Merge 50a88a596dea718c83e535136e9cb46b513cef6f into 03b662a769946b7a6a8569a354860e8eeeb743aa

* 为sovits_v3 适配并行推理

* 清理无用代码

.gitignore

@@ -18,5 +18,183 @@ TEMP
 weight.json
 ffmpeg*
 ffprobe*
+cfg.json
+speakers.json
+ref_audios
 tools/AP_BWE_main/24kto48k/*
 !tools/AP_BWE_main/24kto48k/readme.txt
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc

GPT_SoVITS/TTS_infer_pack/TTS.py

@@ -3,7 +3,7 @@ import math
 import os, sys, gc
 import random
 import traceback
-
+import time
 import torchaudio
 from tqdm import tqdm
 now_dir = os.getcwd()
@@ -908,11 +908,14 @@ class TTS:
                 split_bucket = False
                 print(i18n("分段返回模式不支持分桶处理，已自动关闭分桶处理"))
 
-        if split_bucket and speed_factor==1.0:
+        if split_bucket and speed_factor==1.0 and not (self.configs.is_v3_synthesizer and parallel_infer):
             print(i18n("分桶处理模式已开启"))
         elif speed_factor!=1.0:
             print(i18n("语速调节不支持分桶处理，已自动关闭分桶处理"))
             split_bucket = False
+        elif self.configs.is_v3_synthesizer and parallel_infer:
+            print(i18n("当开启并行推理模式时，SoVits V3模型不支持分桶处理，已自动关闭分桶处理"))
+            split_bucket = False
         else:
             print(i18n("分桶处理模式已关闭"))
 
@@ -936,7 +939,7 @@ class TTS:
             raise ValueError("ref_audio_path cannot be empty, when the reference audio is not set using set_ref_audio()")
 
         ###### setting reference audio and prompt text preprocessing ########
-        t0 = ttime()
+        t0 = time.perf_counter()
         if (ref_audio_path is not None) and (ref_audio_path != self.prompt_cache["ref_audio_path"]):
             if not os.path.exists(ref_audio_path):
                 raise ValueError(f"{ref_audio_path} not exists")
@@ -975,7 +978,7 @@ class TTS:
 
 
         ###### text preprocessing ########
-        t1 = ttime()
+        t1 = time.perf_counter()
         data:list = None
         if not return_fragment:
             data = self.text_preprocessor.preprocess(text, text_lang, text_split_method, self.configs.version)
@@ -1027,7 +1030,7 @@ class TTS:
                 return batch[0]
 
 
-        t2 = ttime()
+        t2 = time.perf_counter()
         try:
             print("############ 推理 ############")
             ###### inference ######
@@ -1036,7 +1039,7 @@ class TTS:
             audio = []
             output_sr = self.configs.sampling_rate if not self.configs.is_v3_synthesizer else 24000
             for item in data:
-                t3 = ttime()
+                t3 = time.perf_counter()
                 if return_fragment:
                     item = make_batch(item)
                     if item is None:
@@ -1071,7 +1074,7 @@ class TTS:
                     max_len=max_len,
                     repetition_penalty=repetition_penalty,
                 )
-                t4 = ttime()
+                t4 = time.perf_counter()
                 t_34 += t4 - t3
 
                 refer_audio_spec:torch.Tensor = [item.to(dtype=self.precision, device=self.configs.device) for item in self.prompt_cache["refer_spec"]]
@@ -1094,6 +1097,7 @@ class TTS:
                 print(f"############ {i18n('合成音频')} ############")
                 if not self.configs.is_v3_synthesizer:
                     if speed_factor == 1.0:
+                        print(f"{i18n('并行合成中')}...")
                         # ## 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)
@@ -1117,6 +1121,17 @@ class TTS:
                             batch_audio_fragment.append(
                                 audio_fragment
                             )  ###试试重建不带上prompt部分
+                else:
+                    if parallel_infer:
+                        print(f"{i18n('并行合成中')}...")
+                        audio_fragments = self.v3_synthesis_batched_infer(
+                                                                        idx_list,
+                                                                        pred_semantic_list, 
+                                                                        batch_phones, 
+                                                                        speed=speed_factor, 
+                                                                        sample_steps=sample_steps
+                                                                    )
+                        batch_audio_fragment.extend(audio_fragments)
                     else:
                         for i, idx in enumerate(tqdm(idx_list)):
                             phones = batch_phones[i].unsqueeze(0).to(self.configs.device)
@@ -1128,7 +1143,7 @@ class TTS:
                                 audio_fragment
                             ) 
 
-                t5 = ttime()
+                t5 = time.perf_counter()
                 t_45 += t5 - t4
                 if return_fragment:
                     print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t4 - t3, t5 - t4))
@@ -1219,13 +1234,13 @@ class TTS:
 
         if super_sampling:
             print(f"############ {i18n('音频超采样')} ############")
-            t1 = ttime()
+            t1 = time.perf_counter()
             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()
+            t2 = time.perf_counter()
             print(f"超采样用时：{t2-t1:.3f}s")
         else:
             audio = audio.cpu().numpy()
@@ -1260,7 +1275,7 @@ class TTS:
             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])
@@ -1285,15 +1300,156 @@ class TTS:
 
             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:]
 
+            mel2 = cfm_res[:, :, -T_min:]
             fea_ref = fea_todo_chunk[:, :, -T_min:]
+
             cfm_resss.append(cfm_res)
-        cmf_res = torch.cat(cfm_resss, 2)
+        cfm_res = torch.cat(cfm_resss, 2)
-        cmf_res = denorm_spec(cmf_res)
+        cfm_res = denorm_spec(cfm_res)
+
         
         with torch.inference_mode():
-            wav_gen = self.bigvgan_model(cmf_res)
+            wav_gen = self.bigvgan_model(cfm_res)
             audio=wav_gen[0][0]#.cpu().detach().numpy()
     
         return audio
+
+    
+
+    def v3_synthesis_batched_infer(self, 
+                    idx_list:List[int],
+                    semantic_tokens_list:List[torch.Tensor], 
+                    batch_phones:List[torch.Tensor], 
+                    speed:float=1.0,
+                    sample_steps:int=32
+                    )->List[torch.Tensor]:
+            
+        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)
+
+        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
+
+        mel2=mel2.to(self.precision)
+
+
+        # #### batched inference
+        overlapped_len = 12
+        feat_chunks = []
+        feat_lens = []
+        feat_list = []
+
+        for i, idx in enumerate(idx_list):
+            phones = batch_phones[i].unsqueeze(0).to(self.configs.device)
+            semantic_tokens = semantic_tokens_list[i][-idx:].unsqueeze(0).unsqueeze(0)   # .unsqueeze(0)#mq要多unsqueeze一次
+            feat, _ = self.vits_model.decode_encp(semantic_tokens, phones, refer_audio_spec, ge, speed)
+            feat_list.append(feat)
+            feat_lens.append(feat.shape[2])
+
+        feats = torch.cat(feat_list, 2)
+        feats_padded = F.pad(feats, (overlapped_len,0), "constant", 0)
+        pos = 0
+        padding_len = 0
+        while True:
+            if pos ==0:
+                chunk = feats_padded[:, :, pos:pos + chunk_len]
+            else:
+                pos = pos - overlapped_len
+                chunk = feats_padded[:, :, pos:pos + chunk_len]
+            pos += chunk_len
+            if (chunk.shape[-1] == 0): break
+
+            # padding for the last chunk
+            padding_len = chunk_len - chunk.shape[2]
+            if padding_len != 0:
+                chunk = F.pad(chunk, (0,padding_len), "constant", 0)
+            feat_chunks.append(chunk)
+            
+
+
+        feat_chunks = torch.cat(feat_chunks, 0)
+        bs = feat_chunks.shape[0]
+        fea_ref = fea_ref.repeat(bs,1,1)
+        fea = torch.cat([fea_ref, feat_chunks], 2).transpose(2, 1)
+        pred_spec = self.vits_model.cfm.inference(fea, torch.LongTensor([fea.size(1)]).to(fea.device), mel2, sample_steps, inference_cfg_rate=0)
+        pred_spec = pred_spec[:, :, -chunk_len:]
+        dd = pred_spec.shape[1]
+        pred_spec = pred_spec.permute(1, 0, 2).contiguous().view(dd, -1).unsqueeze(0)
+        # pred_spec = pred_spec[..., :-padding_len]
+
+
+        pred_spec = denorm_spec(pred_spec)
+        
+        with torch.no_grad():
+            wav_gen = self.bigvgan_model(pred_spec)
+            audio = wav_gen[0][0]#.cpu().detach().numpy()
+
+
+        audio_fragments = []
+        upsample_rate = 256
+        pos = 0
+
+        while pos < audio.shape[-1]:
+            audio_fragment = audio[pos:pos+chunk_len*upsample_rate]
+            audio_fragments.append(audio_fragment)
+            pos += chunk_len*upsample_rate
+
+        audio = self.sola_algorithm(audio_fragments, overlapped_len*upsample_rate)
+        audio = audio[overlapped_len*upsample_rate:-padding_len*upsample_rate]
+
+        audio_fragments = []
+        for feat_len in feat_lens:
+            audio_fragment = audio[:feat_len*upsample_rate]
+            audio_fragments.append(audio_fragment)
+            audio = audio[feat_len*upsample_rate:]
+
+
+        return audio_fragments
+    
+
+
+    def sola_algorithm(self, 
+                    audio_fragments:List[torch.Tensor],
+                    overlap_len:int,
+                    ):
+        
+        for i in range(len(audio_fragments)-1):
+            f1 = audio_fragments[i]
+            f2 = audio_fragments[i+1]
+            w1 = f1[-overlap_len:]
+            w2 = f2[:overlap_len]
+            assert w1.shape == w2.shape
+            corr = F.conv1d(w1.view(1,1,-1), w2.view(1,1,-1),padding=w2.shape[-1]//2).view(-1)[:-1]
+            idx = corr.argmax()
+            f1_ = f1[:-(overlap_len-idx)]
+            audio_fragments[i] = f1_
+
+            f2_ = f2[idx:]
+            window = torch.hann_window((overlap_len-idx)*2, device=f1.device, dtype=f1.dtype)
+            f2_[:(overlap_len-idx)] = window[:(overlap_len-idx)]*f2_[:(overlap_len-idx)] + window[(overlap_len-idx):]*f1[-(overlap_len-idx):]
+            audio_fragments[i+1] = f2_
+
+
+        return torch.cat(audio_fragments, 0)
+            
+                
+

GPT_SoVITS/text/symbols.py

@@ -398,4 +398,5 @@ arpa = {
 symbols = [pad] + c + v + ja_symbols + pu_symbols + list(arpa)
 symbols = sorted(set(symbols))
 if __name__ == "__main__":
+    print(symbols)
     print(len(symbols))

GPT_SoVITS_Inference.ipynb

@@ -1,42 +1,37 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "accelerator": "GPU"
-  },
   "cells": [
     {
       "cell_type": "markdown",
-      "source": [
-        "# Credits for bubarino giving me the huggingface import code (感谢 bubarino 给了我 huggingface 导入代码)"
-      ],
       "metadata": {
         "id": "himHYZmra7ix"
-      }
+      },
+      "source": [
+        "# Credits for bubarino giving me the huggingface import code (感谢 bubarino 给了我 huggingface 导入代码)"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "e9b7iFV3dm1f"
       },
+      "outputs": [],
       "source": [
         "!git clone https://github.com/RVC-Boss/GPT-SoVITS.git\n",
         "%cd GPT-SoVITS\n",
         "!apt-get update && apt-get install -y --no-install-recommends tzdata ffmpeg libsox-dev parallel aria2 git git-lfs && git lfs install\n",
+        "!pip install -r extra-req.txt --no-deps\n",
         "!pip install -r requirements.txt"
-      ],
+      ]
-      "execution_count": null,
-      "outputs": []
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "0NgxXg5sjv7z"
+      },
+      "outputs": [],
       "source": [
         "# @title Download pretrained models 下载预训练模型\n",
         "!mkdir -p /content/GPT-SoVITS/GPT_SoVITS/pretrained_models\n",
@@ -53,16 +48,16 @@
         "!git clone https://huggingface.co/Delik/uvr5_weights\n",
         "!git config core.sparseCheckout true\n",
         "!mv /content/GPT-SoVITS/GPT_SoVITS/pretrained_models/GPT-SoVITS/* /content/GPT-SoVITS/GPT_SoVITS/pretrained_models/"
-      ],
+      ]
-      "metadata": {
-        "id": "0NgxXg5sjv7z",
-        "cellView": "form"
-      },
-      "execution_count": null,
-      "outputs": []
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "cPDEH-9czOJF"
+      },
+      "outputs": [],
       "source": [
         "#@title Create folder models 创建文件夹模型\n",
         "import os\n",
@@ -77,16 +72,16 @@
         "    print(f\"The folder '{folder_name}' was created successfully! (文件夹'{folder_name}'已成功创建！)\")\n",
         "\n",
         "print(\"All folders have been created. (所有文件夹均已创建。)\")"
-      ],
+      ]
-      "metadata": {
-        "cellView": "form",
-        "id": "cPDEH-9czOJF"
-      },
-      "execution_count": null,
-      "outputs": []
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "vbZY-LnM0tzq"
+      },
+      "outputs": [],
       "source": [
         "import requests\n",
         "import zipfile\n",
@@ -124,29 +119,35 @@
         "        shutil.move(source_path, destination_path)\n",
         "\n",
         "print(f'Model downloaded. (模型已下载。)')"
-      ],
+      ]
-      "metadata": {
-        "cellView": "form",
-        "id": "vbZY-LnM0tzq"
-      },
-      "execution_count": null,
-      "outputs": []
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "4oRGUzkrk8C7"
+      },
+      "outputs": [],
       "source": [
         "# @title launch WebUI 启动WebUI\n",
         "!/usr/local/bin/pip install ipykernel\n",
         "!sed -i '10s/False/True/' /content/GPT-SoVITS/config.py\n",
         "%cd /content/GPT-SoVITS/\n",
         "!/usr/local/bin/python  webui.py"
-      ],
-      "metadata": {
-        "id": "4oRGUzkrk8C7",
-        "cellView": "form"
-      },
-      "execution_count": null,
-      "outputs": []
-    }
       ]
     }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

README.md

@@ -1,6 +1,5 @@
 <div align="center">
 
-
 <h1>GPT-SoVITS-WebUI</h1>
 A Powerful Few-shot Voice Conversion and Text-to-Speech WebUI.<br><br>
 
@@ -77,6 +76,7 @@ bash install.sh
 ```bash
 conda create -n GPTSoVits python=3.9
 conda activate GPTSoVits
+pip install -r extra-req.txt --no-deps
 pip install -r requirements.txt
 ```
 
@@ -105,6 +105,7 @@ Download and place [ffmpeg.exe](https://huggingface.co/lj1995/VoiceConversionWeb
 Install [Visual Studio 2017](https://aka.ms/vs/17/release/vc_redist.x86.exe) (Korean TTS Only)
 
 ##### MacOS Users
+
 ```bash
 brew install ffmpeg
 ```
@@ -112,6 +113,7 @@ brew install ffmpeg
 #### Install Dependences
 
 ```bash
+pip install -r extra-req.txt --no-deps
 pip install -r requirements.txt
 ```
 
@@ -200,6 +202,7 @@ if you want to switch to V1,then
 ```bash
 python webui.py v1 <language(optional)>
 ```
+
 Or maunally switch version in WebUI
 
 ### Finetune
@@ -224,11 +227,13 @@ Double-click `go-webui-v2.bat` or use `go-webui-v2.ps1` ,then open the inference
 ```bash
 python GPT_SoVITS/inference_webui.py <language(optional)>
 ```
+
 OR
 
 ```bash
 python webui.py
 ```
+
 then open the inference webui at `1-GPT-SoVITS-TTS/1C-inference`
 
 ## V2 Release Notes
@@ -243,7 +248,7 @@ New Features:
 
 4. Improved synthesis quality for low-quality reference audio
 
-    [more details](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v2%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7))
+   [more details](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v2%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7)>)
 
 Use v2 from v1 environment:
 
@@ -263,7 +268,7 @@ New Features:
 
 2. GPT model is more stable, with fewer repetitions and omissions, and it is easier to generate speech with richer emotional expression.
 
-    [more details](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v3%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7))
+   [more details](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v3%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7)>)
 
 Use v3 from v2 environment:
 
@@ -275,7 +280,6 @@ Use v3 from v2 environment:
 
    additional: for Audio Super Resolution model, you can read [how to download](./tools/AP_BWE_main/24kto48k/readme.txt)
 
-
 ## Todo List
 
 - [x] **High Priority:**
@@ -297,15 +301,20 @@ Use v3 from v2 environment:
   - [ ] model mix
 
 ## (Additional) Method for running from the command line
+
 Use the command line to open the WebUI for UVR5
+
 ```
 python tools/uvr5/webui.py "<infer_device>" <is_half> <webui_port_uvr5>
 ```
+
 <!-- If you can't open a browser, follow the format below for UVR processing,This is using mdxnet for audio processing
 ```
 python mdxnet.py --model --input_root --output_vocal --output_ins --agg_level --format --device --is_half_precision
 ``` -->
+
 This is how the audio segmentation of the dataset is done using the command line
+
 ```
 python audio_slicer.py \
     --input_path "<path_to_original_audio_file_or_directory>" \
@@ -315,16 +324,21 @@ python audio_slicer.py \
     --min_interval <shortest_time_gap_between_adjacent_subclips>
     --hop_size <step_size_for_computing_volume_curve>
 ```
+
 This is how dataset ASR processing is done using the command line(Only Chinese)
+
 ```
 python tools/asr/funasr_asr.py -i <input> -o <output>
 ```
+
 ASR processing is performed through Faster_Whisper(ASR marking except Chinese)
 
 (No progress bars, GPU performance may cause time delays)
+
 ```
 python ./tools/asr/fasterwhisper_asr.py -i <input> -o <output> -l <language> -p <precision>
 ```
+
 A custom list save path is enabled
 
 ## Credits
@@ -332,6 +346,7 @@ A custom list save path is enabled
 Special thanks to the following projects and contributors:
 
 ### Theoretical Research
+
 - [ar-vits](https://github.com/innnky/ar-vits)
 - [SoundStorm](https://github.com/yangdongchao/SoundStorm/tree/master/soundstorm/s1/AR)
 - [vits](https://github.com/jaywalnut310/vits)
@@ -341,17 +356,23 @@ Special thanks to the following projects and contributors:
 - [fish-speech](https://github.com/fishaudio/fish-speech/blob/main/tools/llama/generate.py#L41)
 - [f5-TTS](https://github.com/SWivid/F5-TTS/blob/main/src/f5_tts/model/backbones/dit.py)
 - [shortcut flow matching](https://github.com/kvfrans/shortcut-models/blob/main/targets_shortcut.py)
+
 ### Pretrained Models
+
 - [Chinese Speech Pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain)
 - [Chinese-Roberta-WWM-Ext-Large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)
 - [BigVGAN](https://github.com/NVIDIA/BigVGAN)
+
 ### Text Frontend for Inference
+
 - [paddlespeech zh_normalization](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization)
 - [split-lang](https://github.com/DoodleBears/split-lang)
 - [g2pW](https://github.com/GitYCC/g2pW)
 - [pypinyin-g2pW](https://github.com/mozillazg/pypinyin-g2pW)
 - [paddlespeech g2pw](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/g2pw)
+
 ### WebUI Tools
+
 - [ultimatevocalremovergui](https://github.com/Anjok07/ultimatevocalremovergui)
 - [audio-slicer](https://github.com/openvpi/audio-slicer)
 - [SubFix](https://github.com/cronrpc/SubFix)

Ref_Audio_Selector/common/common.py

@@ -0,0 +1,156 @@
+from tools import my_utils
+from config import python_exec, is_half
+import subprocess
+import sys
+import os
+
+
+class RefAudioListManager:
+    def __init__(self, root_dir):
+        self.audio_dict = {'default': []}
+        absolute_root = os.path.abspath(root_dir)
+
+        for subdir, dirs, files in os.walk(absolute_root):
+            relative_path = os.path.relpath(subdir, absolute_root)
+
+            if relative_path == '.':
+                category = 'default'
+            else:
+                category = relative_path.replace(os.sep, '')
+
+            for file in files:
+                if file.endswith('.wav'):
+                    # 将相对路径转换为绝对路径
+                    audio_abs_path = os.path.join(subdir, file)
+                    if category not in self.audio_dict:
+                        self.audio_dict[category] = []
+                    self.audio_dict[category].append(audio_abs_path)
+
+    def get_audio_list(self):
+        return self.audio_dict
+
+    def get_flattened_audio_list(self):
+        all_audio_files = []
+        for category_audios in self.audio_dict.values():
+            all_audio_files.extend(category_audios)
+        return all_audio_files
+
+    def get_ref_audio_list(self):
+        audio_info_list = []
+        for category, audio_paths in self.audio_dict.items():
+            for audio_path in audio_paths:
+                filename_without_extension = os.path.splitext(os.path.basename(audio_path))[0]
+                audio_info = {
+                    'emotion': f"{category}-{filename_without_extension}",
+                    'ref_path': audio_path,
+                    'ref_text': filename_without_extension,
+                }
+                audio_info_list.append(audio_info)
+        return audio_info_list
+
+
+def batch_clean_paths(paths):
+    """
+    批量处理路径列表，对每个路径调用 clean_path() 函数。
+
+    参数:
+        paths (list[str]): 包含待处理路径的列表。
+
+    返回:
+        list[str]: 经过 clean_path() 处理后的路径列表。
+    """
+    cleaned_paths = []
+    for path in paths:
+        cleaned_paths.append(my_utils.clean_path(path))
+    return cleaned_paths
+
+
+def read_text_file_to_list(file_path):
+    # 按照UTF-8编码打开文件（确保能够正确读取中文）
+    with open(file_path, mode='r', encoding='utf-8') as file:
+        # 读取所有行并存储到一个列表中
+        lines = file.read().splitlines()
+    return lines
+
+
+def get_filename_without_extension(file_path):
+    """
+    Given a file path string, returns the file name without its extension.
+
+    Parameters:
+    file_path (str): The full path to the file.
+
+    Returns:
+    str: The file name without its extension.
+    """
+    base_name = os.path.basename(file_path)  # Get the base name (file name with extension)
+    file_name, file_extension = os.path.splitext(base_name)  # Split the base name into file name and extension
+    return file_name  # Return the file name without extension
+
+
+def read_file(file_path):
+    # 使用with语句打开并读取文件
+    with open(file_path, 'r', encoding='utf-8') as file:  # 'r' 表示以读取模式打开文件
+        # 一次性读取文件所有内容
+        file_content = file.read()
+
+    # 文件在with语句结束时会自动关闭
+    # 现在file_content变量中存储了文件的所有文本内容
+    return file_content
+
+
+def write_text_to_file(text, output_file_path):
+    try:
+        with open(output_file_path, 'w', encoding='utf-8') as file:
+            file.write(text)
+    except IOError as e:
+        print(f"Error occurred while writing to the file: {e}")
+    else:
+        print(f"Text successfully written to file: {output_file_path}")
+
+
+def check_path_existence_and_return(path):
+    """
+    检查给定路径（文件或目录）是否存在。如果存在，返回该路径；否则，返回空字符串。
+    :param path: 待检查的文件或目录路径（字符串）
+    :return: 如果路径存在，返回原路径；否则，返回空字符串
+    """
+    if os.path.exists(path):
+        return path
+    else:
+        return ""
+
+
+def open_file(filepath):
+    if sys.platform.startswith('darwin'):
+        subprocess.run(['open', filepath])  # macOS
+    elif os.name == 'nt':  # For Windows
+        os.startfile(filepath)
+    elif os.name == 'posix':  # For Linux, Unix, etc.
+        subprocess.run(['xdg-open', filepath])
+
+
+def start_new_service(script_path):
+    # 对于Windows系统
+    if sys.platform.startswith('win'):
+        cmd = f'start cmd /k {python_exec} {script_path}'
+    # 对于Mac或者Linux系统
+    else:
+        cmd = f'xterm -e {python_exec} {script_path}'
+
+    proc = subprocess.Popen(cmd, shell=True)
+
+    # 关闭之前启动的子进程
+    # proc.terminate()
+
+    # 或者如果需要强制关闭可以使用
+    # proc.kill()
+
+    return proc
+
+
+if __name__ == '__main__':
+    dir = r'C:\Users\Administrator\Desktop/test'
+    dir2 = r'"C:\Users\Administrator\Desktop\test2"'
+    dir, dir2 = batch_clean_paths([dir, dir2])
+    print(dir, dir2)

Ref_Audio_Selector/common/model_manager.py

@@ -0,0 +1,46 @@
+import os
+import re
+
+pretrained_sovits_name = "GPT_SoVITS/pretrained_models/s2G488k.pth"
+pretrained_gpt_name = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
+SoVITS_weight_root = "SoVITS_weights"
+GPT_weight_root = "GPT_weights"
+os.makedirs(SoVITS_weight_root, exist_ok=True)
+os.makedirs(GPT_weight_root, exist_ok=True)
+
+speaker_verification_models = {
+    'speech_campplus_sv_zh-cn_16k-common': {
+        'task': 'speaker-verification',
+        'model': 'Ref_Audio_Selector/tool/speaker_verification/models/speech_campplus_sv_zh-cn_16k-common',
+        'model_revision': 'v1.0.0'
+    },
+    'speech_eres2net_sv_zh-cn_16k-common': {
+        'task': 'speaker-verification',
+        'model': 'Ref_Audio_Selector/tool/speaker_verification/models/speech_eres2net_sv_zh-cn_16k-common',
+        'model_revision': 'v1.0.5'
+    }
+}
+
+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 get_gpt_model_names():
+    gpt_names = [pretrained_gpt_name]
+    for name in os.listdir(GPT_weight_root):
+        if name.endswith(".ckpt"): gpt_names.append("%s/%s" % (GPT_weight_root, name))
+    sorted(gpt_names, key=custom_sort_key)
+    return gpt_names
+
+
+def get_sovits_model_names():
+    sovits_names = [pretrained_sovits_name]
+    for name in os.listdir(SoVITS_weight_root):
+        if name.endswith(".pth"): sovits_names.append("%s/%s" % (SoVITS_weight_root, name))
+    sorted(sovits_names, key=custom_sort_key)
+    return sovits_names
+

Ref_Audio_Selector/common/time_util.py

@@ -0,0 +1,72 @@
+import time
+import os
+from Ref_Audio_Selector.config_param.log_config import p_logger
+import Ref_Audio_Selector.config_param.config_params as params
+
+
+def timeit_decorator(func):
+    """
+    装饰器，用于计算被装饰函数的执行时间。
+
+    参数:
+        func (function): 要计时的函数。
+
+    返回:
+        function: 包含计时功能的新函数。
+    """
+
+    def wrapper(*args, **kwargs):
+        if params.time_log_print_type != 'file':
+            return func(*args, **kwargs)
+
+        start_time = time.perf_counter()  # 使用 perf_counter 获取高精度计时起点
+
+        func_result = func(*args, **kwargs)  # 执行原函数
+
+        end_time = time.perf_counter()  # 获取计时终点
+        elapsed_time = end_time - start_time  # 计算执行耗时
+
+        # 记录日志内容
+        log_message = f"进程ID: {os.getpid()}, {func.__name__} 执行耗时: {elapsed_time:.6f} 秒"
+        p_logger.info(log_message)
+
+        return func_result
+
+    return wrapper
+
+
+def time_monitor(func):
+    """
+        返回结果，追加时间
+    """
+
+    def wrapper(*args, **kwargs):
+
+        start_time = time.perf_counter()  # 使用 perf_counter 获取高精度计时起点
+
+        func_result = func(*args, **kwargs)  # 执行原函数
+
+        end_time = time.perf_counter()  # 获取计时终点
+        elapsed_time = end_time - start_time  # 计算执行耗时
+
+        return elapsed_time, func_result
+
+    return wrapper
+
+
+# 使用装饰器
+@timeit_decorator
+def example_function(n):
+    time.sleep(n)  # 假设这是需要计时的函数，这里模拟耗时操作
+    return n * 2
+
+
+def example_function2(n):
+    time.sleep(n)  # 假设这是需要计时的函数，这里模拟耗时操作
+    return n * 2
+
+
+if __name__ == "__main__":
+    # 调用经过装饰的函数
+    # result = example_function(2)
+    print(time_monitor(example_function2)(2))

Ref_Audio_Selector/config.ini

@@ -0,0 +1,57 @@
+# config.ini
+
+[Base]
+# 服务端口号
+server_port = 9423
+# 参考音频目录
+reference_audio_dir = refer_audio
+# 临时文件目录
+temp_dir = Ref_Audio_Selector/temp
+
+[Log]
+# 日志保存目录路径
+log_dir = Ref_Audio_Selector/log/general
+# 日志级别 CRITICAL、FATAL、ERROR、WARNING、WARN、INFO、DEBUG、NOTSET、
+log_level = INFO
+# 函数时间消耗日志打印类型 file 打印到文件; close 关闭
+time_log_print_type = file
+# 函数时间消耗日志保存目录路径
+time_log_print_dir = Ref_Audio_Selector/log/performance
+
+[AudioSample]
+# list转换待选参考音频目录
+list_to_convert_reference_audio_dir = refer_audio_all
+# 音频相似度目录
+audio_similarity_dir = similarity
+# 是否开启基准音频预采样 true false
+enable_pre_sample = true
+
+[Inference]
+# 默认测试文本位置
+default_test_text_path = Ref_Audio_Selector/file/test_content/test_content.txt
+# 推理音频目录
+inference_audio_dir = inference_audio
+# 推理音频文本聚合目录
+inference_audio_text_aggregation_dir = text
+# 推理音频情绪聚合目录
+inference_audio_emotion_aggregation_dir = emotion
+
+[ResultCheck]
+# asr输出文件
+asr_filename = asr
+# 文本相似度输出目录
+text_similarity_output_dir = text_similarity
+# 文本情绪平均相似度报告文件名
+text_emotion_average_similarity_report_filename = average_similarity
+# 文本相似度按情绪聚合明细文件名
+text_similarity_by_emotion_detail_filename = emotion_group_detail
+# 文本相似度按文本聚合明细文件名
+text_similarity_by_text_detail_filename = text_group_detail
+
+[AudioConfig]
+# 默认模板文件位置
+default_template_path = Ref_Audio_Selector/file/config_template/ref_audio_template.txt
+# 参考音频配置文件名
+reference_audio_config_filename = refer_audio
+
+[Other]

Ref_Audio_Selector/config_param/config_manager.py

@@ -0,0 +1,111 @@
+import configparser
+import os
+import Ref_Audio_Selector.common.common as common
+
+
+class ParamReadWriteManager:
+    def __init__(self):
+        self.base_dir = 'Ref_Audio_Selector/file/base_info'
+        os.makedirs(self.base_dir, exist_ok=True)
+        # 基础信息
+        self.work_dir = 'work_dir'
+        self.role = 'role'
+        # 第一步
+        self.subsection_num = 'subsection_num'
+        self.sample_num = 'sample_num'
+        # 第二步
+        self.api_set_model_base_url = 'api_set_model_base_url'
+        self.api_gpt_param = 'api_gpt_param'
+        self.api_sovits_param = 'api_sovits_param'
+
+        self.api_v2_set_gpt_model_base_url = 'api_v2_set_gpt_model_base_url'
+        self.api_v2_gpt_model_param = 'api_v2_gpt_model_param'
+        self.api_v2_set_sovits_model_base_url = 'api_v2_set_sovits_model_base_url'
+        self.api_v2_sovits_model_param = 'api_v2_sovits_model_param'
+
+        self.text_url = 'text_url'
+        self.text_param = 'text_param'
+        self.refer_type_param = 'refer_type_param'
+        self.ref_path_param = 'ref_path_param'
+        self.ref_text_param = 'ref_text_param'
+        self.emotion_param = 'emotion_param'
+
+        self.test_content_path = 'test_content_path'
+        self.request_concurrency_num = 'request_concurrency_num'
+
+        # 第三步
+        self.text_similarity_amplification_boundary = 'text_similarity_amplification_boundary'
+        # 第四步
+        # 第五步
+        self.text_template = 'text_template'
+
+    def read(self, key):
+        file_path = os.path.join(self.base_dir, key + '.txt')
+        if os.path.exists(file_path):
+            content = common.read_file(file_path)
+            return content.strip()
+        else:
+            return ''
+
+    def write(self, key, content):
+        file_path = os.path.join(self.base_dir, key + '.txt')
+
+        # 确保内容是字符串类型，如果不是，转换为字符串
+        if not isinstance(content, str):
+            clean_content = str(content).strip()  # 转换为字符串并移除首尾空白
+        else:
+            clean_content = content.strip()
+
+        common.write_text_to_file(clean_content, file_path)
+
+
+class ConfigManager:
+    def __init__(self):
+        self.config_path = 'Ref_Audio_Selector/config.ini'
+        self.config = configparser.ConfigParser()
+        self.config.read(self.config_path, encoding='utf-8')
+
+    def get_base(self, key):
+        return self.config.get('Base', key)
+
+    def get_log(self, key):
+        return self.config.get('Log', key)
+
+    def get_audio_sample(self, key):
+        return self.config.get('AudioSample', key)
+
+    def get_inference(self, key):
+        return self.config.get('Inference', key)
+
+    def get_result_check(self, key):
+        return self.config.get('ResultCheck', key)
+
+    def get_audio_config(self, key):
+        return self.config.get('AudioConfig', key)
+
+    def get_other(self, key):
+        return self.config.get('Other', key)
+
+    def print(self):
+        # 打印所有配置
+        for section in self.config.sections():
+            print('[{}]'.format(section))
+            for key in self.config[section]:
+                print('{} = {}'.format(key, self.config[section][key]))
+            print()
+
+
+_config = ConfigManager()
+_param_read_write_manager = ParamReadWriteManager()
+
+
+def get_config():
+    return _config
+
+
+def get_rw_param():
+    return _param_read_write_manager
+
+
+if __name__ == '__main__':
+    print(_config.print())

Ref_Audio_Selector/config_param/config_params.py

@@ -0,0 +1,58 @@
+import Ref_Audio_Selector.config_param.config_manager as config_manager
+
+config = config_manager.get_config()
+
+# [Base]
+# 服务端口号
+server_port = int(config.get_base('server_port'))
+# 参考音频目录
+reference_audio_dir = config.get_base('reference_audio_dir')
+# 临时文件目录
+temp_dir = config.get_base('temp_dir')
+
+# [Log]
+# 日志保存目录路径
+log_dir = config.get_log('log_dir')
+# 日志级别 CRITICAL、FATAL、ERROR、WARNING、WARN、INFO、DEBUG、NOTSET、
+log_level = config.get_log('log_level')
+# 函数时间消耗日志打印类型 file 打印到文件; close 关闭
+time_log_print_type = config.get_log('time_log_print_type')
+# 函数时间消耗日志保存目录路径
+time_log_print_dir = config.get_log('time_log_print_dir')
+
+# [AudioSample]
+# list转换待选参考音频目录
+list_to_convert_reference_audio_dir = config.get_audio_sample('list_to_convert_reference_audio_dir')
+# 音频相似度目录
+audio_similarity_dir = config.get_audio_sample('audio_similarity_dir')
+# 是否开启基准音频预采样 true false
+enable_pre_sample = config.get_audio_sample('enable_pre_sample')
+
+# [Inference]
+# 默认测试文本位置
+default_test_text_path = config.get_inference('default_test_text_path')
+# 推理音频目录
+inference_audio_dir = config.get_inference('inference_audio_dir')
+# 推理音频文本聚合目录
+inference_audio_text_aggregation_dir = config.get_inference('inference_audio_text_aggregation_dir')
+# 推理音频情绪聚合目录
+inference_audio_emotion_aggregation_dir = config.get_inference('inference_audio_emotion_aggregation_dir')
+
+# [ResultCheck]
+# asr输出文件
+asr_filename = config.get_result_check('asr_filename')
+# 文本相似度输出目录
+text_similarity_output_dir = config.get_result_check('text_similarity_output_dir')
+# 文本情绪平均相似度报告文件名
+text_emotion_average_similarity_report_filename = config.get_result_check('text_emotion_average_similarity_report_filename')
+# 文本相似度按情绪聚合明细文件名
+text_similarity_by_emotion_detail_filename = config.get_result_check('text_similarity_by_emotion_detail_filename')
+# 文本相似度按文本聚合明细文件名
+text_similarity_by_text_detail_filename = config.get_result_check('text_similarity_by_text_detail_filename')
+
+# [AudioConfig]
+# 默认模板文件位置
+default_template_path = config.get_audio_config('default_template_path')
+# 参考音频配置文件名
+reference_audio_config_filename = config.get_audio_config('reference_audio_config_filename')
+

Ref_Audio_Selector/config_param/log_config.py

@@ -0,0 +1,65 @@
+import logging
+import os
+import datetime
+import Ref_Audio_Selector.config_param.config_params as params
+
+
+def create_general_logger():
+    # 获取当前日期，用于文件名和日志内容
+    current_date = datetime.datetime.now().strftime('%Y-%m-%d')
+
+    # 创建一个用于控制台输出的处理器，并设置日志级别
+    console_handler = logging.StreamHandler()
+    # console_handler.setLevel(logging.INFO)
+    # 可以设置控制台输出的格式
+    console_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+    console_handler.setFormatter(console_formatter)
+    console_handler.encoding = 'utf-8'  # 设置字符编码为utf-8
+
+    os.makedirs(params.log_dir, exist_ok=True)
+
+    # 创建一个用于常规日志的处理器
+    general_handler = logging.FileHandler(f"{params.log_dir}/{current_date}.log", mode='a', encoding='utf-8')
+    # general_handler.setLevel(logging.INFO)
+    general_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+    general_handler.setFormatter(general_formatter)
+
+    # 配置一个常规的logger
+    general_logger = logging.getLogger('general')
+    level = logging.getLevelName(params.log_level)
+    general_logger.setLevel(level)
+    general_logger.addHandler(console_handler)
+    general_logger.addHandler(general_handler)
+
+    # 配置根logger，以防万一
+    logging.basicConfig(level=logging.WARNING, handlers=[general_handler])
+
+    return general_logger
+
+
+def create_performance_logger():
+    # 获取当前日期，用于文件名和日志内容
+    current_date = datetime.datetime.now().strftime('%Y-%m-%d')
+
+    os.makedirs(params.time_log_print_dir, exist_ok=True)
+
+    # 创建一个专用于性能监控日志的处理器
+    performance_handler = logging.FileHandler(
+        f"{params.time_log_print_dir}/{current_date}.log", mode='a', encoding='utf-8')
+    # performance_handler.setLevel(logging.INFO)
+    performance_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+    performance_handler.setFormatter(performance_formatter)
+
+    # 配置一个专门用于性能监控的logger
+    performance_logger = logging.getLogger('performance')
+    performance_logger.setLevel(logging.INFO)
+    performance_logger.addHandler(performance_handler)
+
+    return performance_logger
+
+
+def setup_logging():
+    return create_general_logger(), create_performance_logger()
+
+
+logger, p_logger = setup_logging()

Ref_Audio_Selector/file/config_template/ref_audio_template.txt

@@ -0,0 +1,5 @@
+"${emotion}": {
+    "ref_wav_path": "${ref_path}",
+    "prompt_text": "${ref_text}",
+    "prompt_language": "中文"
+}

Ref_Audio_Selector/file/test_content/test_content.txt

@@ -0,0 +1,4 @@
+也是只有一次。”白蓉简单地回答，然后迅速转移话锋，搂住罗辑的脖子说，“算了，我不要那生日礼物了，你也回到正常的生活中来，好吗？”
+云天明看到那是一条丑陋的虫子，软乎乎湿漉漉的，在她白皙的手指间蠕动着，旁边一个女生尖叫道：恶心死了，你碰它干吗？！程心把虫子轻轻放到旁边的草丛中，说，它在这里会给踩死的。
+“那么多的星星，像雾似的。”云天明感叹道。程心把目光从银河收回，转头看着他，指着下面的校园和城市说：“你看下面也很漂亮啊，我们的生活是在这儿，可不是在那么远的银河里。”
+“可我们的专业，不就是为了到地球之外去吗？”“那是为了这里的生活更好，可不是为了逃离地球啊。”云天明当然知道程心的话是委婉地指向他的孤僻和自闭，他也只有默然以对。

Ref_Audio_Selector/start_ref_audio_selector_webui.bat

@@ -0,0 +1,5 @@
+CHCP 65001
+@echo off 
+cd ../
+runtime\python.exe ./Ref_Audio_Selector/ref_audio_selector_webui.py
+pause

Ref_Audio_Selector/tool/asr/fasterwhisper_asr_multi_level_dir.py

@@ -0,0 +1,120 @@
+import argparse
+import os
+import traceback
+import Ref_Audio_Selector.config_param.config_params as params
+
+os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+import torch
+from faster_whisper import WhisperModel
+from tqdm import tqdm
+
+from tools.asr.config import check_fw_local_models
+from Ref_Audio_Selector.config_param.log_config import logger
+
+language_code_list = [
+    "af", "am", "ar", "as", "az",
+    "ba", "be", "bg", "bn", "bo",
+    "br", "bs", "ca", "cs", "cy",
+    "da", "de", "el", "en", "es",
+    "et", "eu", "fa", "fi", "fo",
+    "fr", "gl", "gu", "ha", "haw",
+    "he", "hi", "hr", "ht", "hu",
+    "hy", "id", "is", "it", "ja",
+    "jw", "ka", "kk", "km", "kn",
+    "ko", "la", "lb", "ln", "lo",
+    "lt", "lv", "mg", "mi", "mk",
+    "ml", "mn", "mr", "ms", "mt",
+    "my", "ne", "nl", "nn", "no",
+    "oc", "pa", "pl", "ps", "pt",
+    "ro", "ru", "sa", "sd", "si",
+    "sk", "sl", "sn", "so", "sq",
+    "sr", "su", "sv", "sw", "ta",
+    "te", "tg", "th", "tk", "tl",
+    "tr", "tt", "uk", "ur", "uz",
+    "vi", "yi", "yo", "zh", "yue",
+    "auto"]
+
+
+def execute_asr_multi_level_dir(input_folder, output_folder, model_size, language, precision):
+    if '-local' in model_size:
+        model_size = model_size[:-6]
+        model_path = f'tools/asr/models/faster-whisper-{model_size}'
+    else:
+        model_path = model_size
+    if language == 'auto':
+        language = None  # 不设置语种由模型自动输出概率最高的语种
+    logger.info("loading faster whisper model:", model_size, model_path)
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    try:
+        model = WhisperModel(model_path, device=device, compute_type=precision)
+    except:
+        return logger.error(traceback.format_exc())
+
+    output = []
+
+    # 递归遍历输入目录及所有子目录
+    for root, dirs, files in os.walk(input_folder):
+        for file_name in sorted(files):
+            # 只处理wav文件（假设是wav文件）
+            if file_name.endswith(".wav"):
+                try:
+                    file_path = os.path.join(root, file_name)
+                    original_text = os.path.basename(root)
+                    segments, info = model.transcribe(
+                        audio=file_path,
+                        beam_size=5,
+                        vad_filter=True,
+                        vad_parameters=dict(min_silence_duration_ms=700),
+                        language=language)
+                    text = ''
+
+                    if info.language == "zh":
+                        logger.info("检测为中文文本, 转 FunASR 处理")
+                        if ("only_asr" not in globals()):
+                            from Ref_Audio_Selector.tool.asr.funasr_asr_multi_level_dir import \
+                                only_asr  # #如果用英文就不需要导入下载模型
+                        text = only_asr(file_path)
+
+                    if text == '':
+                        for segment in segments:
+                            text += segment.text
+                    output.append(f"{file_path}|{original_text}|{info.language.upper()}|{text}")
+                    print(f"{file_path}|{original_text}|{info.language.upper()}|{text}")
+                except:
+                    return logger.error(traceback.format_exc())
+
+    output_folder = output_folder
+    os.makedirs(output_folder, exist_ok=True)
+    output_file_path = os.path.abspath(f'{output_folder}/{params.asr_filename}.list')
+
+    with open(output_file_path, "w", encoding="utf-8") as f:
+        f.write("\n".join(output))
+        logger.info(f"ASR 任务完成->标注文件路径: {output_file_path}\n")
+    return output_file_path
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-i", "--input_folder", type=str, required=True,
+                        help="Path to the folder containing WAV files.")
+    parser.add_argument("-o", "--output_folder", type=str, required=True,
+                        help="Output folder to store transcriptions.")
+    parser.add_argument("-s", "--model_size", type=str, default='large-v3',
+                        choices=check_fw_local_models(),
+                        help="Model Size of Faster Whisper")
+    parser.add_argument("-l", "--language", type=str, default='ja',
+                        choices=language_code_list,
+                        help="Language of the audio files.")
+    parser.add_argument("-p", "--precision", type=str, default='float16', choices=['float16', 'float32'],
+                        help="fp16 or fp32")
+
+    cmd = parser.parse_args()
+    output_file_path = execute_asr_multi_level_dir(
+        input_folder=cmd.input_folder,
+        output_folder=cmd.output_folder,
+        model_size=cmd.model_size,
+        language=cmd.language,
+        precision=cmd.precision,
+    )

Ref_Audio_Selector/tool/asr/funasr_asr_multi_level_dir.py

@@ -0,0 +1,94 @@
+# -*- coding:utf-8 -*-
+
+import argparse
+import os
+import traceback
+import Ref_Audio_Selector.config_param.config_params as params
+from Ref_Audio_Selector.config_param.log_config import logger
+from Ref_Audio_Selector.common.time_util import timeit_decorator
+from tqdm import tqdm
+from funasr import AutoModel
+
+path_asr = 'tools/asr/models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'
+path_vad = 'tools/asr/models/speech_fsmn_vad_zh-cn-16k-common-pytorch'
+path_punc = 'tools/asr/models/punc_ct-transformer_zh-cn-common-vocab272727-pytorch'
+path_asr = path_asr if os.path.exists(
+    path_asr) else "iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
+path_vad = path_vad if os.path.exists(path_vad) else "iic/speech_fsmn_vad_zh-cn-16k-common-pytorch"
+path_punc = path_punc if os.path.exists(path_punc) else "iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
+
+model = AutoModel(
+    model=path_asr,
+    model_revision="v2.0.4",
+    vad_model=path_vad,
+    vad_model_revision="v2.0.4",
+    punc_model=path_punc,
+    punc_model_revision="v2.0.4",
+)
+
+
+def only_asr(input_file):
+    try:
+        text = model.generate(input=input_file)[0]["text"]
+    except:
+        text = ''
+        logger.error(traceback.format_exc())
+    return text
+
+
+@timeit_decorator
+def execute_asr_multi_level_dir(input_folder, output_folder, model_size, language):
+    output = []
+    # 递归遍历输入目录及所有子目录
+    for root, dirs, files in os.walk(input_folder):
+        for name in sorted(files):
+            # 只处理wav文件（假设是wav文件）
+            if name.endswith(".wav"):
+                try:
+                    original_text = os.path.basename(root)
+                    # 构造完整的输入音频文件路径
+                    input_file_path = os.path.join(root, name)
+                    input_file_path = os.path.normpath(input_file_path)  # 先标准化可能存在混合斜杠的情况
+                    asr_text = model.generate(input=input_file_path)[0]["text"]
+
+                    output.append(f"{input_file_path}|{original_text}|{language.upper()}|{asr_text}")
+
+                except:
+                    logger.error(traceback.format_exc())
+
+    # 创建或打开指定的输出目录
+    output_folder = output_folder
+    output_dir_abs = os.path.abspath(output_folder)
+    os.makedirs(output_dir_abs, exist_ok=True)
+
+    # 构造输出文件路径
+    output_file_path = os.path.join(output_dir_abs, f'{params.asr_filename}.list')
+
+    # 将输出写入文件
+    with open(output_file_path, "w", encoding="utf-8") as f:
+        f.write("\n".join(output))
+        logger.info(f"ASR 任务完成->标注文件路径: {output_file_path}\n")
+
+    return output_file_path
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-i", "--input_folder", type=str, required=True,
+                        help="Path to the folder containing WAV files.")
+    parser.add_argument("-o", "--output_folder", type=str, required=True,
+                        help="Output folder to store transcriptions.")
+    parser.add_argument("-s", "--model_size", type=str, default='large',
+                        help="Model Size of FunASR is Large")
+    parser.add_argument("-l", "--language", type=str, default='zh', choices=['zh'],
+                        help="Language of the audio files.")
+    parser.add_argument("-p", "--precision", type=str, default='float16', choices=['float16', 'float32'],
+                        help="fp16 or fp32")  # 还没接入
+
+    cmd = parser.parse_args()
+    execute_asr_multi_level_dir(
+        input_folder=cmd.input_folder,
+        output_folder=cmd.output_folder,
+        model_size=cmd.model_size,
+        language=cmd.language,
+    )

Ref_Audio_Selector/tool/audio_check.py

@@ -0,0 +1,54 @@
+import os
+import shutil
+import Ref_Audio_Selector.common.common as common
+import Ref_Audio_Selector.config_param.config_params as params
+from Ref_Audio_Selector.config_param.log_config import logger
+
+
+def remove_matching_audio_files_in_text_dir(text_dir, emotions_list):
+    count = 0
+    emotions = [item['emotion'] for item in emotions_list]
+    for root, dirs, files in os.walk(text_dir):
+        for file in files:
+            if file.endswith(".wav"):
+                emotion_tag = os.path.basename(file)[:-4]
+                if emotion_tag not in emotions:
+                    file_path = os.path.join(root, file)
+                    logger.info(f"Deleting file: {file_path}")
+                    try:
+                        os.remove(file_path)
+                        count += 1
+                    except Exception as e:
+                        logger.error(f"Error deleting file {file_path}: {e}")
+
+    return count
+
+
+def delete_emotion_subdirectories(emotion_dir, emotions_list):
+    count = 0
+
+    emotions = [item['emotion'] for item in emotions_list]
+
+    for entry in os.listdir(emotion_dir):
+        entry_path = os.path.join(emotion_dir, entry)
+        if os.path.isdir(entry_path):
+            if entry not in emotions:
+                logger.info(f"Deleting directory: {entry_path}")
+                try:
+                    # 使用shutil.rmtree删除整个子目录及其内容
+                    shutil.rmtree(entry_path)
+                    count += 1
+                except Exception as e:
+                    logger.error(f"Error deleting directory {entry_path}: {e}")
+
+    return count
+
+
+def sync_ref_audio(ref_audio_dir, inference_audio_dir):
+    ref_audio_manager = common.RefAudioListManager(ref_audio_dir)
+    ref_list = ref_audio_manager.get_ref_audio_list()
+    text_dir = os.path.join(inference_audio_dir, params.inference_audio_text_aggregation_dir)
+    emotion_dir = os.path.join(inference_audio_dir, params.inference_audio_emotion_aggregation_dir)
+    delete_text_wav_num = remove_matching_audio_files_in_text_dir(text_dir, ref_list)
+    delete_emotion_dir_num = delete_emotion_subdirectories(emotion_dir, ref_list)
+    return delete_text_wav_num, delete_emotion_dir_num

Ref_Audio_Selector/tool/audio_config.py

@@ -0,0 +1,31 @@
+import os
+import platform
+
+
+def generate_audio_config(work_space_dir, template_str, audio_list, output_file_path):
+    # 定义一个空字符串来存储最终要写入文件的内容
+    file_content = ""
+
+    # 遍历参考音频列表
+    for audio_info in audio_list:
+        emotion = audio_info['emotion']
+        ref_path = audio_info['ref_path']
+        ref_text = audio_info['ref_text']
+
+        relative_path = os.path.relpath(ref_path, work_space_dir)
+        if platform.system() == 'Windows':
+            relative_path = relative_path.replace('\\', '/')
+
+        # 使用字符串模板替换变量
+        formatted_line = template_str.replace('${emotion}', emotion).replace('${ref_path}', relative_path).replace(
+            '${ref_text}', ref_text)
+
+        # 将格式化后的行添加到内容中，使用逗号和换行符分隔
+        file_content += formatted_line + ",\n"
+
+    # 删除最后一个逗号和换行符，确保格式整洁
+    file_content = file_content[:-2]
+
+    # 将内容写入输出文件
+    with open(output_file_path, 'w', encoding='utf-8') as output_file:
+        output_file.write(file_content)

Ref_Audio_Selector/tool/audio_inference.py

@@ -0,0 +1,238 @@
+import time
+import os
+import requests
+import itertools
+import multiprocessing
+from concurrent.futures import ProcessPoolExecutor
+import numpy as np
+import Ref_Audio_Selector.config_param.config_params as params
+from urllib.parse import urlparse, parse_qs, urlencode, urlunparse, quote
+from Ref_Audio_Selector.config_param.log_config import logger, p_logger
+
+
+class SetModelURLComposer:
+    def __init__(self, type, base_url, gpt_param_name, sovits_param_name):
+        self.type = type
+        self.base_url = base_url
+        self.gpt_param_name = gpt_param_name
+        self.sovits_param_name = sovits_param_name
+
+    def is_valid(self):
+        if self.base_url is None or self.base_url == '':
+            raise Exception("请求地址不能为空")
+        if self.type in ['gpt', 'all']:
+            if self.gpt_param_name is None or self.gpt_param_name == '':
+                raise Exception("GPT参数名不能为空")
+        if self.type in ['sovits', 'all']:
+            if self.sovits_param_name is None or self.sovits_param_name == '':
+                raise Exception("Sovits参数名不能为空")
+
+    def build_get_url(self, value_array, need_url_encode=True):
+        params = {}
+        if self.type == 'gpt':
+            params[self.gpt_param_name] = value_array[0]
+        if self.type == 'sovits':
+            params[self.sovits_param_name] = value_array[0]
+        if self.type == 'all':
+            params[self.gpt_param_name] = value_array[0]
+            params[self.sovits_param_name] = value_array[1]
+        return append_params_to_url(self.base_url, params, need_url_encode)
+
+    def build_post_url(self, value_array, need_url_encode=True):
+        url = append_params_to_url(self.base_url, {}, need_url_encode)
+        params = {}
+        if self.type == 'gpt':
+            params[self.gpt_param_name] = value_array[0]
+        if self.type == 'sovits':
+            params[self.sovits_param_name] = value_array[0]
+        if self.type == 'all':
+            params[self.gpt_param_name] = value_array[0]
+            params[self.sovits_param_name] = value_array[1]
+        return url, params
+
+
+class TTSURLComposer:
+    def __init__(self, base_url, refer_type_param, emotion_param_name, text_param_name, ref_path_param_name, ref_text_param_name):
+        self.base_url = base_url
+        # 角色情绪 or 参考音频
+        self.refer_type_param = refer_type_param 
+        self.emotion_param_name = emotion_param_name
+        self.text_param_name = text_param_name
+        self.ref_path_param_name = ref_path_param_name
+        self.ref_text_param_name = ref_text_param_name
+
+    def is_valid(self):
+        if self.base_url is None or self.base_url == '':
+            raise ValueError("请输入url")
+
+        if self.text_param_name is None or self.text_param_name == '':
+            raise ValueError("请输入text参数名")
+
+        if self.emotion_param_name is None and self.ref_path_param_name is None and self.ref_text_param_name is None:
+            raise ValueError("请输入至少一个参考or情绪的参数")
+
+    def is_emotion(self):
+        return self.refer_type_param == '角色情绪'
+
+    def build_url_with_emotion(self, text_value, emotion_value, need_url_encode=True):
+        params = {
+            self.text_param_name: text_value,
+            self.emotion_param_name: emotion_value,
+        }
+        return append_params_to_url(self.base_url, params, need_url_encode)
+
+    def build_url_with_ref(self, text_value, ref_path_value, ref_text_value, need_url_encode=True):
+        params = {
+            self.text_param_name: text_value,
+            self.ref_path_param_name: ref_path_value,
+            self.ref_text_param_name: ref_text_value,
+        }
+        return append_params_to_url(self.base_url, params, need_url_encode)
+
+
+def append_params_to_url(url_with_params, params, need_url_encode):
+    if params:
+        query_params = '&'.join([f"{k}={v}" for k, v in params.items()])
+        url_with_params += '?' + query_params if '?' not in url_with_params else '&' + query_params
+    return url_with_params if not need_url_encode else safe_encode_query_params(url_with_params)
+
+
+def safe_encode_query_params(original_url):
+    # 分析URL以获取查询字符串部分
+    parsed_url = urlparse(original_url)
+    query_params = parse_qs(parsed_url.query)
+
+    # 将查询参数转换为编码过的字典（键值对会被转码）
+    encoded_params = {k: quote(v[0]) for k, v in query_params.items()}
+
+    # 重新编码查询字符串
+    new_query_string = urlencode(encoded_params, doseq=False)
+
+    # 重建完整的URL
+    new_parsed_url = parsed_url._replace(query=new_query_string)
+    encoded_url = urlunparse(new_parsed_url)
+
+    logger.info(encoded_url)
+    return encoded_url
+
+
+def generate_audio_files_parallel(url_composer, text_list, emotion_list, output_dir_path, num_processes=1):
+
+    # 将emotion_list均匀分成num_processes个子集
+    emotion_groups = np.array_split(emotion_list, num_processes)
+
+    with ProcessPoolExecutor(max_workers=num_processes) as executor:
+        futures = [
+            executor.submit(generate_audio_files_for_emotion_group, url_composer, text_list, group, output_dir_path)
+            for group in emotion_groups]
+        for future in futures:
+            future.result()  # 等待所有进程完成
+
+
+def generate_audio_files_for_emotion_group(url_composer, text_list, emotion_list, output_dir_path):
+    start_time = time.perf_counter()  # 使用 perf_counter 获取高精度计时起点
+    # Ensure the output directory exists
+    output_dir = os.path.abspath(output_dir_path)
+    os.makedirs(output_dir, exist_ok=True)
+
+    # Create subdirectories for text and emotion categories
+    text_subdir = os.path.join(output_dir, params.inference_audio_text_aggregation_dir)
+    os.makedirs(text_subdir, exist_ok=True)
+    emotion_subdir = os.path.join(output_dir, params.inference_audio_emotion_aggregation_dir)
+    os.makedirs(emotion_subdir, exist_ok=True)
+
+    all_count = len(text_list) * len(emotion_list)
+    has_generated_count = 0
+    all_text_count = sum(len(item) for item in text_list)
+
+    # 计算笛卡尔积
+    cartesian_product = list(itertools.product(text_list, emotion_list))
+
+    for text, emotion in cartesian_product:
+        # Generate audio byte stream using the create_audio function
+
+        emotion_name = emotion['emotion']
+
+        text_subdir_text = os.path.join(text_subdir, text)
+        os.makedirs(text_subdir_text, exist_ok=True)
+        text_subdir_text_file_path = os.path.join(text_subdir_text, emotion_name + '.wav')
+
+        emotion_subdir_emotion = os.path.join(emotion_subdir, emotion_name)
+        os.makedirs(emotion_subdir_emotion, exist_ok=True)
+        emotion_subdir_emotion_file_path = os.path.join(emotion_subdir_emotion, text + '.wav')
+
+        # 检查是否已经存在对应的音频文件，如果存在则跳过
+        if os.path.exists(text_subdir_text_file_path) and os.path.exists(emotion_subdir_emotion_file_path):
+            has_generated_count += 1
+            logger.info(f"进程ID: {os.getpid()}, 进度: {has_generated_count}/{all_count}")
+            continue
+
+        if url_composer.is_emotion():
+            real_url = url_composer.build_url_with_emotion(text, emotion['emotion'], False)
+        else:
+            real_url = url_composer.build_url_with_ref(text, emotion['ref_path'], emotion['ref_text'], False)
+
+        audio_bytes = inference_audio_from_api(real_url)
+
+        # Write audio bytes to the respective files
+        with open(text_subdir_text_file_path, 'wb') as f:
+            f.write(audio_bytes)
+        with open(emotion_subdir_emotion_file_path, 'wb') as f:
+            f.write(audio_bytes)
+
+        has_generated_count += 1
+        logger.info(f"进程ID: {os.getpid()}, 进度: {has_generated_count}/{all_count}")
+    end_time = time.perf_counter()  # 获取计时终点
+    elapsed_time = end_time - start_time  # 计算执行耗时
+    # 记录日志内容
+    log_message = f"进程ID: {os.getpid()}, generate_audio_files_for_emotion_group 执行耗时: {elapsed_time:.6f} 秒；推理数量: {has_generated_count}； 字符总数：{all_text_count}；每秒推理字符数：{all_text_count*len(emotion_list) / elapsed_time:.3f}；"
+    p_logger.info(log_message)
+    logger.info(log_message)
+
+
+def inference_audio_from_api(url):
+    logger.info(f'inference_audio_from_api url: {url}')
+    # 发起GET请求
+    response = requests.get(url, stream=True)
+
+    # 检查响应状态码是否正常（例如200表示成功）
+    if response.status_code == 200:
+        # 返回音频数据的字节流
+        return response.content
+    else:
+        raise Exception(f"Failed to fetch audio from API. Server responded with status code {response.status_code}.message: {response.json()}")
+
+
+def start_api_set_model(set_model_url_composer, gpt_models, sovits_models):
+    url, post_body = set_model_url_composer.build_post_url([gpt_models, sovits_models], True)
+    logger.info(f'set_model_url_composer url: {set_model_url_composer}')
+    logger.info(f'start_api_set_model url: {url}')
+    logger.info(f'start_api_set_model post_body: {post_body}')
+    response = requests.post(url, json=post_body)
+    if response.status_code == 200:
+        result = response.text
+        return result
+    else:
+        return f'请求失败，状态码：{response.status_code}'
+
+
+def start_api_v2_set_gpt_model(set_model_url_composer, gpt_models):
+    url = set_model_url_composer.build_get_url([gpt_models], False)
+    logger.info(f'start_api_v2_set_gpt_model url: {url}')
+    response = requests.get(url)
+    if response.status_code == 200:
+        result = response.text
+        return result
+    else:
+        return f'请求失败，状态码：{response.status_code}'
+
+
+def start_api_v2_set_sovits_model(set_model_url_composer, sovits_models):
+    url = set_model_url_composer.build_get_url([sovits_models], False)
+    logger.info(f'start_api_v2_set_sovits_model url: {url}')
+    response = requests.get(url)
+    if response.status_code == 200:
+        result = response.text
+        return result
+    else:
+        return f'请求失败，状态码：{response.status_code}'

Ref_Audio_Selector/tool/audio_sample.py

@@ -0,0 +1,162 @@
+import os
+import shutil
+import random
+import librosa
+from Ref_Audio_Selector.config_param.log_config import logger
+
+
+def check_audio_duration(path, min_duration=3, max_duration=10):
+    try:
+
+        # 直接计算音频文件的时长（单位：秒）
+        duration = librosa.get_duration(filename=path)
+
+        # 判断时长是否在3s至10s之间
+        if min_duration <= duration <= max_duration:
+            return True
+        else:
+            return False
+
+    except Exception as e:
+        logger.error(f"无法打开或处理音频文件：{e}")
+        return None
+
+
+def convert_from_list(list_file, output_dir):
+    # 创建输出目录，如果它不存在的话
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    # 解析.list文件，并操作文件
+    with open(list_file, 'r', encoding='utf-8') as file:
+        lines = file.readlines()
+
+    for line in lines:
+        parts = line.strip().split('|')
+        if len(parts) != 4:
+            logger.error(f"Line format incorrect: {line}")
+            continue
+
+        audio_path, _, _, transcription = parts
+
+        # 构建新的文件名和路径
+        new_filename = transcription.strip() + '.wav'
+        # new_filename = new_filename.replace(' ', '_')  # 移除空格
+        # new_filename = ''.join(e for e in new_filename if e.isalnum() or e in ['_', '.'])  # 移除非法字符
+        new_path = os.path.join(output_dir, new_filename)
+
+        # 如果目标文件已存在，不要覆盖
+        if os.path.exists(new_path):
+            logger.info(f"File already exists: {new_path}")
+            continue
+
+        try:
+            # 检查音频文件是否存在
+            if not os.path.exists(audio_path):
+                logger.info(f"Audio file does not exist: {audio_path}")
+                continue
+
+            if check_audio_duration(audio_path):
+                # 复制音频文件到output目录并重命名
+                shutil.copy2(audio_path, new_path)
+                logger.info(f"File copied and renamed to: {new_path}")
+            else:
+                logger.info(f"File skipped due to duration: {audio_path}")
+
+        except Exception as e:
+            logger.error(f"An error occurred while processing: {audio_path}")
+            logger.error(e)
+
+    logger.info("Processing complete.")
+
+
+def sample(output_audio_dir, similarity_list, subsection_num, sample_num):
+    # 按照相似度分值降序排序相似度列表
+    similarity_list.sort(key=lambda x: x['score'], reverse=True)
+
+    # 计算每段的起始索引
+    step = len(similarity_list) // subsection_num
+    if len(similarity_list) % subsection_num != 0:
+        step += 1
+
+    # 分段并随机采样
+    for i in range(subsection_num):
+        start = i * step
+        end = (i + 1) * step
+        end = min(end, len(similarity_list))  # 防止最后一段越界
+
+        # 创建子列表
+        subsection = similarity_list[start:end]
+        # 在子列表上随机打乱
+        random.shuffle(subsection)
+
+        # 从打乱后的子列表中抽取相应数量的个体
+        num = min(sample_num, len(subsection))
+        sampled_subsection = subsection[:num]
+
+        # 创建并进入子目录
+        subdir_name = f'emotion_{i + 1}'
+        subdir_path = os.path.join(output_audio_dir, subdir_name)
+        os.makedirs(subdir_path, exist_ok=True)
+
+        # 复制采样结果的音频到子目录
+        for item in sampled_subsection:
+            src_path = item['wav_path']
+            dst_path = os.path.join(subdir_path, os.path.basename(src_path))
+            shutil.copyfile(src_path, dst_path)
+
+    logger.info("Sampling completed.")
+
+
+def parse_similarity_file(file_path):
+    """
+    解析指定文本文件，将其中的内容以元组形式存入列表。
+
+    参数:
+        file_path (str): 待解析的文本文件路径。
+
+    返回:
+        list[tuple[float, str]]: 存储浮点数和路径的元组列表。
+    """
+    result_list = []
+
+    with open(file_path, 'r', encoding='utf-8') as file:
+        for line in file:
+            # 去除行尾换行符并按'|'分割
+            score, filepath = line.strip().split('|')
+
+            # 将浮点数字符串转换为浮点数类型
+            score = float(score)
+
+            # 将得分和路径作为元组添加到结果列表
+            result_list.append({
+                'score': score,
+                'wav_path': filepath
+            })
+
+    return result_list
+
+
+def copy_and_move(output_audio_directory, similarity_scores):
+    # 确保新目录存在
+    if not os.path.exists(output_audio_directory):
+        os.makedirs(output_audio_directory)
+
+    # 遍历并复制文件
+    for item in similarity_scores:
+        # 构造新的文件名
+        base_name = os.path.basename(item['wav_path'])[:-4]  # 去掉.wav扩展名
+        new_name = f"{item['score'] * 10000:04.0f}-{base_name}.wav"
+
+        # 新文件的完整路径
+        new_path = os.path.join(output_audio_directory, new_name)
+
+        # 复制文件到新目录
+        shutil.copyfile(item['wav_path'], new_path)
+
+    logger.info("已完成复制和重命名操作。")
+
+
+if __name__ == '__main__':
+    similarity_list = parse_similarity_file("D:/tt/similarity/啊，除了伊甸和樱，竟然还有其他人会提起我？.txt")
+    sample('D:/tt/similarity/output', similarity_list, 10, 4)

Ref_Audio_Selector/tool/speaker_verification/voice_similarity.py

@@ -0,0 +1,142 @@
+import argparse
+import os
+import torchaudio
+import torchaudio.transforms as T
+import platform
+import Ref_Audio_Selector.config_param.config_params as params
+import Ref_Audio_Selector.config_param.log_config as log_config
+from Ref_Audio_Selector.common.time_util import timeit_decorator
+from Ref_Audio_Selector.common.model_manager import speaker_verification_models as models
+
+from modelscope.pipelines import pipeline
+
+
+def init_model(model_type='speech_campplus_sv_zh-cn_16k-common'):
+    log_config.logger.info(f'人声识别模型类型：{model_type}')
+    return pipeline(
+        task=models[model_type]['task'],
+        model=models[model_type]['model'],
+        model_revision=models[model_type]['model_revision']
+    )
+
+
+@timeit_decorator
+def compare_audio_and_generate_report(reference_audio_path, comparison_dir_path, output_file_path, model_type):
+    sv_pipeline = init_model(model_type)
+
+    # Step 1: 获取比较音频目录下所有音频文件的路径
+    comparison_audio_paths = [os.path.join(comparison_dir_path, f) for f in os.listdir(comparison_dir_path) if
+                              f.endswith('.wav')]
+
+    if platform.system() == 'Windows':
+        # 因为这个模型是基于16k音频数据训练的，为了避免后续比较时，每次都对参考音频进行重采样，所以，提前进行了采样
+        # windows不支持torchaudio.sox_effects.apply_effects_tensor，所以改写了依赖文件中的重采样方法
+        # 改用torchaudio.transforms.Resample进行重采样，如果在非windows环境下，没有更改依赖包的采样方法的话，
+        # 使用这段代码进行预采样会出现因为采样方法不同，而导致的模型相似度计算不准确的问题
+        # 当然如果在windows下，使用了其他的采样方法，也会出现不准确的问题
+        if params.enable_pre_sample == 'true':
+            reference_audio_16k = ensure_16k_wav(reference_audio_path)
+        else:
+            reference_audio_16k = reference_audio_path
+    else:
+        reference_audio_16k = reference_audio_path
+
+    # Step 2: 用参考音频依次比较音频目录下的每个音频，获取相似度分数及对应路径
+    all_count = len(comparison_audio_paths)
+    has_processed_count = 0
+    similarity_scores = []
+    for audio_path in comparison_audio_paths:
+        score = sv_pipeline([reference_audio_16k, audio_path])['score']
+        similarity_scores.append({
+            'score': score,
+            'path': audio_path
+        })
+        has_processed_count += 1
+        log_config.logger.info(f'进度：{has_processed_count}/{all_count}')
+
+    # Step 3: 根据相似度分数降序排列
+    similarity_scores.sort(key=lambda x: x['score'], reverse=True)
+
+    # Step 4: 处理输出文件不存在的情况，创建新文件
+    if not os.path.exists(output_file_path):
+        open(output_file_path, 'w').close()  # Create an empty file
+
+    # Step 5: 将排序后的结果写入输出结果文件（支持中文）
+    formatted_scores = [f'{item["score"]}|{item["path"]}' for item in similarity_scores]
+    with open(output_file_path, 'w', encoding='utf-8') as f:
+        # 使用'\n'将每个字符串分开，使其写入不同行
+        content = '\n'.join(formatted_scores)
+        f.write(content)
+
+
+def ensure_16k_wav(audio_file_path, target_sample_rate=16000):
+    """
+    输入一个音频文件地址，判断其采样率并决定是否进行重采样，然后将结果保存到指定的输出文件。
+
+    参数:
+        audio_file_path (str): 音频文件路径。
+        output_file_path (str): 保存重采样后音频数据的目标文件路径。
+        target_sample_rate (int, optional): 目标采样率，默认为16000Hz。
+    """
+    # 读取音频文件并获取其采样率
+    waveform, sample_rate = torchaudio.load(audio_file_path)
+
+    # 判断是否需要重采样
+    if sample_rate == target_sample_rate:
+        return audio_file_path
+    else:
+
+        # 创建Resample实例
+        resampler = T.Resample(orig_freq=sample_rate, new_freq=target_sample_rate)
+
+        # 应用重采样
+        resampled_waveform = resampler(waveform)
+
+        # 创建临时文件夹
+        os.makedirs(params.temp_dir, exist_ok=True)
+
+        # 设置临时文件名
+        temp_file_path = os.path.join(params.temp_dir, os.path.basename(audio_file_path))
+
+        # 保存重采样后的音频到指定文件
+        torchaudio.save(temp_file_path, resampled_waveform, target_sample_rate)
+
+    return temp_file_path
+
+
+def parse_arguments():
+    parser = argparse.ArgumentParser(description="Audio processing script arguments")
+
+    # Reference audio path
+    parser.add_argument("-r", "--reference_audio", type=str, required=True,
+                        help="Path to the reference WAV file.")
+
+    # Comparison directory path
+    parser.add_argument("-c", "--comparison_dir", type=str, required=True,
+                        help="Path to the directory containing comparison WAV files.")
+
+    # Output file path
+    parser.add_argument("-o", "--output_file", type=str, required=True,
+                        help="Path to the output file where results will be written.")
+
+    # Model Type
+    parser.add_argument("-m", "--model_type", type=str, required=True,
+                        help="Path to the model type.")
+
+    return parser.parse_args()
+
+
+if __name__ == '__main__':
+    cmd = parse_arguments()
+    compare_audio_and_generate_report(
+        reference_audio_path=cmd.reference_audio,
+        comparison_dir_path=cmd.comparison_dir,
+        output_file_path=cmd.output_file,
+        model_type=cmd.model_type,
+    )
+
+    # compare_audio_and_generate_report(
+    #     reference_audio_path="D:/tt/渡鸦/refer_audio_all/也对，你的身份和我们不同吗？.wav",
+    #     comparison_dir_path='D:/tt/渡鸦/refer_audio_all',
+    #     output_file_path='D:/tt/渡鸦/test.txt',
+    # )

Ref_Audio_Selector/tool/text_check.py

@@ -0,0 +1,77 @@
+import os
+import Ref_Audio_Selector.common.common as common
+import Ref_Audio_Selector.tool.audio_check as audio_check
+from Ref_Audio_Selector.config_param.log_config import logger
+
+
+def parse_text_similarity_result_txt(file_path):
+    """
+    解析指定格式的txt文件，每行格式：f"{item['average_similarity_score']}|{item['count']}|{item['emotion']}"
+
+    :param file_path: txt文件的路径
+    :return: 包含解析后数据的字典列表
+    """
+    data_list = []
+    with open(file_path, 'r', encoding='utf-8') as file:
+        for line in file:
+            # 使用'|'作为分隔符分割每行数据
+            parts = line.strip().split('|')
+            if len(parts) == 3:
+                # 将分割后的字符串转换为浮点数、整数和字符串
+                try:
+                    item = {
+                        'average_similarity_score': float(parts[0]),
+                        'count': int(parts[1]),
+                        'emotion': parts[2]
+                    }
+                    data_list.append(item)
+                except ValueError as e:
+                    # 如果转换失败，打印错误信息并跳过该行
+                    logger.error(f"Error parsing line: {line.strip()} - {e}")
+
+    return data_list
+
+
+def remove_low_similarity_files(ref_audio_list, report_list, audio_text_similarity_boundary):
+    """
+    根据条件删除低相似度音频文件并返回删除数量。
+    
+    :param ref_audio_list: 包含音频路径和情感属性的列表
+    :param report_list: 包含相似度评分和情感属性的列表
+    :param audio_text_similarity_boundary: 相似度阈值
+    :return: 删除的文件数量
+    """
+    deleted_count = 0
+
+    # 筛选出平均相似度低于阈值的报告
+    low_similarity_reports = [report for report in report_list if
+                              report['average_similarity_score'] < audio_text_similarity_boundary]
+
+    # 遍历低相似度报告，查找并删除对应音频文件
+    for report in low_similarity_reports:
+        emotion = report['emotion']
+        # 查找ref_audio_list中相同情感的音频文件路径
+        matching_refs = [ref for ref in ref_audio_list if ref['emotion'] == emotion]
+        for match in matching_refs:
+            ref_path = match['ref_path']
+            # 检查文件是否存在，然后尝试删除
+            if os.path.exists(ref_path):
+                try:
+                    os.remove(ref_path)
+                    deleted_count += 1
+                    logger.info(f"Deleted file: {ref_path}")
+                except Exception as e:
+                    logger.error(f"Error deleting file {ref_path}: {e}")
+            else:
+                logger.error(f"File not found: {ref_path}")
+
+    return deleted_count
+
+
+def delete_ref_audio_below_boundary(ref_audio_path, text_similarity_result_path, sync_inference_audio_dir,
+                                    audio_text_similarity_boundary):
+    ref_audio_list = common.RefAudioListManager(ref_audio_path).get_ref_audio_list()
+    report_list = parse_text_similarity_result_txt(text_similarity_result_path)
+    count = remove_low_similarity_files(ref_audio_list, report_list, audio_text_similarity_boundary)
+    audio_check.sync_ref_audio(ref_audio_path, sync_inference_audio_dir)
+    return count

Ref_Audio_Selector/tool/text_comparison/asr_text_process.py

@@ -0,0 +1,161 @@
+import os
+import argparse
+from collections import defaultdict
+from operator import itemgetter
+from Ref_Audio_Selector.common.time_util import timeit_decorator
+import Ref_Audio_Selector.tool.text_comparison.text_comparison as text_comparison
+import Ref_Audio_Selector.config_param.config_params as params
+import Ref_Audio_Selector.common.common as common
+from Ref_Audio_Selector.config_param.log_config import logger
+
+
+def parse_asr_file(file_path):
+    output = []
+
+    with open(file_path, 'r', encoding='utf-8') as file:
+        for line in file:
+            # 假设每行都是正确的格式，且"|"'是固定分隔符
+            input_file_path, original_text, language, asr_text = line.strip().split('|')
+
+            emotion = common.get_filename_without_extension(input_file_path)
+
+            # 将解析出的数据构造成新的字典或元组等结构
+            parsed_data = {
+                'emotion': emotion,
+                'input_file_path': input_file_path,
+                'original_text': original_text,
+                'language': language,
+                'asr_text': asr_text,
+                'similarity_score': 0
+            }
+
+            output.append(parsed_data)
+
+    return output
+
+
+@timeit_decorator
+def calculate_similarity_and_append_to_list(input_list, boundary):
+    all_count = len(input_list)
+    has_been_processed_count = 0
+    for item in input_list:
+        original_score, similarity_score = text_comparison.calculate_result(item['original_text'], item['asr_text'], boundary)
+        item['similarity_score'] = similarity_score
+        item['original_score'] = original_score
+        has_been_processed_count += 1
+        logger.info(f'进度：{has_been_processed_count}/{all_count}')
+
+    return input_list
+
+
+def calculate_average_similarity_by_emotion(data_list):
+    result_dict = defaultdict(list)
+
+    for item in data_list:
+        emotion = item['emotion']
+        similarity_score = item['similarity_score']
+        result_dict[emotion].append(similarity_score)
+
+    average_scores = [{'emotion': emotion, 'average_similarity_score': sum(scores) / len(scores), 'count': len(scores)}
+                      for emotion, scores in result_dict.items()]
+
+    average_scores.sort(key=lambda x: x['average_similarity_score'], reverse=True)
+
+    return average_scores
+
+
+def group_and_sort_by_field(data, group_by_field):
+    # 创建一个空的结果字典，键是group_by_field指定的字段，值是一个列表
+    result_dict = defaultdict(list)
+
+    # 遍历输入列表
+    for item in data:
+        # 根据指定的group_by_field将当前元素添加到对应键的列表中
+        key_to_group = item[group_by_field]
+        result_dict[key_to_group].append(item)
+
+    # 对每个键对应的列表中的元素按similarity_score降序排序
+    for key in result_dict:
+        result_dict[key].sort(key=itemgetter('similarity_score'), reverse=True)
+
+    # 将结果字典转换为列表，每个元素是一个包含键（emotion或original_text）和排序后数组的元组
+    result_list = [(k, v) for k, v in result_dict.items()]
+
+    return result_list
+
+
+def format_list_to_text(data_list, output_filename):
+    with open(output_filename, 'w', encoding='utf-8') as output_file:
+        output_file.write('放大后的相似度分值|原始分值|ASR文本|原文文本\n')
+        for key, items in data_list:
+            # 写入情绪标题
+            output_file.write(key + '\n')
+
+            # 写入每条记录
+            for item in items:
+                formatted_line = f"{item['similarity_score']}|{item['original_score']}|{item['asr_text']}|{item['original_text']}\n"
+                output_file.write(formatted_line)
+
+
+def format_list_to_emotion(data_list, output_filename):
+    with open(output_filename, 'w', encoding='utf-8') as output_file:
+        output_file.write('放大后的相似度分值|原始分值|ASR文本|情绪类型\n')
+        for key, items in data_list:
+            # 写入情绪标题
+            output_file.write(key + '\n')
+
+            # 写入每条记录
+            for item in items:
+                formatted_line = f"{item['similarity_score']}|{item['original_score']}|{item['asr_text']}|{item['emotion']}\n"
+                output_file.write(formatted_line)
+
+
+@timeit_decorator
+def process(asr_file_path, output_dir, similarity_enlarge_boundary):
+    # 检查输出目录是否存在，如果不存在则创建
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    records = parse_asr_file(asr_file_path)
+    calculate_similarity_and_append_to_list(records, similarity_enlarge_boundary)
+    average_similarity_list = calculate_average_similarity_by_emotion(records)
+
+    average_similarity_file = os.path.join(output_dir,
+                                           f'{params.text_emotion_average_similarity_report_filename}.txt')
+    average_similarity_content = \
+        '\n'.join([f"{item['average_similarity_score']}|{item['count']}|{item['emotion']}" for item in average_similarity_list])
+    common.write_text_to_file(average_similarity_content, average_similarity_file)
+
+    emotion_detail_list = group_and_sort_by_field(records, 'emotion')
+
+    emotion_detail_file = os.path.join(output_dir, f'{params.text_similarity_by_emotion_detail_filename}.txt')
+    format_list_to_text(emotion_detail_list, emotion_detail_file)
+
+    original_text_detail_list = group_and_sort_by_field(records, 'original_text')
+
+    original_text_detail_file = os.path.join(output_dir, f'{params.text_similarity_by_text_detail_filename}.txt')
+    format_list_to_emotion(original_text_detail_list, original_text_detail_file)
+
+    logger.info('文本相似度分析完成。')
+
+
+def parse_arguments():
+    parser = argparse.ArgumentParser(description="Process ASR files and analyze similarity.")
+
+    parser.add_argument("-a", "--asr_file_path", type=str, required=True,
+                        help="Path to the directory containing ASR files or path to a single ASR file.")
+
+    parser.add_argument("-o", "--output_dir", type=str, required=True,
+                        help="Path to the directory where the analysis results should be saved.")
+
+    parser.add_argument("-b", "--similarity_enlarge_boundary", type=float, required=True,
+                        help="Similarity score boundary value to be used in your calculations.")
+
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    cmd = parse_arguments()
+    # print(cmd)
+    process(cmd.asr_file_path, cmd.output_dir, cmd.similarity_enlarge_boundary)

Ref_Audio_Selector/tool/text_comparison/text_comparison.py

@@ -0,0 +1,128 @@
+import os
+import torch
+from transformers import AutoTokenizer, AutoModel
+from scipy.spatial.distance import cosine
+from Ref_Audio_Selector.config_param.log_config import logger
+
+bert_path = os.environ.get(
+    "bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
+)
+
+# Set device to GPU if available, else CPU
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+logger.info(f'使用计算设备: {device}')
+
+tokenizer = AutoTokenizer.from_pretrained(bert_path)
+model = AutoModel.from_pretrained(bert_path).to(device)
+
+
+def calculate_similarity(text1, text2, max_length=512):
+    # 预处理文本，设置最大长度
+    inputs1 = tokenizer(text1, padding=True, truncation=True, max_length=max_length, return_tensors='pt').to(device)
+    inputs2 = tokenizer(text2, padding=True, truncation=True, max_length=max_length, return_tensors='pt').to(device)
+
+    # 获取句子向量（这里是取CLS token的向量并展平为一维）
+    with torch.no_grad():
+        encoded_text1 = model(**inputs1)[0][:, 0, :].flatten()
+        encoded_text2 = model(**inputs2)[0][:, 0, :].flatten()
+
+    # 确保转换为numpy数组并且是一维的
+    similarity = 1 - cosine(encoded_text1.cpu().numpy().flatten(), encoded_text2.cpu().numpy().flatten())
+
+    return similarity
+
+
+# 对boundary到1区间的值进行放大
+def adjusted_similarity(similarity_score2, boundary=0.8):
+    if similarity_score2 < boundary:
+        return 0
+
+    # 倍数
+    multiple = 1 / (1 - boundary)
+
+    adjusted_score = (similarity_score2 - boundary) * multiple
+
+    return adjusted_score
+
+
+def calculate_result(t1, t2, boundary):
+    # 计算并打印相似度
+    similarity_score2 = calculate_similarity(t1, t2)
+
+    # 调整相似度
+    adjusted_similarity_score2 = adjusted_similarity(similarity_score2, boundary)
+
+    return similarity_score2, adjusted_similarity_score2
+
+
+def print_result(t1, t2, boundary):
+    print(f't2: {t2}')
+    # 计算并打印相似度
+    similarity_score2 = calculate_similarity(t1, t2)
+    print(f"两句话的相似度为: {similarity_score2:.4f}")
+
+    # 调整相似度
+    adjusted_similarity_score2 = adjusted_similarity(similarity_score2, boundary)
+    print(f"调整后的相似度为: {adjusted_similarity_score2:.4f}")
+
+
+def test(boundary):
+    # 原始文本
+    text1 = "这是第一个句子"
+    list = """
+    这是第一个句子
+    这是第二个句子。
+    那么，这是第三个表达。
+    当前呈现的是第四个句子。
+    接下来，我们有第五句话。
+    在此，展示第六条陈述。
+    继续下去，这是第七个短句。
+    不容忽视的是第八个表述。
+    顺延着序列，这是第九句。
+    此处列举的是第十个说法。
+    进入新的篇章，这是第十一个句子。
+    下一段内容即为第十二个句子。
+    显而易见，这是第十三个叙述。
+    渐进地，我们来到第十四句话。
+    向下滚动，您会看到第十五个表达。
+    此刻，呈现在眼前的是第十六个句子。
+    它们中的一个——第十七个句子在此。
+    如同链条般连接，这是第十八个断言。
+    按照顺序排列，接下来是第十九个话语。
+    逐一列举，这是第二十个陈述句。
+    结构相似，本例给出第二十一个实例句。
+    这是最初的陈述句。
+    首先表达的是这一个句子。
+    第一句内容即为此处所示。
+    这是起始的叙述段落。
+    开篇所展示的第一句话就是这个。
+    明媚的阳光洒满大地
+    窗外飘落粉色樱花瓣
+    笔尖轻触纸面思绪万千
+    深夜的月光如水般静谧
+    穿越丛林的小径蜿蜒曲折
+    浅酌清茶品味人生百态
+    破晓时分雄鸡一唱天下白
+    草原上奔驰的骏马无拘无束
+    秋叶纷飞描绘季节更替画卷
+    寒冬雪夜炉火旁围坐共话家常
+    kszdRjYXw
+    pfsMgTlVHnB
+    uQaGxIbWz
+    ZtqNhPmKcOe
+    jfyrXsStVUo
+    wDiEgLkZbn
+    yhNvAfUmqC
+    TpKjxMrWgs
+    eBzHUaFJtYd
+    oQnXcVSiPkL
+    00000
+    """
+    list2 = list.strip().split('\n')
+    for item in list2:
+        print_result(text1, item, boundary)
+
+
+if __name__ == '__main__':
+    test(0.9)

Ref_Audio_Selector/ui_init/init_ui_param.py

@@ -0,0 +1,197 @@
+import os
+import multiprocessing
+import Ref_Audio_Selector.config_param.config_params as params
+import Ref_Audio_Selector.tool.audio_inference as audio_inference
+import Ref_Audio_Selector.common.common as common
+
+rw_param = params.config_manager.get_rw_param()
+# -------------------基本信息---------------------------
+
+# 角色所在工作目录
+base_dir_default = None
+# 工作目录
+text_work_space_dir_default = None
+# 角色名称
+text_role_default = None
+# 参考音频所在目录
+text_refer_audio_file_dir_default = None
+# 推理音频所在目录
+text_inference_audio_file_dir_default = None
+
+# -------------------第一步------------------------------
+
+# 参考音频抽样目录
+text_sample_dir_default = None
+# 分段数
+slider_subsection_num_default = None
+# 每段随机抽样个数
+slider_sample_num_default = None
+
+# -------------------第二步------------------------------
+
+# api服务模型切换接口地址
+text_api_set_model_base_url_default = None
+# GPT模型参数名
+text_api_gpt_param_default = None
+# SoVITS模型参数名
+text_api_sovits_param_default = None
+# api服务GPT模型切换接口地址
+text_api_v2_set_gpt_model_base_url_default = None
+# GPT模型参数名
+text_api_v2_gpt_model_param_default = None
+# api服务SoVITS模型切换接口地址
+text_api_v2_set_sovits_model_base_url_default = None
+# SoVITS模型参数名
+text_api_v2_sovits_model_param_default = None
+# 推理服务请求地址与参数
+text_url_default = None
+# 推理服务请求完整地址
+text_whole_url_default = None
+# 文本参数名
+text_text_default = None
+# 参考参数类型
+dropdown_refer_type_param_default = None
+# 参考音频路径参数名
+text_ref_path_default = None
+# 参考音频文本参数名
+text_ref_text_default = None
+# 角色情绪参数名
+text_emotion_default = None
+# 待推理文本路径
+text_test_content_default = None
+# 请求并发数
+slider_request_concurrency_num_default = 3
+# 最大并发数
+slider_request_concurrency_max_num = None
+
+# -------------------第三步------------------------------
+
+# 待asr的音频所在目录
+text_asr_audio_dir_default = None
+# 待分析的文件路径
+text_text_similarity_analysis_path_default = None
+# 文本相似度放大边界
+slider_text_similarity_amplification_boundary_default = 0.90
+# 文本相似度分析结果文件所在路径
+text_text_similarity_result_path_default = None
+
+# -------------------第四步------------------------------
+# -------------------第五步------------------------------
+# 模板内容
+text_template_default = None
+
+
+def empty_default(vale, default_value):
+    if vale is None or vale == "":
+        return default_value
+    else:
+        return vale
+
+
+def init_base():
+    global text_work_space_dir_default, text_role_default, base_dir_default, text_refer_audio_file_dir_default, text_inference_audio_file_dir_default
+
+    text_work_space_dir_default = rw_param.read(rw_param.work_dir)
+    text_role_default = rw_param.read(rw_param.role)
+    base_dir_default = os.path.join(text_work_space_dir_default, text_role_default)
+
+    text_refer_audio_file_dir_default = common.check_path_existence_and_return(
+        os.path.join(base_dir_default, params.reference_audio_dir))
+
+    text_inference_audio_file_dir_default = common.check_path_existence_and_return(
+        os.path.join(base_dir_default, params.inference_audio_dir))
+
+
+def init_first():
+    global text_sample_dir_default, slider_subsection_num_default, slider_sample_num_default
+
+    text_sample_dir_default = common.check_path_existence_and_return(
+        os.path.join(base_dir_default, params.list_to_convert_reference_audio_dir))
+
+    slider_subsection_num_default = int(empty_default(rw_param.read(rw_param.subsection_num), 10))
+
+    slider_sample_num_default = (empty_default(rw_param.read(rw_param.sample_num), 4))
+
+
+def init_second():
+    global text_api_set_model_base_url_default, text_api_gpt_param_default, text_api_sovits_param_default, text_api_v2_set_gpt_model_base_url_default, text_api_v2_gpt_model_param_default
+    global text_api_v2_set_sovits_model_base_url_default, text_api_v2_sovits_model_param_default, text_url_default, text_whole_url_default, text_text_default, dropdown_refer_type_param_default, text_ref_path_default
+    global text_ref_text_default, text_emotion_default, text_test_content_default, slider_request_concurrency_num_default, slider_request_concurrency_max_num
+
+    text_api_set_model_base_url_default = empty_default(rw_param.read(rw_param.api_set_model_base_url),
+                                                        'http://localhost:9880/set_model')
+    text_api_gpt_param_default = empty_default(rw_param.read(rw_param.api_gpt_param), 'gpt_model_path')
+    text_api_sovits_param_default = empty_default(rw_param.read(rw_param.api_sovits_param), 'sovits_model_path')
+
+    text_api_v2_set_gpt_model_base_url_default = empty_default(rw_param.read(rw_param.api_v2_set_gpt_model_base_url),
+                                                               'http://localhost:9880/set_gpt_weights')
+    text_api_v2_gpt_model_param_default = empty_default(rw_param.read(rw_param.api_v2_gpt_model_param), 'weights_path')
+
+    text_api_v2_set_sovits_model_base_url_default = empty_default(
+        rw_param.read(rw_param.api_v2_set_sovits_model_base_url), 'http://localhost:9880/set_sovits_weights')
+    text_api_v2_sovits_model_param_default = empty_default(rw_param.read(rw_param.api_v2_sovits_model_param), 'weights_path')
+
+    text_url_default = empty_default(rw_param.read(rw_param.text_url),
+                                     'http://localhost:9880?prompt_language=中文&text_language=中文&cut_punc=,.;?!、，。？！;：…')
+    text_text_default = empty_default(rw_param.read(rw_param.text_param), 'text')
+    dropdown_refer_type_param_default = empty_default(rw_param.read(rw_param.refer_type_param), '参考音频')
+
+    text_ref_path_default = empty_default(rw_param.read(rw_param.ref_path_param), 'refer_wav_path')
+    text_ref_text_default = empty_default(rw_param.read(rw_param.ref_text_param), 'prompt_text')
+    text_emotion_default = empty_default(rw_param.read(rw_param.emotion_param), 'emotion')
+
+    text_whole_url_default = whole_url(text_url_default, dropdown_refer_type_param_default, text_text_default,
+                                       text_ref_path_default, text_ref_text_default, text_emotion_default)
+
+    text_test_content_default = empty_default(rw_param.read(rw_param.test_content_path), params.default_test_text_path)
+
+    slider_request_concurrency_max_num = multiprocessing.cpu_count()
+
+    slider_request_concurrency_num_default = empty_default(rw_param.read(rw_param.request_concurrency_num), 3)
+
+    slider_request_concurrency_num_default = min(int(slider_request_concurrency_num_default), slider_request_concurrency_max_num)
+
+
+# 基于请求路径和参数，合成完整的请求路径
+def whole_url(text_url, dropdown_refer_type_param, text_text, text_ref_path, text_ref_text, text_emotion):
+    url_composer = audio_inference.TTSURLComposer(text_url, dropdown_refer_type_param, text_emotion, text_text,
+                                                  text_ref_path, text_ref_text)
+    if url_composer.is_emotion():
+        text_whole_url = url_composer.build_url_with_emotion('测试内容', '情绪类型', False)
+    else:
+        text_whole_url = url_composer.build_url_with_ref('测试内容', '参考路径', '参考文本', False)
+    return text_whole_url
+
+
+def init_third():
+    global text_asr_audio_dir_default, text_text_similarity_analysis_path_default, slider_text_similarity_amplification_boundary_default, text_text_similarity_result_path_default
+
+    text_asr_audio_dir_default = common.check_path_existence_and_return(
+        os.path.join(base_dir_default, params.inference_audio_dir, params.inference_audio_text_aggregation_dir))
+    text_text_similarity_analysis_path_default = common.check_path_existence_and_return(
+        os.path.join(base_dir_default, params.asr_filename + '.list'))
+    slider_text_similarity_amplification_boundary_default = empty_default(
+        rw_param.read(rw_param.text_similarity_amplification_boundary), 0.90)
+    text_text_similarity_result_path_default = common.check_path_existence_and_return(
+        os.path.join(base_dir_default, params.text_emotion_average_similarity_report_filename + '.txt'))
+
+
+def init_fourth():
+    pass
+
+
+def init_fifth():
+    global text_template_default
+
+    default_template_path = params.default_template_path
+    text_template_default = empty_default(rw_param.read(rw_param.text_template),
+                                          common.read_file(default_template_path))
+
+
+def init_all():
+    init_base()
+    init_first()
+    init_second()
+    init_third()
+    init_fourth()
+    init_fifth()

colab_webui.ipynb

@@ -1,23 +1,10 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": [],
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "accelerator": "GPU"
-  },
   "cells": [
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "view-in-github",
+        "colab_type": "text",
-        "colab_type": "text"
+        "id": "view-in-github"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/colab_webui.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -25,18 +12,20 @@
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "环境配置 environment"
-      ],
       "metadata": {
         "id": "_o6a8GS2lWQM"
-      }
+      },
+      "source": [
+        "环境配置 environment"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "e9b7iFV3dm1f"
       },
+      "outputs": [],
       "source": [
         "!pip install -q condacolab\n",
         "# Setting up condacolab and installing packages\n",
@@ -47,13 +36,17 @@
         "!conda install -y -q -c pytorch -c nvidia cudatoolkit\n",
         "%cd -q /content/GPT-SoVITS\n",
         "!conda install -y -q -c conda-forge gcc gxx ffmpeg cmake -c pytorch -c nvidia\n",
+        "!/usr/local/bin/pip install -r extra-req.txt --no-deps\n",
         "!/usr/local/bin/pip install -r requirements.txt"
-      ],
+      ]
-      "execution_count": null,
-      "outputs": []
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0NgxXg5sjv7z"
+      },
+      "outputs": [],
       "source": [
         "# @title Download pretrained models 下载预训练模型\n",
         "!mkdir -p /content/GPT-SoVITS/GPT_SoVITS/pretrained_models\n",
@@ -71,27 +64,35 @@
         "!git clone https://huggingface.co/Delik/uvr5_weights\n",
         "!git config core.sparseCheckout true\n",
         "!mv /content/GPT-SoVITS/GPT_SoVITS/pretrained_models/GPT-SoVITS/* /content/GPT-SoVITS/GPT_SoVITS/pretrained_models/"
-      ],
+      ]
-      "metadata": {
-        "id": "0NgxXg5sjv7z"
-      },
-      "execution_count": null,
-      "outputs": []
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4oRGUzkrk8C7"
+      },
+      "outputs": [],
       "source": [
         "# @title launch WebUI 启动WebUI\n",
         "!/usr/local/bin/pip install ipykernel\n",
         "!sed -i '10s/False/True/' /content/GPT-SoVITS/config.py\n",
         "%cd /content/GPT-SoVITS/\n",
         "!/usr/local/bin/python  webui.py"
-      ],
-      "metadata": {
-        "id": "4oRGUzkrk8C7"
-      },
-      "execution_count": null,
-      "outputs": []
-    }
       ]
     }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "include_colab_link": true,
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

docs/cn/README.md

@@ -76,6 +76,7 @@ bash install.sh
 ```bash
 conda create -n GPTSoVits python=3.9
 conda activate GPTSoVits
+pip install -r extra-req.txt --no-deps
 pip install -r requirements.txt
 ```
 
@@ -104,6 +105,7 @@ conda install -c conda-forge 'ffmpeg<7'
 安装 [Visual Studio 2017](https://aka.ms/vs/17/release/vc_redist.x86.exe) 环境(仅限韩语 TTS)
 
 ##### MacOS 用户
+
 ```bash
 brew install ffmpeg
 ```
@@ -111,6 +113,7 @@ brew install ffmpeg
 #### 安装依赖
 
 ```bash
+pip install -r extra-req.txt --no-deps
 pip install -r requirements.txt
 ```
 
@@ -155,7 +158,6 @@ docker run --rm -it --gpus=all --env=is_half=False --volume=G:\GPT-SoVITS-Docker
 
    - 建议在模型名称和配置文件名中**直接指定模型类型**，例如`mel_mand_roformer`、`bs_roformer`。如果未指定，将从配置文中比对特征，以确定它是哪种类型的模型。例如，模型`bs_roformer_ep_368_sdr_12.9628.ckpt` 和对应的配置文件`bs_roformer_ep_368_sdr_12.9628.yaml` 是一对。`kim_mel_band_roformer.ckpt` 和 `kim_mel_band_roformer.yaml` 也是一对。
 
-
 4. 对于中文 ASR（额外功能），从 [Damo ASR Model](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/files)、[Damo VAD Model](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/files) 和 [Damo Punc Model](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/files) 下载模型，并将它们放置在 `tools/asr/models` 目录中。
 
 5. 对于英语或日语 ASR（额外功能），从 [Faster Whisper Large V3](https://huggingface.co/Systran/faster-whisper-large-v3) 下载模型，并将其放置在 `tools/asr/models` 目录中。此外，[其他模型](https://huggingface.co/Systran) 可能具有类似效果且占用更少的磁盘空间。
@@ -202,6 +204,7 @@ python webui.py <language(optional)>
 ```bash
 python webui.py v1 <language(optional)>
 ```
+
 或者在 webUI 内动态切换
 
 ### 微调
@@ -226,11 +229,13 @@ python webui.py v1 <language(optional)>
 ```bash
 python GPT_SoVITS/inference_webui.py <language(optional)>
 ```
+
 或者
 
 ```bash
 python webui.py
 ```
+
 然后在 `1-GPT-SoVITS-TTS/1C-推理` 中打开推理 webUI
 
 ## V2 发布说明
@@ -245,7 +250,7 @@ python webui.py
 
 4. 对低音质参考音频（尤其是来源于网络的高频严重缺失、听着很闷的音频）合成出来音质更好
 
-    详见[wiki](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v2%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7))
+   详见[wiki](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v2%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7)>)
 
 从 v1 环境迁移至 v2
 
@@ -265,7 +270,7 @@ python webui.py
 
 2. GPT 合成更稳定，重复漏字更少，也更容易跑出丰富情感
 
-    详见[wiki](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v2%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7))
+   详见[wiki](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v2%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7)>)
 
 从 v2 环境迁移至 v3
 
@@ -277,7 +282,6 @@ python webui.py
 
    如果想用音频超分功能缓解 v3 模型生成 24k 音频觉得闷的问题，需要下载额外的模型参数，参考[how to download](../../tools/AP_BWE_main/24kto48k/readme.txt)
 
-
 ## 待办事项清单
 
 - [x] **高优先级：**
@@ -299,16 +303,21 @@ python webui.py
   - [ ] 模型混合。
 
 ## （附加）命令行运行方式
+
 使用命令行打开 UVR5 的 WebUI
-````
+
+```
 python tools/uvr5/webui.py "<infer_device>" <is_half> <webui_port_uvr5>
-````
+```
+
 <!-- 如果打不开浏览器，请按照下面的格式进行UVR处理，这是使用mdxnet进行音频处理的方式
 ````
 python mdxnet.py --model --input_root --output_vocal --output_ins --agg_level --format --device --is_half_precision
 ```` -->
+
 这是使用命令行完成数据集的音频切分的方式
-````
+
+```
 python audio_slicer.py \
     --input_path "<path_to_original_audio_file_or_directory>" \
     --output_root "<directory_where_subdivided_audio_clips_will_be_saved>" \
@@ -316,17 +325,22 @@ python audio_slicer.py \
     --min_length <minimum_duration_of_each_subclip> \
     --min_interval <shortest_time_gap_between_adjacent_subclips>
     --hop_size <step_size_for_computing_volume_curve>
-````
+```
+
 这是使用命令行完成数据集 ASR 处理的方式（仅限中文）
-````
+
+```
 python tools/asr/funasr_asr.py -i <input> -o <output>
-````
+```
+
 通过 Faster_Whisper 进行 ASR 处理（除中文之外的 ASR 标记）
 
 （没有进度条，GPU 性能可能会导致时间延迟）
+
 ```
 python ./tools/asr/fasterwhisper_asr.py -i <input> -o <output> -l <language> -p <precision>
 ```
+
 启用自定义列表保存路径
 
 ## 致谢
@@ -334,6 +348,7 @@ python ./tools/asr/fasterwhisper_asr.py -i <input> -o <output> -l <language> -p
 特别感谢以下项目和贡献者：
 
 ### 理论研究
+
 - [ar-vits](https://github.com/innnky/ar-vits)
 - [SoundStorm](https://github.com/yangdongchao/SoundStorm/tree/master/soundstorm/s1/AR)
 - [vits](https://github.com/jaywalnut310/vits)
@@ -343,17 +358,23 @@ python ./tools/asr/fasterwhisper_asr.py -i <input> -o <output> -l <language> -p
 - [fish-speech](https://github.com/fishaudio/fish-speech/blob/main/tools/llama/generate.py#L41)
 - [f5-TTS](https://github.com/SWivid/F5-TTS/blob/main/src/f5_tts/model/backbones/dit.py)
 - [shortcut flow matching](https://github.com/kvfrans/shortcut-models/blob/main/targets_shortcut.py)
+
 ### 预训练模型
+
 - [Chinese Speech Pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain)
 - [Chinese-Roberta-WWM-Ext-Large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)
 - [BigVGAN](https://github.com/NVIDIA/BigVGAN)
+
 ### 推理用文本前端
+
 - [paddlespeech zh_normalization](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization)
 - [split-lang](https://github.com/DoodleBears/split-lang)
 - [g2pW](https://github.com/GitYCC/g2pW)
 - [pypinyin-g2pW](https://github.com/mozillazg/pypinyin-g2pW)
 - [paddlespeech g2pw](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/g2pw)
+
 ### WebUI 工具
+
 - [ultimatevocalremovergui](https://github.com/Anjok07/ultimatevocalremovergui)
 - [audio-slicer](https://github.com/openvpi/audio-slicer)
 - [SubFix](https://github.com/cronrpc/SubFix)

docs/ja/README.md

@@ -70,7 +70,7 @@ bash install.sh
 ```bash
 conda create -n GPTSoVits python=3.9
 conda activate GPTSoVits
-
+pip install -r extra-req.txt --no-deps
 pip install -r requirements.txt
 ```
 
@@ -97,6 +97,7 @@ conda install -c conda-forge 'ffmpeg<7'
 [ffmpeg.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffmpeg.exe) と [ffprobe.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffprobe.exe) をダウンロードし、GPT-SoVITS のルートフォルダに置きます。
 
 ##### MacOS ユーザー
+
 ```bash
 brew install ffmpeg
 ```
@@ -104,6 +105,7 @@ brew install ffmpeg
 #### 依存関係をインストールします
 
 ```bash
+pip install -r extra-req.txt --no-deps
 pip install -r requirementx.txt
 ```
 
@@ -169,6 +171,7 @@ vocal_path|speaker_name|language|text
 ```
 D:\GPT-SoVITS\xxx/xxx.wav|xxx|en|I like playing Genshin.
 ```
+
 ## 微調整と推論
 
 ### WebUI を開く
@@ -189,6 +192,7 @@ V1に切り替えたい場合は
 ```bash
 python webui.py v1 <言語(オプション)>
 ```
+
 または WebUI で手動でバージョンを切り替えてください。
 
 ### 微調整
@@ -213,11 +217,13 @@ python webui.py v1 <言語(オプション)>
 ```bash
 python GPT_SoVITS/inference_webui.py <言語(オプション)>
 ```
+
 または
 
 ```bash
 python webui.py
 ```
+
 その後、`1-GPT-SoVITS-TTS/1C-inference`で推論 webui を開きます。
 
 ## V2 リリースノート
@@ -232,7 +238,7 @@ python webui.py
 
 4. 低品質の参照音声に対する合成品質の向上
 
-    [詳細はこちら](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v2%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7))
+   [詳細はこちら](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v2%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7)>)
 
 V1 環境から V2 を使用するには:
 
@@ -252,7 +258,7 @@ V1環境からV2を使用するには:
 
 2. GPT モデルがより安定し、繰り返しや省略が減少し、より豊かな感情表現を持つ音声の生成が容易になりました。
 
-    [詳細情報はこちら](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v3%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7))
+   [詳細情報はこちら](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v3%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7)>)
 
 v2 環境から v3 を使用する方法:
 
@@ -285,15 +291,20 @@ v2 環境から v3 を使用する方法:
   - [ ] モデルミックス
 
 ## (追加の) コマンドラインから実行する方法
+
 コマンド ラインを使用して UVR5 の WebUI を開きます
+
 ```
 python tools/uvr5/webui.py "<infer_device>" <is_half> <webui_port_uvr5>
 ```
+
 <!-- ブラウザを開けない場合は、以下の形式に従って UVR 処理を行ってください。これはオーディオ処理に mdxnet を使用しています。
 ```
 python mdxnet.py --model --input_root --output_vocal --output_ins --agg_level --format --device --is_half_precision
 ``` -->
+
 コマンド ラインを使用してデータセットのオーディオ セグメンテーションを行う方法は次のとおりです。
+
 ```
 python audio_slicer.py \
     --input_path "<path_to_original_audio_file_or_directory>" \
@@ -303,16 +314,21 @@ python audio_slicer.py \
     --min_interval <shortest_time_gap_between_adjacent_subclips>
     --hop_size <step_size_for_computing_volume_curve>
 ```
+
 コマンドラインを使用してデータセット ASR 処理を行う方法です (中国語のみ)
+
 ```
 python tools/asr/funasr_asr.py -i <input> -o <output>
 ```
+
 ASR 処理は Faster_Whisper を通じて実行されます(中国語を除く ASR マーキング)
 
 (進行状況バーは表示されません。GPU のパフォーマンスにより時間遅延が発生する可能性があります)
+
 ```
 python ./tools/asr/fasterwhisper_asr.py -i <input> -o <output> -l <language> -p <precision>
 ```
+
 カスタムリストの保存パスが有効になっています
 
 ## クレジット
@@ -320,6 +336,7 @@ python ./tools/asr/fasterwhisper_asr.py -i <input> -o <output> -l <language> -p
 特に以下のプロジェクトと貢献者に感謝します：
 
 ### 理論研究
+
 - [ar-vits](https://github.com/innnky/ar-vits)
 - [SoundStorm](https://github.com/yangdongchao/SoundStorm/tree/master/soundstorm/s1/AR)
 - [vits](https://github.com/jaywalnut310/vits)
@@ -329,17 +346,23 @@ python ./tools/asr/fasterwhisper_asr.py -i <input> -o <output> -l <language> -p
 - [fish-speech](https://github.com/fishaudio/fish-speech/blob/main/tools/llama/generate.py#L41)
 - [f5-TTS](https://github.com/SWivid/F5-TTS/blob/main/src/f5_tts/model/backbones/dit.py)
 - [shortcut flow matching](https://github.com/kvfrans/shortcut-models/blob/main/targets_shortcut.py)
+
 ### 事前学習モデル
+
 - [Chinese Speech Pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain)
 - [Chinese-Roberta-WWM-Ext-Large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)
 - [BigVGAN](https://github.com/NVIDIA/BigVGAN)
+
 ### 推論用テキストフロントエンド
+
 - [paddlespeech zh_normalization](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization)
 - [split-lang](https://github.com/DoodleBears/split-lang)
 - [g2pW](https://github.com/GitYCC/g2pW)
 - [pypinyin-g2pW](https://github.com/mozillazg/pypinyin-g2pW)
 - [paddlespeech g2pw](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/g2pw)
+
 ### WebUI ツール
+
 - [ultimatevocalremovergui](https://github.com/Anjok07/ultimatevocalremovergui)
 - [audio-slicer](https://github.com/openvpi/audio-slicer)
 - [SubFix](https://github.com/cronrpc/SubFix)

docs/ko/README.md

@@ -70,7 +70,7 @@ bash install.sh
 ```bash
 conda create -n GPTSoVits python=3.9
 conda activate GPTSoVits
-
+pip install -r extra-req.txt --no-deps
 pip install -r requirements.txt
 ```
 
@@ -99,6 +99,7 @@ conda install -c conda-forge 'ffmpeg<7'
 [Visual Studio 2017](https://aka.ms/vs/17/release/vc_redist.x86.exe) 설치 (Korean TTS 전용)
 
 ##### MacOS 사용자
+
 ```bash
 brew install ffmpeg
 ```
@@ -106,6 +107,7 @@ brew install ffmpeg
 #### 의존성 설치
 
 ```bash
+pip install -r extra-req.txt --no-deps
 pip install -r requirements.txt
 ```
 
@@ -195,6 +197,7 @@ V1으로 전환하려면,
 ```bash
 python webui.py v1 <언어(옵션)>
 ```
+
 또는 WebUI에서 수동으로 버전을 전환하십시오.
 
 ### 미세 조정
@@ -219,11 +222,13 @@ python webui.py v1 <언어(옵션)>
 ```bash
 python GPT_SoVITS/inference_webui.py <언어(옵션)>
 ```
+
 또는
 
 ```bash
 python webui.py
 ```
+
 그런 다음 `1-GPT-SoVITS-TTS/1C-inference`에서 추론 webui를 엽니다.
 
 ## V2 릴리스 노트
@@ -238,7 +243,7 @@ python webui.py
 
 4. 저품질 참조 오디오에 대한 합성 품질 향상
 
-    [자세한 내용](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v2%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7))
+   [자세한 내용](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v2%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7)>)
 
 V1 환경에서 V2를 사용하려면:
 
@@ -258,7 +263,7 @@ V1 환경에서 V2를 사용하려면:
 
 2. GPT 모델이 더 안정적이며 반복 및 생략이 적고, 더 풍부한 감정 표현을 가진 음성을 생성하기가 더 쉽습니다.
 
-    [자세한 내용](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v3%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7))
+   [자세한 내용](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v3%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7)>)
 
 v2 환경에서 v3 사용하기:
 
@@ -270,7 +275,6 @@ v2 환경에서 v3 사용하기:
 
    추가: 오디오 슈퍼 해상도 모델에 대해서는 [다운로드 방법](../../tools/AP_BWE_main/24kto48k/readme.txt)을 참고하세요.
 
-
 ## 할 일 목록
 
 - [x] **최우선순위:**
@@ -293,15 +297,20 @@ v2 환경에서 v3 사용하기:
   - [ ] 모델 블렌딩.
 
 ## (추가적인) 명령줄에서 실행하는 방법
+
 명령줄을 사용하여 UVR5용 WebUI 열기
+
 ```
 python tools/uvr5/webui.py "<infer_device>" <is_half> <webui_port_uvr5>
 ```
+
 <!-- 브라우저를 열 수 없는 경우 UVR 처리를 위해 아래 형식을 따르십시오. 이는 오디오 처리를 위해 mdxnet을 사용하는 것입니다.
 ```
 python mdxnet.py --model --input_root --output_vocal --output_ins --agg_level --format --device --is_half_precision
 ``` -->
+
 명령줄을 사용하여 데이터세트의 오디오 분할을 수행하는 방법은 다음과 같습니다.
+
 ```
 python audio_slicer.py \
     --input_path "<path_to_original_audio_file_or_directory>" \
@@ -311,16 +320,21 @@ python audio_slicer.py \
     --min_interval <shortest_time_gap_between_adjacent_subclips>
     --hop_size <step_size_for_computing_volume_curve>
 ```
+
 명령줄을 사용하여 데이터 세트 ASR 처리를 수행하는 방법입니다(중국어만 해당).
+
 ```
 python tools/asr/funasr_asr.py -i <input> -o <output>
 ```
+
 ASR 처리는 Faster_Whisper(중국어를 제외한 ASR 마킹)를 통해 수행됩니다.
 
 (진행률 표시줄 없음, GPU 성능으로 인해 시간 지연이 발생할 수 있음)
+
 ```
 python ./tools/asr/fasterwhisper_asr.py -i <input> -o <output> -l <language> -p <precision>
 ```
+
 사용자 정의 목록 저장 경로가 활성화되었습니다.
 
 ## 감사의 말
@@ -328,6 +342,7 @@ python ./tools/asr/fasterwhisper_asr.py -i <input> -o <output> -l <language> -p
 다음 프로젝트와 기여자들에게 특별히 감사드립니다:
 
 ### 이론 연구
+
 - [ar-vits](https://github.com/innnky/ar-vits)
 - [SoundStorm](https://github.com/yangdongchao/SoundStorm/tree/master/soundstorm/s1/AR)
 - [vits](https://github.com/jaywalnut310/vits)
@@ -337,17 +352,23 @@ python ./tools/asr/fasterwhisper_asr.py -i <input> -o <output> -l <language> -p
 - [fish-speech](https://github.com/fishaudio/fish-speech/blob/main/tools/llama/generate.py#L41)
 - [f5-TTS](https://github.com/SWivid/F5-TTS/blob/main/src/f5_tts/model/backbones/dit.py)
 - [shortcut flow matching](https://github.com/kvfrans/shortcut-models/blob/main/targets_shortcut.py)
+
 ### 사전 학습 모델
+
 - [Chinese Speech Pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain)
 - [Chinese-Roberta-WWM-Ext-Large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)
 - [BigVGAN](https://github.com/NVIDIA/BigVGAN)
+
 ### 추론용 텍스트 프론트엔드
+
 - [paddlespeech zh_normalization](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization)
 - [split-lang](https://github.com/DoodleBears/split-lang)
 - [g2pW](https://github.com/GitYCC/g2pW)
 - [pypinyin-g2pW](https://github.com/mozillazg/pypinyin-g2pW)
 - [paddlespeech g2pw](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/g2pw)
+
 ### WebUI 도구
+
 - [ultimatevocalremovergui](https://github.com/Anjok07/ultimatevocalremovergui)
 - [audio-slicer](https://github.com/openvpi/audio-slicer)
 - [SubFix](https://github.com/cronrpc/SubFix)

docs/tr/README.md

@@ -72,7 +72,7 @@ bash install.sh
 ```bash
 conda create -n GPTSoVits python=3.9
 conda activate GPTSoVits
-
+pip install -r extra-req.txt --no-deps
 pip install -r requirements.txt
 ```
 
@@ -99,6 +99,7 @@ conda install -c conda-forge 'ffmpeg<7'
 [ffmpeg.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffmpeg.exe) ve [ffprobe.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffprobe.exe) dosyalarını indirin ve GPT-SoVITS kök dizinine yerleştirin.
 
 ##### MacOS Kullanıcıları
+
 ```bash
 brew install ffmpeg
 ```
@@ -106,6 +107,7 @@ brew install ffmpeg
 #### Bağımlılıkları Yükleme
 
 ```bash
+pip install -r extra-req.txt --no-deps
 pip install -r requirements.txt
 ```
 
@@ -192,6 +194,7 @@ V1'e geçmek istiyorsanız,
 ```bash
 python webui.py v1 <dil(isteğe bağlı)>
 ```
+
 veya WebUI'de manuel olarak sürüm değiştirin.
 
 ### İnce Ayar
@@ -216,11 +219,13 @@ veya WebUI'de manuel olarak sürüm değiştirin.
 ```bash
 python GPT_SoVITS/inference_webui.py <dil(isteğe bağlı)>
 ```
+
 VEYA
 
 ```bash
 python webui.py
 ```
+
 ardından çıkarım webui'sini `1-GPT-SoVITS-TTS/1C-inference` adresinde açın.
 
 ## V2 Sürüm Notları
@@ -235,7 +240,7 @@ Yeni Özellikler:
 
 4. Düşük kaliteli referans sesler için geliştirilmiş sentez kalitesi
 
-    [detaylar burada](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v2%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7))
+   [detaylar burada](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v2%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7)>)
 
 V1 ortamından V2'yi kullanmak için:
 
@@ -255,7 +260,7 @@ V1 ortamından V2'yi kullanmak için:
 
 2. GPT modeli daha **kararlı** hale geldi, tekrarlar ve atlamalar azaldı ve **daha zengin duygusal ifadeler** ile konuşma üretmek daha kolay hale geldi.
 
-    [daha fazla detay](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v3%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7))
+   [daha fazla detay](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v3%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7)>)
 
 ### v2 ortamında v3 kullanımı:
 
@@ -288,15 +293,20 @@ V1 ortamından V2'yi kullanmak için:
   - [ ] model karışımı
 
 ## (Ekstra) Komut satırından çalıştırma yöntemi
+
 UVR5 için Web Arayüzünü açmak için komut satırını kullanın
+
 ```
 python tools/uvr5/webui.py "<infer_device>" <is_half> <webui_port_uvr5>
 ```
+
 <!-- Bir tarayıcı açamıyorsanız, UVR işleme için aşağıdaki formatı izleyin,Bu ses işleme için mdxnet kullanıyor
 ```
 python mdxnet.py --model --input_root --output_vocal --output_ins --agg_level --format --device --is_half_precision
 ``` -->
+
 Veri setinin ses segmentasyonu komut satırı kullanılarak bu şekilde yapılır
+
 ```
 python audio_slicer.py \
     --input_path "<orijinal_ses_dosyası_veya_dizininin_yolu>" \
@@ -306,16 +316,21 @@ python audio_slicer.py \
     --min_interval <bitişik_alt_klipler_arasındaki_en_kısa_zaman_aralığı>
     --hop_size <ses_eğrisini_hesaplamak_için_adım_boyutu>
 ```
+
 Veri seti ASR işleme komut satırı kullanılarak bu şekilde yapılır (Yalnızca Çince)
+
 ```
 python tools/asr/funasr_asr.py -i <girdi> -o <çıktı>
 ```
+
 ASR işleme Faster_Whisper aracılığıyla gerçekleştirilir (Çince dışındaki ASR işaretleme)
 
 (İlerleme çubukları yok, GPU performansı zaman gecikmelerine neden olabilir)
+
 ```
 python ./tools/asr/fasterwhisper_asr.py -i <girdi> -o <çıktı> -l <dil>
 ```
+
 Özel bir liste kaydetme yolu etkinleştirildi
 
 ## Katkı Verenler
@@ -323,6 +338,7 @@ python ./tools/asr/fasterwhisper_asr.py -i <girdi> -o <çıktı> -l <dil>
 Özellikle aşağıdaki projelere ve katkıda bulunanlara teşekkür ederiz:
 
 ### Teorik Araştırma
+
 - [ar-vits](https://github.com/innnky/ar-vits)
 - [SoundStorm](https://github.com/yangdongchao/SoundStorm/tree/master/soundstorm/s1/AR)
 - [vits](https://github.com/jaywalnut310/vits)
@@ -332,17 +348,23 @@ python ./tools/asr/fasterwhisper_asr.py -i <girdi> -o <çıktı> -l <dil>
 - [fish-speech](https://github.com/fishaudio/fish-speech/blob/main/tools/llama/generate.py#L41)
 - [f5-TTS](https://github.com/SWivid/F5-TTS/blob/main/src/f5_tts/model/backbones/dit.py)
 - [shortcut flow matching](https://github.com/kvfrans/shortcut-models/blob/main/targets_shortcut.py)
+
 ### Önceden Eğitilmiş Modeller
+
 - [Chinese Speech Pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain)
 - [Chinese-Roberta-WWM-Ext-Large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)
 - [BigVGAN](https://github.com/NVIDIA/BigVGAN)
+
 ### Tahmin İçin Metin Ön Ucu
+
 - [paddlespeech zh_normalization](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization)
 - [split-lang](https://github.com/DoodleBears/split-lang)
 - [g2pW](https://github.com/GitYCC/g2pW)
 - [pypinyin-g2pW](https://github.com/mozillazg/pypinyin-g2pW)
 - [paddlespeech g2pw](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/g2pw)
+
 ### WebUI Araçları
+
 - [ultimatevocalremovergui](https://github.com/Anjok07/ultimatevocalremovergui)
 - [audio-slicer](https://github.com/openvpi/audio-slicer)
 - [SubFix](https://github.com/cronrpc/SubFix)

extra-req.txt

@@ -0,0 +1 @@
+faster-whisper

gpt-sovits_kaggle.ipynb

@@ -27,7 +27,8 @@
     "!git clone https://github.com/RVC-Boss/GPT-SoVITS.git\n",
     "%cd GPT-SoVITS\n",
     "!apt-get update && apt-get install -y --no-install-recommends tzdata ffmpeg libsox-dev parallel aria2 git git-lfs && git lfs install\n",
-    "!pip install -r requirements.txt"
+    "!pip install -r requirements.txt\n",
+    "!pip install -r extra-req.txt --no-deps"
    ]
   },
   {

install.sh

@@ -1,15 +1,17 @@
 #!/bin/bash
 
+set -e
+
 # 安装构建工具
 # Install build tools
 echo "Installing GCC..."
-conda install -c conda-forge gcc=14
+conda install -c conda-forge gcc=14 -y
 
 echo "Installing G++..."
-conda install -c conda-forge gxx
+conda install -c conda-forge gxx -y
 
 echo "Installing ffmpeg and cmake..."
-conda install ffmpeg cmake
+conda install ffmpeg cmake -y
 
 # 设置编译环境
 # Set up build environment
@@ -26,7 +28,6 @@ else
     USE_CUDA=false
 fi
 
-
 if [ "$USE_CUDA" = false ]; then
     echo "Checking for ROCm installation..."
     if [ -d "/opt/rocm" ]; then
@@ -56,21 +57,53 @@ else
     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
+
+# pyopenjtalk Installation
+conda install jq -y
+
+OS_TYPE=$(uname)
+
+PACKAGE_NAME="pyopenjtalk"
+
+VERSION=$(curl -s https://pypi.org/pypi/$PACKAGE_NAME/json | jq -r .info.version)
+
+wget "https://files.pythonhosted.org/packages/source/${PACKAGE_NAME:0:1}/$PACKAGE_NAME/$PACKAGE_NAME-$VERSION.tar.gz"
+
+TAR_FILE=$(ls ${PACKAGE_NAME}-*.tar.gz)
+DIR_NAME="${TAR_FILE%.tar.gz}"
+
+tar -xzf "$TAR_FILE"
+rm "$TAR_FILE"
+
+CMAKE_FILE="$DIR_NAME/lib/open_jtalk/src/CMakeLists.txt"
+
+if [[ "$OS_TYPE" == "darwin"* ]]; then
+    sed -i '' -E 's/cmake_minimum_required\(VERSION[^\)]*\)/cmake_minimum_required(VERSION 3.5...3.31)/' "$CMAKE_FILE"
+else
+    sed -i -E 's/cmake_minimum_required\(VERSION[^\)]*\)/cmake_minimum_required(VERSION 3.5...3.31)/' "$CMAKE_FILE"
+fi
+
+tar -czf "$TAR_FILE" "$DIR_NAME"
+
+pip install "$TAR_FILE"
+
+rm -rf "$TAR_FILE" "$DIR_NAME"
+
+pip install -r extra-req.txt --no-deps
+
 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 $2}'`
+    location=$(pip show torch | grep Location | awk -F ": " '{print $2}')
-    cd ${location}/torch/lib/
+    cd "${location}"/torch/lib/ || exit
     rm libhsa-runtime64.so*
     cp /opt/rocm/lib/libhsa-runtime64.so.1.2 libhsa-runtime64.so
 fi
 
 echo "Installation completed successfully!"
-

requirements.txt

@@ -3,7 +3,7 @@ scipy
 tensorboard
 librosa==0.9.2
 numba==0.56.4
-pytorch-lightning
+pytorch-lightning>2.0
 gradio>=4.0,<=4.24.0
 ffmpeg-python
 onnxruntime; sys_platform == 'darwin'
@@ -26,7 +26,6 @@ jieba_fast
 jieba
 split-lang
 fast_langdetect>=0.3.0
-Faster_Whisper
 wordsegment
 rotary_embedding_torch
 ToJyutping 
@@ -38,4 +37,9 @@ python_mecab_ko; sys_platform != 'win32'
 fastapi<0.112.2
 x_transformers
 torchmetrics<=1.5
-attrdict
+pydantic<=2.10.6
+ctranslate2>=4.0,<5
+huggingface_hub>=0.13
+tokenizers>=0.13,<1
+av>=11
+tqdm