voras_betaの追加

server/const.py

@@ -65,9 +65,9 @@ class EnumInferenceTypes(Enum):
     pyTorchRVCNono = "pyTorchRVCNono"
     pyTorchRVCv2 = "pyTorchRVCv2"
     pyTorchRVCv2Nono = "pyTorchRVCv2Nono"
-    pyTorchRVCv3 = "pyTorchRVCv3"
     pyTorchWebUI = "pyTorchWebUI"
     pyTorchWebUINono = "pyTorchWebUINono"
+    pyTorchVoRASbeta = "pyTorchVoRASbeta"
     onnxRVC = "onnxRVC"
     onnxRVCNono = "onnxRVCNono"
 

server/voice_changer/RVC/ModelSlotGenerator.py

@@ -10,18 +10,15 @@ from data.ModelSlot import ModelSlot
 def _setInfoByPytorch(slot: ModelSlot):
     cpt = torch.load(slot.modelFile, map_location="cpu")
     config_len = len(cpt["config"])
-
-    if cpt["version"] == "v3":
+    print(cpt["version"])
+    if cpt["version"] == "voras_beta":
         slot.f0 = True if cpt["f0"] == 1 else False
-        slot.modelType = EnumInferenceTypes.pyTorchRVCv3.value
-        slot.embChannels = cpt["config"][17]
+        slot.modelType = EnumInferenceTypes.pyTorchVoRASbeta.value
+        slot.embChannels = 768
         slot.embOutputLayer = (
             cpt["embedder_output_layer"] if "embedder_output_layer" in cpt else 9
         )
-        if slot.embChannels == 256:
-            slot.useFinalProj = True
-        else:
-            slot.useFinalProj = False
+        slot.useFinalProj = False
 
         slot.embedder = cpt["embedder_name"]
         if slot.embedder.endswith("768"):
@@ -33,7 +30,6 @@ def _setInfoByPytorch(slot: ModelSlot):
             slot.embedder = EnumEmbedderTypes.contentvec.value
         elif slot.embedder == EnumEmbedderTypes.hubert_jp.value:
             slot.embedder = EnumEmbedderTypes.hubert_jp.value
-            print("nadare v3 loaded")
         else:
             raise RuntimeError("[Voice Changer][setInfoByONNX] unknown embedder")
 

server/voice_changer/RVC/inferencer/Inferencer.py

@@ -8,7 +8,7 @@ from const import EnumInferenceTypes
 class Inferencer(Protocol):
     inferencerType: EnumInferenceTypes = EnumInferenceTypes.pyTorchRVC
     file: str
-    isHalf: bool = True
+    isHalf: bool = False
     gpu: int = 0
 
     model: onnxruntime.InferenceSession | Any | None = None

server/voice_changer/RVC/inferencer/InferencerManager.py

@@ -8,7 +8,7 @@ from voice_changer.RVC.inferencer.RVCInferencerv2 import RVCInferencerv2
 from voice_changer.RVC.inferencer.RVCInferencerv2Nono import RVCInferencerv2Nono
 from voice_changer.RVC.inferencer.WebUIInferencer import WebUIInferencer
 from voice_changer.RVC.inferencer.WebUIInferencerNono import WebUIInferencerNono
-from voice_changer.RVC.inferencer.RVCInferencerv3 import RVCInferencerv3
+from voice_changer.RVC.inferencer.VorasInferencebeta import VoRASInferencer
 
 class InferencerManager:
     currentInferencer: Inferencer | None = None
@@ -37,8 +37,8 @@ class InferencerManager:
             return RVCInferencerNono().loadModel(file, gpu)
         elif inferencerType == EnumInferenceTypes.pyTorchRVCv2 or inferencerType == EnumInferenceTypes.pyTorchRVCv2.value:
             return RVCInferencerv2().loadModel(file, gpu)
-        elif inferencerType == EnumInferenceTypes.pyTorchRVCv3 or inferencerType == EnumInferenceTypes.pyTorchRVCv3.value:
-            return RVCInferencerv3().loadModel(file, gpu)
+        elif inferencerType == EnumInferenceTypes.pyTorchVoRASbeta or inferencerType == EnumInferenceTypes.pyTorchVoRASbeta.value:
+            return VoRASInferencer().loadModel(file, gpu)
         elif inferencerType == EnumInferenceTypes.pyTorchRVCv2Nono or inferencerType == EnumInferenceTypes.pyTorchRVCv2Nono.value:
             return RVCInferencerv2Nono().loadModel(file, gpu)
         elif inferencerType == EnumInferenceTypes.pyTorchWebUI or inferencerType == EnumInferenceTypes.pyTorchWebUI.value:

server/voice_changer/RVC/inferencer/VorasInferencebeta.py

@@ -4,26 +4,25 @@ from torch import device
 from const import EnumInferenceTypes
 from voice_changer.RVC.inferencer.Inferencer import Inferencer
 from voice_changer.RVC.deviceManager.DeviceManager import DeviceManager
-from .model_v3.models import SynthesizerTrnMs256NSFSid
+from .voras_beta.models import Synthesizer
 
 
-class RVCInferencerv3(Inferencer):
+class VoRASInferencer(Inferencer):
     def loadModel(self, file: str, gpu: device):
-        print("nadare v3 load start")
-        super().setProps(EnumInferenceTypes.pyTorchRVCv3, file, True, gpu)
+        super().setProps(EnumInferenceTypes.pyTorchVoRASbeta, file, False, gpu)
 
         dev = DeviceManager.get_instance().getDevice(gpu)
-        isHalf = False # DeviceManager.get_instance().halfPrecisionAvailable(gpu)
+        self.isHalf = False # DeviceManager.get_instance().halfPrecisionAvailable(gpu)
 
         cpt = torch.load(file, map_location="cpu")
-        model = SynthesizerTrnMs256NSFSid(**cpt["params"])
+        model = Synthesizer(**cpt["params"])
 
         model.eval()
         model.load_state_dict(cpt["weight"], strict=False)
+        model.remove_weight_norm()
+        model.change_speaker(0)
 
         model = model.to(dev)
-        if isHalf:
-            model = model.half()
 
         self.model = model
         print("load model comprete")

server/voice_changer/RVC/inferencer/model_v3/attentions.py

@@ -1,343 +0,0 @@
-import math
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.nn.utils import remove_weight_norm, weight_norm
-
-from . import commons
-from .modules import LayerNorm, LoRALinear1d
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        gin_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        window_size=25,
-        **kwargs
-    ):
-        super().__init__()
-        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.window_size = window_size
-
-        self.drop = nn.Dropout(p_dropout)
-        self.attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    gin_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    window_size=window_size,
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    gin_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask, g):
-        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.attn_layers[i](x, x, g, attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask, g)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.attn_layers:
-            l.remove_weight_norm()
-        for l in self.ffn_layers:
-            l.remove_weight_norm()
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(
-        self,
-        channels,
-        out_channels,
-        gin_channels,
-        n_heads,
-        p_dropout=0.0,
-        window_size=None,
-        heads_share=False,
-        block_length=None,
-        proximal_bias=False,
-        proximal_init=False,
-    ):
-        super().__init__()
-        assert channels % n_heads == 0
-
-        self.channels = channels
-        self.out_channels = out_channels
-        self.n_heads = n_heads
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.heads_share = heads_share
-        self.block_length = block_length
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-        self.attn = None
-
-        self.k_channels = channels // n_heads
-        self.conv_q = LoRALinear1d(channels, channels, gin_channels, 2)
-        self.conv_k = LoRALinear1d(channels, channels, gin_channels, 2)
-        self.conv_v = LoRALinear1d(channels, channels, gin_channels, 2)
-        self.conv_qkw = weight_norm(nn.Conv1d(channels, channels, 5, 1, groups=channels, padding=2))
-        self.conv_vw = weight_norm(nn.Conv1d(channels, channels, 5, 1, groups=channels, padding=2))
-        self.conv_o = LoRALinear1d(channels, out_channels, gin_channels, 2)
-        self.drop = nn.Dropout(p_dropout)
-
-        if window_size is not None:
-            n_heads_rel = 1 if heads_share else n_heads
-            rel_stddev = self.k_channels**-0.5
-            self.emb_rel_k = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-            self.emb_rel_v = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-
-    def forward(self, x, c, g, attn_mask=None):
-        q = self.conv_qkw(self.conv_q(x, g))
-        k = self.conv_qkw(self.conv_k(c, g))
-        v = self.conv_vw(self.conv_v(c, g))
-
-        x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-        x = self.conv_o(x, g)
-        return x
-
-    def attention(self, query, key, value, mask=None):
-        # reshape [b, d, t] -> [b, n_h, t, d_k]
-        b, d, t_s, t_t = (*key.size(), query.size(2))
-        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-        if self.window_size is not None:
-            assert (
-                t_s == t_t
-            ), "Relative attention is only available for self-attention."
-            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-            rel_logits = self._matmul_with_relative_keys(
-                query / math.sqrt(self.k_channels), key_relative_embeddings
-            )
-            scores_local = self._relative_position_to_absolute_position(rel_logits)
-            scores = scores + scores_local
-        if self.proximal_bias:
-            assert t_s == t_t, "Proximal bias is only available for self-attention."
-            scores = scores + self._attention_bias_proximal(t_s).to(
-                device=scores.device, dtype=scores.dtype
-            )
-        if mask is not None:
-            scores = scores.masked_fill(mask == 0, -1e4)
-            if self.block_length is not None:
-                assert (
-                    t_s == t_t
-                ), "Local attention is only available for self-attention."
-                block_mask = (
-                    torch.ones_like(scores)
-                    .triu(-self.block_length)
-                    .tril(self.block_length)
-                )
-                scores = scores.masked_fill(block_mask == 0, -1e4)
-        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
-        p_attn = self.drop(p_attn)
-        output = torch.matmul(p_attn, value)
-        if self.window_size is not None:
-            relative_weights = self._absolute_position_to_relative_position(p_attn)
-            value_relative_embeddings = self._get_relative_embeddings(
-                self.emb_rel_v, t_s
-            )
-            output = output + self._matmul_with_relative_values(
-                relative_weights, value_relative_embeddings
-            )
-        output = (
-            output.transpose(2, 3).contiguous().view(b, d, t_t)
-        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
-        return output, p_attn
-
-    def _matmul_with_relative_values(self, x, y):
-        """
-        x: [b, h, l, m]
-        y: [h or 1, m, d]
-        ret: [b, h, l, d]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0))
-        return ret
-
-    def _matmul_with_relative_keys(self, x, y):
-        """
-        x: [b, h, l, d]
-        y: [h or 1, m, d]
-        ret: [b, h, l, m]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-        return ret
-
-    def _get_relative_embeddings(self, relative_embeddings, length):
-        max_relative_position = 2 * self.window_size + 1
-        # Pad first before slice to avoid using cond ops.
-        pad_length = max(length - (self.window_size + 1), 0)
-        slice_start_position = max((self.window_size + 1) - length, 0)
-        slice_end_position = slice_start_position + 2 * length - 1
-        if pad_length > 0:
-            padded_relative_embeddings = F.pad(
-                relative_embeddings,
-                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
-            )
-        else:
-            padded_relative_embeddings = relative_embeddings
-        used_relative_embeddings = padded_relative_embeddings[
-            :, slice_start_position:slice_end_position
-        ]
-        return used_relative_embeddings
-
-    def _relative_position_to_absolute_position(self, x):
-        """
-        x: [b, h, l, 2*l-1]
-        ret: [b, h, l, l]
-        """
-        batch, heads, length, _ = x.size()
-        # Concat columns of pad to shift from relative to absolute indexing.
-        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
-        # Concat extra elements so to add up to shape (len+1, 2*len-1).
-        x_flat = x.view([batch, heads, length * 2 * length])
-        x_flat = F.pad(
-            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
-        )
-
-        # Reshape and slice out the padded elements.
-        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
-            :, :, :length, length - 1 :
-        ]
-        return x_final
-
-    def _absolute_position_to_relative_position(self, x):
-        """
-        x: [b, h, l, l]
-        ret: [b, h, l, 2*l-1]
-        """
-        batch, heads, length, _ = x.size()
-        # padd along column
-        x = F.pad(
-            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
-        )
-        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
-        # add 0's in the beginning that will skew the elements after reshape
-        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
-        return x_final
-
-    def _attention_bias_proximal(self, length):
-        """Bias for self-attention to encourage attention to close positions.
-        Args:
-          length: an integer scalar.
-        Returns:
-          a Tensor with shape [1, 1, length, length]
-        """
-        r = torch.arange(length, dtype=torch.float32)
-        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-    def remove_weight_norm(self):
-        self.conv_q.remove_weight_norm()
-        self.conv_k.remove_weight_norm()
-        self.conv_v.remove_weight_norm()
-        self.conv_o.remove_weight_norm()
-        remove_weight_norm(self.conv_qkw)
-        remove_weight_norm(self.conv_vw)
-
-
-class FFN(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        filter_channels,
-        gin_channels,
-        kernel_size,
-        p_dropout=0.0,
-        activation=None,
-        causal=False,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.activation = activation
-        self.causal = causal
-
-        self.conv_1 = LoRALinear1d(in_channels, filter_channels, gin_channels, 2)
-        self.conv_2 = LoRALinear1d(filter_channels, out_channels, gin_channels, 2)
-        self.drop = nn.Dropout(p_dropout)
-
-    def forward(self, x, x_mask, g):
-        x = self.conv_1(x * x_mask, g)
-        if self.activation == "gelu":
-            x = x * torch.sigmoid(1.702 * x)
-        else:
-            x = torch.relu(x)
-        x = self.drop(x)
-        x = self.conv_2(x * x_mask, g)
-        return x * x_mask
-
-    def _causal_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = self.kernel_size - 1
-        pad_r = 0
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
-
-    def _same_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = (self.kernel_size - 1) // 2
-        pad_r = self.kernel_size // 2
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
-
-
-    def remove_weight_norm(self):
-        self.conv_1.remove_weight_norm()
-        self.conv_2.remove_weight_norm()

server/voice_changer/RVC/inferencer/model_v3/models.py

@@ -1,522 +0,0 @@
-import math
-import os
-import sys
-
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import Conv1d, Conv2d, ConvTranspose1d
-from torch.nn import functional as F
-from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
-
-from . import attentions, commons, modules
-from .commons import get_padding, init_weights
-from .modules import (CausalConvTranspose1d, ConvNext2d, DilatedCausalConv1d,
-                      LoRALinear1d, ResBlock1, WaveConv1D)
-
-parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-sys.path.append(parent_dir)
-
-
-class TextEncoder(nn.Module):
-    def __init__(
-        self,
-        out_channels: int,
-        hidden_channels: int,
-        filter_channels: int,
-        emb_channels: int,
-        gin_channels: int,
-        n_heads: int,
-        n_layers: int,
-        kernel_size: int,
-        p_dropout: int,
-        f0: bool = True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.emb_channels = emb_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(emb_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.emb_g = nn.Conv1d(gin_channels, hidden_channels, 1)
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, gin_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-
-    def forward(self, phone, pitch, lengths, g):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        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, g)
-        x = self.proj(x)
-
-        return x, None, x_mask
-
-
-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,
-        gin_channels,
-        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.Conv1d(gin_channels, 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)
-        sine_raw = torch.transpose(sine_wavs, 1, 2).to(device=x.device, dtype=x.dtype)
-        return sine_raw, 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,
-        harmonic_num=16,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.upsample_rates = upsample_rates
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, gin_channels=gin_channels, harmonic_num=harmonic_num, is_half=is_half
-        )
-        self.gpre = Conv1d(gin_channels, initial_channel, 1)
-        self.conv_pre = ResBlock1(initial_channel, upsample_initial_channel, gin_channels, [7] * 5, [1] * 5, [1, 2, 4, 8, 1], 1, 2)
-
-        self.ups = nn.ModuleList()
-        self.resblocks = nn.ModuleList()
-        c_cur = upsample_initial_channel
-        for i, u in enumerate(upsample_rates):
-            c_pre = c_cur
-            c_cur = c_pre // 2
-            self.ups.append(
-                CausalConvTranspose1d(
-                    c_pre,
-                    c_pre,
-                    kernel_rate=3,
-                    stride=u,
-                    groups=c_pre,
-                )
-            )
-            self.resblocks.append(ResBlock1(c_pre, c_cur, gin_channels, [11] * 5, [1] * 5, [1, 2, 4, 8, 1], 1, r=2))
-        self.conv_post = DilatedCausalConv1d(c_cur, 1, 5, stride=1, groups=1, dilation=1, bias=False)
-        self.noise_convs = nn.ModuleList()
-        self.noise_pre = LoRALinear1d(1 + harmonic_num, c_pre, gin_channels, r=2+harmonic_num)
-        for i, u in enumerate(upsample_rates[::-1]):
-            c_pre = c_pre * 2
-            c_cur = c_cur * 2
-            if i + 1 < len(upsample_rates):
-                self.noise_convs.append(DilatedCausalConv1d(c_cur, c_pre, kernel_size=u*3, stride=u, groups=c_cur, dilation=1))
-            else:
-                self.noise_convs.append(DilatedCausalConv1d(c_cur, initial_channel, kernel_size=u*3, stride=u, groups=math.gcd(c_cur, initial_channel), dilation=1))
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, x_mask, f0f, g):
-        har_source, noi_source, uv = self.m_source(f0f, self.upp)
-        har_source = self.noise_pre(har_source, g)
-        x_sources = [har_source]
-        for c in self.noise_convs:
-            har_source = c(har_source)
-            x_sources.append(har_source)
-
-        x = x + x_sources[-1]
-        x = x + self.gpre(g)
-        x = self.conv_pre(x, x_mask, g)
-        for i, u in enumerate(self.upsample_rates):
-            x_mask = torch.repeat_interleave(x_mask, u, 2)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x = self.resblocks[i](x + x_sources[-i-2], x_mask, g)
-
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        if x_mask is not None:
-            x *= x_mask
-        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()
-        remove_weight_norm(self.noise_pre)
-        remove_weight_norm(self.conv_post)
-
-
-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,
-        emb_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.emb_channels = emb_channels
-        self.sr = sr
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-
-        self.emb_pitch = nn.Embedding(256, emb_channels)  # pitch 256
-        self.dec = GeneratorNSF(
-            emb_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-        )
-
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print(
-            "gin_channels:",
-            gin_channels,
-            "self.spk_embed_dim:",
-            self.spk_embed_dim,
-            "emb_channels:",
-            emb_channels,
-        )
-
-    def remove_weight_norm(self):
-        self.dec.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
-        # m_p, _, x_mask = self.enc_p(phone, pitch, phone_lengths, g)
-        # z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        # z_p = self.flow(m_p * x_mask, x_mask, g=g)
-
-        x = phone + self.emb_pitch(pitch)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(phone_lengths, x.size(2)), 1).to(
-            phone.dtype
-        )
-
-        m_p_slice, ids_slice = commons.rand_slice_segments(
-            x, phone_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)
-        mask_slice = commons.slice_segments(x_mask, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(m_p_slice, mask_slice, pitchf, g)
-        return o, ids_slice, x_mask, g
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        x = phone + self.emb_pitch(pitch)
-        x = torch.transpose(x, 1, -1)
-        x_mask = torch.unsqueeze(commons.sequence_mask(phone_lengths, x.size(2)), 1).to(
-            phone.dtype
-        )
-        o = self.dec((x * x_mask)[:, :, :max_len], x_mask, nsff0, g)
-        return o, x_mask, (None, None, None, None)
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(
-            self,
-            hidden_channels: int,
-            filter_channels: int,
-            gin_channels: int,
-            n_heads: int,
-            n_layers: int,
-            kernel_size: int,
-            p_dropout: int,
-            ):
-        super(DiscriminatorS, self).__init__()
-        self.convs = WaveConv1D(2, hidden_channels, gin_channels, [10, 7, 7, 7, 5, 3, 3], [5, 4, 4, 4, 3, 2, 2], [1] * 7, hidden_channels // 2, False)
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, gin_channels, n_heads, n_layers//2, kernel_size, p_dropout
-        )
-        self.cross = weight_norm(torch.nn.Conv1d(gin_channels, hidden_channels, 1, 1))
-        self.conv_post = weight_norm(torch.nn.Conv1d(hidden_channels, 1, 3, 1, padding=get_padding(5, 1)))
-
-    def forward(self, x, g):
-        x = self.convs(x)
-        x_mask = torch.ones([x.shape[0], 1, x.shape[2]], device=x.device, dtype=x.dtype)
-        x = self.encoder(x, x_mask, g)
-        fmap = [x]
-        x = x + x * self.cross(g)
-        y = self.conv_post(x)
-        return y, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, gin_channels, upsample_rates, final_dim=256, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        self.init_kernel_size = upsample_rates[-1] * 3
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        N = len(upsample_rates)
-        self.init_conv = norm_f(Conv2d(1, final_dim // (2 ** (N - 1)), (self.init_kernel_size, 1), (upsample_rates[-1], 1)))
-        self.convs = nn.ModuleList()
-        for i, u in enumerate(upsample_rates[::-1][1:], start=1):
-            self.convs.append(
-                ConvNext2d(
-                    final_dim // (2 ** (N - i)),
-                    final_dim // (2 ** (N - i - 1)),
-                    gin_channels,
-                    (u*3, 1),
-                    (u, 1),
-                    4,
-                    r=2
-                )
-            )
-        self.conv_post = weight_norm(Conv2d(final_dim, 1, (3, 1), (1, 1)))
-
-    def forward(self, x, g):
-        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, (n_pad, 0), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        x = torch.flip(x, dims=[2])
-        x = F.pad(x, [0, 0, 0, self.init_kernel_size - 1], mode="constant")
-        x = self.init_conv(x)
-        x = F.leaky_relu(x, modules.LRELU_SLOPE)
-        x = torch.flip(x, dims=[2])
-
-        for i, l in enumerate(self.convs):
-            x = l(x, g)
-            if i >= 1:
-                fmap.append(x)
-
-        x = F.pad(x, [0, 0, 2, 0], mode="constant")
-        x = self.conv_post(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, upsample_rates, gin_channels, periods=[2, 3, 5, 7, 11, 17], **kwargs):
-        super(MultiPeriodDiscriminator, self).__init__()
-
-        # discs = [DiscriminatorS(hidden_channels, filter_channels, gin_channels, n_heads, n_layers, kernel_size, p_dropout)]
-        discs = [
-            DiscriminatorP(i, gin_channels, upsample_rates, use_spectral_norm=False) for i in periods
-        ]
-        self.ups = np.prod(upsample_rates)
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat, g):
-        fmap_rs = []
-        fmap_gs = []
-        y_d_rs = []
-        y_d_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y, g)
-            y_d_g, fmap_g = d(y_hat, g)
-            # 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

server/voice_changer/RVC/inferencer/model_v3/modules.py

@@ -1,626 +0,0 @@
-import math
-
-import torch
-from torch import nn
-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 . import commons, modules
-from .commons import get_padding, init_weights
-from .transforms import piecewise_rational_quadratic_transform
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-5):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        x = x.transpose(1, -1)
-        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-        return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        hidden_channels,
-        out_channels,
-        kernel_size,
-        n_layers,
-        p_dropout,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_channels = hidden_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-        assert n_layers > 1, "Number of layers should be larger than 0."
-
-        self.conv_layers = nn.ModuleList()
-        self.norm_layers = nn.ModuleList()
-        self.conv_layers.append(
-            nn.Conv1d(
-                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
-            )
-        )
-        self.norm_layers.append(LayerNorm(hidden_channels))
-        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
-        for _ in range(n_layers - 1):
-            self.conv_layers.append(
-                nn.Conv1d(
-                    hidden_channels,
-                    hidden_channels,
-                    kernel_size,
-                    padding=kernel_size // 2,
-                )
-            )
-            self.norm_layers.append(LayerNorm(hidden_channels))
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask):
-        x_org = x
-        for i in range(self.n_layers):
-            x = self.conv_layers[i](x * x_mask)
-            x = self.norm_layers[i](x)
-            x = self.relu_drop(x)
-        x = x_org + self.proj(x)
-        return x * x_mask
-
-
-class DDSConv(nn.Module):
-    """
-    Dialted and Depth-Separable Convolution
-    """
-
-    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
-        super().__init__()
-        self.channels = channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-
-        self.drop = nn.Dropout(p_dropout)
-        self.convs_sep = nn.ModuleList()
-        self.convs_1x1 = nn.ModuleList()
-        self.norms_1 = nn.ModuleList()
-        self.norms_2 = nn.ModuleList()
-        for i in range(n_layers):
-            dilation = kernel_size**i
-            padding = (kernel_size * dilation - dilation) // 2
-            self.convs_sep.append(
-                nn.Conv1d(
-                    channels,
-                    channels,
-                    kernel_size,
-                    groups=channels,
-                    dilation=dilation,
-                    padding=padding,
-                )
-            )
-            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-            self.norms_1.append(LayerNorm(channels))
-            self.norms_2.append(LayerNorm(channels))
-
-    def forward(self, x, x_mask, g=None):
-        if g is not None:
-            x = x + g
-        for i in range(self.n_layers):
-            y = self.convs_sep[i](x * x_mask)
-            y = self.norms_1[i](y)
-            y = F.gelu(y)
-            y = self.convs_1x1[i](y)
-            y = self.norms_2[i](y)
-            y = F.gelu(y)
-            y = self.drop(y)
-            x = x + y
-        return x * x_mask
-
-
-class WN(torch.nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-        p_dropout=0,
-    ):
-        super(WN, self).__init__()
-        assert kernel_size % 2 == 1
-        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.p_dropout = p_dropout
-
-        self.in_layers = torch.nn.ModuleList()
-        self.res_skip_layers = torch.nn.ModuleList()
-        self.drop = nn.Dropout(p_dropout)
-
-        if gin_channels != 0:
-            cond_layer = torch.nn.Conv1d(
-                gin_channels, 2 * hidden_channels * n_layers, 1
-            )
-            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
-
-        for i in range(n_layers):
-            dilation = dilation_rate**i
-            padding = int((kernel_size * dilation - dilation) / 2)
-            in_layer = torch.nn.Conv1d(
-                hidden_channels,
-                2 * hidden_channels,
-                kernel_size,
-                dilation=dilation,
-                padding=padding,
-            )
-            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
-            self.in_layers.append(in_layer)
-
-            # last one is not necessary
-            if i < n_layers - 1:
-                res_skip_channels = 2 * hidden_channels
-            else:
-                res_skip_channels = hidden_channels
-
-            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
-            self.res_skip_layers.append(res_skip_layer)
-
-    def forward(self, x, x_mask, g=None, **kwargs):
-        output = torch.zeros_like(x)
-        n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-        if g is not None:
-            g = self.cond_layer(g)
-
-        for i in range(self.n_layers):
-            x_in = self.in_layers[i](x)
-            if g is not None:
-                cond_offset = i * 2 * self.hidden_channels
-                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
-            else:
-                g_l = torch.zeros_like(x_in)
-
-            acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
-            acts = self.drop(acts)
-
-            res_skip_acts = self.res_skip_layers[i](acts)
-            if i < self.n_layers - 1:
-                res_acts = res_skip_acts[:, : self.hidden_channels, :]
-                x = (x + res_acts) * x_mask
-                output = output + res_skip_acts[:, self.hidden_channels :, :]
-            else:
-                output = output + res_skip_acts
-        return output * x_mask
-
-    def remove_weight_norm(self):
-        if self.gin_channels != 0:
-            torch.nn.utils.remove_weight_norm(self.cond_layer)
-        for l in self.in_layers:
-            torch.nn.utils.remove_weight_norm(l)
-        for l in self.res_skip_layers:
-            torch.nn.utils.remove_weight_norm(l)
-
-
-class DilatedCausalConv1d(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, groups=1, dilation=1, bias=True):
-        super(DilatedCausalConv1d, self).__init__()
-        self.kernel_size = kernel_size
-        self.dilation = dilation
-        self.stride = stride
-        self.conv = weight_norm(nn.Conv1d(in_channels, out_channels, kernel_size, stride=stride, groups=groups, dilation=dilation, bias=bias))
-        init_weights(self.conv)
-
-    def forward(self, x):
-        x = torch.flip(x, [2])
-        x = F.pad(x, [0, (self.kernel_size - 1) * self.dilation], mode="constant", value=0.)
-        size = x.shape[2] // self.stride
-        x = self.conv(x)[:, :, :size]
-        x = torch.flip(x, [2])
-        return x
-
-    def remove_weight_norm(self):
-        remove_weight_norm(self.conv)
-
-
-class CausalConvTranspose1d(nn.Module):
-    """
-    padding = 0, dilation = 1のとき
-
-    Lout = (Lin - 1) * stride + kernel_rate * stride + output_padding
-    Lout = Lin * stride + (kernel_rate - 1) * stride + output_padding
-    output_paddingいらないね
-    """
-    def __init__(self, in_channels, out_channels, kernel_rate=3, stride=1, groups=1):
-        super(CausalConvTranspose1d, self).__init__()
-        kernel_size = kernel_rate * stride
-        self.trim_size = (kernel_rate - 1) * stride
-        self.conv = weight_norm(nn.ConvTranspose1d(in_channels, out_channels, kernel_size, stride=stride, groups=groups))
-
-    def forward(self, x):
-        x = self.conv(x)
-        return x[:, :, :-self.trim_size]
-
-    def remove_weight_norm(self):
-        remove_weight_norm(self.conv)
-
-
-class LoRALinear1d(nn.Module):
-    def __init__(self, in_channels, out_channels, info_channels, r):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.info_channels = info_channels
-        self.r = r
-        self.main_fc = weight_norm(nn.Conv1d(in_channels, out_channels, 1))
-        self.adapter_in = nn.Conv1d(info_channels, in_channels * r, 1)
-        self.adapter_out = nn.Conv1d(info_channels, out_channels * r, 1)
-        nn.init.normal_(self.adapter_in.weight.data, 0, 0.01)
-        nn.init.constant_(self.adapter_out.weight.data, 1e-6)
-        init_weights(self.main_fc)
-        self.adapter_in = weight_norm(self.adapter_in)
-        self.adapter_out = weight_norm(self.adapter_out)
-
-    def forward(self, x, g):
-        a_in = self.adapter_in(g).view(-1, self.in_channels, self.r)
-        a_out = self.adapter_out(g).view(-1, self.r, self.out_channels)
-        x = self.main_fc(x) + torch.einsum("brl,brc->bcl", torch.einsum("bcl,bcr->brl", x, a_in), a_out)
-        return x
-
-    def remove_weight_norm(self):
-        remove_weight_norm(self.main_fc)
-        remove_weight_norm(self.adapter_in)
-        remove_weight_norm(self.adapter_out)
-
-
-class LoRALinear2d(nn.Module):
-    def __init__(self, in_channels, out_channels, info_channels, r):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.info_channels = info_channels
-        self.r = r
-        self.main_fc = weight_norm(nn.Conv2d(in_channels, out_channels, (1, 1), (1, 1)))
-        self.adapter_in = nn.Conv1d(info_channels, in_channels * r, 1)
-        self.adapter_out = nn.Conv1d(info_channels, out_channels * r, 1)
-        nn.init.normal_(self.adapter_in.weight.data, 0, 0.01)
-        nn.init.constant_(self.adapter_out.weight.data, 1e-6)
-        self.adapter_in = weight_norm(self.adapter_in)
-        self.adapter_out = weight_norm(self.adapter_out)
-
-    def forward(self, x, g):
-        a_in = self.adapter_in(g).view(-1, self.in_channels, self.r)
-        a_out = self.adapter_out(g).view(-1, self.r, self.out_channels)
-        x = self.main_fc(x) + torch.einsum("brhw,brc->bchw", torch.einsum("bchw,bcr->brhw", x, a_in), a_out)
-        return x
-
-    def remove_weight_norm(self):
-        remove_weight_norm(self.main_fc)
-        remove_weight_norm(self.adapter_in)
-        remove_weight_norm(self.adapter_out)
-
-
-class WaveConv1D(torch.nn.Module):
-    def __init__(self, in_channels, out_channels, gin_channels, kernel_sizes, strides, dilations, extend_ratio, r, use_spectral_norm=False):
-        super(WaveConv1D, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        inner_channels = int(in_channels * extend_ratio)
-        self.convs = []
-        # self.norms = []
-        self.convs.append(LoRALinear1d(in_channels, inner_channels, gin_channels, r))
-        for i, (k, s, d) in enumerate(zip(kernel_sizes, strides, dilations), start=1):
-            self.convs.append(norm_f(Conv1d(inner_channels, inner_channels, k, s, dilation=d, groups=inner_channels, padding=get_padding(k, d))))
-            if i < len(kernel_sizes):
-                self.convs.append(norm_f(Conv1d(inner_channels, inner_channels, 1, 1)))
-            else:
-                self.convs.append(norm_f(Conv1d(inner_channels, out_channels, 1, 1)))
-        self.convs = nn.ModuleList(self.convs)
-
-    def forward(self, x, g, x_mask=None):
-        for i, l in enumerate(self.convs):
-            if i % 2:
-                x_ = l(x)
-            else:
-                x_ = l(x, g)
-            x = F.leaky_relu(x_, modules.LRELU_SLOPE)
-            if x_mask is not None:
-                x *= x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for i, c in enumerate(self.convs):
-            if i % 2:
-                remove_weight_norm(c)
-            else:
-                c.remove_weight_norm()
-
-
-class MBConv2d(torch.nn.Module):
-    """
-    Causal MBConv2D
-    """
-    def __init__(self, in_channels, out_channels, gin_channels, kernel_size, stride, extend_ratio, r, use_spectral_norm=False):
-        super(MBConv2d, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        inner_channels = int(in_channels * extend_ratio)
-        self.kernel_size = kernel_size
-        self.pre_pointwise = LoRALinear2d(in_channels, inner_channels, gin_channels, r=r)
-        self.depthwise = norm_f(Conv2d(inner_channels, inner_channels, kernel_size, stride, groups=inner_channels))
-        self.post_pointwise = LoRALinear2d(inner_channels, out_channels, gin_channels, r=r)
-
-    def forward(self, x, g):
-        x = self.pre_pointwise(x, g)
-        x = F.pad(x, [0, 0, self.kernel_size[0] - 1, 0], mode="constant")
-        x = self.depthwise(x)
-        x = self.post_pointwise(x, g)
-        return x
-
-
-class ConvNext2d(torch.nn.Module):
-    """
-    Causal ConvNext Block
-    stride = 1 only
-    """
-    def __init__(self, in_channels, out_channels, gin_channels, kernel_size, stride, extend_ratio, r, use_spectral_norm=False):
-        super(ConvNext2d, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        inner_channels = int(in_channels * extend_ratio)
-        self.kernel_size = kernel_size
-        self.dwconv = norm_f(Conv2d(in_channels, in_channels, kernel_size, stride, groups=in_channels))
-        self.pwconv1 = LoRALinear2d(in_channels, inner_channels, gin_channels, r=r)
-        self.pwconv2 = LoRALinear2d(inner_channels, out_channels, gin_channels, r=r)
-        self.act = nn.GELU()
-        self.norm = LayerNorm(in_channels)
-
-    def forward(self, x, g):
-        x = F.pad(x, [0, 0, self.kernel_size[0] - 1, 0], mode="constant")
-        x = self.dwconv(x)
-        x = self.norm(x)
-        x = self.pwconv1(x, g)
-        x = F.leaky_relu(x, modules.LRELU_SLOPE)
-        x = self.pwconv2(x, g)
-        x = F.leaky_relu(x, modules.LRELU_SLOPE)
-        return x
-
-    def remove_weight_norm(self):
-        remove_weight_norm(self.dwconv)
-
-
-class SqueezeExcitation1D(torch.nn.Module):
-    def __init__(self, input_channels, squeeze_channels, gin_channels, use_spectral_norm=False):
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        super(SqueezeExcitation1D, self).__init__()
-        self.fc1 = LoRALinear1d(input_channels, squeeze_channels, gin_channels, 2)
-        self.fc2 = LoRALinear1d(squeeze_channels, input_channels, gin_channels, 2)
-
-    def _scale(self, x, x_mask, g):
-        x_length = torch.sum(x_mask, dim=2, keepdim=True)
-        x_length = torch.maximum(x_length, torch.ones_like(x_length))
-        scale = torch.sum(x * x_mask, dim=2, keepdim=True) / x_length
-        scale = self.fc1(scale, g)
-        scale = F.leaky_relu(scale, modules.LRELU_SLOPE)
-        scale = self.fc2(scale, g)
-        return torch.sigmoid(scale)
-
-    def forward(self, x, x_mask, g):
-        scale = self._scale(x, x_mask, g)
-        return scale * x
-
-    def remove_weight_norm(self):
-        self.fc1.remove_weight_norm()
-        self.fc2.remove_weight_norm()
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, in_channels, out_channels, gin_channels, kernel_sizes, strides, dilations, extend_ratio, r):
-        super(ResBlock1, self).__init__()
-        norm_f = weight_norm 
-        inner_channels = int(in_channels * extend_ratio)
-        self.dconvs = nn.ModuleList()
-        self.pconvs = nn.ModuleList()
-        # self.ses = nn.ModuleList()
-        self.norms = nn.ModuleList()
-        self.init_conv = LoRALinear1d(in_channels, inner_channels, gin_channels, r)
-        for i, (k, s, d) in enumerate(zip(kernel_sizes, strides, dilations)):
-            self.norms.append(LayerNorm(inner_channels))
-            self.dconvs.append(DilatedCausalConv1d(inner_channels, inner_channels, k, stride=s, dilation=d, groups=inner_channels))
-            if i < len(kernel_sizes) - 1:
-                self.pconvs.append(LoRALinear1d(inner_channels, inner_channels, gin_channels, r))
-        self.out_conv = LoRALinear1d(inner_channels, out_channels, gin_channels, r)
-        init_weights(self.init_conv)
-        init_weights(self.out_conv)
-
-    def forward(self, x, x_mask, g):
-        x *= x_mask
-        x = self.init_conv(x, g)
-        for i in range(len(self.dconvs)):
-            x *= x_mask
-            x = self.norms[i](x)
-            x_ = self.dconvs[i](x)
-            x_ = F.leaky_relu(x_, modules.LRELU_SLOPE)
-            if i < len(self.dconvs) - 1:
-                x = x + self.pconvs[i](x_, g)
-        x = self.out_conv(x_, g)
-        return x
-
-    def remove_weight_norm(self):
-        for c in self.dconvs:
-            c.remove_weight_norm()
-        for c in self.pconvs:
-            c.remove_weight_norm()
-        self.init_conv.remove_weight_norm()
-        self.out_conv.remove_weight_norm()
-
-
-class Log(nn.Module):
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-            logdet = torch.sum(-y, [1, 2])
-            return y, logdet
-        else:
-            x = torch.exp(x) * x_mask
-            return x
-
-
-class Flip(nn.Module):
-    def forward(self, x, *args, reverse=False, **kwargs):
-        x = torch.flip(x, [1])
-        if not reverse:
-            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-            return x, logdet
-        else:
-            return x
-
-
-class ElementwiseAffine(nn.Module):
-    def __init__(self, channels):
-        super().__init__()
-        self.channels = channels
-        self.m = nn.Parameter(torch.zeros(channels, 1))
-        self.logs = nn.Parameter(torch.zeros(channels, 1))
-
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = self.m + torch.exp(self.logs) * x
-            y = y * x_mask
-            logdet = torch.sum(self.logs * x_mask, [1, 2])
-            return y, logdet
-        else:
-            x = (x - self.m) * torch.exp(-self.logs) * x_mask
-            return x
-
-
-class ResidualCouplingLayer(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        p_dropout=0,
-        gin_channels=0,
-        mean_only=False,
-    ):
-        assert channels % 2 == 0, "channels should be divisible by 2"
-        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.half_channels = channels // 2
-        self.mean_only = mean_only
-
-        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-        self.enc = WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            p_dropout=p_dropout,
-            gin_channels=gin_channels,
-        )
-        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-        self.post.weight.data.zero_()
-        self.post.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0) * x_mask
-        h = self.enc(h, x_mask, g=g)
-        stats = self.post(h) * x_mask
-        if not self.mean_only:
-            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
-        else:
-            m = stats
-            logs = torch.zeros_like(m)
-
-        if not reverse:
-            x1 = m + x1 * torch.exp(logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            logdet = torch.sum(logs, [1, 2])
-            return x, logdet
-        else:
-            x1 = (x1 - m) * torch.exp(-logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            return x
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class ConvFlow(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        filter_channels,
-        kernel_size,
-        n_layers,
-        num_bins=10,
-        tail_bound=5.0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.num_bins = num_bins
-        self.tail_bound = tail_bound
-        self.half_channels = in_channels // 2
-
-        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
-        self.proj = nn.Conv1d(
-            filter_channels, self.half_channels * (num_bins * 3 - 1), 1
-        )
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0)
-        h = self.convs(h, x_mask, g=g)
-        h = self.proj(h) * x_mask
-
-        b, c, t = x0.shape
-        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
-
-        unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
-        unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
-            self.filter_channels
-        )
-        unnormalized_derivatives = h[..., 2 * self.num_bins :]
-
-        x1, logabsdet = piecewise_rational_quadratic_transform(
-            x1,
-            unnormalized_widths,
-            unnormalized_heights,
-            unnormalized_derivatives,
-            inverse=reverse,
-            tails="linear",
-            tail_bound=self.tail_bound,
-        )
-
-        x = torch.cat([x0, x1], 1) * x_mask
-        logdet = torch.sum(logabsdet * x_mask, [1, 2])
-        if not reverse:
-            return x, logdet
-        else:
-            return x

server/voice_changer/RVC/inferencer/voras_beta/config.py

@@ -32,27 +32,17 @@ class TrainConfigData(BaseModel):
 
 
 class TrainConfigModel(BaseModel):
+    emb_channels: 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]
     use_spectral_norm: bool
     gin_channels: int
-    emb_channels: int
     spk_embed_dim: int
 
 
 class TrainConfig(BaseModel):
-    version: Literal["v1", "v2"] = "v2"
+    version: Literal["voras"] = "voras"
     train: TrainConfigTrain
     data: TrainConfigData
     model: TrainConfigModel

server/voice_changer/RVC/inferencer/voras_beta/models.py

@@ -0,0 +1,238 @@
+import math
+import os
+import sys
+
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import Conv2d
+from torch.nn import functional as F
+from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
+
+from . import commons, modules
+from .commons import get_padding
+from .modules import (ConvNext2d, HarmonicEmbedder, IMDCTSymExpHead,
+                      LoRALinear1d, SnakeFilter, WaveBlock)
+
+parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.append(parent_dir)
+
+sr2sr = {
+    "24k": 24000,
+    "32k": 32000,
+    "40k": 40000,
+    "48k": 48000,
+}
+
+class GeneratorVoras(torch.nn.Module):
+    def __init__(
+        self,
+        emb_channels,
+        inter_channels,
+        gin_channels,
+        n_layers,
+        sr,
+        hop_length,
+    ):
+        super(GeneratorVoras, self).__init__()
+        self.n_layers = n_layers
+        self.emb_pitch = HarmonicEmbedder(768, inter_channels, gin_channels, 16, 15)  #   # pitch 256
+        self.plinear = LoRALinear1d(inter_channels, inter_channels, gin_channels, r=8)
+        self.glinear = weight_norm(nn.Conv1d(gin_channels, inter_channels, 1))
+        self.resblocks = nn.ModuleList()
+        self.init_linear = LoRALinear1d(emb_channels, inter_channels, gin_channels, r=4)
+        for _ in range(self.n_layers):
+            self.resblocks.append(WaveBlock(inter_channels, gin_channels, [9] * 2, [1] * 2, [1, 9], 2, r=4))
+        self.head = IMDCTSymExpHead(inter_channels, gin_channels, hop_length, padding="center", sample_rate=sr)
+        self.post = SnakeFilter(4, 8, 9, 2, eps=1e-5)
+
+    def forward(self, x, pitchf, x_mask, g):
+        x = self.init_linear(x, g) + self.plinear(self.emb_pitch(pitchf, g), g) + self.glinear(g)
+        for i in range(self.n_layers):
+            x = self.resblocks[i](x, x_mask, g)
+        x = x * x_mask
+        x = self.head(x, g)
+        x = self.post(x)
+        return torch.tanh(x)
+
+    def remove_weight_norm(self):
+        self.plinear.remove_weight_norm()
+        remove_weight_norm(self.glinear)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+        self.init_linear.remove_weight_norm()
+        self.head.remove_weight_norm()
+        self.post.remove_weight_norm()
+
+    def fix_speaker(self, g):
+        self.plinear.fix_speaker(g)
+        self.init_linear.fix_speaker(g)
+        for l in self.resblocks:
+            l.fix_speaker(g)
+        self.head.fix_speaker(g)
+
+    def unfix_speaker(self, g):
+        self.plinear.unfix_speaker(g)
+        self.init_linear.unfix_speaker(g)
+        for l in self.resblocks:
+            l.unfix_speaker(g)
+        self.head.unfix_speaker(g)
+
+
+class Synthesizer(nn.Module):
+    def __init__(
+        self,
+        segment_size,
+        n_fft,
+        hop_length,
+        inter_channels,
+        n_layers,
+        spk_embed_dim,
+        gin_channels,
+        emb_channels,
+        sr,
+        **kwargs
+    ):
+        super().__init__()
+        if type(sr) == type("strr"):
+            sr = sr2sr[sr]
+        self.segment_size = segment_size
+        self.n_fft = n_fft
+        self.hop_length = hop_length
+        self.inter_channels = inter_channels
+        self.n_layers = n_layers
+        self.spk_embed_dim = spk_embed_dim
+        self.gin_channels = gin_channels
+        self.emb_channels = emb_channels
+        self.sr = sr
+
+        self.dec = GeneratorVoras(
+            emb_channels,
+            inter_channels,
+            gin_channels,
+            n_layers,
+            sr,
+            hop_length
+        )
+
+        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
+        print(
+            "gin_channels:",
+            gin_channels,
+            "self.spk_embed_dim:",
+            self.spk_embed_dim,
+            "emb_channels:",
+            emb_channels,
+        )
+        self.speaker = None
+
+    def remove_weight_norm(self):
+        self.dec.remove_weight_norm()
+
+    def change_speaker(self, sid: int):
+        if self.speaker is not None:
+            g = self.emb_g(torch.from_numpy(np.array(self.speaker))).unsqueeze(-1)
+            self.dec.unfix_speaker(g)
+        g = self.emb_g(torch.from_numpy(np.array(sid))).unsqueeze(-1)
+        self.dec.fix_speaker(g)
+        self.speaker = sid
+
+    def forward(
+        self, phone, phone_lengths, pitch, pitchf, ds
+        ):
+        g = self.emb_g(ds).unsqueeze(-1)
+        x = torch.transpose(phone, 1, -1)
+        x_mask = torch.unsqueeze(commons.sequence_mask(phone_lengths, x.size(2)), 1).to(phone.dtype)
+        x_slice, ids_slice = commons.rand_slice_segments(
+            x, phone_lengths, self.segment_size
+        )
+        pitchf_slice = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
+        mask_slice = commons.slice_segments(x_mask, ids_slice, self.segment_size)
+        o = self.dec(x_slice, pitchf_slice, mask_slice, g)
+        return o, ids_slice, x_mask, g
+
+    def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
+        g = self.emb_g(sid).unsqueeze(-1)
+        x = torch.transpose(phone, 1, -1)
+        x_mask = torch.unsqueeze(commons.sequence_mask(phone_lengths, x.size(2)), 1).to(phone.dtype)
+        o = self.dec((x * x_mask)[:, :, :max_len], nsff0, x_mask, g)
+        return o, x_mask, (None, None, None, None)
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, gin_channels, upsample_rates, final_dim=256, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        self.init_kernel_size = upsample_rates[-1] * 3
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        N = len(upsample_rates)
+        self.init_conv = norm_f(Conv2d(1, final_dim // (2 ** (N - 1)), (self.init_kernel_size, 1), (upsample_rates[-1], 1)))
+        self.convs = nn.ModuleList()
+        for i, u in enumerate(upsample_rates[::-1][1:], start=1):
+            self.convs.append(
+                ConvNext2d(
+                    final_dim // (2 ** (N - i)),
+                    final_dim // (2 ** (N - i - 1)),
+                    gin_channels,
+                    (u*3, 1),
+                    (u, 1),
+                    4,
+                    r=2 + i//2
+                )
+            )
+        self.conv_post = weight_norm(Conv2d(final_dim, 1, (3, 1), (1, 1)))
+
+    def forward(self, x, g):
+        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, (n_pad, 0), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        x = torch.flip(x, dims=[2])
+        x = F.pad(x, [0, 0, 0, self.init_kernel_size - 1], mode="constant")
+        x = self.init_conv(x)
+        x = F.leaky_relu(x, modules.LRELU_SLOPE)
+        x = torch.flip(x, dims=[2])
+        fmap.append(x)
+
+        for i, l in enumerate(self.convs):
+            x = l(x, g)
+            fmap.append(x)
+
+        x = F.pad(x, [0, 0, 2, 0], mode="constant")
+        x = self.conv_post(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, upsample_rates, gin_channels, periods=[2, 3, 5, 7, 11, 17], **kwargs):
+        super(MultiPeriodDiscriminator, self).__init__()
+
+        discs = [
+            DiscriminatorP(i, gin_channels, upsample_rates, use_spectral_norm=False) for i in periods
+        ]
+        self.ups = np.prod(upsample_rates)
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat, g):
+        fmap_rs = []
+        fmap_gs = []
+        y_d_rs = []
+        y_d_gs = []
+        for d in self.discriminators:
+            y_d_r, fmap_r = d(y, g)
+            y_d_g, fmap_g = d(y_hat, g)
+            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

server/voice_changer/RVC/inferencer/voras_beta/modules.py

@@ -0,0 +1,496 @@
+import math
+
+import numpy as np
+import scipy
+import torch
+from torch import nn
+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 torchaudio.functional.functional import _hz_to_mel, _mel_to_hz
+
+from . import commons, modules
+from .commons import get_padding, init_weights
+from .transforms import piecewise_rational_quadratic_transform
+
+LRELU_SLOPE = 0.1
+
+class HarmonicEmbedder(nn.Module):
+    def __init__(self, num_embeddings, embedding_dim, gin_channels, num_head, num_harmonic=0, f0_min=50., f0_max=1100., device="cuda"):
+        super(HarmonicEmbedder, self).__init__()
+        self.embedding_dim = embedding_dim
+        self.num_head = num_head
+        self.num_harmonic = num_harmonic
+
+        f0_mel_min = np.log(1 + f0_min / 700)
+        f0_mel_max = np.log(1 + f0_max * (1 + num_harmonic) / 700)
+        self.sequence = torch.from_numpy(np.linspace(f0_mel_min, f0_mel_max, num_embeddings-2))
+        self.emb_layer = torch.nn.Embedding(num_embeddings, embedding_dim)
+        self.linear_q = Conv1d(gin_channels, num_head * (1 + num_harmonic), 1)
+        self.weight = None
+
+    def forward(self, x, g):
+        b, l = x.size()
+        non_zero = (x != 0.).to(dtype=torch.long).unsqueeze(1)
+        mel = torch.log(1 + x / 700).unsqueeze(1)
+        harmonies = torch.arange(1 + self.num_harmonic, device=x.device, dtype=x.dtype).view(1, 1 + self.num_harmonic, 1) + 1.
+        ix = torch.searchsorted(self.sequence.to(x.device), mel * harmonies).to(x.device) + 1
+        ix = ix * non_zero
+        emb = self.emb_layer(ix).transpose(1, 3).reshape(b, self.num_head, self.embedding_dim // self.num_head, 1 + self.num_harmonic, l)
+        if self.weight is None:
+            weight = torch.nn.functional.softmax(self.linear_q(g).reshape(b, self.num_head, 1, 1 + self.num_harmonic, 1), 3)
+        else:
+            weight = self.weight
+        res = torch.sum(emb * weight, dim=3).reshape(b, self.embedding_dim, l)
+        return res
+
+    def fix_speaker(self, g):
+        self.weight = torch.nn.functional.softmax(self.linear_q(g).reshape(1, self.num_head, 1, 1 + self.num_harmonic, 1), 3)
+
+    def unfix_speaker(self, g):
+        self.weight = None
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-5):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = nn.Parameter(torch.ones(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        x = x.transpose(1, -1)
+        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+        return x.transpose(1, -1)
+
+
+class DilatedCausalConv1d(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, groups=1, dilation=1, bias=True):
+        super(DilatedCausalConv1d, self).__init__()
+        self.kernel_size = kernel_size
+        self.dilation = dilation
+        self.stride = stride
+        self.conv = weight_norm(nn.Conv1d(in_channels, out_channels, kernel_size, stride=stride, groups=groups, dilation=dilation, bias=bias))
+
+    def forward(self, x):
+        x = torch.flip(x, [2])
+        x = F.pad(x, [0, (self.kernel_size - 1) * self.dilation], mode="constant", value=0.)
+        size = x.shape[2] // self.stride
+        x = self.conv(x)[:, :, :size]
+        x = torch.flip(x, [2])
+        return x
+
+    def remove_weight_norm(self):
+        remove_weight_norm(self.conv)
+
+
+class CausalConvTranspose1d(nn.Module):
+    """
+    padding = 0, dilation = 1のとき
+
+    Lout = (Lin - 1) * stride + kernel_rate * stride + output_padding
+    Lout = Lin * stride + (kernel_rate - 1) * stride + output_padding
+    output_paddingいらないね
+    """
+    def __init__(self, in_channels, out_channels, kernel_rate=3, stride=1, groups=1):
+        super(CausalConvTranspose1d, self).__init__()
+        kernel_size = kernel_rate * stride
+        self.trim_size = (kernel_rate - 1) * stride
+        self.conv = weight_norm(nn.ConvTranspose1d(in_channels, out_channels, kernel_size, stride=stride, groups=groups))
+
+    def forward(self, x):
+        x = self.conv(x)
+        return x[:, :, :-self.trim_size]
+
+    def remove_weight_norm(self):
+        remove_weight_norm(self.conv)
+
+
+class LoRALinear1d(nn.Module):
+    def __init__(self, in_channels, out_channels, info_channels, r):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.info_channels = info_channels
+        self.r = r
+        self.main_fc = weight_norm(nn.Conv1d(in_channels, out_channels, 1))
+        self.adapter_in = nn.Conv1d(info_channels, in_channels * r, 1)
+        self.adapter_out = nn.Conv1d(info_channels, out_channels * r, 1)
+        nn.init.normal_(self.adapter_in.weight.data, 0, 0.01)
+        nn.init.constant_(self.adapter_out.weight.data, 1e-6)
+        self.adapter_in = weight_norm(self.adapter_in)
+        self.adapter_out = weight_norm(self.adapter_out)
+        self.speaker_fixed = False
+
+    def forward(self, x, g):
+        x_ = self.main_fc(x)
+        if not self.speaker_fixed:
+            a_in = self.adapter_in(g).view(-1, self.in_channels, self.r)
+            a_out = self.adapter_out(g).view(-1, self.r, self.out_channels)
+            l = torch.einsum("brl,brc->bcl", torch.einsum("bcl,bcr->brl", x, a_in), a_out)
+            x_ = x_ + l
+        return x_
+
+    def remove_weight_norm(self):
+        remove_weight_norm(self.main_fc)
+        remove_weight_norm(self.adapter_in)
+        remove_weight_norm(self.adapter_out)
+
+    def fix_speaker(self, g):
+        self.speaker_fixed = True
+        a_in = self.adapter_in(g).view(-1, self.in_channels, self.r)
+        a_out = self.adapter_out(g).view(-1, self.r, self.out_channels)
+        weight = torch.einsum("bir,bro->oi", a_in, a_out).unsqueeze(2)
+        self.main_fc.weight.data.add_(weight)
+
+    def unfix_speaker(self, g):
+        self.speaker_fixed = False
+        a_in = self.adapter_in(g).view(-1, self.in_channels, self.r)
+        a_out = self.adapter_out(g).view(-1, self.r, self.out_channels)
+        weight = torch.einsum("bir,bro->oi", a_in, a_out).unsqueeze(2)
+        self.main_fc.weight.data.sub_(weight)
+
+
+class LoRALinear2d(nn.Module):
+    def __init__(self, in_channels, out_channels, info_channels, r):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.info_channels = info_channels
+        self.r = r
+        self.main_fc = weight_norm(nn.Conv2d(in_channels, out_channels, (1, 1), (1, 1)))
+        self.adapter_in = nn.Conv1d(info_channels, in_channels * r, 1)
+        self.adapter_out = nn.Conv1d(info_channels, out_channels * r, 1)
+        nn.init.normal_(self.adapter_in.weight.data, 0, 0.01)
+        nn.init.constant_(self.adapter_out.weight.data, 1e-6)
+        self.adapter_in = weight_norm(self.adapter_in)
+        self.adapter_out = weight_norm(self.adapter_out)
+        self.speaker_fixed = False
+
+    def forward(self, x, g):
+        x_ = self.main_fc(x)
+        if not self.speaker_fixed:
+            a_in = self.adapter_in(g).view(-1, self.in_channels, self.r)
+            a_out = self.adapter_out(g).view(-1, self.r, self.out_channels)
+            l = torch.einsum("brhw,brc->bchw", torch.einsum("bchw,bcr->brhw", x, a_in), a_out)
+            x_ = x_ + l
+        return x_
+
+    def remove_weight_norm(self):
+        remove_weight_norm(self.main_fc)
+        remove_weight_norm(self.adapter_in)
+        remove_weight_norm(self.adapter_out)
+
+    def fix_speaker(self, g):
+        a_in = self.adapter_in(g).view(-1, self.in_channels, self.r)
+        a_out = self.adapter_out(g).view(-1, self.r, self.out_channels)
+        weight = torch.einsum("bir,bro->oi", a_in, a_out).unsqueeze(2).unsqueeze(3)
+        self.main_fc.weight.data.add_(weight)
+
+    def unfix_speaker(self, g):
+        a_in = self.adapter_in(g).view(-1, self.in_channels, self.r)
+        a_out = self.adapter_out(g).view(-1, self.r, self.out_channels)
+        weight = torch.einsum("bir,bro->oi", a_in, a_out).unsqueeze(2).unsqueeze(3)
+        self.main_fc.weight.data.sub_(weight)
+
+
+class MBConv2d(torch.nn.Module):
+    """
+    Causal MBConv2D
+    """
+    def __init__(self, in_channels, out_channels, gin_channels, kernel_size, stride, extend_ratio, r, use_spectral_norm=False):
+        super(MBConv2d, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        inner_channels = int(in_channels * extend_ratio)
+        self.kernel_size = kernel_size
+        self.pwconv1 = LoRALinear2d(in_channels, inner_channels, gin_channels, r=r)
+        self.dwconv = norm_f(Conv2d(inner_channels, inner_channels, kernel_size, stride, groups=inner_channels))
+        self.pwconv2 = LoRALinear2d(inner_channels, out_channels, gin_channels, r=r)
+        self.pwnorm = LayerNorm(in_channels)
+        self.dwnorm = LayerNorm(inner_channels)
+
+    def forward(self, x, g):
+        x = self.pwnorm(x)
+        x = self.pwconv1(x, g)
+        x = F.pad(x, [0, 0, self.kernel_size[0] - 1, 0], mode="constant")
+        x = self.dwnorm(x)
+        x = self.dwconv(x)
+        x = F.leaky_relu(x, modules.LRELU_SLOPE)
+        x = self.pwconv2(x, g)
+        x = F.leaky_relu(x, modules.LRELU_SLOPE)
+        return x
+
+class ConvNext2d(torch.nn.Module):
+    """
+    Causal ConvNext Block
+    stride = 1 only
+    """
+    def __init__(self, in_channels, out_channels, gin_channels, kernel_size, stride, extend_ratio, r, use_spectral_norm=False):
+        super(ConvNext2d, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        inner_channels = int(in_channels * extend_ratio)
+        self.kernel_size = kernel_size
+        self.dwconv = norm_f(Conv2d(in_channels, in_channels, kernel_size, stride, groups=in_channels))
+        self.pwconv1 = LoRALinear2d(in_channels, inner_channels, gin_channels, r=r)
+        self.pwconv2 = LoRALinear2d(inner_channels, out_channels, gin_channels, r=r)
+        self.act = nn.GELU()
+        self.norm = LayerNorm(in_channels)
+
+    def forward(self, x, g):
+        x = F.pad(x, [0, 0, self.kernel_size[0] - 1, 0], mode="constant")
+        x = self.dwconv(x)
+        x = self.norm(x)
+        x = self.pwconv1(x, g)
+        x = self.act(x)
+        x = self.pwconv2(x, g)
+        x = self.act(x)
+        return x
+
+    def remove_weight_norm(self):
+        remove_weight_norm(self.dwconv)
+
+
+class WaveBlock(torch.nn.Module):
+    def __init__(self, inner_channels, gin_channels, kernel_sizes, strides, dilations, extend_rate, r):
+        super(WaveBlock, self).__init__()
+        norm_f = weight_norm
+        extend_channels = int(inner_channels * extend_rate)
+        self.dconvs = nn.ModuleList()
+        self.p1convs = nn.ModuleList()
+        self.p2convs = nn.ModuleList()
+        self.norms = nn.ModuleList()
+        self.act = nn.GELU()
+
+        # self.ses = nn.ModuleList()
+        # self.norms = []
+        for i, (k, s, d) in enumerate(zip(kernel_sizes, strides, dilations)):
+            self.dconvs.append(DilatedCausalConv1d(inner_channels, inner_channels, k, stride=s, dilation=d, groups=inner_channels))
+            self.p1convs.append(LoRALinear1d(inner_channels, extend_channels, gin_channels, r))
+            self.p2convs.append(LoRALinear1d(extend_channels, inner_channels, gin_channels, r))
+            self.norms.append(LayerNorm(inner_channels))
+
+    def forward(self, x, x_mask, g):
+        x *= x_mask
+        for i in range(len(self.dconvs)):
+            residual = x.clone()
+            x = self.dconvs[i](x)
+            x = self.norms[i](x)
+            x *= x_mask
+            x = self.p1convs[i](x, g)
+            x = self.act(x)
+            x = self.p2convs[i](x, g)
+            x = residual + x
+        return x
+
+    def remove_weight_norm(self):
+        for c in self.dconvs:
+            c.remove_weight_norm()
+        for c in self.p1convs:
+            c.remove_weight_norm()
+        for c in self.p2convs:
+            c.remove_weight_norm()
+
+    def fix_speaker(self, g):
+        for c in self.p1convs:
+            c.fix_speaker(g)
+        for c in self.p2convs:
+            c.fix_speaker(g)
+
+    def unfix_speaker(self, g):
+        for c in self.p1convs:
+            c.unfix_speaker(g)
+        for c in self.p2convs:
+            c.unfix_speaker(g)
+
+
+class SnakeFilter(torch.nn.Module):
+    """
+    Adaptive filter using snakebeta
+    """
+    def __init__(self, channels, groups, kernel_size, num_layers, eps=1e-6):
+        super(SnakeFilter, self).__init__()
+        self.eps = eps
+        self.num_layers = num_layers
+        inner_channels = channels * groups
+        self.init_conv = DilatedCausalConv1d(1, inner_channels, kernel_size)
+        self.dconvs = torch.nn.ModuleList()
+        self.pconvs = torch.nn.ModuleList()
+        self.post_conv = DilatedCausalConv1d(inner_channels+1, 1, kernel_size, bias=False)
+
+        for i in range(self.num_layers):
+            self.dconvs.append(DilatedCausalConv1d(inner_channels, inner_channels, kernel_size, stride=1, groups=inner_channels, dilation=kernel_size ** (i + 1)))
+            self.pconvs.append(weight_norm(Conv1d(inner_channels, inner_channels, 1, groups=groups)))
+        self.snake_alpha = torch.nn.Parameter(torch.zeros(inner_channels), requires_grad=True)
+        self.snake_beta = torch.nn.Parameter(torch.zeros(inner_channels), requires_grad=True)
+
+    def forward(self, x):
+        y = x.clone()
+        x = self.init_conv(x)
+        for i in range(self.num_layers):
+            # snake activation
+            x = self.dconvs[i](x)
+            x = self.pconvs[i](x)
+        x = x + (1.0 / torch.clip(self.snake_beta.unsqueeze(0).unsqueeze(-1), min=self.eps)) * torch.pow(torch.sin(x * self.snake_alpha.unsqueeze(0).unsqueeze(-1)), 2)
+        x = torch.cat([x, y], 1)
+        x = self.post_conv(x)
+        return x
+
+    def remove_weight_norm(self):
+        self.init_conv.remove_weight_norm()
+        for c in self.dconvs:
+            c.remove_weight_norm()
+        for c in self.pconvs:
+            remove_weight_norm(c)
+        self.post_conv.remove_weight_norm()
+
+"""
+https://github.com/charactr-platform/vocos/blob/main/vocos/heads.py
+"""
+class FourierHead(nn.Module):
+    """Base class for inverse fourier modules."""
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            x (Tensor): Input tensor of shape (B, L, H), where B is the batch size,
+                        L is the sequence length, and H denotes the model dimension.
+
+        Returns:
+            Tensor: Reconstructed time-domain audio signal of shape (B, T), where T is the length of the output signal.
+        """
+        raise NotImplementedError("Subclasses must implement the forward method.")
+
+
+class IMDCT(nn.Module):
+    """
+    Inverse Modified Discrete Cosine Transform (IMDCT) module.
+
+    Args:
+        frame_len (int): Length of the MDCT frame.
+        padding (str, optional): Type of padding. Options are "center" or "same". Defaults to "same".
+    """
+
+    def __init__(self, frame_len: int, padding: str = "same"):
+        super().__init__()
+        if padding not in ["center", "same"]:
+            raise ValueError("Padding must be 'center' or 'same'.")
+        self.padding = padding
+        self.frame_len = frame_len * 2
+        N = frame_len
+        n0 = (N + 1) / 2
+        window = torch.from_numpy(scipy.signal.cosine(N * 2)).float()
+        self.register_buffer("window", window)
+
+        pre_twiddle = torch.exp(1j * torch.pi * n0 * torch.arange(N * 2) / N)
+        post_twiddle = torch.exp(1j * torch.pi * (torch.arange(N * 2) + n0) / (N * 2))
+        self.register_buffer("pre_twiddle", torch.view_as_real(pre_twiddle))
+        self.register_buffer("post_twiddle", torch.view_as_real(post_twiddle))
+
+    def forward(self, X: torch.Tensor) -> torch.Tensor:
+        """
+        Apply the Inverse Modified Discrete Cosine Transform (IMDCT) to the input MDCT coefficients.
+
+        Args:
+            X (Tensor): Input MDCT coefficients of shape (B, N, L), where B is the batch size,
+                L is the number of frames, and N is the number of frequency bins.
+
+        Returns:
+            Tensor: Reconstructed audio waveform of shape (B, T), where T is the length of the audio.
+        """
+        X = X.transpose(1, 2)
+        B, L, N = X.shape
+        Y = torch.zeros((B, L, N * 2), dtype=X.dtype, device=X.device)
+        Y[..., :N] = X
+        Y[..., N:] = -1 * torch.conj(torch.flip(X, dims=(-1,)))
+        y = torch.fft.ifft(Y * torch.view_as_complex(self.pre_twiddle).expand(Y.shape), dim=-1)
+        y = torch.real(y * torch.view_as_complex(self.post_twiddle).expand(y.shape)) * np.sqrt(N) * np.sqrt(2)
+        result = y * self.window.expand(y.shape)
+        output_size = (1, (L + 1) * N)
+        audio = torch.nn.functional.fold(
+            result.transpose(1, 2),
+            output_size=output_size,
+            kernel_size=(1, self.frame_len),
+            stride=(1, self.frame_len // 2),
+        )[:, 0, 0, :]
+
+        if self.padding == "center":
+            pad = self.frame_len // 2
+        elif self.padding == "same":
+            pad = self.frame_len // 4
+        else:
+            raise ValueError("Padding must be 'center' or 'same'.")
+
+        audio = audio[:, pad:-pad]
+        return audio.unsqueeze(1)
+
+
+class IMDCTSymExpHead(FourierHead):
+    """
+    IMDCT Head module for predicting MDCT coefficients with symmetric exponential function
+
+    Args:
+        dim (int): Hidden dimension of the model.
+        mdct_frame_len (int): Length of the MDCT frame.
+        padding (str, optional): Type of padding. Options are "center" or "same". Defaults to "same".
+        sample_rate (int, optional): The sample rate of the audio. If provided, the last layer will be initialized
+                                     based on perceptual scaling. Defaults to None.
+        clip_audio (bool, optional): Whether to clip the audio output within the range of [-1.0, 1.0]. Defaults to False.
+    """
+
+    def __init__(
+        self, dim: int, gin_channels: int, mdct_frame_len: int, padding: str = "same", sample_rate: int = 24000,
+    ):
+        super().__init__()
+        out_dim = mdct_frame_len
+        self.dconv = DilatedCausalConv1d(dim, dim, 5, 1, dim, 1)
+        self.pconv1 = LoRALinear1d(dim, dim * 2, gin_channels, 2)
+        self.pconv2 = LoRALinear1d(dim * 2, out_dim, gin_channels, 2)
+        self.act = torch.nn.GELU()
+        self.imdct = IMDCT(frame_len=mdct_frame_len, padding=padding)
+
+        if sample_rate is not None:
+            # optionally init the last layer following mel-scale
+            m_max = _hz_to_mel(sample_rate // 2)
+            m_pts = torch.linspace(0, m_max, out_dim)
+            f_pts = _mel_to_hz(m_pts)
+            scale = 1 - (f_pts / f_pts.max())
+
+            with torch.no_grad():
+                self.pconv2.main_fc.weight.mul_(scale.view(-1, 1, 1))
+
+    def forward(self, x: torch.Tensor, g: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass of the IMDCTSymExpHead module.
+
+        Args:
+            x (Tensor): Input tensor of shape (B, L, H), where B is the batch size,
+                        L is the sequence length, and H denotes the model dimension.
+
+        Returns:
+            Tensor: Reconstructed time-domain audio signal of shape (B, T), where T is the length of the output signal.
+        """
+        x = self.dconv(x)
+        x = self.pconv1(x, g)
+        x = self.act(x)
+        x = self.pconv2(x, g)
+        x = symexp(x)
+        x = torch.clip(x, min=-1e2, max=1e2)  # safeguard to prevent excessively large magnitudes
+        audio = self.imdct(x)
+        return audio
+
+    def remove_weight_norm(self):
+        self.dconv.remove_weight_norm()
+        self.pconv1.remove_weight_norm()
+        self.pconv2.remove_weight_norm()
+
+    def fix_speaker(self, g):
+        self.pconv1.fix_speaker(g)
+        self.pconv2.fix_speaker(g)
+
+    def unfix_speaker(self, g):
+        self.pconv1.unfix_speaker(g)
+        self.pconv2.unfix_speaker(g)
+
+def symexp(x: torch.Tensor) -> torch.Tensor:
+    return torch.sign(x) * (torch.exp(x.abs()) - 1)

server/voice_changer/RVC/pipeline/Pipeline.py

@@ -3,6 +3,7 @@ from typing import Any
 import math
 import torch
 import torch.nn.functional as F
+from torch.cuda.amp import autocast
 from Exceptions import (
     DeviceCannotSupportHalfPrecisionException,
     DeviceChangingException,
@@ -118,10 +119,6 @@ class Pipeline(object):
 
         # tensor型調整
         feats = audio_pad
-        if self.isHalf is True:
-            feats = feats.half()
-        else:
-            feats = feats.float()
         if feats.dim() == 2:  # double channels
             feats = feats.mean(-1)
         assert feats.dim() == 1, feats.dim()
@@ -129,19 +126,20 @@ class Pipeline(object):
 
         # embedding
         padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-        try:
-            feats = self.embedder.extractFeatures(feats, embOutputLayer, useFinalProj)
-            if torch.isnan(feats).all():
-                raise DeviceCannotSupportHalfPrecisionException()
-        except RuntimeError as e:
-            if "HALF" in e.__str__().upper():
-                raise HalfPrecisionChangingException()
-            elif "same device" in e.__str__():
-                raise DeviceChangingException()
-            else:
-                raise e
-        if protect < 0.5 and search_index:
-            feats0 = feats.clone()
+        with autocast(enabled=self.isHalf):
+            try:
+                feats = self.embedder.extractFeatures(feats, embOutputLayer, useFinalProj)
+                if torch.isnan(feats).all():
+                    raise DeviceCannotSupportHalfPrecisionException()
+            except RuntimeError as e:
+                if "HALF" in e.__str__().upper():
+                    raise HalfPrecisionChangingException()
+                elif "same device" in e.__str__():
+                    raise DeviceChangingException()
+                else:
+                    raise e
+            if protect < 0.5 and search_index:
+                feats0 = feats.clone()
 
         # Index - feature抽出
         # if self.index is not None and self.feature is not None and index_rate != 0:
@@ -167,10 +165,8 @@ class Pipeline(object):
 
             # recover silient font
             npy = np.concatenate([np.zeros([npyOffset, npy.shape[1]]).astype("float32"), npy])
-            if self.isHalf is True:
-                npy = npy.astype("float16")
-
             feats = torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate + (1 - index_rate) * feats
+
         feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
         if protect < 0.5 and search_index:
             feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
@@ -207,14 +203,15 @@ class Pipeline(object):
         # 推論実行
         try:
             with torch.no_grad():
-                audio1 = (
-                    torch.clip(
-                        self.inferencer.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0].to(dtype=torch.float32),
-                        -1.0,
-                        1.0,
-                    )
-                    * 32767.5
-                ).data.to(dtype=torch.int16)
+                with autocast(enabled=self.isHalf):
+                    audio1 = (
+                        torch.clip(
+                            self.inferencer.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0].to(dtype=torch.float32),
+                            -1.0,
+                            1.0,
+                        )
+                        * 32767.5
+                    ).data.to(dtype=torch.int16)
         except RuntimeError as e:
             if "HALF" in e.__str__().upper():
                 print("11", e)