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.gitattributes

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GPT_SoVITS/download.py

@@ -0,0 +1,5 @@
+import os, sys
+now_dir = os.getcwd()
+sys.path.insert(0, now_dir)
+from text.g2pw import G2PWPinyin
+g2pw = G2PWPinyin(model_dir="GPT_SoVITS/text/G2PWModel",model_source="GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",v_to_u=False, neutral_tone_with_five=True)

GPT_SoVITS/download_hubert.py

@@ -0,0 +1,52 @@
+import os
+import requests
+import torch
+from pathlib import Path
+from tqdm import tqdm
+
+def download_file(url, dest_path):
+    """Download a file with progress bar"""
+    response = requests.get(url, stream=True)
+    total_size = int(response.headers.get('content-length', 0))
+    
+    with open(dest_path, 'wb') as f, tqdm(
+        desc=os.path.basename(dest_path),
+        total=total_size,
+        unit='iB',
+        unit_scale=True,
+        unit_divisor=1024,
+    ) as pbar:
+        for data in response.iter_content(chunk_size=1024):
+            size = f.write(data)
+            pbar.update(size)
+
+def download_hubert():
+    """Download Chinese Hubert model"""
+    
+    # Get project root directory (parent of GPT_SoVITS)
+    root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+    
+    # Create model directory
+    model_dir = os.path.join(root_dir, "pretrained_models", "chinese-hubert-base")
+    os.makedirs(model_dir, exist_ok=True)
+    
+    # Model files to download
+    files = {
+        "config.json": "https://huggingface.co/TencentGameMate/chinese-hubert-base/raw/main/config.json",
+        "preprocessor_config.json": "https://huggingface.co/TencentGameMate/chinese-hubert-base/raw/main/preprocessor_config.json",
+        "pytorch_model.bin": "https://huggingface.co/TencentGameMate/chinese-hubert-base/resolve/main/pytorch_model.bin"
+    }
+    
+    print("Downloading Chinese Hubert model...")
+    for filename, url in files.items():
+        dest_path = os.path.join(model_dir, filename)
+        if not os.path.exists(dest_path):
+            print(f"Downloading {filename}...")
+            download_file(url, dest_path)
+        else:
+            print(f"{filename} already exists, skipping...")
+    
+    print("Download complete.")
+
+if __name__ == "__main__":
+    download_hubert() 

GPT_SoVITS/export_torch_script.py

@@ -0,0 +1,832 @@
+# modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py
+# reference: https://github.com/lifeiteng/vall-e
+import argparse
+from typing import Optional
+from my_utils import load_audio
+from text import cleaned_text_to_sequence
+import torch
+import torchaudio
+
+from torch import IntTensor, LongTensor, Tensor, nn
+from torch.nn import functional as F
+
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+from feature_extractor import cnhubert
+
+from AR.models.t2s_lightning_module import Text2SemanticLightningModule
+from module.models_onnx import SynthesizerTrn
+
+from inference_webui import get_phones_and_bert
+
+import os
+import soundfile
+
+default_config = {
+    "embedding_dim": 512,
+    "hidden_dim": 512,
+    "num_head": 8,
+    "num_layers": 12,
+    "num_codebook": 8,
+    "p_dropout": 0.0,
+    "vocab_size": 1024 + 1,
+    "phoneme_vocab_size": 512,
+    "EOS": 1024,
+}
+
+def get_raw_t2s_model(dict_s1) -> Text2SemanticLightningModule: 
+    config = dict_s1["config"]
+    config["model"]["dropout"] = float(config["model"]["dropout"])
+    t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
+    t2s_model.load_state_dict(dict_s1["weight"])
+    t2s_model = t2s_model.eval()
+    return t2s_model
+
+@torch.jit.script
+def logits_to_probs(
+    logits,
+    previous_tokens: Optional[torch.Tensor] = None,
+    temperature: float = 1.0,
+    top_k: Optional[int] = None,
+    top_p: Optional[int] = None,
+    repetition_penalty: float = 1.0,
+):
+    # if previous_tokens is not None:
+    #     previous_tokens = previous_tokens.squeeze()
+    # print(logits.shape,previous_tokens.shape)
+    # pdb.set_trace()
+    if previous_tokens is not None and repetition_penalty != 1.0:
+        previous_tokens = previous_tokens.long()
+        score = torch.gather(logits, dim=1, index=previous_tokens)
+        score = torch.where(
+            score < 0, score * repetition_penalty, score / repetition_penalty
+        )
+        logits.scatter_(dim=1, index=previous_tokens, src=score)
+
+    if top_p is not None and top_p < 1.0:
+        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+        cum_probs = torch.cumsum(
+            torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1
+        )
+        sorted_indices_to_remove = cum_probs > top_p
+        sorted_indices_to_remove[:, 0] = False  # keep at least one option
+        indices_to_remove = sorted_indices_to_remove.scatter(
+            dim=1, index=sorted_indices, src=sorted_indices_to_remove
+        )
+        logits = logits.masked_fill(indices_to_remove, -float("Inf"))
+
+    logits = logits / max(temperature, 1e-5)
+
+    if top_k is not None:
+        v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+        pivot = v[: , -1].unsqueeze(-1)
+        logits = torch.where(logits < pivot, -float("Inf"), logits)
+
+    probs = torch.nn.functional.softmax(logits, dim=-1)
+    return probs
+
+@torch.jit.script
+def multinomial_sample_one_no_sync(probs_sort):  
+    # Does multinomial sampling without a cuda synchronization
+    q = torch.randn_like(probs_sort)
+    return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int)
+
+@torch.jit.script
+def sample(
+    logits,
+    previous_tokens,
+    temperature: float = 1.0,
+    top_k: Optional[int] = None,
+    top_p: Optional[int] = None,
+    repetition_penalty: float = 1.0,
+):
+    probs = logits_to_probs(
+        logits=logits, previous_tokens=previous_tokens, temperature=temperature, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty
+    )
+    idx_next = multinomial_sample_one_no_sync(probs)
+    return idx_next, probs
+
+
+@torch.jit.script
+def spectrogram_torch(y:Tensor, n_fft:int, sampling_rate:int, hop_size:int, win_size:int, center:bool=False):
+    hann_window = torch.hann_window(win_size,device=y.device,dtype=y.dtype)
+    y = torch.nn.functional.pad(
+        y.unsqueeze(1),
+        (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
+        mode="reflect",
+    )
+    y = y.squeeze(1)
+    spec = torch.stft(
+        y,
+        n_fft,
+        hop_length=hop_size,
+        win_length=win_size,
+        window=hann_window,
+        center=center,
+        pad_mode="reflect",
+        normalized=False,
+        onesided=True,
+        return_complex=False,
+    )
+    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+    return spec
+
+
+class DictToAttrRecursive(dict):
+    def __init__(self, input_dict):
+        super().__init__(input_dict)
+        for key, value in input_dict.items():
+            if isinstance(value, dict):
+                value = DictToAttrRecursive(value)
+            self[key] = value
+            setattr(self, key, value)
+
+    def __getattr__(self, item):
+        try:
+            return self[item]
+        except KeyError:
+            raise AttributeError(f"Attribute {item} not found")
+
+    def __setattr__(self, key, value):
+        if isinstance(value, dict):
+            value = DictToAttrRecursive(value)
+        super(DictToAttrRecursive, self).__setitem__(key, value)
+        super().__setattr__(key, value)
+
+    def __delattr__(self, item):
+        try:
+            del self[item]
+        except KeyError:
+            raise AttributeError(f"Attribute {item} not found")
+
+@torch.jit.script
+class T2SMLP:
+    def __init__(self, w1, b1, w2, b2):
+        self.w1 = w1
+        self.b1 = b1
+        self.w2 = w2
+        self.b2 = b2
+
+    def forward(self, x):
+        x = F.relu(F.linear(x, self.w1, self.b1))
+        x = F.linear(x, self.w2, self.b2)
+        return x
+
+@torch.jit.script
+class T2SBlock:
+    def __init__(
+            self,
+            num_heads: int,
+            hidden_dim: int,
+            mlp: T2SMLP,
+            qkv_w,
+            qkv_b,
+            out_w,
+            out_b,
+            norm_w1,
+            norm_b1,
+            norm_eps1: float,
+            norm_w2,
+            norm_b2,
+            norm_eps2: float,
+    ):
+        self.num_heads = num_heads
+        self.mlp = mlp
+        self.hidden_dim: int = hidden_dim
+        self.qkv_w = qkv_w
+        self.qkv_b = qkv_b
+        self.out_w = out_w
+        self.out_b = out_b
+        self.norm_w1 = norm_w1
+        self.norm_b1 = norm_b1
+        self.norm_eps1 = norm_eps1
+        self.norm_w2 = norm_w2
+        self.norm_b2 = norm_b2
+        self.norm_eps2 = norm_eps2
+
+        self.false = torch.tensor(False, dtype=torch.bool)
+
+    @torch.jit.ignore
+    def to_mask(self, x:torch.Tensor, padding_mask:Optional[torch.Tensor]):
+        if padding_mask is None:
+            return x
+        
+        if padding_mask.dtype == torch.bool:
+            return x.masked_fill(padding_mask, 0)
+        else:
+            return x * padding_mask
+        
+    def process_prompt(self, x:torch.Tensor, attn_mask : torch.Tensor, padding_mask:Optional[torch.Tensor]=None):
+        q, k, v = F.linear(self.to_mask(x, padding_mask), self.qkv_w, self.qkv_b).chunk(3, dim=-1)
+
+        batch_size = q.shape[0]
+        q_len = q.shape[1]
+        kv_len = k.shape[1]
+        
+        q = self.to_mask(q, padding_mask)
+        k_cache = self.to_mask(k, padding_mask)
+        v_cache = self.to_mask(v, padding_mask)
+
+        q = q.view(batch_size, q_len, self.num_heads, -1).transpose(1, 2)
+        k = k_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
+        v = v_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
+
+        attn = F.scaled_dot_product_attention(q, k, v, ~attn_mask)
+
+        attn = attn.permute(2, 0, 1, 3).reshape(batch_size*q_len, self.hidden_dim)
+        attn = attn.view(q_len, batch_size, self.hidden_dim).transpose(1, 0)
+        attn = F.linear(self.to_mask(attn, padding_mask), self.out_w, self.out_b)
+
+        if padding_mask is not None:
+            for i in range(batch_size):
+                # mask = padding_mask[i,:,0]
+                if self.false.device!= padding_mask.device:
+                    self.false = self.false.to(padding_mask.device)
+                idx = torch.where(padding_mask[i,:,0]==self.false)[0]
+                x_item = x[i,idx,:].unsqueeze(0)
+                attn_item = attn[i,idx,:].unsqueeze(0)
+                x_item = x_item + attn_item
+                x_item = F.layer_norm(
+                    x_item, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1
+                )
+                x_item = x_item + self.mlp.forward(x_item)
+                x_item = F.layer_norm(
+                    x_item,
+                    [self.hidden_dim],
+                    self.norm_w2,
+                    self.norm_b2,
+                    self.norm_eps2,
+                )
+                x[i,idx,:] = x_item.squeeze(0)
+            x = self.to_mask(x, padding_mask)
+        else:
+            x = x + attn
+            x = F.layer_norm(
+                x, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1
+            )
+            x = x + self.mlp.forward(x)
+            x = F.layer_norm(
+                x,
+                [self.hidden_dim],
+                self.norm_w2,
+                self.norm_b2,
+                self.norm_eps2,
+            )
+        return x, k_cache, v_cache
+    
+    def decode_next_token(self, x:torch.Tensor, k_cache:torch.Tensor, v_cache:torch.Tensor):
+        q, k, v = F.linear(x, self.qkv_w, self.qkv_b).chunk(3, dim=-1)
+
+        k_cache = torch.cat([k_cache, k], dim=1)
+        v_cache = torch.cat([v_cache, v], dim=1)
+        
+        batch_size = q.shape[0]
+        q_len = q.shape[1]
+        kv_len = k_cache.shape[1]
+
+        q = q.view(batch_size, q_len, self.num_heads, -1).transpose(1, 2)
+        k = k_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
+        v = v_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
+
+        attn = F.scaled_dot_product_attention(q, k, v)
+
+        attn = attn.permute(2, 0, 1, 3).reshape(batch_size*q_len, self.hidden_dim)
+        attn = attn.view(q_len, batch_size, self.hidden_dim).transpose(1, 0)
+        attn = F.linear(attn, self.out_w, self.out_b)
+
+        x = x + attn
+        x = F.layer_norm(
+            x, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1
+        )
+        x = x + self.mlp.forward(x)
+        x = F.layer_norm(
+            x,
+            [self.hidden_dim],
+            self.norm_w2,
+            self.norm_b2,
+            self.norm_eps2,
+        )
+        return x, k_cache, v_cache
+
+@torch.jit.script
+class T2STransformer:
+    def __init__(self, num_blocks : int, blocks: list[T2SBlock]):
+        self.num_blocks : int = num_blocks
+        self.blocks = blocks
+
+    def process_prompt(
+        self, x:torch.Tensor, attn_mask : torch.Tensor,padding_mask : Optional[torch.Tensor]=None):
+        k_cache : list[torch.Tensor] = []
+        v_cache : list[torch.Tensor] = []
+        for i in range(self.num_blocks):
+            x, k_cache_, v_cache_ = self.blocks[i].process_prompt(x, attn_mask, padding_mask)
+            k_cache.append(k_cache_)
+            v_cache.append(v_cache_)
+        return x, k_cache, v_cache
+
+    def decode_next_token(
+        self, x:torch.Tensor, 
+        k_cache: list[torch.Tensor], 
+        v_cache: list[torch.Tensor]):
+        for i in range(self.num_blocks):
+            x, k_cache[i], v_cache[i] = self.blocks[i].decode_next_token(x, k_cache[i], v_cache[i])
+        return x, k_cache, v_cache
+
+class VitsModel(nn.Module):
+    def __init__(self, vits_path):
+        super().__init__()
+        # dict_s2 = torch.load(vits_path,map_location="cpu")
+        dict_s2 = torch.load(vits_path)
+        self.hps = dict_s2["config"]
+        if dict_s2['weight']['enc_p.text_embedding.weight'].shape[0] == 322:
+            self.hps["model"]["version"] = "v1"
+        else:
+            self.hps["model"]["version"] = "v2"
+        
+        self.hps = DictToAttrRecursive(self.hps)
+        self.hps.model.semantic_frame_rate = "25hz"
+        self.vq_model = SynthesizerTrn(
+            self.hps.data.filter_length // 2 + 1,
+            self.hps.train.segment_size // self.hps.data.hop_length,
+            n_speakers=self.hps.data.n_speakers,
+            **self.hps.model
+        )
+        self.vq_model.eval()
+        self.vq_model.load_state_dict(dict_s2["weight"], strict=False)
+
+    def forward(self, text_seq, pred_semantic, ref_audio, speed=1.0):
+        refer = spectrogram_torch(
+            ref_audio,
+            self.hps.data.filter_length,
+            self.hps.data.sampling_rate,
+            self.hps.data.hop_length,
+            self.hps.data.win_length,
+            center=False
+        )
+        return self.vq_model(pred_semantic, text_seq, refer, speed)[0, 0]
+
+class T2SModel(nn.Module):
+    def __init__(self,raw_t2s:Text2SemanticLightningModule):
+        super(T2SModel, self).__init__()
+        self.model_dim = raw_t2s.model.model_dim
+        self.embedding_dim = raw_t2s.model.embedding_dim
+        self.num_head = raw_t2s.model.num_head
+        self.num_layers = raw_t2s.model.num_layers
+        self.vocab_size = raw_t2s.model.vocab_size
+        self.phoneme_vocab_size = raw_t2s.model.phoneme_vocab_size
+        # self.p_dropout = float(raw_t2s.model.p_dropout)
+        self.EOS:int = int(raw_t2s.model.EOS)
+        self.norm_first = raw_t2s.model.norm_first
+        assert self.EOS == self.vocab_size - 1
+        self.hz = 50
+
+        self.bert_proj = raw_t2s.model.bert_proj
+        self.ar_text_embedding = raw_t2s.model.ar_text_embedding
+        self.ar_text_position = raw_t2s.model.ar_text_position
+        self.ar_audio_embedding = raw_t2s.model.ar_audio_embedding
+        self.ar_audio_position = raw_t2s.model.ar_audio_position
+        
+        # self.t2s_transformer = T2STransformer(self.num_layers, blocks)
+        # self.t2s_transformer = raw_t2s.model.t2s_transformer
+
+        blocks = []
+        h = raw_t2s.model.h
+
+        for i in range(self.num_layers):
+            layer = h.layers[i]
+            t2smlp = T2SMLP(
+                layer.linear1.weight,
+                layer.linear1.bias,
+                layer.linear2.weight,
+                layer.linear2.bias
+            )
+
+            block = T2SBlock(
+                self.num_head,
+                self.model_dim,
+                t2smlp,
+                layer.self_attn.in_proj_weight,
+                layer.self_attn.in_proj_bias,
+                layer.self_attn.out_proj.weight,
+                layer.self_attn.out_proj.bias,
+                layer.norm1.weight,
+                layer.norm1.bias,
+                layer.norm1.eps,
+                layer.norm2.weight,
+                layer.norm2.bias,
+                layer.norm2.eps
+            )
+
+            blocks.append(block)
+        
+        self.t2s_transformer = T2STransformer(self.num_layers, blocks)
+
+        # self.ar_predict_layer = nn.Linear(self.model_dim, self.vocab_size, bias=False)
+        self.ar_predict_layer = raw_t2s.model.ar_predict_layer
+        # self.loss_fct = nn.CrossEntropyLoss(reduction="sum")
+        self.max_sec = raw_t2s.config["data"]["max_sec"]
+        self.top_k = int(raw_t2s.config["inference"]["top_k"])
+        self.early_stop_num = torch.LongTensor([self.hz * self.max_sec])
+    
+    def forward(self,prompts:LongTensor, ref_seq:LongTensor, text_seq:LongTensor, ref_bert:torch.Tensor, text_bert:torch.Tensor):
+        bert = torch.cat([ref_bert.T, text_bert.T], 1)
+        all_phoneme_ids = torch.cat([ref_seq, text_seq], 1)
+        bert = bert.unsqueeze(0)
+
+        x = self.ar_text_embedding(all_phoneme_ids)
+        x = x + self.bert_proj(bert.transpose(1, 2))
+        x:torch.Tensor = self.ar_text_position(x)
+
+        early_stop_num = self.early_stop_num
+
+
+        #[1,N,512] [1,N]
+        # y, k, v, y_emb, x_example = self.first_stage_decoder(x, prompts)
+        y = prompts
+        # x_example = x[:,:,0] * 0.0
+
+        x_len = x.shape[1]
+        x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool)
+
+        y_emb = self.ar_audio_embedding(y)
+        y_len = y_emb.shape[1]
+        prefix_len = y.shape[1]
+        y_pos = self.ar_audio_position(y_emb)
+        xy_pos = torch.concat([x, y_pos], dim=1)
+
+        bsz = x.shape[0]
+        src_len = x_len + y_len
+        x_attn_mask_pad = F.pad(
+            x_attn_mask,
+            (0, y_len),  ###xx的纯0扩展到xx纯0+xy纯1，(x,x+y)
+            value=True,
+        )
+        y_attn_mask = F.pad(  ###yy的右上1扩展到左边xy的0,(y,x+y)
+            torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
+            (x_len, 0),
+            value=False,
+        )
+        xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0)\
+                                                .unsqueeze(0)\
+                                                .expand(bsz*self.num_head, -1, -1)\
+                                                .view(bsz, self.num_head, src_len, src_len)\
+                                                .to(device=x.device, dtype=torch.bool)
+        
+        idx = 0
+        
+        xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask, None)
+
+        logits = self.ar_predict_layer(xy_dec[:, -1])
+        logits = logits[:, :-1]
+        samples = sample(logits, y, top_k=self.top_k, top_p=1, repetition_penalty=1.35, temperature=1.0)[0]
+        y = torch.concat([y, samples], dim=1)
+        y_emb = self.ar_audio_embedding(y[:, -1:])
+        xy_pos = y_emb * self.ar_audio_position.x_scale + self.ar_audio_position.alpha * self.ar_audio_position.pe[:, y_len + idx].to(dtype=y_emb.dtype,device=y_emb.device)
+
+        stop = False
+        # for idx in range(1, 50):
+        for idx in range(1, 1500):
+            #[1, N] [N_layer, N, 1, 512] [N_layer, N, 1, 512] [1, N, 512] [1] [1, N, 512] [1, N]
+            # y, k, v, y_emb, logits, samples = self.stage_decoder(y, k, v, y_emb, x_example)
+            xy_dec, k_cache, v_cache = self.t2s_transformer.decode_next_token(xy_pos, k_cache, v_cache)
+            logits = self.ar_predict_layer(xy_dec[:, -1])
+
+            if(idx<11):###至少预测出10个token不然不给停止（0.4s）
+                logits = logits[:, :-1]
+            
+            samples = sample(logits, y, top_k=self.top_k, top_p=1, repetition_penalty=1.35, temperature=1.0)[0]
+
+            y = torch.concat([y, samples], dim=1)
+            
+            if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
+                stop = True
+            if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS:
+                stop = True
+            if stop:
+                if y.shape[1] == 0:
+                    y = torch.concat([y, torch.zeros_like(samples)], dim=1)
+                break
+
+            y_emb = self.ar_audio_embedding(y[:, -1:])
+            xy_pos = y_emb * self.ar_audio_position.x_scale + self.ar_audio_position.alpha * self.ar_audio_position.pe[:, y_len + idx].to(dtype=y_emb.dtype,device=y_emb.device)
+        
+        y[0,-1] = 0
+        
+        return y[:, -idx:].unsqueeze(0)
+
+bert_path = os.environ.get(
+    "bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
+)
+cnhubert_base_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
+cnhubert.cnhubert_base_path = cnhubert_base_path
+
+@torch.jit.script
+def build_phone_level_feature(res:Tensor, word2ph:IntTensor):
+    phone_level_feature = []
+    for i in range(word2ph.shape[0]):
+        repeat_feature = res[i].repeat(word2ph[i].item(), 1)
+        phone_level_feature.append(repeat_feature)
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+    # [sum(word2ph), 1024]
+    return phone_level_feature
+
+class MyBertModel(torch.nn.Module):
+    def __init__(self, bert_model):
+        super(MyBertModel, self).__init__()
+        self.bert = bert_model
+
+    def forward(self, input_ids:torch.Tensor, attention_mask:torch.Tensor, token_type_ids:torch.Tensor, word2ph:IntTensor):
+        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
+        # res = torch.cat(outputs["hidden_states"][-3:-2], -1)[0][1:-1]
+        res = torch.cat(outputs[1][-3:-2], -1)[0][1:-1]
+        return build_phone_level_feature(res, word2ph)
+
+class SSLModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.ssl = cnhubert.get_model().model
+
+    def forward(self, ref_audio_16k)-> torch.Tensor:
+        ssl_content = self.ssl(ref_audio_16k)["last_hidden_state"].transpose(1, 2)
+        return ssl_content
+
+class ExportSSLModel(torch.nn.Module):
+    def __init__(self,ssl:SSLModel):
+        super().__init__()
+        self.ssl = ssl
+
+    def forward(self, ref_audio:torch.Tensor):
+        return self.ssl(ref_audio)
+    
+    @torch.jit.export
+    def resample(self,ref_audio:torch.Tensor,src_sr:int,dst_sr:int)->torch.Tensor:
+        audio = resamplex(ref_audio,src_sr,dst_sr).float()
+        return audio
+
+def export_bert(output_path):
+    tokenizer = AutoTokenizer.from_pretrained(bert_path)
+    
+    text = "叹息声一声接着一声传出,木兰对着房门织布.听不见织布机织布的声音,只听见木兰在叹息.问木兰在想什么?问木兰在惦记什么?木兰答道,我也没有在想什么,也没有在惦记什么."
+    ref_bert_inputs = tokenizer(text, return_tensors="pt")
+    word2ph = []
+    for c in text:
+        if c in ['，','。','：','？',",",".","?"]:
+            word2ph.append(1)
+        else:
+            word2ph.append(2)
+    ref_bert_inputs['word2ph'] = torch.Tensor(word2ph).int()
+
+    bert_model = AutoModelForMaskedLM.from_pretrained(bert_path,output_hidden_states=True,torchscript=True)
+    my_bert_model = MyBertModel(bert_model)
+
+    ref_bert_inputs = {
+        'input_ids': ref_bert_inputs['input_ids'],
+        'attention_mask': ref_bert_inputs['attention_mask'],
+        'token_type_ids': ref_bert_inputs['token_type_ids'],
+        'word2ph': ref_bert_inputs['word2ph']
+    }
+
+    torch._dynamo.mark_dynamic(ref_bert_inputs['input_ids'], 1)
+    torch._dynamo.mark_dynamic(ref_bert_inputs['attention_mask'], 1)
+    torch._dynamo.mark_dynamic(ref_bert_inputs['token_type_ids'], 1)
+    torch._dynamo.mark_dynamic(ref_bert_inputs['word2ph'], 0)
+
+    my_bert_model = torch.jit.trace(my_bert_model,example_kwarg_inputs=ref_bert_inputs)
+    output_path = os.path.join(output_path, "bert_model.pt")
+    my_bert_model.save(output_path)
+    print('#### exported bert ####')
+
+def export(gpt_path, vits_path, ref_audio_path, ref_text, output_path, export_bert_and_ssl=False, device='cpu'):
+    if not os.path.exists(output_path):
+        os.makedirs(output_path)
+        print(f"目录已创建: {output_path}")
+    else:
+        print(f"目录已存在: {output_path}")
+    
+    ref_audio = torch.tensor([load_audio(ref_audio_path, 16000)]).float()
+    ssl = SSLModel()
+    if export_bert_and_ssl:
+        s = ExportSSLModel(torch.jit.trace(ssl,example_inputs=(ref_audio)))
+        ssl_path = os.path.join(output_path, "ssl_model.pt")
+        torch.jit.script(s).save(ssl_path)
+        print('#### exported ssl ####')
+        export_bert(output_path)
+    else:
+        s = ExportSSLModel(ssl)
+
+    print(f"device: {device}")
+
+
+    ref_seq_id,ref_bert_T,ref_norm_text = get_phones_and_bert(ref_text,"all_zh",'v2')
+    ref_seq = torch.LongTensor([ref_seq_id]).to(device)
+    ref_bert = ref_bert_T.T.to(ref_seq.device)
+    text_seq_id,text_bert_T,norm_text = get_phones_and_bert("这是一条测试语音，说什么无所谓，只是给它一个例子","all_zh",'v2')
+    text_seq = torch.LongTensor([text_seq_id]).to(device)
+    text_bert = text_bert_T.T.to(text_seq.device)
+
+    ssl_content = ssl(ref_audio).to(device)
+
+     # vits_path = "SoVITS_weights_v2/xw_e8_s216.pth"
+    vits = VitsModel(vits_path).to(device)
+    vits.eval()
+
+    # gpt_path = "GPT_weights_v2/xw-e15.ckpt"
+    # dict_s1 = torch.load(gpt_path, map_location=device)
+    dict_s1 = torch.load(gpt_path)
+    raw_t2s = get_raw_t2s_model(dict_s1).to(device)
+    print('#### get_raw_t2s_model ####')
+    print(raw_t2s.config)
+    t2s_m = T2SModel(raw_t2s)
+    t2s_m.eval()
+    t2s = torch.jit.script(t2s_m).to(device)
+    print('#### script t2s_m ####')
+    
+    print("vits.hps.data.sampling_rate:",vits.hps.data.sampling_rate)
+    gpt_sovits = GPT_SoVITS(t2s,vits).to(device)
+    gpt_sovits.eval()
+    
+    ref_audio_sr = s.resample(ref_audio,16000,32000).to(device)
+
+    torch._dynamo.mark_dynamic(ssl_content, 2)
+    torch._dynamo.mark_dynamic(ref_audio_sr, 1)
+    torch._dynamo.mark_dynamic(ref_seq, 1)
+    torch._dynamo.mark_dynamic(text_seq, 1)
+    torch._dynamo.mark_dynamic(ref_bert, 0)
+    torch._dynamo.mark_dynamic(text_bert, 0)
+
+    with torch.no_grad():
+        gpt_sovits_export = torch.jit.trace(
+        gpt_sovits,
+        example_inputs=(
+            ssl_content,
+            ref_audio_sr,
+            ref_seq,
+            text_seq,
+            ref_bert,
+            text_bert))
+    
+        gpt_sovits_path = os.path.join(output_path, "gpt_sovits_model.pt")
+        gpt_sovits_export.save(gpt_sovits_path)
+        print('#### exported gpt_sovits ####')
+
+@torch.jit.script
+def parse_audio(ref_audio):
+    ref_audio_16k = torchaudio.functional.resample(ref_audio,48000,16000).float()#.to(ref_audio.device)
+    ref_audio_sr = torchaudio.functional.resample(ref_audio,48000,32000).float()#.to(ref_audio.device)
+    return ref_audio_16k,ref_audio_sr
+
+@torch.jit.script
+def resamplex(ref_audio:torch.Tensor,src_sr:int,dst_sr:int)->torch.Tensor:
+    return torchaudio.functional.resample(ref_audio,src_sr,dst_sr).float()
+
+class GPT_SoVITS(nn.Module):
+    def __init__(self, t2s:T2SModel,vits:VitsModel):
+        super().__init__()
+        self.t2s = t2s
+        self.vits = vits
+
+    def forward(self, ssl_content:torch.Tensor, ref_audio_sr:torch.Tensor, ref_seq:Tensor, text_seq:Tensor, ref_bert:Tensor, text_bert:Tensor, speed=1.0):
+        codes = self.vits.vq_model.extract_latent(ssl_content)
+        prompt_semantic = codes[0, 0]
+        prompts = prompt_semantic.unsqueeze(0)
+
+        pred_semantic = self.t2s(prompts, ref_seq, text_seq, ref_bert, text_bert)
+        audio = self.vits(text_seq, pred_semantic, ref_audio_sr, speed)
+        return audio
+
+def test():
+    parser = argparse.ArgumentParser(description="GPT-SoVITS Command Line Tool")
+    parser.add_argument('--gpt_model', required=True, help="Path to the GPT model file")
+    parser.add_argument('--sovits_model', required=True, help="Path to the SoVITS model file")
+    parser.add_argument('--ref_audio', required=True, help="Path to the reference audio file")
+    parser.add_argument('--ref_text', required=True, help="Path to the reference text file")
+    parser.add_argument('--output_path', required=True, help="Path to the output directory")
+
+
+    args = parser.parse_args()
+    gpt_path = args.gpt_model
+    vits_path = args.sovits_model
+    ref_audio_path = args.ref_audio
+    ref_text = args.ref_text
+
+    tokenizer = AutoTokenizer.from_pretrained(bert_path)
+    # bert_model = AutoModelForMaskedLM.from_pretrained(bert_path,output_hidden_states=True,torchscript=True)
+    # bert = MyBertModel(bert_model)
+    my_bert = torch.jit.load("onnx/bert_model.pt",map_location='cuda')
+
+    # dict_s1 = torch.load(gpt_path, map_location="cuda")
+    # raw_t2s = get_raw_t2s_model(dict_s1)
+    # t2s = T2SModel(raw_t2s)
+    # t2s.eval()
+    # t2s = torch.jit.load("onnx/xw/t2s_model.pt",map_location='cuda')
+
+    # vits_path = "SoVITS_weights_v2/xw_e8_s216.pth"
+    # vits = VitsModel(vits_path)
+    # vits.eval()
+
+    # ssl = ExportSSLModel(SSLModel()).to('cuda')
+    # ssl.eval()
+    ssl = torch.jit.load("onnx/by/ssl_model.pt",map_location='cuda')
+
+    # gpt_sovits = GPT_SoVITS(t2s,vits)
+    gpt_sovits = torch.jit.load("onnx/by/gpt_sovits_model.pt",map_location='cuda')
+
+    ref_seq_id,ref_bert_T,ref_norm_text = get_phones_and_bert(ref_text,"all_zh",'v2')
+    ref_seq = torch.LongTensor([ref_seq_id])
+    ref_bert = ref_bert_T.T.to(ref_seq.device)
+    # text_seq_id,text_bert_T,norm_text = get_phones_and_bert("昨天晚上看见征兵文书,知道君主在大规模征兵,那么多卷征兵文册,每一卷上都有父亲的名字.","all_zh",'v2')
+    text = "昨天晚上看见征兵文书,知道君主在大规模征兵,那么多卷征兵文册,每一卷上都有父亲的名字."
+
+    text_seq_id,text_bert_T,norm_text = get_phones_and_bert(text,"all_zh",'v2')
+    
+    test_bert = tokenizer(text, return_tensors="pt")
+    word2ph = []
+    for c in text:
+        if c in ['，','。','：','？',"?",",","."]:
+            word2ph.append(1)
+        else:
+            word2ph.append(2)
+    test_bert['word2ph'] = torch.Tensor(word2ph).int()
+
+    test_bert = my_bert(
+        test_bert['input_ids'].to('cuda'),
+        test_bert['attention_mask'].to('cuda'),
+        test_bert['token_type_ids'].to('cuda'),
+        test_bert['word2ph'].to('cuda')
+    )
+    
+    text_seq = torch.LongTensor([text_seq_id])
+    text_bert = text_bert_T.T.to(text_seq.device)
+
+    print('text_bert:',text_bert.shape,text_bert)
+    print('test_bert:',test_bert.shape,test_bert)
+    print(torch.allclose(text_bert.to('cuda'),test_bert))
+
+    print('text_seq:',text_seq.shape)
+    print('text_bert:',text_bert.shape,text_bert.type())
+
+    #[1,N]
+    ref_audio = torch.tensor([load_audio(ref_audio_path, 16000)]).float().to('cuda')
+    print('ref_audio:',ref_audio.shape)
+    
+    ref_audio_sr = ssl.resample(ref_audio,16000,32000)
+    print('start ssl')
+    ssl_content = ssl(ref_audio)
+
+    print('start gpt_sovits:')
+    print('ssl_content:',ssl_content.shape)
+    print('ref_audio_sr:',ref_audio_sr.shape)
+    print('ref_seq:',ref_seq.shape)
+    ref_seq=ref_seq.to('cuda')
+    print('text_seq:',text_seq.shape)
+    text_seq=text_seq.to('cuda')
+    print('ref_bert:',ref_bert.shape)
+    ref_bert=ref_bert.to('cuda')
+    print('text_bert:',text_bert.shape)
+    text_bert=text_bert.to('cuda')
+
+    with torch.no_grad():
+        audio = gpt_sovits(ssl_content, ref_audio_sr, ref_seq, text_seq, ref_bert, test_bert)
+    print('start write wav')
+    soundfile.write("out.wav", audio.detach().cpu().numpy(), 32000)
+
+
+import text
+import json
+
+def export_symbel(version='v2'):
+    if version=='v1':
+        symbols = text._symbol_to_id_v1
+        with open(f"onnx/symbols_v1.json", "w") as file:
+            json.dump(symbols, file, indent=4)
+    else:
+        symbols = text._symbol_to_id_v2
+        with open(f"onnx/symbols_v2.json", "w") as file:
+            json.dump(symbols, file, indent=4)
+
+def main():
+    parser = argparse.ArgumentParser(description="GPT-SoVITS Command Line Tool")
+    parser.add_argument('--gpt_model', required=True, help="Path to the GPT model file")
+    parser.add_argument('--sovits_model', required=True, help="Path to the SoVITS model file")
+    parser.add_argument('--ref_audio', required=True, help="Path to the reference audio file")
+    parser.add_argument('--ref_text', required=True, help="Path to the reference text file")
+    parser.add_argument('--output_path', required=True, help="Path to the output directory")
+    parser.add_argument('--export_common_model', action='store_true', help="Export Bert and SSL model")
+    parser.add_argument('--device', help="Device to use")
+
+    args = parser.parse_args()
+    export(
+        gpt_path=args.gpt_model,
+        vits_path=args.sovits_model,
+        ref_audio_path=args.ref_audio,
+        ref_text=args.ref_text,
+        output_path=args.output_path,
+        device=args.device,
+        export_bert_and_ssl=args.export_common_model,
+    )
+
+import inference_webui
+if __name__ == "__main__":
+    inference_webui.is_half=False
+    inference_webui.dtype=torch.float32
+    main()
+    # test()

GPT_SoVITS/extract_hubert.py

@@ -0,0 +1,150 @@
+import os
+import sys
+import torch
+import numpy as np
+import traceback
+from scipy.io import wavfile
+import librosa
+from pathlib import Path
+from time import time as ttime
+import shutil
+from tools.my_utils import load_audio, clean_path
+from feature_extractor import cnhubert
+
+def my_save(fea, path, i_part):
+    """Fix issue: torch.save doesn't support chinese path"""
+    dir = os.path.dirname(path)
+    name = os.path.basename(path)
+    tmp_path = f"{ttime()}{i_part}.pth"
+    torch.save(fea, tmp_path)
+    shutil.move(tmp_path, f"{dir}/{name}")
+
+def extract_hubert_features(data_dir="data8", exp_dir="logs/s2"):
+    """Extract Hubert features for stage 2 training"""
+    
+    # Get project root directory (parent of GPT_SoVITS)
+    root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+    
+    # Convert relative paths to absolute
+    data_dir = os.path.join(root_dir, data_dir)
+    exp_dir = os.path.join(root_dir, exp_dir)
+    
+    # Set environment variables for Hubert extraction
+    inp_text = os.path.join(exp_dir, "2-name2text.txt")
+    inp_wav_dir = os.path.join(exp_dir, "5-wav32k")
+    exp_name = "s2"
+    i_part = "0"
+    all_parts = "1"
+    opt_dir = exp_dir
+    cnhubert.cnhubert_base_path = os.path.join(root_dir, "pretrained_models", "chinese-hubert-base")
+    is_half = torch.cuda.is_available()
+    
+    print("Starting Hubert feature extraction...")
+    print(f"Input text file: {inp_text}")
+    print(f"Input wav directory: {inp_wav_dir}")
+    print(f"Output directory: {opt_dir}")
+    
+    hubert_dir = f"{opt_dir}/4-cnhubert"
+    wav32dir = f"{opt_dir}/5-wav32k"
+    os.makedirs(opt_dir, exist_ok=True)
+    os.makedirs(hubert_dir, exist_ok=True)
+    os.makedirs(wav32dir, exist_ok=True)
+
+    maxx = 0.95
+    alpha = 0.5
+    if torch.cuda.is_available():
+        device = "cuda:0"
+    else:
+        device = "cpu"
+        
+    print(f"Loading Hubert model from: {cnhubert.cnhubert_base_path}")
+    model = cnhubert.get_model()
+    if is_half:
+        model = model.half().to(device)
+    else:
+        model = model.to(device)
+
+    nan_fails = []
+    
+    def name2go(wav_name, wav_path):
+        print(f"Processing: {wav_name} from {wav_path}")
+        hubert_path = f"{hubert_dir}/{wav_name}.pt"
+        if os.path.exists(hubert_path):
+            print(f"Skipping {wav_name} - already processed")
+            return
+            
+        if not os.path.exists(wav_path):
+            print(f"Error: WAV file not found: {wav_path}")
+            return
+            
+        tmp_audio = load_audio(wav_path, 32000)
+        if tmp_audio is None:
+            print(f"Error: Failed to load audio: {wav_path}")
+            return
+            
+        tmp_max = np.abs(tmp_audio).max()
+        if tmp_max > 2.2:
+            print(f"{wav_name}-filtered,{tmp_max}")
+            return
+            
+        tmp_audio32 = (tmp_audio / tmp_max * (maxx * alpha * 32768)) + ((1 - alpha) * 32768) * tmp_audio
+        tmp_audio32b = (tmp_audio / tmp_max * (maxx * alpha * 1145.14)) + ((1 - alpha) * 1145.14) * tmp_audio
+        tmp_audio = librosa.resample(tmp_audio32b, orig_sr=32000, target_sr=16000)
+        
+        tensor_wav16 = torch.from_numpy(tmp_audio)
+        if is_half:
+            tensor_wav16 = tensor_wav16.half().to(device)
+        else:
+            tensor_wav16 = tensor_wav16.to(device)
+            
+        ssl = model.model(tensor_wav16.unsqueeze(0))["last_hidden_state"].transpose(1, 2).cpu()
+        
+        if np.isnan(ssl.detach().numpy()).sum() != 0:
+            nan_fails.append((wav_name, wav_path))
+            print(f"nan filtered:{wav_name}")
+            return
+            
+        wavfile.write(
+            f"{wav32dir}/{wav_name}",
+            32000,
+            tmp_audio32.astype("int16"),
+        )
+        my_save(ssl, hubert_path, i_part)
+        print(f"Successfully processed {wav_name}")
+
+    print(f"Reading text file: {inp_text}")
+    with open(inp_text, "r", encoding="utf8") as f:
+        lines = f.read().strip("\n").split("\n")
+    print(f"Found {len(lines)} lines in text file")
+
+    for line in lines[int(i_part)::int(all_parts)]:
+        try:
+            print(f"Processing line: {line}")
+            wav_name, text, _, _ = line.split("\t")
+            wav_name = clean_path(wav_name)
+            if inp_wav_dir:
+                wav_name = os.path.basename(wav_name)
+                wav_path = f"{inp_wav_dir}/{wav_name}"
+            else:
+                wav_path = wav_name
+                wav_name = os.path.basename(wav_name)
+            name2go(wav_name, wav_path)
+        except Exception as e:
+            print(f"Error processing line: {line}")
+            print(traceback.format_exc())
+
+    if len(nan_fails) > 0 and is_half:
+        print("Retrying failed files in float32 mode...")
+        is_half = False
+        model = model.float()
+        for wav in nan_fails:
+            try:
+                name2go(wav[0], wav[1])
+            except:
+                print(f"Error retrying {wav_name}")
+                print(traceback.format_exc())
+    
+    print("Hubert feature extraction complete.")
+
+if __name__ == "__main__":
+    extract_hubert_features() 

GPT_SoVITS/inference_cli.py

@@ -0,0 +1,55 @@
+import argparse
+import os
+import soundfile as sf
+
+from tools.i18n.i18n import I18nAuto
+from GPT_SoVITS.inference_webui import change_gpt_weights, change_sovits_weights, get_tts_wav
+
+i18n = I18nAuto()
+
+def synthesize(GPT_model_path, SoVITS_model_path, ref_audio_path, ref_text_path, ref_language, target_text_path, target_language, output_path):
+    # Read reference text
+    with open(ref_text_path, 'r', encoding='utf-8') as file:
+        ref_text = file.read()
+
+    # Read target text
+    with open(target_text_path, 'r', encoding='utf-8') as file:
+        target_text = file.read()
+
+    # Change model weights
+    change_gpt_weights(gpt_path=GPT_model_path)
+    change_sovits_weights(sovits_path=SoVITS_model_path)
+
+    # Synthesize audio
+    synthesis_result = get_tts_wav(ref_wav_path=ref_audio_path, 
+                                   prompt_text=ref_text, 
+                                   prompt_language=i18n(ref_language), 
+                                   text=target_text, 
+                                   text_language=i18n(target_language), top_p=1, temperature=1)
+    
+    result_list = list(synthesis_result)
+
+    if result_list:
+        last_sampling_rate, last_audio_data = result_list[-1]
+        output_wav_path = os.path.join(output_path, "output.wav")
+        sf.write(output_wav_path, last_audio_data, last_sampling_rate)
+        print(f"Audio saved to {output_wav_path}")
+
+def main():
+    parser = argparse.ArgumentParser(description="GPT-SoVITS Command Line Tool")
+    parser.add_argument('--gpt_model', required=True, help="Path to the GPT model file")
+    parser.add_argument('--sovits_model', required=True, help="Path to the SoVITS model file")
+    parser.add_argument('--ref_audio', required=True, help="Path to the reference audio file")
+    parser.add_argument('--ref_text', required=True, help="Path to the reference text file")
+    parser.add_argument('--ref_language', required=True, choices=["中文", "英文", "日文"], help="Language of the reference audio")
+    parser.add_argument('--target_text', required=True, help="Path to the target text file")
+    parser.add_argument('--target_language', required=True, choices=["中文", "英文", "日文", "中英混合", "日英混合", "多语种混合"], help="Language of the target text")
+    parser.add_argument('--output_path', required=True, help="Path to the output directory")
+
+    args = parser.parse_args()
+
+    synthesize(args.gpt_model, args.sovits_model, args.ref_audio, args.ref_text, args.ref_language, args.target_text, args.target_language, args.output_path)
+
+if __name__ == '__main__':
+    main()
+

GPT_SoVITS/inference_gui.py

@@ -0,0 +1,310 @@
+import os
+import sys
+from PyQt5.QtCore import QEvent
+from PyQt5.QtWidgets import QApplication, QMainWindow, QLabel, QLineEdit, QPushButton, QTextEdit
+from PyQt5.QtWidgets import QGridLayout, QVBoxLayout, QWidget, QFileDialog, QStatusBar, QComboBox
+import soundfile as sf
+
+from tools.i18n.i18n import I18nAuto
+i18n = I18nAuto()
+
+from inference_webui import gpt_path, sovits_path, change_gpt_weights, change_sovits_weights, get_tts_wav
+
+
+class GPTSoVITSGUI(QMainWindow):
+    GPT_Path = gpt_path
+    SoVITS_Path = sovits_path
+
+    def __init__(self):
+        super().__init__()
+
+        self.setWindowTitle('GPT-SoVITS GUI')
+        self.setGeometry(800, 450, 950, 850)
+
+        self.setStyleSheet("""
+            QWidget {
+                background-color: #a3d3b1; 
+            }
+
+            QTabWidget::pane {
+                background-color: #a3d3b1;  
+            }
+
+            QTabWidget::tab-bar {
+                alignment: left;
+            }
+
+            QTabBar::tab {
+                background: #8da4bf; 
+                color: #ffffff;  
+                padding: 8px;
+            }
+
+            QTabBar::tab:selected {
+                background: #2a3f54; 
+            }
+
+            QLabel {
+                color: #000000;  
+            }
+
+            QPushButton {
+                background-color: #4CAF50; 
+                color: white;  
+                padding: 8px;
+                border: 1px solid #4CAF50;
+                border-radius: 4px;
+            }
+
+            QPushButton:hover {
+                background-color: #45a049;  
+                border: 1px solid #45a049;
+                box-shadow: 2px 2px 2px rgba(0, 0, 0, 0.1);
+            }
+        """)    
+
+        license_text = (
+        "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. "
+        "如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.")
+        license_label = QLabel(license_text)
+        license_label.setWordWrap(True)
+
+        self.GPT_model_label = QLabel("选择GPT模型:")
+        self.GPT_model_input = QLineEdit()
+        self.GPT_model_input.setPlaceholderText("拖拽或选择文件")
+        self.GPT_model_input.setText(self.GPT_Path)
+        self.GPT_model_input.setReadOnly(True)
+        self.GPT_model_button = QPushButton("选择GPT模型文件")
+        self.GPT_model_button.clicked.connect(self.select_GPT_model)
+
+        self.SoVITS_model_label = QLabel("选择SoVITS模型:")
+        self.SoVITS_model_input = QLineEdit()
+        self.SoVITS_model_input.setPlaceholderText("拖拽或选择文件")
+        self.SoVITS_model_input.setText(self.SoVITS_Path)
+        self.SoVITS_model_input.setReadOnly(True)
+        self.SoVITS_model_button = QPushButton("选择SoVITS模型文件")
+        self.SoVITS_model_button.clicked.connect(self.select_SoVITS_model)
+
+        self.ref_audio_label = QLabel("上传参考音频:")
+        self.ref_audio_input = QLineEdit()
+        self.ref_audio_input.setPlaceholderText("拖拽或选择文件")
+        self.ref_audio_input.setReadOnly(True)
+        self.ref_audio_button = QPushButton("选择音频文件")
+        self.ref_audio_button.clicked.connect(self.select_ref_audio)
+
+        self.ref_text_label = QLabel("参考音频文本:")
+        self.ref_text_input = QLineEdit()
+        self.ref_text_input.setPlaceholderText("直接输入文字或上传文本")
+        self.ref_text_button = QPushButton("上传文本")
+        self.ref_text_button.clicked.connect(self.upload_ref_text)
+
+        self.ref_language_label = QLabel("参考音频语言:")
+        self.ref_language_combobox = QComboBox()
+        self.ref_language_combobox.addItems(["中文", "英文", "日文", "中英混合", "日英混合", "多语种混合"])
+        self.ref_language_combobox.setCurrentText("多语种混合")
+
+        self.target_text_label = QLabel("合成目标文本:")
+        self.target_text_input = QLineEdit()
+        self.target_text_input.setPlaceholderText("直接输入文字或上传文本")
+        self.target_text_button = QPushButton("上传文本")
+        self.target_text_button.clicked.connect(self.upload_target_text)
+
+        self.target_language_label = QLabel("合成音频语言:")
+        self.target_language_combobox = QComboBox()
+        self.target_language_combobox.addItems(["中文", "英文", "日文", "中英混合", "日英混合", "多语种混合"])
+        self.target_language_combobox.setCurrentText("多语种混合")
+
+        self.output_label = QLabel("输出音频路径:")
+        self.output_input = QLineEdit()
+        self.output_input.setPlaceholderText("拖拽或选择文件")
+        self.output_input.setReadOnly(True)
+        self.output_button = QPushButton("选择文件夹")
+        self.output_button.clicked.connect(self.select_output_path)
+
+        self.output_text = QTextEdit()
+        self.output_text.setReadOnly(True)
+
+        self.add_drag_drop_events([
+            self.GPT_model_input,
+            self.SoVITS_model_input,
+            self.ref_audio_input,
+            self.ref_text_input,
+            self.target_text_input,
+            self.output_input,
+        ])
+
+        self.synthesize_button = QPushButton("合成")
+        self.synthesize_button.clicked.connect(self.synthesize)
+
+        self.clear_output_button = QPushButton("清空输出")
+        self.clear_output_button.clicked.connect(self.clear_output)
+
+        self.status_bar = QStatusBar()
+
+        main_layout = QVBoxLayout()
+
+        input_layout = QGridLayout(self)
+        input_layout.setSpacing(10)
+
+        input_layout.addWidget(license_label, 0, 0, 1, 3)
+
+        input_layout.addWidget(self.GPT_model_label, 1, 0)
+        input_layout.addWidget(self.GPT_model_input, 2, 0, 1, 2)
+        input_layout.addWidget(self.GPT_model_button, 2, 2)
+
+        input_layout.addWidget(self.SoVITS_model_label, 3, 0)
+        input_layout.addWidget(self.SoVITS_model_input, 4, 0, 1, 2)
+        input_layout.addWidget(self.SoVITS_model_button, 4, 2)
+
+        input_layout.addWidget(self.ref_audio_label, 5, 0)
+        input_layout.addWidget(self.ref_audio_input, 6, 0, 1, 2)
+        input_layout.addWidget(self.ref_audio_button, 6, 2)
+
+        input_layout.addWidget(self.ref_language_label, 7, 0)
+        input_layout.addWidget(self.ref_language_combobox, 8, 0, 1, 1)
+        input_layout.addWidget(self.ref_text_label, 9, 0)
+        input_layout.addWidget(self.ref_text_input, 10, 0, 1, 2)
+        input_layout.addWidget(self.ref_text_button, 10, 2)
+
+        input_layout.addWidget(self.target_language_label, 11, 0)
+        input_layout.addWidget(self.target_language_combobox, 12, 0, 1, 1)
+        input_layout.addWidget(self.target_text_label, 13, 0)
+        input_layout.addWidget(self.target_text_input, 14, 0, 1, 2)
+        input_layout.addWidget(self.target_text_button, 14, 2)
+
+        input_layout.addWidget(self.output_label, 15, 0)
+        input_layout.addWidget(self.output_input, 16, 0, 1, 2)
+        input_layout.addWidget(self.output_button, 16, 2)
+
+        main_layout.addLayout(input_layout)
+
+        output_layout = QVBoxLayout()
+        output_layout.addWidget(self.output_text)
+        main_layout.addLayout(output_layout)
+
+        main_layout.addWidget(self.synthesize_button)
+
+        main_layout.addWidget(self.clear_output_button)
+
+        main_layout.addWidget(self.status_bar)
+
+        self.central_widget = QWidget()
+        self.central_widget.setLayout(main_layout)
+        self.setCentralWidget(self.central_widget)
+
+    def dragEnterEvent(self, event):
+        if event.mimeData().hasUrls():
+            event.acceptProposedAction()
+
+    def dropEvent(self, event):
+        if event.mimeData().hasUrls():
+            file_paths = [url.toLocalFile() for url in event.mimeData().urls()]
+            if len(file_paths) == 1:
+                self.update_ref_audio(file_paths[0])
+            else:
+                self.update_ref_audio(", ".join(file_paths))
+
+    def add_drag_drop_events(self, widgets):
+        for widget in widgets:
+            widget.setAcceptDrops(True)
+            widget.installEventFilter(self)
+
+    def eventFilter(self, obj, event):
+        if event.type() in (QEvent.DragEnter, QEvent.Drop):
+            mime_data = event.mimeData()
+            if mime_data.hasUrls():
+                event.acceptProposedAction()
+
+        return super().eventFilter(obj, event)
+
+    def select_GPT_model(self):
+        file_path, _ = QFileDialog.getOpenFileName(self, "选择GPT模型文件", "", "GPT Files (*.ckpt)")
+        if file_path:
+            self.GPT_model_input.setText(file_path)
+
+    def select_SoVITS_model(self):
+        file_path, _ = QFileDialog.getOpenFileName(self, "选择SoVITS模型文件", "", "SoVITS Files (*.pth)")
+        if file_path:
+            self.SoVITS_model_input.setText(file_path)
+
+    def select_ref_audio(self):
+        file_path, _ = QFileDialog.getOpenFileName(self, "选择参考音频文件", "", "Audio Files (*.wav *.mp3)")
+        if file_path:
+            self.update_ref_audio(file_path)
+
+    def upload_ref_text(self):
+        file_path, _ = QFileDialog.getOpenFileName(self, "选择文本文件", "", "Text Files (*.txt)")
+        if file_path:
+            with open(file_path, 'r', encoding='utf-8') as file:
+                content = file.read()
+                self.ref_text_input.setText(content)
+
+    def upload_target_text(self):
+        file_path, _ = QFileDialog.getOpenFileName(self, "选择文本文件", "", "Text Files (*.txt)")
+        if file_path:
+            with open(file_path, 'r', encoding='utf-8') as file:
+                content = file.read()
+                self.target_text_input.setText(content)
+
+    def select_output_path(self):
+        options = QFileDialog.Options()
+        options |= QFileDialog.DontUseNativeDialog
+        options |= QFileDialog.ShowDirsOnly
+
+        folder_dialog = QFileDialog()
+        folder_dialog.setOptions(options)
+        folder_dialog.setFileMode(QFileDialog.Directory)
+
+        if folder_dialog.exec_():
+            folder_path = folder_dialog.selectedFiles()[0]
+            self.output_input.setText(folder_path)
+
+    def update_ref_audio(self, file_path):
+        self.ref_audio_input.setText(file_path)
+
+    def clear_output(self):
+        self.output_text.clear()
+
+    def synthesize(self):
+        GPT_model_path = self.GPT_model_input.text()
+        SoVITS_model_path = self.SoVITS_model_input.text()
+        ref_audio_path = self.ref_audio_input.text()
+        language_combobox = self.ref_language_combobox.currentText()
+        language_combobox = i18n(language_combobox)
+        ref_text = self.ref_text_input.text()
+        target_language_combobox = self.target_language_combobox.currentText()
+        target_language_combobox = i18n(target_language_combobox)
+        target_text = self.target_text_input.text()
+        output_path = self.output_input.text()
+
+        if GPT_model_path != self.GPT_Path:
+            change_gpt_weights(gpt_path=GPT_model_path)
+            self.GPT_Path = GPT_model_path
+        if SoVITS_model_path != self.SoVITS_Path:
+            change_sovits_weights(sovits_path=SoVITS_model_path)
+            self.SoVITS_Path = SoVITS_model_path
+
+        synthesis_result = get_tts_wav(ref_wav_path=ref_audio_path, 
+                                       prompt_text=ref_text, 
+                                       prompt_language=language_combobox, 
+                                       text=target_text, 
+                                       text_language=target_language_combobox)
+
+        result_list = list(synthesis_result)
+
+        if result_list:
+            last_sampling_rate, last_audio_data = result_list[-1]
+            output_wav_path = os.path.join(output_path, "output.wav") 
+            sf.write(output_wav_path, last_audio_data, last_sampling_rate)
+
+            result = "Audio saved to " + output_wav_path
+
+        self.status_bar.showMessage("合成完成！输出路径：" + output_wav_path, 5000)
+        self.output_text.append("处理结果：\n" + result)
+
+
+if __name__ == '__main__':
+    app = QApplication(sys.argv)
+    mainWin = GPTSoVITSGUI()
+    mainWin.show()
+    sys.exit(app.exec_())

GPT_SoVITS/inference_webui.py

@@ -0,0 +1,772 @@
+'''
+按中英混合识别
+按日英混合识别
+多语种启动切分识别语种
+全部按中文识别
+全部按英文识别
+全部按日文识别
+'''
+import logging
+import traceback
+
+logging.getLogger("markdown_it").setLevel(logging.ERROR)
+logging.getLogger("urllib3").setLevel(logging.ERROR)
+logging.getLogger("httpcore").setLevel(logging.ERROR)
+logging.getLogger("httpx").setLevel(logging.ERROR)
+logging.getLogger("asyncio").setLevel(logging.ERROR)
+logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
+logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
+logging.getLogger("multipart.multipart").setLevel(logging.ERROR)
+import LangSegment, os, re, sys, json
+import pdb
+import torch
+
+try:
+    import gradio.analytics as analytics
+    analytics.version_check = lambda:None
+except:...
+
+version=os.environ.get("version","v2")
+pretrained_sovits_name=["GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth", "GPT_SoVITS/pretrained_models/s2G488k.pth"]
+pretrained_gpt_name=["GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"]
+
+_ =[[],[]]
+for i in range(2):
+    if os.path.exists(pretrained_gpt_name[i]):
+        _[0].append(pretrained_gpt_name[i])
+    if os.path.exists(pretrained_sovits_name[i]):
+        _[-1].append(pretrained_sovits_name[i])
+pretrained_gpt_name,pretrained_sovits_name = _
+    
+        
+
+if os.path.exists(f"./weight.json"):
+    pass
+else:
+    with open(f"./weight.json", 'w', encoding="utf-8") as file:json.dump({'GPT':{},'SoVITS':{}},file)
+
+with open(f"./weight.json", 'r', encoding="utf-8") as file:
+    weight_data = file.read()
+    weight_data=json.loads(weight_data)
+    gpt_path = os.environ.get(
+        "gpt_path", weight_data.get('GPT',{}).get(version,pretrained_gpt_name))
+    sovits_path = os.environ.get(
+        "sovits_path", weight_data.get('SoVITS',{}).get(version,pretrained_sovits_name))
+    if isinstance(gpt_path,list):
+        gpt_path = gpt_path[0]
+    if isinstance(sovits_path,list):
+        sovits_path = sovits_path[0]
+
+# gpt_path = os.environ.get(
+#     "gpt_path", pretrained_gpt_name
+# )
+# sovits_path = os.environ.get("sovits_path", pretrained_sovits_name)
+cnhubert_base_path = os.environ.get(
+    "cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base"
+)
+bert_path = os.environ.get(
+    "bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
+)
+infer_ttswebui = os.environ.get("infer_ttswebui", 9872)
+infer_ttswebui = int(infer_ttswebui)
+is_share = os.environ.get("is_share", "False")
+is_share = eval(is_share)
+if "_CUDA_VISIBLE_DEVICES" in os.environ:
+    os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
+is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
+punctuation = set(['!', '?', '…', ',', '.', '-'," "])
+import gradio as gr
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+import numpy as np
+import librosa
+from feature_extractor import cnhubert
+
+cnhubert.cnhubert_base_path = cnhubert_base_path
+
+from module.models import SynthesizerTrn
+from AR.models.t2s_lightning_module import Text2SemanticLightningModule
+from text import cleaned_text_to_sequence
+from text.cleaner import clean_text
+from time import time as ttime
+from module.mel_processing import spectrogram_torch
+from tools.my_utils import load_audio
+from tools.i18n.i18n import I18nAuto, scan_language_list
+
+language=os.environ.get("language","Auto")
+language=sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
+i18n = I18nAuto(language=language)
+
+# os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1'  # 确保直接启动推理UI时也能够设置。
+
+if torch.cuda.is_available():
+    device = "cuda"
+else:
+    device = "cpu"
+
+dict_language_v1 = {
+    i18n("中文"): "all_zh",#全部按中文识别
+    i18n("英文"): "en",#全部按英文识别#######不变
+    i18n("日文"): "all_ja",#全部按日文识别
+    i18n("中英混合"): "zh",#按中英混合识别####不变
+    i18n("日英混合"): "ja",#按日英混合识别####不变
+    i18n("多语种混合"): "auto",#多语种启动切分识别语种
+}
+dict_language_v2 = {
+    i18n("中文"): "all_zh",#全部按中文识别
+    i18n("英文"): "en",#全部按英文识别#######不变
+    i18n("日文"): "all_ja",#全部按日文识别
+    i18n("粤语"): "all_yue",#全部按中文识别
+    i18n("韩文"): "all_ko",#全部按韩文识别
+    i18n("中英混合"): "zh",#按中英混合识别####不变
+    i18n("日英混合"): "ja",#按日英混合识别####不变
+    i18n("粤英混合"): "yue",#按粤英混合识别####不变
+    i18n("韩英混合"): "ko",#按韩英混合识别####不变
+    i18n("多语种混合"): "auto",#多语种启动切分识别语种
+    i18n("多语种混合(粤语)"): "auto_yue",#多语种启动切分识别语种
+}
+dict_language = dict_language_v1 if version =='v1' else dict_language_v2
+
+tokenizer = AutoTokenizer.from_pretrained(bert_path)
+bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
+if is_half == True:
+    bert_model = bert_model.half().to(device)
+else:
+    bert_model = bert_model.to(device)
+
+
+def get_bert_feature(text, word2ph):
+    with torch.no_grad():
+        inputs = tokenizer(text, return_tensors="pt")
+        for i in inputs:
+            inputs[i] = inputs[i].to(device)
+        res = bert_model(**inputs, output_hidden_states=True)
+        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
+    assert len(word2ph) == len(text)
+    phone_level_feature = []
+    for i in range(len(word2ph)):
+        repeat_feature = res[i].repeat(word2ph[i], 1)
+        phone_level_feature.append(repeat_feature)
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+    return phone_level_feature.T
+
+
+class DictToAttrRecursive(dict):
+    def __init__(self, input_dict):
+        super().__init__(input_dict)
+        for key, value in input_dict.items():
+            if isinstance(value, dict):
+                value = DictToAttrRecursive(value)
+            self[key] = value
+            setattr(self, key, value)
+
+    def __getattr__(self, item):
+        try:
+            return self[item]
+        except KeyError:
+            raise AttributeError(f"Attribute {item} not found")
+
+    def __setattr__(self, key, value):
+        if isinstance(value, dict):
+            value = DictToAttrRecursive(value)
+        super(DictToAttrRecursive, self).__setitem__(key, value)
+        super().__setattr__(key, value)
+
+    def __delattr__(self, item):
+        try:
+            del self[item]
+        except KeyError:
+            raise AttributeError(f"Attribute {item} not found")
+
+
+ssl_model = cnhubert.get_model()
+if is_half == True:
+    ssl_model = ssl_model.half().to(device)
+else:
+    ssl_model = ssl_model.to(device)
+
+
+def change_sovits_weights(sovits_path,prompt_language=None,text_language=None):
+    global vq_model, hps, version, dict_language
+    dict_s2 = torch.load(sovits_path, map_location="cpu")
+    hps = dict_s2["config"]
+    hps = DictToAttrRecursive(hps)
+    hps.model.semantic_frame_rate = "25hz"
+    if dict_s2['weight']['enc_p.text_embedding.weight'].shape[0] == 322:
+        hps.model.version = "v1"
+    else:
+        hps.model.version = "v2"
+    version = hps.model.version
+    # print("sovits版本:",hps.model.version)
+    vq_model = SynthesizerTrn(
+        hps.data.filter_length // 2 + 1,
+        hps.train.segment_size // hps.data.hop_length,
+        n_speakers=hps.data.n_speakers,
+        **hps.model
+    )
+    if ("pretrained" not in sovits_path):
+        del vq_model.enc_q
+    if is_half == True:
+        vq_model = vq_model.half().to(device)
+    else:
+        vq_model = vq_model.to(device)
+    vq_model.eval()
+    print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
+    dict_language = dict_language_v1 if version =='v1' else dict_language_v2
+    with open("./weight.json")as f:
+        data=f.read()
+        data=json.loads(data)
+        data["SoVITS"][version]=sovits_path
+    with open("./weight.json","w")as f:f.write(json.dumps(data))
+    if prompt_language is not None and text_language is not None:
+        if prompt_language in list(dict_language.keys()):
+            prompt_text_update, prompt_language_update = {'__type__':'update'},  {'__type__':'update', 'value':prompt_language}
+        else:
+            prompt_text_update = {'__type__':'update', 'value':''}
+            prompt_language_update = {'__type__':'update', 'value':i18n("中文")}
+        if text_language in list(dict_language.keys()):
+            text_update, text_language_update = {'__type__':'update'}, {'__type__':'update', 'value':text_language}
+        else:
+            text_update = {'__type__':'update', 'value':''}
+            text_language_update = {'__type__':'update', 'value':i18n("中文")}
+        return  {'__type__':'update', 'choices':list(dict_language.keys())}, {'__type__':'update', 'choices':list(dict_language.keys())}, prompt_text_update, prompt_language_update, text_update, text_language_update
+
+
+
+change_sovits_weights(sovits_path)
+
+
+def change_gpt_weights(gpt_path):
+    global hz, max_sec, t2s_model, config
+    hz = 50
+    dict_s1 = torch.load(gpt_path, map_location="cpu")
+    config = dict_s1["config"]
+    max_sec = config["data"]["max_sec"]
+    t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
+    t2s_model.load_state_dict(dict_s1["weight"])
+    if is_half == True:
+        t2s_model = t2s_model.half()
+    t2s_model = t2s_model.to(device)
+    t2s_model.eval()
+    total = sum([param.nelement() for param in t2s_model.parameters()])
+    print("Number of parameter: %.2fM" % (total / 1e6))
+    with open("./weight.json")as f:
+        data=f.read()
+        data=json.loads(data)
+        data["GPT"][version]=gpt_path
+    with open("./weight.json","w")as f:f.write(json.dumps(data))
+
+
+change_gpt_weights(gpt_path)
+
+
+def get_spepc(hps, filename):
+    audio = load_audio(filename, int(hps.data.sampling_rate))
+    audio = torch.FloatTensor(audio)
+    maxx=audio.abs().max()
+    if(maxx>1):audio/=min(2,maxx)
+    audio_norm = audio
+    audio_norm = audio_norm.unsqueeze(0)
+    spec = spectrogram_torch(
+        audio_norm,
+        hps.data.filter_length,
+        hps.data.sampling_rate,
+        hps.data.hop_length,
+        hps.data.win_length,
+        center=False,
+    )
+    return spec
+
+def clean_text_inf(text, language, version):
+    phones, word2ph, norm_text = clean_text(text, language, version)
+    phones = cleaned_text_to_sequence(phones, version)
+    return phones, word2ph, norm_text
+
+dtype=torch.float16 if is_half == True else torch.float32
+def get_bert_inf(phones, word2ph, norm_text, language):
+    language=language.replace("all_","")
+    if language == "zh":
+        bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype)
+    else:
+        bert = torch.zeros(
+            (1024, len(phones)),
+            dtype=torch.float16 if is_half == True else torch.float32,
+        ).to(device)
+
+    return bert
+
+
+splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }
+
+
+def get_first(text):
+    pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
+    text = re.split(pattern, text)[0].strip()
+    return text
+
+from text import chinese
+def get_phones_and_bert(text,language,version,final=False):
+    if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
+        language = language.replace("all_","")
+        if language == "en":
+            LangSegment.setfilters(["en"])
+            formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
+        else:
+            # 因无法区别中日韩文汉字,以用户输入为准
+            formattext = text
+        while "  " in formattext:
+            formattext = formattext.replace("  ", " ")
+        if language == "zh":
+            if re.search(r'[A-Za-z]', formattext):
+                formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
+                formattext = chinese.mix_text_normalize(formattext)
+                return get_phones_and_bert(formattext,"zh",version)
+            else:
+                phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
+                bert = get_bert_feature(norm_text, word2ph).to(device)
+        elif language == "yue" and re.search(r'[A-Za-z]', formattext):
+                formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
+                formattext = chinese.mix_text_normalize(formattext)
+                return get_phones_and_bert(formattext,"yue",version)
+        else:
+            phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
+            bert = torch.zeros(
+                (1024, len(phones)),
+                dtype=torch.float16 if is_half == True else torch.float32,
+            ).to(device)
+    elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}:
+        textlist=[]
+        langlist=[]
+        LangSegment.setfilters(["zh","ja","en","ko"])
+        if language == "auto":
+            for tmp in LangSegment.getTexts(text):
+                langlist.append(tmp["lang"])
+                textlist.append(tmp["text"])
+        elif language == "auto_yue":
+            for tmp in LangSegment.getTexts(text):
+                if tmp["lang"] == "zh":
+                    tmp["lang"] = "yue"
+                langlist.append(tmp["lang"])
+                textlist.append(tmp["text"])
+        else:
+            for tmp in LangSegment.getTexts(text):
+                if tmp["lang"] == "en":
+                    langlist.append(tmp["lang"])
+                else:
+                    # 因无法区别中日韩文汉字,以用户输入为准
+                    langlist.append(language)
+                textlist.append(tmp["text"])
+        print(textlist)
+        print(langlist)
+        phones_list = []
+        bert_list = []
+        norm_text_list = []
+        for i in range(len(textlist)):
+            lang = langlist[i]
+            phones, word2ph, norm_text = clean_text_inf(textlist[i], lang, version)
+            bert = get_bert_inf(phones, word2ph, norm_text, lang)
+            phones_list.append(phones)
+            norm_text_list.append(norm_text)
+            bert_list.append(bert)
+        bert = torch.cat(bert_list, dim=1)
+        phones = sum(phones_list, [])
+        norm_text = ''.join(norm_text_list)
+
+    if not final and len(phones) < 6:
+        return get_phones_and_bert("." + text,language,version,final=True)
+
+    return phones,bert.to(dtype),norm_text
+
+
+def merge_short_text_in_array(texts, threshold):
+    if (len(texts)) < 2:
+        return texts
+    result = []
+    text = ""
+    for ele in texts:
+        text += ele
+        if len(text) >= threshold:
+            result.append(text)
+            text = ""
+    if (len(text) > 0):
+        if len(result) == 0:
+            result.append(text)
+        else:
+            result[len(result) - 1] += text
+    return result
+
+##ref_wav_path+prompt_text+prompt_language+text(单个)+text_language+top_k+top_p+temperature
+# cache_tokens={}#暂未实现清理机制
+cache= {}
+def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6, ref_free 
+    =False,speed=1,if_freeze=False,inp_refs=None):
+    global cache
+    if ref_wav_path:pass
+    else:gr.Warning(i18n('请上传参考音频'))
+    if text:pass
+    else:gr.Warning(i18n('请填入推理文本'))
+    t = []
+    if prompt_text is None or len(prompt_text) == 0:
+        ref_free = True
+    t0 = ttime()
+    prompt_language = dict_language[prompt_language]
+    text_language = dict_language[text_language]
+
+
+    if not ref_free:
+        prompt_text = prompt_text.strip("\n")
+        if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
+        print(i18n("实际输入的参考文本:"), prompt_text)
+    text = text.strip("\n")
+    # if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text
+    
+    print(i18n("实际输入的目标文本:"), text)
+    zero_wav = np.zeros(
+        int(hps.data.sampling_rate * 0.3),
+        dtype=np.float16 if is_half == True else np.float32,
+    )
+    if not ref_free:
+        with torch.no_grad():
+            wav16k, sr = librosa.load(ref_wav_path, sr=16000)
+            if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
+                gr.Warning(i18n("参考音频在3~10秒范围外，请更换！"))
+                raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
+            wav16k = torch.from_numpy(wav16k)
+            zero_wav_torch = torch.from_numpy(zero_wav)
+            if is_half == True:
+                wav16k = wav16k.half().to(device)
+                zero_wav_torch = zero_wav_torch.half().to(device)
+            else:
+                wav16k = wav16k.to(device)
+                zero_wav_torch = zero_wav_torch.to(device)
+            wav16k = torch.cat([wav16k, zero_wav_torch])
+            ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
+                "last_hidden_state"
+            ].transpose(
+                1, 2
+            )  # .float()
+            codes = vq_model.extract_latent(ssl_content)
+            prompt_semantic = codes[0, 0]
+            prompt = prompt_semantic.unsqueeze(0).to(device)
+
+    t1 = ttime()
+    t.append(t1-t0)
+
+    if (how_to_cut == i18n("凑四句一切")):
+        text = cut1(text)
+    elif (how_to_cut == i18n("凑50字一切")):
+        text = cut2(text)
+    elif (how_to_cut == i18n("按中文句号。切")):
+        text = cut3(text)
+    elif (how_to_cut == i18n("按英文句号.切")):
+        text = cut4(text)
+    elif (how_to_cut == i18n("按标点符号切")):
+        text = cut5(text)
+    while "\n\n" in text:
+        text = text.replace("\n\n", "\n")
+    print(i18n("实际输入的目标文本(切句后):"), text)
+    texts = text.split("\n")
+    texts = process_text(texts)
+    texts = merge_short_text_in_array(texts, 5)
+    audio_opt = []
+    if not ref_free:
+        phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language, version)
+
+    for i_text,text in enumerate(texts):
+        # 解决输入目标文本的空行导致报错的问题
+        if (len(text.strip()) == 0):
+            continue
+        if (text[-1] not in splits): text += "。" if text_language != "en" else "."
+        print(i18n("实际输入的目标文本(每句):"), text)
+        phones2,bert2,norm_text2=get_phones_and_bert(text, text_language, version)
+        print(i18n("前端处理后的文本(每句):"), norm_text2)
+        if not ref_free:
+            bert = torch.cat([bert1, bert2], 1)
+            all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0)
+        else:
+            bert = bert2
+            all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0)
+
+        bert = bert.to(device).unsqueeze(0)
+        all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
+
+        t2 = ttime()
+        # cache_key="%s-%s-%s-%s-%s-%s-%s-%s"%(ref_wav_path,prompt_text,prompt_language,text,text_language,top_k,top_p,temperature)
+        # print(cache.keys(),if_freeze)
+        if(i_text in cache and if_freeze==True):pred_semantic=cache[i_text]
+        else:
+            with torch.no_grad():
+                pred_semantic, idx = t2s_model.model.infer_panel(
+                    all_phoneme_ids,
+                    all_phoneme_len,
+                    None if ref_free else prompt,
+                    bert,
+                    # prompt_phone_len=ph_offset,
+                    top_k=top_k,
+                    top_p=top_p,
+                    temperature=temperature,
+                    early_stop_num=hz * max_sec,
+                )
+                pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)
+                cache[i_text]=pred_semantic
+        t3 = ttime()
+        refers=[]
+        if(inp_refs):
+            for path in inp_refs:
+                try:
+                    refer = get_spepc(hps, path.name).to(dtype).to(device)
+                    refers.append(refer)
+                except:
+                    traceback.print_exc()
+        if(len(refers)==0):refers = [get_spepc(hps, ref_wav_path).to(dtype).to(device)]
+        audio = (vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers,speed=speed).detach().cpu().numpy()[0, 0])
+        max_audio=np.abs(audio).max()#简单防止16bit爆音
+        if max_audio>1:audio/=max_audio
+        audio_opt.append(audio)
+        audio_opt.append(zero_wav)
+        t4 = ttime()
+        t.extend([t2 - t1,t3 - t2, t4 - t3])
+        t1 = ttime()
+    print("%.3f\t%.3f\t%.3f\t%.3f" % 
+           (t[0], sum(t[1::3]), sum(t[2::3]), sum(t[3::3]))
+           )
+    yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(
+        np.int16
+    )
+
+
+def split(todo_text):
+    todo_text = todo_text.replace("……", "。").replace("——", "，")
+    if todo_text[-1] not in splits:
+        todo_text += "。"
+    i_split_head = i_split_tail = 0
+    len_text = len(todo_text)
+    todo_texts = []
+    while 1:
+        if i_split_head >= len_text:
+            break  # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入
+        if todo_text[i_split_head] in splits:
+            i_split_head += 1
+            todo_texts.append(todo_text[i_split_tail:i_split_head])
+            i_split_tail = i_split_head
+        else:
+            i_split_head += 1
+    return todo_texts
+
+
+def cut1(inp):
+    inp = inp.strip("\n")
+    inps = split(inp)
+    split_idx = list(range(0, len(inps), 4))
+    split_idx[-1] = None
+    if len(split_idx) > 1:
+        opts = []
+        for idx in range(len(split_idx) - 1):
+            opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
+    else:
+        opts = [inp]
+    opts = [item for item in opts if not set(item).issubset(punctuation)]
+    return "\n".join(opts)
+
+
+def cut2(inp):
+    inp = inp.strip("\n")
+    inps = split(inp)
+    if len(inps) < 2:
+        return inp
+    opts = []
+    summ = 0
+    tmp_str = ""
+    for i in range(len(inps)):
+        summ += len(inps[i])
+        tmp_str += inps[i]
+        if summ > 50:
+            summ = 0
+            opts.append(tmp_str)
+            tmp_str = ""
+    if tmp_str != "":
+        opts.append(tmp_str)
+    # print(opts)
+    if len(opts) > 1 and len(opts[-1]) < 50:  ##如果最后一个太短了，和前一个合一起
+        opts[-2] = opts[-2] + opts[-1]
+        opts = opts[:-1]
+    opts = [item for item in opts if not set(item).issubset(punctuation)]
+    return "\n".join(opts)
+
+
+def cut3(inp):
+    inp = inp.strip("\n")
+    opts = ["%s" % item for item in inp.strip("。").split("。")]
+    opts = [item for item in opts if not set(item).issubset(punctuation)]
+    return  "\n".join(opts)
+
+def cut4(inp):
+    inp = inp.strip("\n")
+    opts = ["%s" % item for item in inp.strip(".").split(".")]
+    opts = [item for item in opts if not set(item).issubset(punctuation)]
+    return "\n".join(opts)
+
+
+# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
+def cut5(inp):
+    inp = inp.strip("\n")
+    punds = {',', '.', ';', '?', '!', '、', '，', '。', '？', '！', ';', '：', '…'}
+    mergeitems = []
+    items = []
+
+    for i, char in enumerate(inp):
+        if char in punds:
+            if char == '.' and i > 0 and i < len(inp) - 1 and inp[i - 1].isdigit() and inp[i + 1].isdigit():
+                items.append(char)
+            else:
+                items.append(char)
+                mergeitems.append("".join(items))
+                items = []
+        else:
+            items.append(char)
+
+    if items:
+        mergeitems.append("".join(items))
+
+    opt = [item for item in mergeitems if not set(item).issubset(punds)]
+    return "\n".join(opt)
+
+
+def custom_sort_key(s):
+    # 使用正则表达式提取字符串中的数字部分和非数字部分
+    parts = re.split('(\d+)', s)
+    # 将数字部分转换为整数，非数字部分保持不变
+    parts = [int(part) if part.isdigit() else part for part in parts]
+    return parts
+
+def process_text(texts):
+    _text=[]
+    if all(text in [None, " ", "\n",""] for text in texts):
+        raise ValueError(i18n("请输入有效文本"))
+    for text in texts:
+        if text in  [None, " ", ""]:
+            pass
+        else:
+            _text.append(text)
+    return _text
+
+
+def change_choices():
+    SoVITS_names, GPT_names = get_weights_names(GPT_weight_root, SoVITS_weight_root)
+    return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names, key=custom_sort_key), "__type__": "update"}
+
+
+SoVITS_weight_root=["SoVITS_weights_v2","SoVITS_weights"]
+GPT_weight_root=["GPT_weights_v2","GPT_weights"]
+for path in SoVITS_weight_root+GPT_weight_root:
+    os.makedirs(path,exist_ok=True)
+
+
+def get_weights_names(GPT_weight_root, SoVITS_weight_root):
+    SoVITS_names = [i for i in pretrained_sovits_name]
+    for path in SoVITS_weight_root:
+        for name in os.listdir(path):
+            if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (path, name))
+    GPT_names = [i for i in pretrained_gpt_name]
+    for path in GPT_weight_root:
+        for name in os.listdir(path):
+            if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (path, name))
+    return SoVITS_names, GPT_names
+
+
+SoVITS_names, GPT_names = get_weights_names(GPT_weight_root, SoVITS_weight_root)
+
+def html_center(text, label='p'):
+    return f"""<div style="text-align: center; margin: 100; padding: 50;">
+                <{label} style="margin: 0; padding: 0;">{text}</{label}>
+                </div>"""
+
+def html_left(text, label='p'):
+    return f"""<div style="text-align: left; margin: 0; padding: 0;">
+                <{label} style="margin: 0; padding: 0;">{text}</{label}>
+                </div>"""
+
+
+with gr.Blocks(title="GPT-SoVITS WebUI") as app:
+    gr.Markdown(
+        value=i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>.")
+    )
+    with gr.Group():
+        gr.Markdown(html_center(i18n("模型切换"),'h3'))
+        with gr.Row():
+            GPT_dropdown = gr.Dropdown(label=i18n("GPT模型列表"), choices=sorted(GPT_names, key=custom_sort_key), value=gpt_path, interactive=True, scale=14)
+            SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True, scale=14)
+            refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary", scale=14)
+            refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
+        gr.Markdown(html_center(i18n("*请上传并填写参考信息"),'h3'))
+        with gr.Row():
+            inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath", scale=13)
+            with gr.Column(scale=13):
+                ref_text_free = gr.Checkbox(label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。"), value=False, interactive=True, show_label=True,scale=1)
+                gr.Markdown(html_left(i18n("使用无参考文本模式时建议使用微调的GPT，听不清参考音频说的啥(不晓得写啥)可以开。<br>开启后无视填写的参考文本。")))
+                prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="", lines=5, max_lines=5,scale=1)
+            with gr.Column(scale=14):
+                prompt_language = gr.Dropdown(
+                    label=i18n("参考音频的语种"), choices=list(dict_language.keys()), value=i18n("中文"),
+                )
+                inp_refs = gr.File(label=i18n("可选项：通过拖拽多个文件上传多个参考音频（建议同性），平均融合他们的音色。如不填写此项，音色由左侧单个参考音频控制。如是微调模型，建议参考音频全部在微调训练集音色内，底模不用管。"),file_count="multiple")
+        gr.Markdown(html_center(i18n("*请填写需要合成的目标文本和语种模式"),'h3'))
+        with gr.Row():
+            with gr.Column(scale=13):
+                text = gr.Textbox(label=i18n("需要合成的文本"), value="", lines=26, max_lines=26)
+            with gr.Column(scale=7):
+                text_language = gr.Dropdown(
+                        label=i18n("需要合成的语种")+i18n(".限制范围越小判别效果越好。"), choices=list(dict_language.keys()), value=i18n("中文"), scale=1
+                    )
+                how_to_cut = gr.Dropdown(
+                        label=i18n("怎么切"),
+                        choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ],
+                        value=i18n("凑四句一切"),
+                        interactive=True, scale=1
+                    )
+                gr.Markdown(value=html_center(i18n("语速调整，高为更快")))
+                if_freeze=gr.Checkbox(label=i18n("是否直接对上次合成结果调整语速和音色。防止随机性。"), value=False, interactive=True,show_label=True, scale=1)
+                speed = gr.Slider(minimum=0.6,maximum=1.65,step=0.05,label=i18n("语速"),value=1,interactive=True, scale=1)
+                gr.Markdown(html_center(i18n("GPT采样参数(无参考文本时不要太低。不懂就用默认)：")))
+                top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=15,interactive=True, scale=1)
+                top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True, scale=1)
+                temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True,  scale=1) 
+            # with gr.Column():
+            #     gr.Markdown(value=i18n("手工调整音素。当音素框不为空时使用手工音素输入推理，无视目标文本框。"))
+            #     phoneme=gr.Textbox(label=i18n("音素框"), value="")
+            #     get_phoneme_button = gr.Button(i18n("目标文本转音素"), variant="primary")
+        with gr.Row():
+            inference_button = gr.Button(i18n("合成语音"), variant="primary", size='lg', scale=25)
+            output = gr.Audio(label=i18n("输出的语音"), scale=14)
+
+        inference_button.click(
+            get_tts_wav,
+            [inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, ref_text_free,speed,if_freeze,inp_refs],
+            [output],
+        )
+        SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown,prompt_language,text_language], [prompt_language,text_language,prompt_text,prompt_language,text,text_language])
+        GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], [])
+
+        # gr.Markdown(value=i18n("文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。"))
+        # with gr.Row():
+        #     text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="")
+        #     button1 = gr.Button(i18n("凑四句一切"), variant="primary")
+        #     button2 = gr.Button(i18n("凑50字一切"), variant="primary")
+        #     button3 = gr.Button(i18n("按中文句号。切"), variant="primary")
+        #     button4 = gr.Button(i18n("按英文句号.切"), variant="primary")
+        #     button5 = gr.Button(i18n("按标点符号切"), variant="primary")
+        #     text_opt = gr.Textbox(label=i18n("切分后文本"), value="")
+        #     button1.click(cut1, [text_inp], [text_opt])
+        #     button2.click(cut2, [text_inp], [text_opt])
+        #     button3.click(cut3, [text_inp], [text_opt])
+        #     button4.click(cut4, [text_inp], [text_opt])
+        #     button5.click(cut5, [text_inp], [text_opt])
+        # gr.Markdown(html_center(i18n("后续将支持转音素、手工修改音素、语音合成分步执行。")))
+
+if __name__ == '__main__':
+    app.queue().launch(#concurrency_count=511, max_size=1022
+        server_name="0.0.0.0",
+        inbrowser=True,
+        share=is_share,
+        server_port=infer_ttswebui,
+        quiet=True,
+    )

GPT_SoVITS/inference_webui_fast.py

@@ -0,0 +1,336 @@
+'''
+按中英混合识别
+按日英混合识别
+多语种启动切分识别语种
+全部按中文识别
+全部按英文识别
+全部按日文识别
+'''
+import random
+import os, re, logging
+import sys
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+sys.path.append("%s/GPT_SoVITS" % (now_dir))
+
+logging.getLogger("markdown_it").setLevel(logging.ERROR)
+logging.getLogger("urllib3").setLevel(logging.ERROR)
+logging.getLogger("httpcore").setLevel(logging.ERROR)
+logging.getLogger("httpx").setLevel(logging.ERROR)
+logging.getLogger("asyncio").setLevel(logging.ERROR)
+logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
+logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
+import pdb
+import torch
+
+try:
+    import gradio.analytics as analytics
+    analytics.version_check = lambda:None
+except:...
+
+
+infer_ttswebui = os.environ.get("infer_ttswebui", 9872)
+infer_ttswebui = int(infer_ttswebui)
+is_share = os.environ.get("is_share", "False")
+is_share = eval(is_share)
+if "_CUDA_VISIBLE_DEVICES" in os.environ:
+    os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
+
+is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
+gpt_path = os.environ.get("gpt_path", None)
+sovits_path = os.environ.get("sovits_path", None)
+cnhubert_base_path = os.environ.get("cnhubert_base_path", None)
+bert_path = os.environ.get("bert_path", None)
+version=os.environ.get("version","v2")
+        
+import gradio as gr
+from TTS_infer_pack.TTS import TTS, TTS_Config
+from TTS_infer_pack.text_segmentation_method import get_method
+from tools.i18n.i18n import I18nAuto, scan_language_list
+
+language=os.environ.get("language","Auto")
+language=sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
+i18n = I18nAuto(language=language)
+
+
+# os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1'  # 确保直接启动推理UI时也能够设置。
+
+if torch.cuda.is_available():
+    device = "cuda"
+# elif torch.backends.mps.is_available():
+#     device = "mps"
+else:
+    device = "cpu"
+    
+dict_language_v1 = {
+    i18n("中文"): "all_zh",#全部按中文识别
+    i18n("英文"): "en",#全部按英文识别#######不变
+    i18n("日文"): "all_ja",#全部按日文识别
+    i18n("中英混合"): "zh",#按中英混合识别####不变
+    i18n("日英混合"): "ja",#按日英混合识别####不变
+    i18n("多语种混合"): "auto",#多语种启动切分识别语种
+}
+dict_language_v2 = {
+    i18n("中文"): "all_zh",#全部按中文识别
+    i18n("英文"): "en",#全部按英文识别#######不变
+    i18n("日文"): "all_ja",#全部按日文识别
+    i18n("粤语"): "all_yue",#全部按中文识别
+    i18n("韩文"): "all_ko",#全部按韩文识别
+    i18n("中英混合"): "zh",#按中英混合识别####不变
+    i18n("日英混合"): "ja",#按日英混合识别####不变
+    i18n("粤英混合"): "yue",#按粤英混合识别####不变
+    i18n("韩英混合"): "ko",#按韩英混合识别####不变
+    i18n("多语种混合"): "auto",#多语种启动切分识别语种
+    i18n("多语种混合(粤语)"): "auto_yue",#多语种启动切分识别语种
+}
+dict_language = dict_language_v1 if version =='v1' else dict_language_v2
+
+cut_method = {
+    i18n("不切"):"cut0",
+    i18n("凑四句一切"): "cut1",
+    i18n("凑50字一切"): "cut2",
+    i18n("按中文句号。切"): "cut3",
+    i18n("按英文句号.切"): "cut4",
+    i18n("按标点符号切"): "cut5",
+}
+
+tts_config = TTS_Config("GPT_SoVITS/configs/tts_infer.yaml")
+tts_config.device = device
+tts_config.is_half = is_half
+tts_config.version = version
+if gpt_path is not None:
+    tts_config.t2s_weights_path = gpt_path
+if sovits_path is not None:
+    tts_config.vits_weights_path = sovits_path
+if cnhubert_base_path is not None:
+    tts_config.cnhuhbert_base_path = cnhubert_base_path
+if bert_path is not None:
+    tts_config.bert_base_path = bert_path
+    
+print(tts_config)
+tts_pipeline = TTS(tts_config)
+gpt_path = tts_config.t2s_weights_path
+sovits_path = tts_config.vits_weights_path
+version = tts_config.version
+
+def inference(text, text_lang, 
+              ref_audio_path, 
+              aux_ref_audio_paths,
+              prompt_text, 
+              prompt_lang, top_k, 
+              top_p, temperature, 
+              text_split_method, batch_size, 
+              speed_factor, ref_text_free,
+              split_bucket,fragment_interval,
+              seed, keep_random, parallel_infer,
+              repetition_penalty
+              ):
+
+    seed = -1 if keep_random else seed
+    actual_seed = seed if seed not in [-1, "", None] else random.randrange(1 << 32)
+    inputs={
+        "text": text,
+        "text_lang": dict_language[text_lang],
+        "ref_audio_path": ref_audio_path,
+        "aux_ref_audio_paths": [item.name for item in aux_ref_audio_paths] if aux_ref_audio_paths is not None else [],
+        "prompt_text": prompt_text if not ref_text_free else "",
+        "prompt_lang": dict_language[prompt_lang],
+        "top_k": top_k,
+        "top_p": top_p,
+        "temperature": temperature,
+        "text_split_method": cut_method[text_split_method],
+        "batch_size":int(batch_size),
+        "speed_factor":float(speed_factor),
+        "split_bucket":split_bucket,
+        "return_fragment":False,
+        "fragment_interval":fragment_interval,
+        "seed":actual_seed,
+        "parallel_infer": parallel_infer,
+        "repetition_penalty": repetition_penalty,
+    }
+    for item in tts_pipeline.run(inputs):
+        yield item, actual_seed
+        
+def custom_sort_key(s):
+    # 使用正则表达式提取字符串中的数字部分和非数字部分
+    parts = re.split('(\d+)', s)
+    # 将数字部分转换为整数，非数字部分保持不变
+    parts = [int(part) if part.isdigit() else part for part in parts]
+    return parts
+
+
+def change_choices():
+    SoVITS_names, GPT_names = get_weights_names(GPT_weight_root, SoVITS_weight_root)
+    return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names, key=custom_sort_key), "__type__": "update"}
+
+
+pretrained_sovits_name=["GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth", "GPT_SoVITS/pretrained_models/s2G488k.pth"]
+pretrained_gpt_name=["GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"]
+_ =[[],[]]
+for i in range(2):
+    if os.path.exists(pretrained_gpt_name[i]):
+        _[0].append(pretrained_gpt_name[i])
+    if os.path.exists(pretrained_sovits_name[i]):
+        _[-1].append(pretrained_sovits_name[i])
+pretrained_gpt_name,pretrained_sovits_name = _
+
+SoVITS_weight_root=["SoVITS_weights_v2","SoVITS_weights"]
+GPT_weight_root=["GPT_weights_v2","GPT_weights"]
+for path in SoVITS_weight_root+GPT_weight_root:
+    os.makedirs(path,exist_ok=True)
+
+def get_weights_names(GPT_weight_root, SoVITS_weight_root):
+    SoVITS_names = [i for i in pretrained_sovits_name]
+    for path in SoVITS_weight_root:
+        for name in os.listdir(path):
+            if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (path, name))
+    GPT_names = [i for i in pretrained_gpt_name]
+    for path in GPT_weight_root:
+        for name in os.listdir(path):
+            if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (path, name))
+    return SoVITS_names, GPT_names
+
+
+SoVITS_names, GPT_names = get_weights_names(GPT_weight_root, SoVITS_weight_root)
+
+
+
+def change_sovits_weights(sovits_path,prompt_language=None,text_language=None):
+    tts_pipeline.init_vits_weights(sovits_path)
+    global version, dict_language
+    dict_language = dict_language_v1 if tts_pipeline.configs.version =='v1' else dict_language_v2
+    if prompt_language is not None and text_language is not None:
+        if prompt_language in list(dict_language.keys()):
+            prompt_text_update, prompt_language_update = {'__type__':'update'},  {'__type__':'update', 'value':prompt_language}
+        else:
+            prompt_text_update = {'__type__':'update', 'value':''}
+            prompt_language_update = {'__type__':'update', 'value':i18n("中文")}
+        if text_language in list(dict_language.keys()):
+            text_update, text_language_update = {'__type__':'update'}, {'__type__':'update', 'value':text_language}
+        else:
+            text_update = {'__type__':'update', 'value':''}
+            text_language_update = {'__type__':'update', 'value':i18n("中文")}
+        return  {'__type__':'update', 'choices':list(dict_language.keys())}, {'__type__':'update', 'choices':list(dict_language.keys())}, prompt_text_update, prompt_language_update, text_update, text_language_update
+
+
+
+with gr.Blocks(title="GPT-SoVITS WebUI") as app:
+    gr.Markdown(
+        value=i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>.")
+    )
+    
+    with gr.Column():
+        # with gr.Group():
+        gr.Markdown(value=i18n("模型切换"))
+        with gr.Row():
+            GPT_dropdown = gr.Dropdown(label=i18n("GPT模型列表"), choices=sorted(GPT_names, key=custom_sort_key), value=gpt_path, interactive=True)
+            SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True)
+            refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
+            refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
+
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown(value=i18n("*请上传并填写参考信息"))
+            with gr.Row():
+                inp_ref = gr.Audio(label=i18n("主参考音频(请上传3~10秒内参考音频，超过会报错！)"), type="filepath")
+                inp_refs = gr.File(label=i18n("辅参考音频(可选多个，或不选)"),file_count="multiple")
+            prompt_text = gr.Textbox(label=i18n("主参考音频的文本"), value="", lines=2)
+            with gr.Row():
+                prompt_language = gr.Dropdown(
+                    label=i18n("主参考音频的语种"), choices=list(dict_language.keys()), value=i18n("中文")
+                )
+                with gr.Column():
+                    ref_text_free = gr.Checkbox(label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。"), value=False, interactive=True, show_label=True)
+                    gr.Markdown(i18n("使用无参考文本模式时建议使用微调的GPT，听不清参考音频说的啥(不晓得写啥)可以开，开启后无视填写的参考文本。"))
+    
+        with gr.Column():
+            gr.Markdown(value=i18n("*请填写需要合成的目标文本和语种模式"))
+            text = gr.Textbox(label=i18n("需要合成的文本"), value="", lines=20, max_lines=20)
+            text_language = gr.Dropdown(
+                label=i18n("需要合成的文本的语种"), choices=list(dict_language.keys()), value=i18n("中文")
+            )
+
+        
+    with gr.Group():
+        gr.Markdown(value=i18n("推理设置"))
+        with gr.Row():
+
+            with gr.Column():
+                batch_size = gr.Slider(minimum=1,maximum=200,step=1,label=i18n("batch_size"),value=20,interactive=True)
+                fragment_interval = gr.Slider(minimum=0.01,maximum=1,step=0.01,label=i18n("分段间隔(秒)"),value=0.3,interactive=True)
+                speed_factor = gr.Slider(minimum=0.6,maximum=1.65,step=0.05,label="speed_factor",value=1.0,interactive=True)
+                top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=5,interactive=True)
+                top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True)
+                temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True)
+                repetition_penalty = gr.Slider(minimum=0,maximum=2,step=0.05,label=i18n("重复惩罚"),value=1.35,interactive=True)
+            with gr.Column():
+                with gr.Row():
+                    how_to_cut = gr.Dropdown(
+                            label=i18n("怎么切"),
+                            choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ],
+                            value=i18n("凑四句一切"),
+                            interactive=True, scale=1
+                        )
+                    parallel_infer = gr.Checkbox(label=i18n("并行推理"), value=True, interactive=True, show_label=True)
+                    split_bucket = gr.Checkbox(label=i18n("数据分桶(并行推理时会降低一点计算量)"), value=True, interactive=True, show_label=True)
+                
+                with gr.Row():  
+                    seed = gr.Number(label=i18n("随机种子"),value=-1)
+                    keep_random = gr.Checkbox(label=i18n("保持随机"), value=True, interactive=True, show_label=True)
+
+                output = gr.Audio(label=i18n("输出的语音"))
+                with gr.Row():
+                    inference_button = gr.Button(i18n("合成语音"), variant="primary")
+                    stop_infer = gr.Button(i18n("终止合成"), variant="primary")
+                
+        
+        inference_button.click(
+            inference,
+            [
+                text,text_language, inp_ref, inp_refs,
+                prompt_text, prompt_language, 
+                top_k, top_p, temperature, 
+                how_to_cut, batch_size, 
+                speed_factor, ref_text_free,
+                split_bucket,fragment_interval,
+                seed, keep_random, parallel_infer,
+                repetition_penalty
+             ],
+            [output, seed],
+        )
+        stop_infer.click(tts_pipeline.stop, [], [])
+        SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown,prompt_language,text_language], [prompt_language,text_language,prompt_text,prompt_language,text,text_language])
+        GPT_dropdown.change(tts_pipeline.init_t2s_weights, [GPT_dropdown], [])
+
+    with gr.Group():
+        gr.Markdown(value=i18n("文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。"))
+        with gr.Row():
+            text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="", lines=4)
+            with gr.Column():
+                _how_to_cut = gr.Radio(
+                            label=i18n("怎么切"),
+                            choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ],
+                            value=i18n("凑四句一切"),
+                            interactive=True,
+                        )
+                cut_text= gr.Button(i18n("切分"), variant="primary")
+            
+            def to_cut(text_inp, how_to_cut):
+                if len(text_inp.strip()) == 0 or text_inp==[]:
+                    return ""
+                method = get_method(cut_method[how_to_cut])
+                return method(text_inp)
+        
+            text_opt = gr.Textbox(label=i18n("切分后文本"), value="", lines=4)
+            cut_text.click(to_cut, [text_inp, _how_to_cut], [text_opt])
+        gr.Markdown(value=i18n("后续将支持转音素、手工修改音素、语音合成分步执行。"))
+
+if __name__ == '__main__':
+    app.queue().launch(#concurrency_count=511, max_size=1022
+        server_name="0.0.0.0",
+        inbrowser=True,
+        share=is_share,
+        server_port=infer_ttswebui,
+        quiet=True,
+    )

GPT_SoVITS/onnx_export.py

@@ -0,0 +1,344 @@
+from module.models_onnx import SynthesizerTrn, symbols_v1, symbols_v2
+from AR.models.t2s_lightning_module_onnx import Text2SemanticLightningModule
+import torch
+import torchaudio
+from torch import nn
+from feature_extractor import cnhubert
+
+cnhubert_base_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
+cnhubert.cnhubert_base_path = cnhubert_base_path
+ssl_model = cnhubert.get_model()
+from text import cleaned_text_to_sequence
+import soundfile
+from tools.my_utils import load_audio
+import os
+import json
+
+def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
+    hann_window = torch.hann_window(win_size).to(
+            dtype=y.dtype, device=y.device
+        )
+    y = torch.nn.functional.pad(
+        y.unsqueeze(1),
+        (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
+        mode="reflect",
+    )
+    y = y.squeeze(1)
+    spec = torch.stft(
+        y,
+        n_fft,
+        hop_length=hop_size,
+        win_length=win_size,
+        window=hann_window,
+        center=center,
+        pad_mode="reflect",
+        normalized=False,
+        onesided=True,
+        return_complex=False,
+    )
+    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+    return spec
+
+
+class DictToAttrRecursive(dict):
+    def __init__(self, input_dict):
+        super().__init__(input_dict)
+        for key, value in input_dict.items():
+            if isinstance(value, dict):
+                value = DictToAttrRecursive(value)
+            self[key] = value
+            setattr(self, key, value)
+
+    def __getattr__(self, item):
+        try:
+            return self[item]
+        except KeyError:
+            raise AttributeError(f"Attribute {item} not found")
+
+    def __setattr__(self, key, value):
+        if isinstance(value, dict):
+            value = DictToAttrRecursive(value)
+        super(DictToAttrRecursive, self).__setitem__(key, value)
+        super().__setattr__(key, value)
+
+    def __delattr__(self, item):
+        try:
+            del self[item]
+        except KeyError:
+            raise AttributeError(f"Attribute {item} not found")
+
+
+class T2SEncoder(nn.Module):
+    def __init__(self, t2s, vits):
+        super().__init__()
+        self.encoder = t2s.onnx_encoder
+        self.vits = vits
+    
+    def forward(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content):
+        codes = self.vits.extract_latent(ssl_content)
+        prompt_semantic = codes[0, 0]
+        bert = torch.cat([ref_bert.transpose(0, 1), text_bert.transpose(0, 1)], 1)
+        all_phoneme_ids = torch.cat([ref_seq, text_seq], 1)
+        bert = bert.unsqueeze(0)
+        prompt = prompt_semantic.unsqueeze(0)
+        return self.encoder(all_phoneme_ids, bert), prompt
+
+
+class T2SModel(nn.Module):
+    def __init__(self, t2s_path, vits_model):
+        super().__init__()
+        dict_s1 = torch.load(t2s_path, map_location="cpu")
+        self.config = dict_s1["config"]
+        self.t2s_model = Text2SemanticLightningModule(self.config, "ojbk", is_train=False)
+        self.t2s_model.load_state_dict(dict_s1["weight"])
+        self.t2s_model.eval()
+        self.vits_model = vits_model.vq_model
+        self.hz = 50
+        self.max_sec = self.config["data"]["max_sec"]
+        self.t2s_model.model.top_k = torch.LongTensor([self.config["inference"]["top_k"]])
+        self.t2s_model.model.early_stop_num = torch.LongTensor([self.hz * self.max_sec])
+        self.t2s_model = self.t2s_model.model
+        self.t2s_model.init_onnx()
+        self.onnx_encoder = T2SEncoder(self.t2s_model, self.vits_model)
+        self.first_stage_decoder = self.t2s_model.first_stage_decoder
+        self.stage_decoder = self.t2s_model.stage_decoder
+        #self.t2s_model = torch.jit.script(self.t2s_model)
+
+    def forward(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content):
+        early_stop_num = self.t2s_model.early_stop_num
+
+        #[1,N] [1,N] [N, 1024] [N, 1024] [1, 768, N]
+        x, prompts = self.onnx_encoder(ref_seq, text_seq, ref_bert, text_bert, ssl_content)
+
+        prefix_len = prompts.shape[1]
+
+        #[1,N,512] [1,N]
+        y, k, v, y_emb, x_example = self.first_stage_decoder(x, prompts)
+
+        stop = False
+        for idx in range(1, 1500):
+            #[1, N] [N_layer, N, 1, 512] [N_layer, N, 1, 512] [1, N, 512] [1] [1, N, 512] [1, N]
+            enco = self.stage_decoder(y, k, v, y_emb, x_example)
+            y, k, v, y_emb, logits, samples = enco
+            if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
+                stop = True
+            if torch.argmax(logits, dim=-1)[0] == self.t2s_model.EOS or samples[0, 0] == self.t2s_model.EOS:
+                stop = True
+            if stop:
+                break
+        y[0, -1] = 0
+
+        return y[:, -idx:].unsqueeze(0)
+
+    def export(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content, project_name, dynamo=False):
+        #self.onnx_encoder = torch.jit.script(self.onnx_encoder)
+        if dynamo:
+            export_options = torch.onnx.ExportOptions(dynamic_shapes=True)
+            onnx_encoder_export_output = torch.onnx.dynamo_export(
+                self.onnx_encoder,
+                (ref_seq, text_seq, ref_bert, text_bert, ssl_content),
+                export_options=export_options
+            )
+            onnx_encoder_export_output.save(f"onnx/{project_name}/{project_name}_t2s_encoder.onnx")
+            return
+
+        torch.onnx.export(
+            self.onnx_encoder,
+            (ref_seq, text_seq, ref_bert, text_bert, ssl_content),
+            f"onnx/{project_name}/{project_name}_t2s_encoder.onnx",
+            input_names=["ref_seq", "text_seq", "ref_bert", "text_bert", "ssl_content"],
+            output_names=["x", "prompts"],
+            dynamic_axes={
+                "ref_seq": {1 : "ref_length"},
+                "text_seq": {1 : "text_length"},
+                "ref_bert": {0 : "ref_length"},
+                "text_bert": {0 : "text_length"},
+                "ssl_content": {2 : "ssl_length"},
+            },
+            opset_version=16
+        )
+        x, prompts = self.onnx_encoder(ref_seq, text_seq, ref_bert, text_bert, ssl_content)
+
+        torch.onnx.export(
+            self.first_stage_decoder,
+            (x, prompts),
+            f"onnx/{project_name}/{project_name}_t2s_fsdec.onnx",
+            input_names=["x", "prompts"],
+            output_names=["y", "k", "v", "y_emb", "x_example"],
+            dynamic_axes={
+                "x": {1 : "x_length"},
+                "prompts": {1 : "prompts_length"},
+            },
+            verbose=False,
+            opset_version=16
+        )
+        y, k, v, y_emb, x_example = self.first_stage_decoder(x, prompts)
+
+        torch.onnx.export(
+            self.stage_decoder,
+            (y, k, v, y_emb, x_example),
+            f"onnx/{project_name}/{project_name}_t2s_sdec.onnx",
+            input_names=["iy", "ik", "iv", "iy_emb", "ix_example"],
+            output_names=["y", "k", "v", "y_emb", "logits", "samples"],
+            dynamic_axes={
+                "iy": {1 : "iy_length"},
+                "ik": {1 : "ik_length"},
+                "iv": {1 : "iv_length"},
+                "iy_emb": {1 : "iy_emb_length"},
+                "ix_example": {1 : "ix_example_length"},
+            },
+            verbose=False,
+            opset_version=16
+        )
+
+
+class VitsModel(nn.Module):
+    def __init__(self, vits_path):
+        super().__init__()
+        dict_s2 = torch.load(vits_path,map_location="cpu")
+        self.hps = dict_s2["config"]
+        if dict_s2['weight']['enc_p.text_embedding.weight'].shape[0] == 322:
+            self.hps["model"]["version"] = "v1"
+        else:
+            self.hps["model"]["version"] = "v2"
+        
+        self.hps = DictToAttrRecursive(self.hps)
+        self.hps.model.semantic_frame_rate = "25hz"
+        self.vq_model = SynthesizerTrn(
+            self.hps.data.filter_length // 2 + 1,
+            self.hps.train.segment_size // self.hps.data.hop_length,
+            n_speakers=self.hps.data.n_speakers,
+            **self.hps.model
+        )
+        self.vq_model.eval()
+        self.vq_model.load_state_dict(dict_s2["weight"], strict=False)
+        
+    def forward(self, text_seq, pred_semantic, ref_audio):
+        refer = spectrogram_torch(
+            ref_audio,
+            self.hps.data.filter_length,
+            self.hps.data.sampling_rate,
+            self.hps.data.hop_length,
+            self.hps.data.win_length,
+            center=False
+        )
+        return self.vq_model(pred_semantic, text_seq, refer)[0, 0]
+
+
+class GptSoVits(nn.Module):
+    def __init__(self, vits, t2s):
+        super().__init__()
+        self.vits = vits
+        self.t2s = t2s
+    
+    def forward(self, ref_seq, text_seq, ref_bert, text_bert, ref_audio, ssl_content, debug=False):
+        pred_semantic = self.t2s(ref_seq, text_seq, ref_bert, text_bert, ssl_content)
+        audio = self.vits(text_seq, pred_semantic, ref_audio)
+        if debug:
+            import onnxruntime
+            sess = onnxruntime.InferenceSession("onnx/koharu/koharu_vits.onnx", providers=["CPU"])
+            audio1 = sess.run(None, {
+                "text_seq" : text_seq.detach().cpu().numpy(),
+                "pred_semantic" : pred_semantic.detach().cpu().numpy(), 
+                "ref_audio" : ref_audio.detach().cpu().numpy()
+            })
+            return audio, audio1
+        return audio
+
+    def export(self, ref_seq, text_seq, ref_bert, text_bert, ref_audio, ssl_content, project_name):
+        self.t2s.export(ref_seq, text_seq, ref_bert, text_bert, ssl_content, project_name)
+        pred_semantic = self.t2s(ref_seq, text_seq, ref_bert, text_bert, ssl_content)
+        torch.onnx.export(
+            self.vits,
+            (text_seq, pred_semantic, ref_audio),
+            f"onnx/{project_name}/{project_name}_vits.onnx",
+            input_names=["text_seq", "pred_semantic", "ref_audio"],
+            output_names=["audio"],
+            dynamic_axes={
+                "text_seq": {1 : "text_length"},
+                "pred_semantic": {2 : "pred_length"},
+                "ref_audio": {1 : "audio_length"},
+            },
+            opset_version=17,
+            verbose=False
+        )
+
+
+class SSLModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.ssl = ssl_model
+
+    def forward(self, ref_audio_16k):
+        return self.ssl.model(ref_audio_16k)["last_hidden_state"].transpose(1, 2)
+
+
+def export(vits_path, gpt_path, project_name, vits_model="v2"):
+    vits = VitsModel(vits_path)
+    gpt = T2SModel(gpt_path, vits)
+    gpt_sovits = GptSoVits(vits, gpt)
+    ssl = SSLModel()
+    ref_seq = torch.LongTensor([cleaned_text_to_sequence(["n", "i2", "h", "ao3", ",", "w", "o3", "sh", "i4", "b", "ai2", "y", "e4"],version=vits_model)])
+    text_seq = torch.LongTensor([cleaned_text_to_sequence(["w", "o3", "sh", "i4", "b", "ai2", "y", "e4", "w", "o3", "sh", "i4", "b", "ai2", "y", "e4", "w", "o3", "sh", "i4", "b", "ai2", "y", "e4"],version=vits_model)])
+    ref_bert = torch.randn((ref_seq.shape[1], 1024)).float()
+    text_bert = torch.randn((text_seq.shape[1], 1024)).float()
+    ref_audio = torch.randn((1, 48000 * 5)).float()
+    # ref_audio = torch.tensor([load_audio("rec.wav", 48000)]).float()
+    ref_audio_16k = torchaudio.functional.resample(ref_audio,48000,16000).float()
+    ref_audio_sr = torchaudio.functional.resample(ref_audio,48000,vits.hps.data.sampling_rate).float()
+
+    try:
+        os.mkdir(f"onnx/{project_name}")
+    except:
+        pass
+
+    ssl_content = ssl(ref_audio_16k).float()
+
+    # debug = False
+    debug = True
+
+    # gpt_sovits.export(ref_seq, text_seq, ref_bert, text_bert, ref_audio_sr, ssl_content, project_name)
+
+    if debug:
+        a, b = gpt_sovits(ref_seq, text_seq, ref_bert, text_bert, ref_audio_sr, ssl_content, debug=debug)
+        soundfile.write("out1.wav", a.cpu().detach().numpy(), vits.hps.data.sampling_rate)
+        soundfile.write("out2.wav", b[0], vits.hps.data.sampling_rate)
+    else:
+        a = gpt_sovits(ref_seq, text_seq, ref_bert, text_bert, ref_audio_sr, ssl_content).detach().cpu().numpy()
+        soundfile.write("out.wav", a, vits.hps.data.sampling_rate)
+
+    if vits_model == "v1":
+        symbols = symbols_v1
+    else:
+        symbols = symbols_v2
+
+    MoeVSConf = {
+        "Folder": f"{project_name}",
+        "Name": f"{project_name}",
+        "Type": "GPT-SoVits",
+        "Rate": vits.hps.data.sampling_rate,
+        "NumLayers": gpt.t2s_model.num_layers,
+        "EmbeddingDim": gpt.t2s_model.embedding_dim,
+        "Dict": "BasicDict",
+        "BertPath": "chinese-roberta-wwm-ext-large",
+        # "Symbol": symbols,
+        "AddBlank": False,
+    }
+
+    MoeVSConfJson = json.dumps(MoeVSConf)
+    with open(f"onnx/{project_name}.json", 'w') as MoeVsConfFile:
+        json.dump(MoeVSConf, MoeVsConfFile, indent = 4)
+
+
+if __name__ == "__main__":
+    try:
+        os.mkdir("onnx")
+    except:
+        pass
+
+    gpt_path = "GPT_weights/nahida-e25.ckpt"
+    vits_path = "SoVITS_weights/nahida_e30_s3930.pth"
+    exp_path = "nahida"
+    export(vits_path, gpt_path, exp_path)
+
+    # soundfile.write("out.wav", a, vits.hps.data.sampling_rate)

GPT_SoVITS/prepare_data.py

@@ -0,0 +1,66 @@
+import os
+import shutil
+import torch
+import torchaudio
+from pathlib import Path
+
+def prepare_data_stage2(data_dir="data8", exp_dir="logs/s2"):
+    """Prepare data for stage 2 training"""
+    
+    # Get project root directory (parent of GPT_SoVITS)
+    root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+    
+    # Convert relative paths to absolute
+    data_dir = os.path.join(root_dir, data_dir)
+    exp_dir = os.path.join(root_dir, exp_dir)
+    
+    print(f"Data directory: {data_dir}")
+    print(f"Experiment directory: {exp_dir}")
+    
+    # Create required directories
+    os.makedirs(exp_dir, exist_ok=True)
+    os.makedirs(os.path.join(exp_dir, "4-cnhubert"), exist_ok=True)
+    os.makedirs(os.path.join(exp_dir, "5-wav32k"), exist_ok=True)
+    
+    # Convert phoneme.txt to name2text.txt format
+    phoneme_path = os.path.join(data_dir, "phoneme.txt")
+    name2text_path = os.path.join(exp_dir, "2-name2text.txt")
+    
+    print(f"Reading phoneme data from: {phoneme_path}")
+    print(f"Writing text data to: {name2text_path}")
+    
+    with open(phoneme_path, "r", encoding="utf8") as f_in, \
+         open(name2text_path, "w", encoding="utf8") as f_out:
+        for line in f_in:
+            parts = line.strip().split("|")
+            if len(parts) >= 2:
+                wav_name = os.path.basename(parts[0])
+                text = parts[1]
+                # Format: wav_name \t text \t speaker_id \t language_id
+                f_out.write(f"{wav_name}\t{text}\t0\tHindi\n")
+    
+    # Copy wav files to 5-wav32k
+    wav_dir = os.path.join(data_dir, "wavs")
+    wav32k_dir = os.path.join(exp_dir, "5-wav32k")
+    
+    print(f"Processing wav files from: {wav_dir}")
+    print(f"Saving to: {wav32k_dir}")
+    
+    for wav_file in os.listdir(wav_dir):
+        if wav_file.endswith(".wav"):
+            src_path = os.path.join(wav_dir, wav_file)
+            dst_path = os.path.join(wav32k_dir, wav_file)
+            
+            # Load and resample if needed
+            waveform, sr = torchaudio.load(src_path)
+            if sr != 32000:
+                resampler = torchaudio.transforms.Resample(sr, 32000)
+                waveform = resampler(waveform)
+            
+            # Save as 32kHz wav
+            torchaudio.save(dst_path, waveform, 32000)
+    
+    print("Data preparation complete. Please run the Hubert feature extraction before training.")
+
+if __name__ == "__main__":
+    prepare_data_stage2() 

GPT_SoVITS/pretrained_models/.gitignore

@@ -0,0 +1,2 @@
+*
+!.gitignore

GPT_SoVITS/pretrained_models/README.md

@@ -0,0 +1,5 @@
+---
+license: mit
+pipeline_tag: text-to-speech
+---
+pretrained models used in https://github.com/RVC-Boss/GPT-SoVITS

GPT_SoVITS/process_ckpt.py

@@ -0,0 +1,31 @@
+import traceback
+from collections import OrderedDict
+from time import time as ttime
+import shutil,os
+import torch
+from tools.i18n.i18n import I18nAuto
+
+i18n = I18nAuto()
+
+def my_save(fea,path):#####fix issue: torch.save doesn't support chinese path
+    dir=os.path.dirname(path)
+    name=os.path.basename(path)
+    tmp_path="%s.pth"%(ttime())
+    torch.save(fea,tmp_path)
+    shutil.move(tmp_path,"%s/%s"%(dir,name))
+
+def savee(ckpt, name, epoch, steps, hps):
+    try:
+        opt = OrderedDict()
+        opt["weight"] = {}
+        for key in ckpt.keys():
+            if "enc_q" in key:
+                continue
+            opt["weight"][key] = ckpt[key].half()
+        opt["config"] = hps
+        opt["info"] = "%sepoch_%siteration" % (epoch, steps)
+        # torch.save(opt, "%s/%s.pth" % (hps.save_weight_dir, name))
+        my_save(opt, "%s/%s.pth" % (hps.save_weight_dir, name))
+        return "Success."
+    except:
+        return traceback.format_exc()

GPT_SoVITS/s1_train.py

@@ -0,0 +1,113 @@
+# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/train_t2s.py
+import os
+import pdb
+import logging
+import argparse
+from pathlib import Path
+import torch, platform
+from pytorch_lightning import seed_everything
+from pytorch_lightning import Trainer
+from pytorch_lightning.callbacks import ModelCheckpoint
+from pytorch_lightning.strategies import DDPStrategy
+from AR.data.data_module import Text2SemanticDataModule
+from AR.models.t2s_lightning_module import Text2SemanticLightningModule
+from AR.utils.io import load_yaml_config
+from GPT_SoVITS.utils.wandb_logger import WandbLoggerWithConfig
+
+logging.getLogger("numba").setLevel(logging.WARNING)
+logging.getLogger("matplotlib").setLevel(logging.WARNING)
+torch.set_float32_matmul_precision("high")
+
+def my_model_ckpt(
+    config,
+    if_save_latest,
+    if_save_every_weights,
+    half_weights_save_dir,
+    exp_name,
+    **kwargs,
+):
+    if if_save_latest:
+        kwargs["save_last"] = True
+    callbacks = []
+    callbacks.append(
+        ModelCheckpoint(
+            **kwargs,
+            filename=exp_name + "_{epoch}-{step}",
+        )
+    )
+    return callbacks[0]
+
+def main(args):
+    config = load_yaml_config(args.config_file)
+    
+    output_dir = Path(config["output_dir"])
+    output_dir.mkdir(parents=True, exist_ok=True)
+    
+    ckpt_dir = output_dir / "ckpt"
+    ckpt_dir.mkdir(parents=True, exist_ok=True)
+    
+    seed_everything(config["train"]["seed"], workers=True)
+    
+    # Initialize wandb logger
+    wandb_logger = WandbLoggerWithConfig(config=config)
+    
+    ckpt_callback = my_model_ckpt(
+        config=config,
+        if_save_latest=config["train"]["if_save_latest"],
+        if_save_every_weights=config["train"]["if_save_every_weights"],
+        half_weights_save_dir=config["train"]["half_weights_save_dir"],
+        exp_name=config["train"]["exp_name"],
+        save_top_k=-1,
+        monitor="loss",
+        mode="min",
+        save_on_train_epoch_end=True,
+        every_n_epochs=config["train"]["save_every_n_epoch"],
+        dirpath=ckpt_dir,
+    )
+    
+    # Create data module
+    data_module = Text2SemanticDataModule(
+        config=config,
+        train_semantic_path=config.get("train_semantic_path", ""),
+        train_phoneme_path=config.get("train_phoneme_path", "")
+    )
+    
+    # Initialize model with correct parameters
+    model = Text2SemanticLightningModule(
+        config=config,
+        output_dir=output_dir,
+        is_train=True
+    )
+    
+    # Watch the model in wandb
+    wandb_logger.watch_model(model)
+    
+    trainer = Trainer(
+        max_epochs=config["train"]["epochs"],
+        accelerator="gpu" if torch.cuda.is_available() else "cpu",
+        devices=-1 if torch.cuda.is_available() else 1,
+        benchmark=False,
+        fast_dev_run=False,
+        strategy=DDPStrategy(
+            process_group_backend="nccl" if platform.system() != "Windows" else "gloo"
+        ) if torch.cuda.is_available() else "auto",
+        precision=config["train"]["precision"],
+        logger=wandb_logger,
+        callbacks=[ckpt_callback],
+        use_distributed_sampler=False,
+    )
+    
+    trainer.fit(model, data_module)
+    wandb.finish()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-c",
+        "--config_file",
+        type=str,
+        default="configs/s1.yaml",
+        help="path of config file",
+    )
+    args = parser.parse_args()
+    main(args)

GPT_SoVITS/s2_train.py

@@ -0,0 +1,610 @@
+import warnings
+warnings.filterwarnings("ignore")
+import utils
+from utils import get_hparams
+import os
+os.chdir(os.path.dirname(os.path.abspath(__file__)))
+hps = get_hparams(stage=2)
+
+# Set GPU device - use default if not specified in config
+if hasattr(hps.train, 'gpu_numbers'):
+    os.environ["CUDA_VISIBLE_DEVICES"] = hps.train.gpu_numbers.replace("-", ",")
+else:
+    os.environ["CUDA_VISIBLE_DEVICES"] = "0"  # Default to first GPU
+
+import torch
+from torch.nn import functional as F
+from torch.utils.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter
+import torch.multiprocessing as mp
+import torch.distributed as dist, traceback
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.cuda.amp import autocast, GradScaler
+from tqdm import tqdm
+import logging, traceback
+
+logging.getLogger("matplotlib").setLevel(logging.INFO)
+logging.getLogger("h5py").setLevel(logging.INFO)
+logging.getLogger("numba").setLevel(logging.INFO)
+from random import randint
+from module import commons
+
+from module.data_utils import (
+    TextAudioSpeakerLoader,
+    TextAudioSpeakerCollate,
+    DistributedBucketSampler,
+)
+from module.models import (
+    SynthesizerTrn,
+    MultiPeriodDiscriminator,
+)
+from module.losses import generator_loss, discriminator_loss, feature_loss, kl_loss
+from module.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
+from process_ckpt import savee
+
+torch.backends.cudnn.benchmark = False
+torch.backends.cudnn.deterministic = False
+###反正A100fp32更快，那试试tf32吧
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+torch.set_float32_matmul_precision("medium")  # 最低精度但最快（也就快一丁点），对于结果造成不了影响
+# from config import pretrained_s2G,pretrained_s2D
+global_step = 0
+
+device = "cpu"  # cuda以外的设备，等mps优化后加入
+
+
+def main():
+
+    if torch.cuda.is_available():
+        n_gpus = torch.cuda.device_count()
+    else:
+        n_gpus = 1
+    os.environ["MASTER_ADDR"] = "localhost"
+    os.environ["MASTER_PORT"] = str(randint(20000, 55555))
+
+    mp.spawn(
+        run,
+        nprocs=n_gpus,
+        args=(
+            n_gpus,
+            hps,
+        ),
+    )
+
+
+def run(rank, n_gpus, hps):
+    global global_step
+    if rank == 0:
+        logger = utils.get_logger(hps.data.exp_dir)
+        logger.info(hps)
+        # utils.check_git_hash(hps.s2_ckpt_dir)
+        writer = SummaryWriter(log_dir=hps.s2_ckpt_dir)
+        writer_eval = SummaryWriter(log_dir=os.path.join(hps.s2_ckpt_dir, "eval"))
+
+    dist.init_process_group(
+        backend = "gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl",
+        init_method="env://",
+        world_size=n_gpus,
+        rank=rank,
+    )
+    torch.manual_seed(hps.train.seed)
+    if torch.cuda.is_available():
+        torch.cuda.set_device(rank)
+
+    train_dataset = TextAudioSpeakerLoader(hps.data)  ########
+    train_sampler = DistributedBucketSampler(
+        train_dataset,
+        hps.train.batch_size,
+        [
+            32,
+            300,
+            400,
+            500,
+            600,
+            700,
+            800,
+            900,
+            1000,
+            1100,
+            1200,
+            1300,
+            1400,
+            1500,
+            1600,
+            1700,
+            1800,
+            1900,
+        ],
+        num_replicas=n_gpus,
+        rank=rank,
+        shuffle=True,
+    )
+    collate_fn = TextAudioSpeakerCollate()
+    train_loader = DataLoader(
+        train_dataset,
+        num_workers=6,
+        shuffle=False,
+        pin_memory=True,
+        collate_fn=collate_fn,
+        batch_sampler=train_sampler,
+        persistent_workers=True,
+        prefetch_factor=4,
+    )
+    # if rank == 0:
+    #     eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data, val=True)
+    #     eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False,
+    #                              batch_size=1, pin_memory=True,
+    #                              drop_last=False, collate_fn=collate_fn)
+
+    net_g = SynthesizerTrn(
+        hps.data.filter_length // 2 + 1,
+        hps.train.segment_size // hps.data.hop_length,
+        n_speakers=hps.data.n_speakers,
+        **hps.model,
+    ).cuda(rank) if torch.cuda.is_available() else SynthesizerTrn(
+        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_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank) if torch.cuda.is_available() else MultiPeriodDiscriminator(hps.model.use_spectral_norm).to(device)
+    for name, param in net_g.named_parameters():
+        if not param.requires_grad:
+            print(name, "not requires_grad")
+
+    te_p = list(map(id, net_g.enc_p.text_embedding.parameters()))
+    et_p = list(map(id, net_g.enc_p.encoder_text.parameters()))
+    mrte_p = list(map(id, net_g.enc_p.mrte.parameters()))
+    base_params = filter(
+        lambda p: id(p) not in te_p + et_p + mrte_p and p.requires_grad,
+        net_g.parameters(),
+    )
+
+    # te_p=net_g.enc_p.text_embedding.parameters()
+    # et_p=net_g.enc_p.encoder_text.parameters()
+    # mrte_p=net_g.enc_p.mrte.parameters()
+
+    optim_g = torch.optim.AdamW(
+        # filter(lambda p: p.requires_grad, net_g.parameters()),###默认所有层lr一致
+        [
+            {"params": base_params, "lr": hps.train.learning_rate},
+            {
+                "params": net_g.enc_p.text_embedding.parameters(),
+                "lr": hps.train.learning_rate * hps.train.text_low_lr_rate,
+            },
+            {
+                "params": net_g.enc_p.encoder_text.parameters(),
+                "lr": hps.train.learning_rate * hps.train.text_low_lr_rate,
+            },
+            {
+                "params": net_g.enc_p.mrte.parameters(),
+                "lr": hps.train.learning_rate * hps.train.text_low_lr_rate,
+            },
+        ],
+        hps.train.learning_rate,
+        betas=hps.train.betas,
+        eps=hps.train.eps,
+    )
+    optim_d = torch.optim.AdamW(
+        net_d.parameters(),
+        hps.train.learning_rate,
+        betas=hps.train.betas,
+        eps=hps.train.eps,
+    )
+    if torch.cuda.is_available():
+        net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
+        net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
+    else:
+        net_g = net_g.to(device)
+        net_d = net_d.to(device)
+
+    try:  # 如果能加载自动resume
+        _, _, _, epoch_str = utils.load_checkpoint(
+            utils.latest_checkpoint_path("%s/logs_s2" % hps.data.exp_dir, "D_*.pth"),
+            net_d,
+            optim_d,
+        )  # D多半加载没事
+        if rank == 0:
+            logger.info("loaded D")
+        # _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g,load_opt=0)
+        _, _, _, epoch_str = utils.load_checkpoint(
+            utils.latest_checkpoint_path("%s/logs_s2" % hps.data.exp_dir, "G_*.pth"),
+            net_g,
+            optim_g,
+        )
+        global_step = (epoch_str - 1) * len(train_loader)
+        # epoch_str = 1
+        # global_step = 0
+    except:  # 如果首次不能加载，加载pretrain
+        # traceback.print_exc()
+        epoch_str = 1
+        global_step = 0
+        if hps.train.pretrained_s2G != ""and hps.train.pretrained_s2G != None and os.path.exists(hps.train.pretrained_s2G):
+            if rank == 0:
+                logger.info("loaded pretrained %s" % hps.train.pretrained_s2G)
+            print(
+                net_g.module.load_state_dict(
+                    torch.load(hps.train.pretrained_s2G, map_location="cpu")["weight"],
+                    strict=False,
+                ) if torch.cuda.is_available() else net_g.load_state_dict(
+                    torch.load(hps.train.pretrained_s2G, map_location="cpu")["weight"],
+                    strict=False,
+                )
+            )  ##测试不加载优化器
+        if hps.train.pretrained_s2D != ""and hps.train.pretrained_s2D != None and os.path.exists(hps.train.pretrained_s2D):
+            if rank == 0:
+                logger.info("loaded pretrained %s" % hps.train.pretrained_s2D)
+            print(
+                net_d.module.load_state_dict(
+                    torch.load(hps.train.pretrained_s2D, map_location="cpu")["weight"]
+                ) if torch.cuda.is_available() else net_d.load_state_dict(
+                    torch.load(hps.train.pretrained_s2D, map_location="cpu")["weight"]
+                )
+            )
+
+    # scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
+    # scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
+
+    scheduler_g = torch.optim.lr_scheduler.ExponentialLR(
+        optim_g, gamma=hps.train.lr_decay, last_epoch=-1
+    )
+    scheduler_d = torch.optim.lr_scheduler.ExponentialLR(
+        optim_d, gamma=hps.train.lr_decay, last_epoch=-1
+    )
+    for _ in range(epoch_str):
+        scheduler_g.step()
+        scheduler_d.step()
+
+    scaler = GradScaler(enabled=hps.train.fp16_run)
+
+    for epoch in range(epoch_str, hps.train.epochs + 1):
+        if rank == 0:
+            train_and_evaluate(
+                rank,
+                epoch,
+                hps,
+                [net_g, net_d],
+                [optim_g, optim_d],
+                [scheduler_g, scheduler_d],
+                scaler,
+                # [train_loader, eval_loader], logger, [writer, writer_eval])
+                [train_loader, None],
+                logger,
+                [writer, writer_eval],
+            )
+        else:
+            train_and_evaluate(
+                rank,
+                epoch,
+                hps,
+                [net_g, net_d],
+                [optim_g, optim_d],
+                [scheduler_g, scheduler_d],
+                scaler,
+                [train_loader, None],
+                None,
+                None,
+            )
+        scheduler_g.step()
+        scheduler_d.step()
+
+
+def train_and_evaluate(
+    rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers
+):
+    net_g, net_d = nets
+    optim_g, optim_d = optims
+    # scheduler_g, scheduler_d = schedulers
+    train_loader, eval_loader = loaders
+    if writers is not None:
+        writer, writer_eval = writers
+
+    train_loader.batch_sampler.set_epoch(epoch)
+    global global_step
+
+    net_g.train()
+    net_d.train()
+    for batch_idx, (
+        ssl,
+        ssl_lengths,
+        spec,
+        spec_lengths,
+        y,
+        y_lengths,
+        text,
+        text_lengths,
+    ) in enumerate(tqdm(train_loader)):
+        if torch.cuda.is_available():
+            spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(
+                rank, non_blocking=True
+            )
+            y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(
+                rank, non_blocking=True
+            )
+            ssl = ssl.cuda(rank, non_blocking=True)
+            ssl.requires_grad = False
+            # ssl_lengths = ssl_lengths.cuda(rank, non_blocking=True)
+            text, text_lengths = text.cuda(rank, non_blocking=True), text_lengths.cuda(
+                rank, non_blocking=True
+            )
+        else:
+            spec, spec_lengths = spec.to(device), spec_lengths.to(device)
+            y, y_lengths = y.to(device), y_lengths.to(device)
+            ssl = ssl.to(device)
+            ssl.requires_grad = False
+            # ssl_lengths = ssl_lengths.cuda(rank, non_blocking=True)
+            text, text_lengths = text.to(device), text_lengths.to(device)
+
+        with autocast(enabled=hps.train.fp16_run):
+            (
+                y_hat,
+                kl_ssl,
+                ids_slice,
+                x_mask,
+                z_mask,
+                (z, z_p, m_p, logs_p, m_q, logs_q),
+                stats_ssl,
+            ) = net_g(ssl, spec, spec_lengths, text, text_lengths)
+
+            mel = spec_to_mel_torch(
+                spec,
+                hps.data.filter_length,
+                hps.data.n_mel_channels,
+                hps.data.sampling_rate,
+                hps.data.mel_fmin,
+                hps.data.mel_fmax,
+            )
+            y_mel = commons.slice_segments(
+                mel, ids_slice, hps.train.segment_size // hps.data.hop_length
+            )
+            y_hat_mel = mel_spectrogram_torch(
+                y_hat.squeeze(1),
+                hps.data.filter_length,
+                hps.data.n_mel_channels,
+                hps.data.sampling_rate,
+                hps.data.hop_length,
+                hps.data.win_length,
+                hps.data.mel_fmin,
+                hps.data.mel_fmax,
+            )
+
+            y = commons.slice_segments(
+                y, ids_slice * hps.data.hop_length, hps.train.segment_size
+            )  # slice
+
+            # Discriminator
+            y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
+            with autocast(enabled=False):
+                loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
+                    y_d_hat_r, y_d_hat_g
+                )
+                loss_disc_all = loss_disc
+        optim_d.zero_grad()
+        scaler.scale(loss_disc_all).backward()
+        scaler.unscale_(optim_d)
+        grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
+        scaler.step(optim_d)
+
+        with autocast(enabled=hps.train.fp16_run):
+            # Generator
+            y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
+            with autocast(enabled=False):
+                loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
+                loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
+
+                loss_fm = feature_loss(fmap_r, fmap_g)
+                loss_gen, losses_gen = generator_loss(y_d_hat_g)
+                loss_gen_all = loss_gen + loss_fm + loss_mel + kl_ssl * 1 + loss_kl
+
+        optim_g.zero_grad()
+        scaler.scale(loss_gen_all).backward()
+        scaler.unscale_(optim_g)
+        grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
+        scaler.step(optim_g)
+        scaler.update()
+
+        if rank == 0:
+            if global_step % hps.train.log_interval == 0:
+                lr = optim_g.param_groups[0]["lr"]
+                losses = [loss_disc, loss_gen, loss_fm, loss_mel, kl_ssl, loss_kl]
+                logger.info(
+                    "Train Epoch: {} [{:.0f}%]".format(
+                        epoch, 100.0 * batch_idx / len(train_loader)
+                    )
+                )
+                logger.info([x.item() for x in losses] + [global_step, lr])
+
+                scalar_dict = {
+                    "loss/g/total": loss_gen_all,
+                    "loss/d/total": loss_disc_all,
+                    "learning_rate": lr,
+                    "grad_norm_d": grad_norm_d,
+                    "grad_norm_g": grad_norm_g,
+                }
+                scalar_dict.update(
+                    {
+                        "loss/g/fm": loss_fm,
+                        "loss/g/mel": loss_mel,
+                        "loss/g/kl_ssl": kl_ssl,
+                        "loss/g/kl": loss_kl,
+                    }
+                )
+
+                # scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
+                # scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
+                # scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
+                image_dict = {
+                    "slice/mel_org": utils.plot_spectrogram_to_numpy(
+                        y_mel[0].data.cpu().numpy()
+                    ),
+                    "slice/mel_gen": utils.plot_spectrogram_to_numpy(
+                        y_hat_mel[0].data.cpu().numpy()
+                    ),
+                    "all/mel": utils.plot_spectrogram_to_numpy(
+                        mel[0].data.cpu().numpy()
+                    ),
+                    "all/stats_ssl": utils.plot_spectrogram_to_numpy(
+                        stats_ssl[0].data.cpu().numpy()
+                    ),
+                }
+                utils.summarize(
+                    writer=writer,
+                    global_step=global_step,
+                    images=image_dict,
+                    scalars=scalar_dict,
+                )
+        global_step += 1
+    if epoch % hps.train.save_every_epoch == 0 and rank == 0:
+        if hps.train.if_save_latest == 0:
+            utils.save_checkpoint(
+                net_g,
+                optim_g,
+                hps.train.learning_rate,
+                epoch,
+                os.path.join(
+                    "%s/logs_s2" % hps.data.exp_dir, "G_{}.pth".format(global_step)
+                ),
+            )
+            utils.save_checkpoint(
+                net_d,
+                optim_d,
+                hps.train.learning_rate,
+                epoch,
+                os.path.join(
+                    "%s/logs_s2" % hps.data.exp_dir, "D_{}.pth".format(global_step)
+                ),
+            )
+        else:
+            utils.save_checkpoint(
+                net_g,
+                optim_g,
+                hps.train.learning_rate,
+                epoch,
+                os.path.join(
+                    "%s/logs_s2" % hps.data.exp_dir, "G_{}.pth".format(233333333333)
+                ),
+            )
+            utils.save_checkpoint(
+                net_d,
+                optim_d,
+                hps.train.learning_rate,
+                epoch,
+                os.path.join(
+                    "%s/logs_s2" % hps.data.exp_dir, "D_{}.pth".format(233333333333)
+                ),
+            )
+        if rank == 0 and hps.train.if_save_every_weights == True:
+            if hasattr(net_g, "module"):
+                ckpt = net_g.module.state_dict()
+            else:
+                ckpt = net_g.state_dict()
+            logger.info(
+                "saving ckpt %s_e%s:%s"
+                % (
+                    hps.name,
+                    epoch,
+                    savee(
+                        ckpt,
+                        hps.name + "_e%s_s%s" % (epoch, global_step),
+                        epoch,
+                        global_step,
+                        hps,
+                    ),
+                )
+            )
+
+    if rank == 0:
+        logger.info("====> Epoch: {}".format(epoch))
+
+
+def evaluate(hps, generator, eval_loader, writer_eval):
+    generator.eval()
+    image_dict = {}
+    audio_dict = {}
+    print("Evaluating ...")
+    with torch.no_grad():
+        for batch_idx, (
+            ssl,
+            ssl_lengths,
+            spec,
+            spec_lengths,
+            y,
+            y_lengths,
+            text,
+            text_lengths,
+        ) in enumerate(eval_loader):
+            print(111)
+            if torch.cuda.is_available():
+                spec, spec_lengths = spec.cuda(), spec_lengths.cuda()
+                y, y_lengths = y.cuda(), y_lengths.cuda()
+                ssl = ssl.cuda()
+                text, text_lengths = text.cuda(), text_lengths.cuda()
+            else:
+                spec, spec_lengths = spec.to(device), spec_lengths.to(device)
+                y, y_lengths = y.to(device), y_lengths.to(device)
+                ssl = ssl.to(device)
+                text, text_lengths = text.to(device), text_lengths.to(device)
+            for test in [0, 1]:
+                y_hat, mask, *_ = generator.module.infer(
+                    ssl, spec, spec_lengths, text, text_lengths, test=test
+                ) if torch.cuda.is_available() else generator.infer(
+                    ssl, spec, spec_lengths, text, text_lengths, test=test
+                )
+                y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length
+
+                mel = spec_to_mel_torch(
+                    spec,
+                    hps.data.filter_length,
+                    hps.data.n_mel_channels,
+                    hps.data.sampling_rate,
+                    hps.data.mel_fmin,
+                    hps.data.mel_fmax,
+                )
+                y_hat_mel = mel_spectrogram_torch(
+                    y_hat.squeeze(1).float(),
+                    hps.data.filter_length,
+                    hps.data.n_mel_channels,
+                    hps.data.sampling_rate,
+                    hps.data.hop_length,
+                    hps.data.win_length,
+                    hps.data.mel_fmin,
+                    hps.data.mel_fmax,
+                )
+                image_dict.update(
+                    {
+                        f"gen/mel_{batch_idx}_{test}": utils.plot_spectrogram_to_numpy(
+                            y_hat_mel[0].cpu().numpy()
+                        )
+                    }
+                )
+                audio_dict.update(
+                    {f"gen/audio_{batch_idx}_{test}": y_hat[0, :, : y_hat_lengths[0]]}
+                )
+                image_dict.update(
+                    {
+                        f"gt/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(
+                            mel[0].cpu().numpy()
+                        )
+                    }
+                )
+                audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, : y_lengths[0]]})
+
+        # y_hat, mask, *_ = generator.module.infer(ssl, spec_lengths, speakers, y=None)
+        # audio_dict.update({
+        #     f"gen/audio_{batch_idx}_style_pred": y_hat[0, :, :]
+        # })
+
+    utils.summarize(
+        writer=writer_eval,
+        global_step=global_step,
+        images=image_dict,
+        audios=audio_dict,
+        audio_sampling_rate=hps.data.sampling_rate,
+    )
+    generator.train()
+
+
+if __name__ == "__main__":
+    main()

GPT_SoVITS/text/.gitignore

@@ -0,0 +1,3 @@
+G2PWModel
+__pycache__
+*.zip

GPT_SoVITS/text/__init__.py

@@ -0,0 +1,27 @@
+import os
+import importlib
+
+# Force reload the symbol modules to get updated symbols
+from text import symbols as symbols_v1
+from text import symbols2 as symbols_v2
+importlib.reload(symbols_v1)
+importlib.reload(symbols_v2)
+
+_symbol_to_id_v1 = {s: i for i, s in enumerate(symbols_v1.symbols)}
+_symbol_to_id_v2 = {s: i for i, s in enumerate(symbols_v2.symbols)}
+
+def cleaned_text_to_sequence(cleaned_text, version=None):
+  '''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
+  '''
+  if version is None:version=os.environ.get('version', 'v2')
+  if version == "v1":
+    phones = [_symbol_to_id_v1[symbol] for symbol in cleaned_text]
+  else:
+    phones = [_symbol_to_id_v2[symbol] for symbol in cleaned_text]
+
+  return phones
+

GPT_SoVITS/text/cantonese.py

@@ -0,0 +1,209 @@
+# reference: https://huggingface.co/spaces/Naozumi0512/Bert-VITS2-Cantonese-Yue/blob/main/text/chinese.py
+
+import sys
+import re
+import cn2an
+
+from pyjyutping import jyutping
+from text.symbols import punctuation
+from text.zh_normalization.text_normlization import TextNormalizer
+
+normalizer = lambda x: cn2an.transform(x, "an2cn")
+
+INITIALS = [
+    "aa",
+    "aai",
+    "aak",
+    "aap",
+    "aat",
+    "aau",
+    "ai",
+    "au",
+    "ap",
+    "at",
+    "ak",
+    "a",
+    "p",
+    "b",
+    "e",
+    "ts",
+    "t",
+    "dz",
+    "d",
+    "kw",
+    "k",
+    "gw",
+    "g",
+    "f",
+    "h",
+    "l",
+    "m",
+    "ng",
+    "n",
+    "s",
+    "y",
+    "w",
+    "c",
+    "z",
+    "j",
+    "ong",
+    "on",
+    "ou",
+    "oi",
+    "ok",
+    "o",
+    "uk",
+    "ung",
+]
+INITIALS += ["sp", "spl", "spn", "sil"]
+
+
+rep_map = {
+    "：": ",",
+    "；": ",",
+    "，": ",",
+    "。": ".",
+    "！": "!",
+    "？": "?",
+    "\n": ".",
+    "·": ",",
+    "、": ",",
+    "...": "…",
+    "$": ".",
+    "“": "'",
+    "”": "'",
+    '"': "'",
+    "‘": "'",
+    "’": "'",
+    "（": "'",
+    "）": "'",
+    "(": "'",
+    ")": "'",
+    "《": "'",
+    "》": "'",
+    "【": "'",
+    "】": "'",
+    "[": "'",
+    "]": "'",
+    "—": "-",
+    "～": "-",
+    "~": "-",
+    "「": "'",
+    "」": "'",
+}
+
+
+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 text_normalize(text):
+    tx = TextNormalizer()
+    sentences = tx.normalize(text)
+    dest_text = ""
+    for sentence in sentences:
+        dest_text += replace_punctuation(sentence)
+    return dest_text
+
+
+punctuation_set=set(punctuation)
+def jyuping_to_initials_finals_tones(jyuping_syllables):
+    initials_finals = []
+    tones = []
+    word2ph = []
+
+    for syllable in jyuping_syllables:
+        if syllable in punctuation:
+            initials_finals.append(syllable)
+            tones.append(0)
+            word2ph.append(1)  # Add 1 for punctuation
+        elif syllable == "_":
+            initials_finals.append(syllable)
+            tones.append(0)
+            word2ph.append(1)  # Add 1 for underscore
+        else:
+            try:
+                tone = int(syllable[-1])
+                syllable_without_tone = syllable[:-1]
+            except ValueError:
+                tone = 0
+                syllable_without_tone = syllable
+
+            for initial in INITIALS:
+                if syllable_without_tone.startswith(initial):
+                    if syllable_without_tone.startswith("nga"):
+                        initials_finals.extend(
+                            [
+                                syllable_without_tone[:2],
+                                syllable_without_tone[2:] or syllable_without_tone[-1],
+                            ]
+                        )
+                        # tones.extend([tone, tone])
+                        tones.extend([-1, tone])
+                        word2ph.append(2)
+                    else:
+                        final = syllable_without_tone[len(initial) :] or initial[-1]
+                        initials_finals.extend([initial, final])
+                        # tones.extend([tone, tone])
+                        tones.extend([-1, tone])
+                        word2ph.append(2)
+                    break
+    assert len(initials_finals) == len(tones)
+
+    ###魔改为辅音+带音调的元音
+    phones=[]
+    for a,b in zip(initials_finals,tones):
+        if(b not in [-1,0]):###防止粤语和普通话重合开头加Y，如果是标点，不加。
+            todo="%s%s"%(a,b)
+        else:todo=a
+        if(todo not in punctuation_set):todo="Y%s"%todo
+        phones.append(todo)
+
+    # return initials_finals, tones, word2ph
+    return phones, word2ph
+
+
+def get_jyutping(text):
+    jp = jyutping.convert(text)
+    # print(1111111,jp)
+    for symbol in punctuation:
+        jp = jp.replace(symbol, " " + symbol + " ")
+    jp_array = jp.split()
+    return jp_array
+
+
+def get_bert_feature(text, word2ph):
+    from text import chinese_bert
+
+    return chinese_bert.get_bert_feature(text, word2ph)
+
+
+def g2p(text):
+    # word2ph = []
+    jyuping = get_jyutping(text)
+    # print(jyuping)
+    # phones, tones, word2ph = jyuping_to_initials_finals_tones(jyuping)
+    phones, word2ph = jyuping_to_initials_finals_tones(jyuping)
+    # phones = ["_"] + phones + ["_"]
+    # tones = [0] + tones + [0]
+    # word2ph = [1] + word2ph + [1]
+    return phones, word2ph
+
+
+if __name__ == "__main__":
+    # 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 = "佢個鋤頭太短啦。"
+    text = text_normalize(text)
+    # phones, tones, word2ph = g2p(text)
+    phones, word2ph = g2p(text)
+    # print(phones, tones, word2ph)
+    print(phones, word2ph)

GPT_SoVITS/text/chinese.py

@@ -0,0 +1,211 @@
+import os
+import pdb
+import re
+
+import cn2an
+from pypinyin import lazy_pinyin, Style
+
+from text.symbols import punctuation
+from text.tone_sandhi import ToneSandhi
+from text.zh_normalization.text_normlization import TextNormalizer
+
+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_fast.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 replace_punctuation_with_en(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-\u9fa5A-Za-z" + "".join(punctuation) + r"]+", "", replaced_text
+    )
+
+    return replaced_text
+
+
+def replace_consecutive_punctuation(text):
+    punctuations = ''.join(re.escape(p) for p in punctuation)
+    pattern = f'([{punctuations}])([{punctuations}])+'
+    result = re.sub(pattern, r'\1', text)
+    return result
+
+
+def g2p(text):
+    pattern = r"(?<=[{0}])\s*".format("".join(punctuation))
+    sentences = [i for i in re.split(pattern, text) if i.strip() != ""]
+    phones, word2ph = _g2p(sentences)
+    return phones, 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 = []
+    word2ph = []
+    for seg in segments:
+        pinyins = []
+        # 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]
+                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)
+                new_c, new_v = pinyin_to_symbol_map[pinyin].split(" ")
+                new_v = new_v + tone
+                phone = [new_c, new_v]
+                word2ph.append(len(phone))
+
+            phones_list += phone
+    return phones_list, word2ph
+
+
+def text_normalize(text):
+    # https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization
+    tx = TextNormalizer()
+    sentences = tx.normalize(text)
+    dest_text = ""
+    for sentence in sentences:
+        dest_text += replace_punctuation(sentence)
+
+    # 避免重复标点引起的参考泄露
+    dest_text = replace_consecutive_punctuation(dest_text)
+    return dest_text
+
+
+# 不排除英文的文本格式化
+def mix_text_normalize(text):
+    # https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization
+    tx = TextNormalizer()
+    sentences = tx.normalize(text)
+    dest_text = ""
+    for sentence in sentences:
+        dest_text += replace_punctuation_with_en(sentence)
+
+    # 避免重复标点引起的参考泄露
+    dest_text = replace_consecutive_punctuation(dest_text)
+    return dest_text
+
+
+if __name__ == "__main__":
+    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 = "呣呣呣～就是…大人的鼹鼠党吧？"
+    text = "你好"
+    text = text_normalize(text)
+    print(g2p(text))
+
+
+# # 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

GPT_SoVITS/text/chinese2.py

@@ -0,0 +1,308 @@
+import os
+import pdb
+import re
+
+import cn2an
+from pypinyin import lazy_pinyin, Style
+from pypinyin.contrib.tone_convert import to_normal, to_finals_tone3, to_initials, to_finals
+
+from text.symbols import punctuation
+from text.tone_sandhi import ToneSandhi
+from text.zh_normalization.text_normlization import TextNormalizer
+
+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_fast.posseg as psg
+
+# is_g2pw_str = os.environ.get("is_g2pw", "True")##默认开启
+# is_g2pw = False#True if is_g2pw_str.lower() == 'true' else False
+is_g2pw = True#True if is_g2pw_str.lower() == 'true' else False
+if is_g2pw:
+    print("当前使用g2pw进行拼音推理")
+    from text.g2pw import G2PWPinyin, correct_pronunciation
+    parent_directory = os.path.dirname(current_file_path)
+    g2pw = G2PWPinyin(model_dir="GPT_SoVITS/text/G2PWModel",model_source=os.environ.get("bert_path","GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"),v_to_u=False, neutral_tone_with_five=True)
+
+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, word2ph = _g2p(sentences)
+    return phones, 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
+
+
+must_erhua = {
+    "小院儿", "胡同儿", "范儿", "老汉儿", "撒欢儿", "寻老礼儿", "妥妥儿", "媳妇儿"
+}
+not_erhua = {
+    "虐儿", "为儿", "护儿", "瞒儿", "救儿", "替儿", "有儿", "一儿", "我儿", "俺儿", "妻儿",
+    "拐儿", "聋儿", "乞儿", "患儿", "幼儿", "孤儿", "婴儿", "婴幼儿", "连体儿", "脑瘫儿",
+    "流浪儿", "体弱儿", "混血儿", "蜜雪儿", "舫儿", "祖儿", "美儿", "应采儿", "可儿", "侄儿",
+    "孙儿", "侄孙儿", "女儿", "男儿", "红孩儿", "花儿", "虫儿", "马儿", "鸟儿", "猪儿", "猫儿",
+    "狗儿", "少儿"
+}
+def _merge_erhua(initials: list[str],
+                finals: list[str],
+                word: str,
+                pos: str) -> list[list[str]]:
+    """
+    Do erhub.
+    """
+    # fix er1
+    for i, phn in enumerate(finals):
+        if i == len(finals) - 1 and word[i] == "儿" and phn == 'er1':
+            finals[i] = 'er2'
+
+    # 发音
+    if word not in must_erhua and (word in not_erhua or
+                                        pos in {"a", "j", "nr"}):
+        return initials, finals
+
+    # "……" 等情况直接返回
+    if len(finals) != len(word):
+        return initials, finals
+
+    assert len(finals) == len(word)
+
+    # 与前一个字发同音
+    new_initials = []
+    new_finals = []
+    for i, phn in enumerate(finals):
+        if i == len(finals) - 1 and word[i] == "儿" and phn in {
+                "er2", "er5"
+        } and word[-2:] not in not_erhua and new_finals:
+            phn = "er" + new_finals[-1][-1]
+
+        new_initials.append(initials[i])
+        new_finals.append(phn)
+
+    return new_initials, new_finals
+
+
+def _g2p(segments):
+    phones_list = []
+    word2ph = []
+    for seg in segments:
+        pinyins = []
+        # Replace all English words in the sentence
+        seg = re.sub("[a-zA-Z]+", "", seg)
+        seg_cut = psg.lcut(seg)
+        seg_cut = tone_modifier.pre_merge_for_modify(seg_cut)
+        initials = []
+        finals = []
+
+        if not is_g2pw:
+            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)
+                # 儿化
+                sub_initials, sub_finals = _merge_erhua(sub_initials, sub_finals, word, pos)
+                initials.append(sub_initials)
+                finals.append(sub_finals)
+                # assert len(sub_initials) == len(sub_finals) == len(word)
+            initials = sum(initials, [])
+            finals = sum(finals, [])
+            print("pypinyin结果",initials,finals)
+        else:
+            # g2pw采用整句推理
+            pinyins = g2pw.lazy_pinyin(seg, neutral_tone_with_five=True, style=Style.TONE3)
+
+            pre_word_length = 0
+            for word, pos in seg_cut:
+                sub_initials = []
+                sub_finals = []
+                now_word_length = pre_word_length + len(word)
+
+                if pos == 'eng':
+                    pre_word_length = now_word_length
+                    continue
+
+                word_pinyins = pinyins[pre_word_length:now_word_length]
+
+                # 多音字消歧
+                word_pinyins = correct_pronunciation(word,word_pinyins)
+
+                for pinyin in word_pinyins:
+                    if pinyin[0].isalpha():
+                        sub_initials.append(to_initials(pinyin))
+                        sub_finals.append(to_finals_tone3(pinyin,neutral_tone_with_five=True))
+                    else:
+                        sub_initials.append(pinyin)
+                        sub_finals.append(pinyin)
+
+                pre_word_length = now_word_length
+                sub_finals = tone_modifier.modified_tone(word, pos, sub_finals)
+                # 儿化
+                sub_initials, sub_finals = _merge_erhua(sub_initials, sub_finals, word, pos)
+                initials.append(sub_initials)
+                finals.append(sub_finals)
+
+            initials = sum(initials, [])
+            finals = sum(finals, [])
+            # print("g2pw结果",initials,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]
+                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)
+                new_c, new_v = pinyin_to_symbol_map[pinyin].split(" ")
+                new_v = new_v + tone
+                phone = [new_c, new_v]
+                word2ph.append(len(phone))
+
+            phones_list += phone
+    return phones_list, word2ph
+
+
+def replace_punctuation_with_en(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-\u9fa5A-Za-z" + "".join(punctuation) + r"]+", "", replaced_text
+    )
+
+    return replaced_text
+
+def replace_consecutive_punctuation(text):
+    punctuations = ''.join(re.escape(p) for p in punctuation)
+    pattern = f'([{punctuations}])([{punctuations}])+'
+    result = re.sub(pattern, r'\1', text)
+    return result
+
+def text_normalize(text):
+    # https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization
+    tx = TextNormalizer()
+    sentences = tx.normalize(text)
+    dest_text = ""
+    for sentence in sentences:
+        dest_text += replace_punctuation(sentence)
+
+    # 避免重复标点引起的参考泄露
+    dest_text = replace_consecutive_punctuation(dest_text)
+    return dest_text
+
+# 不排除英文的文本格式化
+def mix_text_normalize(text):
+    # https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization
+    tx = TextNormalizer()
+    sentences = tx.normalize(text)
+    dest_text = ""
+    for sentence in sentences:
+        dest_text += replace_punctuation_with_en(sentence)
+
+    # 避免重复标点引起的参考泄露
+    dest_text = replace_consecutive_punctuation(dest_text)
+    return dest_text
+
+
+if __name__ == "__main__":
+    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 = "呣呣呣～就是…大人的鼹鼠党吧？"
+    text = "你好"
+    text = text_normalize(text)
+    print(g2p(text))
+
+
+# # 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

GPT_SoVITS/text/cleaner.py

@@ -0,0 +1,91 @@
+from text import cleaned_text_to_sequence
+import os
+# if os.environ.get("version","v1")=="v1":
+#     from text import chinese
+#     from text.symbols import symbols
+# else:
+#     from text import chinese2 as chinese
+#     from text.symbols2 import symbols
+
+from text import symbols as symbols_v1
+from text import symbols2 as symbols_v2
+
+special = [
+    # ("%", "zh", "SP"),
+    ("￥", "zh", "SP2"),
+    ("^", "zh", "SP3"),
+    # ('@', 'zh', "SP4")#不搞鬼畜了，和第二版保持一致吧
+]
+
+
+def clean_text(text, language, version=None):
+    if version is None:version=os.environ.get('version', 'v2')
+    if version == "v1":
+        symbols = symbols_v1.symbols
+        language_module_map = {"zh": "chinese", "ja": "japanese", "en": "english"}
+    else:
+        symbols = symbols_v2.symbols
+        language_module_map = {"zh": "chinese2", "ja": "japanese", "en": "english", "ko": "korean","yue":"cantonese"}
+
+    if(language not in language_module_map):
+        language="en"
+        text=" "
+    for special_s, special_l, target_symbol in special:
+        if special_s in text and language == special_l:
+            return clean_special(text, language, special_s, target_symbol, version)
+    language_module = __import__("text."+language_module_map[language],fromlist=[language_module_map[language]])
+    if hasattr(language_module,"text_normalize"):
+        norm_text = language_module.text_normalize(text)
+    else:
+        norm_text=text
+    if language == "zh" or language=="yue":##########
+        phones, word2ph = language_module.g2p(norm_text)
+        assert len(phones) == sum(word2ph)
+        assert len(norm_text) == len(word2ph)
+    elif language == "en":
+        phones = language_module.g2p(norm_text)
+        if len(phones) < 4:
+            phones = [','] + phones
+        word2ph = None
+    else:
+        phones = language_module.g2p(norm_text)
+        word2ph = None
+    phones = ['UNK' if ph not in symbols else ph for ph in phones]
+    return phones, word2ph, norm_text
+
+
+def clean_special(text, language, special_s, target_symbol, version=None):
+    if version is None:version=os.environ.get('version', 'v2')
+    if version == "v1":
+        symbols = symbols_v1.symbols
+        language_module_map = {"zh": "chinese", "ja": "japanese", "en": "english"}
+    else:
+        symbols = symbols_v2.symbols
+        language_module_map = {"zh": "chinese2", "ja": "japanese", "en": "english", "ko": "korean","yue":"cantonese"}
+
+    """
+    特殊静音段sp符号处理
+    """
+    text = text.replace(special_s, ",")
+    language_module = __import__("text."+language_module_map[language],fromlist=[language_module_map[language]])
+    norm_text = language_module.text_normalize(text)
+    phones = language_module.g2p(norm_text)
+    new_ph = []
+    for ph in phones[0]:
+        assert ph in symbols
+        if ph == ",":
+            new_ph.append(target_symbol)
+        else:
+            new_ph.append(ph)
+    return new_ph, phones[1], norm_text
+
+
+def text_to_sequence(text, language, version=None):
+    version = os.environ.get('version',version)
+    if version is None:version='v2'
+    phones = clean_text(text)
+    return cleaned_text_to_sequence(phones, version)
+
+
+if __name__ == "__main__":
+    print(clean_text("你好%啊啊啊额、还是到付红四方。", "zh"))

GPT_SoVITS/text/engdict-hot.rep

@@ -0,0 +1,3 @@
+CHATGPT CH AE1 T JH IY1 P IY1 T IY1
+JSON JH EY1 S AH0 N
+CONDA K AA1 N D AH0

GPT_SoVITS/text/english.py

@@ -0,0 +1,374 @@
+import pickle
+import os
+import re
+import wordsegment
+from g2p_en import G2p
+
+from text.symbols import punctuation
+
+from text.symbols2 import symbols
+
+import unicodedata
+from builtins import str as unicode
+from g2p_en.expand import normalize_numbers
+from nltk.tokenize import TweetTokenizer
+word_tokenize = TweetTokenizer().tokenize
+from nltk import pos_tag
+
+current_file_path = os.path.dirname(__file__)
+CMU_DICT_PATH = os.path.join(current_file_path, "cmudict.rep")
+CMU_DICT_FAST_PATH = os.path.join(current_file_path, "cmudict-fast.rep")
+CMU_DICT_HOT_PATH = os.path.join(current_file_path, "engdict-hot.rep")
+CACHE_PATH = os.path.join(current_file_path, "engdict_cache.pickle")
+NAMECACHE_PATH = os.path.join(current_file_path, "namedict_cache.pickle")
+
+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 replace_phs(phs):
+    rep_map = {"'": "-"}
+    phs_new = []
+    for ph in phs:
+        if ph in symbols:
+            phs_new.append(ph)
+        elif ph in rep_map.keys():
+            phs_new.append(rep_map[ph])
+        else:
+            print("ph not in symbols: ", ph)
+    return phs_new
+
+
+def replace_consecutive_punctuation(text):
+    punctuations = ''.join(re.escape(p) for p in punctuation)
+    pattern = f'([{punctuations}])([{punctuations}])+'
+    result = re.sub(pattern, r'\1', text)
+    return result
+
+
+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].lower()
+
+                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 read_dict_new():
+    g2p_dict = {}
+    with open(CMU_DICT_PATH) as f:
+        line = f.readline()
+        line_index = 1
+        while line:
+            if line_index >= 57:
+                line = line.strip()
+                word_split = line.split("  ")
+                word = word_split[0].lower()
+                g2p_dict[word] = [word_split[1].split(" ")]
+
+            line_index = line_index + 1
+            line = f.readline()
+
+    with open(CMU_DICT_FAST_PATH) as f:
+        line = f.readline()
+        line_index = 1
+        while line:
+            if line_index >= 0:
+                line = line.strip()
+                word_split = line.split(" ")
+                word = word_split[0].lower()
+                if word not in g2p_dict:
+                    g2p_dict[word] = [word_split[1:]]
+
+            line_index = line_index + 1
+            line = f.readline()
+
+    return g2p_dict
+
+def hot_reload_hot(g2p_dict):
+    with open(CMU_DICT_HOT_PATH) as f:
+        line = f.readline()
+        line_index = 1
+        while line:
+            if line_index >= 0:
+                line = line.strip()
+                word_split = line.split(" ")
+                word = word_split[0].lower()
+                # 自定义发音词直接覆盖字典
+                g2p_dict[word] = [word_split[1:]]
+
+            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_new()
+        cache_dict(g2p_dict, CACHE_PATH)
+
+    g2p_dict = hot_reload_hot(g2p_dict)
+
+    return g2p_dict
+
+
+def get_namedict():
+    if os.path.exists(NAMECACHE_PATH):
+        with open(NAMECACHE_PATH, "rb") as pickle_file:
+            name_dict = pickle.load(pickle_file)
+    else:
+        name_dict = {}
+
+    return name_dict
+
+
+def text_normalize(text):
+    # todo: eng text normalize
+    # 适配中文及 g2p_en 标点
+    rep_map = {
+        "[;:：��；]": ",",
+        '["’]': "'",
+        "。": ".",
+        "！": "!",
+        "？": "?",
+    }
+    for p, r in rep_map.items():
+        text = re.sub(p, r, text)
+
+    # 来自 g2p_en 文本格式化处理
+    # 增加大写兼容
+    text = unicode(text)
+    text = normalize_numbers(text)
+    text = ''.join(char for char in unicodedata.normalize('NFD', text)
+                    if unicodedata.category(char) != 'Mn')  # Strip accents
+    text = re.sub("[^ A-Za-z'.,?!\-]", "", text)
+    text = re.sub(r"(?i)i\.e\.", "that is", text)
+    text = re.sub(r"(?i)e\.g\.", "for example", text)
+
+    # 避免重复标点引起的参考泄露
+    text = replace_consecutive_punctuation(text)
+
+    return text
+
+
+class en_G2p(G2p):
+    def __init__(self):
+        super().__init__()
+        # 分词初始化
+        wordsegment.load()
+
+        # 扩展过时字典, 添加姓名字典
+        self.cmu = get_dict()
+        self.namedict = get_namedict()
+
+        # 剔除读音错误的几个缩写
+        for word in ["AE", "AI", "AR", "IOS", "HUD", "OS"]:
+            del self.cmu[word.lower()]
+
+        # 修正多音字
+        self.homograph2features["read"] = (['R', 'IY1', 'D'], ['R', 'EH1', 'D'], 'VBP')
+        self.homograph2features["complex"] = (['K', 'AH0', 'M', 'P', 'L', 'EH1', 'K', 'S'], ['K', 'AA1', 'M', 'P', 'L', 'EH0', 'K', 'S'], 'JJ')
+
+
+    def __call__(self, text):
+        # tokenization
+        words = word_tokenize(text)
+        tokens = pos_tag(words)  # tuples of (word, tag)
+
+        # steps
+        prons = []
+        for o_word, pos in tokens:
+            # 还原 g2p_en 小写操作逻辑
+            word = o_word.lower()
+
+            if re.search("[a-z]", word) is None:
+                pron = [word]
+            # 先把单字母推出去
+            elif len(word) == 1:
+                # 单读 A 发音修正, 这里需要原格式 o_word 判断大写
+                if o_word == "A":
+                    pron = ['EY1']
+                else:
+                    pron = self.cmu[word][0]
+            # g2p_en 原版多音字处理
+            elif word in self.homograph2features:  # Check homograph
+                pron1, pron2, pos1 = self.homograph2features[word]
+                if pos.startswith(pos1):
+                    pron = pron1
+                # pos1比pos长仅出现在read
+                elif len(pos) < len(pos1) and pos == pos1[:len(pos)]:
+                    pron = pron1
+                else:
+                    pron = pron2
+            else:
+                # 递归查找预测
+                pron = self.qryword(o_word)
+
+            prons.extend(pron)
+            prons.extend([" "])
+
+        return prons[:-1]
+
+
+    def qryword(self, o_word):
+        word = o_word.lower()
+
+        # 查字典, 单字母除外
+        if len(word) > 1 and word in self.cmu:  # lookup CMU dict
+            return self.cmu[word][0]
+
+        # 单词仅首字母大写时查找姓名字典
+        if o_word.istitle() and word in self.namedict:
+            return self.namedict[word][0]
+
+        # oov 长度小于等于 3 直接读字母
+        if len(word) <= 3:
+            phones = []
+            for w in word:
+                # 单读 A 发音修正, 此处不存在大写的情况
+                if w == "a":
+                    phones.extend(['EY1'])
+                elif not w.isalpha():
+                    phones.extend([w])
+                else:
+                    phones.extend(self.cmu[w][0])
+            return phones
+
+        # 尝试分离所有格
+        if re.match(r"^([a-z]+)('s)$", word):
+            phones = self.qryword(word[:-2])[:]
+            # P T K F TH HH 无声辅音结尾 's 发 ['S']
+            if phones[-1] in ['P', 'T', 'K', 'F', 'TH', 'HH']:
+                phones.extend(['S'])
+            # S Z SH ZH CH JH 擦声结尾 's 发 ['IH1', 'Z'] 或 ['AH0', 'Z']
+            elif phones[-1] in ['S', 'Z', 'SH', 'ZH', 'CH', 'JH']:
+                phones.extend(['AH0', 'Z'])
+            # B D G DH V M N NG L R W Y 有声辅音结尾 's 发 ['Z']
+            # AH0 AH1 AH2 EY0 EY1 EY2 AE0 AE1 AE2 EH0 EH1 EH2 OW0 OW1 OW2 UH0 UH1 UH2 IY0 IY1 IY2 AA0 AA1 AA2 AO0 AO1 AO2
+            # ER ER0 ER1 ER2 UW0 UW1 UW2 AY0 AY1 AY2 AW0 AW1 AW2 OY0 OY1 OY2 IH IH0 IH1 IH2 元音结尾 's 发 ['Z']
+            else:
+                phones.extend(['Z'])
+            return phones
+
+        # 尝试进行分词，应对复合词
+        comps = wordsegment.segment(word.lower())
+
+        # 无法分词的送回去预测
+        if len(comps)==1:
+            return self.predict(word)
+
+        # 可以分词的递归处理
+        return [phone for comp in comps for phone in self.qryword(comp)]
+
+
+_g2p = en_G2p()
+
+
+def g2p(text):
+    # g2p_en 整段推理，剔除不存在的arpa返回
+    phone_list = _g2p(text)
+    phones = [ph if ph != "<unk>" else "UNK" for ph in phone_list if ph not in [" ", "<pad>", "UW", "</s>", "<s>"]]
+
+    return replace_phs(phones)
+
+
+if __name__ == "__main__":
+    print(g2p("hello"))
+    print(g2p(text_normalize("e.g. I used openai's AI tool to draw a picture.")))
+    print(g2p(text_normalize("In this; paper, we propose 1 DSPGAN, a GAN-based universal vocoder.")))

GPT_SoVITS/text/g2pw/__init__.py

@@ -0,0 +1 @@
+from text.g2pw.g2pw import *

GPT_SoVITS/text/g2pw/dataset.py

@@ -0,0 +1,166 @@
+# Copyright (c) 2022 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.
+"""
+Credits
+    This code is modified from https://github.com/GitYCC/g2pW
+"""
+from typing import Dict
+from typing import List
+from typing import Tuple
+
+import numpy as np
+
+from .utils import tokenize_and_map
+
+ANCHOR_CHAR = '▁'
+
+
+def prepare_onnx_input(tokenizer,
+                       labels: List[str],
+                       char2phonemes: Dict[str, List[int]],
+                       chars: List[str],
+                       texts: List[str],
+                       query_ids: List[int],
+                       use_mask: bool=False,
+                       window_size: int=None,
+                       max_len: int=512) -> Dict[str, np.array]:
+    if window_size is not None:
+        truncated_texts, truncated_query_ids = _truncate_texts(
+            window_size=window_size, texts=texts, query_ids=query_ids)
+    input_ids = []
+    token_type_ids = []
+    attention_masks = []
+    phoneme_masks = []
+    char_ids = []
+    position_ids = []
+
+    for idx in range(len(texts)):
+        text = (truncated_texts if window_size else texts)[idx].lower()
+        query_id = (truncated_query_ids if window_size else query_ids)[idx]
+
+        try:
+            tokens, text2token, token2text = tokenize_and_map(
+                tokenizer=tokenizer, text=text)
+        except Exception:
+            print(f'warning: text "{text}" is invalid')
+            return {}
+
+        text, query_id, tokens, text2token, token2text = _truncate(
+            max_len=max_len,
+            text=text,
+            query_id=query_id,
+            tokens=tokens,
+            text2token=text2token,
+            token2text=token2text)
+
+        processed_tokens = ['[CLS]'] + tokens + ['[SEP]']
+
+        input_id = list(
+            np.array(tokenizer.convert_tokens_to_ids(processed_tokens)))
+        token_type_id = list(np.zeros((len(processed_tokens), ), dtype=int))
+        attention_mask = list(np.ones((len(processed_tokens), ), dtype=int))
+
+        query_char = text[query_id]
+        phoneme_mask = [1 if i in char2phonemes[query_char] else 0 for i in range(len(labels))] \
+            if use_mask else [1] * len(labels)
+        char_id = chars.index(query_char)
+        position_id = text2token[
+            query_id] + 1  # [CLS] token locate at first place
+
+        input_ids.append(input_id)
+        token_type_ids.append(token_type_id)
+        attention_masks.append(attention_mask)
+        phoneme_masks.append(phoneme_mask)
+        char_ids.append(char_id)
+        position_ids.append(position_id)
+
+    outputs = {
+        'input_ids': np.array(input_ids).astype(np.int64),
+        'token_type_ids': np.array(token_type_ids).astype(np.int64),
+        'attention_masks': np.array(attention_masks).astype(np.int64),
+        'phoneme_masks': np.array(phoneme_masks).astype(np.float32),
+        'char_ids': np.array(char_ids).astype(np.int64),
+        'position_ids': np.array(position_ids).astype(np.int64),
+    }
+    return outputs
+
+
+def _truncate_texts(window_size: int, texts: List[str],
+                    query_ids: List[int]) -> Tuple[List[str], List[int]]:
+    truncated_texts = []
+    truncated_query_ids = []
+    for text, query_id in zip(texts, query_ids):
+        start = max(0, query_id - window_size // 2)
+        end = min(len(text), query_id + window_size // 2)
+        truncated_text = text[start:end]
+        truncated_texts.append(truncated_text)
+
+        truncated_query_id = query_id - start
+        truncated_query_ids.append(truncated_query_id)
+    return truncated_texts, truncated_query_ids
+
+
+def _truncate(max_len: int,
+              text: str,
+              query_id: int,
+              tokens: List[str],
+              text2token: List[int],
+              token2text: List[Tuple[int]]):
+    truncate_len = max_len - 2
+    if len(tokens) <= truncate_len:
+        return (text, query_id, tokens, text2token, token2text)
+
+    token_position = text2token[query_id]
+
+    token_start = token_position - truncate_len // 2
+    token_end = token_start + truncate_len
+    font_exceed_dist = -token_start
+    back_exceed_dist = token_end - len(tokens)
+    if font_exceed_dist > 0:
+        token_start += font_exceed_dist
+        token_end += font_exceed_dist
+    elif back_exceed_dist > 0:
+        token_start -= back_exceed_dist
+        token_end -= back_exceed_dist
+
+    start = token2text[token_start][0]
+    end = token2text[token_end - 1][1]
+
+    return (text[start:end], query_id - start, tokens[token_start:token_end], [
+        i - token_start if i is not None else None
+        for i in text2token[start:end]
+    ], [(s - start, e - start) for s, e in token2text[token_start:token_end]])
+
+
+def get_phoneme_labels(polyphonic_chars: List[List[str]]
+                       ) -> Tuple[List[str], Dict[str, List[int]]]:
+    labels = sorted(list(set([phoneme for char, phoneme in polyphonic_chars])))
+    char2phonemes = {}
+    for char, phoneme in polyphonic_chars:
+        if char not in char2phonemes:
+            char2phonemes[char] = []
+        char2phonemes[char].append(labels.index(phoneme))
+    return labels, char2phonemes
+
+
+def get_char_phoneme_labels(polyphonic_chars: List[List[str]]
+                            ) -> Tuple[List[str], Dict[str, List[int]]]:
+    labels = sorted(
+        list(set([f'{char} {phoneme}' for char, phoneme in polyphonic_chars])))
+    char2phonemes = {}
+    for char, phoneme in polyphonic_chars:
+        if char not in char2phonemes:
+            char2phonemes[char] = []
+        char2phonemes[char].append(labels.index(f'{char} {phoneme}'))
+    return labels, char2phonemes

GPT_SoVITS/text/g2pw/g2pw.py

@@ -0,0 +1,154 @@
+# This code is modified from https://github.com/mozillazg/pypinyin-g2pW
+
+import pickle
+import os
+
+from pypinyin.constants import RE_HANS
+from pypinyin.core import Pinyin, Style
+from pypinyin.seg.simpleseg import simple_seg
+from pypinyin.converter import UltimateConverter
+from pypinyin.contrib.tone_convert import to_tone
+from .onnx_api import G2PWOnnxConverter
+
+current_file_path = os.path.dirname(__file__)
+CACHE_PATH = os.path.join(current_file_path, "polyphonic.pickle")
+PP_DICT_PATH = os.path.join(current_file_path, "polyphonic.rep")
+PP_FIX_DICT_PATH = os.path.join(current_file_path, "polyphonic-fix.rep")
+
+
+class G2PWPinyin(Pinyin):
+    def __init__(self, model_dir='G2PWModel/', model_source=None,
+                 enable_non_tradional_chinese=True,
+                 v_to_u=False, neutral_tone_with_five=False, tone_sandhi=False, **kwargs):
+        self._g2pw = G2PWOnnxConverter(
+            model_dir=model_dir,
+            style='pinyin',
+            model_source=model_source,
+            enable_non_tradional_chinese=enable_non_tradional_chinese,
+        )
+        self._converter = Converter(
+            self._g2pw, v_to_u=v_to_u,
+            neutral_tone_with_five=neutral_tone_with_five,
+            tone_sandhi=tone_sandhi,
+        )
+
+    def get_seg(self, **kwargs):
+        return simple_seg
+
+
+class Converter(UltimateConverter):
+    def __init__(self, g2pw_instance, v_to_u=False,
+                 neutral_tone_with_five=False,
+                 tone_sandhi=False, **kwargs):
+        super(Converter, self).__init__(
+            v_to_u=v_to_u,
+            neutral_tone_with_five=neutral_tone_with_five,
+            tone_sandhi=tone_sandhi, **kwargs)
+
+        self._g2pw = g2pw_instance
+
+    def convert(self, words, style, heteronym, errors, strict, **kwargs):
+        pys = []
+        if RE_HANS.match(words):
+            pys = self._to_pinyin(words, style=style, heteronym=heteronym,
+                                  errors=errors, strict=strict)
+            post_data = self.post_pinyin(words, heteronym, pys)
+            if post_data is not None:
+                pys = post_data
+
+            pys = self.convert_styles(
+                pys, words, style, heteronym, errors, strict)
+
+        else:
+            py = self.handle_nopinyin(words, style=style, errors=errors,
+                                      heteronym=heteronym, strict=strict)
+            if py:
+                pys.extend(py)
+
+        return _remove_dup_and_empty(pys)
+
+    def _to_pinyin(self, han, style, heteronym, errors, strict, **kwargs):
+        pinyins = []
+
+        g2pw_pinyin = self._g2pw(han)
+
+        if not g2pw_pinyin:  # g2pw 不支持的汉字改为使用 pypinyin 原有逻辑
+            return super(Converter, self).convert(
+                         han, Style.TONE, heteronym, errors, strict, **kwargs)
+
+        for i, item in enumerate(g2pw_pinyin[0]):
+            if item is None:  # g2pw 不支持的汉字改为使用 pypinyin 原有逻辑
+                py = super(Converter, self).convert(
+                           han[i], Style.TONE, heteronym, errors, strict, **kwargs)
+                pinyins.extend(py)
+            else:
+                pinyins.append([to_tone(item)])
+
+        return pinyins
+
+
+def _remove_dup_items(lst, remove_empty=False):
+    new_lst = []
+    for item in lst:
+        if remove_empty and not item:
+            continue
+        if item not in new_lst:
+            new_lst.append(item)
+    return new_lst
+
+
+def _remove_dup_and_empty(lst_list):
+    new_lst_list = []
+    for lst in lst_list:
+        lst = _remove_dup_items(lst, remove_empty=True)
+        if lst:
+            new_lst_list.append(lst)
+        else:
+            new_lst_list.append([''])
+
+    return new_lst_list
+
+
+def cache_dict(polyphonic_dict, file_path):
+    with open(file_path, "wb") as pickle_file:
+        pickle.dump(polyphonic_dict, pickle_file)
+
+
+def get_dict():
+    if os.path.exists(CACHE_PATH):
+        with open(CACHE_PATH, "rb") as pickle_file:
+            polyphonic_dict = pickle.load(pickle_file)
+    else:
+        polyphonic_dict = read_dict()
+        cache_dict(polyphonic_dict, CACHE_PATH)
+
+    return polyphonic_dict
+
+
+def read_dict():
+    polyphonic_dict = {}
+    with open(PP_DICT_PATH,encoding="utf-8") as f:
+        line = f.readline()
+        while line:
+            key, value_str = line.split(':')
+            value = eval(value_str.strip())
+            polyphonic_dict[key.strip()] = value
+            line = f.readline()
+    with open(PP_FIX_DICT_PATH,encoding="utf-8") as f:
+        line = f.readline()
+        while line:
+            key, value_str = line.split(':')
+            value = eval(value_str.strip())
+            polyphonic_dict[key.strip()] = value
+            line = f.readline()
+    return polyphonic_dict
+
+
+def correct_pronunciation(word,word_pinyins):
+    if word in pp_dict:
+        word_pinyins = pp_dict[word]
+
+    return word_pinyins
+
+
+pp_dict = get_dict()

GPT_SoVITS/text/g2pw/onnx_api.py

@@ -0,0 +1,241 @@
+# This code is modified from https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/g2pw
+# This code is modified from https://github.com/GitYCC/g2pW
+
+import warnings
+warnings.filterwarnings("ignore")
+import json
+import os
+import zipfile,requests
+from typing import Any
+from typing import Dict
+from typing import List
+from typing import Tuple
+
+import numpy as np
+import onnxruntime
+onnxruntime.set_default_logger_severity(3)
+from opencc import OpenCC
+from transformers import AutoTokenizer
+from pypinyin import pinyin
+from pypinyin import Style
+
+from .dataset import get_char_phoneme_labels
+from .dataset import get_phoneme_labels
+from .dataset import prepare_onnx_input
+from .utils import load_config
+from ..zh_normalization.char_convert import tranditional_to_simplified
+
+model_version = '1.1'
+
+
+def predict(session, onnx_input: Dict[str, Any],
+            labels: List[str]) -> Tuple[List[str], List[float]]:
+    all_preds = []
+    all_confidences = []
+    probs = session.run([], {
+        "input_ids": onnx_input['input_ids'],
+        "token_type_ids": onnx_input['token_type_ids'],
+        "attention_mask": onnx_input['attention_masks'],
+        "phoneme_mask": onnx_input['phoneme_masks'],
+        "char_ids": onnx_input['char_ids'],
+        "position_ids": onnx_input['position_ids']
+    })[0]
+
+    preds = np.argmax(probs, axis=1).tolist()
+    max_probs = []
+    for index, arr in zip(preds, probs.tolist()):
+        max_probs.append(arr[index])
+    all_preds += [labels[pred] for pred in preds]
+    all_confidences += max_probs
+
+    return all_preds, all_confidences
+
+
+def download_and_decompress(model_dir: str='G2PWModel/'):
+    if not os.path.exists(model_dir):
+        parent_directory = os.path.dirname(model_dir)
+        zip_dir = os.path.join(parent_directory,"G2PWModel_1.1.zip")
+        extract_dir = os.path.join(parent_directory,"G2PWModel_1.1")
+        extract_dir_new = os.path.join(parent_directory,"G2PWModel")
+        print("Downloading g2pw model...")
+        modelscope_url = "https://paddlespeech.bj.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip"
+        with requests.get(modelscope_url, stream=True) as r:
+            r.raise_for_status()
+            with open(zip_dir, 'wb') as f:
+                for chunk in r.iter_content(chunk_size=8192):
+                    if chunk:
+                        f.write(chunk)
+
+        print("Extracting g2pw model...")
+        with zipfile.ZipFile(zip_dir, "r") as zip_ref:
+            zip_ref.extractall(parent_directory)
+        
+        os.rename(extract_dir, extract_dir_new)
+
+    return model_dir
+
+class G2PWOnnxConverter:
+    def __init__(self,
+                 model_dir: str='G2PWModel/',
+                 style: str='bopomofo',
+                 model_source: str=None,
+                 enable_non_tradional_chinese: bool=False):
+        uncompress_path = download_and_decompress(model_dir)
+
+        sess_options = onnxruntime.SessionOptions()
+        sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
+        sess_options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL
+        sess_options.intra_op_num_threads = 2
+        try:
+            self.session_g2pW = onnxruntime.InferenceSession(os.path.join(uncompress_path, 'g2pW.onnx'),sess_options=sess_options, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+        except:
+            self.session_g2pW = onnxruntime.InferenceSession(os.path.join(uncompress_path, 'g2pW.onnx'),sess_options=sess_options, providers=['CPUExecutionProvider'])
+        self.config = load_config(
+            config_path=os.path.join(uncompress_path, 'config.py'),
+            use_default=True)
+
+        self.model_source = model_source if model_source else self.config.model_source
+        self.enable_opencc = enable_non_tradional_chinese
+
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_source)
+
+        polyphonic_chars_path = os.path.join(uncompress_path,
+                                             'POLYPHONIC_CHARS.txt')
+        monophonic_chars_path = os.path.join(uncompress_path,
+                                             'MONOPHONIC_CHARS.txt')
+        self.polyphonic_chars = [
+            line.split('\t')
+            for line in open(polyphonic_chars_path, encoding='utf-8').read()
+            .strip().split('\n')
+        ]
+        self.non_polyphonic = {
+            '一', '不', '和', '咋', '嗲', '剖', '差', '攢', '倒', '難', '奔', '勁', '拗',
+            '肖', '瘙', '誒', '泊', '听', '噢'
+        }
+        self.non_monophonic = {'似', '攢'}
+        self.monophonic_chars = [
+            line.split('\t')
+            for line in open(monophonic_chars_path, encoding='utf-8').read()
+            .strip().split('\n')
+        ]
+        self.labels, self.char2phonemes = get_char_phoneme_labels(
+            polyphonic_chars=self.polyphonic_chars
+        ) if self.config.use_char_phoneme else get_phoneme_labels(
+            polyphonic_chars=self.polyphonic_chars)
+
+        self.chars = sorted(list(self.char2phonemes.keys()))
+
+        self.polyphonic_chars_new = set(self.chars)
+        for char in self.non_polyphonic:
+            if char in self.polyphonic_chars_new:
+                self.polyphonic_chars_new.remove(char)
+
+        self.monophonic_chars_dict = {
+            char: phoneme
+            for char, phoneme in self.monophonic_chars
+        }
+        for char in self.non_monophonic:
+            if char in self.monophonic_chars_dict:
+                self.monophonic_chars_dict.pop(char)
+
+        self.pos_tags = [
+            'UNK', 'A', 'C', 'D', 'I', 'N', 'P', 'T', 'V', 'DE', 'SHI'
+        ]
+
+        with open(
+                os.path.join(uncompress_path,
+                             'bopomofo_to_pinyin_wo_tune_dict.json'),
+                'r',
+                encoding='utf-8') as fr:
+            self.bopomofo_convert_dict = json.load(fr)
+        self.style_convert_func = {
+            'bopomofo': lambda x: x,
+            'pinyin': self._convert_bopomofo_to_pinyin,
+        }[style]
+
+        with open(
+                os.path.join(uncompress_path, 'char_bopomofo_dict.json'),
+                'r',
+                encoding='utf-8') as fr:
+            self.char_bopomofo_dict = json.load(fr)
+
+        if self.enable_opencc:
+            self.cc = OpenCC('s2tw')
+
+    def _convert_bopomofo_to_pinyin(self, bopomofo: str) -> str:
+        tone = bopomofo[-1]
+        assert tone in '12345'
+        component = self.bopomofo_convert_dict.get(bopomofo[:-1])
+        if component:
+            return component + tone
+        else:
+            print(f'Warning: "{bopomofo}" cannot convert to pinyin')
+            return None
+
+    def __call__(self, sentences: List[str]) -> List[List[str]]:
+        if isinstance(sentences, str):
+            sentences = [sentences]
+
+        if self.enable_opencc:
+            translated_sentences = []
+            for sent in sentences:
+                translated_sent = self.cc.convert(sent)
+                assert len(translated_sent) == len(sent)
+                translated_sentences.append(translated_sent)
+            sentences = translated_sentences
+
+        texts, query_ids, sent_ids, partial_results = self._prepare_data(
+            sentences=sentences)
+        if len(texts) == 0:
+            # sentences no polyphonic words
+            return partial_results
+
+        onnx_input = prepare_onnx_input(
+            tokenizer=self.tokenizer,
+            labels=self.labels,
+            char2phonemes=self.char2phonemes,
+            chars=self.chars,
+            texts=texts,
+            query_ids=query_ids,
+            use_mask=self.config.use_mask,
+            window_size=None)
+
+        preds, confidences = predict(
+            session=self.session_g2pW,
+            onnx_input=onnx_input,
+            labels=self.labels)
+        if self.config.use_char_phoneme:
+            preds = [pred.split(' ')[1] for pred in preds]
+
+        results = partial_results
+        for sent_id, query_id, pred in zip(sent_ids, query_ids, preds):
+            results[sent_id][query_id] = self.style_convert_func(pred)
+
+        return results
+
+    def _prepare_data(
+            self, sentences: List[str]
+    ) -> Tuple[List[str], List[int], List[int], List[List[str]]]:
+        texts, query_ids, sent_ids, partial_results = [], [], [], []
+        for sent_id, sent in enumerate(sentences):
+            # pypinyin works well for Simplified Chinese than Traditional Chinese
+            sent_s = tranditional_to_simplified(sent)
+            pypinyin_result = pinyin(
+                sent_s, neutral_tone_with_five=True, style=Style.TONE3)
+            partial_result = [None] * len(sent)
+            for i, char in enumerate(sent):
+                if char in self.polyphonic_chars_new:
+                    texts.append(sent)
+                    query_ids.append(i)
+                    sent_ids.append(sent_id)
+                elif char in self.monophonic_chars_dict:
+                    partial_result[i] = self.style_convert_func(
+                        self.monophonic_chars_dict[char])
+                elif char in self.char_bopomofo_dict:
+                    partial_result[i] = pypinyin_result[i][0]
+                    # partial_result[i] =  self.style_convert_func(self.char_bopomofo_dict[char][0])
+                else:
+                    partial_result[i] = pypinyin_result[i][0]
+
+            partial_results.append(partial_result)
+        return texts, query_ids, sent_ids, partial_results

GPT_SoVITS/text/g2pw/polyphonic.rep

@@ -0,0 +1,53 @@
+湖泊: ['hu2','po1']
+地壳: ['di4','qiao4']
+柏树: ['bai3','shu4']
+曝光: ['bao4','guang1']
+弹力: ['tan2','li4']
+字帖: ['zi4','tie4']
+口吃: ['kou3','chi1']
+包扎: ['bao1','za1']
+哪吒: ['ne2','zha1']
+说服: ['shuo1','fu2']
+识字: ['shi2','zi4']
+骨头: ['gu3','tou5']
+对称: ['dui4','chen4']
+口供: ['kou3','gong4']
+抹布: ['ma1','bu4']
+露背: ['lu4','bei4']
+圈养: ['juan4', 'yang3']
+眼眶: ['yan3', 'kuang4']
+品行: ['pin3','xing2']
+颤抖: ['chan4','dou3']
+差不多: ['cha4','bu5','duo1']
+鸭绿江: ['ya1','lu4','jiang1']
+撒切尔: ['sa4','qie4','er3']
+比比皆是: ['bi3','bi3','jie1','shi4']
+身无长物: ['shen1','wu2','chang2','wu4']
+手里: ['shou2','li3']
+关卡: ['guan1','qia3']
+怀揣: ['huai2','chuai1']
+挑剔: ['tiao1','ti4']
+供称: ['gong4','cheng1']
+作坊: ['zuo1', 'fang5']
+中医: ['zhong1','yi1']
+嚷嚷: ['rang1','rang5']
+商厦: ['shang1','sha4']
+大厦: ['da4','sha4']
+刹车: ['sha1','che1']
+嘚瑟: ['de4','se5']
+朝鲜: ['chao2','xian3']
+阿房宫: ['e1','pang2','gong1']
+阿胶: ['e1','jiao1']
+咖喱: ['ga1','li5']
+时分: ['shi2','fen1']
+蚌埠: ['beng4','bu4']
+驯服: ['xun4','fu2']
+幸免于难: ['xing4','mian3','yu2','nan4']
+恶行: ['e4','xing2']
+唉: ['ai4']
+扎实: ['zha1','shi2']
+干将: ['gan4','jiang4']
+陈威行: ['chen2', 'wei1', 'hang2']
+郭晟: ['guo1', 'sheng4']
+中标: ['zhong4', 'biao1']
+抗住: ['kang2', 'zhu4']

GPT_SoVITS/text/g2pw/utils.py

@@ -0,0 +1,145 @@
+# Copyright (c) 2022 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.
+"""
+Credits
+    This code is modified from https://github.com/GitYCC/g2pW
+"""
+import os
+import re
+
+
+def wordize_and_map(text: str):
+    words = []
+    index_map_from_text_to_word = []
+    index_map_from_word_to_text = []
+    while len(text) > 0:
+        match_space = re.match(r'^ +', text)
+        if match_space:
+            space_str = match_space.group(0)
+            index_map_from_text_to_word += [None] * len(space_str)
+            text = text[len(space_str):]
+            continue
+
+        match_en = re.match(r'^[a-zA-Z0-9]+', text)
+        if match_en:
+            en_word = match_en.group(0)
+
+            word_start_pos = len(index_map_from_text_to_word)
+            word_end_pos = word_start_pos + len(en_word)
+            index_map_from_word_to_text.append((word_start_pos, word_end_pos))
+
+            index_map_from_text_to_word += [len(words)] * len(en_word)
+
+            words.append(en_word)
+            text = text[len(en_word):]
+        else:
+            word_start_pos = len(index_map_from_text_to_word)
+            word_end_pos = word_start_pos + 1
+            index_map_from_word_to_text.append((word_start_pos, word_end_pos))
+
+            index_map_from_text_to_word += [len(words)]
+
+            words.append(text[0])
+            text = text[1:]
+    return words, index_map_from_text_to_word, index_map_from_word_to_text
+
+
+def tokenize_and_map(tokenizer, text: str):
+    words, text2word, word2text = wordize_and_map(text=text)
+
+    tokens = []
+    index_map_from_token_to_text = []
+    for word, (word_start, word_end) in zip(words, word2text):
+        word_tokens = tokenizer.tokenize(word)
+
+        if len(word_tokens) == 0 or word_tokens == ['[UNK]']:
+            index_map_from_token_to_text.append((word_start, word_end))
+            tokens.append('[UNK]')
+        else:
+            current_word_start = word_start
+            for word_token in word_tokens:
+                word_token_len = len(re.sub(r'^##', '', word_token))
+                index_map_from_token_to_text.append(
+                    (current_word_start, current_word_start + word_token_len))
+                current_word_start = current_word_start + word_token_len
+                tokens.append(word_token)
+
+    index_map_from_text_to_token = text2word
+    for i, (token_start, token_end) in enumerate(index_map_from_token_to_text):
+        for token_pos in range(token_start, token_end):
+            index_map_from_text_to_token[token_pos] = i
+
+    return tokens, index_map_from_text_to_token, index_map_from_token_to_text
+
+
+def _load_config(config_path: os.PathLike):
+    import importlib.util
+    spec = importlib.util.spec_from_file_location('__init__', config_path)
+    config = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(config)
+    return config
+
+
+default_config_dict = {
+    'manual_seed': 1313,
+    'model_source': 'bert-base-chinese',
+    'window_size': 32,
+    'num_workers': 2,
+    'use_mask': True,
+    'use_char_phoneme': False,
+    'use_conditional': True,
+    'param_conditional': {
+        'affect_location': 'softmax',
+        'bias': True,
+        'char-linear': True,
+        'pos-linear': False,
+        'char+pos-second': True,
+        'char+pos-second_lowrank': False,
+        'lowrank_size': 0,
+        'char+pos-second_fm': False,
+        'fm_size': 0,
+        'fix_mode': None,
+        'count_json': 'train.count.json'
+    },
+    'lr': 5e-5,
+    'val_interval': 200,
+    'num_iter': 10000,
+    'use_focal': False,
+    'param_focal': {
+        'alpha': 0.0,
+        'gamma': 0.7
+    },
+    'use_pos': True,
+    'param_pos ': {
+        'weight': 0.1,
+        'pos_joint_training': True,
+        'train_pos_path': 'train.pos',
+        'valid_pos_path': 'dev.pos',
+        'test_pos_path': 'test.pos'
+    }
+}
+
+
+def load_config(config_path: os.PathLike, use_default: bool=False):
+    config = _load_config(config_path)
+    if use_default:
+        for attr, val in default_config_dict.items():
+            if not hasattr(config, attr):
+                setattr(config, attr, val)
+            elif isinstance(val, dict):
+                d = getattr(config, attr)
+                for dict_k, dict_v in val.items():
+                    if dict_k not in d:
+                        d[dict_k] = dict_v
+    return config

GPT_SoVITS/text/hindi.py

@@ -0,0 +1,222 @@
+import re
+from typing import List, Set
+
+# Hindi text normalization rules
+_hindi_numbers = "०१२३४५६७८९"
+_english_numbers = "0123456789"
+_number_map = str.maketrans(_hindi_numbers, _english_numbers)
+
+# Common abbreviations and their expansions
+_abbreviations = {
+    # Titles and honorifics
+    "डॉ": "डॉक्टर",
+    "श्री": "श्रीमान",
+    "श्रीमती": "श्रीमती",
+    "कु": "कुमारी",
+    "प्रो": "प्रोफेसर",
+    "चौ": "चौधरी",
+    "स्व": "स्वर्गीय",
+    
+    # Common organizations
+    "भा": "भारत",
+    "सं": "संघ",
+    "वि": "विश्वविद्यालय",
+    "म": "महा",
+    
+    # Common words
+    "क्र": "क्रमांक",
+    "रु": "रुपये",
+    "ज़ि": "ज़िला",
+    "उ": "उत्तर",
+    "द": "दक्षिण",
+    "पू": "पूर्व",
+    "प": "पश्चिम"
+}
+
+# Common conjunct consonants (consonant clusters)
+_common_conjuncts = {
+    # क-based conjuncts
+    "क्क", "क्त", "क्र", "क्ल", "क्व", "क्ष", "क्स",
+    # ग-based conjuncts
+    "ग्र", "ग्ल", "ग्व", "ग्न", "ग्म",
+    # च-based conjuncts
+    "च्च", "च्छ", "च्य", "च्र",
+    # ज-based conjuncts
+    "ज्ज", "ज्ञ", "ज्य", "ज्र", "ज्व",
+    # त-based conjuncts
+    "त्त", "त्र", "त्य", "त्व", "त्न", "त्म",
+    # द-based conjuncts
+    "द्द", "द्य", "द्व", "द्र", "द्म", "द्ध",
+    # न-based conjuncts
+    "न्न", "न्त", "न्द", "न्य", "न्र", "न्व",
+    # प-based conjuncts
+    "प्प", "प्त", "प्र", "प्ल", "प्स",
+    # ब-based conjuncts
+    "ब्र", "ब्ल", "ब्ज",
+    # म-based conjuncts
+    "म्प", "म्ब", "म्म", "म्ल", "म्र",
+    # य-based conjuncts
+    "य्य", "य्र",
+    # र-based conjuncts (reph forms)
+    "र्क", "र्ग", "र्च", "र्ज", "र्त", "र्द", "र्प", "र्ब", "र्म", "र्य", "र्ल", "र्व", "र्श", "र्स", "र्ह",
+    # ल-based conjuncts
+    "ल्क", "ल्ग", "ल्ट", "ल्ड", "ल्प", "ल्म", "ल्ल", "ल्व",
+    # श-based conjuncts
+    "श्च", "श्न", "श्प", "श्म", "श्य", "श्र", "श्ल", "श्व",
+    # स-based conjuncts
+    "स्क", "स्ट", "स्त", "स्थ", "स्न", "स्प", "स्फ", "स्म", "स्य", "स्र", "स्व", "स्स",
+    # ह-based conjuncts
+    "ह्य", "ह्र", "ह्व", "ह्ल", "ह्न", "ह्म"
+}
+
+def _is_final_position(text: str, pos: int) -> bool:
+    """Check if the position is at the end of a word."""
+    return pos == len(text) - 1 or text[pos + 1] in {' ', ',', '.', '!', '?', '-'}
+
+def _is_light_syllable(text: str, pos: int) -> bool:
+    """Check if the syllable at given position is light (no long vowels or conjuncts)."""
+    if pos >= len(text) - 1:
+        return True
+    next_char = text[pos + 1]
+    return not (('\u093E' <= next_char <= '\u094C') or next_char == '\u094D')
+
+def get_schwa_deletion_positions(text: str) -> Set[int]:
+    """Determine positions where schwa should be deleted in Hindi words.
+    Enhanced with more accurate rules."""
+    positions = set()
+    words = text.split()
+    
+    for word in words:
+        word_start = text.find(word)
+        length = len(word)
+        
+        for i in range(length):
+            pos = word_start + i
+            
+            # Basic conditions for schwa deletion
+            if ('\u0915' <= word[i] <= '\u0939' and  # Current char is consonant
+                i < length - 1 and
+                not '\u093E' <= word[i + 1] <= '\u094D'):  # Next char is not a vowel mark
+                
+                # Rule 1: Delete schwa in final position of word
+                if i == length - 1:
+                    positions.add(pos)
+                    continue
+                
+                # Rule 2: Delete schwa between consonants in non-final light syllables
+                if (i < length - 2 and
+                    '\u0915' <= word[i + 1] <= '\u0939' and  # Next char is consonant
+                    _is_light_syllable(word, i)):
+                    positions.add(pos)
+                    continue
+                
+                # Rule 3: Delete schwa in compound words at morpheme boundaries
+                if (i < length - 2 and
+                    word[i + 1] == '\u094D' and  # Virama
+                    '\u0915' <= word[i + 2] <= '\u0939'):  # Followed by consonant
+                    positions.add(pos)
+                    continue
+
+    return positions
+
+def normalize_hindi_text(text: str) -> str:
+    """Normalize Hindi text by applying various rules."""
+    # Convert Hindi numbers to English numbers
+    text = text.translate(_number_map)
+    
+    # Replace abbreviations with their full forms
+    for abbr, full in _abbreviations.items():
+        text = re.sub(rf'\b{abbr}\b', full, text)
+    
+    # Remove extra spaces
+    text = re.sub(r'\s+', ' ', text)
+    
+    # Normalize chandrabindu to anusvara
+    text = text.replace('\u0901', '\u0902')
+    
+    # Normalize nukta variations
+    nukta_chars = {
+        'क़': 'क', 'ख़': 'ख', 'ग़': 'ग', 'ज़': 'ज',
+        'ड़': 'ड', 'ढ़': 'ढ', 'फ़': 'फ'
+    }
+    for nuk, base in nukta_chars.items():
+        text = text.replace(nuk, base)
+    
+    # Remove any non-Devanagari characters except basic punctuation
+    text = re.sub(r'[^\u0900-\u097F\s.,!?-]', '', text)
+    
+    return text.strip()
+
+def hindi_to_phonemes(text: str) -> str:
+    """Convert Hindi text to phonemes."""
+    text = normalize_hindi_text(text)
+    phonemes = []
+    
+    i = 0
+    while i < len(text):
+        char = text[i]
+        
+        # Skip spaces and punctuation
+        if char in ' .,!?-':
+            if char == ' ':
+                phonemes.append('SP')
+            else:
+                phonemes.append(char)
+            i += 1
+            continue
+        
+        # Handle consonants
+        if '\u0915' <= char <= '\u0939':
+            # Check for conjuncts
+            if i + 2 < len(text) and text[i + 1] == '\u094D':
+                # Look ahead for multi-consonant conjuncts
+                j = i + 2
+                conjunct = text[i:j + 1]
+                while (j < len(text) and text[j] != ' ' and
+                       conjunct in _common_conjuncts):
+                    j += 1
+                    if j < len(text) and text[j-1] == '\u094D':
+                        conjunct = text[i:j + 1]
+                    else:
+                        break
+                
+                if conjunct[:-1] in _common_conjuncts:
+                    phonemes.append(conjunct[:-1])
+                    i = j
+                else:
+                    # Handle as single consonant if not a known conjunct
+                    phonemes.append(char)
+                    i += 1
+            else:
+                # Single consonant
+                phonemes.append(char)
+                
+                # Check for vowel marks
+                if i + 1 < len(text) and '\u093E' <= text[i + 1] <= '\u094C':
+                    phonemes.append(text[i + 1])
+                    i += 2
+                else:
+                    # Add implicit 'अ' if no vowel mark and not a schwa deletion position
+                    if i not in get_schwa_deletion_positions(text):
+                        phonemes.append('अ')
+                    i += 1
+        
+        # Handle independent vowels
+        elif '\u0904' <= char <= '\u0914':
+            phonemes.append(char)
+            i += 1
+        
+        # Handle anusvara and visarga
+        elif char in ['\u0902', '\u0903']:
+            phonemes.append(char)
+            i += 1
+        
+        else:
+            i += 1
+    
+    return ' '.join(phonemes)
+
+def get_phoneme_sequence(text: str) -> List[str]:
+    """Convert text to a sequence of phonemes for the model."""
+    phoneme_string = hindi_to_phonemes(text)
+    return phoneme_string.split() 

GPT_SoVITS/text/japanese.py

@@ -0,0 +1,226 @@
+# modified from https://github.com/CjangCjengh/vits/blob/main/text/japanese.py
+import re
+import os
+import hashlib
+try:
+    import pyopenjtalk
+    current_file_path = os.path.dirname(__file__)
+    def get_hash(fp: str) -> str:
+        hash_md5 = hashlib.md5()
+        with open(fp, "rb") as f:
+            for chunk in iter(lambda: f.read(4096), b""):
+                hash_md5.update(chunk)
+        return hash_md5.hexdigest()
+
+    USERDIC_CSV_PATH = os.path.join(current_file_path, "ja_userdic", "userdict.csv")
+    USERDIC_BIN_PATH = os.path.join(current_file_path, "ja_userdic", "user.dict")
+    USERDIC_HASH_PATH = os.path.join(current_file_path, "ja_userdic", "userdict.md5")
+    # 如果没有用户词典，就生成一个；如果有，就检查md5，如果不一样，就重新生成
+    if os.path.exists(USERDIC_CSV_PATH):
+        if not os.path.exists(USERDIC_BIN_PATH) or get_hash(USERDIC_CSV_PATH) != open(USERDIC_HASH_PATH, "r",encoding='utf-8').read():
+            pyopenjtalk.mecab_dict_index(USERDIC_CSV_PATH, USERDIC_BIN_PATH)
+            with open(USERDIC_HASH_PATH, "w", encoding='utf-8') as f:
+                f.write(get_hash(USERDIC_CSV_PATH))
+
+    if os.path.exists(USERDIC_BIN_PATH):
+        pyopenjtalk.update_global_jtalk_with_user_dict(USERDIC_BIN_PATH)   
+except Exception as e:
+    # print(e)
+    import pyopenjtalk
+    # failed to load user dictionary, ignore.
+    pass
+
+
+from text.symbols import punctuation
+# Regular expression matching Japanese without punctuation marks:
+_japanese_characters = re.compile(
+    r"[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]"
+)
+
+# Regular expression matching non-Japanese characters or punctuation marks:
+_japanese_marks = re.compile(
+    r"[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]"
+)
+
+# List of (symbol, Japanese) pairs for marks:
+_symbols_to_japanese = [(re.compile("%s" % x[0]), x[1]) for x in [("％", "パーセント")]]
+
+
+# List of (consonant, sokuon) pairs:
+_real_sokuon = [
+    (re.compile("%s" % x[0]), x[1])
+    for x in [
+        (r"Q([↑↓]*[kg])", r"k#\1"),
+        (r"Q([↑↓]*[tdjʧ])", r"t#\1"),
+        (r"Q([↑↓]*[sʃ])", r"s\1"),
+        (r"Q([↑↓]*[pb])", r"p#\1"),
+    ]
+]
+
+# List of (consonant, hatsuon) pairs:
+_real_hatsuon = [
+    (re.compile("%s" % x[0]), x[1])
+    for x in [
+        (r"N([↑↓]*[pbm])", r"m\1"),
+        (r"N([↑↓]*[ʧʥj])", r"n^\1"),
+        (r"N([↑↓]*[tdn])", r"n\1"),
+        (r"N([↑↓]*[kg])", r"ŋ\1"),
+    ]
+]
+
+
+def post_replace_ph(ph):
+    rep_map = {
+        "：": ",",
+        "；": ",",
+        "，": ",",
+        "。": ".",
+        "！": "!",
+        "？": "?",
+        "\n": ".",
+        "·": ",",
+        "、": ",",
+        "...": "…",
+    }
+
+    if ph in rep_map.keys():
+        ph = rep_map[ph]
+    return ph
+
+
+def replace_consecutive_punctuation(text):
+    punctuations = ''.join(re.escape(p) for p in punctuation)
+    pattern = f'([{punctuations}])([{punctuations}])+'
+    result = re.sub(pattern, r'\1', text)
+    return result
+
+
+def symbols_to_japanese(text):
+    for regex, replacement in _symbols_to_japanese:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def preprocess_jap(text, with_prosody=False):
+    """Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html"""
+    text = symbols_to_japanese(text)
+    # English words to lower case, should have no influence on japanese words.
+    text = text.lower()
+    sentences = re.split(_japanese_marks, text)
+    marks = re.findall(_japanese_marks, text)
+    text = []
+    for i, sentence in enumerate(sentences):
+        if re.match(_japanese_characters, sentence):
+            if with_prosody:
+                text += pyopenjtalk_g2p_prosody(sentence)[1:-1]
+            else:
+                p = pyopenjtalk.g2p(sentence)
+                text += p.split(" ")
+
+        if i < len(marks):
+            if marks[i] == " ":# 防止意外的UNK
+                continue
+            text += [marks[i].replace(" ", "")]
+    return text
+
+
+def text_normalize(text):
+    # todo: jap text normalize
+
+    # 避免重复标点引起的参考泄露
+    text = replace_consecutive_punctuation(text)
+    return text
+
+# Copied from espnet https://github.com/espnet/espnet/blob/master/espnet2/text/phoneme_tokenizer.py
+def pyopenjtalk_g2p_prosody(text, drop_unvoiced_vowels=True):
+    """Extract phoneme + prosoody symbol sequence from input full-context labels.
+
+    The algorithm is based on `Prosodic features control by symbols as input of
+    sequence-to-sequence acoustic modeling for neural TTS`_ with some r9y9's tweaks.
+
+    Args:
+        text (str): Input text.
+        drop_unvoiced_vowels (bool): whether to drop unvoiced vowels.
+
+    Returns:
+        List[str]: List of phoneme + prosody symbols.
+
+    Examples:
+        >>> from espnet2.text.phoneme_tokenizer import pyopenjtalk_g2p_prosody
+        >>> pyopenjtalk_g2p_prosody("こんにちは。")
+        ['^', 'k', 'o', '[', 'N', 'n', 'i', 'ch', 'i', 'w', 'a', '$']
+
+    .. _`Prosodic features control by symbols as input of sequence-to-sequence acoustic
+        modeling for neural TTS`: https://doi.org/10.1587/transinf.2020EDP7104
+
+    """
+    labels = pyopenjtalk.make_label(pyopenjtalk.run_frontend(text))
+    N = len(labels)
+
+    phones = []
+    for n in range(N):
+        lab_curr = labels[n]
+
+        # current phoneme
+        p3 = re.search(r"\-(.*?)\+", lab_curr).group(1)
+        # deal unvoiced vowels as normal vowels
+        if drop_unvoiced_vowels and p3 in "AEIOU":
+            p3 = p3.lower()
+
+        # deal with sil at the beginning and the end of text
+        if p3 == "sil":
+            assert n == 0 or n == N - 1
+            if n == 0:
+                phones.append("^")
+            elif n == N - 1:
+                # check question form or not
+                e3 = _numeric_feature_by_regex(r"!(\d+)_", lab_curr)
+                if e3 == 0:
+                    phones.append("$")
+                elif e3 == 1:
+                    phones.append("?")
+            continue
+        elif p3 == "pau":
+            phones.append("_")
+            continue
+        else:
+            phones.append(p3)
+
+        # accent type and position info (forward or backward)
+        a1 = _numeric_feature_by_regex(r"/A:([0-9\-]+)\+", lab_curr)
+        a2 = _numeric_feature_by_regex(r"\+(\d+)\+", lab_curr)
+        a3 = _numeric_feature_by_regex(r"\+(\d+)/", lab_curr)
+
+        # number of mora in accent phrase
+        f1 = _numeric_feature_by_regex(r"/F:(\d+)_", lab_curr)
+
+        a2_next = _numeric_feature_by_regex(r"\+(\d+)\+", labels[n + 1])
+        # accent phrase border
+        if a3 == 1 and a2_next == 1 and p3 in "aeiouAEIOUNcl":
+            phones.append("#")
+        # pitch falling
+        elif a1 == 0 and a2_next == a2 + 1 and a2 != f1:
+            phones.append("]")
+        # pitch rising
+        elif a2 == 1 and a2_next == 2:
+            phones.append("[")
+
+    return phones
+
+# Copied from espnet https://github.com/espnet/espnet/blob/master/espnet2/text/phoneme_tokenizer.py
+def _numeric_feature_by_regex(regex, s):
+    match = re.search(regex, s)
+    if match is None:
+        return -50
+    return int(match.group(1))
+
+def g2p(norm_text, with_prosody=True):
+    phones = preprocess_jap(norm_text, with_prosody)
+    phones = [post_replace_ph(i) for i in phones]
+    # todo: implement tones and word2ph
+    return phones
+
+
+if __name__ == "__main__":
+    phones = g2p("Hello.こんにちは！今日もNiCe天気ですね！tokyotowerに行きましょう！")
+    print(phones)

GPT_SoVITS/text/korean.py

@@ -0,0 +1,265 @@
+# reference: https://github.com/ORI-Muchim/MB-iSTFT-VITS-Korean/blob/main/text/korean.py
+
+import re
+from jamo import h2j, j2hcj
+import ko_pron
+from g2pk2 import G2p
+
+from text.symbols2 import symbols 
+
+# This is a list of Korean classifiers preceded by pure Korean numerals.
+_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
+
+# List of (hangul, hangul divided) pairs:
+_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [
+    # ('ㄳ', 'ㄱㅅ'),   # g2pk2, A Syllable-ending Rule
+    # ('ㄵ', 'ㄴㅈ'),
+    # ('ㄶ', 'ㄴㅎ'),
+    # ('ㄺ', 'ㄹㄱ'),
+    # ('ㄻ', 'ㄹㅁ'),
+    # ('ㄼ', 'ㄹㅂ'),
+    # ('ㄽ', 'ㄹㅅ'),
+    # ('ㄾ', 'ㄹㅌ'),
+    # ('ㄿ', 'ㄹㅍ'),
+    # ('ㅀ', 'ㄹㅎ'),
+    # ('ㅄ', 'ㅂㅅ'),
+    ('ㅘ', 'ㅗㅏ'),
+    ('ㅙ', 'ㅗㅐ'),
+    ('ㅚ', 'ㅗㅣ'),
+    ('ㅝ', 'ㅜㅓ'),
+    ('ㅞ', 'ㅜㅔ'),
+    ('ㅟ', 'ㅜㅣ'),
+    ('ㅢ', 'ㅡㅣ'),
+    ('ㅑ', 'ㅣㅏ'),
+    ('ㅒ', 'ㅣㅐ'),
+    ('ㅕ', 'ㅣㅓ'),
+    ('ㅖ', 'ㅣㅔ'),
+    ('ㅛ', 'ㅣㅗ'),
+    ('ㅠ', 'ㅣㅜ')
+]]
+
+# List of (Latin alphabet, hangul) pairs:
+_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
+    ('a', '에이'),
+    ('b', '비'),
+    ('c', '시'),
+    ('d', '디'),
+    ('e', '이'),
+    ('f', '에프'),
+    ('g', '지'),
+    ('h', '에이치'),
+    ('i', '아이'),
+    ('j', '제이'),
+    ('k', '케이'),
+    ('l', '엘'),
+    ('m', '엠'),
+    ('n', '엔'),
+    ('o', '오'),
+    ('p', '피'),
+    ('q', '큐'),
+    ('r', '아르'),
+    ('s', '에스'),
+    ('t', '티'),
+    ('u', '유'),
+    ('v', '브이'),
+    ('w', '더블유'),
+    ('x', '엑스'),
+    ('y', '와이'),
+    ('z', '제트')
+]]
+
+# List of (ipa, lazy ipa) pairs:
+_ipa_to_lazy_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
+    ('t͡ɕ','ʧ'),
+    ('d͡ʑ','ʥ'),
+    ('ɲ','n^'),
+    ('ɕ','ʃ'),
+    ('ʷ','w'),
+    ('ɭ','l`'),
+    ('ʎ','ɾ'),
+    ('ɣ','ŋ'),
+    ('ɰ','ɯ'),
+    ('ʝ','j'),
+    ('ʌ','ə'),
+    ('ɡ','g'),
+    ('\u031a','#'),
+    ('\u0348','='),
+    ('\u031e',''),
+    ('\u0320',''),
+    ('\u0339','')
+]]
+
+
+def fix_g2pk2_error(text):
+    new_text = ""
+    i = 0
+    while i < len(text) - 4:
+        if (text[i:i+3] == 'ㅇㅡㄹ' or text[i:i+3] == 'ㄹㅡㄹ') and text[i+3] == ' ' and text[i+4] == 'ㄹ':
+            new_text += text[i:i+3] + ' ' + 'ㄴ'
+            i += 5
+        else:
+            new_text += text[i]
+            i += 1
+
+    new_text += text[i:]
+    return new_text
+
+
+def latin_to_hangul(text):
+    for regex, replacement in _latin_to_hangul:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def divide_hangul(text):
+    text = j2hcj(h2j(text))
+    for regex, replacement in _hangul_divided:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def hangul_number(num, sino=True):
+    '''Reference https://github.com/Kyubyong/g2pK'''
+    num = re.sub(',', '', num)
+
+    if num == '0':
+        return '영'
+    if not sino and num == '20':
+        return '스무'
+
+    digits = '123456789'
+    names = '일이삼사오육칠팔구'
+    digit2name = {d: n for d, n in zip(digits, names)}
+
+    modifiers = '한 두 세 네 다섯 여섯 일곱 여덟 아홉'
+    decimals = '열 스물 서른 마흔 쉰 예순 일흔 여든 아흔'
+    digit2mod = {d: mod for d, mod in zip(digits, modifiers.split())}
+    digit2dec = {d: dec for d, dec in zip(digits, decimals.split())}
+
+    spelledout = []
+    for i, digit in enumerate(num):
+        i = len(num) - i - 1
+        if sino:
+            if i == 0:
+                name = digit2name.get(digit, '')
+            elif i == 1:
+                name = digit2name.get(digit, '') + '십'
+                name = name.replace('일십', '십')
+        else:
+            if i == 0:
+                name = digit2mod.get(digit, '')
+            elif i == 1:
+                name = digit2dec.get(digit, '')
+        if digit == '0':
+            if i % 4 == 0:
+                last_three = spelledout[-min(3, len(spelledout)):]
+                if ''.join(last_three) == '':
+                    spelledout.append('')
+                    continue
+            else:
+                spelledout.append('')
+                continue
+        if i == 2:
+            name = digit2name.get(digit, '') + '백'
+            name = name.replace('일백', '백')
+        elif i == 3:
+            name = digit2name.get(digit, '') + '천'
+            name = name.replace('일천', '천')
+        elif i == 4:
+            name = digit2name.get(digit, '') + '만'
+            name = name.replace('일만', '만')
+        elif i == 5:
+            name = digit2name.get(digit, '') + '십'
+            name = name.replace('일십', '십')
+        elif i == 6:
+            name = digit2name.get(digit, '') + '백'
+            name = name.replace('일백', '백')
+        elif i == 7:
+            name = digit2name.get(digit, '') + '천'
+            name = name.replace('일천', '천')
+        elif i == 8:
+            name = digit2name.get(digit, '') + '억'
+        elif i == 9:
+            name = digit2name.get(digit, '') + '십'
+        elif i == 10:
+            name = digit2name.get(digit, '') + '백'
+        elif i == 11:
+            name = digit2name.get(digit, '') + '천'
+        elif i == 12:
+            name = digit2name.get(digit, '') + '조'
+        elif i == 13:
+            name = digit2name.get(digit, '') + '십'
+        elif i == 14:
+            name = digit2name.get(digit, '') + '백'
+        elif i == 15:
+            name = digit2name.get(digit, '') + '천'
+        spelledout.append(name)
+    return ''.join(elem for elem in spelledout)
+
+
+def number_to_hangul(text):
+    '''Reference https://github.com/Kyubyong/g2pK'''
+    tokens = set(re.findall(r'(\d[\d,]*)([\uac00-\ud71f]+)', text))
+    for token in tokens:
+        num, classifier = token
+        if classifier[:2] in _korean_classifiers or classifier[0] in _korean_classifiers:
+            spelledout = hangul_number(num, sino=False)
+        else:
+            spelledout = hangul_number(num, sino=True)
+        text = text.replace(f'{num}{classifier}', f'{spelledout}{classifier}')
+    # digit by digit for remaining digits
+    digits = '0123456789'
+    names = '영일이삼사오육칠팔구'
+    for d, n in zip(digits, names):
+        text = text.replace(d, n)
+    return text
+
+
+def korean_to_lazy_ipa(text):
+    text = latin_to_hangul(text)
+    text = number_to_hangul(text)
+    text=re.sub('[\uac00-\ud7af]+',lambda x:ko_pron.romanise(x.group(0),'ipa').split('] ~ [')[0],text)
+    for regex, replacement in _ipa_to_lazy_ipa:
+        text = re.sub(regex, replacement, text)
+    return text
+
+_g2p=G2p()
+def korean_to_ipa(text):
+    text = latin_to_hangul(text)
+    text = number_to_hangul(text)
+    text = _g2p(text)
+    text = fix_g2pk2_error(text)
+    text = korean_to_lazy_ipa(text)
+    return text.replace('ʧ','tʃ').replace('ʥ','dʑ')
+
+def post_replace_ph(ph):
+    rep_map = {
+        "：": ",",
+        "；": ",",
+        "，": ",",
+        "。": ".",
+        "！": "!",
+        "？": "?",
+        "\n": ".",
+        "·": ",",
+        "、": ",",
+        "...": "…",
+        " ": "空",
+    }
+    if ph in rep_map.keys():
+        ph = rep_map[ph]
+    if ph in symbols:
+        return ph
+    if ph not in symbols:
+        ph = "停"
+    return ph
+
+def g2p(text):
+    text = latin_to_hangul(text)
+    text = _g2p(text)
+    text = divide_hangul(text)
+    text = fix_g2pk2_error(text)
+    text = re.sub(r'([\u3131-\u3163])$', r'\1.', text)
+    # text = "".join([post_replace_ph(i) for i in text])
+    text = [post_replace_ph(i) for i in text]
+    return text

GPT_SoVITS/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

GPT_SoVITS/text/symbols.py

@@ -0,0 +1,427 @@
+import os
+
+# punctuation = ['!', '?', '…', ",", ".","@"]#@是SP停顿
+punctuation = ["!", "?", "…", ",", "."]  # @是SP停顿
+punctuation.append("-")
+pu_symbols = punctuation + ["SP", "SP2", "SP3", "UNK"]
+# pu_symbols = punctuation + ["SP", 'SP2', 'SP3','SP4', "UNK"]
+pad = "_"
+
+c = [
+    "AA",
+    "EE",
+    "OO",
+    "b",
+    "c",
+    "ch",
+    "d",
+    "f",
+    "g",
+    "h",
+    "j",
+    "k",
+    "l",
+    "m",
+    "n",
+    "p",
+    "q",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "w",
+    "x",
+    "y",
+    "z",
+    "zh",
+]
+v = [
+    "E1",
+    "En1",
+    "a1",
+    "ai1",
+    "an1",
+    "ang1",
+    "ao1",
+    "e1",
+    "ei1",
+    "en1",
+    "eng1",
+    "er1",
+    "i1",
+    "i01",
+    "ia1",
+    "ian1",
+    "iang1",
+    "iao1",
+    "ie1",
+    "in1",
+    "ing1",
+    "iong1",
+    "ir1",
+    "iu1",
+    "o1",
+    "ong1",
+    "ou1",
+    "u1",
+    "ua1",
+    "uai1",
+    "uan1",
+    "uang1",
+    "ui1",
+    "un1",
+    "uo1",
+    "v1",
+    "van1",
+    "ve1",
+    "vn1",
+    "E2",
+    "En2",
+    "a2",
+    "ai2",
+    "an2",
+    "ang2",
+    "ao2",
+    "e2",
+    "ei2",
+    "en2",
+    "eng2",
+    "er2",
+    "i2",
+    "i02",
+    "ia2",
+    "ian2",
+    "iang2",
+    "iao2",
+    "ie2",
+    "in2",
+    "ing2",
+    "iong2",
+    "ir2",
+    "iu2",
+    "o2",
+    "ong2",
+    "ou2",
+    "u2",
+    "ua2",
+    "uai2",
+    "uan2",
+    "uang2",
+    "ui2",
+    "un2",
+    "uo2",
+    "v2",
+    "van2",
+    "ve2",
+    "vn2",
+    "E3",
+    "En3",
+    "a3",
+    "ai3",
+    "an3",
+    "ang3",
+    "ao3",
+    "e3",
+    "ei3",
+    "en3",
+    "eng3",
+    "er3",
+    "i3",
+    "i03",
+    "ia3",
+    "ian3",
+    "iang3",
+    "iao3",
+    "ie3",
+    "in3",
+    "ing3",
+    "iong3",
+    "ir3",
+    "iu3",
+    "o3",
+    "ong3",
+    "ou3",
+    "u3",
+    "ua3",
+    "uai3",
+    "uan3",
+    "uang3",
+    "ui3",
+    "un3",
+    "uo3",
+    "v3",
+    "van3",
+    "ve3",
+    "vn3",
+    "E4",
+    "En4",
+    "a4",
+    "ai4",
+    "an4",
+    "ang4",
+    "ao4",
+    "e4",
+    "ei4",
+    "en4",
+    "eng4",
+    "er4",
+    "i4",
+    "i04",
+    "ia4",
+    "ian4",
+    "iang4",
+    "iao4",
+    "ie4",
+    "in4",
+    "ing4",
+    "iong4",
+    "ir4",
+    "iu4",
+    "o4",
+    "ong4",
+    "ou4",
+    "u4",
+    "ua4",
+    "uai4",
+    "uan4",
+    "uang4",
+    "ui4",
+    "un4",
+    "uo4",
+    "v4",
+    "van4",
+    "ve4",
+    "vn4",
+    "E5",
+    "En5",
+    "a5",
+    "ai5",
+    "an5",
+    "ang5",
+    "ao5",
+    "e5",
+    "ei5",
+    "en5",
+    "eng5",
+    "er5",
+    "i5",
+    "i05",
+    "ia5",
+    "ian5",
+    "iang5",
+    "iao5",
+    "ie5",
+    "in5",
+    "ing5",
+    "iong5",
+    "ir5",
+    "iu5",
+    "o5",
+    "ong5",
+    "ou5",
+    "u5",
+    "ua5",
+    "uai5",
+    "uan5",
+    "uang5",
+    "ui5",
+    "un5",
+    "uo5",
+    "v5",
+    "van5",
+    "ve5",
+    "vn5",
+]
+
+v_without_tone = [
+    "E",
+    "En",
+    "a",
+    "ai",
+    "an",
+    "ang",
+    "ao",
+    "e",
+    "ei",
+    "en",
+    "eng",
+    "er",
+    "i",
+    "i0",
+    "ia",
+    "ian",
+    "iang",
+    "iao",
+    "ie",
+    "in",
+    "ing",
+    "iong",
+    "ir",
+    "iu",
+    "o",
+    "ong",
+    "ou",
+    "u",
+    "ua",
+    "uai",
+    "uan",
+    "uang",
+    "ui",
+    "un",
+    "uo",
+    "v",
+    "van",
+    "ve",
+    "vn",
+]
+
+# japanese
+ja_symbols = [
+    "I",
+    "N",
+    "U",
+    "a",
+    "b",
+    "by",
+    "ch",
+    "cl",
+    "d",
+    "dy",
+    "e",
+    "f",
+    "g",
+    "gy",
+    "h",
+    "hy",
+    "i",
+    "j",
+    "k",
+    "ky",
+    "m",
+    "my",
+    "n",
+    "ny",
+    "o",
+    "p",
+    "py",
+    "r",
+    "ry",
+    "s",
+    "sh",
+    "t",
+    "ts",
+    "u",
+    "v",
+    "w",
+    "y",
+    "z",
+    # "[", #上升调型
+    # "]", #下降调型
+    # "$", #结束符
+    # "^", #开始符
+]
+
+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",
+}
+
+# Hindi phonemes
+hi_consonants = [
+    "क", "ख", "ग", "घ", "ङ",
+    "च", "छ", "ज", "झ", "ञ",
+    "ट", "ठ", "ड", "ढ", "ण",
+    "त", "थ", "द", "ध", "न",
+    "प", "फ", "ब", "भ", "म",
+    "य", "र", "ल", "व",
+    "श", "ष", "स", "ह",
+    "क्ष", "त्र", "ज्ञ"
+]
+
+hi_vowels = [
+    "अ", "आ", "इ", "ई", "उ", "ऊ",
+    "ए", "ऐ", "ओ", "औ",
+    "ऋ", "ॠ",
+    "ं", "ः", "ँ",  # anusvara, visarga, chandrabindu
+    "्",  # virama
+    "ा", "ि", "ी", "ु", "ू",
+    "े", "ै", "ो", "ौ",
+    "ृ", "ॄ",
+    "ॉ", "ऑ"  # Added candra o and o with candra
+]
+
+hi_numbers = ["०", "१", "२", "३", "४", "५", "६", "७", "८", "९"]
+
+symbols = [pad] + c + v + ja_symbols + pu_symbols + list(arpa) + hi_consonants + hi_vowels + hi_numbers
+symbols = sorted(set(symbols))
+if __name__ == "__main__":
+    print(len(symbols))

GPT_SoVITS/text/symbols2.py

@@ -0,0 +1,444 @@
+import os
+
+# punctuation = ['!', '?', '…', ",", ".","@"]#@是SP停顿
+punctuation = ["!", "?", "…", ",", "."]  # @是SP停顿
+punctuation.append("-")
+pu_symbols = punctuation + ["SP", "SP2", "SP3", "UNK"]
+# pu_symbols = punctuation + ["SP", 'SP2', 'SP3','SP4', "UNK"]
+pad = "_"
+
+c = [
+    "AA",
+    "EE",
+    "OO",
+    "b",
+    "c",
+    "ch",
+    "d",
+    "f",
+    "g",
+    "h",
+    "j",
+    "k",
+    "l",
+    "m",
+    "n",
+    "p",
+    "q",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "w",
+    "x",
+    "y",
+    "z",
+    "zh",
+]
+v = [
+    "E1",
+    "En1",
+    "a1",
+    "ai1",
+    "an1",
+    "ang1",
+    "ao1",
+    "e1",
+    "ei1",
+    "en1",
+    "eng1",
+    "er1",
+    "i1",
+    "i01",
+    "ia1",
+    "ian1",
+    "iang1",
+    "iao1",
+    "ie1",
+    "in1",
+    "ing1",
+    "iong1",
+    "ir1",
+    "iu1",
+    "o1",
+    "ong1",
+    "ou1",
+    "u1",
+    "ua1",
+    "uai1",
+    "uan1",
+    "uang1",
+    "ui1",
+    "un1",
+    "uo1",
+    "v1",
+    "van1",
+    "ve1",
+    "vn1",
+    "E2",
+    "En2",
+    "a2",
+    "ai2",
+    "an2",
+    "ang2",
+    "ao2",
+    "e2",
+    "ei2",
+    "en2",
+    "eng2",
+    "er2",
+    "i2",
+    "i02",
+    "ia2",
+    "ian2",
+    "iang2",
+    "iao2",
+    "ie2",
+    "in2",
+    "ing2",
+    "iong2",
+    "ir2",
+    "iu2",
+    "o2",
+    "ong2",
+    "ou2",
+    "u2",
+    "ua2",
+    "uai2",
+    "uan2",
+    "uang2",
+    "ui2",
+    "un2",
+    "uo2",
+    "v2",
+    "van2",
+    "ve2",
+    "vn2",
+    "E3",
+    "En3",
+    "a3",
+    "ai3",
+    "an3",
+    "ang3",
+    "ao3",
+    "e3",
+    "ei3",
+    "en3",
+    "eng3",
+    "er3",
+    "i3",
+    "i03",
+    "ia3",
+    "ian3",
+    "iang3",
+    "iao3",
+    "ie3",
+    "in3",
+    "ing3",
+    "iong3",
+    "ir3",
+    "iu3",
+    "o3",
+    "ong3",
+    "ou3",
+    "u3",
+    "ua3",
+    "uai3",
+    "uan3",
+    "uang3",
+    "ui3",
+    "un3",
+    "uo3",
+    "v3",
+    "van3",
+    "ve3",
+    "vn3",
+    "E4",
+    "En4",
+    "a4",
+    "ai4",
+    "an4",
+    "ang4",
+    "ao4",
+    "e4",
+    "ei4",
+    "en4",
+    "eng4",
+    "er4",
+    "i4",
+    "i04",
+    "ia4",
+    "ian4",
+    "iang4",
+    "iao4",
+    "ie4",
+    "in4",
+    "ing4",
+    "iong4",
+    "ir4",
+    "iu4",
+    "o4",
+    "ong4",
+    "ou4",
+    "u4",
+    "ua4",
+    "uai4",
+    "uan4",
+    "uang4",
+    "ui4",
+    "un4",
+    "uo4",
+    "v4",
+    "van4",
+    "ve4",
+    "vn4",
+    "E5",
+    "En5",
+    "a5",
+    "ai5",
+    "an5",
+    "ang5",
+    "ao5",
+    "e5",
+    "ei5",
+    "en5",
+    "eng5",
+    "er5",
+    "i5",
+    "i05",
+    "ia5",
+    "ian5",
+    "iang5",
+    "iao5",
+    "ie5",
+    "in5",
+    "ing5",
+    "iong5",
+    "ir5",
+    "iu5",
+    "o5",
+    "ong5",
+    "ou5",
+    "u5",
+    "ua5",
+    "uai5",
+    "uan5",
+    "uang5",
+    "ui5",
+    "un5",
+    "uo5",
+    "v5",
+    "van5",
+    "ve5",
+    "vn5",
+]
+
+v_without_tone = [
+    "E",
+    "En",
+    "a",
+    "ai",
+    "an",
+    "ang",
+    "ao",
+    "e",
+    "ei",
+    "en",
+    "eng",
+    "er",
+    "i",
+    "i0",
+    "ia",
+    "ian",
+    "iang",
+    "iao",
+    "ie",
+    "in",
+    "ing",
+    "iong",
+    "ir",
+    "iu",
+    "o",
+    "ong",
+    "ou",
+    "u",
+    "ua",
+    "uai",
+    "uan",
+    "uang",
+    "ui",
+    "un",
+    "uo",
+    "v",
+    "van",
+    "ve",
+    "vn",
+]
+
+# japanese
+ja_symbols = [
+    "I",
+    "N",
+    "U",
+    "a",
+    "b",
+    "by",
+    "ch",
+    "cl",
+    "d",
+    "dy",
+    "e",
+    "f",
+    "g",
+    "gy",
+    "h",
+    "hy",
+    "i",
+    "j",
+    "k",
+    "ky",
+    "m",
+    "my",
+    "n",
+    "ny",
+    "o",
+    "p",
+    "py",
+    "r",
+    "ry",
+    "s",
+    "sh",
+    "t",
+    "ts",
+    "u",
+    "v",
+    "w",
+    "y",
+    "z",
+    ###楼下2个留到后面加
+    # "[", #上升调型
+    # "]", #下降调型
+    # "$", #结束符
+    # "^", #开始符
+]
+
+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",
+}
+
+ko_symbols='ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ空停'
+# ko_symbols='ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
+
+yue_symbols={'Yeot3', 'Yip1', 'Yyu3', 'Yeng4', 'Yut5', 'Yaan5', 'Ym5', 'Yaan6', 'Yang1', 'Yun4', 'Yon2', 'Yui5', 'Yun2', 'Yat3', 'Ye', 'Yeot1', 'Yoeng5', 'Yoek2', 'Yam2', 'Yeon6', 'Yu6', 'Yiu3', 'Yaang6', 'Yp5', 'Yai4', 'Yoek4', 'Yit6', 'Yam5', 'Yoeng6', 'Yg1', 'Yk3', 'Yoe4', 'Yam3', 'Yc', 'Yyu4', 'Yyut1', 'Yiu4', 'Ying3', 'Yip3', 'Yaap3', 'Yau3', 'Yan4', 'Yau1', 'Yap4', 'Yk6', 'Yok3', 'Yai1', 'Yeot6', 'Yan2', 'Yoek6', 'Yt1', 'Yoi1', 'Yit5', 'Yn4', 'Yaau3', 'Yau4', 'Yuk6', 'Ys', 'Yuk', 'Yin6', 'Yung6', 'Ya', 'You', 'Yaai5', 'Yau5', 'Yoi3', 'Yaak3', 'Yaat3', 'Ying2', 'Yok5', 'Yeng2', 'Yyut3', 'Yam1', 'Yip5', 'You1', 'Yam6', 'Yaa5', 'Yi6', 'Yek4', 'Yyu2', 'Yuk5', 'Yaam1', 'Yang2', 'Yai', 'Yiu6', 'Yin4', 'Yok4', 'Yot3', 'Yui2', 'Yeoi5', 'Yyun6', 'Yyu5', 'Yoi5', 'Yeot2', 'Yim4', 'Yeoi2', 'Yaan1', 'Yang6', 'Yong1', 'Yaang4', 'Yung5', 'Yeon1', 'Yin2', 'Ya3', 'Yaang3', 'Yg', 'Yk2', 'Yaau5', 'Yut1', 'Yt5', 'Yip4', 'Yung4', 'Yj', 'Yong3', 'Ya1', 'Yg6', 'Yaau6', 'Yit3', 'Yun3', 'Ying1', 'Yn2', 'Yg4', 'Yl', 'Yp3', 'Yn3', 'Yak1', 'Yang5', 'Yoe6', 'You2', 'Yap2', 'Yak2', 'Yt3', 'Yot5', 'Yim2', 'Yi1', 'Yn6', 'Yaat5', 'Yaam3', 'Yoek5', 'Ye3', 'Yeon4', 'Yaa2', 'Yu3', 'Yim6', 'Ym', 'Yoe3', 'Yaai2', 'Ym2', 'Ya6', 'Yeng6', 'Yik4', 'Yot4', 'Yaai4', 'Yyun3', 'Yu1', 'Yoeng1', 'Yaap2', 'Yuk3', 'Yoek3', 'Yeng5', 'Yeoi1', 'Yiu2', 'Yok1', 'Yo1', 'Yoek1', 'Yoeng2', 'Yeon5', 'Yiu1', 'Yoeng4', 'Yuk2', 'Yat4', 'Yg5', 'Yut4', 'Yan6', 'Yin3', 'Yaa6', 'Yap1', 'Yg2', 'Yoe5', 'Yt4', 'Ya5', 'Yo4', 'Yyu1', 'Yak3', 'Yeon2', 'Yong4', 'Ym1', 'Ye2', 'Yaang5', 'Yoi2', 'Yeng3', 'Yn', 'Yyut4', 'Yau', 'Yaak2', 'Yaan4', 'Yek2', 'Yin1', 'Yi5', 'Yoe2', 'Yei5', 'Yaat6', 'Yak5', 'Yp6', 'Yok6', 'Yei2', 'Yaap1', 'Yyut5', 'Yi4', 'Yim1', 'Yk5', 'Ye4', 'Yok2', 'Yaam6', 'Yat2', 'Yon6', 'Yei3', 'Yyu6', 'Yeot5', 'Yk4', 'Yai6', 'Yd', 'Yg3', 'Yei6', 'Yau2', 'Yok', 'Yau6', 'Yung3', 'Yim5', 'Yut6', 'Yit1', 'Yon3', 'Yat1', 'Yaam2', 'Yyut2', 'Yui6', 'Yt2', 'Yek6', 'Yt', 'Ye6', 'Yang3', 'Ying6', 'Yaau1', 'Yeon3', 'Yng', 'Yh', 'Yang4', 'Ying5', 'Yaap6', 'Yoeng3', 'Yyun4', 'You3', 'Yan5', 'Yat5', 'Yot1', 'Yun1', 'Yi3', 'Yaa1', 'Yaap4', 'You6', 'Yaang2', 'Yaap5', 'Yaa3', 'Yaak6', 'Yeng1', 'Yaak1', 'Yo5', 'Yoi4', 'Yam4', 'Yik1', 'Ye1', 'Yai5', 'Yung1', 'Yp2', 'Yui4', 'Yaak4', 'Yung2', 'Yak4', 'Yaat4', 'Yeoi4', 'Yut2', 'Yin5', 'Yaau4', 'Yap6', 'Yb', 'Yaam4', 'Yw', 'Yut3', 'Yong2', 'Yt6', 'Yaai6', 'Yap5', 'Yik5', 'Yun6', 'Yaam5', 'Yun5', 'Yik3', 'Ya2', 'Yyut6', 'Yon4', 'Yk1', 'Yit4', 'Yak6', 'Yaan2', 'Yuk1', 'Yai2', 'Yik2', 'Yaat2', 'Yo3', 'Ykw', 'Yn5', 'Yaa', 'Ye5', 'Yu4', 'Yei1', 'Yai3', 'Yyun5', 'Yip2', 'Yaau2', 'Yiu5', 'Ym4', 'Yeoi6', 'Yk', 'Ym6', 'Yoe1', 'Yeoi3', 'Yon', 'Yuk4', 'Yaai3', 'Yaa4', 'Yot6', 'Yaang1', 'Yei4', 'Yek1', 'Yo', 'Yp', 'Yo6', 'Yp4', 'Yan3', 'Yoi', 'Yap3', 'Yek3', 'Yim3', 'Yz', 'Yot2', 'Yoi6', 'Yit2', 'Yu5', 'Yaan3', 'Yan1', 'Yon5', 'Yp1', 'Yong5', 'Ygw', 'Yak', 'Yat6', 'Ying4', 'Yu2', 'Yf', 'Ya4', 'Yon1', 'You4', 'Yik6', 'Yui1', 'Yaat1', 'Yeot4', 'Yi2', 'Yaai1', 'Yek5', 'Ym3', 'Yong6', 'You5', 'Yyun1', 'Yn1', 'Yo2', 'Yip6', 'Yui3', 'Yaak5', 'Yyun2'}
+
+# Hindi consonants
+hi_consonants = [
+    "क", "ख", "ग", "घ", "ङ",
+    "च", "छ", "ज", "झ", "ञ",
+    "ट", "ठ", "ड", "ढ", "ण",
+    "त", "थ", "द", "ध", "न",
+    "प", "फ", "ब", "भ", "म",
+    "य", "र", "ल", "व",
+    "श", "ष", "स", "ह",
+    "क्ष", "त्र", "ज्ञ"
+]
+
+# Hindi vowels and modifiers
+hi_vowels = [
+    "अ", "आ", "इ", "ई", "उ", "ऊ",
+    "ए", "ऐ", "ओ", "औ",
+    "ऋ", "ॠ",
+    "ं", "ः", "ँ",  # anusvara, visarga, chandrabindu
+    "्",  # virama
+    "ा", "ि", "ी", "ु", "ू",
+    "े", "ै", "ो", "ौ",
+    "ृ", "ॄ",
+    "ॉ", "ऑ"  # candra o and o with candra
+]
+
+# Hindi numbers
+hi_numbers = ["०", "१", "२", "३", "४", "५", "६", "७", "८", "९"]
+
+# Add Hindi symbols to symbols list
+symbols = [pad] + c + v + ja_symbols + pu_symbols + list(arpa) + hi_consonants + hi_vowels + hi_numbers
+symbols = sorted(set(symbols))
+# print(len(symbols))
+
+if __name__ == "__main__":
+    print(len(symbols))
+'''
+粤语：
+    732-353=379
+韩文+粤语：
+    732-322=410
+'''

GPT_SoVITS/text/text_processing.py

@@ -0,0 +1,37 @@
+import re
+from typing import List, Optional
+from .symbols import symbols
+from .hindi import get_phoneme_sequence as hindi_to_phonemes
+
+def is_hindi_text(text: str) -> bool:
+    """Check if the text contains Hindi characters."""
+    return bool(re.search(r'[\u0900-\u097F]', text))
+
+def text_to_sequence(text: str, language: Optional[str] = None) -> List[str]:
+    """Convert text to sequence of symbols."""
+    if language is None:
+        # Auto-detect language
+        if is_hindi_text(text):
+            language = 'hi'
+        # Add more language detection as needed
+        else:
+            language = 'en'  # Default to English
+    
+    # Convert text to phonemes based on language
+    if language == 'hi':
+        phonemes = hindi_to_phonemes(text)
+    else:
+        # Handle other languages or use default
+        phonemes = ['UNK']  # Replace with proper handling for other languages
+    
+    # Verify all phonemes are in the symbols list
+    for p in phonemes:
+        if p not in symbols:
+            print(f"Warning: phoneme '{p}' not in symbols list")
+    
+    # Filter out unknown phonemes
+    return [p for p in phonemes if p in symbols]
+
+def sequence_to_text(sequence: List[str]) -> str:
+    """Convert sequence of symbols back to text."""
+    return ' '.join(sequence) 

GPT_SoVITS/text/tone_sandhi.py

@@ -0,0 +1,807 @@
+# 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_fast as 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",
+            "���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",
+            "幺幺",
+            "干嘛",
+            "学子",
+            "哈哈",
+            "数数",
+            "袅袅",
+            "局地",
+            "以下",
+            "娃哈哈",
+            "花花草草",
+            "留得",
+            "耕地",
+            "想想",
+            "熙熙",
+            "攘攘",
+            "卵子",
+            "死死",
+            "冉冉",
+            "恳恳",
+            "佼佼",
+            "吵吵",
+            "打打",
+            "考考",
+            "整整",
+            "莘莘",
+            "落地",
+            "算子",
+            "家家户户",
+            "青青",
+        }
+        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 "吧呢哈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 shold 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 = []
+        # function 1
+        for i, (word, pos) in enumerate(seg):
+            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"
+                and seg[i + 1][1] == "v"
+            ):
+                new_seg[i - 1][0] = new_seg[i - 1][0] + "一" + new_seg[i - 1][0]
+            else:
+                if (
+                    i - 2 >= 0
+                    and seg[i - 1][0] == "一"
+                    and seg[i - 2][0] == word
+                    and pos == "v"
+                    and seg[i - 2][1] == "v"
+                ):
+                    continue
+                else:
+                    new_seg.append([word, pos])
+        seg = new_seg
+        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)
+        try:
+            seg = self._merge_continuous_three_tones(seg)
+        except:
+            print("_merge_continuous_three_tones failed")
+        try:
+            seg = self._merge_continuous_three_tones_2(seg)
+        except:
+            print("_merge_continuous_three_tones_2 failed")
+
+        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

GPT_SoVITS/text/zh_normalization/README.md

@@ -0,0 +1,16 @@
+## Supported NSW (Non-Standard-Word) Normalization
+
+|NSW type|raw|normalized|
+|:--|:-|:-|
+|serial number|电影中梁朝伟扮演的陈永仁的编号27149|电影中梁朝伟扮演的陈永仁的编号二七一四九|
+|cardinal|这块黄金重达324.75克<br>我们班的最高总分为583分|这块黄金重达三百二十四点七五克<br>我们班的最高总分为五百八十三分|
+|numeric range |12\~23<br>-1.5\~2|十二到二十三<br>负一点五到二|
+|date|她出生于86年8月18日，她弟弟出生于1995年3月1日|她出生于八六年八月十八日， 她弟弟出生于一九九五年三月一日|
+|time|等会请在12:05请通知我|等会请在十二点零五分请通知我
+|temperature|今天的最低气温达到-10°C|今天的最低气温达到零下十度
+|fraction|现场有7/12的观众投出了赞成票|现场有十二分之七的观众投出了赞成票|
+|percentage|明天有62％的概率降雨|明天有百分之六十二的概率降雨|
+|money|随便来几个价格12块5，34.5元，20.1万|随便来几个价格十二块五，三十四点五元，二十点一万|
+|telephone|这是固话0421-33441122<br>这是手机+86 18544139121|这是固话零四二一三三四四一一二二<br>这是手机八六一八五四四一三九一二一|
+## References
+[Pull requests #658 of DeepSpeech](https://github.com/PaddlePaddle/DeepSpeech/pull/658/files)

GPT_SoVITS/text/zh_normalization/__init__.py

@@ -0,0 +1,14 @@
+# Copyright (c) 2020 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 text.zh_normalization.text_normlization import *

GPT_SoVITS/text/zh_normalization/char_convert.py

@@ -0,0 +1,46 @@
+# coding=utf-8
+# Copyright (c) 2020 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.
+"""Traditional and simplified Chinese conversion, a simplified character may correspond to multiple traditional characters.
+"""
+simplified_charcters = 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+
+traditional_characters = 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鐃鐄鐇鐏鐒鐓鐔鐗馗鐙鐝鐠鐡鐦鐨鐩鐫鐬鐱鐳鐶鐻鐽鐿鑀鑅鑌鑐鑕鑚鑛鑢鑤鑥鑪鑭鑯鑱鑴鑵鑷钁钃镻閆閈閌閎閒閔閗閟閡関閤閤閧閬閲閹閺閻閼閽閿闇闉闋闐闑闒闓闘闚闞闟闠闤闥阞阢阤阨阬阯阹阼阽陁陑陔陛陜陡陥陬騭陴険陼陾隂隃隈隒隗隞隠隣隤隩隮隰顴隳隷隹雂雈雉雊雎雑雒雗雘雚雝雟雩雰雱驛霂霅霈霊霑霒霓霙霝霢霣霤霨霩霪霫霮靁靆靉靑靚靣靦靪靮靰靳靷靸靺靼靿鞀鞃鞄鞌鞗鞙鞚鞝鞞鞡鞣鞨鞫鞬鞮鞶鞹鞾韃韅韉馱韍韎韔韖韘韝韞韡韣韭韮韱韹韺頀颳頄頇頊頍頎頏頒頖頞頠頫頬顱頯頲頴頼顇顋顑顒顓顔顕顚顜顢顣顬顳颭颮颱颶颸颺颻颽颾颿飀飂飈飌飜飡飣飤飥飩飫飮飱飶餀餂餄餎餇餈餑餔餕餖餗餚餛餜餟餠餤餧餩餪餫餬餮餱餲餳餺餻餼餽餿饁饅饇饉饊饍饎饐饘饟饢馘馥馝馡馣騮騾馵馹駃駄駅駆駉駋駑駓駔駗駘駙駜駡駢駪駬駰駴駸駹駽駾騂騄騅騆騉騋騍騏驎騑騒験騕騖騠騢騣騤騧驤騵騶騸騺驀驂驃驄驆驈驊驌驍驎驏驒驔驖驙驦驩驫骺鯁骫骭骯骱骴骶骷髏骾髁髂髄髆髈髐髑髕髖髙髝髞髟髡髣髧髪髫髭髯髲髳髹髺髽髾鬁鬃鬅鬈鬋鬎鬏鬐鬑鬒鬖鬗鬘鬙鬠鬣鬪鬫鬬鬮鬯鬰鬲鬵鬷魆魈魊魋魍魎魑魖鰾魛魟魣魦魨魬魴魵魸鮀鮁鮆鮌鮎鮑鮒鮓鮚鮞鮟鱇鮠鮦鮨鮪鮭鮶鮸鮿鯀鯄鯆鯇鯈鯔鯕鯖鯗鯙鯠鯤鯥鯫鯰鯷鯸鯿鰂鰆鶼鰉鰋鰐鰒鰕鰛鰜鰣鰤鰥鰦鰨鰩鰮鰳鰶鰷鱺鰼鰽鱀鱄鱅鱆鱈鱎鱐鱓鱔鱖鱘鱟鱠鱣鱨鱭鱮鱲鱵鱻鲅鳦鳧鳯鳲鳷鳻鴂鴃鴄鴆鴈鴎鴒鴔鴗鴛鴦鴝鵒鴟鴠鴢鴣鴥鴯鶓鴳鴴鴷鴽鵀鵁鵂鵓鵖鵙鵜鶘鵞鵟鵩鵪鵫鵵鵷鵻鵾鶂鶊鶏鶒鶖鶗鶡鶤鶦鶬鶱鶲鶵鶸鶹鶺鶿鷀鷁鷃鷄鷇鷈鷉鷊鷏鷓鷕鷖鷙鷞鷟鷥鷦鷯鷩鷫鷭鷳鷴鷽鷾鷿鸂鸇鸊鸏鸑鸒鸓鸕鸛鸜鸝鹸鹹鹺麀麂麃麄麇麋麌麐麑麒麚麛麝麤麩麪麫麮麯麰麺麾黁黈黌黢黒黓黕黙黝黟黥黦黧黮黰黱黲黶黹黻黼黽黿鼂鼃鼅鼈鼉鼏鼐鼒鼕鼖鼙鼚鼛鼡鼩鼱鼪鼫鼯鼷鼽齁齆齇齈齉齌齎齏齔齕齗齙齚齜齞齟齬齠齢齣齧齩齮齯齰齱齵齾龎龑龒龔龖龘龝龡龢龤'
+
+assert len(simplified_charcters) == len(simplified_charcters)
+
+s2t_dict = {}
+t2s_dict = {}
+for i, item in enumerate(simplified_charcters):
+    s2t_dict[item] = traditional_characters[i]
+    t2s_dict[traditional_characters[i]] = item
+
+
+def tranditional_to_simplified(text: str) -> str:
+    return "".join(
+        [t2s_dict[item] if item in t2s_dict else item for item in text])
+
+
+def simplified_to_traditional(text: str) -> str:
+    return "".join(
+        [s2t_dict[item] if item in s2t_dict else item for item in text])
+
+
+if __name__ == "__main__":
+    text = "一般是指存取一個應用程式啟動時始終顯示在網站或網頁瀏覽器中的一個或多個初始網頁等畫面存在的站點"
+    print(text)
+    text_simple = tranditional_to_simplified(text)
+    print(text_simple)
+    text_traditional = simplified_to_traditional(text_simple)
+    print(text_traditional)

GPT_SoVITS/text/zh_normalization/chronology.py

@@ -0,0 +1,134 @@
+# 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.
+import re
+
+from .num import DIGITS
+from .num import num2str
+from .num import verbalize_cardinal
+from .num import verbalize_digit
+
+
+def _time_num2str(num_string: str) -> str:
+    """A special case for verbalizing number in time."""
+    result = num2str(num_string.lstrip('0'))
+    if num_string.startswith('0'):
+        result = DIGITS['0'] + result
+    return result
+
+
+# 时刻表达式
+RE_TIME = re.compile(r'([0-1]?[0-9]|2[0-3])'
+                     r':([0-5][0-9])'
+                     r'(:([0-5][0-9]))?')
+
+# 时间范围，如8:30-12:30
+RE_TIME_RANGE = re.compile(r'([0-1]?[0-9]|2[0-3])'
+                           r':([0-5][0-9])'
+                           r'(:([0-5][0-9]))?'
+                           r'(~|-)'
+                           r'([0-1]?[0-9]|2[0-3])'
+                           r':([0-5][0-9])'
+                           r'(:([0-5][0-9]))?')
+
+
+def replace_time(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+
+    is_range = len(match.groups()) > 5
+
+    hour = match.group(1)
+    minute = match.group(2)
+    second = match.group(4)
+
+    if is_range:
+        hour_2 = match.group(6)
+        minute_2 = match.group(7)
+        second_2 = match.group(9)
+
+    result = f"{num2str(hour)}点"
+    if minute.lstrip('0'):
+        if int(minute) == 30:
+            result += "半"
+        else:
+            result += f"{_time_num2str(minute)}分"
+    if second and second.lstrip('0'):
+        result += f"{_time_num2str(second)}秒"
+
+    if is_range:
+        result += "至"
+        result += f"{num2str(hour_2)}点"
+        if minute_2.lstrip('0'):
+            if int(minute) == 30:
+                result += "半"
+            else:
+                result += f"{_time_num2str(minute_2)}分"
+        if second_2 and second_2.lstrip('0'):
+            result += f"{_time_num2str(second_2)}秒"
+
+    return result
+
+
+RE_DATE = re.compile(r'(\d{4}|\d{2})年'
+                     r'((0?[1-9]|1[0-2])月)?'
+                     r'(((0?[1-9])|((1|2)[0-9])|30|31)([日号]))?')
+
+
+def replace_date(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    year = match.group(1)
+    month = match.group(3)
+    day = match.group(5)
+    result = ""
+    if year:
+        result += f"{verbalize_digit(year)}年"
+    if month:
+        result += f"{verbalize_cardinal(month)}月"
+    if day:
+        result += f"{verbalize_cardinal(day)}{match.group(9)}"
+    return result
+
+
+# 用 / 或者 - 分隔的 YY/MM/DD 或者 YY-MM-DD 日期
+RE_DATE2 = re.compile(
+    r'(\d{4})([- /.])(0[1-9]|1[012])\2(0[1-9]|[12][0-9]|3[01])')
+
+
+def replace_date2(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    year = match.group(1)
+    month = match.group(3)
+    day = match.group(4)
+    result = ""
+    if year:
+        result += f"{verbalize_digit(year)}年"
+    if month:
+        result += f"{verbalize_cardinal(month)}月"
+    if day:
+        result += f"{verbalize_cardinal(day)}日"
+    return result

GPT_SoVITS/text/zh_normalization/constants.py

@@ -0,0 +1,62 @@
+# 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.
+import re
+import string
+
+from pypinyin.constants import SUPPORT_UCS4
+
+# 全角半角转换
+# 英文字符全角 -> 半角映射表 (num: 52)
+F2H_ASCII_LETTERS = {
+    ord(char) + 65248: ord(char)
+    for char in string.ascii_letters
+}
+
+# 英文字符半角 -> 全角映射表
+H2F_ASCII_LETTERS = {value: key for key, value in F2H_ASCII_LETTERS.items()}
+
+# 数字字符全角 -> 半角映射表 (num: 10)
+F2H_DIGITS = {ord(char) + 65248: ord(char) for char in string.digits}
+# 数字字符半角 -> 全角映射表
+H2F_DIGITS = {value: key for key, value in F2H_DIGITS.items()}
+
+# 标点符号全角 -> 半角映射表 (num: 32)
+F2H_PUNCTUATIONS = {ord(char) + 65248: ord(char) for char in string.punctuation}
+# 标点符号半角 -> 全角映射表
+H2F_PUNCTUATIONS = {value: key for key, value in F2H_PUNCTUATIONS.items()}
+
+# 空格 (num: 1)
+F2H_SPACE = {'\u3000': ' '}
+H2F_SPACE = {' ': '\u3000'}
+
+# 非"有拼音的汉字"的字符串，可用于NSW提取
+if SUPPORT_UCS4:
+    RE_NSW = re.compile(r'(?:[^'
+                        r'\u3007'  # 〇
+                        r'\u3400-\u4dbf'  # CJK扩展A:[3400-4DBF]
+                        r'\u4e00-\u9fff'  # CJK基本:[4E00-9FFF]
+                        r'\uf900-\ufaff'  # CJK兼容:[F900-FAFF]
+                        r'\U00020000-\U0002A6DF'  # CJK扩展B:[20000-2A6DF]
+                        r'\U0002A703-\U0002B73F'  # CJK扩展C:[2A700-2B73F]
+                        r'\U0002B740-\U0002B81D'  # CJK扩展D:[2B740-2B81D]
+                        r'\U0002F80A-\U0002FA1F'  # CJK兼容扩展:[2F800-2FA1F]
+                        r'])+')
+else:
+    RE_NSW = re.compile(  # pragma: no cover
+        r'(?:[^'
+        r'\u3007'  # 〇
+        r'\u3400-\u4dbf'  # CJK扩展A:[3400-4DBF]
+        r'\u4e00-\u9fff'  # CJK基本:[4E00-9FFF]
+        r'\uf900-\ufaff'  # CJK兼容:[F900-FAFF]
+        r'])+')

GPT_SoVITS/text/zh_normalization/num.py

@@ -0,0 +1,318 @@
+# 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.
+"""
+Rules to verbalize numbers into Chinese characters.
+https://zh.wikipedia.org/wiki/中文数字#現代中文
+"""
+import re
+from collections import OrderedDict
+from typing import List
+
+DIGITS = {str(i): tran for i, tran in enumerate('零一二三四五六七八九')}
+UNITS = OrderedDict({
+    1: '十',
+    2: '百',
+    3: '千',
+    4: '万',
+    8: '亿',
+})
+
+COM_QUANTIFIERS = '(处|台|架|枚|趟|幅|平|方|堵|间|床|株|批|项|例|列|篇|栋|注|亩|封|艘|把|目|套|段|人|所|朵|匹|张|座|回|场|尾|条|个|首|阙|阵|网|炮|顶|丘|棵|只|支|袭|辆|挑|担|颗|壳|窠|曲|墙|群|腔|砣|座|客|贯|扎|捆|刀|令|打|手|罗|坡|山|岭|江|溪|钟|队|单|双|对|出|口|头|脚|板|跳|枝|件|贴|针|线|管|名|位|身|堂|课|本|页|家|户|层|丝|毫|厘|分|钱|两|斤|担|铢|石|钧|锱|忽|(千|毫|微)克|毫|厘|(公)分|分|寸|尺|丈|里|寻|常|铺|程|(千|分|厘|毫|微)米|米|撮|勺|合|升|斗|石|盘|碗|碟|叠|桶|笼|盆|盒|杯|钟|斛|锅|簋|篮|盘|桶|罐|瓶|壶|卮|盏|箩|箱|煲|啖|袋|钵|年|月|日|季|刻|时|周|天|秒|分|小时|旬|纪|岁|世|更|夜|春|夏|秋|冬|代|伏|辈|丸|泡|粒|颗|幢|堆|条|根|支|道|面|片|张|颗|块|元|(亿|千万|百万|万|千|百)|(亿|千万|百万|万|千|百|美|)元|(亿|千万|百万|万|千|百|十|)吨|(亿|千万|百万|万|千|百|)块|角|毛|分)'
+
+# 分数表达式
+RE_FRAC = re.compile(r'(-?)(\d+)/(\d+)')
+
+
+def replace_frac(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    sign = match.group(1)
+    nominator = match.group(2)
+    denominator = match.group(3)
+    sign: str = "负" if sign else ""
+    nominator: str = num2str(nominator)
+    denominator: str = num2str(denominator)
+    result = f"{sign}{denominator}分之{nominator}"
+    return result
+
+
+# 百分数表达式
+RE_PERCENTAGE = re.compile(r'(-?)(\d+(\.\d+)?)%')
+
+
+def replace_percentage(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    sign = match.group(1)
+    percent = match.group(2)
+    sign: str = "负" if sign else ""
+    percent: str = num2str(percent)
+    result = f"{sign}百分之{percent}"
+    return result
+
+
+# 整数表达式
+# 带负号的整数 -10
+RE_INTEGER = re.compile(r'(-)' r'(\d+)')
+
+
+def replace_negative_num(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    sign = match.group(1)
+    number = match.group(2)
+    sign: str = "负" if sign else ""
+    number: str = num2str(number)
+    result = f"{sign}{number}"
+    return result
+
+
+# 编号-无符号整形
+# 00078
+RE_DEFAULT_NUM = re.compile(r'\d{3}\d*')
+
+
+def replace_default_num(match):
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    number = match.group(0)
+    return verbalize_digit(number, alt_one=True)
+
+
+# 加减乘除
+# RE_ASMD = re.compile(
+#     r'((-?)((\d+)(\.\d+)?)|(\.(\d+)))([\+\-\×÷=])((-?)((\d+)(\.\d+)?)|(\.(\d+)))')
+RE_ASMD = re.compile(
+    r'((-?)((\d+)(\.\d+)?[⁰¹²³⁴⁵⁶⁷⁸⁹ˣʸⁿ]*)|(\.\d+[⁰¹²³⁴⁵⁶⁷⁸⁹ˣʸⁿ]*)|([A-Za-z][⁰¹²³⁴⁵⁶⁷⁸⁹ˣʸⁿ]*))([\+\-\×÷=])((-?)((\d+)(\.\d+)?[⁰¹²³⁴⁵⁶⁷⁸⁹ˣʸⁿ]*)|(\.\d+[⁰¹²³⁴⁵⁶⁷⁸⁹ˣʸⁿ]*)|([A-Za-z][⁰¹²³⁴⁵⁶⁷⁸⁹ˣʸⁿ]*))')
+
+asmd_map = {
+    '+': '加',
+    '-': '减',
+    '×': '乘',
+    '÷': '除',
+    '=': '等于'
+}
+
+def replace_asmd(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    result = match.group(1) + asmd_map[match.group(8)] + match.group(9)
+    return result
+
+
+# 次方专项
+RE_POWER = re.compile(r'[⁰¹²³⁴⁵⁶⁷⁸⁹ˣʸⁿ]+')
+
+power_map = {
+    '⁰': '0',
+    '¹': '1',
+    '²': '2',
+    '³': '3',
+    '⁴': '4',
+    '⁵': '5',
+    '⁶': '6',
+    '⁷': '7',
+    '⁸': '8',
+    '⁹': '9',
+    'ˣ': 'x',
+    'ʸ': 'y',
+    'ⁿ': 'n'
+}
+
+def replace_power(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    power_num = ""
+    for m in match.group(0):
+        power_num += power_map[m]
+    result = "的" + power_num + "次方"
+    return result
+
+
+# 数字表达式
+# 纯小数
+RE_DECIMAL_NUM = re.compile(r'(-?)((\d+)(\.\d+))' r'|(\.(\d+))')
+# 正整数 + 量词
+RE_POSITIVE_QUANTIFIERS = re.compile(r"(\d+)([多余几\+])?" + COM_QUANTIFIERS)
+RE_NUMBER = re.compile(r'(-?)((\d+)(\.\d+)?)' r'|(\.(\d+))')
+
+
+def replace_positive_quantifier(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    number = match.group(1)
+    match_2 = match.group(2)
+    if match_2 == "+":
+        match_2 = "多"
+    match_2: str = match_2 if match_2 else ""
+    quantifiers: str = match.group(3)
+    number: str = num2str(number)
+    number = "两" if number == "二" else number
+    result = f"{number}{match_2}{quantifiers}"
+    return result
+
+
+def replace_number(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    sign = match.group(1)
+    number = match.group(2)
+    pure_decimal = match.group(5)
+    if pure_decimal:
+        result = num2str(pure_decimal)
+    else:
+        sign: str = "负" if sign else ""
+        number: str = num2str(number)
+        result = f"{sign}{number}"
+    return result
+
+
+# 范围表达式
+# match.group(1) and match.group(8) are copy from RE_NUMBER
+
+RE_RANGE = re.compile(
+    r"""
+    (?<![\d\+\-\×÷=])      # 使用反向前瞻以确保数字范围之前没有其他数字和操作符
+    ((-?)((\d+)(\.\d+)?))  # 匹配范围起始的负数或正数（整数或小数）
+    [-~]                   # 匹配范围分隔符
+    ((-?)((\d+)(\.\d+)?))  # 匹配范围结束的负数或正数（整数或小数）
+    (?![\d\+\-\×÷=])       # 使用正向前瞻以确保数字范围之后没有其他数字和操作符
+    """, re.VERBOSE)
+
+
+def replace_range(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    first, second = match.group(1), match.group(6)
+    first = RE_NUMBER.sub(replace_number, first)
+    second = RE_NUMBER.sub(replace_number, second)
+    result = f"{first}到{second}"
+    return result
+
+
+# ~至表达式
+RE_TO_RANGE = re.compile(
+    r'((-?)((\d+)(\.\d+)?)|(\.(\d+)))(%|°C|℃|度|摄氏度|cm2|cm²|cm3|cm³|cm|db|ds|kg|km|m2|m²|m³|m3|ml|m|mm|s)[~]((-?)((\d+)(\.\d+)?)|(\.(\d+)))(%|°C|℃|度|摄氏度|cm2|cm²|cm3|cm³|cm|db|ds|kg|km|m2|m²|m³|m3|ml|m|mm|s)')
+
+def replace_to_range(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    result = match.group(0).replace('~', '至')
+    return result
+
+
+def _get_value(value_string: str, use_zero: bool=True) -> List[str]:
+    stripped = value_string.lstrip('0')
+    if len(stripped) == 0:
+        return []
+    elif len(stripped) == 1:
+        if use_zero and len(stripped) < len(value_string):
+            return [DIGITS['0'], DIGITS[stripped]]
+        else:
+            return [DIGITS[stripped]]
+    else:
+        largest_unit = next(
+            power for power in reversed(UNITS.keys()) if power < len(stripped))
+        first_part = value_string[:-largest_unit]
+        second_part = value_string[-largest_unit:]
+        return _get_value(first_part) + [UNITS[largest_unit]] + _get_value(
+            second_part)
+
+
+def verbalize_cardinal(value_string: str) -> str:
+    if not value_string:
+        return ''
+
+    # 000 -> '零' , 0 -> '零'
+    value_string = value_string.lstrip('0')
+    if len(value_string) == 0:
+        return DIGITS['0']
+
+    result_symbols = _get_value(value_string)
+    # verbalized number starting with '一十*' is abbreviated as `十*`
+    if len(result_symbols) >= 2 and result_symbols[0] == DIGITS[
+            '1'] and result_symbols[1] == UNITS[1]:
+        result_symbols = result_symbols[1:]
+    return ''.join(result_symbols)
+
+
+def verbalize_digit(value_string: str, alt_one=False) -> str:
+    result_symbols = [DIGITS[digit] for digit in value_string]
+    result = ''.join(result_symbols)
+    if alt_one:
+        result = result.replace("一", "幺")
+    return result
+
+
+def num2str(value_string: str) -> str:
+    integer_decimal = value_string.split('.')
+    if len(integer_decimal) == 1:
+        integer = integer_decimal[0]
+        decimal = ''
+    elif len(integer_decimal) == 2:
+        integer, decimal = integer_decimal
+    else:
+        raise ValueError(
+            f"The value string: '${value_string}' has more than one point in it."
+        )
+
+    result = verbalize_cardinal(integer)
+
+    decimal = decimal.rstrip('0')
+    if decimal:
+        # '.22' is verbalized as '零点二二'
+        # '3.20' is verbalized as '三点二
+        result = result if result else "零"
+        result += '点' + verbalize_digit(decimal)
+    return result

GPT_SoVITS/text/zh_normalization/phonecode.py

@@ -0,0 +1,63 @@
+# 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.
+import re
+
+from .num import verbalize_digit
+
+# 规范化固话/手机号码
+# 手机
+# http://www.jihaoba.com/news/show/13680
+# 移动：139、138、137、136、135、134、159、158、157、150、151、152、188、187、182、183、184、178、198
+# 联通：130、131、132、156、155、186、185、176
+# 电信：133、153、189、180、181、177
+RE_MOBILE_PHONE = re.compile(
+    r"(?<!\d)((\+?86 ?)?1([38]\d|5[0-35-9]|7[678]|9[89])\d{8})(?!\d)")
+RE_TELEPHONE = re.compile(
+    r"(?<!\d)((0(10|2[1-3]|[3-9]\d{2})-?)?[1-9]\d{6,7})(?!\d)")
+
+# 全国统一的号码400开头
+RE_NATIONAL_UNIFORM_NUMBER = re.compile(r"(400)(-)?\d{3}(-)?\d{4}")
+
+
+def phone2str(phone_string: str, mobile=True) -> str:
+    if mobile:
+        sp_parts = phone_string.strip('+').split()
+        result = '，'.join(
+            [verbalize_digit(part, alt_one=True) for part in sp_parts])
+        return result
+    else:
+        sil_parts = phone_string.split('-')
+        result = '，'.join(
+            [verbalize_digit(part, alt_one=True) for part in sil_parts])
+        return result
+
+
+def replace_phone(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    return phone2str(match.group(0), mobile=False)
+
+
+def replace_mobile(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    return phone2str(match.group(0))

GPT_SoVITS/text/zh_normalization/quantifier.py

@@ -0,0 +1,63 @@
+# 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.
+import re
+
+from .num import num2str
+
+# 温度表达式，温度会影响负号的读法
+# -3°C 零下三度
+RE_TEMPERATURE = re.compile(r'(-?)(\d+(\.\d+)?)(°C|℃|度|摄氏度)')
+measure_dict = {
+    "cm2": "平方厘米",
+    "cm²": "平方厘米",
+    "cm3": "立方厘米",
+    "cm³": "立方厘米",
+    "cm": "厘米",
+    "db": "分贝",
+    "ds": "毫秒",
+    "kg": "千克",
+    "km": "千米",
+    "m2": "平方米",
+    "m²": "平方米",
+    "m³": "立方米",
+    "m3": "立方米",
+    "ml": "毫升",
+    "m": "米",
+    "mm": "毫米",
+    "s": "秒"
+}
+
+
+def replace_temperature(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    sign = match.group(1)
+    temperature = match.group(2)
+    unit = match.group(3)
+    sign: str = "零下" if sign else ""
+    temperature: str = num2str(temperature)
+    unit: str = "摄氏度" if unit == "摄氏度" else "度"
+    result = f"{sign}{temperature}{unit}"
+    return result
+
+
+def replace_measure(sentence) -> str:
+    for q_notation in measure_dict:
+        if q_notation in sentence:
+            sentence = sentence.replace(q_notation, measure_dict[q_notation])
+    return sentence

GPT_SoVITS/text/zh_normalization/text_normlization.py

@@ -0,0 +1,175 @@
+# 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.
+import re
+from typing import List
+
+from .char_convert import tranditional_to_simplified
+from .chronology import RE_DATE
+from .chronology import RE_DATE2
+from .chronology import RE_TIME
+from .chronology import RE_TIME_RANGE
+from .chronology import replace_date
+from .chronology import replace_date2
+from .chronology import replace_time
+from .constants import F2H_ASCII_LETTERS
+from .constants import F2H_DIGITS
+from .constants import F2H_SPACE
+from .num import RE_DECIMAL_NUM
+from .num import RE_DEFAULT_NUM
+from .num import RE_FRAC
+from .num import RE_INTEGER
+from .num import RE_NUMBER
+from .num import RE_PERCENTAGE
+from .num import RE_POSITIVE_QUANTIFIERS
+from .num import RE_RANGE
+from .num import RE_TO_RANGE
+from .num import RE_ASMD
+from .num import RE_POWER
+from .num import replace_default_num
+from .num import replace_frac
+from .num import replace_negative_num
+from .num import replace_number
+from .num import replace_percentage
+from .num import replace_positive_quantifier
+from .num import replace_range
+from .num import replace_to_range
+from .num import replace_asmd
+from .num import replace_power
+from .phonecode import RE_MOBILE_PHONE
+from .phonecode import RE_NATIONAL_UNIFORM_NUMBER
+from .phonecode import RE_TELEPHONE
+from .phonecode import replace_mobile
+from .phonecode import replace_phone
+from .quantifier import RE_TEMPERATURE
+from .quantifier import replace_measure
+from .quantifier import replace_temperature
+
+
+class TextNormalizer():
+    def __init__(self):
+        self.SENTENCE_SPLITOR = re.compile(r'([：、，；。？！,;?!][”’]?)')
+
+    def _split(self, text: str, lang="zh") -> List[str]:
+        """Split long text into sentences with sentence-splitting punctuations.
+        Args:
+            text (str): The input text.
+        Returns:
+            List[str]: Sentences.
+        """
+        # Only for pure Chinese here
+        if lang == "zh":
+            text = text.replace(" ", "")
+            # 过滤掉特殊字符
+            text = re.sub(r'[——《》【】<>{}()（）#&@“”^_|\\]', '', text)
+        text = self.SENTENCE_SPLITOR.sub(r'\1\n', text)
+        text = text.strip()
+        sentences = [sentence.strip() for sentence in re.split(r'\n+', text)]
+        return sentences
+
+    def _post_replace(self, sentence: str) -> str:
+        sentence = sentence.replace('/', '每')
+        # sentence = sentence.replace('~', '至')
+        # sentence = sentence.replace('～', '至')
+        sentence = sentence.replace('①', '一')
+        sentence = sentence.replace('②', '二')
+        sentence = sentence.replace('③', '三')
+        sentence = sentence.replace('④', '四')
+        sentence = sentence.replace('⑤', '五')
+        sentence = sentence.replace('⑥', '六')
+        sentence = sentence.replace('⑦', '七')
+        sentence = sentence.replace('⑧', '八')
+        sentence = sentence.replace('⑨', '九')
+        sentence = sentence.replace('⑩', '十')
+        sentence = sentence.replace('α', '阿尔法')
+        sentence = sentence.replace('β', '贝塔')
+        sentence = sentence.replace('γ', '伽玛').replace('Γ', '伽玛')
+        sentence = sentence.replace('δ', '德尔塔').replace('Δ', '德尔塔')
+        sentence = sentence.replace('ε', '艾普西龙')
+        sentence = sentence.replace('ζ', '捷塔')
+        sentence = sentence.replace('η', '依塔')
+        sentence = sentence.replace('θ', '西塔').replace('Θ', '西塔')
+        sentence = sentence.replace('ι', '艾欧塔')
+        sentence = sentence.replace('κ', '喀帕')
+        sentence = sentence.replace('λ', '拉姆达').replace('Λ', '拉姆达')
+        sentence = sentence.replace('μ', '缪')
+        sentence = sentence.replace('ν', '拗')
+        sentence = sentence.replace('ξ', '克西').replace('Ξ', '克西')
+        sentence = sentence.replace('ο', '欧米克伦')
+        sentence = sentence.replace('π', '派').replace('Π', '派')
+        sentence = sentence.replace('ρ', '肉')
+        sentence = sentence.replace('ς', '西格玛').replace('Σ', '西格玛').replace(
+            'σ', '西格玛')
+        sentence = sentence.replace('τ', '套')
+        sentence = sentence.replace('υ', '宇普西龙')
+        sentence = sentence.replace('φ', '服艾').replace('Φ', '服艾')
+        sentence = sentence.replace('χ', '器')
+        sentence = sentence.replace('ψ', '普赛').replace('Ψ', '普赛')
+        sentence = sentence.replace('ω', '欧米伽').replace('Ω', '欧米伽')
+        # 兜底数学运算，顺便兼容懒人用语
+        sentence = sentence.replace('+', '加')
+        sentence = sentence.replace('-', '减')
+        sentence = sentence.replace('×', '乘')
+        sentence = sentence.replace('÷', '除')
+        sentence = sentence.replace('=', '等')
+        # re filter special characters, have one more character "-" than line 68
+        sentence = re.sub(r'[-——《》【】<=>{}()（）#&@“”^_|\\]', '', sentence)
+        return sentence
+
+    def normalize_sentence(self, sentence: str) -> str:
+        # basic character conversions
+        sentence = tranditional_to_simplified(sentence)
+        sentence = sentence.translate(F2H_ASCII_LETTERS).translate(
+            F2H_DIGITS).translate(F2H_SPACE)
+
+        # number related NSW verbalization
+        sentence = RE_DATE.sub(replace_date, sentence)
+        sentence = RE_DATE2.sub(replace_date2, sentence)
+
+        # range first
+        sentence = RE_TIME_RANGE.sub(replace_time, sentence)
+        sentence = RE_TIME.sub(replace_time, sentence)
+
+        # 处理~波浪号作为至的替换
+        sentence = RE_TO_RANGE.sub(replace_to_range, sentence)
+        sentence = RE_TEMPERATURE.sub(replace_temperature, sentence)
+        sentence = replace_measure(sentence)
+
+        # 处理数学运算
+        while RE_ASMD.search(sentence):
+            sentence = RE_ASMD.sub(replace_asmd, sentence)
+        sentence = RE_POWER.sub(replace_power, sentence)
+
+        sentence = RE_FRAC.sub(replace_frac, sentence)
+        sentence = RE_PERCENTAGE.sub(replace_percentage, sentence)
+        sentence = RE_MOBILE_PHONE.sub(replace_mobile, sentence)
+
+        sentence = RE_TELEPHONE.sub(replace_phone, sentence)
+        sentence = RE_NATIONAL_UNIFORM_NUMBER.sub(replace_phone, sentence)
+
+        sentence = RE_RANGE.sub(replace_range, sentence)
+
+        sentence = RE_INTEGER.sub(replace_negative_num, sentence)
+        sentence = RE_DECIMAL_NUM.sub(replace_number, sentence)
+        sentence = RE_POSITIVE_QUANTIFIERS.sub(replace_positive_quantifier,
+                                               sentence)
+        sentence = RE_DEFAULT_NUM.sub(replace_default_num, sentence)
+        sentence = RE_NUMBER.sub(replace_number, sentence)
+        sentence = self._post_replace(sentence)
+
+        return sentence
+
+    def normalize(self, text: str) -> List[str]:
+        sentences = self._split(text)
+        sentences = [self.normalize_sentence(sent) for sent in sentences]
+        return sentences