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