v3モデルのデモンストレーション

server/const.py

@@ -65,6 +65,7 @@ class EnumInferenceTypes(Enum):
     pyTorchRVCNono = "pyTorchRVCNono"
     pyTorchRVCv2 = "pyTorchRVCv2"
     pyTorchRVCv2Nono = "pyTorchRVCv2Nono"
+    pyTorchRVCv3 = "pyTorchRVCv3"
     pyTorchWebUI = "pyTorchWebUI"
     pyTorchWebUINono = "pyTorchWebUINono"
     onnxRVC = "onnxRVC"

server/voice_changer/RVC/ModelSlotGenerator.py

@@ -11,7 +11,33 @@ def _setInfoByPytorch(slot: ModelSlot):
     cpt = torch.load(slot.modelFile, map_location="cpu")
     config_len = len(cpt["config"])
 
-    if config_len == 18:
+    if cpt["version"] == "v3":
+        slot.f0 = True if cpt["f0"] == 1 else False
+        slot.modelType = EnumInferenceTypes.pyTorchRVCv3.value
+        slot.embChannels = cpt["config"][17]
+        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.embedder = cpt["embedder_name"]
+        if slot.embedder.endswith("768"):
+            slot.embedder = slot.embedder[:-3]
+
+        if slot.embedder == EnumEmbedderTypes.hubert.value:
+            slot.embedder = EnumEmbedderTypes.hubert.value
+        elif slot.embedder == EnumEmbedderTypes.contentvec.value:
+            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")
+
+    elif config_len == 18:
         # Original RVC
         slot.f0 = True if cpt["f0"] == 1 else False
         version = cpt.get("version", "v1")

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
 
 class InferencerManager:
     currentInferencer: Inferencer | None = None
@@ -37,6 +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.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/RVCInferencerv3.py

@@ -0,0 +1,40 @@
+import torch
+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
+
+
+class RVCInferencerv3(Inferencer):
+    def loadModel(self, file: str, gpu: device):
+        print("nadare v3 load start")
+        super().setProps(EnumInferenceTypes.pyTorchRVCv3, file, True, gpu)
+
+        dev = DeviceManager.get_instance().getDevice(gpu)
+        isHalf = False # DeviceManager.get_instance().halfPrecisionAvailable(gpu)
+
+        cpt = torch.load(file, map_location="cpu")
+        model = SynthesizerTrnMs256NSFSid(**cpt["params"])
+
+        model.eval()
+        model.load_state_dict(cpt["weight"], strict=False)
+
+        model = model.to(dev)
+        if isHalf:
+            model = model.half()
+
+        self.model = model
+        print("load model comprete")
+        return self
+
+    def infer(
+        self,
+        feats: torch.Tensor,
+        pitch_length: torch.Tensor,
+        pitch: torch.Tensor,
+        pitchf: torch.Tensor,
+        sid: torch.Tensor,
+    ) -> torch.Tensor:
+        return self.model.infer(feats, pitch_length, pitch, pitchf, sid)

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

@@ -0,0 +1,343 @@
+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/commons.py

@@ -0,0 +1,165 @@
+import math
+
+import torch
+from torch.nn import functional as F
+
+
+def init_weights(m, mean=0.0, std=0.01):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        m.weight.data.normal_(mean, std)
+
+
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size * dilation - dilation) / 2)
+
+
+def convert_pad_shape(pad_shape):
+    l = pad_shape[::-1]
+    pad_shape = [item for sublist in l for item in sublist]
+    return pad_shape
+
+
+def kl_divergence(m_p, logs_p, m_q, logs_q):
+    """KL(P||Q)"""
+    kl = (logs_q - logs_p) - 0.5
+    kl += (
+        0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
+    )
+    return kl
+
+
+def rand_gumbel(shape):
+    """Sample from the Gumbel distribution, protect from overflows."""
+    uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
+    return -torch.log(-torch.log(uniform_samples))
+
+
+def rand_gumbel_like(x):
+    g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
+    return g
+
+
+def slice_segments(x, ids_str, segment_size=4):
+    ret = torch.zeros_like(x[:, :, :segment_size])
+    for i in range(x.size(0)):
+        idx_str = ids_str[i]
+        idx_end = idx_str + segment_size
+        r = x[i, :, idx_str:idx_end]
+        ret[i, :, :r.size(1)] = r
+    return ret
+
+
+def slice_segments2(x, ids_str, segment_size=4):
+    ret = torch.zeros_like(x[:, :segment_size])
+    for i in range(x.size(0)):
+        idx_str = ids_str[i]
+        idx_end = idx_str + segment_size
+        r = x[i, idx_str:idx_end]
+        ret[i, :r.size(0)] = r
+    return ret
+
+
+def rand_slice_segments(x, x_lengths, segment_size=4, ids_str=None):
+    b, d, t = x.size()
+    if ids_str is None:
+        ids_str = torch.zeros([b]).to(device=x.device, dtype=x_lengths.dtype)
+    ids_str_max = torch.maximum(torch.zeros_like(x_lengths).to(device=x_lengths.device ,dtype=x_lengths.dtype), x_lengths - segment_size + 1 - ids_str)
+    ids_str += (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
+    ret = slice_segments(x, ids_str, segment_size)
+    return ret, ids_str
+
+
+def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
+    position = torch.arange(length, dtype=torch.float)
+    num_timescales = channels // 2
+    log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
+        num_timescales - 1
+    )
+    inv_timescales = min_timescale * torch.exp(
+        torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
+    )
+    scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
+    signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
+    signal = F.pad(signal, [0, 0, 0, channels % 2])
+    signal = signal.view(1, channels, length)
+    return signal
+
+
+def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
+    b, channels, length = x.size()
+    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+    return x + signal.to(dtype=x.dtype, device=x.device)
+
+
+def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
+    b, channels, length = x.size()
+    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+    return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
+
+
+def subsequent_mask(length):
+    mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
+    return mask
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+def convert_pad_shape(pad_shape):
+    l = pad_shape[::-1]
+    pad_shape = [item for sublist in l for item in sublist]
+    return pad_shape
+
+
+def shift_1d(x):
+    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
+    return x
+
+
+def sequence_mask(length, max_length=None):
+    if max_length is None:
+        max_length = length.max()
+    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
+    return x.unsqueeze(0) < length.unsqueeze(1)
+
+
+def generate_path(duration, mask):
+    """
+    duration: [b, 1, t_x]
+    mask: [b, 1, t_y, t_x]
+    """
+    b, _, t_y, t_x = mask.shape
+    cum_duration = torch.cumsum(duration, -1)
+
+    cum_duration_flat = cum_duration.view(b * t_x)
+    path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
+    path = path.view(b, t_x, t_y)
+    path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
+    path = path.unsqueeze(1).transpose(2, 3) * mask
+    return path
+
+
+def clip_grad_value_(parameters, clip_value, norm_type=2):
+    if isinstance(parameters, torch.Tensor):
+        parameters = [parameters]
+    parameters = list(filter(lambda p: p.grad is not None, parameters))
+    norm_type = float(norm_type)
+    if clip_value is not None:
+        clip_value = float(clip_value)
+
+    total_norm = 0
+    for p in parameters:
+        param_norm = p.grad.data.norm(norm_type)
+        total_norm += param_norm.item() ** norm_type
+        if clip_value is not None:
+            p.grad.data.clamp_(min=-clip_value, max=clip_value)
+    total_norm = total_norm ** (1.0 / norm_type)
+    return total_norm

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

@@ -0,0 +1,71 @@
+from typing import *
+
+from pydantic import BaseModel
+
+
+class TrainConfigTrain(BaseModel):
+    log_interval: int
+    seed: int
+    epochs: int
+    learning_rate: float
+    betas: List[float]
+    eps: float
+    batch_size: int
+    fp16_run: bool
+    lr_decay: float
+    segment_size: int
+    init_lr_ratio: int
+    warmup_epochs: int
+    c_mel: int
+    c_kl: float
+
+
+class TrainConfigData(BaseModel):
+    max_wav_value: float
+    sampling_rate: int
+    filter_length: int
+    hop_length: int
+    win_length: int
+    n_mel_channels: int
+    mel_fmin: float
+    mel_fmax: Any
+
+
+class TrainConfigModel(BaseModel):
+    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"
+    train: TrainConfigTrain
+    data: TrainConfigData
+    model: TrainConfigModel
+
+
+class DatasetMetaItem(BaseModel):
+    gt_wav: str
+    co256: str
+    f0: Optional[str]
+    f0nsf: Optional[str]
+    speaker_id: int
+
+
+class DatasetMetadata(BaseModel):
+    files: Dict[str, DatasetMetaItem]
+    # mute: DatasetMetaItem

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

@@ -0,0 +1,522 @@
+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

@@ -0,0 +1,626 @@
+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/model_v3/transforms.py

@@ -0,0 +1,207 @@
+import numpy as np
+import torch
+from torch.nn import functional as F
+
+DEFAULT_MIN_BIN_WIDTH = 1e-3
+DEFAULT_MIN_BIN_HEIGHT = 1e-3
+DEFAULT_MIN_DERIVATIVE = 1e-3
+
+
+def piecewise_rational_quadratic_transform(
+    inputs,
+    unnormalized_widths,
+    unnormalized_heights,
+    unnormalized_derivatives,
+    inverse=False,
+    tails=None,
+    tail_bound=1.0,
+    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+    min_derivative=DEFAULT_MIN_DERIVATIVE,
+):
+    if tails is None:
+        spline_fn = rational_quadratic_spline
+        spline_kwargs = {}
+    else:
+        spline_fn = unconstrained_rational_quadratic_spline
+        spline_kwargs = {"tails": tails, "tail_bound": tail_bound}
+
+    outputs, logabsdet = spline_fn(
+        inputs=inputs,
+        unnormalized_widths=unnormalized_widths,
+        unnormalized_heights=unnormalized_heights,
+        unnormalized_derivatives=unnormalized_derivatives,
+        inverse=inverse,
+        min_bin_width=min_bin_width,
+        min_bin_height=min_bin_height,
+        min_derivative=min_derivative,
+        **spline_kwargs
+    )
+    return outputs, logabsdet
+
+
+def searchsorted(bin_locations, inputs, eps=1e-6):
+    bin_locations[..., -1] += eps
+    return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
+
+
+def unconstrained_rational_quadratic_spline(
+    inputs,
+    unnormalized_widths,
+    unnormalized_heights,
+    unnormalized_derivatives,
+    inverse=False,
+    tails="linear",
+    tail_bound=1.0,
+    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+    min_derivative=DEFAULT_MIN_DERIVATIVE,
+):
+    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
+    outside_interval_mask = ~inside_interval_mask
+
+    outputs = torch.zeros_like(inputs)
+    logabsdet = torch.zeros_like(inputs)
+
+    if tails == "linear":
+        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
+        constant = np.log(np.exp(1 - min_derivative) - 1)
+        unnormalized_derivatives[..., 0] = constant
+        unnormalized_derivatives[..., -1] = constant
+
+        outputs[outside_interval_mask] = inputs[outside_interval_mask]
+        logabsdet[outside_interval_mask] = 0
+    else:
+        raise RuntimeError("{} tails are not implemented.".format(tails))
+
+    (
+        outputs[inside_interval_mask],
+        logabsdet[inside_interval_mask],
+    ) = rational_quadratic_spline(
+        inputs=inputs[inside_interval_mask],
+        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
+        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
+        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
+        inverse=inverse,
+        left=-tail_bound,
+        right=tail_bound,
+        bottom=-tail_bound,
+        top=tail_bound,
+        min_bin_width=min_bin_width,
+        min_bin_height=min_bin_height,
+        min_derivative=min_derivative,
+    )
+
+    return outputs, logabsdet
+
+
+def rational_quadratic_spline(
+    inputs,
+    unnormalized_widths,
+    unnormalized_heights,
+    unnormalized_derivatives,
+    inverse=False,
+    left=0.0,
+    right=1.0,
+    bottom=0.0,
+    top=1.0,
+    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+    min_derivative=DEFAULT_MIN_DERIVATIVE,
+):
+    if torch.min(inputs) < left or torch.max(inputs) > right:
+        raise ValueError("Input to a transform is not within its domain")
+
+    num_bins = unnormalized_widths.shape[-1]
+
+    if min_bin_width * num_bins > 1.0:
+        raise ValueError("Minimal bin width too large for the number of bins")
+    if min_bin_height * num_bins > 1.0:
+        raise ValueError("Minimal bin height too large for the number of bins")
+
+    widths = F.softmax(unnormalized_widths, dim=-1)
+    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
+    cumwidths = torch.cumsum(widths, dim=-1)
+    cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0)
+    cumwidths = (right - left) * cumwidths + left
+    cumwidths[..., 0] = left
+    cumwidths[..., -1] = right
+    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
+
+    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
+
+    heights = F.softmax(unnormalized_heights, dim=-1)
+    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
+    cumheights = torch.cumsum(heights, dim=-1)
+    cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0)
+    cumheights = (top - bottom) * cumheights + bottom
+    cumheights[..., 0] = bottom
+    cumheights[..., -1] = top
+    heights = cumheights[..., 1:] - cumheights[..., :-1]
+
+    if inverse:
+        bin_idx = searchsorted(cumheights, inputs)[..., None]
+    else:
+        bin_idx = searchsorted(cumwidths, inputs)[..., None]
+
+    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
+    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
+
+    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
+    delta = heights / widths
+    input_delta = delta.gather(-1, bin_idx)[..., 0]
+
+    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
+    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
+
+    input_heights = heights.gather(-1, bin_idx)[..., 0]
+
+    if inverse:
+        a = (inputs - input_cumheights) * (
+            input_derivatives + input_derivatives_plus_one - 2 * input_delta
+        ) + input_heights * (input_delta - input_derivatives)
+        b = input_heights * input_derivatives - (inputs - input_cumheights) * (
+            input_derivatives + input_derivatives_plus_one - 2 * input_delta
+        )
+        c = -input_delta * (inputs - input_cumheights)
+
+        discriminant = b.pow(2) - 4 * a * c
+        assert (discriminant >= 0).all()
+
+        root = (2 * c) / (-b - torch.sqrt(discriminant))
+        outputs = root * input_bin_widths + input_cumwidths
+
+        theta_one_minus_theta = root * (1 - root)
+        denominator = input_delta + (
+            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
+            * theta_one_minus_theta
+        )
+        derivative_numerator = input_delta.pow(2) * (
+            input_derivatives_plus_one * root.pow(2)
+            + 2 * input_delta * theta_one_minus_theta
+            + input_derivatives * (1 - root).pow(2)
+        )
+        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
+
+        return outputs, -logabsdet
+    else:
+        theta = (inputs - input_cumwidths) / input_bin_widths
+        theta_one_minus_theta = theta * (1 - theta)
+
+        numerator = input_heights * (
+            input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta
+        )
+        denominator = input_delta + (
+            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
+            * theta_one_minus_theta
+        )
+        outputs = input_cumheights + numerator / denominator
+
+        derivative_numerator = input_delta.pow(2) * (
+            input_derivatives_plus_one * theta.pow(2)
+            + 2 * input_delta * theta_one_minus_theta
+            + input_derivatives * (1 - theta).pow(2)
+        )
+        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
+
+        return outputs, logabsdet

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

@@ -0,0 +1,286 @@
+import glob
+import logging
+import os
+import shutil
+import socket
+import sys
+
+import ffmpeg
+import matplotlib
+import matplotlib.pylab as plt
+import numpy as np
+import torch
+from scipy.io.wavfile import read
+from torch.nn import functional as F
+
+from modules.shared import ROOT_DIR
+
+from .config import TrainConfig
+
+matplotlib.use("Agg")
+logging.getLogger("matplotlib").setLevel(logging.WARNING)
+
+logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
+logger = logging
+
+
+class AWP:
+    """
+    Fast AWP
+    https://www.kaggle.com/code/junkoda/fast-awp
+    """
+    def __init__(self, model, optimizer, *, adv_param='weight',
+                 adv_lr=0.01, adv_eps=0.01):
+        self.model = model
+        self.optimizer = optimizer
+        self.adv_param = adv_param
+        self.adv_lr = adv_lr
+        self.adv_eps = adv_eps
+        self.backup = {}
+
+    def perturb(self):
+        """
+        Perturb model parameters for AWP gradient
+        Call before loss and loss.backward()
+        """
+        self._save()  # save model parameters
+        self._attack_step()  # perturb weights
+
+    def _attack_step(self):
+        e = 1e-6
+        for name, param in self.model.named_parameters():
+            if param.requires_grad and param.grad is not None and self.adv_param in name:
+                grad = self.optimizer.state[param]['exp_avg']
+                norm_grad = torch.norm(grad)
+                norm_data = torch.norm(param.detach())
+
+                if norm_grad != 0 and not torch.isnan(norm_grad):
+                    # Set lower and upper limit in change
+                    limit_eps = self.adv_eps * param.detach().abs()
+                    param_min = param.data - limit_eps
+                    param_max = param.data + limit_eps
+
+                    # Perturb along gradient
+                    # w += (adv_lr * |w| / |grad|) * grad
+                    param.data.add_(grad, alpha=(self.adv_lr * (norm_data + e) / (norm_grad + e)))
+
+                    # Apply the limit to the change
+                    param.data.clamp_(param_min, param_max)
+
+    def _save(self):
+        for name, param in self.model.named_parameters():
+            if param.requires_grad and param.grad is not None and self.adv_param in name:
+                if name not in self.backup:
+                    self.backup[name] = param.clone().detach()
+                else:
+                    self.backup[name].copy_(param.data)
+
+    def restore(self):
+        """
+        Restore model parameter to correct position; AWP do not perturbe weights, it perturb gradients
+        Call after loss.backward(), before optimizer.step()
+        """
+        for name, param in self.model.named_parameters():
+            if name in self.backup:
+                param.data.copy_(self.backup[name])
+
+
+def load_audio(file: str, sr):
+    try:
+        # https://github.com/openai/whisper/blob/main/whisper/audio.py#L26
+        # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
+        # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
+        file = (
+            file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+        )  # Prevent small white copy path head and tail with spaces and " and return
+        out, _ = (
+            ffmpeg.input(file, threads=0)
+            .output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
+            .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
+        )
+    except Exception as e:
+        raise RuntimeError(f"Failed to load audio: {e}")
+
+    return np.frombuffer(out, np.float32).flatten()
+
+
+def find_empty_port():
+    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    s.bind(("", 0))
+    s.listen(1)
+    port = s.getsockname()[1]
+    s.close()
+    return port
+
+
+def load_checkpoint(checkpoint_path, model, optimizer=None, load_opt=1):
+    assert os.path.isfile(checkpoint_path)
+    checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
+
+    saved_state_dict = checkpoint_dict["model"]
+    if hasattr(model, "module"):
+        state_dict = model.module.state_dict()
+    else:
+        state_dict = model.state_dict()
+    new_state_dict = {}
+    for k, v in state_dict.items():  # 模型需要的shape
+        try:
+            new_state_dict[k] = saved_state_dict[k]
+            if saved_state_dict[k].shape != state_dict[k].shape:
+                print(
+                    f"shape-{k}-mismatch|need-{state_dict[k].shape}|get-{saved_state_dict[k].shape}"
+                )
+                if saved_state_dict[k].dim() == 2:  # NOTE: check is this ok?
+                    # for embedded input 256 <==> 768
+                    # this achieves we can continue training from original's pretrained checkpoints when using embedder that 768-th dim output etc.
+                    if saved_state_dict[k].dtype == torch.half:
+                        new_state_dict[k] = (
+                            F.interpolate(
+                                saved_state_dict[k].float().unsqueeze(0).unsqueeze(0),
+                                size=state_dict[k].shape,
+                                mode="bilinear",
+                            )
+                            .half()
+                            .squeeze(0)
+                            .squeeze(0)
+                        )
+                    else:
+                        new_state_dict[k] = (
+                            F.interpolate(
+                                saved_state_dict[k].unsqueeze(0).unsqueeze(0),
+                                size=state_dict[k].shape,
+                                mode="bilinear",
+                            )
+                            .squeeze(0)
+                            .squeeze(0)
+                        )
+                    print(
+                        "interpolated new_state_dict",
+                        k,
+                        "from",
+                        saved_state_dict[k].shape,
+                        "to",
+                        new_state_dict[k].shape,
+                    )
+                else:
+                    raise KeyError
+        except Exception as e:
+            # print(traceback.format_exc())
+            print(f"{k} is not in the checkpoint")
+            print("error: %s" % e)
+            new_state_dict[k] = v  # 模型自带的随机值
+    if hasattr(model, "module"):
+        model.module.load_state_dict(new_state_dict, strict=False)
+    else:
+        model.load_state_dict(new_state_dict, strict=False)
+    print("Loaded model weights")
+
+    epoch = checkpoint_dict["epoch"]
+    learning_rate = checkpoint_dict["learning_rate"]
+    if optimizer is not None and load_opt == 1:
+        optimizer.load_state_dict(checkpoint_dict["optimizer"])
+    print("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, epoch))
+    return model, optimizer, learning_rate, epoch
+
+
+def save_state(model, optimizer, learning_rate, epoch, checkpoint_path):
+    print(
+        "Saving model and optimizer state at epoch {} to {}".format(
+            epoch, checkpoint_path
+        )
+    )
+    if hasattr(model, "module"):
+        state_dict = model.module.state_dict()
+    else:
+        state_dict = model.state_dict()
+    torch.save(
+        {
+            "model": state_dict,
+            "epoch": epoch,
+            "optimizer": optimizer.state_dict(),
+            "learning_rate": learning_rate,
+        },
+        checkpoint_path,
+    )
+
+
+def summarize(
+    writer,
+    global_step,
+    scalars={},
+    histograms={},
+    images={},
+    audios={},
+    audio_sampling_rate=22050,
+):
+    for k, v in scalars.items():
+        writer.add_scalar(k, v, global_step)
+    for k, v in histograms.items():
+        writer.add_histogram(k, v, global_step)
+    for k, v in images.items():
+        writer.add_image(k, v, global_step, dataformats="HWC")
+    for k, v in audios.items():
+        writer.add_audio(k, v, global_step, audio_sampling_rate)
+
+
+def latest_checkpoint_path(dir_path, regex="G_*.pth"):
+    filelist = glob.glob(os.path.join(dir_path, regex))
+    if len(filelist) == 0:
+        return None
+    filelist.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
+    filepath = filelist[-1]
+    return filepath
+
+
+def plot_spectrogram_to_numpy(spectrogram):
+    fig, ax = plt.subplots(figsize=(10, 2))
+    im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
+    plt.colorbar(im, ax=ax)
+    plt.xlabel("Frames")
+    plt.ylabel("Channels")
+    plt.tight_layout()
+
+    fig.canvas.draw()
+    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
+    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+    plt.close()
+    return data
+
+
+def plot_alignment_to_numpy(alignment, info=None):
+    fig, ax = plt.subplots(figsize=(6, 4))
+    im = ax.imshow(
+        alignment.transpose(), aspect="auto", origin="lower", interpolation="none"
+    )
+    fig.colorbar(im, ax=ax)
+    xlabel = "Decoder timestep"
+    if info is not None:
+        xlabel += "\n\n" + info
+    plt.xlabel(xlabel)
+    plt.ylabel("Encoder timestep")
+    plt.tight_layout()
+
+    fig.canvas.draw()
+    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
+    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+    plt.close()
+    return data
+
+
+def load_wav_to_torch(full_path):
+    sampling_rate, data = read(full_path)
+    return torch.FloatTensor(data.astype(np.float32)), sampling_rate
+
+
+def load_config(training_dir: str, sample_rate: int, emb_channels: int):
+    if emb_channels == 256:
+        config_path = os.path.join(ROOT_DIR, "configs", f"{sample_rate}.json")
+    else:
+        config_path = os.path.join(
+            ROOT_DIR, "configs", f"{sample_rate}-{emb_channels}.json"
+        )
+    config_save_path = os.path.join(training_dir, "config.json")
+
+    shutil.copyfile(config_path, config_save_path)
+
+    return TrainConfig.parse_file(config_save_path)