import math import logging from typing import Optional logger = logging.getLogger(__name__) import numpy as np import torch from torch import nn from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d from torch.nn import functional as F from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm from infer.lib.infer_pack import attentions, commons, modules from infer.lib.infer_pack.commons import get_padding, init_weights has_xpu = bool(hasattr(torch, "xpu") and torch.xpu.is_available()) class TextEncoder(nn.Module): def __init__( self, in_channels, out_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, f0=True, ): super(TextEncoder, self).__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 = float(p_dropout) self.emb_phone = nn.Linear(in_channels, 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, float(p_dropout), ) self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) def forward( self, phone: torch.Tensor, pitch: torch.Tensor, lengths: torch.Tensor, skip_head: Optional[torch.Tensor] = None, ): if pitch is 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) if skip_head is not None: assert isinstance(skip_head, torch.Tensor) head = int(skip_head.item()) x = x[:, :, head:] x_mask = x_mask[:, :, head:] stats = self.proj(x) * x_mask m, logs = torch.split(stats, self.out_channels, dim=1) return m, logs, x_mask class ResidualCouplingBlock(nn.Module): def __init__( self, channels, hidden_channels, kernel_size, dilation_rate, n_layers, n_flows=4, gin_channels=0, ): super(ResidualCouplingBlock, self).__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: torch.Tensor, x_mask: torch.Tensor, g: Optional[torch.Tensor] = None, reverse: bool = False, ): if not reverse: for flow in self.flows: x, _ = flow(x, x_mask, g=g, reverse=reverse) else: for flow in self.flows[::-1]: x, _ = flow.forward(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() def __prepare_scriptable__(self): for i in range(self.n_flows): for hook in self.flows[i * 2]._forward_pre_hooks.values(): if ( hook.__module__ == "torch.nn.utils.weight_norm" and hook.__class__.__name__ == "WeightNorm" ): torch.nn.utils.remove_weight_norm(self.flows[i * 2]) return self class PosteriorEncoder(nn.Module): def __init__( self, in_channels, out_channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, ): super(PosteriorEncoder, self).__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: torch.Tensor, x_lengths: torch.Tensor, g: Optional[torch.Tensor] = 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) z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask return z, m, logs, x_mask def remove_weight_norm(self): self.enc.remove_weight_norm() def __prepare_scriptable__(self): for hook in self.enc._forward_pre_hooks.values(): if ( hook.__module__ == "torch.nn.utils.weight_norm" and hook.__class__.__name__ == "WeightNorm" ): torch.nn.utils.remove_weight_norm(self.enc) return self 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: torch.Tensor, g: Optional[torch.Tensor] = 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 __prepare_scriptable__(self): for l in self.ups: for hook in l._forward_pre_hooks.values(): # The hook we want to remove is an instance of WeightNorm class, so # normally we would do `if isinstance(...)` but this class is not accessible # because of shadowing, so we check the module name directly. # https://github.com/pytorch/pytorch/blob/be0ca00c5ce260eb5bcec3237357f7a30cc08983/torch/nn/utils/__init__.py#L3 if ( hook.__module__ == "torch.nn.utils.weight_norm" and hook.__class__.__name__ == "WeightNorm" ): torch.nn.utils.remove_weight_norm(l) for l in self.resblocks: for hook in l._forward_pre_hooks.values(): if ( hook.__module__ == "torch.nn.utils.weight_norm" and hook.__class__.__name__ == "WeightNorm" ): torch.nn.utils.remove_weight_norm(l) return self 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(torch.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) if uv.device.type == "privateuseone": # for DirectML uv = uv.float() return uv def forward(self, f0: torch.Tensor, upp: int): """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 range(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=float(upp), mode="linear", align_corners=True, ).transpose(2, 1) rad_values = F.interpolate( rad_values.transpose(2, 1), scale_factor=float(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 * torch.pi ) sine_waves = sine_waves * self.sine_amp uv = self._f02uv(f0) uv = F.interpolate( uv.transpose(2, 1), scale_factor=float(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() # self.ddtype:int = -1 def forward(self, x: torch.Tensor, upp: int = 1): # if self.ddtype ==-1: # self.ddtype = self.l_linear.weight.dtype sine_wavs, uv, _ = self.l_sin_gen(x, upp) # print(x.dtype,sine_wavs.dtype,self.l_linear.weight.dtype) # if self.is_half: # sine_wavs = sine_wavs.half() # sine_merge = self.l_tanh(self.l_linear(sine_wavs.to(x))) # print(sine_wavs.dtype,self.ddtype) # if sine_wavs.dtype != self.l_linear.weight.dtype: sine_wavs = sine_wavs.to(dtype=self.l_linear.weight.dtype) 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=math.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() 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, ) ) ) if i + 1 < len(upsample_rates): stride_f0 = math.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 = math.prod(upsample_rates) self.lrelu_slope = modules.LRELU_SLOPE def forward(self, x, f0, g: Optional[torch.Tensor] = 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) # torch.jit.script() does not support direct indexing of torch modules # That's why I wrote this for i, (ups, noise_convs) in enumerate(zip(self.ups, self.noise_convs)): if i < self.num_upsamples: x = F.leaky_relu(x, self.lrelu_slope) x = ups(x) x_source = noise_convs(har_source) x = x + x_source xs: Optional[torch.Tensor] = None l = [i * self.num_kernels + j for j in range(self.num_kernels)] for j, resblock in enumerate(self.resblocks): if j in l: if xs is None: xs = resblock(x) else: xs += resblock(x) # This assertion cannot be ignored! \ # If ignored, it will cause torch.jit.script() compilation errors assert isinstance(xs, torch.Tensor) 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() def __prepare_scriptable__(self): for l in self.ups: for hook in l._forward_pre_hooks.values(): # The hook we want to remove is an instance of WeightNorm class, so # normally we would do `if isinstance(...)` but this class is not accessible # because of shadowing, so we check the module name directly. # https://github.com/pytorch/pytorch/blob/be0ca00c5ce260eb5bcec3237357f7a30cc08983/torch/nn/utils/__init__.py#L3 if ( hook.__module__ == "torch.nn.utils.weight_norm" and hook.__class__.__name__ == "WeightNorm" ): torch.nn.utils.remove_weight_norm(l) for l in self.resblocks: for hook in self.resblocks._forward_pre_hooks.values(): if ( hook.__module__ == "torch.nn.utils.weight_norm" and hook.__class__.__name__ == "WeightNorm" ): torch.nn.utils.remove_weight_norm(l) return self sr2sr = { "32k": 32000, "40k": 40000, "48k": 48000, } 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(SynthesizerTrnMs256NSFsid, self).__init__() if isinstance(sr, str): 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 = float(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 = TextEncoder( 256, inter_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, float(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) logger.debug( "gin_channels: " + str(gin_channels) + ", self.spk_embed_dim: " + str(self.spk_embed_dim) ) def remove_weight_norm(self): self.dec.remove_weight_norm() self.flow.remove_weight_norm() if hasattr(self, "enc_q"): self.enc_q.remove_weight_norm() def __prepare_scriptable__(self): for hook in self.dec._forward_pre_hooks.values(): # The hook we want to remove is an instance of WeightNorm class, so # normally we would do `if isinstance(...)` but this class is not accessible # because of shadowing, so we check the module name directly. # https://github.com/pytorch/pytorch/blob/be0ca00c5ce260eb5bcec3237357f7a30cc08983/torch/nn/utils/__init__.py#L3 if ( hook.__module__ == "torch.nn.utils.weight_norm" and hook.__class__.__name__ == "WeightNorm" ): torch.nn.utils.remove_weight_norm(self.dec) for hook in self.flow._forward_pre_hooks.values(): if ( hook.__module__ == "torch.nn.utils.weight_norm" and hook.__class__.__name__ == "WeightNorm" ): torch.nn.utils.remove_weight_norm(self.flow) if hasattr(self, "enc_q"): for hook in self.enc_q._forward_pre_hooks.values(): if ( hook.__module__ == "torch.nn.utils.weight_norm" and hook.__class__.__name__ == "WeightNorm" ): torch.nn.utils.remove_weight_norm(self.enc_q) return self @torch.jit.ignore def forward( self, phone: torch.Tensor, phone_lengths: torch.Tensor, pitch: torch.Tensor, pitchf: torch.Tensor, y: torch.Tensor, y_lengths: torch.Tensor, ds: Optional[torch.Tensor] = None, ): # 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 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 ) # 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) @torch.jit.export def infer( self, phone: torch.Tensor, phone_lengths: torch.Tensor, pitch: torch.Tensor, nsff0: torch.Tensor, sid: torch.Tensor, skip_head: Optional[torch.Tensor] = None, return_length: Optional[torch.Tensor] = None, ): g = self.emb_g(sid).unsqueeze(-1) if skip_head is not None and return_length is not None: assert isinstance(skip_head, torch.Tensor) assert isinstance(return_length, torch.Tensor) head = int(skip_head.item()) length = int(return_length.item()) flow_head = torch.clamp(skip_head - 24, min=0) dec_head = head - int(flow_head.item()) m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths, flow_head) z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask z = self.flow(z_p, x_mask, g=g, reverse=True) z = z[:, :, dec_head : dec_head + length] x_mask = x_mask[:, :, dec_head : dec_head + length] nsff0 = nsff0[:, head : head + length] else: 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 z = self.flow(z_p, x_mask, g=g, reverse=True) o = self.dec(z * x_mask, nsff0, g=g) return o, x_mask, (z, z_p, m_p, logs_p) class SynthesizerTrnMs768NSFsid(SynthesizerTrnMs256NSFsid): 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(SynthesizerTrnMs768NSFsid, self).__init__( 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 ) del self.enc_p self.enc_p = TextEncoder( 768, inter_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, float(p_dropout), ) 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(SynthesizerTrnMs256NSFsid_nono, self).__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 = float(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 = TextEncoder( 256, inter_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, float(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) logger.debug( "gin_channels: " + str(gin_channels) + ", self.spk_embed_dim: " + str(self.spk_embed_dim) ) def remove_weight_norm(self): self.dec.remove_weight_norm() self.flow.remove_weight_norm() if hasattr(self, "enc_q"): self.enc_q.remove_weight_norm() def __prepare_scriptable__(self): for hook in self.dec._forward_pre_hooks.values(): # The hook we want to remove is an instance of WeightNorm class, so # normally we would do `if isinstance(...)` but this class is not accessible # because of shadowing, so we check the module name directly. # https://github.com/pytorch/pytorch/blob/be0ca00c5ce260eb5bcec3237357f7a30cc08983/torch/nn/utils/__init__.py#L3 if ( hook.__module__ == "torch.nn.utils.weight_norm" and hook.__class__.__name__ == "WeightNorm" ): torch.nn.utils.remove_weight_norm(self.dec) for hook in self.flow._forward_pre_hooks.values(): if ( hook.__module__ == "torch.nn.utils.weight_norm" and hook.__class__.__name__ == "WeightNorm" ): torch.nn.utils.remove_weight_norm(self.flow) if hasattr(self, "enc_q"): for hook in self.enc_q._forward_pre_hooks.values(): if ( hook.__module__ == "torch.nn.utils.weight_norm" and hook.__class__.__name__ == "WeightNorm" ): torch.nn.utils.remove_weight_norm(self.enc_q) return self @torch.jit.ignore 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) @torch.jit.export def infer( self, phone: torch.Tensor, phone_lengths: torch.Tensor, sid: torch.Tensor, skip_head: Optional[torch.Tensor] = None, return_length: Optional[torch.Tensor] = None, ): g = self.emb_g(sid).unsqueeze(-1) if skip_head is not None and return_length is not None: assert isinstance(skip_head, torch.Tensor) assert isinstance(return_length, torch.Tensor) head = int(skip_head.item()) length = int(return_length.item()) flow_head = torch.clamp(skip_head - 24, min=0) dec_head = head - int(flow_head.item()) m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths, flow_head) z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask z = self.flow(z_p, x_mask, g=g, reverse=True) z = z[:, :, dec_head : dec_head + length] x_mask = x_mask[:, :, dec_head : dec_head + length] else: 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 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(SynthesizerTrnMs256NSFsid_nono): 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(SynthesizerTrnMs768NSFsid_nono, self).__init__( 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 ) del self.enc_p self.enc_p = TextEncoder( 768, inter_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, float(p_dropout), f0=False, ) 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) if has_xpu and x.dtype == torch.bfloat16: x = F.pad(x.to(dtype=torch.float16), (0, n_pad), "reflect").to( dtype=torch.bfloat16 ) else: 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