Merge pull request 'master' (#1) from bing/TakwayDisplayPlatform:master into master

Reviewed-on: #1

.gitignore

@@ -7,4 +7,8 @@ takway.db
 
 /storage/**
 
-/app.log
+/app.log
+
+**/*.pth
+**/bert/*
+**/slm/*

README_BERT_VITS2.md

@@ -0,0 +1,20 @@
+## 下载 BERT 模型
+#### 中文 RoBERTa 
++ Huggingface路径(https://huggingface.co/hfl/chinese-roberta-wwm-ext-large) 
++ 本地路径(/home/deep01/softwareGroup/Takway/Bert-VITS2/bert/chinese-roberta-wwm-ext-large)
+#### 英文 DeBERTa
++ Huggingface 路径(https://huggingface.co/microsoft/deberta-v3-large)
++ 本地路径(/home/deep01/softwareGroup/Takway/Bert-VITS2/bert/deberta-v3-large)
+#### 日文 DeBERTa
++ Huggingface 路径(https://huggingface.co/ku-nlp/deberta-v2-large-japanese-char-wwm)
++ 本地路径(/home/deep01/softwareGroup/Takway/Bert-VITS2/bert/deberta-v2-large-japanese-char-wwm)
+#### WavLM
++ Huggingface 路径(https://huggingface.co/microsoft/wavlm-base-plus)
++ 本地路径(/home/deep01/softwareGroup/Takway/Bert-VITS2/slm/wavlm-base-plus)
+
+#### 将 BERT 模型放置到 bert 文件夹下，WavLM 模型放置到 slm 文件夹下，覆盖同名文件夹。
+
+## 下载预训练模型
+路径：/home/deep01/softwareGroup/Takway/Bert-VITS2/data/mix/saved_models/250000_G.pth
+
+将其放在 ./utils/bert_vits2/data/mix/models内

test.py

@@ -0,0 +1,13 @@
+from utils.bert_vits2_utils import TextToSpeech
+import soundfile as sf
+tts = TextToSpeech()
+tts.print_speakers_info()
+
+audio, sample_rate= tts.synthesize("你好，我好开心",        # 文本
+                                   0,                      # 说话人 id
+                                   style_text="我很难过！！！！呜呜呜！！！",   # 情绪prompt，当language=="ZH" 才有效 
+                                   style_weight=0.9,        # 情绪prompt权重
+                                   language="mix",          # 语言类型，包括 "ZH" "EN" "mix"
+                                   en_ratio=1.)             # mix语言类型下，英文文本速度，越大速度越慢
+save_path = "./tmp2.wav"
+sf.write(save_path, audio, sample_rate)

utils/bert_vits2/attentions.py

@@ -0,0 +1,464 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from . import commons
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+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)
+
+
+@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
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size=1,
+        p_dropout=0.0,
+        window_size=4,
+        isflow=True,
+        **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
+        # if isflow:
+        #  cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
+        #  self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
+        #  self.cond_layer = weight_norm(cond_layer, name='weight')
+        #  self.gin_channels = 256
+        self.cond_layer_idx = self.n_layers
+        if "gin_channels" in kwargs:
+            self.gin_channels = kwargs["gin_channels"]
+            if self.gin_channels != 0:
+                self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
+                # vits2 says 3rd block, so idx is 2 by default
+                self.cond_layer_idx = (
+                    kwargs["cond_layer_idx"] if "cond_layer_idx" in kwargs else 2
+                )
+                logging.debug(self.gin_channels, self.cond_layer_idx)
+                assert (
+                    self.cond_layer_idx < self.n_layers
+                ), "cond_layer_idx should be less than n_layers"
+        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,
+                    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,
+                    kernel_size,
+                    p_dropout=p_dropout,
+                )
+            )
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask, g=None):
+        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            if i == self.cond_layer_idx and g is not None:
+                g = self.spk_emb_linear(g.transpose(1, 2))
+                g = g.transpose(1, 2)
+                x = x + g
+                x = x * x_mask
+            y = self.attn_layers[i](x, x, attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size=1,
+        p_dropout=0.0,
+        proximal_bias=False,
+        proximal_init=True,
+        **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.proximal_bias = proximal_bias
+        self.proximal_init = proximal_init
+
+        self.drop = nn.Dropout(p_dropout)
+        self.self_attn_layers = nn.ModuleList()
+        self.norm_layers_0 = nn.ModuleList()
+        self.encdec_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.self_attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels,
+                    hidden_channels,
+                    n_heads,
+                    p_dropout=p_dropout,
+                    proximal_bias=proximal_bias,
+                    proximal_init=proximal_init,
+                )
+            )
+            self.norm_layers_0.append(LayerNorm(hidden_channels))
+            self.encdec_attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
+                )
+            )
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(
+                    hidden_channels,
+                    hidden_channels,
+                    filter_channels,
+                    kernel_size,
+                    p_dropout=p_dropout,
+                    causal=True,
+                )
+            )
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask, h, h_mask):
+        """
+        x: decoder input
+        h: encoder output
+        """
+        self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
+            device=x.device, dtype=x.dtype
+        )
+        encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            y = self.self_attn_layers[i](x, x, self_attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_0[i](x + y)
+
+            y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(
+        self,
+        channels,
+        out_channels,
+        n_heads,
+        p_dropout=0.0,
+        window_size=None,
+        heads_share=True,
+        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 = nn.Conv1d(channels, channels, 1)
+        self.conv_k = nn.Conv1d(channels, channels, 1)
+        self.conv_v = nn.Conv1d(channels, channels, 1)
+        self.conv_o = nn.Conv1d(channels, out_channels, 1)
+        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
+            )
+
+        nn.init.xavier_uniform_(self.conv_q.weight)
+        nn.init.xavier_uniform_(self.conv_k.weight)
+        nn.init.xavier_uniform_(self.conv_v.weight)
+        if proximal_init:
+            with torch.no_grad():
+                self.conv_k.weight.copy_(self.conv_q.weight)
+                self.conv_k.bias.copy_(self.conv_q.bias)
+
+    def forward(self, x, c, attn_mask=None):
+        q = self.conv_q(x)
+        k = self.conv_k(c)
+        v = self.conv_v(c)
+
+        x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+        x = self.conv_o(x)
+        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):
+        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()
+        # pad 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)
+
+
+class FFN(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        filter_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
+
+        if causal:
+            self.padding = self._causal_padding
+        else:
+            self.padding = self._same_padding
+
+        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+        self.drop = nn.Dropout(p_dropout)
+
+    def forward(self, x, x_mask):
+        x = self.conv_1(self.padding(x * x_mask))
+        if self.activation == "gelu":
+            x = x * torch.sigmoid(1.702 * x)
+        else:
+            x = torch.relu(x)
+        x = self.drop(x)
+        x = self.conv_2(self.padding(x * x_mask))
+        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

utils/bert_vits2/commons.py

@@ -0,0 +1,158 @@
+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):
+    layer = pad_shape[::-1]
+    pad_shape = [item for sublist in layer for item in sublist]
+    return pad_shape
+
+
+def intersperse(lst, item):
+    result = [item] * (len(lst) * 2 + 1)
+    result[1::2] = lst
+    return result
+
+
+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):
+    gather_indices = ids_str.view(x.size(0), 1, 1).repeat(
+        1, x.size(1), 1
+    ) + torch.arange(segment_size, device=x.device)
+    return torch.gather(x, 2, gather_indices)
+
+
+def rand_slice_segments(x, x_lengths=None, segment_size=4):
+    b, d, t = x.size()
+    if x_lengths is None:
+        x_lengths = t
+    ids_str_max = torch.clamp(x_lengths - segment_size + 1, min=0)
+    ids_str = (torch.rand([b], 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):
+    layer = pad_shape[::-1]
+    pad_shape = [item for sublist in layer 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

utils/bert_vits2/config.py

@@ -0,0 +1,262 @@
+"""
+@Desc: 全局配置文件读取
+"""
+
+import argparse
+import yaml
+from typing import Dict, List
+import os
+import shutil
+import sys
+
+
+class Resample_config:
+    """重采样配置"""
+
+    def __init__(self, in_dir: str, out_dir: str, sampling_rate: int = 44100):
+        self.sampling_rate: int = sampling_rate  # 目标采样率
+        self.in_dir: str = in_dir  # 待处理音频目录路径
+        self.out_dir: str = out_dir  # 重采样输出路径
+
+    @classmethod
+    def from_dict(cls, dataset_path: str, data: Dict[str, any]):
+        """从字典中生成实例"""
+
+        # 不检查路径是否有效，此逻辑在resample.py中处理
+        data["in_dir"] = os.path.join(dataset_path, data["in_dir"])
+        data["out_dir"] = os.path.join(dataset_path, data["out_dir"])
+
+        return cls(**data)
+
+
+class Preprocess_text_config:
+    """数据预处理配置"""
+
+    def __init__(
+        self,
+        transcription_path: str,
+        cleaned_path: str,
+        train_path: str,
+        val_path: str,
+        config_path: str,
+        val_per_lang: int = 5,
+        max_val_total: int = 10000,
+        clean: bool = True,
+    ):
+        self.transcription_path: str = (
+            transcription_path  # 原始文本文件路径，文本格式应为{wav_path}|{speaker_name}|{language}|{text}。
+        )
+        self.cleaned_path: str = (
+            cleaned_path  # 数据清洗后文本路径，可以不填。不填则将在原始文本目录生成
+        )
+        self.train_path: str = (
+            train_path  # 训练集路径，可以不填。不填则将在原始文本目录生成
+        )
+        self.val_path: str = (
+            val_path  # 验证集路径，可以不填。不填则将在原始文本目录生成
+        )
+        self.config_path: str = config_path  # 配置文件路径
+        self.val_per_lang: int = val_per_lang  # 每个speaker的验证集条数
+        self.max_val_total: int = (
+            max_val_total  # 验证集最大条数，多于的会被截断并放到训练集中
+        )
+        self.clean: bool = clean  # 是否进行数据清洗
+
+    @classmethod
+    def from_dict(cls, dataset_path: str, data: Dict[str, any]):
+        """从字典中生成实例"""
+
+        data["transcription_path"] = os.path.join(
+            dataset_path, data["transcription_path"]
+        )
+        if data["cleaned_path"] == "" or data["cleaned_path"] is None:
+            data["cleaned_path"] = None
+        else:
+            data["cleaned_path"] = os.path.join(dataset_path, data["cleaned_path"])
+        data["train_path"] = os.path.join(dataset_path, data["train_path"])
+        data["val_path"] = os.path.join(dataset_path, data["val_path"])
+        data["config_path"] = os.path.join(dataset_path, data["config_path"])
+
+        return cls(**data)
+
+
+class Bert_gen_config:
+    """bert_gen 配置"""
+
+    def __init__(
+        self,
+        config_path: str,
+        num_processes: int = 2,
+        device: str = "cuda",
+        use_multi_device: bool = False,
+    ):
+        self.config_path = config_path
+        self.num_processes = num_processes
+        self.device = device
+        self.use_multi_device = use_multi_device
+
+    @classmethod
+    def from_dict(cls, dataset_path: str, data: Dict[str, any]):
+        data["config_path"] = os.path.join(dataset_path, data["config_path"])
+
+        return cls(**data)
+
+
+class Emo_gen_config:
+    """emo_gen 配置"""
+
+    def __init__(
+        self,
+        config_path: str,
+        num_processes: int = 2,
+        device: str = "cuda",
+        use_multi_device: bool = False,
+    ):
+        self.config_path = config_path
+        self.num_processes = num_processes
+        self.device = device
+        self.use_multi_device = use_multi_device
+
+    @classmethod
+    def from_dict(cls, dataset_path: str, data: Dict[str, any]):
+        data["config_path"] = os.path.join(dataset_path, data["config_path"])
+
+        return cls(**data)
+
+
+class Train_ms_config:
+    """训练配置"""
+
+    def __init__(
+        self,
+        config_path: str,
+        env: Dict[str, any],
+        base: Dict[str, any],
+        model: str,
+        num_workers: int,
+        spec_cache: bool,
+        keep_ckpts: int,
+    ):
+        self.env = env  # 需要加载的环境变量
+        self.base = base  # 底模配置
+        self.model = (
+            model  # 训练模型存储目录，该路径为相对于dataset_path的路径，而非项目根目录
+        )
+        self.config_path = config_path  # 配置文件路径
+        self.num_workers = num_workers  # worker数量
+        self.spec_cache = spec_cache  # 是否启用spec缓存
+        self.keep_ckpts = keep_ckpts  # ckpt数量
+
+    @classmethod
+    def from_dict(cls, dataset_path: str, data: Dict[str, any]):
+        # data["model"] = os.path.join(dataset_path, data["model"])
+        data["config_path"] = os.path.join(dataset_path, data["config_path"])
+
+        return cls(**data)
+
+
+class Webui_config:
+    """webui 配置"""
+
+    def __init__(
+        self,
+        device: str,
+        model: str,
+        config_path: str,
+        language_identification_library: str,
+        port: int = 7860,
+        share: bool = False,
+        debug: bool = False,
+    ):
+        self.device: str = device
+        self.model: str = model  # 端口号
+        self.config_path: str = config_path  # 是否公开部署，对外网开放
+        self.port: int = port  # 是否开启debug模式
+        self.share: bool = share  # 模型路径
+        self.debug: bool = debug  # 配置文件路径
+        self.language_identification_library: str = (
+            language_identification_library  # 语种识别库
+        )
+
+    @classmethod
+    def from_dict(cls, dataset_path: str, data: Dict[str, any]):
+        data["config_path"] = os.path.join(dataset_path, data["config_path"])
+        data["model"] = os.path.join(dataset_path, data["model"])
+        return cls(**data)
+
+
+class Server_config:
+    def __init__(
+        self, models: List[Dict[str, any]], port: int = 5000, device: str = "cuda"
+    ):
+        self.models: List[Dict[str, any]] = models  # 需要加载的所有模型的配置
+        self.port: int = port  # 端口号
+        self.device: str = device  # 模型默认使用设备
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, any]):
+        return cls(**data)
+
+
+class Translate_config:
+    """翻译api配置"""
+
+    def __init__(self, app_key: str, secret_key: str):
+        self.app_key = app_key
+        self.secret_key = secret_key
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, any]):
+        return cls(**data)
+
+
+class Config:
+    def __init__(self, config_path: str):
+        if not os.path.isfile(config_path) and os.path.isfile("default_config.yml"):
+            shutil.copy(src="default_config.yml", dst=config_path)
+            print(
+                f"已根据默认配置文件default_config.yml生成配置文件{config_path}。请按该配置文件的说明进行配置后重新运行。"
+            )
+            print("如无特殊需求，请勿修改default_config.yml或备份该文件。")
+            sys.exit(0)
+        print(os.getcwd())
+        with open(file=config_path, mode="r", encoding="utf-8") as file:
+            yaml_config: Dict[str, any] = yaml.safe_load(file.read())
+            dataset_path: str = yaml_config["dataset_path"]
+            openi_token: str = yaml_config["openi_token"]
+            self.dataset_path: str = dataset_path
+            self.mirror: str = yaml_config["mirror"]
+            self.openi_token: str = openi_token
+            self.resample_config: Resample_config = Resample_config.from_dict(
+                dataset_path, yaml_config["resample"]
+            )
+            self.preprocess_text_config: Preprocess_text_config = (
+                Preprocess_text_config.from_dict(
+                    dataset_path, yaml_config["preprocess_text"]
+                )
+            )
+            self.bert_gen_config: Bert_gen_config = Bert_gen_config.from_dict(
+                dataset_path, yaml_config["bert_gen"]
+            )
+            self.emo_gen_config: Emo_gen_config = Emo_gen_config.from_dict(
+                dataset_path, yaml_config["emo_gen"]
+            )
+            self.train_ms_config: Train_ms_config = Train_ms_config.from_dict(
+                dataset_path, yaml_config["train_ms"]
+            )
+            self.webui_config: Webui_config = Webui_config.from_dict(
+                dataset_path, yaml_config["webui"]
+            )
+            self.server_config: Server_config = Server_config.from_dict(
+                yaml_config["server"]
+            )
+            self.translate_config: Translate_config = Translate_config.from_dict(
+                yaml_config["translate"]
+            )
+
+
+parser = argparse.ArgumentParser()
+# 为避免与以前的config.json起冲突，将其更名如下
+parser.add_argument("-y", "--yml_config", type=str, default="./utils/bert_vits2/config.yml")
+args, _ = parser.parse_known_args()
+config = Config(args.yml_config)

utils/bert_vits2/config.yml

@@ -0,0 +1,180 @@
+# 全局配置
+# 对于希望在同一时间使用多个配置文件的情况，例如两个GPU同时跑两个训练集：通过环境变量指定配置文件，不指定则默认为./config.yml
+
+# 拟提供通用路径配置，统一存放数据，避免数据放得很乱
+# 每个数据集与其对应的模型存放至统一路径下，后续所有的路径配置均为相对于datasetPath的路径
+# 不填或者填空则路径为相对于项目根目录的路径
+# dataset_path: "data/Genshin-KL"
+dataset_path: "utils/bert_vits2/data/mix"
+
+# 模型镜像源，默认huggingface，使用openi镜像源需指定openi_token
+mirror: ""
+openi_token: ""  # openi token
+
+# resample 音频重采样配置
+# 注意， “:” 后需要加空格
+resample:
+  # 目标重采样率
+  sampling_rate: 44100
+  # sampling_rate: 16000
+  # 音频文件输入路径，重采样会将该路径下所有.wav音频文件重采样
+  # 请填入相对于datasetPath的相对路径
+  in_dir: "audios/raw" # 相对于根目录的路径为 /datasetPath/in_dir
+  # 音频文件重采样后输出路径
+  out_dir: "audios/wavs"
+
+
+# preprocess_text 数据集预处理相关配置
+# 注意， “:” 后需要加空格
+preprocess_text:
+  # 原始文本文件路径，文本格式应为{wav_path}|{speaker_name}|{language}|{text}。
+  transcription_path: "filelists/你的数据集文本.list"
+  # 数据清洗后文本路径，可以不填。不填则将在原始文本目录生成
+  cleaned_path: ""
+  # 训练集路径
+  train_path: "filelists/train.list"
+  # 验证集路径
+  val_path: "filelists/val.list"
+  # 配置文件路径
+  config_path: "config.json"
+  # 每个语言的验证集条数
+  val_per_lang: 4
+  # 验证集最大条数，多于的会被截断并放到训练集中
+  max_val_total: 12
+  # 是否进行数据清洗
+  clean: true
+
+
+# bert_gen 相关配置
+# 注意， “:” 后需要加空格
+bert_gen:
+  # 训练数据集配置文件路径
+  config_path: "config.json"
+  # 并行数
+  num_processes: 4
+  # 使用设备：可选项 "cuda" 显卡推理，"cpu" cpu推理
+  # 该选项同时决定了get_bert_feature的默认设备
+  device: "cuda"
+  # 使用多卡推理
+  use_multi_device: false
+
+# emo_gen 相关配置
+# 注意， “:” 后需要加空格
+emo_gen:
+  # 训练数据集配置文件路径
+  config_path: "config.json"
+  # 并行数
+  num_processes: 4
+  # 使用设备：可选项 "cuda" 显卡推理，"cpu" cpu推理
+  device: "cuda"
+  # 使用多卡推理
+  use_multi_device: false
+
+# train 训练配置
+# 注意， “:” 后需要加空格
+train_ms:
+  env:
+    MASTER_ADDR: "localhost"
+    MASTER_PORT: 10086
+    WORLD_SIZE: 1
+    LOCAL_RANK: 0
+    RANK: 0
+    # 可以填写任意名的环境变量
+    # THE_ENV_VAR_YOU_NEED_TO_USE: "1234567"
+  # 底模设置
+  base:
+    use_base_model: false
+    repo_id: "Stardust_minus/Bert-VITS2"
+    model_image: "Bert-VITS2_2.3底模" # openi网页的模型名
+  # 训练模型存储目录：与旧版本的区别，原先数据集是存放在logs/model_name下的，现在改为统一存放在Data/你的数据集/models下
+  model: "models"
+  # 配置文件路径
+  config_path: "configs/config.json"
+  # 训练使用的worker，不建议超过CPU核心数
+  num_workers: 16
+  # 关闭此项可以节约接近70%的磁盘空间，但是可能导致实际训练速度变慢和更高的CPU使用率。
+  spec_cache: False
+  # 保存的检查点数量，多于此数目的权重会被删除来节省空间。
+  keep_ckpts: 3
+
+
+# webui webui配置
+# 注意， “:” 后需要加空格
+webui:
+  # 推理设备
+  device: "cuda"
+  # 模型路径
+  # model: "models/G_32000.pth"
+  model: "models/G_250000.pth"
+  # 配置文件路径
+  config_path: "configs/config.json"
+  # 端口号
+  port: 7861
+  # 是否公开部署，对外网开放
+  share: false
+  # 是否开启debug模式
+  debug: false
+  # 语种识别库，可选langid, fastlid
+  language_identification_library: "langid"
+
+
+# server-fastapi配置
+# 注意， “:” 后需要加空格
+# 注意，本配置下的所有配置均为相对于根目录的路径
+server:
+  # 端口号
+  port: 5005
+  # 模型默认使用设备：但是当前并没有实现这个配置。
+  device: "cuda"
+  # 需要加载的所有模型的配置，可以填多个模型，也可以不填模型，等网页成功后手动加载模型
+  # 不加载模型的配置格式：删除默认给的两个模型配置，给models赋值 [ ]，也就是空列表。参考模型2的speakers 即 models: [ ]
+  # 注意，所有模型都必须正确配置model与config的路径，空路径会导致加载错误。
+  # 也可以不填模型，等网页加载成功后手动填写models。
+  models:
+    - # 模型的路径
+      model: ""
+      # 模型config.json的路径
+      config: ""
+      # 模型使用设备，若填写则会覆盖默认配置
+      device: "cuda"
+      # 模型默认使用的语言
+      language: "ZH"
+      # 模型人物默认参数
+      # 不必填写所有人物，不填的使用默认值
+      # 暂时不用填写，当前尚未实现按人区分配置
+      speakers:
+        - speaker: "科比"
+          sdp_ratio: 0.2
+          noise_scale: 0.6
+          noise_scale_w: 0.8
+          length_scale: 1
+        - speaker: "五条悟"
+          sdp_ratio: 0.3
+          noise_scale: 0.7
+          noise_scale_w: 0.8
+          length_scale: 0.5
+        - speaker: "安倍晋三"
+          sdp_ratio: 0.2
+          noise_scale: 0.6
+          noise_scale_w: 0.8
+          length_scale: 1.2
+    - # 模型的路径
+      model: ""
+      # 模型config.json的路径
+      config: ""
+      # 模型使用设备，若填写则会覆盖默认配置
+      device: "cpu"
+      # 模型默认使用的语言
+      language: "JP"
+      # 模型人物默认参数
+      # 不必填写所有人物，不填的使用默认值
+      speakers: [ ] # 也可以不填
+
+# 百度翻译开放平台 api配置
+# api接入文档 https://api.fanyi.baidu.com/doc/21
+# 请不要在github等网站公开分享你的app id 与 key
+translate:
+  # 你的APPID
+  "app_key": ""
+  # 你的密钥
+  "secret_key": ""

utils/bert_vits2/data/mix/configs/config.json

@@ -0,0 +1,112 @@
+{
+  "train": {
+    "log_interval": 400,
+    "eval_interval": 2000,
+    "seed": 42,
+    "epochs": 1000,
+    "learning_rate": 0.00002,
+    "betas": [
+      0.8,
+      0.99
+    ],
+    "eps": 1e-09,
+    "batch_size": 24,
+    "bf16_run": false,
+    "lr_decay": 0.99995,
+    "segment_size": 16384,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "c_commit": 100,
+    "skip_optimizer": true,
+    "freeze_ZH_bert": false,
+    "freeze_JP_bert": false,
+    "freeze_EN_bert": false,
+    "freeze_emo": false
+  },
+  "data": {
+    "training_files": "data/mix/train.list",
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+}

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utils/bert_vits2/emotional/clap-htsat-fused/README.md

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+---
+license: apache-2.0
+---
+# Model card for CLAP
+
+Model card for CLAP: Contrastive Language-Audio Pretraining
+
+![clap_image](https://s3.amazonaws.com/moonup/production/uploads/1678811100805-62441d1d9fdefb55a0b7d12c.png)
+
+
+#  Table of Contents
+
+0. [TL;DR](#TL;DR)
+1. [Model Details](#model-details)
+2. [Usage](#usage)
+3. [Uses](#uses)
+4. [Citation](#citation)
+
+# TL;DR
+
+The abstract of the paper states that:
+
+> Contrastive learning has shown remarkable success in the field of multimodal representation learning. In this paper, we propose a pipeline of contrastive language-audio pretraining to develop an audio representation by combining audio data with natural language descriptions. To accomplish this target, we first release LAION-Audio-630K, a large collection of 633,526 audio-text pairs from different data sources. Second, we construct a contrastive language-audio pretraining model by considering different audio encoders and text encoders. We incorporate the feature fusion mechanism and keyword-to-caption augmentation into the model design to further enable the model to process audio inputs of variable lengths and enhance the performance. Third, we perform comprehensive experiments to evaluate our model across three tasks: text-to-audio retrieval, zero-shot audio classification, and supervised audio classification. The results demonstrate that our model achieves superior performance in text-to-audio retrieval task. In audio classification tasks, the model achieves state-of-the-art performance in the zero-shot setting and is able to obtain performance comparable to models' results in the non-zero-shot setting. LAION-Audio-630K and the proposed model are both available to the public.
+
+
+# Usage
+
+You can use this model for zero shot audio classification or extracting audio and/or textual features.
+
+# Uses
+
+## Perform zero-shot audio classification
+
+### Using `pipeline`
+
+```python
+from datasets import load_dataset
+from transformers import pipeline
+
+dataset = load_dataset("ashraq/esc50")
+audio = dataset["train"]["audio"][-1]["array"]
+
+audio_classifier = pipeline(task="zero-shot-audio-classification", model="laion/clap-htsat-fused")
+output = audio_classifier(audio, candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"])
+print(output)
+>>> [{"score": 0.999, "label": "Sound of a dog"}, {"score": 0.001, "label": "Sound of vaccum cleaner"}]
+```
+
+## Run the model:
+
+You can also get the audio and text embeddings using `ClapModel`
+
+### Run the model on CPU:
+
+```python
+from datasets import load_dataset
+from transformers import ClapModel, ClapProcessor
+
+librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+audio_sample = librispeech_dummy[0]
+
+model = ClapModel.from_pretrained("laion/clap-htsat-fused")
+processor = ClapProcessor.from_pretrained("laion/clap-htsat-fused")
+
+inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt")
+audio_embed = model.get_audio_features(**inputs)
+```
+
+### Run the model on GPU:
+
+```python
+from datasets import load_dataset
+from transformers import ClapModel, ClapProcessor
+
+librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+audio_sample = librispeech_dummy[0]
+
+model = ClapModel.from_pretrained("laion/clap-htsat-fused").to(0)
+processor = ClapProcessor.from_pretrained("laion/clap-htsat-fused")
+
+inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt").to(0)
+audio_embed = model.get_audio_features(**inputs)
+```
+
+
+# Citation
+
+If you are using this model for your work, please consider citing the original paper:
+```
+@misc{https://doi.org/10.48550/arxiv.2211.06687,
+  doi = {10.48550/ARXIV.2211.06687},
+
+  url = {https://arxiv.org/abs/2211.06687},
+
+  author = {Wu, Yusong and Chen, Ke and Zhang, Tianyu and Hui, Yuchen and Berg-Kirkpatrick, Taylor and Dubnov, Shlomo},
+
+  keywords = {Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},
+
+  title = {Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation},
+
+  publisher = {arXiv},
+
+  year = {2022},
+
+  copyright = {Creative Commons Attribution 4.0 International}
+}
+```

utils/bert_vits2/emotional/clap-htsat-fused/config.json

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utils/bert_vits2/emotional/clap-htsat-fused/preprocessor_config.json

@@ -0,0 +1,22 @@
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+  "nb_max_samples": 480000,
+  "padding": "repeatpad",
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "ClapProcessor",
+  "return_attention_mask": false,
+  "sampling_rate": 48000,
+  "top_db": null,
+  "truncation": "fusion"
+}

utils/bert_vits2/emotional/clap-htsat-fused/special_tokens_map.json

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+}

utils/bert_vits2/emotional/clap-htsat-fused/tokenizer_config.json

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+  "unk_token": "<unk>"
+}

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utils/bert_vits2/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/LICENSE

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+Attribution-NonCommercial-ShareAlike 4.0 International
+
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utils/bert_vits2/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/README.md

@@ -0,0 +1,127 @@
+---
+language: en
+datasets:
+- msp-podcast
+inference: true
+tags:
+- speech
+- audio
+- wav2vec2
+- audio-classification
+- emotion-recognition
+license: cc-by-nc-sa-4.0
+pipeline_tag: audio-classification
+---
+
+# Model for Dimensional Speech Emotion Recognition based on Wav2vec 2.0
+
+The model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0...1. In addition, it also provides the pooled states of the last transformer layer. The model was created by fine-tuning [
+Wav2Vec2-Large-Robust](https://huggingface.co/facebook/wav2vec2-large-robust) on [MSP-Podcast](https://ecs.utdallas.edu/research/researchlabs/msp-lab/MSP-Podcast.html) (v1.7). The model was pruned from 24 to 12 transformer layers before fine-tuning. An [ONNX](https://onnx.ai/") export of the model is available from [doi:10.5281/zenodo.6221127](https://zenodo.org/record/6221127). Further details are given in the associated [paper](https://arxiv.org/abs/2203.07378) and [tutorial](https://github.com/audeering/w2v2-how-to).
+
+# Usage
+
+```python
+import numpy as np
+import torch
+import torch.nn as nn
+from transformers import Wav2Vec2Processor
+from transformers.models.wav2vec2.modeling_wav2vec2 import (
+    Wav2Vec2Model,
+    Wav2Vec2PreTrainedModel,
+)
+
+
+class RegressionHead(nn.Module):
+    r"""Classification head."""
+
+    def __init__(self, config):
+
+        super().__init__()
+
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.final_dropout)
+        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
+
+    def forward(self, features, **kwargs):
+
+        x = features
+        x = self.dropout(x)
+        x = self.dense(x)
+        x = torch.tanh(x)
+        x = self.dropout(x)
+        x = self.out_proj(x)
+
+        return x
+
+
+class EmotionModel(Wav2Vec2PreTrainedModel):
+    r"""Speech emotion classifier."""
+
+    def __init__(self, config):
+
+        super().__init__(config)
+
+        self.config = config
+        self.wav2vec2 = Wav2Vec2Model(config)
+        self.classifier = RegressionHead(config)
+        self.init_weights()
+
+    def forward(
+            self,
+            input_values,
+    ):
+
+        outputs = self.wav2vec2(input_values)
+        hidden_states = outputs[0]
+        hidden_states = torch.mean(hidden_states, dim=1)
+        logits = self.classifier(hidden_states)
+
+        return hidden_states, logits
+
+
+
+# load model from hub
+device = 'cpu'
+model_name = 'audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim'
+processor = Wav2Vec2Processor.from_pretrained(model_name)
+model = EmotionModel.from_pretrained(model_name)
+
+# dummy signal
+sampling_rate = 16000
+signal = np.zeros((1, sampling_rate), dtype=np.float32)
+
+
+def process_func(
+    x: np.ndarray,
+    sampling_rate: int,
+    embeddings: bool = False,
+) -> np.ndarray:
+    r"""Predict emotions or extract embeddings from raw audio signal."""
+
+    # run through processor to normalize signal
+    # always returns a batch, so we just get the first entry
+    # then we put it on the device
+    y = processor(x, sampling_rate=sampling_rate)
+    y = y['input_values'][0]
+    y = y.reshape(1, -1)
+    y = torch.from_numpy(y).to(device)
+
+    # run through model
+    with torch.no_grad():
+        y = model(y)[0 if embeddings else 1]
+
+    # convert to numpy
+    y = y.detach().cpu().numpy()
+
+    return y
+
+
+print(process_func(signal, sampling_rate))
+#  Arousal    dominance valence
+# [[0.5460754  0.6062266  0.40431657]]
+
+print(process_func(signal, sampling_rate, embeddings=True))
+# Pooled hidden states of last transformer layer
+# [[-0.00752167  0.0065819  -0.00746342 ...  0.00663632  0.00848748
+#    0.00599211]]
+```

utils/bert_vits2/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/config.json

@@ -0,0 +1,122 @@
+{
+  "_name_or_path": "torch",
+  "activation_dropout": 0.1,
+  "adapter_kernel_size": 3,
+  "adapter_stride": 2,
+  "add_adapter": false,
+  "apply_spec_augment": true,
+  "architectures": [
+    "Wav2Vec2ForSpeechClassification"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 1,
+  "classifier_proj_size": 256,
+  "codevector_dim": 768,
+  "contrastive_logits_temperature": 0.1,
+  "conv_bias": true,
+  "conv_dim": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "conv_kernel": [
+    10,
+    3,
+    3,
+    3,
+    3,
+    2,
+    2
+  ],
+  "conv_stride": [
+    5,
+    2,
+    2,
+    2,
+    2,
+    2,
+    2
+  ],
+  "ctc_loss_reduction": "sum",
+  "ctc_zero_infinity": false,
+  "diversity_loss_weight": 0.1,
+  "do_stable_layer_norm": true,
+  "eos_token_id": 2,
+  "feat_extract_activation": "gelu",
+  "feat_extract_dropout": 0.0,
+  "feat_extract_norm": "layer",
+  "feat_proj_dropout": 0.1,
+  "feat_quantizer_dropout": 0.0,
+  "final_dropout": 0.1,
+  "finetuning_task": "wav2vec2_reg",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.1,
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "arousal",
+    "1": "dominance",
+    "2": "valence"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "arousal": 0,
+    "dominance": 1,
+    "valence": 2
+  },
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.1,
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.05,
+  "model_type": "wav2vec2",
+  "num_adapter_layers": 3,
+  "num_attention_heads": 16,
+  "num_codevector_groups": 2,
+  "num_codevectors_per_group": 320,
+  "num_conv_pos_embedding_groups": 16,
+  "num_conv_pos_embeddings": 128,
+  "num_feat_extract_layers": 7,
+  "num_hidden_layers": 12,
+  "num_negatives": 100,
+  "output_hidden_size": 1024,
+  "pad_token_id": 0,
+  "pooling_mode": "mean",
+  "problem_type": "regression",
+  "proj_codevector_dim": 768,
+  "tdnn_dilation": [
+    1,
+    2,
+    3,
+    1,
+    1
+  ],
+  "tdnn_dim": [
+    512,
+    512,
+    512,
+    512,
+    1500
+  ],
+  "tdnn_kernel": [
+    5,
+    3,
+    3,
+    1,
+    1
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.17.0.dev0",
+  "use_weighted_layer_sum": false,
+  "vocab_size": null,
+  "xvector_output_dim": 512
+}

utils/bert_vits2/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/preprocessor_config.json

@@ -0,0 +1,9 @@
+{
+  "do_normalize": true,
+  "feature_extractor_type": "Wav2Vec2FeatureExtractor",
+  "feature_size": 1,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "return_attention_mask": true,
+  "sampling_rate": 16000
+}

utils/bert_vits2/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/vocab.json

@@ -0,0 +1 @@
+{}

utils/bert_vits2/infer.py

@@ -0,0 +1,439 @@
+"""
+版本管理、兼容推理及模型加载实现。
+版本说明：
+    1. 版本号与github的release版本号对应，使用哪个release版本训练的模型即对应其版本号
+    2. 请在模型的config.json中显示声明版本号，添加一个字段"version" : "你的版本号"
+特殊版本说明：
+    1.1.1-fix： 1.1.1版本训练的模型，但是在推理时使用dev的日语修复
+    2.3：当前版本
+"""
+import torch
+from . import commons
+from .text import cleaned_text_to_sequence, get_bert
+
+# from clap_wrapper import get_clap_audio_feature, get_clap_text_feature
+from typing import Union
+from .text.cleaner import clean_text
+from . import utils
+
+from .models import SynthesizerTrn
+from .text.symbols import symbols
+
+# from utils.tts.bert_vits2.oldVersion.V220.models import SynthesizerTrn as V220SynthesizerTrn
+# from utils.tts.bert_vits2.oldVersion.V220.text import symbols as V220symbols
+# from utils.tts.bert_vits2.oldVersion.V210.models import SynthesizerTrn as V210SynthesizerTrn
+# from utils.tts.bert_vits2.oldVersion.V210.text import symbols as V210symbols
+# from utils.tts.bert_vits2.oldVersion.V200.models import SynthesizerTrn as V200SynthesizerTrn
+# from utils.tts.bert_vits2.oldVersion.V200.text import symbols as V200symbols
+# from utils.tts.bert_vits2.oldVersion.V111.models import SynthesizerTrn as V111SynthesizerTrn
+# from utils.tts.bert_vits2.oldVersion.V111.text import symbols as V111symbols
+# from utils.tts.bert_vits2.oldVersion.V110.models import SynthesizerTrn as V110SynthesizerTrn
+# from utils.tts.bert_vits2.oldVersion.V110.text import symbols as V110symbols
+# from utils.tts.bert_vits2.oldVersion.V101.models import SynthesizerTrn as V101SynthesizerTrn
+# from utils.tts.bert_vits2.oldVersion.V101.text import symbols as V101symbols
+
+# from oldVersion import V111, V110, V101, V200, V210, V220
+
+# 当前版本信息
+latest_version = "2.3"
+
+# 版本兼容
+# SynthesizerTrnMap = {
+#     "2.2": V220SynthesizerTrn,
+#     "2.1": V210SynthesizerTrn,
+#     "2.0.2-fix": V200SynthesizerTrn,
+#     "2.0.1": V200SynthesizerTrn,
+#     "2.0": V200SynthesizerTrn,
+#     "1.1.1-fix": V111SynthesizerTrn,
+#     "1.1.1": V111SynthesizerTrn,
+#     "1.1": V110SynthesizerTrn,
+#     "1.1.0": V110SynthesizerTrn,
+#     "1.0.1": V101SynthesizerTrn,
+#     "1.0": V101SynthesizerTrn,
+#     "1.0.0": V101SynthesizerTrn,
+# }
+
+# symbolsMap = {
+#     "2.2": V220symbols,
+#     "2.1": V210symbols,
+#     "2.0.2-fix": V200symbols,
+#     "2.0.1": V200symbols,
+#     "2.0": V200symbols,
+#     "1.1.1-fix": V111symbols,
+#     "1.1.1": V111symbols,
+#     "1.1": V110symbols,
+#     "1.1.0": V110symbols,
+#     "1.0.1": V101symbols,
+#     "1.0": V101symbols,
+#     "1.0.0": V101symbols,
+# }
+
+
+# def get_emo_(reference_audio, emotion, sid):
+#     emo = (
+#         torch.from_numpy(get_emo(reference_audio))
+#         if reference_audio and emotion == -1
+#         else torch.FloatTensor(
+#             np.load(f"emo_clustering/{sid}/cluster_center_{emotion}.npy")
+#         )
+#     )
+#     return emo
+
+
+def get_net_g(model_path: str, version: str, device: str, hps):
+    if version != latest_version:
+        net_g = SynthesizerTrnMap[version](
+            len(symbolsMap[version]),
+            hps.data.filter_length // 2 + 1,
+            hps.train.segment_size // hps.data.hop_length,
+            n_speakers=hps.data.n_speakers,
+            **hps.model,
+        ).to(device)
+    else:
+        # 当前版本模型 net_g
+        net_g = SynthesizerTrn(
+            len(symbols),
+            hps.data.filter_length // 2 + 1,
+            hps.train.segment_size // hps.data.hop_length,
+            n_speakers=hps.data.n_speakers,
+            **hps.model,
+        ).to(device)
+    _ = net_g.eval()
+    _ = utils.load_checkpoint(model_path, net_g, None, skip_optimizer=True)
+    return net_g
+
+
+def get_text(text, language_str, hps, device, style_text=None, style_weight=0.7):
+    style_text = None if style_text == "" else style_text
+    # 在此处实现当前版本的get_text
+    norm_text, phone, tone, word2ph = clean_text(text, language_str)
+    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
+
+    if hps.data.add_blank:
+        phone = commons.intersperse(phone, 0)
+        tone = commons.intersperse(tone, 0)
+        language = commons.intersperse(language, 0)
+        for i in range(len(word2ph)):
+            word2ph[i] = word2ph[i] * 2
+        word2ph[0] += 1
+    bert_ori = get_bert(
+        norm_text, word2ph, language_str, device, style_text, style_weight
+    )
+    del word2ph
+    assert bert_ori.shape[-1] == len(phone), phone
+
+    if language_str == "ZH":
+        bert = bert_ori
+        ja_bert = torch.randn(1024, len(phone))
+        en_bert = torch.randn(1024, len(phone))
+    elif language_str == "JP":
+        bert = torch.randn(1024, len(phone))
+        ja_bert = bert_ori
+        en_bert = torch.randn(1024, len(phone))
+    elif language_str == "EN":
+        bert = torch.randn(1024, len(phone))
+        ja_bert = torch.randn(1024, len(phone))
+        en_bert = bert_ori
+    else:
+        raise ValueError("language_str should be ZH, JP or EN")
+
+    assert bert.shape[-1] == len(
+        phone
+    ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
+
+    phone = torch.LongTensor(phone)
+    tone = torch.LongTensor(tone)
+    language = torch.LongTensor(language)
+    return bert, ja_bert, en_bert, phone, tone, language
+
+
+def infer(
+    text,
+    emotion: Union[int, str],
+    sdp_ratio,
+    noise_scale,
+    noise_scale_w,
+    length_scale,
+    sid,
+    language,
+    hps,
+    net_g,
+    device,
+    reference_audio=None,
+    skip_start=False,
+    skip_end=False,
+    style_text=None,
+    style_weight=0.7,
+):
+    # # 2.2版本参数位置变了
+    # inferMap_V4 = {
+    #     "2.2": V220.infer,
+    # }
+    # # 2.1 参数新增 emotion reference_audio skip_start skip_end
+    # inferMap_V3 = {
+    #     "2.1": V210.infer,
+    # }
+    # # 支持中日英三语版本
+    # inferMap_V2 = {
+    #     "2.0.2-fix": V200.infer,
+    #     "2.0.1": V200.infer,
+    #     "2.0": V200.infer,
+    #     "1.1.1-fix": V111.infer_fix,
+    #     "1.1.1": V111.infer,
+    #     "1.1": V110.infer,
+    #     "1.1.0": V110.infer,
+    # }
+    # # 仅支持中文版本
+    # # 在测试中，并未发现两个版本的模型不能互相通用
+    # inferMap_V1 = {
+    #     "1.0.1": V101.infer,
+    #     "1.0": V101.infer,
+    #     "1.0.0": V101.infer,
+    # }
+    version = hps.version if hasattr(hps, "version") else latest_version
+    # 非当前版本，根据版本号选择合适的infer
+    if version != latest_version:
+        if version in inferMap_V4.keys():
+            return inferMap_V4[version](
+                text,
+                emotion,
+                sdp_ratio,
+                noise_scale,
+                noise_scale_w,
+                length_scale,
+                sid,
+                language,
+                hps,
+                net_g,
+                device,
+                reference_audio,
+                skip_start,
+                skip_end,
+                style_text,
+                style_weight,
+            )
+        if version in inferMap_V3.keys():
+            return inferMap_V3[version](
+                text,
+                sdp_ratio,
+                noise_scale,
+                noise_scale_w,
+                length_scale,
+                sid,
+                language,
+                hps,
+                net_g,
+                device,
+                reference_audio,
+                emotion,
+                skip_start,
+                skip_end,
+                style_text,
+                style_weight,
+            )
+        if version in inferMap_V2.keys():
+            return inferMap_V2[version](
+                text,
+                sdp_ratio,
+                noise_scale,
+                noise_scale_w,
+                length_scale,
+                sid,
+                language,
+                hps,
+                net_g,
+                device,
+            )
+        if version in inferMap_V1.keys():
+            return inferMap_V1[version](
+                text,
+                sdp_ratio,
+                noise_scale,
+                noise_scale_w,
+                length_scale,
+                sid,
+                hps,
+                net_g,
+                device,
+            )
+    # 在此处实现当前版本的推理
+    # emo = get_emo_(reference_audio, emotion, sid)
+    # if isinstance(reference_audio, np.ndarray):
+    #     emo = get_clap_audio_feature(reference_audio, device)
+    # else:
+    #     emo = get_clap_text_feature(emotion, device)
+    # emo = torch.squeeze(emo, dim=1)
+
+    bert, ja_bert, en_bert, phones, tones, lang_ids = get_text(
+        text,
+        language,
+        hps,
+        device,
+        style_text=style_text,
+        style_weight=style_weight,
+    )
+    if skip_start:
+        phones = phones[3:]
+        tones = tones[3:]
+        lang_ids = lang_ids[3:]
+        bert = bert[:, 3:]
+        ja_bert = ja_bert[:, 3:]
+        en_bert = en_bert[:, 3:]
+    if skip_end:
+        phones = phones[:-2]
+        tones = tones[:-2]
+        lang_ids = lang_ids[:-2]
+        bert = bert[:, :-2]
+        ja_bert = ja_bert[:, :-2]
+        en_bert = en_bert[:, :-2]
+    with torch.no_grad():
+        x_tst = phones.to(device).unsqueeze(0)
+        tones = tones.to(device).unsqueeze(0)
+        lang_ids = lang_ids.to(device).unsqueeze(0)
+        bert = bert.to(device).unsqueeze(0)
+        ja_bert = ja_bert.to(device).unsqueeze(0)
+        en_bert = en_bert.to(device).unsqueeze(0)
+        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
+        # emo = emo.to(device).unsqueeze(0)
+        del phones
+        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
+        audio = (
+            net_g.infer(
+                x_tst,
+                x_tst_lengths,
+                speakers,
+                tones,
+                lang_ids,
+                bert,
+                ja_bert,
+                en_bert,
+                sdp_ratio=sdp_ratio,
+                noise_scale=noise_scale,
+                noise_scale_w=noise_scale_w,
+                length_scale=length_scale,
+                en_ratio=1.0
+            )[0][0, 0]
+            .data.cpu()
+            .float()
+            .numpy()
+        )
+        del (
+            x_tst,
+            tones,
+            lang_ids,
+            bert,
+            x_tst_lengths,
+            speakers,
+            ja_bert,
+            en_bert,
+        )  # , emo
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        return audio
+
+
+def infer_multilang(
+    text,
+    sdp_ratio,
+    noise_scale,
+    noise_scale_w,
+    length_scale,
+    sid,
+    language,
+    hps,
+    net_g,
+    device,
+    reference_audio=None,
+    emotion=None,
+    skip_start=False,
+    skip_end=False,
+    en_ratio=1.0
+):
+    bert, ja_bert, en_bert, phones, tones, lang_ids = [], [], [], [], [], []
+    # emo = get_emo_(reference_audio, emotion, sid)
+    # if isinstance(reference_audio, np.ndarray):
+    #     emo = get_clap_audio_feature(reference_audio, device)
+    # else:
+    #     emo = get_clap_text_feature(emotion, device)
+    # emo = torch.squeeze(emo, dim=1)
+    for idx, (txt, lang) in enumerate(zip(text, language)):
+        _skip_start = (idx != 0) or (skip_start and idx == 0)
+        _skip_end = (idx != len(language) - 1) or skip_end
+        (
+            temp_bert,
+            temp_ja_bert,
+            temp_en_bert,
+            temp_phones,
+            temp_tones,
+            temp_lang_ids,
+        ) = get_text(txt, lang, hps, device)
+        if _skip_start:
+            temp_bert = temp_bert[:, 3:]
+            temp_ja_bert = temp_ja_bert[:, 3:]
+            temp_en_bert = temp_en_bert[:, 3:]
+            temp_phones = temp_phones[3:]
+            temp_tones = temp_tones[3:]
+            temp_lang_ids = temp_lang_ids[3:]
+        if _skip_end:
+            temp_bert = temp_bert[:, :-2]
+            temp_ja_bert = temp_ja_bert[:, :-2]
+            temp_en_bert = temp_en_bert[:, :-2]
+            temp_phones = temp_phones[:-2]
+            temp_tones = temp_tones[:-2]
+            temp_lang_ids = temp_lang_ids[:-2]
+        bert.append(temp_bert)
+        ja_bert.append(temp_ja_bert)
+        en_bert.append(temp_en_bert)
+        phones.append(temp_phones)
+        tones.append(temp_tones)
+        lang_ids.append(temp_lang_ids)
+    bert = torch.concatenate(bert, dim=1)
+    ja_bert = torch.concatenate(ja_bert, dim=1)
+    en_bert = torch.concatenate(en_bert, dim=1)
+    phones = torch.concatenate(phones, dim=0)
+    tones = torch.concatenate(tones, dim=0)
+    lang_ids = torch.concatenate(lang_ids, dim=0)
+    with torch.no_grad():
+        x_tst = phones.to(device).unsqueeze(0)
+        tones = tones.to(device).unsqueeze(0)
+        lang_ids = lang_ids.to(device).unsqueeze(0)
+        bert = bert.to(device).unsqueeze(0)
+        ja_bert = ja_bert.to(device).unsqueeze(0)
+        en_bert = en_bert.to(device).unsqueeze(0)
+        # emo = emo.to(device).unsqueeze(0)
+        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
+        del phones
+        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
+        audio = (
+            net_g.infer(
+                x_tst,
+                x_tst_lengths,
+                speakers,
+                tones,
+                lang_ids,
+                bert,
+                ja_bert,
+                en_bert,
+                sdp_ratio=sdp_ratio,
+                noise_scale=noise_scale,
+                noise_scale_w=noise_scale_w,
+                length_scale=length_scale,
+                en_ratio=en_ratio
+            )[0][0, 0]
+            .data.cpu()
+            .float()
+            .numpy()
+        )
+        del (
+            x_tst,
+            tones,
+            lang_ids,
+            bert,
+            x_tst_lengths,
+            speakers,
+            ja_bert,
+            en_bert,
+        )  # , emo
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        return audio

utils/bert_vits2/modules.py

@@ -0,0 +1,580 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from torch.nn import Conv1d
+from torch.nn.utils import weight_norm, remove_weight_norm
+
+from . import commons
+from .commons import init_weights, get_padding
+from .transforms import piecewise_rational_quadratic_transform
+from .attentions import Encoder
+
+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):
+    """
+    Dilated 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 ResBlock1(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super(ResBlock1, self).__init__()
+        self.convs1 = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[0],
+                        padding=get_padding(kernel_size, dilation[0]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[1],
+                        padding=get_padding(kernel_size, dilation[1]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[2],
+                        padding=get_padding(kernel_size, dilation[2]),
+                    )
+                ),
+            ]
+        )
+        self.convs1.apply(init_weights)
+
+        self.convs2 = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1),
+                    )
+                ),
+            ]
+        )
+        self.convs2.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c2(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs1:
+            remove_weight_norm(l)
+        for l in self.convs2:
+            remove_weight_norm(l)
+
+
+class ResBlock2(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
+        super(ResBlock2, self).__init__()
+        self.convs = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[0],
+                        padding=get_padding(kernel_size, dilation[0]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[1],
+                        padding=get_padding(kernel_size, dilation[1]),
+                    )
+                ),
+            ]
+        )
+        self.convs.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c in self.convs:
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs:
+            remove_weight_norm(l)
+
+
+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
+
+
+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
+
+
+class TransformerCouplingLayer(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        kernel_size,
+        n_layers,
+        n_heads,
+        p_dropout=0,
+        filter_channels=0,
+        mean_only=False,
+        wn_sharing_parameter=None,
+        gin_channels=0,
+    ):
+        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.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 = (
+            Encoder(
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers,
+                kernel_size,
+                p_dropout,
+                isflow=True,
+                gin_channels=gin_channels,
+            )
+            if wn_sharing_parameter is None
+            else wn_sharing_parameter
+        )
+        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

utils/bert_vits2/monotonic_align/__init__.py

@@ -0,0 +1,16 @@
+from numpy import zeros, int32, float32
+from torch import from_numpy
+
+from .core import maximum_path_jit
+
+
+def maximum_path(neg_cent, mask):
+    device = neg_cent.device
+    dtype = neg_cent.dtype
+    neg_cent = neg_cent.data.cpu().numpy().astype(float32)
+    path = zeros(neg_cent.shape, dtype=int32)
+
+    t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32)
+    t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32)
+    maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
+    return from_numpy(path).to(device=device, dtype=dtype)

utils/bert_vits2/monotonic_align/core.py

@@ -0,0 +1,46 @@
+import numba
+
+
+@numba.jit(
+    numba.void(
+        numba.int32[:, :, ::1],
+        numba.float32[:, :, ::1],
+        numba.int32[::1],
+        numba.int32[::1],
+    ),
+    nopython=True,
+    nogil=True,
+)
+def maximum_path_jit(paths, values, t_ys, t_xs):
+    b = paths.shape[0]
+    max_neg_val = -1e9
+    for i in range(int(b)):
+        path = paths[i]
+        value = values[i]
+        t_y = t_ys[i]
+        t_x = t_xs[i]
+
+        v_prev = v_cur = 0.0
+        index = t_x - 1
+
+        for y in range(t_y):
+            for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
+                if x == y:
+                    v_cur = max_neg_val
+                else:
+                    v_cur = value[y - 1, x]
+                if x == 0:
+                    if y == 0:
+                        v_prev = 0.0
+                    else:
+                        v_prev = max_neg_val
+                else:
+                    v_prev = value[y - 1, x - 1]
+                value[y, x] += max(v_prev, v_cur)
+
+        for y in range(t_y - 1, -1, -1):
+            path[y, index] = 1
+            if index != 0 and (
+                index == y or value[y - 1, index] < value[y - 1, index - 1]
+            ):
+                index = index - 1

utils/bert_vits2/re_matching.py

@@ -0,0 +1,81 @@
+import re
+
+
+def extract_language_and_text_updated(speaker, dialogue):
+    # 使用正则表达式匹配<语言>标签和其后的文本
+    pattern_language_text = r"<(\S+?)>([^<]+)"
+    matches = re.findall(pattern_language_text, dialogue, re.DOTALL)
+    speaker = speaker[1:-1]
+    # 清理文本：去除两边的空白字符
+    matches_cleaned = [(lang.upper(), text.strip()) for lang, text in matches]
+    matches_cleaned.append(speaker)
+    return matches_cleaned
+
+
+def validate_text(input_text):
+    # 验证说话人的正则表达式
+    pattern_speaker = r"(\[\S+?\])((?:\s*<\S+?>[^<\[\]]+?)+)"
+
+    # 使用re.DOTALL标志使.匹配包括换行符在内的所有字符
+    matches = re.findall(pattern_speaker, input_text, re.DOTALL)
+
+    # 对每个匹配到的说话人内容进行进一步验证
+    for _, dialogue in matches:
+        language_text_matches = extract_language_and_text_updated(_, dialogue)
+        if not language_text_matches:
+            return (
+                False,
+                "Error: Invalid format detected in dialogue content. Please check your input.",
+            )
+
+    # 如果输入的文本中没有找到任何匹配项
+    if not matches:
+        return (
+            False,
+            "Error: No valid speaker format detected. Please check your input.",
+        )
+
+    return True, "Input is valid."
+
+
+def text_matching(text: str) -> list:
+    speaker_pattern = r"(\[\S+?\])(.+?)(?=\[\S+?\]|$)"
+    matches = re.findall(speaker_pattern, text, re.DOTALL)
+    result = []
+    for speaker, dialogue in matches:
+        result.append(extract_language_and_text_updated(speaker, dialogue))
+    return result
+
+
+def cut_para(text):
+    splitted_para = re.split("[\n]", text)  # 按段分
+    splitted_para = [
+        sentence.strip() for sentence in splitted_para if sentence.strip()
+    ]  # 删除空字符串
+    return splitted_para
+
+
+def cut_sent(para):
+    para = re.sub("([。！;？\?])([^”’])", r"\1\n\2", para)  # 单字符断句符
+    para = re.sub("(\.{6})([^”’])", r"\1\n\2", para)  # 英文省略号
+    para = re.sub("(\…{2})([^”’])", r"\1\n\2", para)  # 中文省略号
+    para = re.sub("([。！？\?][”’])([^，。！？\?])", r"\1\n\2", para)
+    para = para.rstrip()  # 段尾如果有多余的\n就去掉它
+    return para.split("\n")
+
+
+if __name__ == "__main__":
+    text = """
+    [说话人1]
+    [说话人2]<zh>你好吗？<jp>元気ですか？<jp>こんにちは，世界。<zh>你好吗？
+    [说话人3]<zh>谢谢。<jp>どういたしまして。
+    """
+    text_matching(text)
+    # 测试函数
+    test_text = """
+    [说话人1]<zh>你好，こんにちは！<jp>こんにちは，世界。
+    [说话人2]<zh>你好吗？
+    """
+    text_matching(test_text)
+    res = validate_text(test_text)
+    print(res)

utils/bert_vits2/text/__init__.py

@@ -0,0 +1,63 @@
+from ..text.symbols import *
+
+_symbol_to_id = {s: i for i, s in enumerate(symbols)}
+
+
+def cleaned_text_to_sequence(cleaned_text, tones, language):
+    """Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
+    Args:
+      text: string to convert to a sequence
+    Returns:
+      List of integers corresponding to the symbols in the text
+    """
+    phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
+    tone_start = language_tone_start_map[language]
+    tones = [i + tone_start for i in tones]
+    lang_id = language_id_map[language]
+    lang_ids = [lang_id for i in phones]
+    return phones, tones, lang_ids
+
+
+def get_bert(norm_text, word2ph, language, device, style_text=None, style_weight=0.7):
+    from .chinese_bert import get_bert_feature as zh_bert
+    from .english_bert_mock import get_bert_feature as en_bert
+    from .japanese_bert import get_bert_feature as jp_bert
+
+    lang_bert_func_map = {"ZH": zh_bert, "EN": en_bert, "JP": jp_bert}
+    bert = lang_bert_func_map[language](
+        norm_text, word2ph, device, style_text, style_weight
+    )
+    return bert
+
+
+def check_bert_models():
+    relative_path = r"./utils/bert_vits2/"
+    import json
+    from pathlib import Path
+
+    from ..config import config
+    from .bert_utils import _check_bert
+
+    if config.mirror.lower() == "openi":
+        import openi
+
+        kwargs = {"token": config.openi_token} if config.openi_token else {}
+        openi.login(**kwargs)
+    with open(relative_path+"bert/bert_models.json", "r") as fp:
+        models = json.load(fp)
+        for k, v in models.items():
+            local_path = Path(relative_path+"bert").joinpath(k)
+            _check_bert(v["repo_id"], v["files"], local_path)
+
+
+def init_openjtalk():
+    import platform
+
+    if platform.platform() == "Linux":
+        import pyopenjtalk
+
+        pyopenjtalk.g2p("こんにちは，世界。")
+
+
+init_openjtalk()
+check_bert_models()

utils/bert_vits2/text/bert_utils.py

@@ -0,0 +1,23 @@
+from pathlib import Path
+
+from huggingface_hub import hf_hub_download
+
+from ..config import config
+
+
+MIRROR: str = config.mirror
+
+
+def _check_bert(repo_id, files, local_path):
+    for file in files:
+        if not Path(local_path).joinpath(file).exists():
+            if MIRROR.lower() == "openi":
+                import openi
+
+                openi.model.download_model(
+                    "Stardust_minus/Bert-VITS2", repo_id.split("/")[-1], "./bert"
+                )
+            else:
+                hf_hub_download(
+                    repo_id, file, local_dir=local_path, local_dir_use_symlinks=False
+                )

utils/bert_vits2/text/chinese.py

@@ -0,0 +1,206 @@
+import os
+import re
+
+from pypinyin import lazy_pinyin, Style
+
+from ..text.symbols import punctuation
+from ..text.tone_sandhi import ToneSandhi
+
+try:
+    from tn.chinese.normalizer import Normalizer
+
+    normalizer = Normalizer().normalize
+except ImportError:
+    import cn2an
+
+    print("tn.chinese.normalizer not found, use cn2an normalizer")
+    normalizer = lambda x: cn2an.transform(x, "an2cn")
+
+current_file_path = os.path.dirname(__file__)
+pinyin_to_symbol_map = {
+    line.split("\t")[0]: line.strip().split("\t")[1]
+    for line in open(os.path.join(current_file_path, "opencpop-strict.txt")).readlines()
+}
+
+import jieba.posseg as psg
+
+
+rep_map = {
+    "：": ",",
+    "；": ",",
+    "，": ",",
+    "。": ".",
+    "！": "!",
+    "？": "?",
+    "\n": ".",
+    "·": ",",
+    "、": ",",
+    "...": "…",
+    "$": ".",
+    "“": "'",
+    "”": "'",
+    '"': "'",
+    "‘": "'",
+    "’": "'",
+    "（": "'",
+    "）": "'",
+    "(": "'",
+    ")": "'",
+    "《": "'",
+    "》": "'",
+    "【": "'",
+    "】": "'",
+    "[": "'",
+    "]": "'",
+    "—": "-",
+    "～": "-",
+    "~": "-",
+    "「": "'",
+    "」": "'",
+}
+
+tone_modifier = ToneSandhi()
+
+
+def replace_punctuation(text):
+    text = text.replace("嗯", "恩").replace("呣", "母")
+    pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
+
+    replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
+
+    replaced_text = re.sub(
+        r"[^\u4e00-\u9fa5" + "".join(punctuation) + r"]+", "", replaced_text
+    )
+
+    return replaced_text
+
+
+def g2p(text):
+    pattern = r"(?<=[{0}])\s*".format("".join(punctuation))
+    sentences = [i for i in re.split(pattern, text) if i.strip() != ""]
+    phones, tones, word2ph = _g2p(sentences)
+    assert sum(word2ph) == len(phones)
+    assert len(word2ph) == len(text)  # Sometimes it will crash,you can add a try-catch.
+    phones = ["_"] + phones + ["_"]
+    tones = [0] + tones + [0]
+    word2ph = [1] + word2ph + [1]
+    return phones, tones, word2ph
+
+
+def _get_initials_finals(word):
+    initials = []
+    finals = []
+    orig_initials = lazy_pinyin(word, neutral_tone_with_five=True, style=Style.INITIALS)
+    orig_finals = lazy_pinyin(
+        word, neutral_tone_with_five=True, style=Style.FINALS_TONE3
+    )
+    for c, v in zip(orig_initials, orig_finals):
+        initials.append(c)
+        finals.append(v)
+    return initials, finals
+
+
+def _g2p(segments):
+    phones_list = []
+    tones_list = []
+    word2ph = []
+    for seg in segments:
+        # Replace all English words in the sentence
+        seg = re.sub("[a-zA-Z]+", "", seg)
+        seg_cut = psg.lcut(seg)
+        initials = []
+        finals = []
+        seg_cut = tone_modifier.pre_merge_for_modify(seg_cut)
+        for word, pos in seg_cut:
+            if pos == "eng":
+                continue
+            sub_initials, sub_finals = _get_initials_finals(word)
+            sub_finals = tone_modifier.modified_tone(word, pos, sub_finals)
+            initials.append(sub_initials)
+            finals.append(sub_finals)
+
+            # assert len(sub_initials) == len(sub_finals) == len(word)
+        initials = sum(initials, [])
+        finals = sum(finals, [])
+        #
+        for c, v in zip(initials, finals):
+            raw_pinyin = c + v
+            # NOTE: post process for pypinyin outputs
+            # we discriminate i, ii and iii
+            if c == v:
+                assert c in punctuation
+                phone = [c]
+                tone = "0"
+                word2ph.append(1)
+            else:
+                v_without_tone = v[:-1]
+                tone = v[-1]
+
+                pinyin = c + v_without_tone
+                assert tone in "12345"
+
+                if c:
+                    # 多音节
+                    v_rep_map = {
+                        "uei": "ui",
+                        "iou": "iu",
+                        "uen": "un",
+                    }
+                    if v_without_tone in v_rep_map.keys():
+                        pinyin = c + v_rep_map[v_without_tone]
+                else:
+                    # 单音节
+                    pinyin_rep_map = {
+                        "ing": "ying",
+                        "i": "yi",
+                        "in": "yin",
+                        "u": "wu",
+                    }
+                    if pinyin in pinyin_rep_map.keys():
+                        pinyin = pinyin_rep_map[pinyin]
+                    else:
+                        single_rep_map = {
+                            "v": "yu",
+                            "e": "e",
+                            "i": "y",
+                            "u": "w",
+                        }
+                        if pinyin[0] in single_rep_map.keys():
+                            pinyin = single_rep_map[pinyin[0]] + pinyin[1:]
+
+                assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, seg, raw_pinyin)
+                phone = pinyin_to_symbol_map[pinyin].split(" ")
+                word2ph.append(len(phone))
+
+            phones_list += phone
+            tones_list += [int(tone)] * len(phone)
+    return phones_list, tones_list, word2ph
+
+
+def text_normalize(text):
+    text = normalizer(text)
+    text = replace_punctuation(text)
+    return text
+
+
+def get_bert_feature(text, word2ph):
+    from text import chinese_bert
+
+    return chinese_bert.get_bert_feature(text, word2ph)
+
+
+if __name__ == "__main__":
+    from text.chinese_bert import get_bert_feature
+
+    text = "啊！但是《原神》是由,米哈\游自主，  [研发]的一款全.新开放世界.冒险游戏"
+    text = text_normalize(text)
+    print(text)
+    phones, tones, word2ph = g2p(text)
+    bert = get_bert_feature(text, word2ph)
+
+    print(phones, tones, word2ph, bert.shape)
+
+
+# # 示例用法
+# text = "这是一个示例文本：,你好！这是一个测试...."
+# print(g2p_paddle(text))  # 输出: 这是一个示例文本你好这是一个测试

utils/bert_vits2/text/chinese_bert.py

@@ -0,0 +1,119 @@
+import sys
+
+import torch
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+
+from ..config import config
+
+LOCAL_PATH = "./utils/bert_vits2/bert/chinese-roberta-wwm-ext-large"
+
+tokenizer = AutoTokenizer.from_pretrained(LOCAL_PATH)
+
+models = dict()
+
+
+def get_bert_feature(
+    text,
+    word2ph,
+    device=config.bert_gen_config.device,
+    style_text=None,
+    style_weight=0.7,
+):
+    if (
+        sys.platform == "darwin"
+        and torch.backends.mps.is_available()
+        and device == "cpu"
+    ):
+        device = "mps"
+    if not device:
+        device = "cuda"
+    if device not in models.keys():
+        models[device] = AutoModelForMaskedLM.from_pretrained(LOCAL_PATH).to(device)
+    with torch.no_grad():
+        inputs = tokenizer(text, return_tensors="pt")
+        for i in inputs:
+            inputs[i] = inputs[i].to(device)
+        res = models[device](**inputs, output_hidden_states=True)
+        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
+        if style_text:
+            style_inputs = tokenizer(style_text, return_tensors="pt")
+            for i in style_inputs:
+                style_inputs[i] = style_inputs[i].to(device)
+            style_res = models[device](**style_inputs, output_hidden_states=True)
+            style_res = torch.cat(style_res["hidden_states"][-3:-2], -1)[0].cpu()
+            style_res_mean = style_res.mean(0)
+    assert len(word2ph) == len(text) + 2
+    word2phone = word2ph
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        if style_text:
+            repeat_feature = (
+                res[i].repeat(word2phone[i], 1) * (1 - style_weight)
+                + style_res_mean.repeat(word2phone[i], 1) * style_weight
+            )
+        else:
+            repeat_feature = res[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+
+    return phone_level_feature.T
+
+
+if __name__ == "__main__":
+    word_level_feature = torch.rand(38, 1024)  # 12个词,每个词1024维特征
+    word2phone = [
+        1,
+        2,
+        1,
+        2,
+        2,
+        1,
+        2,
+        2,
+        1,
+        2,
+        2,
+        1,
+        2,
+        2,
+        2,
+        2,
+        2,
+        1,
+        1,
+        2,
+        2,
+        1,
+        2,
+        2,
+        2,
+        2,
+        1,
+        2,
+        2,
+        2,
+        2,
+        2,
+        1,
+        2,
+        2,
+        2,
+        2,
+        1,
+    ]
+
+    # 计算总帧数
+    total_frames = sum(word2phone)
+    print(word_level_feature.shape)
+    print(word2phone)
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        print(word_level_feature[i].shape)
+
+        # 对每个词重复word2phone[i]次
+        repeat_feature = word_level_feature[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+    print(phone_level_feature.shape)  # torch.Size([36, 1024])

utils/bert_vits2/text/cleaner.py

@@ -0,0 +1,29 @@
+from ..text import chinese, japanese, english, cleaned_text_to_sequence
+# from text import chinese, cleaned_text_to_sequence
+
+language_module_map = {"ZH": chinese, "JP": japanese, "EN": english}
+# language_module_map = {"ZH": chinese}
+
+
+def clean_text(text, language):
+    language_module = language_module_map[language]
+    norm_text = language_module.text_normalize(text)
+    phones, tones, word2ph = language_module.g2p(norm_text)
+    return norm_text, phones, tones, word2ph
+
+
+def clean_text_bert(text, language):
+    language_module = language_module_map[language]
+    norm_text = language_module.text_normalize(text)
+    phones, tones, word2ph = language_module.g2p(norm_text)
+    bert = language_module.get_bert_feature(norm_text, word2ph)
+    return phones, tones, bert
+
+
+def text_to_sequence(text, language):
+    norm_text, phones, tones, word2ph = clean_text(text, language)
+    return cleaned_text_to_sequence(phones, tones, language)
+
+
+if __name__ == "__main__":
+    pass

utils/bert_vits2/text/english.py

@@ -0,0 +1,494 @@
+import pickle
+import os
+import re
+from g2p_en import G2p
+from transformers import DebertaV2Tokenizer
+
+from ..text import symbols
+from ..text.symbols import punctuation
+
+current_file_path = os.path.dirname(__file__)
+CMU_DICT_PATH = os.path.join(current_file_path, "cmudict.rep")
+CACHE_PATH = os.path.join(current_file_path, "cmudict_cache.pickle")
+_g2p = G2p()
+LOCAL_PATH = "./utils/bert_vits2/bert/deberta-v3-large"
+tokenizer = DebertaV2Tokenizer.from_pretrained(LOCAL_PATH)
+
+arpa = {
+    "AH0",
+    "S",
+    "AH1",
+    "EY2",
+    "AE2",
+    "EH0",
+    "OW2",
+    "UH0",
+    "NG",
+    "B",
+    "G",
+    "AY0",
+    "M",
+    "AA0",
+    "F",
+    "AO0",
+    "ER2",
+    "UH1",
+    "IY1",
+    "AH2",
+    "DH",
+    "IY0",
+    "EY1",
+    "IH0",
+    "K",
+    "N",
+    "W",
+    "IY2",
+    "T",
+    "AA1",
+    "ER1",
+    "EH2",
+    "OY0",
+    "UH2",
+    "UW1",
+    "Z",
+    "AW2",
+    "AW1",
+    "V",
+    "UW2",
+    "AA2",
+    "ER",
+    "AW0",
+    "UW0",
+    "R",
+    "OW1",
+    "EH1",
+    "ZH",
+    "AE0",
+    "IH2",
+    "IH",
+    "Y",
+    "JH",
+    "P",
+    "AY1",
+    "EY0",
+    "OY2",
+    "TH",
+    "HH",
+    "D",
+    "ER0",
+    "CH",
+    "AO1",
+    "AE1",
+    "AO2",
+    "OY1",
+    "AY2",
+    "IH1",
+    "OW0",
+    "L",
+    "SH",
+}
+
+
+def post_replace_ph(ph):
+    rep_map = {
+        "：": ",",
+        "；": ",",
+        "，": ",",
+        "。": ".",
+        "！": "!",
+        "？": "?",
+        "\n": ".",
+        "·": ",",
+        "、": ",",
+        "…": "...",
+        "···": "...",
+        "・・・": "...",
+        "v": "V",
+    }
+    if ph in rep_map.keys():
+        ph = rep_map[ph]
+    if ph in symbols:
+        return ph
+    if ph not in symbols:
+        ph = "UNK"
+    return ph
+
+
+rep_map = {
+    "：": ",",
+    "；": ",",
+    "，": ",",
+    "。": ".",
+    "！": "!",
+    "？": "?",
+    "\n": ".",
+    "．": ".",
+    "…": "...",
+    "···": "...",
+    "・・・": "...",
+    "·": ",",
+    "・": ",",
+    "、": ",",
+    "$": ".",
+    "“": "'",
+    "”": "'",
+    '"': "'",
+    "‘": "'",
+    "’": "'",
+    "（": "'",
+    "）": "'",
+    "(": "'",
+    ")": "'",
+    "《": "'",
+    "》": "'",
+    "【": "'",
+    "】": "'",
+    "[": "'",
+    "]": "'",
+    "—": "-",
+    "−": "-",
+    "～": "-",
+    "~": "-",
+    "「": "'",
+    "」": "'",
+}
+
+
+def replace_punctuation(text):
+    pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
+
+    replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
+
+    # replaced_text = re.sub(
+    #     r"[^\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\u3005"
+    #     + "".join(punctuation)
+    #     + r"]+",
+    #     "",
+    #     replaced_text,
+    # )
+
+    return replaced_text
+
+
+def read_dict():
+    g2p_dict = {}
+    start_line = 49
+    with open(CMU_DICT_PATH) as f:
+        line = f.readline()
+        line_index = 1
+        while line:
+            if line_index >= start_line:
+                line = line.strip()
+                word_split = line.split("  ")
+                word = word_split[0]
+
+                syllable_split = word_split[1].split(" - ")
+                g2p_dict[word] = []
+                for syllable in syllable_split:
+                    phone_split = syllable.split(" ")
+                    g2p_dict[word].append(phone_split)
+
+            line_index = line_index + 1
+            line = f.readline()
+
+    return g2p_dict
+
+
+def cache_dict(g2p_dict, file_path):
+    with open(file_path, "wb") as pickle_file:
+        pickle.dump(g2p_dict, pickle_file)
+
+
+def get_dict():
+    if os.path.exists(CACHE_PATH):
+        with open(CACHE_PATH, "rb") as pickle_file:
+            g2p_dict = pickle.load(pickle_file)
+    else:
+        g2p_dict = read_dict()
+        cache_dict(g2p_dict, CACHE_PATH)
+
+    return g2p_dict
+
+
+eng_dict = get_dict()
+
+
+def refine_ph(phn):
+    tone = 0
+    if re.search(r"\d$", phn):
+        tone = int(phn[-1]) + 1
+        phn = phn[:-1]
+    else:
+        tone = 3
+    return phn.lower(), tone
+
+
+def refine_syllables(syllables):
+    tones = []
+    phonemes = []
+    for phn_list in syllables:
+        for i in range(len(phn_list)):
+            phn = phn_list[i]
+            phn, tone = refine_ph(phn)
+            phonemes.append(phn)
+            tones.append(tone)
+    return phonemes, tones
+
+
+import inflect
+
+_inflect = inflect.engine()
+_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])")
+_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)")
+_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)")
+_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)")
+_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)")
+_number_re = re.compile(r"[0-9]+")
+
+# List of (regular expression, replacement) pairs for abbreviations:
+_abbreviations = [
+    (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
+    for x in [
+        ("mrs", "misess"),
+        ("mr", "mister"),
+        ("dr", "doctor"),
+        ("st", "saint"),
+        ("co", "company"),
+        ("jr", "junior"),
+        ("maj", "major"),
+        ("gen", "general"),
+        ("drs", "doctors"),
+        ("rev", "reverend"),
+        ("lt", "lieutenant"),
+        ("hon", "honorable"),
+        ("sgt", "sergeant"),
+        ("capt", "captain"),
+        ("esq", "esquire"),
+        ("ltd", "limited"),
+        ("col", "colonel"),
+        ("ft", "fort"),
+    ]
+]
+
+
+# List of (ipa, lazy ipa) pairs:
+_lazy_ipa = [
+    (re.compile("%s" % x[0]), x[1])
+    for x in [
+        ("r", "ɹ"),
+        ("æ", "e"),
+        ("ɑ", "a"),
+        ("ɔ", "o"),
+        ("ð", "z"),
+        ("θ", "s"),
+        ("ɛ", "e"),
+        ("ɪ", "i"),
+        ("ʊ", "u"),
+        ("ʒ", "ʥ"),
+        ("ʤ", "ʥ"),
+        ("ˈ", "↓"),
+    ]
+]
+
+# List of (ipa, lazy ipa2) pairs:
+_lazy_ipa2 = [
+    (re.compile("%s" % x[0]), x[1])
+    for x in [
+        ("r", "ɹ"),
+        ("ð", "z"),
+        ("θ", "s"),
+        ("ʒ", "ʑ"),
+        ("ʤ", "dʑ"),
+        ("ˈ", "↓"),
+    ]
+]
+
+# List of (ipa, ipa2) pairs
+_ipa_to_ipa2 = [
+    (re.compile("%s" % x[0]), x[1]) for x in [("r", "ɹ"), ("ʤ", "dʒ"), ("ʧ", "tʃ")]
+]
+
+
+def _expand_dollars(m):
+    match = m.group(1)
+    parts = match.split(".")
+    if len(parts) > 2:
+        return match + " dollars"  # Unexpected format
+    dollars = int(parts[0]) if parts[0] else 0
+    cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
+    if dollars and cents:
+        dollar_unit = "dollar" if dollars == 1 else "dollars"
+        cent_unit = "cent" if cents == 1 else "cents"
+        return "%s %s, %s %s" % (dollars, dollar_unit, cents, cent_unit)
+    elif dollars:
+        dollar_unit = "dollar" if dollars == 1 else "dollars"
+        return "%s %s" % (dollars, dollar_unit)
+    elif cents:
+        cent_unit = "cent" if cents == 1 else "cents"
+        return "%s %s" % (cents, cent_unit)
+    else:
+        return "zero dollars"
+
+
+def _remove_commas(m):
+    return m.group(1).replace(",", "")
+
+
+def _expand_ordinal(m):
+    return _inflect.number_to_words(m.group(0))
+
+
+def _expand_number(m):
+    num = int(m.group(0))
+    if num > 1000 and num < 3000:
+        if num == 2000:
+            return "two thousand"
+        elif num > 2000 and num < 2010:
+            return "two thousand " + _inflect.number_to_words(num % 100)
+        elif num % 100 == 0:
+            return _inflect.number_to_words(num // 100) + " hundred"
+        else:
+            return _inflect.number_to_words(
+                num, andword="", zero="oh", group=2
+            ).replace(", ", " ")
+    else:
+        return _inflect.number_to_words(num, andword="")
+
+
+def _expand_decimal_point(m):
+    return m.group(1).replace(".", " point ")
+
+
+def normalize_numbers(text):
+    text = re.sub(_comma_number_re, _remove_commas, text)
+    text = re.sub(_pounds_re, r"\1 pounds", text)
+    text = re.sub(_dollars_re, _expand_dollars, text)
+    text = re.sub(_decimal_number_re, _expand_decimal_point, text)
+    text = re.sub(_ordinal_re, _expand_ordinal, text)
+    text = re.sub(_number_re, _expand_number, text)
+    return text
+
+
+def text_normalize(text):
+    text = normalize_numbers(text)
+    text = replace_punctuation(text)
+    text = re.sub(r"([,;.\?\!])([\w])", r"\1 \2", text)
+    return text
+
+
+def distribute_phone(n_phone, n_word):
+    phones_per_word = [0] * n_word
+    for task in range(n_phone):
+        min_tasks = min(phones_per_word)
+        min_index = phones_per_word.index(min_tasks)
+        phones_per_word[min_index] += 1
+    return phones_per_word
+
+
+def sep_text(text):
+    words = re.split(r"([,;.\?\!\s+])", text)
+    words = [word for word in words if word.strip() != ""]
+    return words
+
+
+def text_to_words(text):
+    tokens = tokenizer.tokenize(text)
+    words = []
+    for idx, t in enumerate(tokens):
+        if t.startswith("▁"):
+            words.append([t[1:]])
+        else:
+            if t in punctuation:
+                if idx == len(tokens) - 1:
+                    words.append([f"{t}"])
+                else:
+                    if (
+                        not tokens[idx + 1].startswith("▁")
+                        and tokens[idx + 1] not in punctuation
+                    ):
+                        if idx == 0:
+                            words.append([])
+                        words[-1].append(f"{t}")
+                    else:
+                        words.append([f"{t}"])
+            else:
+                if idx == 0:
+                    words.append([])
+                words[-1].append(f"{t}")
+    return words
+
+
+def g2p(text):
+    phones = []
+    tones = []
+    phone_len = []
+    # words = sep_text(text)
+    # tokens = [tokenizer.tokenize(i) for i in words]
+    words = text_to_words(text)
+
+    for word in words:
+        temp_phones, temp_tones = [], []
+        if len(word) > 1:
+            if "'" in word:
+                word = ["".join(word)]
+        for w in word:
+            if w in punctuation:
+                temp_phones.append(w)
+                temp_tones.append(0)
+                continue
+            if w.upper() in eng_dict:
+                phns, tns = refine_syllables(eng_dict[w.upper()])
+                temp_phones += [post_replace_ph(i) for i in phns]
+                temp_tones += tns
+                # w2ph.append(len(phns))
+            else:
+                phone_list = list(filter(lambda p: p != " ", _g2p(w)))
+                phns = []
+                tns = []
+                for ph in phone_list:
+                    if ph in arpa:
+                        ph, tn = refine_ph(ph)
+                        phns.append(ph)
+                        tns.append(tn)
+                    else:
+                        phns.append(ph)
+                        tns.append(0)
+                temp_phones += [post_replace_ph(i) for i in phns]
+                temp_tones += tns
+        phones += temp_phones
+        tones += temp_tones
+        phone_len.append(len(temp_phones))
+        # phones = [post_replace_ph(i) for i in phones]
+
+    word2ph = []
+    for token, pl in zip(words, phone_len):
+        word_len = len(token)
+
+        aaa = distribute_phone(pl, word_len)
+        word2ph += aaa
+
+    phones = ["_"] + phones + ["_"]
+    tones = [0] + tones + [0]
+    word2ph = [1] + word2ph + [1]
+    assert len(phones) == len(tones), text
+    assert len(phones) == sum(word2ph), text
+
+    return phones, tones, word2ph
+
+
+def get_bert_feature(text, word2ph):
+    from text import english_bert_mock
+
+    return english_bert_mock.get_bert_feature(text, word2ph)
+
+
+if __name__ == "__main__":
+    # print(get_dict())
+    # print(eng_word_to_phoneme("hello"))
+    print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder."))
+    # all_phones = set()
+    # for k, syllables in eng_dict.items():
+    #     for group in syllables:
+    #         for ph in group:
+    #             all_phones.add(ph)
+    # print(all_phones)

utils/bert_vits2/text/english_bert_mock.py

@@ -0,0 +1,61 @@
+import sys
+
+import torch
+from transformers import DebertaV2Model, DebertaV2Tokenizer
+
+from ..config import config
+
+
+LOCAL_PATH = "./utils/bert_vits2/bert/deberta-v3-large"
+
+tokenizer = DebertaV2Tokenizer.from_pretrained(LOCAL_PATH)
+
+models = dict()
+
+
+def get_bert_feature(
+    text,
+    word2ph,
+    device=config.bert_gen_config.device,
+    style_text=None,
+    style_weight=0.7,
+):
+    if (
+        sys.platform == "darwin"
+        and torch.backends.mps.is_available()
+        and device == "cpu"
+    ):
+        device = "mps"
+    if not device:
+        device = "cuda"
+    if device not in models.keys():
+        models[device] = DebertaV2Model.from_pretrained(LOCAL_PATH).to(device)
+    with torch.no_grad():
+        inputs = tokenizer(text, return_tensors="pt")
+        for i in inputs:
+            inputs[i] = inputs[i].to(device)
+        res = models[device](**inputs, output_hidden_states=True)
+        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
+        if style_text:
+            style_inputs = tokenizer(style_text, return_tensors="pt")
+            for i in style_inputs:
+                style_inputs[i] = style_inputs[i].to(device)
+            style_res = models[device](**style_inputs, output_hidden_states=True)
+            style_res = torch.cat(style_res["hidden_states"][-3:-2], -1)[0].cpu()
+            style_res_mean = style_res.mean(0)
+    assert len(word2ph) == res.shape[0], (text, res.shape[0], len(word2ph))
+    word2phone = word2ph
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        if style_text:
+            repeat_feature = (
+                res[i].repeat(word2phone[i], 1) * (1 - style_weight)
+                + style_res_mean.repeat(word2phone[i], 1) * style_weight
+            )
+        else:
+            repeat_feature = res[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+
+    return phone_level_feature.T

utils/bert_vits2/text/japanese.py

@@ -0,0 +1,720 @@
+# Convert Japanese text to phonemes which is
+# compatible with Julius https://github.com/julius-speech/segmentation-kit
+import re
+import unicodedata
+
+from transformers import AutoTokenizer
+
+from ..text import punctuation, symbols
+
+from num2words import num2words
+
+import pyopenjtalk
+import jaconv
+
+
+# Mapping of hiragana to phonetic representation
+hiragana_map = {
+    "う゛ぁ": " v a",
+    "う゛ぃ": " v i",
+    "う゛ぇ": " v e",
+    "う゛ぉ": " v o",
+    "う゛ゅ": " by u",
+    "ぅ゛": " v u",
+    # ゔ等の処理を追加
+    "ゔぁ": " v a",
+    "ゔぃ": " v i",
+    "ゔぇ": " v e",
+    "ゔぉ": " v o",
+    "ゔゅ": " by u",
+    # 2文字からなる変換規則
+    "あぁ": " a a",
+    "いぃ": " i i",
+    "いぇ": " i e",
+    "いゃ": " y a",
+    "うぅ": " u:",
+    "えぇ": " e e",
+    "おぉ": " o:",
+    "かぁ": " k a:",
+    "きぃ": " k i:",
+    "くぅ": " k u:",
+    "くゃ": " ky a",
+    "くゅ": " ky u",
+    "くょ": " ky o",
+    "けぇ": " k e:",
+    "こぉ": " k o:",
+    "がぁ": " g a:",
+    "ぎぃ": " g i:",
+    "ぐぅ": " g u:",
+    "ぐゃ": " gy a",
+    "ぐゅ": " gy u",
+    "ぐょ": " gy o",
+    "げぇ": " g e:",
+    "ごぉ": " g o:",
+    "さぁ": " s a:",
+    "しぃ": " sh i",
+    "すぅ": " s u:",
+    "すゃ": " sh a",
+    "すゅ": " sh u",
+    "すょ": " sh o",
+    "せぇ": " s e:",
+    "そぉ": " s o:",
+    "ざぁ": " z a:",
+    "じぃ": " j i:",
+    "ずぅ": " z u:",
+    "ずゃ": " zy a",
+    "ずゅ": " zy u",
+    "ずょ": " zy o",
+    "ぜぇ": " z e:",
+    "ぞぉ": " z o:",
+    "たぁ": " t a:",
+    "ちぃ": " ch i",
+    "つぁ": " ts a",
+    "つぃ": " ts i",
+    "つぅ": " ts u",
+    "つゃ": " ch a",
+    "つゅ": " ch u",
+    "つょ": " ch o",
+    "つぇ": " ts e",
+    "つぉ": " ts o",
+    "てぇ": " t e:",
+    "とぉ": " t o:",
+    "だぁ": " d a:",
+    "ぢぃ": " j i:",
+    "づぅ": " d u:",
+    "づゃ": " zy a",
+    "づゅ": " zy u",
+    "づょ": " zy o",
+    "でぇ": " d e:",
+    "なぁ": " n a:",
+    "にぃ": " n i:",
+    "ぬぅ": " n u:",
+    "ぬゃ": " ny a",
+    "ぬゅ": " ny u",
+    "ぬょ": " ny o",
+    "ねぇ": " n e:",
+    "のぉ": " n o:",
+    "はぁ": " h a:",
+    "ひぃ": " h i:",
+    "ふぅ": " f u:",
+    "ふゃ": " hy a",
+    "へぇ": " h e:",
+    "ほぉ": " h o:",
+    "ばぁ": " b a:",
+    "びぃ": " b i:",
+    "ぶぅ": " b u:",
+    "ぶゅ": " by u",
+    "べぇ": " b e:",
+    "ぼぉ": " b o:",
+    "ぱぁ": " p a:",
+    "ぴぃ": " p i:",
+    "ぷぅ": " p u:",
+    "ぷゃ": " py a",
+    "ぷゅ": " py u",
+    "ぷょ": " py o",
+    "ぺぇ": " p e:",
+    "ぽぉ": " p o:",
+    "まぁ": " m a:",
+    "みぃ": " m i:",
+    "むぅ": " m u:",
+    "むゃ": " my a",
+    "むゅ": " my u",
+    "むょ": " my o",
+    "めぇ": " m e:",
+    "もぉ": " m o:",
+    "やぁ": " y a:",
+    "ゆぅ": " y u:",
+    "ゆゃ": " y a:",
+    "ゆゅ": " y u:",
+    "ゆょ": " y o:",
+    "よぉ": " y o:",
+    "らぁ": " r a:",
+    "りぃ": " r i:",
+    "るぅ": " r u:",
+    "るゃ": " ry a",
+    "るゅ": " ry u",
+    "るょ": " ry o",
+    "れぇ": " r e:",
+    "ろぉ": " r o:",
+    "わぁ": " w a:",
+    "をぉ": " o:",
+    "う゛": " b u",
+    "でぃ": " d i",
+    "でゃ": " dy a",
+    "でゅ": " dy u",
+    "でょ": " dy o",
+    "てぃ": " t i",
+    "てゃ": " ty a",
+    "てゅ": " ty u",
+    "てょ": " ty o",
+    "すぃ": " s i",
+    "ずぁ": " z u",
+    "ずぃ": " z i",
+    "ずぇ": " z e",
+    "ずぉ": " z o",
+    "きゃ": " ky a",
+    "きゅ": " ky u",
+    "きょ": " ky o",
+    "しゃ": " sh a",
+    "しゅ": " sh u",
+    "しぇ": " sh e",
+    "しょ": " sh o",
+    "ちゃ": " ch a",
+    "ちゅ": " ch u",
+    "ちぇ": " ch e",
+    "ちょ": " ch o",
+    "とぅ": " t u",
+    "とゃ": " ty a",
+    "とゅ": " ty u",
+    "とょ": " ty o",
+    "どぁ": " d o ",
+    "どぅ": " d u",
+    "どゃ": " dy a",
+    "どゅ": " dy u",
+    "どょ": " dy o",
+    "どぉ": " d o:",
+    "にゃ": " ny a",
+    "にゅ": " ny u",
+    "にょ": " ny o",
+    "ひゃ": " hy a",
+    "ひゅ": " hy u",
+    "ひょ": " hy o",
+    "みゃ": " my a",
+    "みゅ": " my u",
+    "みょ": " my o",
+    "りゃ": " ry a",
+    "りゅ": " ry u",
+    "りょ": " ry o",
+    "ぎゃ": " gy a",
+    "ぎゅ": " gy u",
+    "ぎょ": " gy o",
+    "ぢぇ": " j e",
+    "ぢゃ": " j a",
+    "ぢゅ": " j u",
+    "ぢょ": " j o",
+    "じぇ": " j e",
+    "じゃ": " j a",
+    "じゅ": " j u",
+    "じょ": " j o",
+    "びゃ": " by a",
+    "びゅ": " by u",
+    "びょ": " by o",
+    "ぴゃ": " py a",
+    "ぴゅ": " py u",
+    "ぴょ": " py o",
+    "うぁ": " u a",
+    "うぃ": " w i",
+    "うぇ": " w e",
+    "うぉ": " w o",
+    "ふぁ": " f a",
+    "ふぃ": " f i",
+    "ふゅ": " hy u",
+    "ふょ": " hy o",
+    "ふぇ": " f e",
+    "ふぉ": " f o",
+    # 1音からなる変換規則
+    "あ": " a",
+    "い": " i",
+    "う": " u",
+    "ゔ": " v u",  # ゔの処理を追加
+    "え": " e",
+    "お": " o",
+    "か": " k a",
+    "き": " k i",
+    "く": " k u",
+    "け": " k e",
+    "こ": " k o",
+    "さ": " s a",
+    "し": " sh i",
+    "す": " s u",
+    "せ": " s e",
+    "そ": " s o",
+    "た": " t a",
+    "ち": " ch i",
+    "つ": " ts u",
+    "て": " t e",
+    "と": " t o",
+    "な": " n a",
+    "に": " n i",
+    "ぬ": " n u",
+    "ね": " n e",
+    "の": " n o",
+    "は": " h a",
+    "ひ": " h i",
+    "ふ": " f u",
+    "へ": " h e",
+    "ほ": " h o",
+    "ま": " m a",
+    "み": " m i",
+    "む": " m u",
+    "め": " m e",
+    "も": " m o",
+    "ら": " r a",
+    "り": " r i",
+    "る": " r u",
+    "れ": " r e",
+    "ろ": " r o",
+    "が": " g a",
+    "ぎ": " g i",
+    "ぐ": " g u",
+    "げ": " g e",
+    "ご": " g o",
+    "ざ": " z a",
+    "じ": " j i",
+    "ず": " z u",
+    "ぜ": " z e",
+    "ぞ": " z o",
+    "だ": " d a",
+    "ぢ": " j i",
+    "づ": " z u",
+    "で": " d e",
+    "ど": " d o",
+    "ば": " b a",
+    "び": " b i",
+    "ぶ": " b u",
+    "べ": " b e",
+    "ぼ": " b o",
+    "ぱ": " p a",
+    "ぴ": " p i",
+    "ぷ": " p u",
+    "ぺ": " p e",
+    "ぽ": " p o",
+    "や": " y a",
+    "ゆ": " y u",
+    "よ": " y o",
+    "わ": " w a",
+    "ゐ": " i",
+    "ゑ": " e",
+    "ん": " N",
+    "っ": " q",
+    # ここまでに処理されてない ぁぃぅぇぉ はそのまま大文字扱い
+    "ぁ": " a",
+    "ぃ": " i",
+    "ぅ": " u",
+    "ぇ": " e",
+    "ぉ": " o",
+    "ゎ": " w a",
+    # 長音の処理
+    # for (pattern, replace_str) in JULIUS_LONG_VOWEL:
+    #     text = pattern.sub(replace_str, text)
+    # text = text.replace("o u", "o:")  # おう -> おーの音便
+    "ー": ":",
+    "〜": ":",
+    "−": ":",
+    "-": ":",
+    # その他特別な処理
+    "を": " o",
+    # ここまでに処理されていないゅ等もそのまま大文字扱い（追加）
+    "ゃ": " y a",
+    "ゅ": " y u",
+    "ょ": " y o",
+}
+
+
+def hiragana2p(txt: str) -> str:
+    """
+    Modification of `jaconv.hiragana2julius`.
+    - avoid using `:`, instead, `あーーー` -> `a a a a`.
+    - avoid converting `o u` to `o o` (because the input is already actual `yomi`).
+    - avoid using `N` for `ん` (for compatibility)
+    - use `v` for `ゔ` related text.
+    - add bare `ゃ` `ゅ` `ょ` to `y a` `y u` `y o` (for compatibility).
+    """
+
+    result = []
+    skip = 0
+    for i in range(len(txt)):
+        if skip:
+            skip -= 1
+            continue
+
+        for length in range(3, 0, -1):
+            if txt[i : i + length] in hiragana_map:
+                result.append(hiragana_map[txt[i : i + length]])
+                skip = length - 1
+                break
+
+    txt = "".join(result)
+    txt = txt.strip()
+    txt = txt.replace(":+", ":")
+
+    # ここまで`jaconv.hiragana2julius`と音便処理と長音処理をのぞいて同じ
+    # ここから`k a:: k i:`→`k a a a k i i`のように`:`の数だけ繰り返す処理
+    pattern = r"(\w)(:*)"
+    replacement = lambda m: m.group(1) + (" " + m.group(1)) * len(m.group(2))
+
+    txt = re.sub(pattern, replacement, txt)
+    txt = txt.replace("N", "n")  # 促音のNをnに変換
+    return txt
+
+
+def kata2phoneme(text: str) -> str:
+    """Convert katakana text to phonemes."""
+    text = text.strip()
+    if text == "ー":
+        return ["ー"]
+    elif text.startswith("ー"):
+        return ["ー"] + kata2phoneme(text[1:])
+    res = []
+    prev = None
+    while text:
+        if re.match(_MARKS, text):
+            res.append(text)
+            text = text[1:]
+            continue
+        if text.startswith("ー"):
+            if prev:
+                res.append(prev[-1])
+            text = text[1:]
+            continue
+        res += hiragana2p(jaconv.kata2hira(text)).split(" ")
+        break
+    # res = _COLON_RX.sub(":", res)
+    return res
+
+
+_SYMBOL_TOKENS = set(list("・、。？！"))
+_NO_YOMI_TOKENS = set(list("「」『』―（）［］[]"))
+_MARKS = re.compile(
+    r"[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]"
+)
+
+
+def text2sep_kata(text: str):
+    parsed = pyopenjtalk.run_frontend(text)
+    res = []
+    sep = []
+    for parts in parsed:
+        word, yomi = replace_punctuation(parts["string"]), parts["pron"].replace(
+            "’", ""
+        )
+        if yomi:
+            if re.match(_MARKS, yomi):
+                if len(word) > 1:
+                    word = [replace_punctuation(i) for i in list(word)]
+                    yomi = word
+                    res += yomi
+                    sep += word
+                    continue
+                elif word not in rep_map.keys() and word not in rep_map.values():
+                    word = ","
+                yomi = word
+            res.append(yomi)
+        else:
+            if word in _SYMBOL_TOKENS:
+                res.append(word)
+            elif word in ("っ", "ッ"):
+                res.append("ッ")
+            elif word in _NO_YOMI_TOKENS:
+                pass
+            else:
+                res.append(word)
+        sep.append(word)
+    return sep, res, get_accent(parsed)
+
+
+def get_accent(parsed):
+    labels = pyopenjtalk.make_label(parsed)
+
+    phonemes = []
+    accents = []
+    for n, label in enumerate(labels):
+        phoneme = re.search(r"\-([^\+]*)\+", label).group(1)
+        if phoneme not in ["sil", "pau"]:
+            phonemes.append(phoneme.replace("cl", "q").lower())
+        else:
+            continue
+        a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
+        a2 = int(re.search(r"\+(\d+)\+", label).group(1))
+        if re.search(r"\-([^\+]*)\+", labels[n + 1]).group(1) in ["sil", "pau"]:
+            a2_next = -1
+        else:
+            a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
+        # Falling
+        if a1 == 0 and a2_next == a2 + 1:
+            accents.append(-1)
+        # Rising
+        elif a2 == 1 and a2_next == 2:
+            accents.append(1)
+        else:
+            accents.append(0)
+    return list(zip(phonemes, accents))
+
+
+_ALPHASYMBOL_YOMI = {
+    "#": "シャープ",
+    "%": "パーセント",
+    "&": "アンド",
+    "+": "プラス",
+    "-": "マイナス",
+    ":": "コロン",
+    ";": "セミコロン",
+    "<": "小なり",
+    "=": "イコール",
+    ">": "大なり",
+    "@": "アット",
+    "a": "エー",
+    "b": "ビー",
+    "c": "シー",
+    "d": "ディー",
+    "e": "イー",
+    "f": "エフ",
+    "g": "ジー",
+    "h": "エイチ",
+    "i": "アイ",
+    "j": "ジェー",
+    "k": "ケー",
+    "l": "エル",
+    "m": "エム",
+    "n": "エヌ",
+    "o": "オー",
+    "p": "ピー",
+    "q": "キュー",
+    "r": "アール",
+    "s": "エス",
+    "t": "ティー",
+    "u": "ユー",
+    "v": "ブイ",
+    "w": "ダブリュー",
+    "x": "エックス",
+    "y": "ワイ",
+    "z": "ゼット",
+    "α": "アルファ",
+    "β": "ベータ",
+    "γ": "ガンマ",
+    "δ": "デルタ",
+    "ε": "イプシロン",
+    "ζ": "ゼータ",
+    "η": "イータ",
+    "θ": "シータ",
+    "ι": "イオタ",
+    "κ": "カッパ",
+    "λ": "ラムダ",
+    "μ": "ミュー",
+    "ν": "ニュー",
+    "ξ": "クサイ",
+    "ο": "オミクロン",
+    "π": "パイ",
+    "ρ": "ロー",
+    "σ": "シグマ",
+    "τ": "タウ",
+    "υ": "ウプシロン",
+    "φ": "ファイ",
+    "χ": "カイ",
+    "ψ": "プサイ",
+    "ω": "オメガ",
+}
+
+
+_NUMBER_WITH_SEPARATOR_RX = re.compile("[0-9]{1,3}(,[0-9]{3})+")
+_CURRENCY_MAP = {"$": "ドル", "¥": "円", "£": "ポンド", "€": "ユーロ"}
+_CURRENCY_RX = re.compile(r"([$¥£€])([0-9.]*[0-9])")
+_NUMBER_RX = re.compile(r"[0-9]+(\.[0-9]+)?")
+
+
+def japanese_convert_numbers_to_words(text: str) -> str:
+    res = _NUMBER_WITH_SEPARATOR_RX.sub(lambda m: m[0].replace(",", ""), text)
+    res = _CURRENCY_RX.sub(lambda m: m[2] + _CURRENCY_MAP.get(m[1], m[1]), res)
+    res = _NUMBER_RX.sub(lambda m: num2words(m[0], lang="ja"), res)
+    return res
+
+
+def japanese_convert_alpha_symbols_to_words(text: str) -> str:
+    return "".join([_ALPHASYMBOL_YOMI.get(ch, ch) for ch in text.lower()])
+
+
+def is_japanese_character(char):
+    # 定义日语文字系统的 Unicode 范围
+    japanese_ranges = [
+        (0x3040, 0x309F),  # 平假名
+        (0x30A0, 0x30FF),  # 片假名
+        (0x4E00, 0x9FFF),  # 汉字 (CJK Unified Ideographs)
+        (0x3400, 0x4DBF),  # 汉字扩展 A
+        (0x20000, 0x2A6DF),  # 汉字扩展 B
+        # 可以根据需要添加其他汉字扩展范围
+    ]
+
+    # 将字符的 Unicode 编码转换为整数
+    char_code = ord(char)
+
+    # 检查字符是否在任何一个日语范围内
+    for start, end in japanese_ranges:
+        if start <= char_code <= end:
+            return True
+
+    return False
+
+
+rep_map = {
+    "：": ",",
+    "；": ",",
+    "，": ",",
+    "。": ".",
+    "！": "!",
+    "？": "?",
+    "\n": ".",
+    "．": ".",
+    "…": "...",
+    "···": "...",
+    "・・・": "...",
+    "·": ",",
+    "・": ",",
+    "、": ",",
+    "$": ".",
+    "“": "'",
+    "”": "'",
+    '"': "'",
+    "‘": "'",
+    "’": "'",
+    "（": "'",
+    "）": "'",
+    "(": "'",
+    ")": "'",
+    "《": "'",
+    "》": "'",
+    "【": "'",
+    "】": "'",
+    "[": "'",
+    "]": "'",
+    "—": "-",
+    "−": "-",
+    "～": "-",
+    "~": "-",
+    "「": "'",
+    "」": "'",
+}
+
+
+def replace_punctuation(text):
+    pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
+
+    replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
+
+    replaced_text = re.sub(
+        r"[^\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\u3005"
+        + "".join(punctuation)
+        + r"]+",
+        "",
+        replaced_text,
+    )
+
+    return replaced_text
+
+
+def text_normalize(text):
+    res = unicodedata.normalize("NFKC", text)
+    res = japanese_convert_numbers_to_words(res)
+    # res = "".join([i for i in res if is_japanese_character(i)])
+    res = replace_punctuation(res)
+    res = res.replace("゙", "")
+    return res
+
+
+def distribute_phone(n_phone, n_word):
+    phones_per_word = [0] * n_word
+    for task in range(n_phone):
+        min_tasks = min(phones_per_word)
+        min_index = phones_per_word.index(min_tasks)
+        phones_per_word[min_index] += 1
+    return phones_per_word
+
+
+def handle_long(sep_phonemes):
+    for i in range(len(sep_phonemes)):
+        if sep_phonemes[i][0] == "ー":
+            sep_phonemes[i][0] = sep_phonemes[i - 1][-1]
+        if "ー" in sep_phonemes[i]:
+            for j in range(len(sep_phonemes[i])):
+                if sep_phonemes[i][j] == "ー":
+                    sep_phonemes[i][j] = sep_phonemes[i][j - 1][-1]
+    return sep_phonemes
+
+
+tokenizer = AutoTokenizer.from_pretrained("./utils/bert_vits2/bert/deberta-v2-large-japanese-char-wwm")
+
+
+def align_tones(phones, tones):
+    res = []
+    for pho in phones:
+        temp = [0] * len(pho)
+        for idx, p in enumerate(pho):
+            if len(tones) == 0:
+                break
+            if p == tones[0][0]:
+                temp[idx] = tones[0][1]
+                if idx > 0:
+                    temp[idx] += temp[idx - 1]
+                tones.pop(0)
+        temp = [0] + temp
+        temp = temp[:-1]
+        if -1 in temp:
+            temp = [i + 1 for i in temp]
+        res.append(temp)
+    res = [i for j in res for i in j]
+    assert not any([i < 0 for i in res]) and not any([i > 1 for i in res])
+    return res
+
+
+def rearrange_tones(tones, phones):
+    res = [0] * len(tones)
+    for i in range(len(tones)):
+        if i == 0:
+            if tones[i] not in punctuation:
+                res[i] = 1
+        elif tones[i] == prev:
+            if phones[i] in punctuation:
+                res[i] = 0
+            else:
+                res[i] = 1
+        elif tones[i] > prev:
+            res[i] = 2
+        elif tones[i] < prev:
+            res[i - 1] = 3
+            res[i] = 1
+        prev = tones[i]
+    return res
+
+
+def g2p(norm_text):
+    sep_text, sep_kata, acc = text2sep_kata(norm_text)
+    sep_tokenized = []
+    for i in sep_text:
+        if i not in punctuation:
+            sep_tokenized.append(tokenizer.tokenize(i))
+        else:
+            sep_tokenized.append([i])
+
+    sep_phonemes = handle_long([kata2phoneme(i) for i in sep_kata])
+    # 异常处理，MeCab不认识的词的话会一路传到这里来，然后炸掉。目前来看只有那些超级稀有的生僻词会出现这种情况
+    for i in sep_phonemes:
+        for j in i:
+            assert j in symbols, (sep_text, sep_kata, sep_phonemes)
+    tones = align_tones(sep_phonemes, acc)
+
+    word2ph = []
+    for token, phoneme in zip(sep_tokenized, sep_phonemes):
+        phone_len = len(phoneme)
+        word_len = len(token)
+
+        aaa = distribute_phone(phone_len, word_len)
+        word2ph += aaa
+    phones = ["_"] + [j for i in sep_phonemes for j in i] + ["_"]
+    # tones = [0] + rearrange_tones(tones, phones[1:-1]) + [0]
+    tones = [0] + tones + [0]
+    word2ph = [1] + word2ph + [1]
+    assert len(phones) == len(tones)
+    return phones, tones, word2ph
+
+
+if __name__ == "__main__":
+    tokenizer = AutoTokenizer.from_pretrained("./utils/bert_vits2/bert/deberta-v2-large-japanese")
+    text = "hello,こんにちは、世界ー！……"
+    from text.japanese_bert import get_bert_feature
+
+    text = text_normalize(text)
+    print(text)
+
+    phones, tones, word2ph = g2p(text)
+    bert = get_bert_feature(text, word2ph)
+
+    print(phones, tones, word2ph, bert.shape)

utils/bert_vits2/text/japanese_bert.py

@@ -0,0 +1,65 @@
+import sys
+
+import torch
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+
+from ..config import config
+from ..text.japanese import text2sep_kata
+
+LOCAL_PATH = "./utils/bert_vits2/bert/deberta-v2-large-japanese-char-wwm"
+
+tokenizer = AutoTokenizer.from_pretrained(LOCAL_PATH)
+
+models = dict()
+
+
+def get_bert_feature(
+    text,
+    word2ph,
+    device=config.bert_gen_config.device,
+    style_text=None,
+    style_weight=0.7,
+):
+    text = "".join(text2sep_kata(text)[0])
+    if style_text:
+        style_text = "".join(text2sep_kata(style_text)[0])
+    if (
+        sys.platform == "darwin"
+        and torch.backends.mps.is_available()
+        and device == "cpu"
+    ):
+        device = "mps"
+    if not device:
+        device = "cuda"
+    if device not in models.keys():
+        models[device] = AutoModelForMaskedLM.from_pretrained(LOCAL_PATH).to(device)
+    with torch.no_grad():
+        inputs = tokenizer(text, return_tensors="pt")
+        for i in inputs:
+            inputs[i] = inputs[i].to(device)
+        res = models[device](**inputs, output_hidden_states=True)
+        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
+        if style_text:
+            style_inputs = tokenizer(style_text, return_tensors="pt")
+            for i in style_inputs:
+                style_inputs[i] = style_inputs[i].to(device)
+            style_res = models[device](**style_inputs, output_hidden_states=True)
+            style_res = torch.cat(style_res["hidden_states"][-3:-2], -1)[0].cpu()
+            style_res_mean = style_res.mean(0)
+
+    assert len(word2ph) == len(text) + 2
+    word2phone = word2ph
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        if style_text:
+            repeat_feature = (
+                res[i].repeat(word2phone[i], 1) * (1 - style_weight)
+                + style_res_mean.repeat(word2phone[i], 1) * style_weight
+            )
+        else:
+            repeat_feature = res[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+
+    return phone_level_feature.T

utils/bert_vits2/text/opencpop-strict.txt

@@ -0,0 +1,429 @@
+a	AA a
+ai	AA ai
+an	AA an
+ang	AA ang
+ao	AA ao
+ba	b a
+bai	b ai
+ban	b an
+bang	b ang
+bao	b ao
+bei	b ei
+ben	b en
+beng	b eng
+bi	b i
+bian	b ian
+biao	b iao
+bie	b ie
+bin	b in
+bing	b ing
+bo	b o
+bu	b u
+ca	c a
+cai	c ai
+can	c an
+cang	c ang
+cao	c ao
+ce	c e
+cei	c ei
+cen	c en
+ceng	c eng
+cha	ch a
+chai	ch ai
+chan	ch an
+chang	ch ang
+chao	ch ao
+che	ch e
+chen	ch en
+cheng	ch eng
+chi	ch ir
+chong	ch ong
+chou	ch ou
+chu	ch u
+chua	ch ua
+chuai	ch uai
+chuan	ch uan
+chuang	ch uang
+chui	ch ui
+chun	ch un
+chuo	ch uo
+ci	c i0
+cong	c ong
+cou	c ou
+cu	c u
+cuan	c uan
+cui	c ui
+cun	c un
+cuo	c uo
+da	d a
+dai	d ai
+dan	d an
+dang	d ang
+dao	d ao
+de	d e
+dei	d ei
+den	d en
+deng	d eng
+di	d i
+dia	d ia
+dian	d ian
+diao	d iao
+die	d ie
+ding	d ing
+diu	d iu
+dong	d ong
+dou	d ou
+du	d u
+duan	d uan
+dui	d ui
+dun	d un
+duo	d uo
+e	EE e
+ei	EE ei
+en	EE en
+eng	EE eng
+er	EE er
+fa	f a
+fan	f an
+fang	f ang
+fei	f ei
+fen	f en
+feng	f eng
+fo	f o
+fou	f ou
+fu	f u
+ga	g a
+gai	g ai
+gan	g an
+gang	g ang
+gao	g ao
+ge	g e
+gei	g ei
+gen	g en
+geng	g eng
+gong	g ong
+gou	g ou
+gu	g u
+gua	g ua
+guai	g uai
+guan	g uan
+guang	g uang
+gui	g ui
+gun	g un
+guo	g uo
+ha	h a
+hai	h ai
+han	h an
+hang	h ang
+hao	h ao
+he	h e
+hei	h ei
+hen	h en
+heng	h eng
+hong	h ong
+hou	h ou
+hu	h u
+hua	h ua
+huai	h uai
+huan	h uan
+huang	h uang
+hui	h ui
+hun	h un
+huo	h uo
+ji	j i
+jia	j ia
+jian	j ian
+jiang	j iang
+jiao	j iao
+jie	j ie
+jin	j in
+jing	j ing
+jiong	j iong
+jiu	j iu
+ju	j v
+jv	j v
+juan	j van
+jvan	j van
+jue	j ve
+jve	j ve
+jun	j vn
+jvn	j vn
+ka	k a
+kai	k ai
+kan	k an
+kang	k ang
+kao	k ao
+ke	k e
+kei	k ei
+ken	k en
+keng	k eng
+kong	k ong
+kou	k ou
+ku	k u
+kua	k ua
+kuai	k uai
+kuan	k uan
+kuang	k uang
+kui	k ui
+kun	k un
+kuo	k uo
+la	l a
+lai	l ai
+lan	l an
+lang	l ang
+lao	l ao
+le	l e
+lei	l ei
+leng	l eng
+li	l i
+lia	l ia
+lian	l ian
+liang	l iang
+liao	l iao
+lie	l ie
+lin	l in
+ling	l ing
+liu	l iu
+lo	l o
+long	l ong
+lou	l ou
+lu	l u
+luan	l uan
+lun	l un
+luo	l uo
+lv	l v
+lve	l ve
+ma	m a
+mai	m ai
+man	m an
+mang	m ang
+mao	m ao
+me	m e
+mei	m ei
+men	m en
+meng	m eng
+mi	m i
+mian	m ian
+miao	m iao
+mie	m ie
+min	m in
+ming	m ing
+miu	m iu
+mo	m o
+mou	m ou
+mu	m u
+na	n a
+nai	n ai
+nan	n an
+nang	n ang
+nao	n ao
+ne	n e
+nei	n ei
+nen	n en
+neng	n eng
+ni	n i
+nian	n ian
+niang	n iang
+niao	n iao
+nie	n ie
+nin	n in
+ning	n ing
+niu	n iu
+nong	n ong
+nou	n ou
+nu	n u
+nuan	n uan
+nun	n un
+nuo	n uo
+nv	n v
+nve	n ve
+o	OO o
+ou	OO ou
+pa	p a
+pai	p ai
+pan	p an
+pang	p ang
+pao	p ao
+pei	p ei
+pen	p en
+peng	p eng
+pi	p i
+pian	p ian
+piao	p iao
+pie	p ie
+pin	p in
+ping	p ing
+po	p o
+pou	p ou
+pu	p u
+qi	q i
+qia	q ia
+qian	q ian
+qiang	q iang
+qiao	q iao
+qie	q ie
+qin	q in
+qing	q ing
+qiong	q iong
+qiu	q iu
+qu	q v
+qv	q v
+quan	q van
+qvan	q van
+que	q ve
+qve	q ve
+qun	q vn
+qvn	q vn
+ran	r an
+rang	r ang
+rao	r ao
+re	r e
+ren	r en
+reng	r eng
+ri	r ir
+rong	r ong
+rou	r ou
+ru	r u
+rua	r ua
+ruan	r uan
+rui	r ui
+run	r un
+ruo	r uo
+sa	s a
+sai	s ai
+san	s an
+sang	s ang
+sao	s ao
+se	s e
+sen	s en
+seng	s eng
+sha	sh a
+shai	sh ai
+shan	sh an
+shang	sh ang
+shao	sh ao
+she	sh e
+shei	sh ei
+shen	sh en
+sheng	sh eng
+shi	sh ir
+shou	sh ou
+shu	sh u
+shua	sh ua
+shuai	sh uai
+shuan	sh uan
+shuang	sh uang
+shui	sh ui
+shun	sh un
+shuo	sh uo
+si	s i0
+song	s ong
+sou	s ou
+su	s u
+suan	s uan
+sui	s ui
+sun	s un
+suo	s uo
+ta	t a
+tai	t ai
+tan	t an
+tang	t ang
+tao	t ao
+te	t e
+tei	t ei
+teng	t eng
+ti	t i
+tian	t ian
+tiao	t iao
+tie	t ie
+ting	t ing
+tong	t ong
+tou	t ou
+tu	t u
+tuan	t uan
+tui	t ui
+tun	t un
+tuo	t uo
+wa	w a
+wai	w ai
+wan	w an
+wang	w ang
+wei	w ei
+wen	w en
+weng	w eng
+wo	w o
+wu	w u
+xi	x i
+xia	x ia
+xian	x ian
+xiang	x iang
+xiao	x iao
+xie	x ie
+xin	x in
+xing	x ing
+xiong	x iong
+xiu	x iu
+xu	x v
+xv	x v
+xuan	x van
+xvan	x van
+xue	x ve
+xve	x ve
+xun	x vn
+xvn	x vn
+ya	y a
+yan	y En
+yang	y ang
+yao	y ao
+ye	y E
+yi	y i
+yin	y in
+ying	y ing
+yo	y o
+yong	y ong
+you	y ou
+yu	y v
+yv	y v
+yuan	y van
+yvan	y van
+yue	y ve
+yve	y ve
+yun	y vn
+yvn	y vn
+za	z a
+zai	z ai
+zan	z an
+zang	z ang
+zao	z ao
+ze	z e
+zei	z ei
+zen	z en
+zeng	z eng
+zha	zh a
+zhai	zh ai
+zhan	zh an
+zhang	zh ang
+zhao	zh ao
+zhe	zh e
+zhei	zh ei
+zhen	zh en
+zheng	zh eng
+zhi	zh ir
+zhong	zh ong
+zhou	zh ou
+zhu	zh u
+zhua	zh ua
+zhuai	zh uai
+zhuan	zh uan
+zhuang	zh uang
+zhui	zh ui
+zhun	zh un
+zhuo	zh uo
+zi	z i0
+zong	z ong
+zou	z ou
+zu	z u
+zuan	z uan
+zui	z ui
+zun	z un
+zuo	z uo

utils/bert_vits2/text/symbols.py

@@ -0,0 +1,187 @@
+punctuation = ["!", "?", "…", ",", ".", "'", "-"]
+pu_symbols = punctuation + ["SP", "UNK"]
+pad = "_"
+
+# chinese
+zh_symbols = [
+    "E",
+    "En",
+    "a",
+    "ai",
+    "an",
+    "ang",
+    "ao",
+    "b",
+    "c",
+    "ch",
+    "d",
+    "e",
+    "ei",
+    "en",
+    "eng",
+    "er",
+    "f",
+    "g",
+    "h",
+    "i",
+    "i0",
+    "ia",
+    "ian",
+    "iang",
+    "iao",
+    "ie",
+    "in",
+    "ing",
+    "iong",
+    "ir",
+    "iu",
+    "j",
+    "k",
+    "l",
+    "m",
+    "n",
+    "o",
+    "ong",
+    "ou",
+    "p",
+    "q",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "u",
+    "ua",
+    "uai",
+    "uan",
+    "uang",
+    "ui",
+    "un",
+    "uo",
+    "v",
+    "van",
+    "ve",
+    "vn",
+    "w",
+    "x",
+    "y",
+    "z",
+    "zh",
+    "AA",
+    "EE",
+    "OO",
+]
+num_zh_tones = 6
+
+# japanese
+ja_symbols = [
+    "N",
+    "a",
+    "a:",
+    "b",
+    "by",
+    "ch",
+    "d",
+    "dy",
+    "e",
+    "e:",
+    "f",
+    "g",
+    "gy",
+    "h",
+    "hy",
+    "i",
+    "i:",
+    "j",
+    "k",
+    "ky",
+    "m",
+    "my",
+    "n",
+    "ny",
+    "o",
+    "o:",
+    "p",
+    "py",
+    "q",
+    "r",
+    "ry",
+    "s",
+    "sh",
+    "t",
+    "ts",
+    "ty",
+    "u",
+    "u:",
+    "w",
+    "y",
+    "z",
+    "zy",
+]
+num_ja_tones = 2
+
+# English
+en_symbols = [
+    "aa",
+    "ae",
+    "ah",
+    "ao",
+    "aw",
+    "ay",
+    "b",
+    "ch",
+    "d",
+    "dh",
+    "eh",
+    "er",
+    "ey",
+    "f",
+    "g",
+    "hh",
+    "ih",
+    "iy",
+    "jh",
+    "k",
+    "l",
+    "m",
+    "n",
+    "ng",
+    "ow",
+    "oy",
+    "p",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "th",
+    "uh",
+    "uw",
+    "V",
+    "w",
+    "y",
+    "z",
+    "zh",
+]
+num_en_tones = 4
+
+# combine all symbols
+normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
+symbols = [pad] + normal_symbols + pu_symbols
+sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
+
+# combine all tones
+num_tones = num_zh_tones + num_ja_tones + num_en_tones
+
+# language maps
+language_id_map = {"ZH": 0, "JP": 1, "EN": 2}
+num_languages = len(language_id_map.keys())
+
+language_tone_start_map = {
+    "ZH": 0,
+    "JP": num_zh_tones,
+    "EN": num_zh_tones + num_ja_tones,
+}
+
+if __name__ == "__main__":
+    a = set(zh_symbols)
+    b = set(en_symbols)
+    print(sorted(a & b))

utils/bert_vits2/text/tone_sandhi.py

@@ -0,0 +1,776 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import List
+from typing import Tuple
+
+import jieba
+from pypinyin import lazy_pinyin
+from pypinyin import Style
+
+
+class ToneSandhi:
+    def __init__(self):
+        self.must_neural_tone_words = {
+            "麻烦",
+            "麻利",
+            "鸳鸯",
+            "高粱",
+            "骨头",
+            "骆驼",
+            "马虎",
+            "首饰",
+            "馒头",
+            "馄饨",
+            "风筝",
+            "难为",
+            "队伍",
+            "阔气",
+            "闺女",
+            "门道",
+            "锄头",
+            "铺盖",
+            "铃铛",
+            "铁匠",
+            "钥匙",
+            "里脊",
+            "里头",
+            "部分",
+            "那么",
+            "道士",
+            "造化",
+            "迷糊",
+            "连累",
+            "这么",
+            "这个",
+            "运气",
+            "过去",
+            "软和",
+            "转悠",
+            "踏实",
+            "跳蚤",
+            "跟头",
+            "趔趄",
+            "财主",
+            "豆腐",
+            "讲究",
+            "记性",
+            "记号",
+            "认识",
+            "规矩",
+            "见识",
+            "裁缝",
+            "补丁",
+            "衣裳",
+            "衣服",
+            "衙门",
+            "街坊",
+            "行李",
+            "行当",
+            "蛤蟆",
+            "蘑菇",
+            "薄荷",
+            "葫芦",
+            "葡萄",
+            "萝卜",
+            "荸荠",
+            "苗条",
+            "苗头",
+            "苍蝇",
+            "芝麻",
+            "舒服",
+            "舒坦",
+            "舌头",
+            "自在",
+            "膏药",
+            "脾气",
+            "脑袋",
+            "脊梁",
+            "能耐",
+            "胳膊",
+            "胭脂",
+            "胡萝",
+            "胡琴",
+            "胡同",
+            "聪明",
+            "耽误",
+            "耽搁",
+            "耷拉",
+            "耳朵",
+            "老爷",
+            "老实",
+            "老婆",
+            "老头",
+            "老太",
+            "翻腾",
+            "罗嗦",
+            "罐头",
+            "编辑",
+            "结实",
+            "红火",
+            "累赘",
+            "糨糊",
+            "糊涂",
+            "精神",
+            "粮食",
+            "簸箕",
+            "篱笆",
+            "算计",
+            "算盘",
+            "答应",
+            "笤帚",
+            "笑语",
+            "笑话",
+            "窟窿",
+            "窝囊",
+            "窗户",
+            "稳当",
+            "稀罕",
+            "称呼",
+            "秧歌",
+            "秀气",
+            "秀才",
+            "福气",
+            "祖宗",
+            "砚台",
+            "码头",
+            "石榴",
+            "石头",
+            "石匠",
+            "知识",
+            "眼睛",
+            "眯缝",
+            "眨巴",
+            "眉毛",
+            "相声",
+            "盘算",
+            "白净",
+            "痢疾",
+            "痛快",
+            "疟疾",
+            "疙瘩",
+            "疏忽",
+            "畜生",
+            "生意",
+            "甘蔗",
+            "琵琶",
+            "琢磨",
+            "琉璃",
+            "玻璃",
+            "玫瑰",
+            "玄乎",
+            "狐狸",
+            "状元",
+            "特务",
+            "牲口",
+            "牙碜",
+            "牌楼",
+            "爽快",
+            "爱人",
+            "热闹",
+            "烧饼",
+            "烟筒",
+            "烂糊",
+            "点心",
+            "炊帚",
+            "灯笼",
+            "火候",
+            "漂亮",
+            "滑溜",
+            "溜达",
+            "温和",
+            "清楚",
+            "消息",
+            "浪头",
+            "活泼",
+            "比方",
+            "正经",
+            "欺负",
+            "模糊",
+            "槟榔",
+            "棺材",
+            "棒槌",
+            "棉花",
+            "核桃",
+            "栅栏",
+            "柴火",
+            "架势",
+            "枕头",
+            "枇杷",
+            "机灵",
+            "本事",
+            "木头",
+            "木匠",
+            "朋友",
+            "月饼",
+            "月亮",
+            "暖和",
+            "明白",
+            "时候",
+            "新鲜",
+            "故事",
+            "收拾",
+            "收成",
+            "提防",
+            "挖苦",
+            "挑剔",
+            "指甲",
+            "指头",
+            "拾掇",
+            "拳头",
+            "拨弄",
+            "招牌",
+            "招呼",
+            "抬举",
+            "护士",
+            "折腾",
+            "扫帚",
+            "打量",
+            "打算",
+            "打点",
+            "打扮",
+            "打听",
+            "打发",
+            "扎实",
+            "扁担",
+            "戒指",
+            "懒得",
+            "意识",
+            "意思",
+            "情形",
+            "悟性",
+            "怪物",
+            "思量",
+            "怎么",
+            "念头",
+            "念叨",
+            "快活",
+            "忙活",
+            "志气",
+            "心思",
+            "得罪",
+            "张罗",
+            "弟兄",
+            "开通",
+            "应酬",
+            "庄稼",
+            "干事",
+            "帮手",
+            "帐篷",
+            "希罕",
+            "师父",
+            "师傅",
+            "巴结",
+            "巴掌",
+            "差事",
+            "工夫",
+            "岁数",
+            "屁股",
+            "尾巴",
+            "少爷",
+            "小气",
+            "小伙",
+            "将就",
+            "对头",
+            "对付",
+            "寡妇",
+            "家伙",
+            "客气",
+            "实在",
+            "官司",
+            "学问",
+            "学生",
+            "字号",
+            "嫁妆",
+            "媳妇",
+            "媒人",
+            "婆家",
+            "娘家",
+            "委屈",
+            "姑娘",
+            "姐夫",
+            "妯娌",
+            "妥当",
+            "妖精",
+            "奴才",
+            "女婿",
+            "头发",
+            "太阳",
+            "大爷",
+            "大方",
+            "大意",
+            "大夫",
+            "多少",
+            "多么",
+            "外甥",
+            "壮实",
+            "地道",
+            "地方",
+            "在乎",
+            "困难",
+            "嘴巴",
+            "嘱咐",
+            "嘟囔",
+            "嘀咕",
+            "喜欢",
+            "喇嘛",
+            "喇叭",
+            "商量",
+            "唾沫",
+            "哑巴",
+            "哈欠",
+            "哆嗦",
+            "咳嗽",
+            "和尚",
+            "告诉",
+            "告示",
+            "含糊",
+            "吓唬",
+            "后头",
+            "名字",
+            "名堂",
+            "合同",
+            "吆喝",
+            "叫唤",
+            "口袋",
+            "厚道",
+            "厉害",
+            "千斤",
+            "包袱",
+            "包涵",
+            "匀称",
+            "勤快",
+            "动静",
+            "动弹",
+            "功夫",
+            "力气",
+            "前头",
+            "刺猬",
+            "刺激",
+            "别扭",
+            "利落",
+            "利索",
+            "利害",
+            "分析",
+            "出息",
+            "凑合",
+            "凉快",
+            "冷战",
+            "冤枉",
+            "冒失",
+            "养活",
+            "关系",
+            "先生",
+            "兄弟",
+            "便宜",
+            "使唤",
+            "佩服",
+            "作坊",
+            "体面",
+            "位置",
+            "似的",
+            "伙计",
+            "休息",
+            "什么",
+            "人家",
+            "亲戚",
+            "亲家",
+            "交情",
+            "云彩",
+            "事情",
+            "买卖",
+            "主意",
+            "丫头",
+            "丧气",
+            "两口",
+            "东西",
+            "东家",
+            "世故",
+            "不由",
+            "不在",
+            "下水",
+            "下巴",
+            "上头",
+            "上司",
+            "丈夫",
+            "丈人",
+            "一辈",
+            "那个",
+            "菩萨",
+            "父亲",
+            "母亲",
+            "咕噜",
+            "邋遢",
+            "费用",
+            "冤家",
+            "甜头",
+            "介绍",
+            "荒唐",
+            "大人",
+            "泥鳅",
+            "幸福",
+            "熟悉",
+            "计划",
+            "扑腾",
+            "蜡烛",
+            "姥爷",
+            "照顾",
+            "喉咙",
+            "吉他",
+            "弄堂",
+            "蚂蚱",
+            "凤凰",
+            "拖沓",
+            "寒碜",
+            "糟蹋",
+            "倒腾",
+            "报复",
+            "逻辑",
+            "盘缠",
+            "喽啰",
+            "牢骚",
+            "咖喱",
+            "扫把",
+            "惦记",
+        }
+        self.must_not_neural_tone_words = {
+            "男子",
+            "女子",
+            "分子",
+            "原子",
+            "量子",
+            "莲子",
+            "石子",
+            "瓜子",
+            "电子",
+            "人人",
+            "虎虎",
+        }
+        self.punc = "：，；。？！“”‘’':,;.?!"
+
+    # the meaning of jieba pos tag: https://blog.csdn.net/weixin_44174352/article/details/113731041
+    # e.g.
+    # word: "家里"
+    # pos: "s"
+    # finals: ['ia1', 'i3']
+    def _neural_sandhi(self, word: str, pos: str, finals: List[str]) -> List[str]:
+        # reduplication words for n. and v. e.g. 奶奶, 试试, 旺旺
+        for j, item in enumerate(word):
+            if (
+                j - 1 >= 0
+                and item == word[j - 1]
+                and pos[0] in {"n", "v", "a"}
+                and word not in self.must_not_neural_tone_words
+            ):
+                finals[j] = finals[j][:-1] + "5"
+        ge_idx = word.find("个")
+        if len(word) >= 1 and word[-1] in "吧呢啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶":
+            finals[-1] = finals[-1][:-1] + "5"
+        elif len(word) >= 1 and word[-1] in "的地得":
+            finals[-1] = finals[-1][:-1] + "5"
+        # e.g. 走了, 看着, 去过
+        # elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}:
+        #     finals[-1] = finals[-1][:-1] + "5"
+        elif (
+            len(word) > 1
+            and word[-1] in "们子"
+            and pos in {"r", "n"}
+            and word not in self.must_not_neural_tone_words
+        ):
+            finals[-1] = finals[-1][:-1] + "5"
+        # e.g. 桌上, 地下, 家里
+        elif len(word) > 1 and word[-1] in "上下里" and pos in {"s", "l", "f"}:
+            finals[-1] = finals[-1][:-1] + "5"
+        # e.g. 上来, 下去
+        elif len(word) > 1 and word[-1] in "来去" and word[-2] in "上下进出回过起开":
+            finals[-1] = finals[-1][:-1] + "5"
+        # 个做量词
+        elif (
+            ge_idx >= 1
+            and (
+                word[ge_idx - 1].isnumeric()
+                or word[ge_idx - 1] in "几有两半多各整每做是"
+            )
+        ) or word == "个":
+            finals[ge_idx] = finals[ge_idx][:-1] + "5"
+        else:
+            if (
+                word in self.must_neural_tone_words
+                or word[-2:] in self.must_neural_tone_words
+            ):
+                finals[-1] = finals[-1][:-1] + "5"
+
+        word_list = self._split_word(word)
+        finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]]
+        for i, word in enumerate(word_list):
+            # conventional neural in Chinese
+            if (
+                word in self.must_neural_tone_words
+                or word[-2:] in self.must_neural_tone_words
+            ):
+                finals_list[i][-1] = finals_list[i][-1][:-1] + "5"
+        finals = sum(finals_list, [])
+        return finals
+
+    def _bu_sandhi(self, word: str, finals: List[str]) -> List[str]:
+        # e.g. 看不懂
+        if len(word) == 3 and word[1] == "不":
+            finals[1] = finals[1][:-1] + "5"
+        else:
+            for i, char in enumerate(word):
+                # "不" before tone4 should be bu2, e.g. 不怕
+                if char == "不" and i + 1 < len(word) and finals[i + 1][-1] == "4":
+                    finals[i] = finals[i][:-1] + "2"
+        return finals
+
+    def _yi_sandhi(self, word: str, finals: List[str]) -> List[str]:
+        # "一" in number sequences, e.g. 一零零, 二一零
+        if word.find("一") != -1 and all(
+            [item.isnumeric() for item in word if item != "一"]
+        ):
+            return finals
+        # "一" between reduplication words should be yi5, e.g. 看一看
+        elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]:
+            finals[1] = finals[1][:-1] + "5"
+        # when "一" is ordinal word, it should be yi1
+        elif word.startswith("第一"):
+            finals[1] = finals[1][:-1] + "1"
+        else:
+            for i, char in enumerate(word):
+                if char == "一" and i + 1 < len(word):
+                    # "一" before tone4 should be yi2, e.g. 一段
+                    if finals[i + 1][-1] == "4":
+                        finals[i] = finals[i][:-1] + "2"
+                    # "一" before non-tone4 should be yi4, e.g. 一天
+                    else:
+                        # "一" 后面如果是标点，还读一声
+                        if word[i + 1] not in self.punc:
+                            finals[i] = finals[i][:-1] + "4"
+        return finals
+
+    def _split_word(self, word: str) -> List[str]:
+        word_list = jieba.cut_for_search(word)
+        word_list = sorted(word_list, key=lambda i: len(i), reverse=False)
+        first_subword = word_list[0]
+        first_begin_idx = word.find(first_subword)
+        if first_begin_idx == 0:
+            second_subword = word[len(first_subword) :]
+            new_word_list = [first_subword, second_subword]
+        else:
+            second_subword = word[: -len(first_subword)]
+            new_word_list = [second_subword, first_subword]
+        return new_word_list
+
+    def _three_sandhi(self, word: str, finals: List[str]) -> List[str]:
+        if len(word) == 2 and self._all_tone_three(finals):
+            finals[0] = finals[0][:-1] + "2"
+        elif len(word) == 3:
+            word_list = self._split_word(word)
+            if self._all_tone_three(finals):
+                #  disyllabic + monosyllabic, e.g. 蒙古/包
+                if len(word_list[0]) == 2:
+                    finals[0] = finals[0][:-1] + "2"
+                    finals[1] = finals[1][:-1] + "2"
+                #  monosyllabic + disyllabic, e.g. 纸/老虎
+                elif len(word_list[0]) == 1:
+                    finals[1] = finals[1][:-1] + "2"
+            else:
+                finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]]
+                if len(finals_list) == 2:
+                    for i, sub in enumerate(finals_list):
+                        # e.g. 所有/人
+                        if self._all_tone_three(sub) and len(sub) == 2:
+                            finals_list[i][0] = finals_list[i][0][:-1] + "2"
+                        # e.g. 好/喜欢
+                        elif (
+                            i == 1
+                            and not self._all_tone_three(sub)
+                            and finals_list[i][0][-1] == "3"
+                            and finals_list[0][-1][-1] == "3"
+                        ):
+                            finals_list[0][-1] = finals_list[0][-1][:-1] + "2"
+                        finals = sum(finals_list, [])
+        # split idiom into two words who's length is 2
+        elif len(word) == 4:
+            finals_list = [finals[:2], finals[2:]]
+            finals = []
+            for sub in finals_list:
+                if self._all_tone_three(sub):
+                    sub[0] = sub[0][:-1] + "2"
+                finals += sub
+
+        return finals
+
+    def _all_tone_three(self, finals: List[str]) -> bool:
+        return all(x[-1] == "3" for x in finals)
+
+    # merge "不" and the word behind it
+    # if don't merge, "不" sometimes appears alone according to jieba, which may occur sandhi error
+    def _merge_bu(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
+        new_seg = []
+        last_word = ""
+        for word, pos in seg:
+            if last_word == "不":
+                word = last_word + word
+            if word != "不":
+                new_seg.append((word, pos))
+            last_word = word[:]
+        if last_word == "不":
+            new_seg.append((last_word, "d"))
+            last_word = ""
+        return new_seg
+
+    # function 1: merge "一" and reduplication words in it's left and right, e.g. "听","一","听" ->"听一听"
+    # function 2: merge single  "一" and the word behind it
+    # if don't merge, "一" sometimes appears alone according to jieba, which may occur sandhi error
+    # e.g.
+    # input seg: [('听', 'v'), ('一', 'm'), ('听', 'v')]
+    # output seg: [['听一听', 'v']]
+    def _merge_yi(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
+        new_seg = [] * len(seg)
+        # function 1
+        i = 0
+        while i < len(seg):
+            word, pos = seg[i]
+            if (
+                i - 1 >= 0
+                and word == "一"
+                and i + 1 < len(seg)
+                and seg[i - 1][0] == seg[i + 1][0]
+                and seg[i - 1][1] == "v"
+            ):
+                new_seg[i - 1][0] = new_seg[i - 1][0] + "一" + new_seg[i - 1][0]
+                i += 2
+            else:
+                if (
+                    i - 2 >= 0
+                    and seg[i - 1][0] == "一"
+                    and seg[i - 2][0] == word
+                    and pos == "v"
+                ):
+                    continue
+                else:
+                    new_seg.append([word, pos])
+                i += 1
+        seg = [i for i in new_seg if len(i) > 0]
+        new_seg = []
+        # function 2
+        for i, (word, pos) in enumerate(seg):
+            if new_seg and new_seg[-1][0] == "一":
+                new_seg[-1][0] = new_seg[-1][0] + word
+            else:
+                new_seg.append([word, pos])
+        return new_seg
+
+    # the first and the second words are all_tone_three
+    def _merge_continuous_three_tones(
+        self, seg: List[Tuple[str, str]]
+    ) -> List[Tuple[str, str]]:
+        new_seg = []
+        sub_finals_list = [
+            lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
+            for (word, pos) in seg
+        ]
+        assert len(sub_finals_list) == len(seg)
+        merge_last = [False] * len(seg)
+        for i, (word, pos) in enumerate(seg):
+            if (
+                i - 1 >= 0
+                and self._all_tone_three(sub_finals_list[i - 1])
+                and self._all_tone_three(sub_finals_list[i])
+                and not merge_last[i - 1]
+            ):
+                # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
+                if (
+                    not self._is_reduplication(seg[i - 1][0])
+                    and len(seg[i - 1][0]) + len(seg[i][0]) <= 3
+                ):
+                    new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
+                    merge_last[i] = True
+                else:
+                    new_seg.append([word, pos])
+            else:
+                new_seg.append([word, pos])
+
+        return new_seg
+
+    def _is_reduplication(self, word: str) -> bool:
+        return len(word) == 2 and word[0] == word[1]
+
+    # the last char of first word and the first char of second word is tone_three
+    def _merge_continuous_three_tones_2(
+        self, seg: List[Tuple[str, str]]
+    ) -> List[Tuple[str, str]]:
+        new_seg = []
+        sub_finals_list = [
+            lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
+            for (word, pos) in seg
+        ]
+        assert len(sub_finals_list) == len(seg)
+        merge_last = [False] * len(seg)
+        for i, (word, pos) in enumerate(seg):
+            if (
+                i - 1 >= 0
+                and sub_finals_list[i - 1][-1][-1] == "3"
+                and sub_finals_list[i][0][-1] == "3"
+                and not merge_last[i - 1]
+            ):
+                # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
+                if (
+                    not self._is_reduplication(seg[i - 1][0])
+                    and len(seg[i - 1][0]) + len(seg[i][0]) <= 3
+                ):
+                    new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
+                    merge_last[i] = True
+                else:
+                    new_seg.append([word, pos])
+            else:
+                new_seg.append([word, pos])
+        return new_seg
+
+    def _merge_er(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
+        new_seg = []
+        for i, (word, pos) in enumerate(seg):
+            if i - 1 >= 0 and word == "儿" and seg[i - 1][0] != "#":
+                new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
+            else:
+                new_seg.append([word, pos])
+        return new_seg
+
+    def _merge_reduplication(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
+        new_seg = []
+        for i, (word, pos) in enumerate(seg):
+            if new_seg and word == new_seg[-1][0]:
+                new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
+            else:
+                new_seg.append([word, pos])
+        return new_seg
+
+    def pre_merge_for_modify(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
+        seg = self._merge_bu(seg)
+        try:
+            seg = self._merge_yi(seg)
+        except:
+            print("_merge_yi failed")
+        seg = self._merge_reduplication(seg)
+        seg = self._merge_continuous_three_tones(seg)
+        seg = self._merge_continuous_three_tones_2(seg)
+        seg = self._merge_er(seg)
+        return seg
+
+    def modified_tone(self, word: str, pos: str, finals: List[str]) -> List[str]:
+        finals = self._bu_sandhi(word, finals)
+        finals = self._yi_sandhi(word, finals)
+        finals = self._neural_sandhi(word, pos, finals)
+        finals = self._three_sandhi(word, finals)
+        return finals

utils/bert_vits2/tools/__init__.py

@@ -0,0 +1,3 @@
+"""
+工具包
+"""

utils/bert_vits2/tools/classify_language.py

@@ -0,0 +1,197 @@
+import regex as re
+
+try:
+    from ..config import config
+
+    LANGUAGE_IDENTIFICATION_LIBRARY = (
+        config.webui_config.language_identification_library
+    )
+except:
+    LANGUAGE_IDENTIFICATION_LIBRARY = "langid"
+
+module = LANGUAGE_IDENTIFICATION_LIBRARY.lower()
+
+langid_languages = [
+    "af",
+    "am",
+    "an",
+    "ar",
+    "as",
+    "az",
+    "be",
+    "bg",
+    "bn",
+    "br",
+    "bs",
+    "ca",
+    "cs",
+    "cy",
+    "da",
+    "de",
+    "dz",
+    "el",
+    "en",
+    "eo",
+    "es",
+    "et",
+    "eu",
+    "fa",
+    "fi",
+    "fo",
+    "fr",
+    "ga",
+    "gl",
+    "gu",
+    "he",
+    "hi",
+    "hr",
+    "ht",
+    "hu",
+    "hy",
+    "id",
+    "is",
+    "it",
+    "ja",
+    "jv",
+    "ka",
+    "kk",
+    "km",
+    "kn",
+    "ko",
+    "ku",
+    "ky",
+    "la",
+    "lb",
+    "lo",
+    "lt",
+    "lv",
+    "mg",
+    "mk",
+    "ml",
+    "mn",
+    "mr",
+    "ms",
+    "mt",
+    "nb",
+    "ne",
+    "nl",
+    "nn",
+    "no",
+    "oc",
+    "or",
+    "pa",
+    "pl",
+    "ps",
+    "pt",
+    "qu",
+    "ro",
+    "ru",
+    "rw",
+    "se",
+    "si",
+    "sk",
+    "sl",
+    "sq",
+    "sr",
+    "sv",
+    "sw",
+    "ta",
+    "te",
+    "th",
+    "tl",
+    "tr",
+    "ug",
+    "uk",
+    "ur",
+    "vi",
+    "vo",
+    "wa",
+    "xh",
+    "zh",
+    "zu",
+]
+
+
+def classify_language(text: str, target_languages: list = None) -> str:
+    if module == "fastlid" or module == "fasttext":
+        from fastlid import fastlid, supported_langs
+
+        classifier = fastlid
+        if target_languages != None:
+            target_languages = [
+                lang for lang in target_languages if lang in supported_langs
+            ]
+            fastlid.set_languages = target_languages
+    elif module == "langid":
+        import langid
+
+        classifier = langid.classify
+        if target_languages != None:
+            target_languages = [
+                lang for lang in target_languages if lang in langid_languages
+            ]
+            langid.set_languages(target_languages)
+    else:
+        raise ValueError(f"Wrong module {module}")
+
+    lang = classifier(text)[0]
+
+    return lang
+
+
+def classify_zh_ja(text: str) -> str:
+    for idx, char in enumerate(text):
+        unicode_val = ord(char)
+
+        # 检测日语字符
+        if 0x3040 <= unicode_val <= 0x309F or 0x30A0 <= unicode_val <= 0x30FF:
+            return "ja"
+
+        # 检测汉字字符
+        if 0x4E00 <= unicode_val <= 0x9FFF:
+            # 检查周围的字符
+            next_char = text[idx + 1] if idx + 1 < len(text) else None
+
+            if next_char and (
+                0x3040 <= ord(next_char) <= 0x309F or 0x30A0 <= ord(next_char) <= 0x30FF
+            ):
+                return "ja"
+
+    return "zh"
+
+
+def split_alpha_nonalpha(text, mode=1):
+    if mode == 1:
+        pattern = r"(?<=[\u4e00-\u9fff\u3040-\u30FF\d\s])(?=[\p{Latin}])|(?<=[\p{Latin}\s])(?=[\u4e00-\u9fff\u3040-\u30FF\d])"
+    elif mode == 2:
+        pattern = r"(?<=[\u4e00-\u9fff\u3040-\u30FF\s])(?=[\p{Latin}\d])|(?<=[\p{Latin}\d\s])(?=[\u4e00-\u9fff\u3040-\u30FF])"
+    else:
+        raise ValueError("Invalid mode. Supported modes are 1 and 2.")
+
+    return re.split(pattern, text)
+
+
+if __name__ == "__main__":
+    text = "这是一个测试文本"
+    print(classify_language(text))
+    print(classify_zh_ja(text))  # "zh"
+
+    text = "これはテストテキストです"
+    print(classify_language(text))
+    print(classify_zh_ja(text))  # "ja"
+
+    text = "vits和Bert-VITS2是tts模型。花费3days.花费3天。Take 3 days"
+
+    print(split_alpha_nonalpha(text, mode=1))
+    # output: ['vits', '和', 'Bert-VITS', '2是', 'tts', '模型。花费3', 'days.花费3天。Take 3 days']
+
+    print(split_alpha_nonalpha(text, mode=2))
+    # output: ['vits', '和', 'Bert-VITS2', '是', 'tts', '模型。花费', '3days.花费', '3', '天。Take 3 days']
+
+    text = "vits 和 Bert-VITS2 是 tts 模型。花费3days.花费3天。Take 3 days"
+    print(split_alpha_nonalpha(text, mode=1))
+    # output: ['vits ', '和 ', 'Bert-VITS', '2 ', '是 ', 'tts ', '模型。花费3', 'days.花费3天。Take ', '3 ', 'days']
+
+    text = "vits 和 Bert-VITS2 是 tts 模型。花费3days.花费3天。Take 3 days"
+    print(split_alpha_nonalpha(text, mode=2))
+    # output: ['vits ', '和 ', 'Bert-VITS2 ', '是 ', 'tts ', '模型。花费', '3days.花费', '3', '天。Take ', '3 ', 'days']

utils/bert_vits2/tools/log.py

@@ -0,0 +1,17 @@
+"""
+logger封装
+"""
+
+from loguru import logger
+import sys
+
+
+# 移除所有默认的处理器
+logger.remove()
+
+# 自定义格式并添加到标准输出
+log_format = (
+    "<g>{time:MM-DD HH:mm:ss}</g> <lvl>{level:<9}</lvl>| {file}:{line} | {message}"
+)
+
+logger.add(sys.stdout, format=log_format, backtrace=True, diagnose=True)

utils/bert_vits2/tools/sentence.py

@@ -0,0 +1,173 @@
+import logging
+
+import regex as re
+
+from ..tools.classify_language import classify_language, split_alpha_nonalpha
+
+
+def check_is_none(item) -> bool:
+    """none -> True, not none -> False"""
+    return (
+        item is None
+        or (isinstance(item, str) and str(item).isspace())
+        or str(item) == ""
+    )
+
+
+def markup_language(text: str, target_languages: list = None) -> str:
+    pattern = (
+        r"[\!\"\#\$\%\&\'\(\)\*\+\,\-\.\/\:\;\<\>\=\?\@\[\]\{\}\\\\\^\_\`"
+        r"\！？。＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」"
+        r"『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘\'\‛\“\”\„\‟…‧﹏.]+"
+    )
+    sentences = re.split(pattern, text)
+
+    pre_lang = ""
+    p = 0
+
+    if target_languages is not None:
+        sorted_target_languages = sorted(target_languages)
+        if sorted_target_languages in [["en", "zh"], ["en", "ja"], ["en", "ja", "zh"]]:
+            new_sentences = []
+            for sentence in sentences:
+                new_sentences.extend(split_alpha_nonalpha(sentence))
+            sentences = new_sentences
+
+    for sentence in sentences:
+        if check_is_none(sentence):
+            continue
+
+        lang = classify_language(sentence, target_languages)
+
+        if pre_lang == "":
+            text = text[:p] + text[p:].replace(
+                sentence, f"[{lang.upper()}]{sentence}", 1
+            )
+            p += len(f"[{lang.upper()}]")
+        elif pre_lang != lang:
+            text = text[:p] + text[p:].replace(
+                sentence, f"[{pre_lang.upper()}][{lang.upper()}]{sentence}", 1
+            )
+            p += len(f"[{pre_lang.upper()}][{lang.upper()}]")
+        pre_lang = lang
+        p += text[p:].index(sentence) + len(sentence)
+    text += f"[{pre_lang.upper()}]"
+
+    return text
+
+
+def split_by_language(text: str, target_languages: list = None) -> list:
+    pattern = (
+        r"[\!\"\#\$\%\&\'\(\)\*\+\,\-\.\/\:\;\<\>\=\?\@\[\]\{\}\\\\\^\_\`"
+        r"\！？\。＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」"
+        r"『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘\'\‛\“\”\„\‟…‧﹏.]+"
+    )
+    sentences = re.split(pattern, text)
+
+    pre_lang = ""
+    start = 0
+    end = 0
+    sentences_list = []
+
+    if target_languages is not None:
+        sorted_target_languages = sorted(target_languages)
+        if sorted_target_languages in [["en", "zh"], ["en", "ja"], ["en", "ja", "zh"]]:
+            new_sentences = []
+            for sentence in sentences:
+                new_sentences.extend(split_alpha_nonalpha(sentence))
+            sentences = new_sentences
+
+    for sentence in sentences:
+        if check_is_none(sentence):
+            continue
+
+        lang = classify_language(sentence, target_languages)
+
+        end += text[end:].index(sentence)
+        if pre_lang != "" and pre_lang != lang:
+            sentences_list.append((text[start:end], pre_lang))
+            start = end
+        end += len(sentence)
+        pre_lang = lang
+    sentences_list.append((text[start:], pre_lang))
+
+    return sentences_list
+
+
+def sentence_split(text: str, max: int) -> list:
+    pattern = r"[!(),—+\-.:;?？。，、；：]+"
+    sentences = re.split(pattern, text)
+    discarded_chars = re.findall(pattern, text)
+
+    sentences_list, count, p = [], 0, 0
+
+    # 按被分割的符号遍历
+    for i, discarded_chars in enumerate(discarded_chars):
+        count += len(sentences[i]) + len(discarded_chars)
+        if count >= max:
+            sentences_list.append(text[p : p + count].strip())
+            p += count
+            count = 0
+
+    # 加入最后剩余的文本
+    if p < len(text):
+        sentences_list.append(text[p:])
+
+    return sentences_list
+
+
+def sentence_split_and_markup(text, max=50, lang="auto", speaker_lang=None):
+    # 如果该speaker只支持一种语言
+    if speaker_lang is not None and len(speaker_lang) == 1:
+        if lang.upper() not in ["AUTO", "MIX"] and lang.lower() != speaker_lang[0]:
+            logging.debug(
+                f'lang "{lang}" is not in speaker_lang {speaker_lang},automatically set lang={speaker_lang[0]}'
+            )
+        lang = speaker_lang[0]
+
+    sentences_list = []
+    if lang.upper() != "MIX":
+        if max <= 0:
+            sentences_list.append(
+                markup_language(text, speaker_lang)
+                if lang.upper() == "AUTO"
+                else f"[{lang.upper()}]{text}[{lang.upper()}]"
+            )
+        else:
+            for i in sentence_split(text, max):
+                if check_is_none(i):
+                    continue
+                sentences_list.append(
+                    markup_language(i, speaker_lang)
+                    if lang.upper() == "AUTO"
+                    else f"[{lang.upper()}]{i}[{lang.upper()}]"
+                )
+    else:
+        sentences_list.append(text)
+
+    for i in sentences_list:
+        logging.debug(i)
+
+    return sentences_list
+
+
+if __name__ == "__main__":
+    text = "这几天心里颇不宁静。今晚在院子里坐着乘凉，忽然想起日日走过的荷塘，在这满月的光里，总该另有一番样子吧。月亮渐渐地升高了，墙外马路上孩子们的欢笑，已经听不见了；妻在屋里拍着闰儿，迷迷糊糊地哼着眠歌。我悄悄地披了大衫，带上门出去。"
+    print(markup_language(text, target_languages=None))
+    print(sentence_split(text, max=50))
+    print(sentence_split_and_markup(text, max=50, lang="auto", speaker_lang=None))
+
+    text = "你好，这是一段用来测试自动标注的文本。こんにちは,これは自動ラベリングのテスト用テキストです.Hello, this is a piece of text to test autotagging.你好！今天我们要介绍VITS项目，其重点是使用了GAN Duration predictor和transformer flow,并且接入了Bert模型来提升韵律。Bert embedding会在稍后介绍。"
+    print(split_by_language(text, ["zh", "ja", "en"]))
+
+    text = "vits和Bert-VITS2是tts模型。花费3days.花费3天。Take 3 days"
+
+    print(split_by_language(text, ["zh", "ja", "en"]))
+    # output: [('vits', 'en'), ('和', 'ja'), ('Bert-VITS', 'en'), ('2是', 'zh'), ('tts', 'en'), ('模型。花费3', 'zh'), ('days.', 'en'), ('花费3天。', 'zh'), ('Take 3 days', 'en')]
+
+    print(split_by_language(text, ["zh", "en"]))
+    # output: [('vits', 'en'), ('和', 'zh'), ('Bert-VITS', 'en'), ('2是', 'zh'), ('tts', 'en'), ('模型。花费3', 'zh'), ('days.', 'en'), ('花费3天。', 'zh'), ('Take 3 days', 'en')]
+
+    text = "vits 和 Bert-VITS2 是 tts 模型。花费 3 days. 花费 3天。Take 3 days"
+    print(split_by_language(text, ["zh", "en"]))
+    # output: [('vits ', 'en'), ('和 ', 'zh'), ('Bert-VITS2 ', 'en'), ('是 ', 'zh'), ('tts ', 'en'), ('模型。花费 ', 'zh'), ('3 days. ', 'en'), ('花费 3天。', 'zh'), ('Take 3 days', 'en')]

utils/bert_vits2/tools/translate.py

@@ -0,0 +1,62 @@
+"""
+翻译api
+"""
+
+from ..config import config
+
+import random
+import hashlib
+import requests
+
+
+def translate(Sentence: str, to_Language: str = "jp", from_Language: str = ""):
+    """
+    :param Sentence: 待翻译语句
+    :param from_Language: 待翻译语句语言
+    :param to_Language: 目标语言
+    :return: 翻译后语句 出错时返回None
+
+    常见语言代码：中文 zh 英语 en 日语 jp
+    """
+    appid = config.translate_config.app_key
+    key = config.translate_config.secret_key
+    if appid == "" or key == "":
+        return "请开发者在config.yml中配置app_key与secret_key"
+    url = "https://fanyi-api.baidu.com/api/trans/vip/translate"
+    texts = Sentence.splitlines()
+    outTexts = []
+    for t in texts:
+        if t != "":
+            # 签名计算 参考文档 https://api.fanyi.baidu.com/product/113
+            salt = str(random.randint(1, 100000))
+            signString = appid + t + salt + key
+            hs = hashlib.md5()
+            hs.update(signString.encode("utf-8"))
+            signString = hs.hexdigest()
+            if from_Language == "":
+                from_Language = "auto"
+            headers = {"Content-Type": "application/x-www-form-urlencoded"}
+            payload = {
+                "q": t,
+                "from": from_Language,
+                "to": to_Language,
+                "appid": appid,
+                "salt": salt,
+                "sign": signString,
+            }
+            # 发送请求
+            try:
+                response = requests.post(
+                    url=url, data=payload, headers=headers, timeout=3
+                )
+                response = response.json()
+                if "trans_result" in response.keys():
+                    result = response["trans_result"][0]
+                    if "dst" in result.keys():
+                        dst = result["dst"]
+                        outTexts.append(dst)
+            except Exception:
+                return Sentence
+        else:
+            outTexts.append(t)
+    return "\n".join(outTexts)

utils/bert_vits2/transforms.py

@@ -0,0 +1,209 @@
+import torch
+from torch.nn import functional as F
+
+import numpy as np
+
+
+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

utils/bert_vits2/utils.py

@@ -0,0 +1,460 @@
+import os
+import glob
+import argparse
+import logging
+import json
+import shutil
+import subprocess
+import numpy as np
+from huggingface_hub import hf_hub_download
+from scipy.io.wavfile import read
+import torch
+import re
+
+MATPLOTLIB_FLAG = False
+
+logger = logging.getLogger(__name__)
+
+
+def download_emo_models(mirror, repo_id, model_name):
+    if mirror == "openi":
+        import openi
+
+        openi.model.download_model(
+            "Stardust_minus/Bert-VITS2",
+            repo_id.split("/")[-1],
+            "./emotional",
+        )
+    else:
+        hf_hub_download(
+            repo_id,
+            "pytorch_model.bin",
+            local_dir=model_name,
+            local_dir_use_symlinks=False,
+        )
+
+
+def download_checkpoint(
+    dir_path, repo_config, token=None, regex="G_*.pth", mirror="openi"
+):
+    repo_id = repo_config["repo_id"]
+    f_list = glob.glob(os.path.join(dir_path, regex))
+    if f_list:
+        print("Use existed model, skip downloading.")
+        return
+    if mirror.lower() == "openi":
+        import openi
+
+        kwargs = {"token": token} if token else {}
+        openi.login(**kwargs)
+
+        model_image = repo_config["model_image"]
+        openi.model.download_model(repo_id, model_image, dir_path)
+
+        fs = glob.glob(os.path.join(dir_path, model_image, "*.pth"))
+        for file in fs:
+            shutil.move(file, dir_path)
+        shutil.rmtree(os.path.join(dir_path, model_image))
+    else:
+        for file in ["DUR_0.pth", "D_0.pth", "G_0.pth"]:
+            hf_hub_download(
+                repo_id, file, local_dir=dir_path, local_dir_use_symlinks=False
+            )
+
+
+def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
+    assert os.path.isfile(checkpoint_path)
+    checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
+    iteration = checkpoint_dict["iteration"]
+    learning_rate = checkpoint_dict["learning_rate"]
+    if (
+        optimizer is not None
+        and not skip_optimizer
+        and checkpoint_dict["optimizer"] is not None
+    ):
+        optimizer.load_state_dict(checkpoint_dict["optimizer"])
+    elif optimizer is None and not skip_optimizer:
+        # else:      Disable this line if Infer and resume checkpoint,then enable the line upper
+        new_opt_dict = optimizer.state_dict()
+        new_opt_dict_params = new_opt_dict["param_groups"][0]["params"]
+        new_opt_dict["param_groups"] = checkpoint_dict["optimizer"]["param_groups"]
+        new_opt_dict["param_groups"][0]["params"] = new_opt_dict_params
+        optimizer.load_state_dict(new_opt_dict)
+
+    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():
+        try:
+            # assert "emb_g" not in k
+            new_state_dict[k] = saved_state_dict[k]
+            assert saved_state_dict[k].shape == v.shape, (
+                saved_state_dict[k].shape,
+                v.shape,
+            )
+        except:
+            # For upgrading from the old version
+            if "ja_bert_proj" in k:
+                v = torch.zeros_like(v)
+                logger.warn(
+                    f"Seems you are using the old version of the model, the {k} is automatically set to zero for backward compatibility"
+                )
+            else:
+                logger.error(f"{k} is not in the checkpoint")
+
+            new_state_dict[k] = v
+
+    if hasattr(model, "module"):
+        model.module.load_state_dict(new_state_dict, strict=False)
+    else:
+        model.load_state_dict(new_state_dict, strict=False)
+
+    logger.info(
+        "Loaded checkpoint '{}' (iteration {})".format(checkpoint_path, iteration)
+    )
+
+    return model, optimizer, learning_rate, iteration
+
+
+def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
+    logger.info(
+        "Saving model and optimizer state at iteration {} to {}".format(
+            iteration, checkpoint_path
+        )
+    )
+    if hasattr(model, "module"):
+        state_dict = model.module.state_dict()
+    else:
+        state_dict = model.state_dict()
+    torch.save(
+        {
+            "model": state_dict,
+            "iteration": iteration,
+            "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"):
+    f_list = glob.glob(os.path.join(dir_path, regex))
+    f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
+    x = f_list[-1]
+    return x
+
+
+def plot_spectrogram_to_numpy(spectrogram):
+    global MATPLOTLIB_FLAG
+    if not MATPLOTLIB_FLAG:
+        import matplotlib
+
+        matplotlib.use("Agg")
+        MATPLOTLIB_FLAG = True
+        mpl_logger = logging.getLogger("matplotlib")
+        mpl_logger.setLevel(logging.WARNING)
+    import matplotlib.pylab as plt
+    import numpy as np
+
+    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):
+    global MATPLOTLIB_FLAG
+    if not MATPLOTLIB_FLAG:
+        import matplotlib
+
+        matplotlib.use("Agg")
+        MATPLOTLIB_FLAG = True
+        mpl_logger = logging.getLogger("matplotlib")
+        mpl_logger.setLevel(logging.WARNING)
+    import matplotlib.pylab as plt
+    import numpy as np
+
+    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_filepaths_and_text(filename, split="|"):
+    with open(filename, encoding="utf-8") as f:
+        filepaths_and_text = [line.strip().split(split) for line in f]
+    return filepaths_and_text
+
+
+def get_hparams(init=True):
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-c",
+        "--config",
+        type=str,
+        default="./configs/base.json",
+        help="JSON file for configuration",
+    )
+    parser.add_argument("-m", "--model", type=str, required=True, help="Model name")
+
+    args = parser.parse_args()
+    model_dir = os.path.join("./logs", args.model)
+
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+
+    config_path = args.config
+    config_save_path = os.path.join(model_dir, "config.json")
+    if init:
+        with open(config_path, "r", encoding="utf-8") as f:
+            data = f.read()
+        with open(config_save_path, "w", encoding="utf-8") as f:
+            f.write(data)
+    else:
+        with open(config_save_path, "r", vencoding="utf-8") as f:
+            data = f.read()
+    config = json.loads(data)
+    hparams = HParams(**config)
+    hparams.model_dir = model_dir
+    return hparams
+
+
+def clean_checkpoints(path_to_models="logs/44k/", n_ckpts_to_keep=2, sort_by_time=True):
+    """Freeing up space by deleting saved ckpts
+
+    Arguments:
+    path_to_models    --  Path to the model directory
+    n_ckpts_to_keep   --  Number of ckpts to keep, excluding G_0.pth and D_0.pth
+    sort_by_time      --  True -> chronologically delete ckpts
+                          False -> lexicographically delete ckpts
+    """
+    import re
+
+    ckpts_files = [
+        f
+        for f in os.listdir(path_to_models)
+        if os.path.isfile(os.path.join(path_to_models, f))
+    ]
+
+    def name_key(_f):
+        return int(re.compile("._(\\d+)\\.pth").match(_f).group(1))
+
+    def time_key(_f):
+        return os.path.getmtime(os.path.join(path_to_models, _f))
+
+    sort_key = time_key if sort_by_time else name_key
+
+    def x_sorted(_x):
+        return sorted(
+            [f for f in ckpts_files if f.startswith(_x) and not f.endswith("_0.pth")],
+            key=sort_key,
+        )
+    to_del = [
+        os.path.join(path_to_models, fn)
+        for fn in (
+            x_sorted("G")[:-n_ckpts_to_keep]
+            + x_sorted("D")[:-n_ckpts_to_keep]
+            + x_sorted("WD")[:-n_ckpts_to_keep]
+        )
+    ]
+
+    def del_info(fn):
+        return logger.info(f".. Free up space by deleting ckpt {fn}")
+
+    def del_routine(x):
+        return [os.remove(x), del_info(x)]
+
+    [del_routine(fn) for fn in to_del]
+
+
+def get_hparams_from_dir(model_dir):
+    config_save_path = os.path.join(model_dir, "config.json")
+    with open(config_save_path, "r", encoding="utf-8") as f:
+        data = f.read()
+    config = json.loads(data)
+
+    hparams = HParams(**config)
+    hparams.model_dir = model_dir
+    return hparams
+
+
+def get_hparams_from_file(config_path):
+    # print("config_path: ", config_path)
+    with open(config_path, "r", encoding="utf-8") as f:
+        data = f.read()
+    config = json.loads(data)
+
+    hparams = HParams(**config)
+    return hparams
+
+
+def check_git_hash(model_dir):
+    source_dir = os.path.dirname(os.path.realpath(__file__))
+    if not os.path.exists(os.path.join(source_dir, ".git")):
+        logger.warn(
+            "{} is not a git repository, therefore hash value comparison will be ignored.".format(
+                source_dir
+            )
+        )
+        return
+
+    cur_hash = subprocess.getoutput("git rev-parse HEAD")
+
+    path = os.path.join(model_dir, "githash")
+    if os.path.exists(path):
+        saved_hash = open(path).read()
+        if saved_hash != cur_hash:
+            logger.warn(
+                "git hash values are different. {}(saved) != {}(current)".format(
+                    saved_hash[:8], cur_hash[:8]
+                )
+            )
+    else:
+        open(path, "w").write(cur_hash)
+
+
+def get_logger(model_dir, filename="train.log"):
+    global logger
+    logger = logging.getLogger(os.path.basename(model_dir))
+    logger.setLevel(logging.DEBUG)
+
+    formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+    h = logging.FileHandler(os.path.join(model_dir, filename))
+    h.setLevel(logging.DEBUG)
+    h.setFormatter(formatter)
+    logger.addHandler(h)
+    return logger
+
+
+class HParams:
+    def __init__(self, **kwargs):
+        for k, v in kwargs.items():
+            if type(v) == dict:
+                v = HParams(**v)
+            self[k] = v
+
+    def keys(self):
+        return self.__dict__.keys()
+
+    def items(self):
+        return self.__dict__.items()
+
+    def values(self):
+        return self.__dict__.values()
+
+    def __len__(self):
+        return len(self.__dict__)
+
+    def __getitem__(self, key):
+        return getattr(self, key)
+
+    def __setitem__(self, key, value):
+        return setattr(self, key, value)
+
+    def __contains__(self, key):
+        return key in self.__dict__
+
+    def __repr__(self):
+        return self.__dict__.__repr__()
+
+
+def load_model(model_path, config_path):
+    hps = get_hparams_from_file(config_path)
+    net = SynthesizerTrn(
+        # len(symbols),
+        108,
+        hps.data.filter_length // 2 + 1,
+        hps.train.segment_size // hps.data.hop_length,
+        n_speakers=hps.data.n_speakers,
+        **hps.model,
+    ).to("cpu")
+    _ = net.eval()
+    _ = load_checkpoint(model_path, net, None, skip_optimizer=True)
+    return net
+
+
+def mix_model(
+    network1, network2, output_path, voice_ratio=(0.5, 0.5), tone_ratio=(0.5, 0.5)
+):
+    if hasattr(network1, "module"):
+        state_dict1 = network1.module.state_dict()
+        state_dict2 = network2.module.state_dict()
+    else:
+        state_dict1 = network1.state_dict()
+        state_dict2 = network2.state_dict()
+    for k in state_dict1.keys():
+        if k not in state_dict2.keys():
+            continue
+        if "enc_p" in k:
+            state_dict1[k] = (
+                state_dict1[k].clone() * tone_ratio[0]
+                + state_dict2[k].clone() * tone_ratio[1]
+            )
+        else:
+            state_dict1[k] = (
+                state_dict1[k].clone() * voice_ratio[0]
+                + state_dict2[k].clone() * voice_ratio[1]
+            )
+    for k in state_dict2.keys():
+        if k not in state_dict1.keys():
+            state_dict1[k] = state_dict2[k].clone()
+    torch.save(
+        {"model": state_dict1, "iteration": 0, "optimizer": None, "learning_rate": 0},
+        output_path,
+    )
+
+
+def get_steps(model_path):
+    matches = re.findall(r"\d+", model_path)
+    return matches[-1] if matches else None

utils/bert_vits2_utils.py

@@ -0,0 +1,463 @@
+import gc
+import os
+import numpy as np
+import torch
+from torch import LongTensor
+from typing import Optional
+import soundfile as sf
+import logging
+import gradio as gr
+import librosa
+# bert_vits2
+from .bert_vits2 import utils
+from .bert_vits2.infer import get_net_g, latest_version, infer_multilang, infer
+from .bert_vits2.config import config
+from .bert_vits2 import re_matching
+from .bert_vits2.tools.sentence import split_by_language
+
+logger = logging.getLogger(__name__)
+class TextToSpeech:
+    def __init__(self,
+                 device='cuda',
+                 ):
+        self.device = device = torch.device(device)
+
+        if config.webui_config.debug:
+            logger.info("Enable DEBUG")
+        
+        hps = utils.get_hparams_from_file(config.webui_config.config_path)
+        self.hps = hps
+        # 若config.json中未指定版本则默认为最新版本
+        version = hps.version if hasattr(hps, "version") else latest_version
+        self.version = version
+
+        net_g = get_net_g(
+            model_path=config.webui_config.model, version=version, device=device, hps=hps
+        )
+        self.net_g = net_g
+
+        self.speaker_ids = speaker_ids = hps.data.spk2id
+        self.speakers = speakers = list(speaker_ids.keys())
+        self.speaker = speakers[0]
+
+        self.languages = languages = ["ZH", "JP", "EN", "mix", "auto"]
+
+    def free_up_memory(self):
+    # Prior inference run might have large variables not cleaned up due to exception during the run.
+    # Free up as much memory as possible to allow this run to be successful.
+        gc.collect()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+    def process_mix(self, slice):
+        _speaker = slice.pop()
+        _text, _lang = [], []
+        for lang, content in slice:
+            content = content.split("|")
+            content = [part for part in content if part != ""]
+            if len(content) == 0:
+                continue
+            if len(_text) == 0:
+                _text = [[part] for part in content]
+                _lang = [[lang] for part in content]
+            else:
+                _text[-1].append(content[0])
+                _lang[-1].append(lang)
+                if len(content) > 1:
+                    _text += [[part] for part in content[1:]]
+                    _lang += [[lang] for part in content[1:]]
+        return _text, _lang, _speaker
+    
+    def process_auto(self, text):
+        _text, _lang = [], []
+        for slice in text.split("|"):
+            if slice == "":
+                continue
+            temp_text, temp_lang = [], []
+            sentences_list = split_by_language(slice, target_languages=["zh", "ja", "en"])
+            for sentence, lang in sentences_list:
+                if sentence == "":
+                    continue
+                temp_text.append(sentence)
+                if lang == "ja":
+                    lang = "jp"
+                temp_lang.append(lang.upper())
+            _text.append(temp_text)
+            _lang.append(temp_lang)
+        return _text, _lang
+
+    def generate_audio(
+        self,
+        slices,
+        sdp_ratio,
+        noise_scale,
+        noise_scale_w,
+        length_scale,
+        speaker,
+        language,
+        reference_audio,
+        emotion,
+        style_text,
+        style_weight,
+        skip_start=False,
+        skip_end=False,
+    ):
+        audio_list = []
+        # silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16)
+
+        self.free_up_memory()
+
+        with torch.no_grad():
+            for idx, piece in enumerate(slices):
+                skip_start = idx != 0
+                skip_end = idx != len(slices) - 1
+                audio = infer(
+                    piece,
+                    reference_audio=reference_audio,
+                    emotion=emotion,
+                    sdp_ratio=sdp_ratio,
+                    noise_scale=noise_scale,
+                    noise_scale_w=noise_scale_w,
+                    length_scale=length_scale,
+                    sid=speaker,
+                    language=language,
+                    hps=self.hps,
+                    net_g=self.net_g,
+                    device=self.device,
+                    skip_start=skip_start,
+                    skip_end=skip_end,
+                    style_text=style_text,
+                    style_weight=style_weight,
+                )
+                audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio)
+                audio_list.append(audio16bit)
+        return audio_list
+
+    
+    def generate_audio_multilang(
+        self,
+        slices,
+        sdp_ratio,
+        noise_scale,
+        noise_scale_w,
+        length_scale,
+        speaker,
+        language,
+        reference_audio,
+        emotion,
+        skip_start=False,
+        skip_end=False,
+        en_ratio=1.0
+    ):
+        audio_list = []
+        # silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16)
+
+        self.free_up_memory()
+
+        with torch.no_grad():
+            for idx, piece in enumerate(slices):
+                skip_start = idx != 0
+                skip_end = idx != len(slices) - 1
+                audio = infer_multilang(
+                    piece,
+                    reference_audio=reference_audio,
+                    emotion=emotion,
+                    sdp_ratio=sdp_ratio,
+                    noise_scale=noise_scale,
+                    noise_scale_w=noise_scale_w,
+                    length_scale=length_scale,
+                    sid=speaker,
+                    language=language[idx],
+                    hps=self.hps,
+                    net_g=self.net_g,
+                    device=self.device,
+                    skip_start=skip_start,
+                    skip_end=skip_end,
+                    en_ratio=en_ratio
+                )
+                audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio)
+                audio_list.append(audio16bit)
+        return audio_list
+
+
+
+    def process_text(self,
+                     text: str,
+                     speaker,
+                     sdp_ratio,
+                     noise_scale,
+                     noise_scale_w,
+                     length_scale,
+                     language,
+                     reference_audio,
+                     emotion,
+                     style_text=None,
+                     style_weight=0,
+                     en_ratio=1.0
+                     ):
+        hps = self.hps
+        audio_list = []
+        if language == "mix":
+            bool_valid, str_valid = re_matching.validate_text(text)
+            if not bool_valid:
+                return str_valid, (
+                    hps.data.sampling_rate,
+                    np.concatenate([np.zeros(hps.data.sampling_rate // 2)]),
+                )
+            for slice in re_matching.text_matching(text):
+                _text, _lang, _speaker = self.process_mix(slice)
+                if _speaker is None:
+                    continue
+                print(f"Text: {_text}\nLang: {_lang}")
+                audio_list.extend(
+                    self.generate_audio_multilang(
+                        _text,
+                        sdp_ratio,
+                        noise_scale,
+                        noise_scale_w,
+                        length_scale,
+                        _speaker,
+                        _lang,
+                        reference_audio,
+                        emotion,
+                        en_ratio=en_ratio
+                    )
+                )
+        elif language.lower() == "auto":
+            _text, _lang = self.process_auto(text)
+            print(f"Text: {_text}\nLang: {_lang}")
+            audio_list.extend(
+                self.generate_audio_multilang(
+                    _text,
+                    sdp_ratio,
+                    noise_scale,
+                    noise_scale_w,
+                    length_scale,
+                    speaker,
+                    _lang,
+                    reference_audio,
+                    emotion,
+                    en_ratio=en_ratio
+                )
+            )
+        else:
+            audio_list.extend(
+                self.generate_audio(
+                    text.split("|"),
+                    sdp_ratio,
+                    noise_scale,
+                    noise_scale_w,
+                    length_scale,
+                    speaker,
+                    language,
+                    reference_audio,
+                    emotion,
+                    style_text,
+                    style_weight,
+                )
+            )
+        return audio_list
+    
+    def tts_split(
+        self,
+        text: str,
+        speaker,
+        sdp_ratio,
+        noise_scale,
+        noise_scale_w,
+        length_scale,
+        language,
+        cut_by_sent,
+        interval_between_para,
+        interval_between_sent,
+        reference_audio,
+        emotion,
+        style_text,
+        style_weight,
+        en_ratio
+    ):
+        while text.find("\n\n") != -1:
+            text = text.replace("\n\n", "\n")
+        text = text.replace("|", "")
+        para_list = re_matching.cut_para(text)
+        para_list = [p for p in para_list if p != ""]
+        audio_list = []
+        for p in para_list:
+            if not cut_by_sent:
+                audio_list += self.process_text(
+                    p,
+                    speaker,
+                    sdp_ratio,
+                    noise_scale,
+                    noise_scale_w,
+                    length_scale,
+                    language,
+                    reference_audio,
+                    emotion,
+                    style_text,
+                    style_weight,
+                    en_ratio
+                )
+                silence = np.zeros((int)(44100 * interval_between_para), dtype=np.int16)
+                audio_list.append(silence)
+            else:
+                audio_list_sent = []
+                sent_list = re_matching.cut_sent(p)
+                sent_list = [s for s in sent_list if s != ""]
+                for s in sent_list:
+                    audio_list_sent += self.process_text(
+                        s,
+                        speaker,
+                        sdp_ratio,
+                        noise_scale,
+                        noise_scale_w,
+                        length_scale,
+                        language,
+                        reference_audio,
+                        emotion,
+                        style_text,
+                        style_weight,
+                        en_ratio
+                    )
+                    silence = np.zeros((int)(44100 * interval_between_sent))
+                    audio_list_sent.append(silence)
+                if (interval_between_para - interval_between_sent) > 0:
+                    silence = np.zeros(
+                        (int)(44100 * (interval_between_para - interval_between_sent))
+                    )
+                    audio_list_sent.append(silence)
+                audio16bit = gr.processing_utils.convert_to_16_bit_wav(
+                    np.concatenate(audio_list_sent)
+                )  # 对完整句子做音量归一
+                audio_list.append(audio16bit)
+        audio_concat = np.concatenate(audio_list)
+        return ("Success", (self.hps.data.sampling_rate, audio_concat))
+
+
+    def tts_fn(
+        self,
+        text: str,
+        speaker,
+        sdp_ratio,
+        noise_scale,
+        noise_scale_w,
+        length_scale,
+        language,
+        reference_audio,
+        emotion,
+        prompt_mode,
+        style_text=None,
+        style_weight=0,
+    ):
+        if style_text == "":
+            style_text = None
+        if prompt_mode == "Audio prompt":
+            if reference_audio == None:
+                return ("Invalid audio prompt", None)
+            else:
+                reference_audio = self.load_audio(reference_audio)[1]
+        else:
+            reference_audio = None
+
+        audio_list = self.process_text(
+            text,
+            speaker,
+            sdp_ratio,
+            noise_scale,
+            noise_scale_w,
+            length_scale,
+            language,
+            reference_audio,
+            emotion,
+            style_text,
+            style_weight,
+        )
+
+        audio_concat = np.concatenate(audio_list)
+        return "Success", (self.hps.data.sampling_rate, audio_concat)
+    
+    def load_audio(self, path):
+        audio, sr = librosa.load(path, 48000)
+        # audio = librosa.resample(audio, 44100, 48000)
+        return sr, audio
+
+    def format_utils(self, text, speaker):
+        _text, _lang = self.process_auto(text)
+        res = f"[{speaker}]"
+        for lang_s, content_s in zip(_lang, _text):
+            for lang, content in zip(lang_s, content_s):
+                # res += f"<{lang.lower()}>{content}"
+                # 部分中文会被识别成日文，强转成中文
+                lang = lang.lower().replace("jp", "zh")
+                res += f"<{lang}>{content}"
+            res += "|"
+        return "mix", res[:-1]
+    
+    def synthesize(self,
+                   text,
+                   speaker_idx=0,       # self.speakers 的 index，指定说话
+                   sdp_ratio=0.5,
+                   noise_scale=0.6,
+                   noise_scale_w=0.9,
+                   length_scale=1.0,    # 越大语速越慢
+                   language="mix",      # ["ZH", "EN", "mix"] 三选一
+                   opt_cut_by_send=False, # 按句切分    在按段落切分的基础上再按句子切分文本
+                   interval_between_para=1.0, # 段间停顿(秒)，需要大于句间停顿才有效
+                   interval_between_sent=0.2, # 句间停顿(秒)，勾选按句切分才生效
+                   audio_prompt=None,
+                   text_prompt="",
+                   prompt_mode="Text prompts",
+                   style_text="",        #    "使用辅助文本的语意来辅助生成对话（语言保持与主文本相同）\n\n"
+                                         # "**注意**：不要使用**指令式文本**（如：开心），要使用**带有强烈情感的文本**（如：我好快乐！！！）\n\n"
+                                         # "效果较不明确，留空即为不使用该功能"
+                   style_weight=0.7,     # "主文本和辅助文本的bert混合比率，0表示仅主文本，1表示仅辅助文本
+                   en_ratio=1.0          # 中英混合时，英文速度控制，越大英文速度越慢 
+                   ):
+        """
+        return: audio, sample_rate
+        """
+        
+        speaker = self.speakers[speaker_idx]
+
+        if language == "mix":
+            language, text = self.format_utils(text, speaker)
+        
+            text_output, audio_output = self.tts_split(
+                text,
+                speaker,
+                sdp_ratio,
+                noise_scale,
+                noise_scale_w,
+                length_scale,
+                language,
+                opt_cut_by_send,
+                interval_between_para,
+                interval_between_sent,
+                audio_prompt,
+                text_prompt,
+                style_text,
+                style_weight,
+                en_ratio
+            )
+        else:
+            text_output, audio_output = self.tts_fn(
+                text,
+                speaker,
+                sdp_ratio,
+                noise_scale,
+                noise_scale_w,
+                length_scale,
+                language,
+                audio_prompt,
+                text_prompt,
+                prompt_mode,
+                style_text,
+                style_weight
+            )
+        
+        # return text_output, audio_output
+        return audio_output[1], audio_output[0]
+    
+    def print_speakers_info(self):
+        for i, speaker in enumerate(self.speakers):
+            print(f"id: {i}, speaker: {speaker}")

utils/speaker_checker_utils.py

@@ -0,0 +1,114 @@
+from modelscope.pipelines import pipeline
+import numpy as np
+import os
+
+ERES2NETV2 = {
+    "task": 'speaker-verification',
+    "model_name": 'damo/speech_eres2netv2_sv_zh-cn_16k-common',
+    "model_revision": 'v1.0.1',
+    "save_embeddings": False
+}
+
+# 保存 embedding 的路径
+DEFALUT_SAVE_PATH = os.path.join(os.path.dirname(os.path.dirname(__name__)), "speaker_embedding")
+
+class SpeakerChecker:
+    def __init__(self, 
+                speaker_wav_path,
+                task='speaker-verification',
+                model_name='damo/speech_eres2netv2_sv_zh-cn_16k-common',
+                model_revision='v1.0.1',
+                device="cuda",
+                save_embeddings=False,):
+        self.pipeline = pipeline(
+                task=task,
+                model=model_name,
+                model_revision=model_revision,
+                device=device)
+        self.save_embeddings = save_embeddings
+
+        
+        self.update_embedding_with_wav(speaker_wav_path)
+
+    # save path 为 none 时     不将 speaker_wav_path 对应音频的 embedding 存在本地
+    # save_path 不为 none 时   将 speaker_wav_path 对应音频的 embedding 存在本地对应位置
+    def update_embedding_with_wav(self, speaker_wav_path, save_path=None):
+        self.speaker_1_emb = self.wav2embeddings(speaker_wav_path, save_path)
+    
+    def update_embedding_with_np(self, speaker_emb_path):
+        self.speaker_1_emb = np.load(speaker_emb_path)
+
+    def wav2embeddings(self, speaker_1_wav, save_path=None):
+        result = self.pipeline([speaker_1_wav], output_emb=True)
+        speaker_1_emb = result['embs'][0]
+        if save_path is not None:
+            np.save(save_path, speaker_1_emb)
+        return speaker_1_emb
+    
+    def checker(self, audio: str, threshold=0.333):
+        result = self.pipeline([audio], output_emb=True)
+        speaker2_emb = result["embs"][0]
+        similarity = np.dot(self.speaker_1_emb, speaker2_emb) / (np.linalg.norm(self.speaker_1_emb) * np.linalg.norm(speaker2_emb))
+        if similarity > threshold:
+            return True
+        else:
+            return False
+
+
+    # def _verifaction(self, speaker_1_wav, speaker_2_wav, threshold, save_path):
+    #     if not self.save_embeddings:
+    #         result = self.pipeline([speaker_1_wav, speaker_2_wav], thr=threshold)
+    #         return result["text"]
+    #     else:
+    #         result = self.pipeline([speaker_1_wav, speaker_2_wav], thr=threshold, output_emb=True)
+    #         speaker1_emb = result["embs"][0]
+    #         speaker2_emb = result["embs"][1]
+    #         np.save(os.path.join(save_path, "speaker_1.npy"), speaker1_emb)
+    #         return result['outputs']["text"]
+        
+    # def _verifaction_from_embedding(self, base_emb, speaker_2_wav, threshold):
+    #     base_emb = np.load(base_emb)
+    #     result = self.pipeline([speaker_2_wav], output_emb=True)
+    #     speaker2_emb = result["embs"][0]
+    #     similarity = np.dot(base_emb, speaker2_emb) / (np.linalg.norm(base_emb) * np.linalg.norm(speaker2_emb))
+    #     if similarity > threshold:
+    #         return "yes"
+    #     else:
+    #         return "no"
+    
+    # def verfication(self, 
+    #                 base_emb=None, 
+    #                 speaker_1_wav=None, 
+    #                 speaker_2_wav=None, 
+    #                 threshold=0.333, 
+    #                 save_path=None):
+    #     if base_emb is not None and speaker_1_wav is not None:
+    #         raise ValueError("Only need one of them, base_emb or speaker_1_wav")
+    #     if base_emb is not None and speaker_2_wav is not None:
+    #         return self._verifaction_from_embedding(base_emb, speaker_2_wav, threshold)
+    #     elif speaker_1_wav is not None and speaker_2_wav is not None:
+    #         return self._verifaction(speaker_1_wav, speaker_2_wav, threshold, save_path)
+    #     else:
+    #         raise NotImplementedError
+        
+if __name__ == '__main__':
+    # verifier = speaker_verfication(**ERES2NETV2)
+
+    # verifier = speaker_verfication(save_embeddings=False)
+    # result = verifier.verfication(base_emb=None, speaker_1_wav=r"C:\Users\bing\Downloads\speaker1_a_cn_16k.wav", 
+    #                               speaker_2_wav=r"C:\Users\bing\Downloads\speaker2_a_cn_16k.wav",
+    #                               threshold=0.333,
+    #                               save_path=r"D:\python\irving\takway_base-main\savePath"
+    #                               )
+    # print("---")
+    # print(result)
+    # print(verifier.verfication(r"D:\python\irving\takway_base-main\savePath\speaker_1.npy",
+    #                            speaker_2_wav=r"C:\Users\bing\Downloads\speaker1_b_cn_16k.wav",
+    #                            threshold=0.333,
+    #                            ))
+    speaker_wav_path = r"C:\Users\bing\Downloads\speaker1_a_cn_16k.wav"
+    speaker_checker = SpeakerChecker(speaker_wav_path)
+    audio = r"C:\Users\bing\Downloads\speaker1_b_cn_16k.wav"
+    is_target = speaker_checker.checker(audio)
+    print(is_target)
+