Merge pull request #1 from RVC-Boss/main

111

.gitignore

@@ -17,4 +17,6 @@ SoVITS_weights_v3
 TEMP
 weight.json
 ffmpeg*
-ffprobe*
+ffprobe*
+tools/AP_BWE_main/24kto48k/*
+!tools/AP_BWE_main/24kto48k/readme.txt

GPT_SoVITS/AR/modules/patched_mha_with_cache.py

@@ -12,33 +12,33 @@ import torch
 
 
 def multi_head_attention_forward_patched(
-    query: Tensor,
-    key: Tensor,
-    value: Tensor,
-    embed_dim_to_check: int,
-    num_heads: int,
-    in_proj_weight: Optional[Tensor],
-    in_proj_bias: Optional[Tensor],
-    bias_k: Optional[Tensor],
-    bias_v: Optional[Tensor],
-    add_zero_attn: bool,
+    query,
+    key,
+    value,
+    embed_dim_to_check,
+    num_heads,
+    in_proj_weight,
+    in_proj_bias,
+    bias_k,
+    bias_v,
+    add_zero_attn,
     dropout_p: float,
-    out_proj_weight: Tensor,
-    out_proj_bias: Optional[Tensor],
-    training: bool = True,
-    key_padding_mask: Optional[Tensor] = None,
-    need_weights: bool = True,
-    attn_mask: Optional[Tensor] = None,
-    use_separate_proj_weight: bool = False,
-    q_proj_weight: Optional[Tensor] = None,
-    k_proj_weight: Optional[Tensor] = None,
-    v_proj_weight: Optional[Tensor] = None,
-    static_k: Optional[Tensor] = None,
-    static_v: Optional[Tensor] = None,
-    average_attn_weights: bool = True,
-    is_causal: bool = False,
+    out_proj_weight,
+    out_proj_bias,
+    training = True,
+    key_padding_mask = None,
+    need_weights = True,
+    attn_mask = None,
+    use_separate_proj_weight = False,
+    q_proj_weight = None,
+    k_proj_weight = None,
+    v_proj_weight = None,
+    static_k = None,
+    static_v = None,
+    average_attn_weights = True,
+    is_causal = False,
     cache=None,
-) -> Tuple[Tensor, Optional[Tensor]]:
+):
     r"""
     Args:
         query, key, value: map a query and a set of key-value pairs to an output.

GPT_SoVITS/BigVGAN/alias_free_activation/torch/act.py

@@ -2,7 +2,7 @@
 #   LICENSE is in incl_licenses directory.
 
 import torch.nn as nn
-from alias_free_activation.torch.resample import UpSample1d, DownSample1d
+from .resample import UpSample1d, DownSample1d
 
 
 class Activation1d(nn.Module):

GPT_SoVITS/BigVGAN/alias_free_activation/torch/resample.py

@@ -3,8 +3,8 @@
 
 import torch.nn as nn
 from torch.nn import functional as F
-from alias_free_activation.torch.filter import LowPassFilter1d
-from alias_free_activation.torch.filter import kaiser_sinc_filter1d
+from .filter import LowPassFilter1d
+from .filter import kaiser_sinc_filter1d
 
 
 class UpSample1d(nn.Module):

GPT_SoVITS/BigVGAN/bigvgan.py

@@ -14,10 +14,10 @@ import torch.nn as nn
 from torch.nn import Conv1d, ConvTranspose1d
 from torch.nn.utils import weight_norm, remove_weight_norm
 
-import activations
-from utils0 import init_weights, get_padding
-from alias_free_activation.torch.act import Activation1d as TorchActivation1d
-from env import AttrDict
+from . import activations
+from .utils0 import init_weights, get_padding
+from .alias_free_activation.torch.act import Activation1d as TorchActivation1d
+from .env import AttrDict
 
 from huggingface_hub import PyTorchModelHubMixin, hf_hub_download
 
@@ -93,7 +93,7 @@ class AMPBlock1(torch.nn.Module):
 
         # Select which Activation1d, lazy-load cuda version to ensure backward compatibility
         if self.h.get("use_cuda_kernel", False):
-            from alias_free_activation.cuda.activation1d import (
+            from .alias_free_activation.cuda.activation1d import (
                 Activation1d as CudaActivation1d,
             )
 
@@ -193,7 +193,7 @@ class AMPBlock2(torch.nn.Module):
 
         # Select which Activation1d, lazy-load cuda version to ensure backward compatibility
         if self.h.get("use_cuda_kernel", False):
-            from alias_free_activation.cuda.activation1d import (
+            from .alias_free_activation.cuda.activation1d import (
                 Activation1d as CudaActivation1d,
             )
 
@@ -271,7 +271,7 @@ class BigVGAN(
 
         # Select which Activation1d, lazy-load cuda version to ensure backward compatibility
         if self.h.get("use_cuda_kernel", False):
-            from alias_free_activation.cuda.activation1d import (
+            from .alias_free_activation.cuda.activation1d import (
                 Activation1d as CudaActivation1d,
             )
 

GPT_SoVITS/BigVGAN/meldataset.py

@@ -15,7 +15,7 @@ from librosa.filters import mel as librosa_mel_fn
 import pathlib
 from tqdm import tqdm
 from typing import List, Tuple, Optional
-from env import AttrDict
+from .env import AttrDict
 
 MAX_WAV_VALUE = 32767.0  # NOTE: 32768.0 -1 to prevent int16 overflow (results in popping sound in corner cases)
 

GPT_SoVITS/BigVGAN/utils0.py

@@ -9,7 +9,7 @@ from torch.nn.utils import weight_norm
 
 matplotlib.use("Agg")
 import matplotlib.pylab as plt
-from meldataset import MAX_WAV_VALUE
+from .meldataset import MAX_WAV_VALUE
 from scipy.io.wavfile import write
 
 

GPT_SoVITS/TTS_infer_pack/TTS.py

@@ -4,6 +4,7 @@ import os, sys, gc
 import random
 import traceback
 
+import torchaudio
 from tqdm import tqdm
 now_dir = os.getcwd()
 sys.path.append(now_dir)
@@ -15,10 +16,11 @@ import torch
 import torch.nn.functional as F
 import yaml
 from transformers import AutoModelForMaskedLM, AutoTokenizer
-
+from tools.audio_sr import AP_BWE
 from AR.models.t2s_lightning_module import Text2SemanticLightningModule
 from feature_extractor.cnhubert import CNHubert
-from module.models import SynthesizerTrn
+from module.models import SynthesizerTrn, SynthesizerTrnV3
+from peft import LoraConfig, get_peft_model
 import librosa
 from time import time as ttime
 from tools.i18n.i18n import I18nAuto, scan_language_list
@@ -26,10 +28,98 @@ from tools.my_utils import load_audio
 from module.mel_processing import spectrogram_torch
 from TTS_infer_pack.text_segmentation_method import splits
 from TTS_infer_pack.TextPreprocessor import TextPreprocessor
+from BigVGAN.bigvgan import BigVGAN
+from module.mel_processing import spectrogram_torch,mel_spectrogram_torch
+from process_ckpt import get_sovits_version_from_path_fast, load_sovits_new
 language=os.environ.get("language","Auto")
 language=sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
 i18n = I18nAuto(language=language)
 
+
+
+spec_min = -12
+spec_max = 2
+def norm_spec(x):
+    return (x - spec_min) / (spec_max - spec_min) * 2 - 1
+def denorm_spec(x):
+    return (x + 1) / 2 * (spec_max - spec_min) + spec_min
+mel_fn=lambda x: mel_spectrogram_torch(x, **{
+    "n_fft": 1024,
+    "win_size": 1024,
+    "hop_size": 256,
+    "num_mels": 100,
+    "sampling_rate": 24000,
+    "fmin": 0,
+    "fmax": None,
+    "center": False
+})
+
+
+def speed_change(input_audio:np.ndarray, speed:float, sr:int):
+    # 将 NumPy 数组转换为原始 PCM 流
+    raw_audio = input_audio.astype(np.int16).tobytes()
+
+    # 设置 ffmpeg 输入流
+    input_stream = ffmpeg.input('pipe:', format='s16le', acodec='pcm_s16le', ar=str(sr), ac=1)
+
+    # 变速处理
+    output_stream = input_stream.filter('atempo', speed)
+
+    # 输出流到管道
+    out, _ = (
+        output_stream.output('pipe:', format='s16le', acodec='pcm_s16le')
+        .run(input=raw_audio, capture_stdout=True, capture_stderr=True)
+    )
+
+    # 将管道输出解码为 NumPy 数组
+    processed_audio = np.frombuffer(out, np.int16)
+
+    return processed_audio
+
+
+
+resample_transform_dict={}
+def resample(audio_tensor, sr0, device):
+    global resample_transform_dict
+    if sr0 not in resample_transform_dict:
+        resample_transform_dict[sr0] = torchaudio.transforms.Resample(
+            sr0, 24000
+        ).to(device)
+    return resample_transform_dict[sr0](audio_tensor)
+
+
+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 NO_PROMPT_ERROR(Exception):
+    pass
+
+
 # configs/tts_infer.yaml
 """
 custom:
@@ -56,11 +146,19 @@ default_v2:
   t2s_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt
   vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth
   version: v2
+default_v3:
+  bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
+  cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
+  device: cpu
+  is_half: false
+  t2s_weights_path: GPT_SoVITS/pretrained_models/s1v3.ckpt
+  vits_weights_path: GPT_SoVITS/pretrained_models/s2Gv3.pth
+  version: v3
 """
 
 def set_seed(seed:int):
     seed = int(seed)
-    seed = seed if seed != -1 else random.randrange(1 << 32)
+    seed = seed if seed != -1 else random.randint(0, 2**32 - 1)
     print(f"Set seed to {seed}")
     os.environ['PYTHONHASHSEED'] = str(seed)
     random.seed(seed)
@@ -82,7 +180,7 @@ def set_seed(seed:int):
 
 class TTS_Config:
     default_configs={
-        "default":{
+        "v1":{
                 "device": "cpu",
                 "is_half": False,
                 "version": "v1",
@@ -91,7 +189,7 @@ class TTS_Config:
                 "cnhuhbert_base_path": "GPT_SoVITS/pretrained_models/chinese-hubert-base",
                 "bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",
             },
-        "default_v2":{
+        "v2":{
                 "device": "cpu",
                 "is_half": False,
                 "version": "v2",
@@ -100,6 +198,15 @@ class TTS_Config:
                 "cnhuhbert_base_path": "GPT_SoVITS/pretrained_models/chinese-hubert-base",
                 "bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",
             },
+        "v3":{
+                "device": "cpu",
+                "is_half": False,
+                "version": "v3",
+                "t2s_weights_path": "GPT_SoVITS/pretrained_models/s1v3.ckpt",
+                "vits_weights_path": "GPT_SoVITS/pretrained_models/s2Gv3.pth",
+                "cnhuhbert_base_path": "GPT_SoVITS/pretrained_models/chinese-hubert-base",
+                "bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",
+            },
     }
     configs:dict = None
     v1_languages:list = ["auto", "en", "zh", "ja",  "all_zh", "all_ja"]
@@ -136,12 +243,9 @@ class TTS_Config:
 
         assert isinstance(configs, dict)
         version = configs.get("version", "v2").lower()
-        assert version in ["v1", "v2"]
-        self.default_configs["default"] = configs.get("default", self.default_configs["default"])
-        self.default_configs["default_v2"] = configs.get("default_v2", self.default_configs["default_v2"])
-
-        default_config_key = "default"if version=="v1" else "default_v2"
-        self.configs:dict = configs.get("custom", deepcopy(self.default_configs[default_config_key]))
+        assert version in ["v1", "v2", "v3"]
+        self.default_configs[version] = configs.get(version, self.default_configs[version])
+        self.configs:dict = configs.get("custom", deepcopy(self.default_configs[version]))
 
 
         self.device = self.configs.get("device", torch.device("cpu"))
@@ -159,20 +263,22 @@ class TTS_Config:
         self.vits_weights_path = self.configs.get("vits_weights_path", None)
         self.bert_base_path = self.configs.get("bert_base_path", None)
         self.cnhuhbert_base_path = self.configs.get("cnhuhbert_base_path", None)
-        self.languages = self.v2_languages if self.version=="v2" else self.v1_languages
+        self.languages = self.v1_languages if self.version=="v1" else self.v2_languages
+
+        self.is_v3_synthesizer:bool = False
 
 
         if (self.t2s_weights_path in [None, ""]) or (not os.path.exists(self.t2s_weights_path)):
-            self.t2s_weights_path = self.default_configs[default_config_key]['t2s_weights_path']
+            self.t2s_weights_path = self.default_configs[version]['t2s_weights_path']
             print(f"fall back to default t2s_weights_path: {self.t2s_weights_path}")
         if (self.vits_weights_path in [None, ""]) or (not os.path.exists(self.vits_weights_path)):
-            self.vits_weights_path = self.default_configs[default_config_key]['vits_weights_path']
+            self.vits_weights_path = self.default_configs[version]['vits_weights_path']
             print(f"fall back to default vits_weights_path: {self.vits_weights_path}")
         if (self.bert_base_path in [None, ""]) or (not os.path.exists(self.bert_base_path)):
-            self.bert_base_path = self.default_configs[default_config_key]['bert_base_path']
+            self.bert_base_path = self.default_configs[version]['bert_base_path']
             print(f"fall back to default bert_base_path: {self.bert_base_path}")
         if (self.cnhuhbert_base_path in [None, ""]) or (not os.path.exists(self.cnhuhbert_base_path)):
-            self.cnhuhbert_base_path = self.default_configs[default_config_key]['cnhuhbert_base_path']
+            self.cnhuhbert_base_path = self.default_configs[version]['cnhuhbert_base_path']
             print(f"fall back to default cnhuhbert_base_path: {self.cnhuhbert_base_path}")
         self.update_configs()
 
@@ -195,7 +301,7 @@ class TTS_Config:
         else:
             print(i18n("路径不存在,使用默认配置"))
             self.save_configs(configs_path)
-        with open(configs_path, 'r') as f:
+        with open(configs_path, 'r', encoding='utf-8') as f:
             configs = yaml.load(f, Loader=yaml.FullLoader)
 
         return configs
@@ -224,7 +330,7 @@ class TTS_Config:
 
     def update_version(self, version:str)->None:
         self.version = version
-        self.languages = self.v2_languages if self.version=="v2" else self.v1_languages
+        self.languages = self.v1_languages if self.version=="v1" else self.v2_languages
 
     def __str__(self):
         self.configs = self.update_configs()
@@ -252,10 +358,13 @@ class TTS:
             self.configs:TTS_Config = TTS_Config(configs)
 
         self.t2s_model:Text2SemanticLightningModule = None
-        self.vits_model:SynthesizerTrn = None
+        self.vits_model:Union[SynthesizerTrn, SynthesizerTrnV3] = None
         self.bert_tokenizer:AutoTokenizer = None
         self.bert_model:AutoModelForMaskedLM = None
         self.cnhuhbert_model:CNHubert = None
+        self.bigvgan_model:BigVGAN = None
+        self.sr_model:AP_BWE = None
+        self.sr_model_not_exist:bool = False
 
         self._init_models()
 
@@ -310,38 +419,83 @@ class TTS:
             self.bert_model = self.bert_model.half()
 
     def init_vits_weights(self, weights_path: str):
-        print(f"Loading VITS weights from {weights_path}")
+        
         self.configs.vits_weights_path = weights_path
-        dict_s2 = torch.load(weights_path, map_location=self.configs.device,weights_only=False)
-        hps = dict_s2["config"]
-        if dict_s2['weight']['enc_p.text_embedding.weight'].shape[0] == 322:
-            self.configs.update_version("v1")
-        else:
-            self.configs.update_version("v2")
-        self.configs.save_configs()
+        version, model_version, if_lora_v3=get_sovits_version_from_path_fast(weights_path)
+        path_sovits_v3=self.configs.default_configs["v3"]["vits_weights_path"]
+
+        if if_lora_v3==True and os.path.exists(path_sovits_v3)==False:
+            info= path_sovits_v3 + i18n("SoVITS V3 底模缺失，无法加载相应 LoRA 权重")
+            raise FileExistsError(info)
+
+        # dict_s2 = torch.load(weights_path, map_location=self.configs.device,weights_only=False)
+        dict_s2 = load_sovits_new(weights_path)
+        hps = dict_s2["config"]
+
+        hps["model"]["semantic_frame_rate"] = "25hz"
+        if 'enc_p.text_embedding.weight'not in dict_s2['weight']:
+            hps["model"]["version"] = "v2"#v3model,v2sybomls
+        elif dict_s2['weight']['enc_p.text_embedding.weight'].shape[0] == 322:
+            hps["model"]["version"] = "v1"
+        else:
+            hps["model"]["version"] = "v2"
+        # version = hps["model"]["version"]
 
-        hps["model"]["version"] = self.configs.version
         self.configs.filter_length = hps["data"]["filter_length"]
         self.configs.segment_size = hps["train"]["segment_size"]
         self.configs.sampling_rate = hps["data"]["sampling_rate"]
         self.configs.hop_length = hps["data"]["hop_length"]
         self.configs.win_length = hps["data"]["win_length"]
         self.configs.n_speakers = hps["data"]["n_speakers"]
-        self.configs.semantic_frame_rate = "25hz"
+        self.configs.semantic_frame_rate = hps["model"]["semantic_frame_rate"]
         kwargs = hps["model"]
-        vits_model = SynthesizerTrn(
-            self.configs.filter_length // 2 + 1,
-            self.configs.segment_size // self.configs.hop_length,
-            n_speakers=self.configs.n_speakers,
-            **kwargs
-        )
+        # print(f"self.configs.sampling_rate:{self.configs.sampling_rate}")
+
+        self.configs.update_version(model_version)
+
+        # print(f"model_version:{model_version}")
+        # print(f'hps["model"]["version"]:{hps["model"]["version"]}')
+        if model_version!="v3":
+            vits_model = SynthesizerTrn(
+                self.configs.filter_length // 2 + 1,
+                self.configs.segment_size // self.configs.hop_length,
+                n_speakers=self.configs.n_speakers,
+                **kwargs
+            )
+            if hasattr(vits_model, "enc_q"):
+                del vits_model.enc_q
+            self.configs.is_v3_synthesizer = False
+        else:
+            vits_model = SynthesizerTrnV3(
+                self.configs.filter_length // 2 + 1,
+                self.configs.segment_size // self.configs.hop_length,
+                n_speakers=self.configs.n_speakers,
+                **kwargs
+            )
+            self.configs.is_v3_synthesizer = True
+            self.init_bigvgan()
+            
+
+        if if_lora_v3==False:
+            print(f"Loading VITS weights from {weights_path}. {vits_model.load_state_dict(dict_s2['weight'], strict=False)}")
+        else:
+            print(f"Loading VITS pretrained weights from {weights_path}. {vits_model.load_state_dict(load_sovits_new(path_sovits_v3)['weight'], strict=False)}")
+            lora_rank=dict_s2["lora_rank"]
+            lora_config = LoraConfig(
+                target_modules=["to_k", "to_q", "to_v", "to_out.0"],
+                r=lora_rank,
+                lora_alpha=lora_rank,
+                init_lora_weights=True,
+            )
+            vits_model.cfm = get_peft_model(vits_model.cfm, lora_config)
+            print(f"Loading LoRA weights from {weights_path}. {vits_model.load_state_dict(dict_s2['weight'], strict=False)}")
+            
+            vits_model.cfm = vits_model.cfm.merge_and_unload()
 
-        if hasattr(vits_model, "enc_q"):
-            del vits_model.enc_q
 
         vits_model = vits_model.to(self.configs.device)
         vits_model = vits_model.eval()
-        vits_model.load_state_dict(dict_s2["weight"], strict=False)
+
         self.vits_model = vits_model
         if self.configs.is_half and str(self.configs.device)!="cpu":
             self.vits_model = self.vits_model.half()
@@ -363,6 +517,30 @@ class TTS:
         if self.configs.is_half and str(self.configs.device)!="cpu":
             self.t2s_model = self.t2s_model.half()
 
+
+    def init_bigvgan(self):
+        if self.bigvgan_model is not None:
+            return
+        self.bigvgan_model = BigVGAN.from_pretrained("%s/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x" % (now_dir,), use_cuda_kernel=False)  # if True, RuntimeError: Ninja is required to load C++ extensions
+        # remove weight norm in the model and set to eval mode
+        self.bigvgan_model.remove_weight_norm()
+        self.bigvgan_model = self.bigvgan_model.eval()
+        if self.configs.is_half == True:
+            self.bigvgan_model = self.bigvgan_model.half().to(self.configs.device)
+        else:
+            self.bigvgan_model = self.bigvgan_model.to(self.configs.device)
+
+    def init_sr_model(self):
+        if self.sr_model is not None:
+            return
+        try:
+            self.sr_model:AP_BWE=AP_BWE(self.configs.device,DictToAttrRecursive)
+            self.sr_model_not_exist = False
+        except FileNotFoundError:
+            print(i18n("你没有下载超分模型的参数，因此不进行超分。如想超分请先参照教程把文件下载好"))
+            self.sr_model_not_exist = True
+
+
     def enable_half_precision(self, enable: bool = True, save: bool = True):
         '''
             To enable half precision for the TTS model.
@@ -387,6 +565,8 @@ class TTS:
                 self.bert_model =self.bert_model.half()
             if self.cnhuhbert_model is not None:
                 self.cnhuhbert_model = self.cnhuhbert_model.half()
+            if self.bigvgan_model is not None:
+                self.bigvgan_model = self.bigvgan_model.half()
         else:
             if self.t2s_model is not None:
                 self.t2s_model = self.t2s_model.float()
@@ -396,6 +576,8 @@ class TTS:
                 self.bert_model = self.bert_model.float()
             if self.cnhuhbert_model is not None:
                 self.cnhuhbert_model = self.cnhuhbert_model.float()
+            if self.bigvgan_model is not None:
+                self.bigvgan_model = self.bigvgan_model.float()
 
     def set_device(self, device: torch.device, save: bool = True):
         '''
@@ -414,6 +596,11 @@ class TTS:
             self.bert_model = self.bert_model.to(device)
         if self.cnhuhbert_model is not None:
             self.cnhuhbert_model = self.cnhuhbert_model.to(device)
+        if self.bigvgan_model is not None:
+            self.bigvgan_model = self.bigvgan_model.to(device)
+        if self.sr_model is not None:
+            self.sr_model = self.sr_model.to(device)
+        
 
     def set_ref_audio(self, ref_audio_path:str):
         '''
@@ -437,6 +624,11 @@ class TTS:
             self.prompt_cache["refer_spec"][0] = spec
 
     def _get_ref_spec(self, ref_audio_path):
+        raw_audio, raw_sr = torchaudio.load(ref_audio_path)
+        raw_audio=raw_audio.to(self.configs.device).float()
+        self.prompt_cache["raw_audio"] = raw_audio
+        self.prompt_cache["raw_sr"] = raw_sr
+
         audio = load_audio(ref_audio_path, int(self.configs.sampling_rate))
         audio = torch.FloatTensor(audio)
         maxx=audio.abs().max()
@@ -625,11 +817,11 @@ class TTS:
         Recovery the order of the audio according to the batch_index_list.
 
         Args:
-            data (List[list(np.ndarray)]): the out of order audio .
+            data (List[list(torch.Tensor)]): the out of order audio .
             batch_index_list (List[list[int]]): the batch index list.
 
         Returns:
-            list (List[np.ndarray]): the data in the original order.
+            list (List[torch.Tensor]): the data in the original order.
         '''
         length = len(sum(batch_index_list, []))
         _data = [None]*length
@@ -671,6 +863,8 @@ class TTS:
                     "seed": -1,                   # int. random seed for reproducibility.
                     "parallel_infer": True,       # bool. whether to use parallel inference.
                     "repetition_penalty": 1.35    # float. repetition penalty for T2S model.
+                    "sample_steps": 32,           # int. number of sampling steps for VITS model V3.
+                    "super_sampling": False,       # bool. whether to use super-sampling for audio when using VITS model V3.
                 }
         returns:
             Tuple[int, np.ndarray]: sampling rate and audio data.
@@ -698,6 +892,8 @@ class TTS:
         actual_seed = set_seed(seed)
         parallel_infer = inputs.get("parallel_infer", True)
         repetition_penalty = inputs.get("repetition_penalty", 1.35)
+        sample_steps = inputs.get("sample_steps", 32)
+        super_sampling = inputs.get("super_sampling", False)
 
         if parallel_infer:
             print(i18n("并行推理模式已开启"))
@@ -732,6 +928,9 @@ class TTS:
         if not no_prompt_text:
             assert prompt_lang in self.configs.languages
 
+        if no_prompt_text and self.configs.is_v3_synthesizer:
+            raise NO_PROMPT_ERROR("prompt_text cannot be empty when using SoVITS_V3")
+
         if ref_audio_path in [None, ""] and \
             ((self.prompt_cache["prompt_semantic"] is None) or (self.prompt_cache["refer_spec"] in [None, []])):
             raise ValueError("ref_audio_path cannot be empty, when the reference audio is not set using set_ref_audio()")
@@ -761,13 +960,13 @@ class TTS:
             if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_lang != "en" else "."
             print(i18n("实际输入的参考文本:"), prompt_text)
             if self.prompt_cache["prompt_text"] != prompt_text:
-                self.prompt_cache["prompt_text"] = prompt_text
-                self.prompt_cache["prompt_lang"] = prompt_lang
                 phones, bert_features, norm_text = \
                     self.text_preprocessor.segment_and_extract_feature_for_text(
                                                                         prompt_text,
                                                                         prompt_lang,
                                                                         self.configs.version)
+                self.prompt_cache["prompt_text"] = prompt_text
+                self.prompt_cache["prompt_lang"] = prompt_lang
                 self.prompt_cache["phones"] = phones
                 self.prompt_cache["bert_features"] = bert_features
                 self.prompt_cache["norm_text"] = norm_text
@@ -781,8 +980,7 @@ class TTS:
         if not return_fragment:
             data = self.text_preprocessor.preprocess(text, text_lang, text_split_method, self.configs.version)
             if len(data) == 0:
-                yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
-                                                            dtype=np.int16)
+                yield 16000, np.zeros(int(16000), dtype=np.int16)
                 return
 
             batch_index_list:list = None
@@ -836,6 +1034,7 @@ class TTS:
             t_34 = 0.0
             t_45 = 0.0
             audio = []
+            output_sr = self.configs.sampling_rate if not self.configs.is_v3_synthesizer else 24000
             for item in data:
                 t3 = ttime()
                 if return_fragment:
@@ -858,7 +1057,7 @@ class TTS:
                 else:
                     prompt = self.prompt_cache["prompt_semantic"].expand(len(all_phoneme_ids), -1).to(self.configs.device)
 
-
+                print(f"############ {i18n('预测语义Token')} ############")
                 pred_semantic_list, idx_list = self.t2s_model.model.infer_panel(
                     all_phoneme_ids,
                     all_phoneme_lens,
@@ -892,70 +1091,80 @@ class TTS:
                 # batch_audio_fragment = (self.vits_model.batched_decode(
                 #         pred_semantic, pred_semantic_len, batch_phones, batch_phones_len,refer_audio_spec
                 #     ))
-
-                if speed_factor == 1.0:
-                    # ## vits并行推理 method 2
-                    pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
-                    upsample_rate = math.prod(self.vits_model.upsample_rates)
-                    audio_frag_idx = [pred_semantic_list[i].shape[0]*2*upsample_rate for i in range(0, len(pred_semantic_list))]
-                    audio_frag_end_idx = [ sum(audio_frag_idx[:i+1]) for i in range(0, len(audio_frag_idx))]
-                    all_pred_semantic = torch.cat(pred_semantic_list).unsqueeze(0).unsqueeze(0).to(self.configs.device)
-                    _batch_phones = torch.cat(batch_phones).unsqueeze(0).to(self.configs.device)
-                    _batch_audio_fragment = (self.vits_model.decode(
-                            all_pred_semantic, _batch_phones, refer_audio_spec, speed=speed_factor
-                        ).detach()[0, 0, :])
-                    audio_frag_end_idx.insert(0, 0)
-                    batch_audio_fragment= [_batch_audio_fragment[audio_frag_end_idx[i-1]:audio_frag_end_idx[i]] for i in range(1, len(audio_frag_end_idx))]
+                print(f"############ {i18n('合成音频')} ############")
+                if not self.configs.is_v3_synthesizer:
+                    if speed_factor == 1.0:
+                        # ## vits并行推理 method 2
+                        pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
+                        upsample_rate = math.prod(self.vits_model.upsample_rates)
+                        audio_frag_idx = [pred_semantic_list[i].shape[0]*2*upsample_rate for i in range(0, len(pred_semantic_list))]
+                        audio_frag_end_idx = [ sum(audio_frag_idx[:i+1]) for i in range(0, len(audio_frag_idx))]
+                        all_pred_semantic = torch.cat(pred_semantic_list).unsqueeze(0).unsqueeze(0).to(self.configs.device)
+                        _batch_phones = torch.cat(batch_phones).unsqueeze(0).to(self.configs.device)
+                        _batch_audio_fragment = (self.vits_model.decode(
+                                all_pred_semantic, _batch_phones, refer_audio_spec, speed=speed_factor
+                            ).detach()[0, 0, :])
+                        audio_frag_end_idx.insert(0, 0)
+                        batch_audio_fragment= [_batch_audio_fragment[audio_frag_end_idx[i-1]:audio_frag_end_idx[i]] for i in range(1, len(audio_frag_end_idx))]
+                    else:
+                    # ## vits串行推理
+                        for i, idx in enumerate(tqdm(idx_list)):
+                            phones = batch_phones[i].unsqueeze(0).to(self.configs.device)
+                            _pred_semantic = (pred_semantic_list[i][-idx:].unsqueeze(0).unsqueeze(0))   # .unsqueeze(0)#mq要多unsqueeze一次
+                            audio_fragment =(self.vits_model.decode(
+                                    _pred_semantic, phones, refer_audio_spec, speed=speed_factor
+                                ).detach()[0, 0, :])
+                            batch_audio_fragment.append(
+                                audio_fragment
+                            )  ###试试重建不带上prompt部分
                 else:
-                # ## vits串行推理
-                    for i, idx in enumerate(idx_list):
+                    for i, idx in enumerate(tqdm(idx_list)):
                         phones = batch_phones[i].unsqueeze(0).to(self.configs.device)
                         _pred_semantic = (pred_semantic_list[i][-idx:].unsqueeze(0).unsqueeze(0))   # .unsqueeze(0)#mq要多unsqueeze一次
-                        audio_fragment =(self.vits_model.decode(
-                                _pred_semantic, phones, refer_audio_spec, speed=speed_factor
-                            ).detach()[0, 0, :])
+                        audio_fragment = self.v3_synthesis(
+                                _pred_semantic, phones, speed=speed_factor, sample_steps=sample_steps
+                            )
                         batch_audio_fragment.append(
                             audio_fragment
-                        )  ###试试重建不带上prompt部分
+                        ) 
 
                 t5 = ttime()
                 t_45 += t5 - t4
                 if return_fragment:
                     print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t4 - t3, t5 - t4))
                     yield self.audio_postprocess([batch_audio_fragment],
-                                                    self.configs.sampling_rate,
+                                                    output_sr,
                                                     None,
                                                     speed_factor,
                                                     False,
-                                                    fragment_interval
+                                                    fragment_interval,
+                                                    super_sampling if self.configs.is_v3_synthesizer else False
                                                     )
                 else:
                     audio.append(batch_audio_fragment)
 
                 if self.stop_flag:
-                    yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
-                                                            dtype=np.int16)
+                    yield 16000, np.zeros(int(16000), dtype=np.int16)
                     return
 
             if not return_fragment:
                 print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45))
                 if len(audio) == 0:
-                    yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
-                                                                dtype=np.int16)
+                    yield 16000, np.zeros(int(16000), dtype=np.int16)
                     return
                 yield self.audio_postprocess(audio,
-                                                self.configs.sampling_rate,
+                                                output_sr,
                                                 batch_index_list,
                                                 speed_factor,
                                                 split_bucket,
-                                                fragment_interval
+                                                fragment_interval,
+                                                super_sampling if self.configs.is_v3_synthesizer else False
                                                 )
 
         except Exception as e:
             traceback.print_exc()
             # 必须返回一个空音频, 否则会导致显存不释放。
-            yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
-                                                            dtype=np.int16)
+            yield 16000, np.zeros(int(16000), dtype=np.int16)
             # 重置模型, 否则会导致显存释放不完全。
             del self.t2s_model
             del self.vits_model
@@ -983,7 +1192,8 @@ class TTS:
                           batch_index_list:list=None,
                           speed_factor:float=1.0,
                           split_bucket:bool=True,
-                          fragment_interval:float=0.3
+                          fragment_interval:float=0.3,
+                          super_sampling:bool=False,
                           )->Tuple[int, np.ndarray]:
         zero_wav = torch.zeros(
                         int(self.configs.sampling_rate * fragment_interval),
@@ -996,7 +1206,7 @@ class TTS:
                 max_audio=torch.abs(audio_fragment).max()#简单防止16bit爆音
                 if max_audio>1: audio_fragment/=max_audio
                 audio_fragment:torch.Tensor = torch.cat([audio_fragment, zero_wav], dim=0)
-                audio[i][j] = audio_fragment.cpu().numpy()
+                audio[i][j] = audio_fragment
 
 
         if split_bucket:
@@ -1005,8 +1215,21 @@ class TTS:
             # audio = [item for batch in audio for item in batch]
             audio = sum(audio, [])
 
+        audio = torch.cat(audio, dim=0)
+
+        if super_sampling:
+            print(f"############ {i18n('音频超采样')} ############")
+            t1 = ttime()
+            self.init_sr_model()
+            if not self.sr_model_not_exist:
+                audio,sr=self.sr_model(audio.unsqueeze(0),sr)
+                max_audio=np.abs(audio).max()
+                if max_audio > 1: audio /= max_audio
+            t2 = ttime()
+            print(f"超采样用时：{t2-t1:.3f}s")
+        else:
+            audio = audio.cpu().numpy()
 
-        audio = np.concatenate(audio, 0)
         audio = (audio * 32768).astype(np.int16)
 
         # try:
@@ -1018,25 +1241,59 @@ class TTS:
         return sr, audio
 
 
+    def v3_synthesis(self, 
+                     semantic_tokens:torch.Tensor, 
+                     phones:torch.Tensor, 
+                     speed:float=1.0,
+                     sample_steps:int=32
+                     ):
+            
+        prompt_semantic_tokens = self.prompt_cache["prompt_semantic"].unsqueeze(0).unsqueeze(0).to(self.configs.device)
+        prompt_phones = torch.LongTensor(self.prompt_cache["phones"]).unsqueeze(0).to(self.configs.device)
+        refer_audio_spec = self.prompt_cache["refer_spec"][0].to(dtype=self.precision, device=self.configs.device)
 
+        fea_ref,ge = self.vits_model.decode_encp(prompt_semantic_tokens, prompt_phones, refer_audio_spec)
+        ref_audio:torch.Tensor = self.prompt_cache["raw_audio"]
+        ref_sr = self.prompt_cache["raw_sr"]
+        ref_audio=ref_audio.to(self.configs.device).float()
+        if (ref_audio.shape[0] == 2):
+            ref_audio = ref_audio.mean(0).unsqueeze(0)
+        if ref_sr!=24000:
+            ref_audio=resample(ref_audio, ref_sr, self.configs.device)
+        # print("ref_audio",ref_audio.abs().mean())
+        mel2 = mel_fn(ref_audio)
+        mel2 = norm_spec(mel2)
+        T_min = min(mel2.shape[2], fea_ref.shape[2])
+        mel2 = mel2[:, :, :T_min]
+        fea_ref = fea_ref[:, :, :T_min]
+        if (T_min > 468):
+            mel2 = mel2[:, :, -468:]
+            fea_ref = fea_ref[:, :, -468:]
+            T_min = 468
+        chunk_len = 934 - T_min
 
-def speed_change(input_audio:np.ndarray, speed:float, sr:int):
-    # 将 NumPy 数组转换为原始 PCM 流
-    raw_audio = input_audio.astype(np.int16).tobytes()
+        mel2=mel2.to(self.precision)
+        fea_todo, ge = self.vits_model.decode_encp(semantic_tokens, phones, refer_audio_spec, ge, speed)
 
-    # 设置 ffmpeg 输入流
-    input_stream = ffmpeg.input('pipe:', format='s16le', acodec='pcm_s16le', ar=str(sr), ac=1)
+        cfm_resss = []
+        idx = 0
+        while (1):
+            fea_todo_chunk = fea_todo[:, :, idx:idx + chunk_len]
+            if (fea_todo_chunk.shape[-1] == 0): break
+            idx += chunk_len
+            fea = torch.cat([fea_ref, fea_todo_chunk], 2).transpose(2, 1)
 
-    # 变速处理
-    output_stream = input_stream.filter('atempo', speed)
+            cfm_res = self.vits_model.cfm.inference(fea, torch.LongTensor([fea.size(1)]).to(fea.device), mel2, sample_steps, inference_cfg_rate=0)
+            cfm_res = cfm_res[:, :, mel2.shape[2]:]
+            mel2 = cfm_res[:, :, -T_min:]
 
-    # 输出流到管道
-    out, _ = (
-        output_stream.output('pipe:', format='s16le', acodec='pcm_s16le')
-        .run(input=raw_audio, capture_stdout=True, capture_stderr=True)
-    )
-
-    # 将管道输出解码为 NumPy 数组
-    processed_audio = np.frombuffer(out, np.int16)
-
-    return processed_audio
+            fea_ref = fea_todo_chunk[:, :, -T_min:]
+            cfm_resss.append(cfm_res)
+        cmf_res = torch.cat(cfm_resss, 2)
+        cmf_res = denorm_spec(cmf_res)
+        
+        with torch.inference_mode():
+            wav_gen = self.bigvgan_model(cmf_res)
+            audio=wav_gen[0][0]#.cpu().detach().numpy()
+    
+        return audio

GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py

@@ -1,5 +1,6 @@
 
 import os, sys
+import threading
 
 from tqdm import tqdm
 now_dir = os.getcwd()
@@ -54,6 +55,7 @@ class TextPreprocessor:
         self.bert_model = bert_model
         self.tokenizer = tokenizer
         self.device = device
+        self.bert_lock = threading.RLock()
 
     def preprocess(self, text:str, lang:str, text_split_method:str, version:str="v2")->List[Dict]:
         print(f'############ {i18n("切分文本")} ############')
@@ -117,70 +119,71 @@ class TextPreprocessor:
         return self.get_phones_and_bert(text, language, version)
 
     def get_phones_and_bert(self, text:str, language:str, version:str, final:bool=False):
-        if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
-            language = language.replace("all_","")
-            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 self.get_phones_and_bert(formattext,"zh",version)
-                else:
-                    phones, word2ph, norm_text = self.clean_text_inf(formattext, language, version)
-                    bert = self.get_bert_feature(norm_text, word2ph).to(self.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 self.get_phones_and_bert(formattext,"yue",version)
-            else:
-                phones, word2ph, norm_text = self.clean_text_inf(formattext, language, version)
-                bert = torch.zeros(
-                    (1024, len(phones)),
-                    dtype=torch.float32,
-                ).to(self.device)
-        elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}:
-            textlist=[]
-            langlist=[]
-            if language == "auto":
-                for tmp in LangSegmenter.getTexts(text):
-                    langlist.append(tmp["lang"])
-                    textlist.append(tmp["text"])
-            elif language == "auto_yue":
-                for tmp in LangSegmenter.getTexts(text):
-                    if tmp["lang"] == "zh":
-                        tmp["lang"] = "yue"
-                    langlist.append(tmp["lang"])
-                    textlist.append(tmp["text"])
-            else:
-                for tmp in LangSegmenter.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 = self.clean_text_inf(textlist[i], lang, version)
-                bert = self.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)
+        with self.bert_lock:
+          if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
+              # language = language.replace("all_","")
+              formattext = text
+              while "  " in formattext:
+                  formattext = formattext.replace("  ", " ")
+              if language == "all_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 self.get_phones_and_bert(formattext,"zh",version)
+                  else:
+                      phones, word2ph, norm_text = self.clean_text_inf(formattext, language, version)
+                      bert = self.get_bert_feature(norm_text, word2ph).to(self.device)
+              elif language == "all_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 self.get_phones_and_bert(formattext,"yue",version)
+              else:
+                  phones, word2ph, norm_text = self.clean_text_inf(formattext, language, version)
+                  bert = torch.zeros(
+                      (1024, len(phones)),
+                      dtype=torch.float32,
+                  ).to(self.device)
+          elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}:
+              textlist=[]
+              langlist=[]
+              if language == "auto":
+                  for tmp in LangSegmenter.getTexts(text):
+                      langlist.append(tmp["lang"])
+                      textlist.append(tmp["text"])
+              elif language == "auto_yue":
+                  for tmp in LangSegmenter.getTexts(text):
+                      if tmp["lang"] == "zh":
+                          tmp["lang"] = "yue"
+                      langlist.append(tmp["lang"])
+                      textlist.append(tmp["text"])
+              else:
+                  for tmp in LangSegmenter.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 = self.clean_text_inf(textlist[i], lang, version)
+                  bert = self.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 self.get_phones_and_bert("." + text,language,version,final=True)
+          if not final and len(phones) < 6:
+              return self.get_phones_and_bert("." + text,language,version,final=True)
 
-        return phones, bert, norm_text
+          return phones, bert, norm_text
 
 
     def get_bert_feature(self, text:str, word2ph:list)->torch.Tensor:
@@ -199,6 +202,7 @@ class TextPreprocessor:
         return phone_level_feature.T
 
     def clean_text_inf(self, text:str, language:str, version:str="v2"):
+        language = language.replace("all_","")
         phones, word2ph, norm_text = clean_text(text, language, version)
         phones = cleaned_text_to_sequence(phones, version)
         return phones, word2ph, norm_text

GPT_SoVITS/configs/tts_infer.yaml

@@ -6,7 +6,7 @@ custom:
   t2s_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt
   version: v2
   vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth
-default:
+v1:
   bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
   cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
   device: cpu
@@ -14,7 +14,7 @@ default:
   t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
   version: v1
   vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth
-default_v2:
+v2:
   bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
   cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
   device: cpu
@@ -22,3 +22,11 @@ default_v2:
   t2s_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt
   version: v2
   vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth
+v3:
+  bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
+  cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
+  device: cpu
+  is_half: false
+  t2s_weights_path: GPT_SoVITS/pretrained_models/s1v3.ckpt
+  version: v3
+  vits_weights_path: GPT_SoVITS/pretrained_models/s2Gv3.pth

GPT_SoVITS/export_torch_script.py

@@ -427,7 +427,7 @@ class T2SModel(nn.Module):
         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):
+    def forward(self,prompts:LongTensor, ref_seq:LongTensor, text_seq:LongTensor, ref_bert:torch.Tensor, text_bert:torch.Tensor,top_k:LongTensor):
         bert = torch.cat([ref_bert.T, text_bert.T], 1)
         all_phoneme_ids = torch.cat([ref_seq, text_seq], 1)
         bert = bert.unsqueeze(0)
@@ -472,12 +472,13 @@ class T2SModel(nn.Module):
                                                 .to(device=x.device, dtype=torch.bool)
         
         idx = 0
+        top_k = int(top_k)
         
         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]
+        samples = sample(logits, y, top_k=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)
@@ -493,7 +494,7 @@ class T2SModel(nn.Module):
             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]
+            samples = sample(logits, y, top_k=top_k, top_p=1, repetition_penalty=1.35, temperature=1.0)[0]
 
             y = torch.concat([y, samples], dim=1)
             
@@ -653,6 +654,8 @@ def export(gpt_path, vits_path, ref_audio_path, ref_text, output_path, export_be
     torch._dynamo.mark_dynamic(ref_bert, 0)
     torch._dynamo.mark_dynamic(text_bert, 0)
 
+    top_k = torch.LongTensor([5]).to(device)
+
     with torch.no_grad():
         gpt_sovits_export = torch.jit.trace(
         gpt_sovits,
@@ -662,7 +665,8 @@ def export(gpt_path, vits_path, ref_audio_path, ref_text, output_path, export_be
             ref_seq,
             text_seq,
             ref_bert,
-            text_bert))
+            text_bert,
+            top_k))
     
         gpt_sovits_path = os.path.join(output_path, "gpt_sovits_model.pt")
         gpt_sovits_export.save(gpt_sovits_path)
@@ -684,15 +688,26 @@ class GPT_SoVITS(nn.Module):
         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):
+    def forward(
+        self,
+        ssl_content: torch.Tensor,
+        ref_audio_sr: torch.Tensor,
+        ref_seq: Tensor,
+        text_seq: Tensor,
+        ref_bert: Tensor,
+        text_bert: Tensor,
+        top_k: LongTensor,
+        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)
+        pred_semantic = self.t2s(prompts, ref_seq, text_seq, ref_bert, text_bert, top_k)
         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")
@@ -784,8 +799,10 @@ def test():
     print('text_bert:',text_bert.shape)
     text_bert=text_bert.to('cuda')
 
+    top_k = torch.LongTensor([5]).to('cuda')
+
     with torch.no_grad():
-        audio = gpt_sovits(ssl_content, ref_audio_sr, ref_seq, text_seq, ref_bert, test_bert)
+        audio = gpt_sovits(ssl_content, ref_audio_sr, ref_seq, text_seq, ref_bert, test_bert, top_k)
     print('start write wav')
     soundfile.write("out.wav", audio.detach().cpu().numpy(), 32000)
 

GPT_SoVITS/f5_tts/model/backbones/dit.py

@@ -138,7 +138,7 @@ class DiT(nn.Module):
         time: float["b"] | float[""],  # time step  # noqa: F821 F722
             dt_base_bootstrap,
         text0,  # : int["b nt"]  # noqa: F722#####condition feature
-        use_grad_ckpt,  # bool
+        use_grad_ckpt=False,  # bool
         ###no-use
         drop_audio_cond=False,  # cfg for cond audio
         drop_text=False,  # cfg for text

GPT_SoVITS/inference_webui_fast.py

@@ -7,7 +7,7 @@
 全部按日文识别
 '''
 import random
-import os, re, logging
+import os, re, logging, json
 import sys
 now_dir = os.getcwd()
 sys.path.append(now_dir)
@@ -44,7 +44,7 @@ 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.TTS import TTS, TTS_Config, NO_PROMPT_ERROR
 from TTS_infer_pack.text_segmentation_method import get_method
 from tools.i18n.i18n import I18nAuto, scan_language_list
 
@@ -62,6 +62,9 @@ if torch.cuda.is_available():
 else:
     device = "cpu"
 
+# is_half = False
+# 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
@@ -123,11 +126,11 @@ def inference(text, text_lang,
               speed_factor, ref_text_free,
               split_bucket,fragment_interval,
               seed, keep_random, parallel_infer,
-              repetition_penalty
+              repetition_penalty, sample_steps, super_sampling,
               ):
 
     seed = -1 if keep_random else seed
-    actual_seed = seed if seed not in [-1, "", None] else random.randrange(1 << 32)
+    actual_seed = seed if seed not in [-1, "", None] else random.randint(0, 2**32 - 1)
     inputs={
         "text": text,
         "text_lang": dict_language[text_lang],
@@ -147,9 +150,14 @@ def inference(text, text_lang,
         "seed":actual_seed,
         "parallel_infer": parallel_infer,
         "repetition_penalty": repetition_penalty,
+        "sample_steps": int(sample_steps),
+        "super_sampling": super_sampling,
     }
-    for item in tts_pipeline.run(inputs):
-        yield item, actual_seed
+    try:
+        for item in tts_pipeline.run(inputs):
+            yield item, actual_seed
+    except NO_PROMPT_ERROR:
+        gr.Warning(i18n('V3不支持无参考文本模式，请填写参考文本！'))
 
 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
@@ -163,19 +171,38 @@ 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"}
 
+path_sovits_v3="GPT_SoVITS/pretrained_models/s2Gv3.pth"
+pretrained_sovits_name=["GPT_SoVITS/pretrained_models/s2G488k.pth", "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",path_sovits_v3]
+pretrained_gpt_name=["GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt","GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt", "GPT_SoVITS/pretrained_models/s1v3.ckpt"]
 
-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])
+for i in range(3):
+    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"]
+
+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]
+
+
+
+SoVITS_weight_root=["SoVITS_weights","SoVITS_weights_v2","SoVITS_weights_v3"]
+GPT_weight_root=["GPT_weights","GPT_weights_v2","GPT_weights_v3"]
 for path in SoVITS_weight_root+GPT_weight_root:
     os.makedirs(path,exist_ok=True)
 
@@ -194,10 +221,18 @@ def get_weights_names(GPT_weight_root, SoVITS_weight_root):
 SoVITS_names, GPT_names = get_weights_names(GPT_weight_root, SoVITS_weight_root)
 
 
-
+from process_ckpt import get_sovits_version_from_path_fast
 def change_sovits_weights(sovits_path,prompt_language=None,text_language=None):
-    tts_pipeline.init_vits_weights(sovits_path)
     global version, dict_language
+    version, model_version, if_lora_v3=get_sovits_version_from_path_fast(sovits_path)
+
+    if if_lora_v3 and not os.path.exists(path_sovits_v3):
+        info= path_sovits_v3 + i18n("SoVITS V3 底模缺失，无法加载相应 LoRA 权重")
+        gr.Warning(info)
+        raise FileExistsError(info)
+
+    tts_pipeline.init_vits_weights(sovits_path)
+
     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()):
@@ -210,9 +245,19 @@ def change_sovits_weights(sovits_path,prompt_language=None,text_language=None):
         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
-
+        if model_version=="v3":
+            visible_sample_steps=True
+            visible_inp_refs=False
+        else:
+            visible_sample_steps=False
+            visible_inp_refs=True
+        yield  {'__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,{"__type__": "update", "visible": visible_sample_steps},{"__type__": "update", "visible": visible_inp_refs},{"__type__": "update", "value": False,"interactive":True if model_version!="v3"else False},{"__type__": "update", "visible":True if model_version=="v3"else False}
 
+    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))
 
 with gr.Blocks(title="GPT-SoVITS WebUI") as app:
     gr.Markdown(
@@ -257,13 +302,19 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
         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.Row():
+                    batch_size = gr.Slider(minimum=1,maximum=200,step=1,label=i18n("batch_size"),value=20,interactive=True)
+                    sample_steps = gr.Radio(label=i18n("采样步数(仅对V3生效)"),value=32,choices=[4,8,16,32],visible=True)
+                with gr.Row():
+                    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="语速",value=1.0,interactive=True)
+                with gr.Row():
+                    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)
+                with gr.Row():
+                    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(
@@ -272,10 +323,14 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
                             value=i18n("凑四句一切"),
                             interactive=True, scale=1
                         )
+                    super_sampling = gr.Checkbox(label=i18n("音频超采样(仅对V3生效))"), value=False, interactive=True, show_label=True)
+
+                with gr.Row():
                     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)
 
@@ -295,7 +350,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
                 speed_factor, ref_text_free,
                 split_bucket,fragment_interval,
                 seed, keep_random, parallel_infer,
-                repetition_penalty
+                repetition_penalty, sample_steps, super_sampling,
              ],
             [output, seed],
         )

GPT_SoVITS/module/models_onnx.py

@@ -9,6 +9,8 @@ from module import commons
 from module import modules
 from module import attentions_onnx as attentions
 
+from f5_tts.model import DiT
+
 from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
 from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
 from module.commons import init_weights, get_padding
@@ -342,6 +344,37 @@ class PosteriorEncoder(nn.Module):
         return z, m, logs, x_mask
 
 
+class Encoder(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 hidden_channels,
+                 kernel_size,
+                 dilation_rate,
+                 n_layers,
+                 gin_channels=0):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+
+        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+        self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
+        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
+
+    def forward(self, x, x_lengths, g=None):
+        if(g!=None):
+            g = g.detach()
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+        x = self.pre(x) * x_mask
+        x = self.enc(x, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+        return stats, x_mask
+
 class WNEncoder(nn.Module):
     def __init__(
         self,
@@ -916,4 +949,175 @@ class SynthesizerTrn(nn.Module):
     def extract_latent(self, x):
         ssl = self.ssl_proj(x)
         quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
-        return codes.transpose(0, 1)
+        return codes.transpose(0, 1)
+
+class CFM(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels,dit
+    ):
+        super().__init__()
+        # self.sigma_min = 1e-6
+
+        self.estimator = dit
+
+        self.in_channels = in_channels
+
+        # self.criterion = torch.nn.MSELoss()
+
+    def forward(self, mu:torch.Tensor, x_lens:torch.LongTensor, prompt:torch.Tensor, n_timesteps:torch.LongTensor, temperature:float=1.0):
+        """Forward diffusion"""
+        B, T = mu.size(0), mu.size(1)
+        x = torch.randn([B, self.in_channels, T], device=mu.device,dtype=mu.dtype)
+
+        ntimesteps = int(n_timesteps)
+
+        prompt_len = prompt.size(-1)
+        prompt_x = torch.zeros_like(x,dtype=mu.dtype)
+        prompt_x[..., :prompt_len] = prompt[..., :prompt_len]
+        x[..., :prompt_len] = 0.0
+        mu=mu.transpose(2,1)
+        t = torch.tensor(0.0,dtype=x.dtype,device=x.device)
+        d = torch.tensor(1.0/ntimesteps,dtype=x.dtype,device=x.device)
+        d_tensor = torch.ones(x.shape[0], device=x.device,dtype=mu.dtype) * d
+
+        for j in range(ntimesteps):
+            t_tensor = torch.ones(x.shape[0], device=x.device,dtype=mu.dtype) * t
+            # d_tensor = torch.ones(x.shape[0], device=x.device,dtype=mu.dtype) * d
+            # v_pred = model(x, t_tensor, d_tensor, **extra_args)
+            v_pred = self.estimator(x, prompt_x, x_lens, t_tensor,d_tensor, mu).transpose(2, 1)
+            # if inference_cfg_rate>1e-5:
+            #     neg = self.estimator(x, prompt_x, x_lens, t_tensor, d_tensor, mu, use_grad_ckpt=False, drop_audio_cond=True, drop_text=True).transpose(2, 1)
+            #     v_pred=v_pred+(v_pred-neg)*inference_cfg_rate
+            x = x + d * v_pred
+            t = t + d
+            x[:, :, :prompt_len] = 0.0
+        return x
+
+
+def set_no_grad(net_g):
+    for name, param in net_g.named_parameters():
+        param.requires_grad=False
+
+@torch.jit.script_if_tracing
+def compile_codes_length(codes):
+    y_lengths1 = torch.LongTensor([codes.size(2)]).to(codes.device)
+    return y_lengths1 * 2.5 * 1.5
+
+@torch.jit.script_if_tracing
+def compile_ref_length(refer):
+    refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
+    return refer_lengths
+
+class SynthesizerTrnV3(nn.Module):
+    """
+    Synthesizer for Training
+    """
+
+    def __init__(self,
+                 spec_channels,
+                 segment_size,
+                 inter_channels,
+                 hidden_channels,
+                 filter_channels,
+                 n_heads,
+                 n_layers,
+                 kernel_size,
+                 p_dropout,
+                 resblock,
+                 resblock_kernel_sizes,
+                 resblock_dilation_sizes,
+                 upsample_rates,
+                 upsample_initial_channel,
+                 upsample_kernel_sizes,
+                 n_speakers=0,
+                 gin_channels=0,
+                 use_sdp=True,
+                 semantic_frame_rate=None,
+                 freeze_quantizer=None,
+                 version="v3",
+                 **kwargs):
+
+        super().__init__()
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.n_speakers = n_speakers
+        self.gin_channels = gin_channels
+        self.version = version
+
+        self.model_dim=512
+        self.use_sdp = use_sdp
+        self.enc_p = TextEncoder(inter_channels,hidden_channels,filter_channels,n_heads,n_layers,kernel_size,p_dropout)
+        # self.ref_enc = modules.MelStyleEncoder(spec_channels, style_vector_dim=gin_channels)###Rollback
+        self.ref_enc = modules.MelStyleEncoder(704, style_vector_dim=gin_channels)###Rollback
+        # self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
+        #                      upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
+        # self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16,
+        #                               gin_channels=gin_channels)
+        # self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
+
+
+        ssl_dim = 768
+        assert semantic_frame_rate in ['25hz', "50hz"]
+        self.semantic_frame_rate = semantic_frame_rate
+        if semantic_frame_rate == '25hz':
+            self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 2, stride=2)
+        else:
+            self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 1, stride=1)
+
+        self.quantizer = ResidualVectorQuantizer(
+            dimension=ssl_dim,
+            n_q=1,
+            bins=1024
+        )
+        freeze_quantizer
+        inter_channels2=512
+        self.bridge=nn.Sequential(
+            nn.Conv1d(inter_channels, inter_channels2, 1, stride=1),
+            nn.LeakyReLU()
+        )
+        self.wns1=Encoder(inter_channels2, inter_channels2, inter_channels2, 5, 1, 8,gin_channels=gin_channels)
+        self.linear_mel=nn.Conv1d(inter_channels2,100,1,stride=1)
+        self.cfm = CFM(100,DiT(**dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=inter_channels2, conv_layers=4)),)#text_dim is condition feature dim
+        if freeze_quantizer==True:
+            set_no_grad(self.ssl_proj)
+            set_no_grad(self.quantizer)
+            set_no_grad(self.enc_p)
+
+    def create_ge(self, refer):
+        refer_lengths = compile_ref_length(refer)
+        refer_mask = torch.unsqueeze(commons.sequence_mask(refer_lengths, refer.size(2)), 1).to(refer.dtype)
+        ge = self.ref_enc(refer[:,:704] * refer_mask, refer_mask)
+        return ge
+
+    def forward(self, codes, text,ge,speed=1):
+
+        y_lengths1=compile_codes_length(codes)
+        
+        quantized = self.quantizer.decode(codes)
+        if self.semantic_frame_rate == '25hz':
+            quantized = F.interpolate(quantized, scale_factor=2, mode="nearest")##BCT
+        x, m_p, logs_p, y_mask = self.enc_p(quantized, text, ge,speed)
+        fea=self.bridge(x)
+        fea = F.interpolate(fea, scale_factor=1.875, mode="nearest")##BCT
+        ####more wn paramter to learn mel
+        fea, y_mask_ = self.wns1(fea, y_lengths1, ge)
+        return fea
+
+    def extract_latent(self, x):
+        ssl =  self.ssl_proj(x)
+        quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
+        return codes.transpose(0,1)

GPT_SoVITS/prepare_datasets/3-get-semantic.py

@@ -81,7 +81,7 @@ if os.path.exists(semantic_path) == False:
     # utils.load_checkpoint(pretrained_s2G, vq_model, None, True)
     print(
         vq_model.load_state_dict(
-            torch.load(pretrained_s2G, map_location="cpu")["weight"], strict=False
+            torch.load(pretrained_s2G, map_location="cpu", weights_only=False)["weight"], strict=False
         )
     )
 

GPT_SoVITS/text/g2pw/onnx_api.py

@@ -58,7 +58,7 @@ def download_and_decompress(model_dir: str='G2PWModel/'):
         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"
+        modelscope_url = "https://paddlespeech.cdn.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:

README.md

@@ -54,7 +54,7 @@ _Note: numba==0.56.4 requires py<3.11_
 
 ### Windows
 
-If you are a Windows user (tested with win>=10), you can [download the integrated package](https://huggingface.co/lj1995/GPT-SoVITS-windows-package/resolve/main/GPT-SoVITS-beta.7z?download=true) and double-click on _go-webui.bat_ to start GPT-SoVITS-WebUI.
+If you are a Windows user (tested with win>=10), you can [download the integrated package](https://huggingface.co/lj1995/GPT-SoVITS-windows-package/resolve/main/GPT-SoVITS-v3lora-20250228.7z?download=true) and double-click on _go-webui.bat_ to start GPT-SoVITS-WebUI.
 
 **Users in China can [download the package here](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e/dkxgpiy9zb96hob4#KTvnO).**
 
@@ -146,7 +146,7 @@ docker run --rm -it --gpus=all --env=is_half=False --volume=G:\GPT-SoVITS-Docker
 
 1. Download pretrained models from [GPT-SoVITS Models](https://huggingface.co/lj1995/GPT-SoVITS) and place them in `GPT_SoVITS/pretrained_models`.
 
-2. Download G2PW models from [G2PWModel_1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip), unzip and rename to `G2PWModel`, and then place them in `GPT_SoVITS/text`.(Chinese TTS Only)
+2. Download G2PW models from [G2PWModel_1.1.zip](https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip), unzip and rename to `G2PWModel`, and then place them in `GPT_SoVITS/text`.(Chinese TTS Only)
 
 3. For UVR5 (Vocals/Accompaniment Separation & Reverberation Removal, additionally), download models from [UVR5 Weights](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/uvr5_weights) and place them in `tools/uvr5/uvr5_weights`.
 
@@ -253,7 +253,7 @@ Use v2 from v1 environment:
 
 3. Download v2 pretrained models from [huggingface](https://huggingface.co/lj1995/GPT-SoVITS/tree/main/gsv-v2final-pretrained) and put them into `GPT_SoVITS\pretrained_models\gsv-v2final-pretrained`.
 
-    Chinese v2 additional: [G2PWModel_1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip)（Download G2PW models,  unzip and rename to `G2PWModel`, and then place them in `GPT_SoVITS/text`.
+    Chinese v2 additional: [G2PWModel_1.1.zip](https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip)（Download G2PW models,  unzip and rename to `G2PWModel`, and then place them in `GPT_SoVITS/text`.
 
 ## V3 Release Notes
 

api_v2.py

@@ -39,6 +39,8 @@ POST:
     "seed": -1,                   # int. random seed for reproducibility.
     "parallel_infer": True,       # bool. whether to use parallel inference.
     "repetition_penalty": 1.35    # float. repetition penalty for T2S model.
+    "sample_steps": 32,           # int. number of sampling steps for VITS model V3.
+    "super_sampling": False,       # bool. whether to use super-sampling for audio when using VITS model V3.
 }
 ```
 
@@ -164,6 +166,8 @@ class TTS_Request(BaseModel):
     streaming_mode:bool = False
     parallel_infer:bool = True
     repetition_penalty:float = 1.35
+    sample_steps:int = 32
+    super_sampling:bool = False
 
 ### modify from https://github.com/RVC-Boss/GPT-SoVITS/pull/894/files
 def pack_ogg(io_buffer:BytesIO, data:np.ndarray, rate:int):
@@ -294,7 +298,9 @@ async def tts_handle(req:dict):
                 "media_type": "wav",          # str. media type of the output audio, support "wav", "raw", "ogg", "aac".
                 "streaming_mode": False,      # bool. whether to return a streaming response.
                 "parallel_infer": True,       # bool.(optional) whether to use parallel inference.
-                "repetition_penalty": 1.35    # float.(optional) repetition penalty for T2S model.          
+                "repetition_penalty": 1.35    # float.(optional) repetition penalty for T2S model.   
+                "sample_steps": 32,           # int. number of sampling steps for VITS model V3.
+                "super_sampling": False,       # bool. whether to use super-sampling for audio when using VITS model V3.       
             }
     returns:
         StreamingResponse: audio stream response.
@@ -316,10 +322,12 @@ async def tts_handle(req:dict):
         
         if streaming_mode:
             def streaming_generator(tts_generator:Generator, media_type:str):
-                if media_type == "wav":
-                    yield wave_header_chunk()
-                    media_type = "raw"
+                if_frist_chunk = True
                 for sr, chunk in tts_generator:
+                    if if_frist_chunk and media_type == "wav":
+                        yield wave_header_chunk(sample_rate=sr)
+                        media_type = "raw"
+                        if_frist_chunk = False
                     yield pack_audio(BytesIO(), chunk, sr, media_type).getvalue()
             # _media_type = f"audio/{media_type}" if not (streaming_mode and media_type in ["wav", "raw"]) else f"audio/x-{media_type}"
             return StreamingResponse(streaming_generator(tts_generator, media_type, ), media_type=f"audio/{media_type}")
@@ -365,7 +373,9 @@ async def tts_get_endpoint(
                         media_type:str = "wav",
                         streaming_mode:bool = False,
                         parallel_infer:bool = True,
-                        repetition_penalty:float = 1.35
+                        repetition_penalty:float = 1.35,
+                        sample_steps:int =32,
+                        super_sampling:bool = False
                         ):
     req = {
         "text": text,
@@ -387,7 +397,9 @@ async def tts_get_endpoint(
         "media_type":media_type,
         "streaming_mode":streaming_mode,
         "parallel_infer":parallel_infer,
-        "repetition_penalty":float(repetition_penalty)
+        "repetition_penalty":float(repetition_penalty),
+        "sample_steps":int(sample_steps),
+        "super_sampling":super_sampling
     }
     return await tts_handle(req)
                 

docs/cn/README.md

@@ -53,7 +53,7 @@ _注: numba==0.56.4 需要 python<3.11_
 
 ### Windows
 
-如果你是 Windows 用户（已在 win>=10 上测试），可以下载[下载整合包](https://huggingface.co/lj1995/GPT-SoVITS-windows-package/resolve/main/GPT-SoVITS-beta.7z?download=true)，解压后双击 go-webui.bat 即可启动 GPT-SoVITS-WebUI。
+如果你是 Windows 用户（已在 win>=10 上测试），可以下载[整合包](https://huggingface.co/lj1995/GPT-SoVITS-windows-package/resolve/main/GPT-SoVITS-v3lora-20250228.7z?download=true)，解压后双击 go-webui.bat 即可启动 GPT-SoVITS-WebUI。
 
 **中国地区的用户可以[在此处下载整合包](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e/dkxgpiy9zb96hob4#KTvnO)。**
 
@@ -147,7 +147,7 @@ docker run --rm -it --gpus=all --env=is_half=False --volume=G:\GPT-SoVITS-Docker
 
 1. 从 [GPT-SoVITS Models](https://huggingface.co/lj1995/GPT-SoVITS) 下载预训练模型，并将其放置在 `GPT_SoVITS/pretrained_models` 目录中。
 
-2. 从 [G2PWModel_1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip) 下载模型，解压并重命名为 `G2PWModel`，然后将其放置在 `GPT_SoVITS/text` 目录中。（仅限中文TTS）
+2. 从 [G2PWModel_1.1.zip](https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip) 下载模型，解压并重命名为 `G2PWModel`，然后将其放置在 `GPT_SoVITS/text` 目录中。（仅限中文TTS）
 
 3. 对于 UVR5（人声/伴奏分离和混响移除，额外功能），从 [UVR5 Weights](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/uvr5_weights) 下载模型，并将其放置在 `tools/uvr5/uvr5_weights` 目录中。
 
@@ -255,7 +255,7 @@ python webui.py
 
 3. 需要从[huggingface](https://huggingface.co/lj1995/GPT-SoVITS/tree/main/gsv-v2final-pretrained) 下载预训练模型文件放到GPT_SoVITS\pretrained_models\gsv-v2final-pretrained下
 
-    中文额外需要下载[G2PWModel_1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip)（下载G2PW模型,解压并重命名为`G2PWModel`,将其放到`GPT_SoVITS/text`目录下）
+    中文额外需要下载[G2PWModel_1.1.zip](https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip)（下载G2PW模型,解压并重命名为`G2PWModel`,将其放到`GPT_SoVITS/text`目录下）
 
 ## V3更新说明
 

docs/ja/README.md

@@ -49,7 +49,7 @@ _注記: numba==0.56.4 は py<3.11 が必要です_
 
 ### Windows
 
-Windows ユーザー:（Windows 10 以降でテスト済み）、[統合パッケージをダウンロード](https://huggingface.co/lj1995/GPT-SoVITS-windows-package/resolve/main/GPT-SoVITS-beta.7z?download=true)し、解凍後に _go-webui.bat_ をダブルクリックすると、GPT-SoVITS-WebUI が起動します。
+Windows ユーザー:（Windows 10 以降でテスト済み）、[統合パッケージをダウンロード](https://huggingface.co/lj1995/GPT-SoVITS-windows-package/resolve/main/GPT-SoVITS-v3lora-20250228.7z?download=true)し、解凍後に _go-webui.bat_ をダブルクリックすると、GPT-SoVITS-WebUI が起動します。
 
 ### Linux
 
@@ -138,7 +138,7 @@ docker run --rm -it --gpus=all --env=is_half=False --volume=G:\GPT-SoVITS-Docker
 
 1. [GPT-SoVITS Models](https://huggingface.co/lj1995/GPT-SoVITS) から事前訓練済みモデルをダウンロードし、`GPT_SoVITS/pretrained_models` ディレクトリに配置してください。
 
-2. [G2PWModel_1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip) からモデルをダウンロードし、解凍して `G2PWModel` にリネームし、`GPT_SoVITS/text` ディレクトリに配置してください。（中国語TTSのみ）
+2. [G2PWModel_1.1.zip](https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip) からモデルをダウンロードし、解凍して `G2PWModel` にリネームし、`GPT_SoVITS/text` ディレクトリに配置してください。（中国語TTSのみ）
 
 3. UVR5（ボーカル/伴奏（BGM等）分離 & リバーブ除去の追加機能）の場合は、[UVR5 Weights](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/uvr5_weights) からモデルをダウンロードし、`tools/uvr5/uvr5_weights` ディレクトリに配置してください。
 
@@ -242,7 +242,7 @@ V1環境からV2を使用するには:
 
 3. [huggingface](https://huggingface.co/lj1995/GPT-SoVITS/tree/main/gsv-v2final-pretrained)からV2の事前学習モデルをダウンロードし、それらを`GPT_SoVITS\pretrained_models\gsv-v2final-pretrained`に配置
 
-    中国語V2追加: [G2PWModel_1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip)（G2PWモデルをダウンロードし、解凍して`G2PWModel`にリネームし、`GPT_SoVITS/text`に配置します）
+    中国語V2追加: [G2PWModel_1.1.zip](https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip)（G2PWモデルをダウンロードし、解凍して`G2PWModel`にリネームし、`GPT_SoVITS/text`に配置します）
 
 ## V3 リリースノート
 

docs/ko/README.md

@@ -49,7 +49,7 @@ _참고: numba==0.56.4 는 python<3.11 을 필요로 합니다._
 
 ### Windows
 
-Windows 사용자라면 (win>=10에서 테스트됨), [통합 패키지를 다운로드](https://huggingface.co/lj1995/GPT-SoVITS-windows-package/resolve/main/GPT-SoVITS-beta.7z?download=true)한 후 압축을 풀고 _go-webui.bat_ 파일을 더블 클릭하면 GPT-SoVITS-WebUI를 시작할 수 있습니다.
+Windows 사용자라면 (win>=10에서 테스트됨), [통합 패키지를 다운로드](https://huggingface.co/lj1995/GPT-SoVITS-windows-package/resolve/main/GPT-SoVITS-v3lora-20250228.7z?download=true)한 후 압축을 풀고 _go-webui.bat_ 파일을 더블 클릭하면 GPT-SoVITS-WebUI를 시작할 수 있습니다.
 
 ### Linux
 
@@ -143,7 +143,7 @@ docker run --rm -it --gpus=all --env=is_half=False --volume=G:\GPT-SoVITS-Docker
 
 1. [GPT-SoVITS Models](https://huggingface.co/lj1995/GPT-SoVITS) 에서 사전 학습된 모델을 다운로드하고, `GPT_SoVITS/pretrained_models` 디렉토리에 배치하세요.
 
-2. [G2PWModel_1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip) 에서 모델을 다운로드하고 압축을 풀어 `G2PWModel`로 이름을 변경한 후, `GPT_SoVITS/text` 디렉토리에 배치하세요. (중국어 TTS 전용)
+2. [G2PWModel_1.1.zip](https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip) 에서 모델을 다운로드하고 압축을 풀어 `G2PWModel`로 이름을 변경한 후, `GPT_SoVITS/text` 디렉토리에 배치하세요. (중국어 TTS 전용)
 
 3. UVR5 (보컬/반주 분리 & 잔향 제거 추가 기능)의 경우, [UVR5 Weights](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/uvr5_weights) 에서 모델을 다운로드하고 `tools/uvr5/uvr5_weights` 디렉토리에 배치하세요.
 
@@ -248,7 +248,7 @@ V1 환경에서 V2를 사용하려면:
 
 3. [huggingface](https://huggingface.co/lj1995/GPT-SoVITS/tree/main/gsv-v2final-pretrained)에서 V2 사전 학습 모델을 다운로드하여 `GPT_SoVITS\pretrained_models\gsv-v2final-pretrained`에 넣으십시오.
 
-    중국어 V2 추가: [G2PWModel_1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip) (G2PW 모델을 다운로드하여 압축을 풀고 `G2PWModel`로 이름을 변경한 다음 `GPT_SoVITS/text`에 배치합니다.)
+    중국어 V2 추가: [G2PWModel_1.1.zip](https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip) (G2PW 모델을 다운로드하여 압축을 풀고 `G2PWModel`로 이름을 변경한 다음 `GPT_SoVITS/text`에 배치합니다.)
 
 ## V3 릴리스 노트
 

docs/tr/README.md

@@ -51,7 +51,7 @@ _Not: numba==0.56.4, py<3.11 gerektirir_
 
 ### Windows
 
-Eğer bir Windows kullanıcısıysanız (win>=10 ile test edilmiştir), [entegre paketi indirin](https://huggingface.co/lj1995/GPT-SoVITS-windows-package/resolve/main/GPT-SoVITS-beta.7z?download=true) ve _go-webui.bat_ dosyasına çift tıklayarak GPT-SoVITS-WebUI'yi başlatın.
+Eğer bir Windows kullanıcısıysanız (win>=10 ile test edilmiştir), [entegre paketi indirin](https://huggingface.co/lj1995/GPT-SoVITS-windows-package/resolve/main/GPT-SoVITS-v3lora-20250228.7z?download=true) ve _go-webui.bat_ dosyasına çift tıklayarak GPT-SoVITS-WebUI'yi başlatın.
 
 ### Linux
 
@@ -138,7 +138,7 @@ docker run --rm -it --gpus=all --env=is_half=False --volume=G:\GPT-SoVITS-Docker
 
 1. [GPT-SoVITS Models](https://huggingface.co/lj1995/GPT-SoVITS) üzerinden önceden eğitilmiş modelleri indirip `GPT_SoVITS/pretrained_models` dizinine yerleştirin.
 
-2. [G2PWModel_1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip) üzerinden modeli indirip sıkıştırmayı açın ve `G2PWModel` olarak yeniden adlandırın, ardından `GPT_SoVITS/text` dizinine yerleştirin. (Sadece Çince TTS için)
+2. [G2PWModel_1.1.zip](https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip) üzerinden modeli indirip sıkıştırmayı açın ve `G2PWModel` olarak yeniden adlandırın, ardından `GPT_SoVITS/text` dizinine yerleştirin. (Sadece Çince TTS için)
 
 3. UVR5 (Vokal/Enstrümantal Ayrımı & Yankı Giderme) için, [UVR5 Weights](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/uvr5_weights) üzerinden modelleri indirip `tools/uvr5/uvr5_weights` dizinine yerleştirin.
 
@@ -245,7 +245,7 @@ V1 ortamından V2'yi kullanmak için:
 
 3. [huggingface](https://huggingface.co/lj1995/GPT-SoVITS/tree/main/gsv-v2final-pretrained) adresinden v2 önceden eğitilmiş modelleri indirin ve bunları `GPT_SoVITS\pretrained_models\gsv-v2final-pretrained` dizinine yerleştirin.
 
-    Ek olarak Çince V2: [G2PWModel_1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip) (G2PW modellerini indirip, zipten çıkarıp, `G2PWModel` olarak yeniden adlandırıp `GPT_SoVITS/text` dizinine yerleştirin.)
+    Ek olarak Çince V2: [G2PWModel_1.1.zip](https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip) (G2PW modellerini indirip, zipten çıkarıp, `G2PWModel` olarak yeniden adlandırıp `GPT_SoVITS/text` dizinine yerleştirin.)
 
 ## V3 Sürüm Notları
 

install.sh

@@ -2,8 +2,13 @@
 
 # 安装构建工具
 # Install build tools
+echo "Installing GCC..."
 conda install -c conda-forge gcc=14
+
+echo "Installing G++..."
 conda install -c conda-forge gxx
+
+echo "Installing ffmpeg and cmake..."
 conda install ffmpeg cmake
 
 # 设置编译环境
@@ -12,10 +17,60 @@ export CMAKE_MAKE_PROGRAM="$CONDA_PREFIX/bin/cmake"
 export CC="$CONDA_PREFIX/bin/gcc"
 export CXX="$CONDA_PREFIX/bin/g++"
 
-conda install pytorch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 pytorch-cuda=11.8 -c pytorch -c nvidia
+echo "Checking for CUDA installation..."
+if command -v nvidia-smi &> /dev/null; then
+    USE_CUDA=true
+    echo "CUDA found."
+else
+    echo "CUDA not found."
+    USE_CUDA=false
+fi
+
+
+if [ "$USE_CUDA" = false ]; then
+    echo "Checking for ROCm installation..."
+    if [ -d "/opt/rocm" ]; then
+        USE_ROCM=true
+        echo "ROCm found."
+        if grep -qi "microsoft" /proc/version; then
+            echo "You are running WSL."
+            IS_WSL=true
+        else
+            echo "You are NOT running WSL."
+            IS_WSL=false
+        fi
+    else
+        echo "ROCm not found."
+        USE_ROCM=false
+    fi
+fi
+
+if [ "$USE_CUDA" = true ]; then
+    echo "Installing PyTorch with CUDA support..."
+    conda install pytorch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 pytorch-cuda=11.8 -c pytorch -c nvidia
+elif [ "$USE_ROCM" = true ] ; then
+    echo "Installing PyTorch with ROCm support..."
+    pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/rocm6.2
+else
+    echo "Installing PyTorch for CPU..."
+    conda install pytorch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 cpuonly -c pytorch
+fi
+
+
+echo "Installing Python dependencies from requirements.txt..."
 
 # 刷新环境
 # Refresh environment
 hash -r
+pip install -r requirements.txt
+
+if [ "$USE_ROCM" = true ] && [ "$IS_WSL" = true ] ; then
+    echo "Update to WSL compatible runtime lib..."
+    location=`pip show torch | grep Location | awk -F ": " '{print $2}'`
+    cd ${location}/torch/lib/
+    rm libhsa-runtime64.so*
+    cp /opt/rocm/lib/libhsa-runtime64.so.1.2 libhsa-runtime64.so
+fi
+
+echo "Installation completed successfully!"
 
-pip install -r requirements.txt

tools/audio_sr.py

@@ -39,6 +39,11 @@ class AP_BWE():
         self.model=model
         self.h=h
 
+    def to(self, *arg, **kwargs):
+        self.model.to(*arg, **kwargs)
+        self.device = self.model.conv_pre_mag.weight.device
+        return self
+
     def __call__(self, audio,orig_sampling_rate):
         with torch.no_grad():
             # audio, orig_sampling_rate = torchaudio.load(inp_path)

tools/my_utils.py

@@ -32,7 +32,7 @@ def clean_path(path_str:str):
     if path_str.endswith(('\\','/')):
         return clean_path(path_str[0:-1])
     path_str = path_str.replace('/', os.sep).replace('\\', os.sep)
-    return path_str.strip(" ").strip('\'').strip("\n").strip('"').strip(" ").strip("\u202a")
+    return path_str.strip(" \'\n\"\u202a")#path_str.strip(" ").strip('\'').strip("\n").strip('"').strip(" ").strip("\u202a")
 
 
 def check_for_existance(file_list:list=None,is_train=False,is_dataset_processing=False):