Merge branch 'RVC-Boss:main' into main

GPT_SoVITS/prepare_datasets/1-get-text.py

@@ -10,13 +10,13 @@ all_parts = os.environ.get("all_parts")
 os.environ["CUDA_VISIBLE_DEVICES"] = os.environ.get("_CUDA_VISIBLE_DEVICES")
 opt_dir = os.environ.get("opt_dir")
 bert_pretrained_dir = os.environ.get("bert_pretrained_dir")
-is_half = eval(os.environ.get("is_half", "True"))
+import torch
+is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
 import sys, numpy as np, traceback, pdb
 import os.path
 from glob import glob
 from tqdm import tqdm
 from text.cleaner import clean_text
-import torch
 from transformers import AutoModelForMaskedLM, AutoTokenizer
 import numpy as np
 

GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py

@@ -10,11 +10,12 @@ os.environ["CUDA_VISIBLE_DEVICES"]= os.environ.get("_CUDA_VISIBLE_DEVICES")
 from feature_extractor import cnhubert
 opt_dir=                            os.environ.get("opt_dir")
 cnhubert.cnhubert_base_path=                os.environ.get("cnhubert_base_dir")
-is_half=eval(os.environ.get("is_half","True"))
+import torch
+is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
 
 import pdb,traceback,numpy as np,logging
 from scipy.io import wavfile
-import librosa,torch
+import librosa
 now_dir = os.getcwd()
 sys.path.append(now_dir)
 from tools.my_utils import load_audio

GPT_SoVITS/prepare_datasets/3-get-semantic.py

@@ -8,7 +8,8 @@ os.environ["CUDA_VISIBLE_DEVICES"] = os.environ.get("_CUDA_VISIBLE_DEVICES")
 opt_dir = os.environ.get("opt_dir")
 pretrained_s2G = os.environ.get("pretrained_s2G")
 s2config_path = os.environ.get("s2config_path")
-is_half = eval(os.environ.get("is_half", "True"))
+import torch
+is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
 import math, traceback
 import multiprocessing
 import sys, pdb
@@ -19,7 +20,7 @@ from random import shuffle
 import torch.multiprocessing as mp
 from glob import glob
 from tqdm import tqdm
-import logging, librosa, utils, torch
+import logging, librosa, utils
 from module.models import SynthesizerTrn
 
 logging.getLogger("numba").setLevel(logging.WARNING)

README.md

@@ -106,7 +106,7 @@ conda install -c conda-forge 'ffmpeg<7'
 
 Download and place [ffmpeg.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffmpeg.exe) and [ffprobe.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffprobe.exe) in the GPT-SoVITS root.
 
-##### Mac Users
+##### MacOS Users
 ```bash
 brew install ffmpeg
 ```
@@ -156,7 +156,7 @@ For English or Japanese ASR (additionally), download models from [Faster Whisper
 
 Users in the China region can download this model by entering the links below
 
-- [Faster Whisper Large V3](https://www.icloud.com/iclouddrive/0c4pQxFs7oWyVU1iMTq2DbmLA#faster-whisper-large-v3) (Click "Download a copy", log out if you encounter errors while downloading.)
+- [Faster Whisper Large V3](https://www.icloud.com/iclouddrive/00bUEp9_mcjMq_dhHu_vrAFDQ#faster-whisper-large-v3) (Click "Download a copy", log out if you encounter errors while downloading.)
 
 - [Faster Whisper Large V3](https://hf-mirror.com/Systran/faster-whisper-large-v3) (HuggingFace mirror site)
 
@@ -227,7 +227,7 @@ 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>
+python ./tools/asr/fasterwhisper_asr.py -i <input> -o <output> -l <language> -p <precision>
 ```
 A custom list save path is enabled
 

docs/cn/README.md

@@ -106,7 +106,7 @@ conda install -c conda-forge 'ffmpeg<7'
 
 下载并将 [ffmpeg.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffmpeg.exe) 和 [ffprobe.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffprobe.exe) 放置在 GPT-SoVITS 根目录下。
 
-##### Mac 用户
+##### MacOS 用户
 ```bash
 brew install ffmpeg
 ```
@@ -155,7 +155,7 @@ docker run --rm -it --gpus=all --env=is_half=False --volume=G:\GPT-SoVITS-Docker
 对于英语与日语自动语音识别（附加）,从 [Faster Whisper Large V3](https://huggingface.co/Systran/faster-whisper-large-v3) 下载模型，并将它们放置在 `tools/asr/models` 中。 此外，[其他模型](https://huggingface.co/Systran)可能具有类似效果，但占用更小的磁盘空间。
 
 中国地区用户可以通过以下链接下载：
-- [Faster Whisper Large V3](https://www.icloud.com/iclouddrive/0c4pQxFs7oWyVU1iMTq2DbmLA#faster-whisper-large-v3)（点击“下载副本”，如果下载时遇到错误，请退出登录）
+- [Faster Whisper Large V3](https://www.icloud.com/iclouddrive/00bUEp9_mcjMq_dhHu_vrAFDQ#faster-whisper-large-v3)(点击“下载副本”，如果下载时遇到错误，请退出登录）
   
 - [Faster Whisper Large V3](https://hf-mirror.com/Systran/faster-whisper-large-v3)(Hugging Face镜像站)
 
@@ -185,7 +185,7 @@ D:\GPT-SoVITS\xxx/xxx.wav|xxx|en|I like playing Genshin.
 - [ ] **高优先级：**
 
   - [x] 日语和英语的本地化。
-  - [ ] 用户指南。
+  - [x] 用户指南。
   - [x] 日语和英语数据集微调训练。
 
 - [ ] **功能:**
@@ -226,9 +226,9 @@ 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>
+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

docs/ja/README.md

@@ -102,7 +102,7 @@ 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 のルートディレクトリに置きます。
 
-##### Mac ユーザー
+##### MacOS ユーザー
 ```bash
 brew install ffmpeg
 ```
@@ -209,7 +209,7 @@ ASR処理はFaster_Whisperを通じて実行されます(中国語を除くASR
 
 (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>
+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
 

docs/ko/README.md

@@ -102,7 +102,7 @@ conda install -c conda-forge 'ffmpeg<7'
 
 [ffmpeg.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffmpeg.exe)와 [ffprobe.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffprobe.exe)를 GPT-SoVITS root 디렉토리에 넣습니다.
 
-##### Mac 사용자
+##### MacOS 사용자
 ```bash
 brew install ffmpeg
 ```
@@ -213,7 +213,7 @@ ASR 처리는 Faster_Whisper(중국어를 제외한 ASR 마킹)를 통해 수행
 
 (진행률 표시줄 없음, GPU 성능으로 인해 시간 지연이 발생할 수 있음)
 ```
-python ./tools/asr/fasterwhisper_asr.py -i <input> -o <output> -l <language>
+python ./tools/asr/fasterwhisper_asr.py -i <input> -o <output> -l <language> -p <precision>
 ```
 사용자 정의 목록 저장 경로가 활성화되었습니다.
 

docs/tr/README.md

@@ -102,7 +102,7 @@ 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.
 
-##### Mac Kullanıcıları
+##### MacOS Kullanıcıları
 ```bash
 brew install ffmpeg
 ```

tools/asr/config.py

@@ -21,11 +21,13 @@ asr_dict = {
         'lang': ['zh'],
         'size': ['large'],
         'path': 'funasr_asr.py',
+        'precision': ['float32']
     },
     "Faster Whisper (多语种)": {
         'lang': ['auto', 'zh', 'en', 'ja'],
         'size': check_fw_local_models(),
-        'path': 'fasterwhisper_asr.py'
+        'path': 'fasterwhisper_asr.py',
-    }
+        'precision': ['float32', 'float16', 'int8']
+    },
 }
 

tools/asr/fasterwhisper_asr.py

@@ -101,8 +101,8 @@ if __name__ == '__main__':
     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'],
+    parser.add_argument("-p", "--precision", type=str, default='float16', choices=['float16','float32','int8'],
-                        help="fp16 or fp32")
+                        help="fp16, int8 or fp32")
 
     cmd = parser.parse_args()
     output_file_path = execute_asr(

tools/asr/funasr_asr.py

@@ -4,7 +4,8 @@ import argparse
 import os
 import traceback
 from tqdm import tqdm
-
+# from funasr.utils import version_checker
+# version_checker.check_for_update = lambda: None
 from funasr import AutoModel
 
 path_asr  = 'tools/asr/models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'
@@ -14,6 +15,7 @@ path_asr  = path_asr  if os.path.exists(path_asr)  else "iic/speech_paraformer-l
 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",

tools/i18n/locale/en_US.json

@@ -34,6 +34,7 @@
     "3、去混响、去延迟模型（by FoxJoy）：": "3. Reverberation and delay removal model(by FoxJoy):",
     "ASR 模型": "ASR model",
     "ASR 模型尺寸": "ASR model size",
+    "数据类型精度": "Computing precision",
     "ASR 语言设置": "ASR language",
     "ASR进程输出信息": "ASR output log",
     "GPT模型列表": "GPT weight list",

tools/i18n/locale/es_ES.json

@@ -34,6 +34,7 @@
     "3、去混响、去延迟模型（by FoxJoy）：": "3. Modelos de eliminación de reverberación y retardo (por FoxJoy)",
     "ASR 模型": "Modelo ASR",
     "ASR 模型尺寸": "Tamaño del modelo ASR",
+    "数据类型精度": "precisión del tipo de datos",
     "ASR 语言设置": "Configuración del idioma ASR",
     "ASR进程输出信息": "Información de salida del proceso ASR",
     "GPT模型列表": "Lista de modelos GPT",

tools/i18n/locale/fr_FR.json

@@ -34,6 +34,7 @@
     "3、去混响、去延迟模型（by FoxJoy）：": "3. Modèle de suppression de réverbération et de retard (par FoxJoy) :",
     "ASR 模型": "Modèle ASR",
     "ASR 模型尺寸": "Taille du modèle ASR",
+    "数据类型精度": "précision du type de données",
     "ASR 语言设置": "Paramètres de langue ASR",
     "ASR进程输出信息": "Informations de processus ASR",
     "GPT模型列表": "Liste des modèles GPT",

tools/i18n/locale/it_IT.json

@@ -34,6 +34,7 @@
     "3、去混响、去延迟模型（by FoxJoy）：": "3. Modello per rimuovere la riverberazione e il ritardo (by FoxJoy):",
     "ASR 模型": "Modello ASR",
     "ASR 模型尺寸": "Dimensioni del modello ASR",
+    "数据类型精度": "precisione del tipo di dati",
     "ASR 语言设置": "Impostazioni linguistiche ASR",
     "ASR进程输出信息": "Informazioni sull'output del processo ASR",
     "GPT模型列表": "Elenco dei modelli GPT",

tools/i18n/locale/ja_JP.json

@@ -34,6 +34,7 @@
     "3、去混响、去延迟模型（by FoxJoy）：": "3、リバーブ除去と遅延除去モデル（by FoxJoy）：",
     "ASR 模型": "ASR モデル",
     "ASR 模型尺寸": "ASRモデルサイズ",
+    "数据类型精度": "データ型の精度",
     "ASR 语言设置": "ASR 言語設定",
     "ASR进程输出信息": "ASRプロセスの出力情報",
     "GPT模型列表": "GPTモデルリスト",

tools/i18n/locale/ko_KR.json

@@ -34,6 +34,7 @@
     "3、去混响、去延迟模型（by FoxJoy）：": "3. 잔향 제거 및 지연 제거 모델 (by FoxJoy):",
     "ASR 模型": "ASR 모델",
     "ASR 模型尺寸": "ASR 모델 크기",
+    "数据类型精度": "데이터 유형 정밀도",
     "ASR 语言设置": "ASR 언어 설정",
     "ASR进程输出信息": "ASR 프로세스 출력 정보",
     "GPT模型列表": "GPT 모델 목록",

tools/i18n/locale/pt_BR.json

@@ -34,6 +34,7 @@
     "3、去混响、去延迟模型（by FoxJoy）：": "3. Modelo de remoção de reverberação e atraso (por FoxJoy):",
     "ASR 模型": "Modelo ASR",
     "ASR 模型尺寸": "Tamanho do modelo ASR",
+    "数据类型精度": "precisão do tipo de dado",
     "ASR 语言设置": "Configurações de idioma do ASR",
     "ASR进程输出信息": "Informações de saída do processo ASR",
     "GPT模型列表": "Lista de modelos GPT",

tools/i18n/locale/ru_RU.json

@@ -34,6 +34,7 @@
     "3、去混响、去延迟模型（by FoxJoy）：": "3. Модель удаления реверберации и задержек (от FoxJoy):",
     "ASR 模型": "Модель ASR",
     "ASR 模型尺寸": "Размер модели ASR",
+    "数据类型精度": "точность типа данных",
     "ASR 语言设置": "Настройки языка ASR",
     "ASR进程输出信息": "Информация о процессе ASR",
     "GPT模型列表": "Список моделей GPT",

tools/i18n/locale/tr_TR.json

@@ -34,6 +34,7 @@
     "3、去混响、去延迟模型（by FoxJoy）：": "3. Yankı ve gecikme giderme modeli (FoxJoy tarafından):",
     "ASR 模型": "ASR modeli",
     "ASR 模型尺寸": "ASR model boyutu",
+    "数据类型精度": "veri türü doğruluğu",
     "ASR 语言设置": "ASR dil ayarları",
     "ASR进程输出信息": "ASR işlemi çıktı bilgisi",
     "GPT模型列表": "GPT model listesi",

tools/i18n/locale/zh_CN.json

@@ -34,6 +34,7 @@
     "3、去混响、去延迟模型（by FoxJoy）：": "3、去混响、去延迟模型（by FoxJoy）：",
     "ASR 模型": "ASR 模型",
     "ASR 模型尺寸": "ASR 模型尺寸",
+    "数据类型精度": "数据类型精度",
     "ASR 语言设置": "ASR 语言设置",
     "ASR进程输出信息": "ASR进程输出信息",
     "GPT模型列表": "GPT模型列表",

tools/i18n/locale/zh_HK.json

@@ -34,6 +34,7 @@
     "3、去混响、去延迟模型（by FoxJoy）：": "3、去混響、去延遲模型（by FoxJoy）：",
     "ASR 模型": "ASR 模型",
     "ASR 模型尺寸": "ASR 模型尺寸",
+    "数据类型精度": "數據類型精度",
     "ASR 语言设置": "ASR 語言設置",
     "ASR进程输出信息": "ASR進程輸出信息",
     "GPT模型列表": "GPT模型列表",

tools/i18n/locale/zh_SG.json

@@ -34,6 +34,7 @@
     "3、去混响、去延迟模型（by FoxJoy）：": "3、去混響、去延遲模型（by FoxJoy）：",
     "ASR 模型": "ASR 模型",
     "ASR 模型尺寸": "ASR 模型尺寸",
+    "数据类型精度": "數據類型精度",
     "ASR 语言设置": "ASR 語言設定",
     "ASR进程输出信息": "ASR進程輸出資訊",
     "GPT模型列表": "GPT模型列表",

tools/i18n/locale/zh_TW.json

@@ -34,6 +34,7 @@
     "3、去混响、去延迟模型（by FoxJoy）：": "3、去混響、去延遲模型（by FoxJoy）：",
     "ASR 模型": "ASR 模型",
     "ASR 模型尺寸": "ASR 模型尺寸",
+    "数据类型精度": "數據類型精度",
     "ASR 语言设置": "ASR 語言設置",
     "ASR进程输出信息": "ASR進程輸出資訊",
     "GPT模型列表": "GPT模型列表",

tools/uvr5/bs_roformer/bs_roformer.py

@@ -479,27 +479,31 @@ class BSRoformer(Module):
                               'b s f t c -> b (f s) t c')  # merge stereo / mono into the frequency, with frequency leading dimension, for band splitting
 
         x = rearrange(stft_repr, 'b f t c -> b t (f c)')
-
+        # print("460:", x.dtype)#fp32
         x = self.band_split(x)
 
         # axial / hierarchical attention
 
+        # print("487:",x.dtype)#fp16
         for transformer_block in self.layers:
 
             if len(transformer_block) == 3:
                 linear_transformer, time_transformer, freq_transformer = transformer_block
 
                 x, ft_ps = pack([x], 'b * d')
+                # print("494:", x.dtype)#fp16
                 x = linear_transformer(x)
+                # print("496:", x.dtype)#fp16
                 x, = unpack(x, ft_ps, 'b * d')
             else:
                 time_transformer, freq_transformer = transformer_block
 
+            # print("501:", x.dtype)#fp16
             x = rearrange(x, 'b t f d -> b f t d')
             x, ps = pack([x], '* t d')
 
             x = time_transformer(x)
-
+            # print("505:", x.dtype)#fp16
             x, = unpack(x, ps, '* t d')
             x = rearrange(x, 'b f t d -> b t f d')
             x, ps = pack([x], '* f d')
@@ -508,10 +512,11 @@ class BSRoformer(Module):
 
             x, = unpack(x, ps, '* f d')
 
+        # print("515:", x.dtype)######fp16
         x = self.final_norm(x)
 
         num_stems = len(self.mask_estimators)
-
+        # print("519:", x.dtype)#fp32
         mask = torch.stack([fn(x) for fn in self.mask_estimators], dim=1)
         mask = rearrange(mask, 'b n t (f c) -> b n f t c', c=2)
 

tools/uvr5/bsroformer.py

@@ -1,4 +1,5 @@
 # This code is modified from https://github.com/ZFTurbo/
+import pdb
 
 import librosa
 from tqdm import tqdm
@@ -10,6 +11,7 @@ import torch.nn as nn
 
 import warnings
 warnings.filterwarnings("ignore")
+from bs_roformer.bs_roformer import BSRoformer
 
 class BsRoformer_Loader:
     def get_model_from_config(self):
@@ -40,7 +42,7 @@ class BsRoformer_Loader:
 
         }
 
-        from bs_roformer.bs_roformer import BSRoformer
+
         model = BSRoformer(
             **dict(config)
         )
@@ -95,6 +97,8 @@ class BsRoformer_Loader:
                             part = nn.functional.pad(input=part, pad=(0, C - length), mode='reflect')
                         else:
                             part = nn.functional.pad(input=part, pad=(0, C - length, 0, 0), mode='constant', value=0)
+                    if(self.is_half==True):
+                        part=part.half()
                     batch_data.append(part)
                     batch_locations.append((i, length))
                     i += step
@@ -102,6 +106,7 @@ class BsRoformer_Loader:
 
                     if len(batch_data) >= batch_size or (i >= mix.shape[1]):
                         arr = torch.stack(batch_data, dim=0)
+                        # print(23333333,arr.dtype)
                         x = model(arr)
 
                         window = window_middle
@@ -192,14 +197,18 @@ class BsRoformer_Loader:
         # print("Elapsed time: {:.2f} sec".format(time.time() - start_time))
 
 
-    def __init__(self, model_path, device):
+    def __init__(self, model_path, device,is_half):
         self.device = device
         self.extract_instrumental=True
 
         model = self.get_model_from_config()
-        state_dict = torch.load(model_path)
+        state_dict = torch.load(model_path,map_location="cpu")
         model.load_state_dict(state_dict)
+        self.is_half=is_half
+        if(is_half==False):
             self.model = model.to(device)
+        else:
+            self.model = model.half().to(device)
 
 
     def _path_audio_(self, input, others_root, vocal_root, format, is_hp3=False):

tools/uvr5/webui.py

@@ -17,8 +17,8 @@ from bsroformer import BsRoformer_Loader
 weight_uvr5_root = "tools/uvr5/uvr5_weights"
 uvr5_names = []
 for name in os.listdir(weight_uvr5_root):
-    if name.endswith(".pth") or "onnx" in name:
+    if name.endswith(".pth") or name.endswith(".ckpt") or "onnx" in name:
-        uvr5_names.append(name.replace(".pth", ""))
+        uvr5_names.append(name.replace(".pth", "").replace(".ckpt", ""))
 
 device=sys.argv[1]
 is_half=eval(sys.argv[2])
@@ -37,8 +37,9 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format
         elif model_name == "Bs_Roformer" or "bs_roformer" in model_name.lower():
             func = BsRoformer_Loader
             pre_fun = func(
-                model_path = os.path.join(weight_uvr5_root, model_name + ".pth"),
+                model_path = os.path.join(weight_uvr5_root, model_name + ".ckpt"),
                 device = device,
+                is_half=is_half
             )
         else:
             func = AudioPre if "DeEcho" not in model_name else AudioPreDeEcho

webui.py

@@ -192,7 +192,7 @@ def change_tts_inference(if_tts,bert_path,cnhubert_base_path,gpu_number,gpt_path
         yield i18n("TTS推理进程已关闭")
 
 from tools.asr.config import asr_dict
-def open_asr(asr_inp_dir, asr_opt_dir, asr_model, asr_model_size, asr_lang):
+def open_asr(asr_inp_dir, asr_opt_dir, asr_model, asr_model_size, asr_lang, asr_precision):
     global p_asr
     if(p_asr==None):
         asr_inp_dir=my_utils.clean_path(asr_inp_dir)
@@ -202,16 +202,18 @@ def open_asr(asr_inp_dir, asr_opt_dir, asr_model, asr_model_size, asr_lang):
         cmd += f' -o "{asr_opt_dir}"'
         cmd += f' -s {asr_model_size}'
         cmd += f' -l {asr_lang}'
-        cmd += " -p %s"%("float16"if is_half==True else "float32")
+        cmd += f" -p {asr_precision}"
-
+        output_file_name = os.path.basename(asr_inp_dir)
-        yield "ASR任务开启：%s"%cmd,{"__type__":"update","visible":False},{"__type__":"update","visible":True}
+        output_folder = asr_opt_dir or "output/asr_opt"
+        output_file_path = os.path.abspath(f'{output_folder}/{output_file_name}.list')
+        yield "ASR任务开启：%s"%cmd, {"__type__":"update","visible":False}, {"__type__":"update","visible":True}, {"__type__":"update"}
         print(cmd)
         p_asr = Popen(cmd, shell=True)
         p_asr.wait()
         p_asr=None
-        yield f"ASR任务完成, 查看终端进行下一步",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+        yield f"ASR任务完成, 查看终端进行下一步", {"__type__":"update","visible":True}, {"__type__":"update","visible":False}, {"__type__":"update","value":output_file_path}
     else:
-        yield "已有正在进行的ASR任务，需先终止才能开启下一次任务",{"__type__":"update","visible":False},{"__type__":"update","visible":True}
+        yield "已有正在进行的ASR任务，需先终止才能开启下一次任务", {"__type__":"update","visible":False}, {"__type__":"update","visible":True}, {"__type__":"update"}
         # return None
 
 def close_asr():
@@ -227,14 +229,14 @@ def open_denoise(denoise_inp_dir, denoise_opt_dir):
         denoise_opt_dir=my_utils.clean_path(denoise_opt_dir)
         cmd = '"%s" tools/cmd-denoise.py -i "%s" -o "%s" -p %s'%(python_exec,denoise_inp_dir,denoise_opt_dir,"float16"if is_half==True else "float32")
 
-        yield "语音降噪任务开启：%s"%cmd,{"__type__":"update","visible":False},{"__type__":"update","visible":True}
+        yield "语音降噪任务开启：%s"%cmd, {"__type__":"update","visible":False}, {"__type__":"update","visible":True}, {"__type__":"update"}
         print(cmd)
         p_denoise = Popen(cmd, shell=True)
         p_denoise.wait()
         p_denoise=None
-        yield f"语音降噪任务完成, 查看终端进行下一步",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+        yield f"语音降噪任务完成, 查看终端进行下一步", {"__type__":"update","visible":True}, {"__type__":"update","visible":False}, {"__type__":"update","value":denoise_opt_dir}
     else:
-        yield "已有正在进行的语音降噪任务，需先终止才能开启下一次任务",{"__type__":"update","visible":False},{"__type__":"update","visible":True}
+        yield "已有正在进行的语音降噪任务，需先终止才能开启下一次任务", {"__type__":"update","visible":False}, {"__type__":"update","visible":True}, {"__type__":"update"}
         # return None
 
 def close_denoise():
@@ -341,12 +343,12 @@ def open_slice(inp,opt_root,threshold,min_length,min_interval,hop_size,max_sil_k
     inp = my_utils.clean_path(inp)
     opt_root = my_utils.clean_path(opt_root)
     if(os.path.exists(inp)==False):
-        yield "输入路径不存在",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+        yield "输入路径不存在", {"__type__":"update","visible":True}, {"__type__":"update","visible":False}, {"__type__": "update"}, {"__type__": "update"}
         return
     if os.path.isfile(inp):n_parts=1
     elif os.path.isdir(inp):pass
     else:
-        yield "输入路径存在但既不是文件也不是文件夹",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+        yield "输入路径存在但既不是文件也不是文件夹", {"__type__":"update","visible":True}, {"__type__":"update","visible":False}, {"__type__": "update"}, {"__type__": "update"}
         return
     if (ps_slice == []):
         for i_part in range(n_parts):
@@ -354,13 +356,13 @@ def open_slice(inp,opt_root,threshold,min_length,min_interval,hop_size,max_sil_k
             print(cmd)
             p = Popen(cmd, shell=True)
             ps_slice.append(p)
-        yield "切割执行中", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+        yield "切割执行中", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}, {"__type__": "update"}, {"__type__": "update"}
         for p in ps_slice:
             p.wait()
         ps_slice=[]
-        yield "切割结束",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+        yield "切割结束", {"__type__":"update","visible":True}, {"__type__":"update","visible":False}, {"__type__": "update", "value":opt_root}, {"__type__": "update", "value":opt_root}
     else:
-        yield "已有正在进行的切割任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+        yield "已有正在进行的切割任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}, {"__type__": "update"}, {"__type__": "update"}
 
 def close_slice():
     global ps_slice
@@ -689,33 +691,37 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
                 uvr5_info = gr.Textbox(label=i18n("UVR5进程输出信息"))
             gr.Markdown(value=i18n("0b-语音切分工具"))
             with gr.Row():
+                with gr.Column(scale=3):
                     with gr.Row():
                         slice_inp_path=gr.Textbox(label=i18n("音频自动切分输入路径，可文件可文件夹"),value="")
                         slice_opt_root=gr.Textbox(label=i18n("切分后的子音频的输出根目录"),value="output/slicer_opt")
+                    with gr.Row():
                         threshold=gr.Textbox(label=i18n("threshold:音量小于这个值视作静音的备选切割点"),value="-34")
                         min_length=gr.Textbox(label=i18n("min_length:每段最小多长，如果第一段太短一直和后面段连起来直到超过这个值"),value="4000")
                         min_interval=gr.Textbox(label=i18n("min_interval:最短切割间隔"),value="300")
                         hop_size=gr.Textbox(label=i18n("hop_size:怎么算音量曲线，越小精度越大计算量越高（不是精度越大效果越好）"),value="10")
                         max_sil_kept=gr.Textbox(label=i18n("max_sil_kept:切完后静音最多留多长"),value="500")
                     with gr.Row():
-                    open_slicer_button=gr.Button(i18n("开启语音切割"), variant="primary",visible=True)
-                    close_slicer_button=gr.Button(i18n("终止语音切割"), variant="primary",visible=False)
                         _max=gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("max:归一化后最大值多少"),value=0.9,interactive=True)
                         alpha=gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("alpha_mix:混多少比例归一化后音频进来"),value=0.25,interactive=True)
                         n_process=gr.Slider(minimum=1,maximum=n_cpu,step=1,label=i18n("切割使用的进程数"),value=4,interactive=True)
+                    with gr.Row():
                         slicer_info = gr.Textbox(label=i18n("语音切割进程输出信息"))
+                open_slicer_button=gr.Button(i18n("开启语音切割"), variant="primary",visible=True)
+                close_slicer_button=gr.Button(i18n("终止语音切割"), variant="primary",visible=False)
             gr.Markdown(value=i18n("0bb-语音降噪工具"))
             with gr.Row():
-                open_denoise_button = gr.Button(i18n("开启语音降噪"), variant="primary",visible=True)
+                with gr.Column(scale=3):
-                close_denoise_button = gr.Button(i18n("终止语音降噪进程"), variant="primary",visible=False)
+                    with gr.Row():
                         denoise_input_dir=gr.Textbox(label=i18n("降噪音频文件输入文件夹"),value="")
                         denoise_output_dir=gr.Textbox(label=i18n("降噪结果输出文件夹"),value="output/denoise_opt")
+                    with gr.Row():
                         denoise_info = gr.Textbox(label=i18n("语音降噪进程输出信息"))
+                open_denoise_button = gr.Button(i18n("开启语音降噪"), variant="primary",visible=True)
+                close_denoise_button = gr.Button(i18n("终止语音降噪进程"), variant="primary",visible=False)
             gr.Markdown(value=i18n("0c-中文批量离线ASR工具"))
             with gr.Row():
-                open_asr_button = gr.Button(i18n("开启离线批量ASR"), variant="primary",visible=True)
+                with gr.Column(scale=3):
-                close_asr_button = gr.Button(i18n("终止ASR进程"), variant="primary",visible=False)
-                with gr.Column():
                     with gr.Row():
                         asr_inp_dir = gr.Textbox(
                             label=i18n("输入文件夹路径"),
@@ -746,17 +752,39 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
                             interactive = True,
                             value="zh"
                         )
+                        asr_precision = gr.Dropdown(
+                            label       = i18n("数据类型精度"),
+                            choices     = ["float32"],
+                            interactive = True,
+                            value="float32"
+                        ) 
                     with gr.Row():
                         asr_info = gr.Textbox(label=i18n("ASR进程输出信息"))     
+                open_asr_button = gr.Button(i18n("开启离线批量ASR"), variant="primary",visible=True)
+                close_asr_button = gr.Button(i18n("终止ASR进程"), variant="primary",visible=False)                  
 
                 def change_lang_choices(key): #根据选择的模型修改可选的语言
                     # return gr.Dropdown(choices=asr_dict[key]['lang'])
                     return {"__type__": "update", "choices": asr_dict[key]['lang'],"value":asr_dict[key]['lang'][0]}
                 def change_size_choices(key): # 根据选择的模型修改可选的模型尺寸
                     # return gr.Dropdown(choices=asr_dict[key]['size'])
-                    return {"__type__": "update", "choices": asr_dict[key]['size']}
+                    return {"__type__": "update", "choices": asr_dict[key]['size'],"value":asr_dict[key]['size'][-1]}
+                def change_precision_choices(key): #根据选择的模型修改可选的语言
+                    if key =="Faster Whisper (多语种)":
+                        if default_batch_size <= 4:
+                            precision = 'int8'
+                        elif is_half:
+                            precision = 'float16'
+                        else:
+                            precision = 'float32'
+                    else:
+                        precision = 'float32'
+                    # return gr.Dropdown(choices=asr_dict[key]['precision'])
+                    return {"__type__": "update", "choices": asr_dict[key]['precision'],"value":precision}
                 asr_model.change(change_lang_choices, [asr_model], [asr_lang])
                 asr_model.change(change_size_choices, [asr_model], [asr_size])
+                asr_model.change(change_precision_choices, [asr_model], [asr_precision])
+
                 
             gr.Markdown(value=i18n("0d-语音文本校对标注工具"))
             with gr.Row():
@@ -769,11 +797,11 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
                 label_info = gr.Textbox(label=i18n("打标工具进程输出信息"))
             if_label.change(change_label, [if_label,path_list], [label_info])
             if_uvr5.change(change_uvr5, [if_uvr5], [uvr5_info])
-            open_asr_button.click(open_asr, [asr_inp_dir, asr_opt_dir, asr_model, asr_size, asr_lang], [asr_info,open_asr_button,close_asr_button])
+            open_asr_button.click(open_asr, [asr_inp_dir, asr_opt_dir, asr_model, asr_size, asr_lang, asr_precision], [asr_info,open_asr_button,close_asr_button,path_list])
             close_asr_button.click(close_asr, [], [asr_info,open_asr_button,close_asr_button])
-            open_slicer_button.click(open_slice, [slice_inp_path,slice_opt_root,threshold,min_length,min_interval,hop_size,max_sil_kept,_max,alpha,n_process], [slicer_info,open_slicer_button,close_slicer_button])
+            open_slicer_button.click(open_slice, [slice_inp_path,slice_opt_root,threshold,min_length,min_interval,hop_size,max_sil_kept,_max,alpha,n_process], [slicer_info,open_slicer_button,close_slicer_button,asr_inp_dir,denoise_input_dir])
             close_slicer_button.click(close_slice, [], [slicer_info,open_slicer_button,close_slicer_button])
-            open_denoise_button.click(open_denoise, [denoise_input_dir,denoise_output_dir], [denoise_info,open_denoise_button,close_denoise_button])
+            open_denoise_button.click(open_denoise, [denoise_input_dir,denoise_output_dir], [denoise_info,open_denoise_button,close_denoise_button,asr_inp_dir])
             close_denoise_button.click(close_denoise, [], [denoise_info,open_denoise_button,close_denoise_button])
 
         with gr.TabItem(i18n("1-GPT-SoVITS-TTS")):