source /etc/profile; export PYTHONPATH=/data/code; cd /data/code/AIMeiSheng/docker_demo; python3 tmp.py"]
\ No newline at end of file
+CMD ["/bin/bash", "-c", "source /etc/profile; export PYTHONPATH=/data/code; cd /data/code/AIMeiSheng/docker_demo; python3 offline_server.py"]
+#CMD ["/bin/bash", "-c", "source /etc/profile; export PYTHONPATH=/data/code; cd /data/code/AIMeiSheng/docker_demo; python3 tmp.py"]
\ No newline at end of file
diff --git a/AIMeiSheng/docker_demo/readme.txt b/AIMeiSheng/docker_demo/readme.txt
index a03e512..bb9fc16 100644
--- a/AIMeiSheng/docker_demo/readme.txt
+++ b/AIMeiSheng/docker_demo/readme.txt
@@ -1,24 +1,24 @@
简介: ai美声功能，其核心是输入一段15-30s的人声作为音色信息，再给定输入音源，将音源转换为指定音色的声音的效果。例如，孙燕姿演唱的东风破
架构方案: http_server.py (1个) 作为服务端，接收外部传来的数据，塞入到redis中，由offline_server.py (多个服务) 进行承接
# 部署要求:
1. http_server.py 部署在sg-prod-songrefresh-gpu-7 上
2. offline_server.py 使用docker 部署在超级节点上，由运维进行控制
# http_server.py 环境要求:
pip install redis
pip install flask
# offline_server.py 环境要求(docker)
cd docker_demo目录下(例子如下):
1. docker build -f Dockerfile -t av_ai_meisheng .
(通过docker images 获取av_ai_meisheng的image_id)
2. docker run --gpus all -it -v /data/rsync/jianli.yang/av_svc:/data/code image_id # 即可启动服务
# 测试代码: docker 环境下, offline_server.py 即可验证
# http测试命令:
-curl http://127.0.0.1:5000/ai_meisheng -H "Content-Type: application/json" -d '{ "record_song_url": "http://starmaker-sg-1256122840.cos.ap-singapore.myqcloud.com/production/ai_voice/6755399445110104/f7ced5f67bcb2351a5b9a03fb8f81620-source.mp4", "target_url": "http://starmaker-sg-1256122840.cos.ap-singapore.myqcloud.com/production/ai_voice/6755399445110104/f7ced5f67bcb2351a5b9a03fb8f81620-target_test.mp4","start": 33300,"end": 208677,"vocal_loudness": -14.57,"female_recording_url": "http://starmaker-sg-1256122840.cos.ap-singapore.myqcloud.com/production/uploading/recordings_origin/4222124723437931/origin_master.mp4", "male_recording_url": "http://starmaker-sg-1256122840.cos.ap-singapore.myqcloud.com/production/uploading/recordings_origin/12666374036224383/origin_master.mp4"}'
+curl http://127.0.0.1:5000/ai_meisheng -H "Content-Type: application/json" -d "{'record_song_url': 'http://starmaker-sg-1256122840.cos.ap-singapore.myqcloud.com/production/ai_voice/10414574146376859/5940142f51165c9dfcbee4702c7df977-source.mp4', 'target_url': 'http://starmaker-sg-1256122840.cos.ap-singapore.myqcloud.com/production/ai_voice/10414574146376859/5940142f51165c9dfcbee4702c7df977-target111.mp4', 'start': 30778, 'end': 221169, 'vocal_loudness': -31.821442613813534, 'female_recording_url': 'http://songbook-starmaker-sg-1256122840.cos.ap-singapore.myqcloud.com/production/songbook/ai-voice/57edc985a8c2e59f5069bb2b77ac5eff.m4a', 'male_recording_url': 'http://songbook-starmaker-sg-1256122840.cos.ap-singapore.myqcloud.com/production/songbook/ai-voice/e24dd7772c52c61ea6cf0b6031c77235.m4a'}"
{"gender":"male","schedule":100,"status":0,"target_song_url":"https://av-audit-sync-sg-1256122840.cos.ap-singapore.myqcloud.com/dataset/AIMeiSheng/vocal_test/out.m4a"}
# 资源消耗: 显存占用约2G,但是最高能到9G, 所以，一台机器部署一个即可
注意: 通过common.py 的prod可以控制是否是线上环境
\ No newline at end of file
diff --git a/AIMeiSheng/lib/infer_pack/models_embed_in_dec_diff_control_enc_spken200x_onlyspk_double.py b/AIMeiSheng/lib/infer_pack/models_embed_in_dec_diff_control_enc_spken200x_onlyspk_double.py
index 1268f17..8348721 100644
--- a/AIMeiSheng/lib/infer_pack/models_embed_in_dec_diff_control_enc_spken200x_onlyspk_double.py
+++ b/AIMeiSheng/lib/infer_pack/models_embed_in_dec_diff_control_enc_spken200x_onlyspk_double.py
@@ -1,1301 +1,1301 @@
import math, pdb, os
from time import time as ttime
import torch
from torch import nn
from torch.nn import functional as F
from lib.infer_pack import modules
from lib.infer_pack import attentions_in_dec_double as attentions
from lib.infer_pack import commons
from lib.infer_pack.commons import init_weights, get_padding
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
from lib.infer_pack.commons import init_weights
import numpy as np
from lib.infer_pack import commons
from thop import profile
from diffuse_fang.diffUse_wraper_double import diff_decoder,ddpm_para
ddpm_dp = ddpm_para()
g2_dim = 256
class TextEncoder256(nn.Module):
def __init__(
self,
out_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
f0=True,
):
super().__init__()
self.out_channels = out_channels
self.hidden_channels = hidden_channels
self.filter_channels = filter_channels
self.n_heads = n_heads
self.n_layers = n_layers
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.emb_phone = nn.Linear(256, hidden_channels)
self.lrelu = nn.LeakyReLU(0.1, inplace=True)
if f0 == True:
self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256
self.encoder = attentions.Encoder(
hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
)
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
def forward(self, phone, pitch, lengths):
if pitch == None:
x = self.emb_phone(phone)
else:
x = self.emb_phone(phone) + self.emb_pitch(pitch)
x = x * math.sqrt(self.hidden_channels) # [b, t, h]
x = self.lrelu(x)
x = torch.transpose(x, 1, -1) # [b, h, t]
x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
x.dtype
)
x = self.encoder(x * x_mask, x_mask)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
return m, logs, x_mask
class TextEncoder768(nn.Module):
def __init__(
self,
out_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
f0=True,
):
super().__init__()
self.out_channels = out_channels
self.hidden_channels = hidden_channels
self.filter_channels = filter_channels
self.n_heads = n_heads
self.n_layers = n_layers
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.emb_phone = nn.Linear(768, hidden_channels)
self.lrelu = nn.LeakyReLU(0.1, inplace=True)
if f0 == True:
self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256
self.encoder = attentions.Encoder(
hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
)
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
#self.emb_g = nn.Linear(256, hidden_channels)
def forward(self, phone, pitch, lengths,g):#fang add
if pitch == None:
x = self.emb_phone(phone)
else:
x = self.emb_phone(phone) + self.emb_pitch(pitch) #+ self.emb_g(g)
#print("@@@x:",x.shape)
x = x * math.sqrt(self.hidden_channels) # [b, t, h]
x = self.lrelu(x)
x = torch.transpose(x, 1, -1) # [b, h, t]
#print("@@@x1:",x.shape)
x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
x.dtype
)
#x = self.encoder(x * x_mask, x_mask,g)
x = self.encoder(x * x_mask, x_mask,g)#fang add
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
return m, logs, x_mask,x
class ResidualCouplingBlock(nn.Module):
def __init__(
self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
n_flows=4,
gin_channels=0,
):
super().__init__()
self.channels = channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.dilation_rate = dilation_rate
self.n_layers = n_layers
self.n_flows = n_flows
self.gin_channels = gin_channels
self.flows = nn.ModuleList()
for i in range(n_flows):
self.flows.append(
modules.ResidualCouplingLayer(
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=gin_channels,
mean_only=True,
)
)
self.flows.append(modules.Flip())
def forward(self, x, x_mask, g=None, reverse=False):
if not reverse:
for flow in self.flows:
x, _ = flow(x, x_mask, g=g, reverse=reverse)
else:
for flow in reversed(self.flows):
x = flow(x, x_mask, g=g, reverse=reverse)
return x
def remove_weight_norm(self):
for i in range(self.n_flows):
self.flows[i * 2].remove_weight_norm()
class PosteriorEncoder(nn.Module):
def __init__(
self,
in_channels,
out_channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=0,
):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.dilation_rate = dilation_rate
self.n_layers = n_layers
self.gin_channels = gin_channels
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
self.enc = modules.WN(
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=gin_channels,
)
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
def forward(self, x, x_lengths, g=None):
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
x.dtype
)
x = self.pre(x) * x_mask
x = self.enc(x, x_mask, g=g)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)#均值和方差 fang
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask ##随机采样 fang
return z, m, logs, x_mask
def remove_weight_norm(self):
self.enc.remove_weight_norm()
class Generator(torch.nn.Module):
def __init__(
self,
initial_channel,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gin_channels=0,
):
super(Generator, self).__init__()
self.num_kernels = len(resblock_kernel_sizes)
self.num_upsamples = len(upsample_rates)
self.conv_pre = Conv1d(
initial_channel, upsample_initial_channel, 7, 1, padding=3
)
resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
self.ups.append(
weight_norm(
ConvTranspose1d(
upsample_initial_channel // (2**i),
upsample_initial_channel // (2 ** (i + 1)),
k,
u,
padding=(k - u) // 2,
)
)
)
self.resblocks = nn.ModuleList()
for i in range(len(self.ups)):
ch = upsample_initial_channel // (2 ** (i + 1))
for j, (k, d) in enumerate(
zip(resblock_kernel_sizes, resblock_dilation_sizes)
):
self.resblocks.append(resblock(ch, k, d))
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
self.ups.apply(init_weights)
if gin_channels != 0:
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
def forward(self, x, g=None):
x = self.conv_pre(x)
if g is not None:
x = x + self.cond(g)
for i in range(self.num_upsamples):
x = F.leaky_relu(x, modules.LRELU_SLOPE)
x = self.ups[i](x)
xs = None
for j in range(self.num_kernels):
if xs is None:
xs = self.resblocks[i * self.num_kernels + j](x)
else:
xs += self.resblocks[i * self.num_kernels + j](x)
x = xs / self.num_kernels
x = F.leaky_relu(x)
x = self.conv_post(x)
x = torch.tanh(x)
return x
def remove_weight_norm(self):
for l in self.ups:
remove_weight_norm(l)
for l in self.resblocks:
l.remove_weight_norm()
class SineGen(torch.nn.Module):
"""Definition of sine generator
SineGen(samp_rate, harmonic_num = 0,
sine_amp = 0.1, noise_std = 0.003,
voiced_threshold = 0,
flag_for_pulse=False)
samp_rate: sampling rate in Hz
harmonic_num: number of harmonic overtones (default 0)
sine_amp: amplitude of sine-wavefrom (default 0.1)
noise_std: std of Gaussian noise (default 0.003)
voiced_thoreshold: F0 threshold for U/V classification (default 0)
flag_for_pulse: this SinGen is used inside PulseGen (default False)
Note: when flag_for_pulse is True, the first time step of a voiced
segment is always sin(np.pi) or cos(0)
"""
def __init__(
self,
samp_rate,
harmonic_num=0,
sine_amp=0.1,
noise_std=0.003,
voiced_threshold=0,
flag_for_pulse=False,
):
super(SineGen, self).__init__()
self.sine_amp = sine_amp
self.noise_std = noise_std
self.harmonic_num = harmonic_num
self.dim = self.harmonic_num + 1
self.sampling_rate = samp_rate
self.voiced_threshold = voiced_threshold
def _f02uv(self, f0):
# generate uv signal
uv = torch.ones_like(f0)
uv = uv * (f0 > self.voiced_threshold)
return uv
def forward(self, f0, upp):
"""sine_tensor, uv = forward(f0)
input F0: tensor(batchsize=1, length, dim=1)
f0 for unvoiced steps should be 0
output sine_tensor: tensor(batchsize=1, length, dim)
output uv: tensor(batchsize=1, length, 1)
"""
with torch.no_grad():
f0 = f0[:, None].transpose(1, 2)
f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
# fundamental component
f0_buf[:, :, 0] = f0[:, :, 0]
for idx in np.arange(self.harmonic_num):
f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
idx + 2
) # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
rad_values = (f0_buf / self.sampling_rate) % 1 ###%1意味着n_har的乘积无法后处理优化
rand_ini = torch.rand(
f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
)
rand_ini[:, 0] = 0
rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
tmp_over_one = torch.cumsum(rad_values, 1) # % 1 #####%1意味着后面的cumsum无法再优化
tmp_over_one *= upp
tmp_over_one = F.interpolate(
tmp_over_one.transpose(2, 1),
scale_factor=upp,
mode="linear",
align_corners=True,
).transpose(2, 1)
rad_values = F.interpolate(
rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
).transpose(
2, 1
) #######
tmp_over_one %= 1
tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
cumsum_shift = torch.zeros_like(rad_values)
cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
sine_waves = torch.sin(
torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
)
sine_waves = sine_waves * self.sine_amp
uv = self._f02uv(f0)
uv = F.interpolate(
uv.transpose(2, 1), scale_factor=upp, mode="nearest"
).transpose(2, 1)
noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
noise = noise_amp * torch.randn_like(sine_waves)
sine_waves = sine_waves * uv + noise
return sine_waves, uv, noise
class SourceModuleHnNSF(torch.nn.Module):
"""SourceModule for hn-nsf
SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
add_noise_std=0.003, voiced_threshod=0)
sampling_rate: sampling_rate in Hz
harmonic_num: number of harmonic above F0 (default: 0)
sine_amp: amplitude of sine source signal (default: 0.1)
add_noise_std: std of additive Gaussian noise (default: 0.003)
note that amplitude of noise in unvoiced is decided
by sine_amp
voiced_threshold: threhold to set U/V given F0 (default: 0)
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
F0_sampled (batchsize, length, 1)
Sine_source (batchsize, length, 1)
noise_source (batchsize, length 1)
uv (batchsize, length, 1)
"""
def __init__(
self,
sampling_rate,
harmonic_num=0,
sine_amp=0.1,
add_noise_std=0.003,
voiced_threshod=0,
is_half=True,
):
super(SourceModuleHnNSF, self).__init__()
self.sine_amp = sine_amp
self.noise_std = add_noise_std
self.is_half = is_half
# to produce sine waveforms
self.l_sin_gen = SineGen(
sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
)
# to merge source harmonics into a single excitation
self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
self.l_tanh = torch.nn.Tanh()
def forward(self, x, upp=None):
sine_wavs, uv, _ = self.l_sin_gen(x, upp)
if self.is_half:
sine_wavs = sine_wavs.half()
sine_merge = self.l_tanh(self.l_linear(sine_wavs))
return sine_merge, None, None # noise, uv
class GeneratorNSF(torch.nn.Module):
def __init__(
self,
initial_channel,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gin_channels,
sr,
is_half=False,
):
super(GeneratorNSF, self).__init__()
self.num_kernels = len(resblock_kernel_sizes)
self.num_upsamples = len(upsample_rates)
self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
self.m_source = SourceModuleHnNSF(
sampling_rate=sr, harmonic_num=0, is_half=is_half
)
self.noise_convs = nn.ModuleList()
self.conv_pre = Conv1d(
initial_channel, upsample_initial_channel, 7, 1, padding=3
)
resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
self.ups = nn.ModuleList()
self.ups_g = nn.ModuleList()# fang add
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
c_cur = upsample_initial_channel // (2 ** (i + 1))
self.ups.append(
weight_norm(
ConvTranspose1d(
upsample_initial_channel // (2**i),
upsample_initial_channel // (2 ** (i + 1)),
k,
u,
padding=(k - u) // 2,
)
)
)
self.ups_g.append(
nn.Conv1d(upsample_initial_channel,upsample_initial_channel // (2 ** (i + 1) ), 1)
#F.interpolate(input, scale_factor=2, mode='nearest')
)# fang add
if i + 1 < len(upsample_rates):
stride_f0 = np.prod(upsample_rates[i + 1 :])
self.noise_convs.append(
Conv1d(
1,
c_cur,
kernel_size=stride_f0 * 2,
stride=stride_f0,
padding=stride_f0 // 2,
)
)
else:
self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
self.resblocks = nn.ModuleList()
for i in range(len(self.ups)):
ch = upsample_initial_channel // (2 ** (i + 1))
for j, (k, d) in enumerate(
zip(resblock_kernel_sizes, resblock_dilation_sizes)
):
self.resblocks.append(resblock(ch, k, d))
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
self.ups.apply(init_weights)
if gin_channels != 0:
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
self.upp = np.prod(upsample_rates)
def forward(self, x, f0, g=None):
har_source, noi_source, uv = self.m_source(f0, self.upp)
har_source = har_source.transpose(1, 2)
x = self.conv_pre(x)
if g is not None:
#x = x + self.cond(g) ##org
tmp_g = self.cond(g) ##fang add
x = x + tmp_g ##fang add
#print('###@@@@##x:',x.shape )
for i in range(self.num_upsamples):
x = F.leaky_relu(x, modules.LRELU_SLOPE)
x = self.ups[i](x)
x_source = self.noise_convs[i](har_source)
x = x + x_source
xg = self.ups_g[i](tmp_g) #fang add
x = x + xg #fang add
xs = None
for j in range(self.num_kernels):
if xs is None:
xs = self.resblocks[i * self.num_kernels + j](x)
else:
xs += self.resblocks[i * self.num_kernels + j](x)
x = xs / self.num_kernels
#print('@@@@##x:',x.shape)
x = F.leaky_relu(x)
x = self.conv_post(x)
x = torch.tanh(x)
return x
def remove_weight_norm(self):
for l in self.ups:
remove_weight_norm(l)
for l in self.resblocks:
l.remove_weight_norm()
sr2sr = {
"32k": 32000,
"40k": 40000,
"48k": 48000,
"24k": 24000,
}
class SynthesizerTrnMs256NSFsid(nn.Module):
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,
spk_embed_dim,
gin_channels,
sr,
**kwargs
):
super().__init__()
if type(sr) == type("strr"):
sr = sr2sr[sr]
self.spec_channels = spec_channels
self.inter_channels = inter_channels
self.hidden_channels = hidden_channels
self.filter_channels = filter_channels
self.n_heads = n_heads
self.n_layers = n_layers
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.resblock = resblock
self.resblock_kernel_sizes = resblock_kernel_sizes
self.resblock_dilation_sizes = resblock_dilation_sizes
self.upsample_rates = upsample_rates
self.upsample_initial_channel = upsample_initial_channel
self.upsample_kernel_sizes = upsample_kernel_sizes
self.segment_size = segment_size
self.gin_channels = gin_channels
# self.hop_length = hop_length#
self.spk_embed_dim = spk_embed_dim
self.enc_p = TextEncoder256(
inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
)
self.dec = GeneratorNSF(
inter_channels,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gin_channels=gin_channels,
sr=sr,
is_half=kwargs["is_half"],
)
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, 3, gin_channels=gin_channels
)
self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
def remove_weight_norm(self):
self.dec.remove_weight_norm()
self.flow.remove_weight_norm()
self.enc_q.remove_weight_norm()
def forward(
self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
): # 这里ds是id，[bs,1]
# print(1,pitch.shape)#[bs,t]
g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
#print("@@@pitch.shape: ",pitch.shape)
#g = ds.unsqueeze(-1)
m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
z_p = self.flow(z, y_mask, g=g)
z_slice, ids_slice = commons.rand_slice_segments(
z, y_lengths, self.segment_size
) #按照self.segment_size这个长度，进行随机切割z，长度固定，开始位置不同存在ids_slice中,z_slice是切割的结果， fang
# print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
# print(-2,pitchf.shape,z_slice.shape)
o = self.dec(z_slice, pitchf, g=g)
return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
def infer(self, phone, phone_lengths, pitch, nsff0, sid, rate=None):
g = self.emb_g(sid).unsqueeze(-1)
m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
if rate:
head = int(z_p.shape[2] * rate)
z_p = z_p[:, :, -head:]
x_mask = x_mask[:, :, -head:]
nsff0 = nsff0[:, -head:]
z = self.flow(z_p, x_mask, g=g, reverse=True)
print('z shape: ',z.shape)
print('x_mask shape: ',x_mask.shape)
z_x_mask = z * x_mask
print('z_x_mask shape: ',z_x_mask.shape)
print('nsff0 shape:p', nsff0.shape)
print('g shape: ',g.shape)
o = self.dec(z * x_mask, nsff0, g=g)
self.get_floats()
return o, x_mask, (z, z_p, m_p, logs_p)
def get_floats(self,):
T = 21.4 #郭宇_但愿人长久_40k.wav
z = torch.randn(1,g2_dim ,2740)# 2s data（同时用2s数据验证，整数倍就对了，防止干扰）
x_mask = torch.randn(1,1 ,2740)
g = torch.randn(1,256 ,1)
inputs_bfcc = z #z * x_mask
nsff0 = torch.randn(1, 2740)
devices = 'cuda' #'cpu'
self.dec = self.dec.to(devices).half()
inputs_bfcc , nsff0, g = inputs_bfcc.to(devices).half(), nsff0.to(devices).half(), g.to(devices).half()
flops, params = profile(self.dec, (inputs_bfcc, nsff0, g))
print(f'@@@hifi-gan nsf decflops: {flops/(T*pow(10,9))} GFLOPS, params: { params/pow(10,6)} M')
return 0
class SynthesizerTrnMs768NSFsid(nn.Module):
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,
spk_embed_dim,
gin_channels,
sr,
**kwargs
):
super().__init__()
if type(sr) == type("strr"):
sr = sr2sr[sr]
self.spec_channels = spec_channels
self.inter_channels = inter_channels
self.hidden_channels = hidden_channels
self.filter_channels = filter_channels
self.n_heads = n_heads
self.n_layers = n_layers
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.resblock = resblock
self.resblock_kernel_sizes = resblock_kernel_sizes
self.resblock_dilation_sizes = resblock_dilation_sizes
self.upsample_rates = upsample_rates
self.upsample_initial_channel = upsample_initial_channel
self.upsample_kernel_sizes = upsample_kernel_sizes
self.segment_size = segment_size
self.gin_channels = gin_channels
# self.hop_length = hop_length#
self.spk_embed_dim = spk_embed_dim
self.enc_p = TextEncoder768(
inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
)
self.dec = GeneratorNSF(
inter_channels,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gin_channels=gin_channels,
sr=sr,
is_half=kwargs["is_half"],
)
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, 3, gin_channels=gin_channels
)
#for p in self.flow.parameters():
# p.requires_grad=False
#for p in self.enc_p.parameters():
# p.requires_grad=False
self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
self.diff_decoder = diff_decoder
#self.diff_cond_g = nn.Conv1d(256,g2_dim, 1)
self.diff_cond_gx = self.zero_module(self.conv_nd(1, 256, g2_dim, 3, padding=1))
self.diff_cond_out = self.zero_module(self.conv_nd(1, g2_dim, g2_dim, 3, padding=1))
self.lzp = 0.1
self.ssl_proj = self.zero_module(nn.Conv1d(256*2, 256, 1, stride=1))
self.ssl_proj1 = self.zero_module(nn.Conv1d(256, 256, 1, stride=1))
self.ssl_proj1_norm = nn.BatchNorm1d(256)#nn.LayerNorm(256)
self.ssl_proj2 = self.zero_module(nn.Conv1d(256, 256, 1, stride=1))
self.ssl_proj2_norm = nn.BatchNorm1d(256)#nn.LayerNorm(256)
def zero_module(self,module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module
def conv_nd(self, dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
return nn.Conv3d(*args, **kwargs)
raise ValueError(f"unsupported dimensions: {dims}")
def remove_weight_norm(self):
self.dec.remove_weight_norm()
self.flow.remove_weight_norm()
self.enc_q.remove_weight_norm()
def forward(
self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
): # 这里ds是id，[bs,1]
# print(1,pitch.shape)#[bs,t]
#g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
#print("@@@@@fang@@@@@")
#print("@@@@g:",ds.size())
#g, ge = ds[0], ds[1]
g = ds.unsqueeze(-1)
#g = self.ssl_proj(g)#[:,256:,:])
g1 = self.ssl_proj1_norm( self.ssl_proj1(g[:,:256,:]))
g2 = self.ssl_proj2_norm( self.ssl_proj2(g[:,256:,:]))
g = g1 + g[:,256:,:]#+ g2
#g = g[:,:256,:] + ge
#print("@@@@g1:",g.size())
#print("g:",g.size())
#print("phone_lengths: ",phone_lengths.size())
#print("pitch: ",pitch.size())
#m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
m_p, logs_p, x_mask, x_embed = self.enc_p(phone, pitch, phone_lengths,g)#fang add
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)#self.enc_q = PosteriorEncoder ##这里面预测出了随机采样的隐变量z,m_q是均值，logs_q是方差，y_mask是mask的数据 fangi
z_p = self.flow(z, y_mask, g=g)# z是y_msk的输入
z_p_sample = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * y_mask
zx = self.flow(z_p_sample, y_mask, g=g, reverse=True)
#print("@@@@@g:",g.shape)
g_z_p = self.diff_cond_gx(g)
#print("@@@@@g_z_p:",g_z_p.shape)
z_res = z - zx
#print('#######x_embed:',x_embed.shape)
#print('#######z_p_sample:',z_p_sample.shape)
#print('#######g_z_p:',g_z_p.shape)
#z_p1 = z_p_sample + g_z_p
z_p1 = x_embed + g_z_p
###diff st
z_p_diff = z_p1.transpose(1,2) ##b,frames,feat
z_diff = z_res.transpose(1,2) ##b,frames,feat
diff_loss,_ = self.diff_decoder(z_p_diff, gt_spec=z_diff, infer=False, infer_speedup=ddpm_dp.infer_speedup, method=ddpm_dp.method, use_tqdm=ddpm_dp.use_tqdm)
#self.diff_decoder = self.diff_decoder.float()
#print("@@@z: ",z.shape)
#b = z_p_diff.shape[0]
t = 200#np.random.randint(100,1000)#200#torch.randint(0, 1000, (b,), device=g.device).long()
z_diff = zx.transpose(1,2)
z_x_diff = self.diff_decoder(z_p_diff, gt_spec=z_diff*self.lzp, infer=True, infer_speedup=ddpm_dp.infer_speedup, method=ddpm_dp.method, k_step=t, use_tqdm=False)
#print("@@@z_x: ",z_x.shape)
z1 = z_x_diff.transpose(1,2)
z1 = self.diff_cond_out(z1)
z_in = (zx + z1)
#z_p = z_p_rec.transpose(1,2)
##diff en
##oneflow
#z_p = self.flow(z, y_mask, g=g)
z_slice, ids_slice = commons.rand_slice_segments(
z_in, y_lengths, self.segment_size
)
# print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
# print(-2,pitchf.shape,z_slice.shape)
o = self.dec(z_slice, pitchf, g=g)
return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q),diff_loss
def infer(self, phone, phone_lengths, pitch, nsff0, sid, rate=None):
#g = self.emb_g(sid).unsqueeze(-1)
g = sid.unsqueeze(-1).unsqueeze(0)
g = self.ssl_proj(g)
#g1 = self.ssl_proj1_norm(g[:,:256,:])
#g2 = self.ssl_proj2_norm(g[:,256:,:])
#g1 = self.ssl_proj1_norm( self.ssl_proj1(g[:,:256,:]))
#g2 = self.ssl_proj2_norm( self.ssl_proj2(g[:,256:,:]))
#g1 = self.ssl_proj1(g[:,:256,:])
#g2 = self.ssl_proj1(g[:,:256,:])
#g = g1 + g2
#g = g[:,256:,:]#+ g2
#m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) #org
print("@@@@@@pitch:",pitch.shape,"phone:",phone.shape)
m_p, logs_p, x_mask, x_embed = self.enc_p(phone, pitch, phone_lengths,g) #fang add
z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
if rate:
head = int(z_p.shape[2] * rate)
z_p = z_p[:, :, -head:]
x_mask = x_mask[:, :, -head:]
nsff0 = nsff0[:, -head:]
z = self.flow(z_p, x_mask, g=g, reverse=True)
g_z_p = self.diff_cond_gx(g)
#z_p1 = z_p + g_z_p
z_p1 = x_embed + g_z_p
#if is_half:
#self.diff_decoder = self.diff_decoder.float()
z_p_diff = z_p1.transpose(1,2).float() ##b,frames,feat
z_diff = z.transpose(1,2) ##b,frames,feat
- print("@@z_p_diff", z_p_diff[0,0,:])
+ # print("@@z_p_diff", z_p_diff[0,0,:])
self.diff_decoder = self.diff_decoder.float()
z_x = self.diff_decoder(z_p_diff, gt_spec=z_diff*self.lzp, infer=True, infer_speedup=ddpm_dp.infer_speedup, method=ddpm_dp.method, k_step=200, use_tqdm=ddpm_dp.use_tqdm)
- print("@@z_x", z_x[0,0,:])
+ # print("@@z_x", z_x[0,0,:])
z1 = z_x.transpose(1,2).half()
z_res = self.diff_cond_out(z1)
z = z + z_res
o = self.dec(z * x_mask, nsff0, g=g)
#self.get_floats()
return o, x_mask, (z, z_p, m_p, logs_p)
class SynthesizerTrnMs256NSFsid_nono(nn.Module):
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,
spk_embed_dim,
gin_channels,
sr=None,
**kwargs
):
super().__init__()
self.spec_channels = spec_channels
self.inter_channels = inter_channels
self.hidden_channels = hidden_channels
self.filter_channels = filter_channels
self.n_heads = n_heads
self.n_layers = n_layers
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.resblock = resblock
self.resblock_kernel_sizes = resblock_kernel_sizes
self.resblock_dilation_sizes = resblock_dilation_sizes
self.upsample_rates = upsample_rates
self.upsample_initial_channel = upsample_initial_channel
self.upsample_kernel_sizes = upsample_kernel_sizes
self.segment_size = segment_size
self.gin_channels = gin_channels
# self.hop_length = hop_length#
self.spk_embed_dim = spk_embed_dim
self.enc_p = TextEncoder256(
inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
f0=False,
)
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, 3, gin_channels=gin_channels
)
self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
def remove_weight_norm(self):
self.dec.remove_weight_norm()
self.flow.remove_weight_norm()
self.enc_q.remove_weight_norm()
def forward(self, phone, phone_lengths, y, y_lengths, ds): # 这里ds是id，[bs,1]
g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
z_p = self.flow(z, y_mask, g=g)
z_slice, ids_slice = commons.rand_slice_segments(
z, y_lengths, self.segment_size
)
o = self.dec(z_slice, g=g)
return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
def infer(self, phone, phone_lengths, sid, rate=None):
g = self.emb_g(sid).unsqueeze(-1)
m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
if rate:
head = int(z_p.shape[2] * rate)
z_p = z_p[:, :, -head:]
x_mask = x_mask[:, :, -head:]
z = self.flow(z_p, x_mask, g=g, reverse=True)
o = self.dec(z * x_mask, g=g)
return o, x_mask, (z, z_p, m_p, logs_p)
class SynthesizerTrnMs768NSFsid_nono(nn.Module):
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,
spk_embed_dim,
gin_channels,
sr=None,
**kwargs
):
super().__init__()
self.spec_channels = spec_channels
self.inter_channels = inter_channels
self.hidden_channels = hidden_channels
self.filter_channels = filter_channels
self.n_heads = n_heads
self.n_layers = n_layers
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.resblock = resblock
self.resblock_kernel_sizes = resblock_kernel_sizes
self.resblock_dilation_sizes = resblock_dilation_sizes
self.upsample_rates = upsample_rates
self.upsample_initial_channel = upsample_initial_channel
self.upsample_kernel_sizes = upsample_kernel_sizes
self.segment_size = segment_size
self.gin_channels = gin_channels
# self.hop_length = hop_length#
self.spk_embed_dim = spk_embed_dim
self.enc_p = TextEncoder768(
inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
f0=False,
)
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, 3, gin_channels=gin_channels
)
self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
def remove_weight_norm(self):
self.dec.remove_weight_norm()
self.flow.remove_weight_norm()
self.enc_q.remove_weight_norm()
def forward(self, phone, phone_lengths, y, y_lengths, ds): # 这里ds是id，[bs,1]
#g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
g = ds.unsqueeze(-1)
#m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths) #org
m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths,g=g)#fang add
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
z_p = self.flow(z, y_mask, g=g)
z_slice, ids_slice = commons.rand_slice_segments(
z, y_lengths, self.segment_size
)
o = self.dec(z_slice, g=g)
return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
def infer(self, phone, phone_lengths, sid, rate=None):
#g = self.emb_g(sid).unsqueeze(-1)
g = sid.unsqueeze(-1).unsqueeze(0)
#m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths,g=g)#fang add
z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
if rate:
head = int(z_p.shape[2] * rate)
z_p = z_p[:, :, -head:]
x_mask = x_mask[:, :, -head:]
z = self.flow(z_p, x_mask, g=g, reverse=True)
o = self.dec(z * x_mask, g=g)
return o, x_mask, (z, z_p, m_p, logs_p)
class MultiPeriodDiscriminator(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super(MultiPeriodDiscriminator, self).__init__()
periods = [2, 3, 5, 7, 11, 17]
# periods = [3, 5, 7, 11, 17, 23, 37]
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
discs = discs + [
DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
]
self.discriminators = nn.ModuleList(discs)
def forward(self, y, y_hat):
y_d_rs = [] #
y_d_gs = []
fmap_rs = []
fmap_gs = []
for i, d in enumerate(self.discriminators):
y_d_r, fmap_r = d(y)
y_d_g, fmap_g = d(y_hat)
# for j in range(len(fmap_r)):
# print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
y_d_rs.append(y_d_r)
y_d_gs.append(y_d_g)
fmap_rs.append(fmap_r)
fmap_gs.append(fmap_g)
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
class MultiPeriodDiscriminatorV2(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super(MultiPeriodDiscriminatorV2, self).__init__()
# periods = [2, 3, 5, 7, 11, 17]
periods = [2, 3, 5, 7, 11, 17, 23, 37]
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
discs = discs + [
DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
]
self.discriminators = nn.ModuleList(discs)
def forward(self, y, y_hat):
y_d_rs = [] #
y_d_gs = []
fmap_rs = []
fmap_gs = []
for i, d in enumerate(self.discriminators):
y_d_r, fmap_r = d(y)
y_d_g, fmap_g = d(y_hat)
# for j in range(len(fmap_r)):
# print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
y_d_rs.append(y_d_r)
y_d_gs.append(y_d_g)
fmap_rs.append(fmap_r)
fmap_gs.append(fmap_g)
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
class DiscriminatorS(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super(DiscriminatorS, self).__init__()
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
self.convs = nn.ModuleList(
[
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
]
)
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
def forward(self, x):
fmap = []
for l in self.convs:
x = l(x)
x = F.leaky_relu(x, modules.LRELU_SLOPE)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
x = torch.flatten(x, 1, -1)
return x, fmap
class DiscriminatorP(torch.nn.Module):
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
super(DiscriminatorP, self).__init__()
self.period = period
self.use_spectral_norm = use_spectral_norm
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
self.convs = nn.ModuleList(
[
norm_f(
Conv2d(
1,
32,
(kernel_size, 1),
(stride, 1),
padding=(get_padding(kernel_size, 1), 0),
)
),
norm_f(
Conv2d(
32,
128,
(kernel_size, 1),
(stride, 1),
padding=(get_padding(kernel_size, 1), 0),
)
),
norm_f(
Conv2d(
128,
512,
(kernel_size, 1),
(stride, 1),
padding=(get_padding(kernel_size, 1), 0),
)
),
norm_f(
Conv2d(
512,
1024,
(kernel_size, 1),
(stride, 1),
padding=(get_padding(kernel_size, 1), 0),
)
),
norm_f(
Conv2d(
1024,
1024,
(kernel_size, 1),
1,
padding=(get_padding(kernel_size, 1), 0),
)
),
]
)
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
def forward(self, x):
fmap = []
# 1d to 2d
b, c, t = x.shape
if t % self.period != 0: # pad first
n_pad = self.period - (t % self.period)
x = F.pad(x, (0, n_pad), "reflect")
t = t + n_pad
x = x.view(b, c, t // self.period, self.period)
for l in self.convs:
x = l(x)
x = F.leaky_relu(x, modules.LRELU_SLOPE)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
x = torch.flatten(x, 1, -1)
return x, fmap

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