optimize(infer): move nsf & gens into rvc

infer/lib/infer_pack/models.py

@@ -1,425 +1,26 @@
-import math
 from typing import Optional, List
 
 import torch
 from torch import nn
-from torch.nn import Conv1d, Conv2d, ConvTranspose1d
+from torch.nn import Conv1d, Conv2d
 from torch.nn import functional as F
-from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
+from torch.nn.utils import spectral_norm, weight_norm
 from rvc import residuals
 
 from rvc.residuals import ResidualCouplingBlock
 from rvc.utils import (
     get_padding,
-    call_weight_data_normal_if_Conv,
     slice_on_last_dim,
     rand_slice_segments_on_last_dim,
 )
 from rvc.encoders import TextEncoder, PosteriorEncoder
+from rvc.generators import Generator
+from rvc.nsf import NSFGenerator
 
 has_xpu = bool(hasattr(torch, "xpu") and torch.xpu.is_available())
 
 
-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 = residuals.ResBlock1 if resblock == "1" else residuals.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(call_weight_data_normal_if_Conv)
-
-        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,
-        # n_res: Optional[torch.Tensor] = None,
-    ):
-        """
-        if n_res is not None:
-            assert isinstance(n_res, torch.Tensor)
-            n = int(n_res.item())
-            if n != x.shape[-1]:
-                x = F.interpolate(x, size=n, mode="linear")
-        """
-        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, residuals.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,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        # 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: torch.Tensor, upp: int = 1):
-        sine_wavs, _, _ = self.l_sin_gen(x, upp)
-        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: int,
-        resblock: str,
-        resblock_kernel_sizes: List[int],
-        resblock_dilation_sizes: List[List[int]],
-        upsample_rates: List[int],
-        upsample_initial_channel: int,
-        upsample_kernel_sizes: List[int],
-        gin_channels: int,
-        sr: int,
-    ):
-        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)
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = residuals.ResBlock1 if resblock == "1" else residuals.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: int = 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(call_weight_data_normal_if_Conv)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = math.prod(upsample_rates)
-
-        self.lrelu_slope = residuals.LRELU_SLOPE
-
-    def forward(
-        self,
-        x,
-        f0,
-        g: Optional[torch.Tensor] = None,
-        # n_res: Optional[torch.Tensor] = None,
-    ):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        """
-        if n_res is not None:
-            assert isinstance(n_res, torch.Tensor)
-            n = int(n_res.item())
-            if n * self.upp != har_source.shape[-1]:
-                har_source = F.interpolate(har_source, size=n * self.upp, mode="linear")
-            if n != x.shape[-1]:
-                x = F.interpolate(x, size=n, mode="linear")
-        """
-        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
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
+class SynthesizerTrnMsNSFsid(nn.Module):
     def __init__(
         self,
         spec_channels: int,
@@ -440,6 +41,7 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
         spk_embed_dim: int,
         gin_channels: int,
         sr: str | int,
+        text_encoder_in_channels: int,
     ):
         super(SynthesizerTrnMs256NSFsid, self).__init__()
         if isinstance(sr, str):
@@ -467,7 +69,7 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
         # self.hop_length = hop_length#
         self.spk_embed_dim = spk_embed_dim
         self.enc_p = TextEncoder(
-            256,
+            text_encoder_in_channels,
             inter_channels,
             hidden_channels,
             filter_channels,
@@ -476,7 +78,7 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
             kernel_size,
             float(p_dropout),
         )
-        self.dec = GeneratorNSF(
+        self.dec = NSFGenerator(
             inter_channels,
             resblock,
             resblock_kernel_sizes,
@@ -597,29 +199,29 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
         return o, x_mask, (z, z_p, m_p, logs_p)
 
 
-class SynthesizerTrnMs768NSFsid(SynthesizerTrnMs256NSFsid):
+class SynthesizerTrnMs256NSFsid(SynthesizerTrnMsNSFsid):
     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,
+        spec_channels: int,
+        segment_size: int,
+        inter_channels: int,
+        hidden_channels: int,
+        filter_channels: int,
+        n_heads: int,
+        n_layers: int,
+        kernel_size: int,
+        p_dropout: int,
+        resblock: str,
+        resblock_kernel_sizes: List[int],
+        resblock_dilation_sizes: List[List[int]],
+        upsample_rates: List[int],
+        upsample_initial_channel: int,
+        upsample_kernel_sizes: List[int],
+        spk_embed_dim: int,
+        gin_channels: int,
+        sr: str | int,
     ):
-        super(SynthesizerTrnMs768NSFsid, self).__init__(
+        super().__init__(
             spec_channels,
             segment_size,
             inter_channels,
@@ -638,42 +240,76 @@ class SynthesizerTrnMs768NSFsid(SynthesizerTrnMs256NSFsid):
             spk_embed_dim,
             gin_channels,
             sr,
+            256,
         )
-        del self.enc_p
-        self.enc_p = TextEncoder(
-            768,
+
+
+class SynthesizerTrnMs768NSFsid(SynthesizerTrnMsNSFsid):
+    def __init__(
+        self,
+        spec_channels: int,
+        segment_size: int,
+        inter_channels: int,
+        hidden_channels: int,
+        filter_channels: int,
+        n_heads: int,
+        n_layers: int,
+        kernel_size: int,
+        p_dropout: int,
+        resblock: str,
+        resblock_kernel_sizes: List[int],
+        resblock_dilation_sizes: List[List[int]],
+        upsample_rates: List[int],
+        upsample_initial_channel: int,
+        upsample_kernel_sizes: List[int],
+        spk_embed_dim: int,
+        gin_channels: int,
+        sr: str | int,
+    ):
+        super().__init__(
+            spec_channels,
+            segment_size,
             inter_channels,
             hidden_channels,
             filter_channels,
             n_heads,
             n_layers,
             kernel_size,
-            float(p_dropout),
+            p_dropout,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            spk_embed_dim,
+            gin_channels,
+            sr,
+            768,
         )
 
 
 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,
+        spec_channels: int,
+        segment_size: int,
+        inter_channels: int,
+        hidden_channels: int,
+        filter_channels: int,
+        n_heads: int,
+        n_layers: int,
+        kernel_size: int,
+        p_dropout: int,
         resblock: str,
-        resblock_kernel_sizes,
+        resblock_kernel_sizes: List[int],
         resblock_dilation_sizes: List[List[int]],
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
+        upsample_rates: List[int],
+        upsample_initial_channel: int,
+        upsample_kernel_sizes: List[int],
+        spk_embed_dim: int,
+        gin_channels: int,
         sr = None,
-        **kwargs
     ):
         super(SynthesizerTrnMs256NSFsid_nono, self).__init__()
         self.spec_channels = spec_channels
@@ -811,25 +447,24 @@ class SynthesizerTrnMs256NSFsid_nono(nn.Module):
 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,
+        spec_channels: int,
+        segment_size: int,
+        inter_channels: int,
+        hidden_channels: int,
+        filter_channels: int,
+        n_heads: int,
+        n_layers: int,
+        kernel_size: int,
+        p_dropout: int,
+        resblock: str,
+        resblock_kernel_sizes: List[int],
+        resblock_dilation_sizes: List[List[int]],
+        upsample_rates: List[int],
+        upsample_initial_channel: int,
+        upsample_kernel_sizes: List[int],
+        spk_embed_dim: int,
+        gin_channels: int,
         sr = None,
-        **kwargs
     ):
         super(SynthesizerTrnMs768NSFsid_nono, self).__init__(
             spec_channels,
@@ -849,8 +484,6 @@ class SynthesizerTrnMs768NSFsid_nono(SynthesizerTrnMs256NSFsid_nono):
             upsample_kernel_sizes,
             spk_embed_dim,
             gin_channels,
-            sr,
-            **kwargs
         )
         del self.enc_p
         self.enc_p = TextEncoder(

infer/lib/infer_pack/models_onnx.py

@@ -1,8 +1,7 @@
 import torch
 from torch import nn
 
-from .models import GeneratorNSF
-
+from rvc.nsf import NSFGenerator
 from rvc.encoders import TextEncoder, PosteriorEncoder
 from rvc.residuals import ResidualCouplingBlock
 
@@ -66,7 +65,7 @@ class SynthesizerTrnMsNSFsidM(nn.Module):
             kernel_size,
             float(p_dropout),
         )
-        self.dec = GeneratorNSF(
+        self.dec = NSFGenerator(
             inter_channels,
             resblock,
             resblock_kernel_sizes,

rvc/attentions.py

@@ -226,6 +226,9 @@ class MultiHeadAttention(nn.Module):
 
 
 class FFN(nn.Module):
+    """
+    Feed-Forward Network
+    """
     def __init__(
         self,
         in_channels: int,

rvc/generators.py

@@ -0,0 +1,225 @@
+from typing import Optional, List, Tuple
+
+import torch
+from torch import nn
+from torch.nn import Conv1d, ConvTranspose1d
+from torch.nn import functional as F
+from torch.nn.utils import remove_weight_norm, weight_norm
+
+from .residuals import ResBlock1, ResBlock2, LRELU_SLOPE
+from .utils import call_weight_data_normal_if_Conv
+
+class Generator(torch.nn.Module):
+    def __init__(
+        self,
+        initial_channel: int,
+        resblock: str,
+        resblock_kernel_sizes: List[int],
+        resblock_dilation_sizes: List[List[int]],
+        upsample_rates: List[int],
+        upsample_initial_channel: int,
+        upsample_kernel_sizes: List[int],
+        gin_channels: int = 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
+        )
+
+        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()
+        resblock_module = ResBlock1 if resblock == "1" else ResBlock2
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for k, d in zip(resblock_kernel_sizes, resblock_dilation_sizes):
+                self.resblocks.append(resblock_module(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(call_weight_data_normal_if_Conv)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def __call__(
+        self,
+        x: torch.Tensor,
+        g: Optional[torch.Tensor] = None,
+        # n_res: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        return super().__call__(x, g=g)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        g: Optional[torch.Tensor] = None,
+        # n_res: Optional[torch.Tensor] = None,
+    ):
+        """
+        if n_res is not None:
+            assert isinstance(n_res, torch.Tensor)
+            n = int(n_res.item())
+            if n != x.shape[-1]:
+                x = F.interpolate(x, size=n, mode="linear")
+        """
+        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, LRELU_SLOPE)
+            x = self.ups[i](x)
+            n = i * self.num_kernels
+            xs = self.resblocks[n](x)
+            for j in range(1, self.num_kernels):
+                xs += self.resblocks[n + 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 SineGenerator(torch.nn.Module):
+    """Definition of sine generator
+    SineGenerator(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: int,
+        harmonic_num: int = 0,
+        sine_amp: float = 0.1,
+        noise_std: float = 0.003,
+        voiced_threshold: int = 0,
+    ):
+        super(SineGenerator, self).__init__()
+        self.sine_amp = sine_amp
+        self.noise_std = noise_std
+        self.harmonic_num = harmonic_num
+        self.dim = harmonic_num + 1
+        self.sampling_rate = samp_rate
+        self.voiced_threshold = voiced_threshold
+    
+    def __call__(self, f0: torch.Tensor, upp: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        return super().__call__(f0, upp)
+
+    def forward(self, f0: torch.Tensor, upp: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """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: torch.Tensor = F.interpolate(
+                tmp_over_one.transpose(2, 1),
+                scale_factor = float(upp),
+                mode="linear",
+                align_corners=True,
+            ).transpose(2, 1)
+            rad_values: torch.Tensor = 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: torch.Tensor = 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
+
+    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

rvc/nsf.py

@@ -0,0 +1,214 @@
+from typing import Optional, List
+import math
+
+import torch
+from torch import nn
+from torch.nn import Conv1d, ConvTranspose1d
+from torch.nn import functional as F
+from torch.nn.utils import remove_weight_norm, weight_norm
+
+from .generators import SineGenerator
+from .residuals import ResBlock1, ResBlock2, LRELU_SLOPE
+from .utils import call_weight_data_normal_if_Conv
+
+
+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: int,
+        harmonic_num: int = 0,
+        sine_amp: float = 0.1,
+        add_noise_std: float = 0.003,
+        voiced_threshod: int = 0,
+    ):
+        super(SourceModuleHnNSF, self).__init__()
+
+        self.sine_amp = sine_amp
+        self.noise_std = add_noise_std
+        # to produce sine waveforms
+        self.l_sin_gen = SineGenerator(
+            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 __call__(self, x: torch.Tensor, upp: int = 1) -> torch.Tensor:
+        return super().__call__(x, upp=upp)
+
+    def forward(self, x: torch.Tensor, upp: int = 1) -> torch.Tensor:
+        sine_wavs, _, _ = self.l_sin_gen(x, upp)
+        sine_wavs = sine_wavs.to(dtype=self.l_linear.weight.dtype)
+        sine_merge: torch.Tensor = self.l_tanh(self.l_linear(sine_wavs))
+        return sine_merge #, None, None  # noise, uv
+
+class NSFGenerator(torch.nn.Module):
+    def __init__(
+        self,
+        initial_channel: int,
+        resblock: str,
+        resblock_kernel_sizes: List[int],
+        resblock_dilation_sizes: List[List[int]],
+        upsample_rates: List[int],
+        upsample_initial_channel: int,
+        upsample_kernel_sizes: List[int],
+        gin_channels: int,
+        sr: int,
+    ):
+        super(NSFGenerator, 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)
+        self.noise_convs = nn.ModuleList()
+        self.conv_pre = Conv1d(
+            initial_channel, upsample_initial_channel, 7, 1, padding=3
+        )
+        resblock = ResBlock1 if resblock == "1" else 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: int = 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(call_weight_data_normal_if_Conv)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+        self.upp = math.prod(upsample_rates)
+
+        self.lrelu_slope = LRELU_SLOPE
+
+    def __call__(
+        self,
+        x: torch.Tensor,
+        f0: torch.Tensor,
+        g: Optional[torch.Tensor] = None,
+        # n_res: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        return super().__call__(x, f0, g=g)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        f0: torch.Tensor,
+        g: Optional[torch.Tensor] = None,
+        # n_res: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        har_source = self.m_source(f0, self.upp)
+        har_source = har_source.transpose(1, 2)
+        """
+        if n_res is not None:
+            assert isinstance(n_res, torch.Tensor)
+            n = int(n_res.item())
+            if n * self.upp != har_source.shape[-1]:
+                har_source = F.interpolate(har_source, size=n * self.upp, mode="linear")
+            if n != x.shape[-1]:
+                x = F.interpolate(x, size=n, mode="linear")
+        """
+        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