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28
README.md
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@ -147,7 +147,17 @@ Users in China region can download these two models by entering the links below
- [UVR5 Weights](https://www.icloud.com.cn/iclouddrive/0bekRKDiJXboFhbfm3lM2fVbA#UVR5_Weights)
For Multilingual ASR, download models from [FunAudioLLM/SenseVoiceSmall](https://huggingface.co/FunAudioLLM/SenseVoiceSmall/tree/main) or [iic/SenseVoiceSmall](https://modelscope.cn/models/iic/SenseVoiceSmall/files) and place them in `tools/asr/models`.
For Chinese ASR (additionally), download models from [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), and [Damo Punc Model](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/files) and place them in `tools/asr/models`.
For English or Japanese ASR (additionally), download models from [Faster Whisper Large V3](https://huggingface.co/Systran/faster-whisper-large-v3) and place them in `tools/asr/models`. Also, [other models](https://huggingface.co/Systran) may have the similar effect with smaller disk footprint.
Users in 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) (clicking "Download a copy")
- [Faster Whisper Large V3](https://hf-mirror.com/Systran/faster-whisper-large-v3) (HuggingFace mirror site)
For SenseVoice Multilingual ASR, download models from [FunAudioLLM/SenseVoiceSmall](https://huggingface.co/FunAudioLLM/SenseVoiceSmall/tree/main) or [iic/SenseVoiceSmall](https://modelscope.cn/models/iic/SenseVoiceSmall/files) and place them in `tools/asr/models`.
## Dataset Format
@ -208,13 +218,23 @@ 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
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>
```
SenseVoice Multilingual ASR
```
python tools/asr/sensevoice.py -i <input> -o <output> -l <language> -d <device>
```
A custom list save path is enabled
## Credits
Special thanks to the following projects and contributors:
@ -239,6 +259,8 @@ Special thanks to the following projects and contributors:
- [SubFix](https://github.com/cronrpc/SubFix)
- [FFmpeg](https://github.com/FFmpeg/FFmpeg)
- [gradio](https://github.com/gradio-app/gradio)
- [faster-whisper](https://github.com/SYSTRAN/faster-whisper)
- [FunASR](https://github.com/alibaba-damo-academy/FunASR)
- [SenseVoice](https://github.com/FunAudioLLM/SenseVoice)
## Thanks to all contributors for their efforts

26
docs/cn/README.md
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@ -147,7 +147,17 @@ docker run --rm -it --gpus=all --env=is_half=False --volume=G:\GPT-SoVITS-Docker
- [UVR5 Weights](https://www.icloud.com.cn/iclouddrive/0bekRKDiJXboFhbfm3lM2fVbA#UVR5_Weights)
对于多语言自动语音识别（附加），从 [FunAudioLLM/SenseVoiceSmall](https://huggingface.co/FunAudioLLM/SenseVoiceSmall/tree/main) 或 [iic/SenseVoiceSmall](https://modelscope.cn/models/iic/SenseVoiceSmall/files) 下载模型，并将它们放置在 `tools/asr/models` 中。
对于中文自动语音识别（附加），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` 中。
对于SenseVoice多语言自动语音识别（附加），从 [FunAudioLLM/SenseVoiceSmall](https://huggingface.co/FunAudioLLM/SenseVoiceSmall/tree/main) 或 [iic/SenseVoiceSmall](https://modelscope.cn/models/iic/SenseVoiceSmall/files) 下载模型，并将它们放置在 `tools/asr/models` 中。
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”)
- [Faster Whisper Large V3](https://hf-mirror.com/Systran/faster-whisper-large-v3)(Hugging Face镜像站)
@ -210,7 +220,17 @@ python audio_slicer.py \
--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
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>
````
使用SenseVoice进行多语言ASR
````
python tools/asr/sensevoice.py -i <input> -o <output> -l <language> -d <device>
````
@ -241,6 +261,8 @@ python tools/asr/sensevoice.py -i <input> -o <output> -l <language> -d <device>
- [SubFix](https://github.com/cronrpc/SubFix)
- [FFmpeg](https://github.com/FFmpeg/FFmpeg)
- [gradio](https://github.com/gradio-app/gradio)
- [faster-whisper](https://github.com/SYSTRAN/faster-whisper)
- [FunASR](https://github.com/alibaba-damo-academy/FunASR)
- [SenseVoice](https://github.com/FunAudioLLM/SenseVoice)
## 感谢所有贡献者的努力

3
docs/ja/README.md
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@ -206,7 +206,7 @@ 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
@ -236,6 +236,7 @@ python ./tools/asr/fasterwhisper_asr.py -i <input> -o <output> -l <language>
- [gradio](https://github.com/gradio-app/gradio)
- [faster-whisper](https://github.com/SYSTRAN/faster-whisper)
- [FunASR](https://github.com/alibaba-damo-academy/FunASR)
- [SenseVoice](https://github.com/FunAudioLLM/SenseVoice)
## すべてのコントリビューターに感謝します

4
docs/ko/README.md
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@ -210,7 +210,7 @@ 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.
@ -240,7 +240,7 @@ python ./tools/asr/fasterwhisper_asr.py -i <input> -o <output> -l <language>
- [gradio](https://github.com/gradio-app/gradio)
- [faster-whisper](https://github.com/SYSTRAN/faster-whisper)
- [FunASR](https://github.com/alibaba-damo-academy/FunASR)
- [SenseVoice](https://github.com/FunAudioLLM/SenseVoice)
## 모든 기여자들에게 감사드립니다 ;)

1
docs/tr/README.md
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@ -250,6 +250,7 @@ python ./tools/asr/fasterwhisper_asr.py -i <girdi> -o <çıktı> -l <dil>
- [gradio](https://github.com/gradio-app/gradio)
- [faster-whisper](https://github.com/SYSTRAN/faster-whisper)
- [FunASR](https://github.com/alibaba-damo-academy/FunASR)
- [SenseVoice](https://github.com/FunAudioLLM/SenseVoice)
## Tüm katkıda bulunanlara çabaları için teşekkürler

1
requirements.txt
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@ -24,4 +24,5 @@ psutil
jieba_fast
jieba
LangSegment>=0.2.0
faster_whisper
wordsegment

39
tools/asr/config.py Normal file
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@ -0,0 +1,39 @@
import os
def check_fw_local_models():
'''
启动时检查本地是否有 Faster Whisper 模型.
'''
model_size_list = [
"tiny", "tiny.en",
"base", "base.en",
"small", "small.en",
"medium", "medium.en",
"large", "large-v1",
"large-v2", "large-v3"]
for i, size in enumerate(model_size_list):
if os.path.exists(f'tools/asr/models/faster-whisper-{size}'):
model_size_list[i] = size + '-local'
return model_size_list
asr_dict = {
"达摩 ASR (中文)": {
'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',
'precision': ['float32', 'float16', 'int8']
},
"Sense Voice": {
'lang': ['auto', 'zh', 'en', 'ja'],
'size': ['small'],
'path': 'sensevoice_asr.py',
'precision': 'float32'
}
}

114
tools/asr/fasterwhisper_asr.py Normal file
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@ -0,0 +1,114 @@
import argparse
import os
import traceback
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
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(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
print("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 print(traceback.format_exc())
input_file_names = os.listdir(input_folder)
input_file_names.sort()
output = []
output_file_name = os.path.basename(input_folder)
for file_name in tqdm(input_file_names):
try:
file_path = os.path.join(input_folder, file_name)
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":
print("检测为中文文本, 转 FunASR 处理")
if("only_asr"not in globals()):
from tools.asr.funasr_asr 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}|{output_file_name}|{info.language.upper()}|{text}")
except:
print(traceback.format_exc())
output_folder = output_folder or "output/asr_opt"
os.makedirs(output_folder, exist_ok=True)
output_file_path = os.path.abspath(f'{output_folder}/{output_file_name}.list')
with open(output_file_path, "w", encoding="utf-8") as f:
f.write("\n".join(output))
print(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','int8'],
help="fp16, int8 or fp32")
cmd = parser.parse_args()
output_file_path = execute_asr(
input_folder = cmd.input_folder,
output_folder = cmd.output_folder,
model_size = cmd.model_size,
language = cmd.language,
precision = cmd.precision,
)

77
tools/asr/funasr_asr.py Normal file
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@ -0,0 +1,77 @@
# -*- coding:utf-8 -*-
import argparse
import os
import traceback
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 = ''
print(traceback.format_exc())
return text
def execute_asr(input_folder, output_folder, model_size, language):
input_file_names = os.listdir(input_folder)
input_file_names.sort()
output = []
output_file_name = os.path.basename(input_folder)
for file_name in tqdm(input_file_names):
try:
file_path = os.path.join(input_folder, file_name)
text = model.generate(input=file_path)[0]["text"]
output.append(f"{file_path}|{output_file_name}|{language.upper()}|{text}")
except:
print(traceback.format_exc())
output_folder = output_folder or "output/asr_opt"
os.makedirs(output_folder, exist_ok=True)
output_file_path = os.path.abspath(f'{output_folder}/{output_file_name}.list')
with open(output_file_path, "w", encoding="utf-8") as f:
f.write("\n".join(output))
print(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(
input_folder = cmd.input_folder,
output_folder = cmd.output_folder,
model_size = cmd.model_size,
language = cmd.language,
)

5
tools/asr/sensevoice.py → tools/asr/sensevoice_asr.py
View File

@ -63,6 +63,11 @@ if __name__ == '__main__':
help="Language of the audio files.")
parser.add_argument("-d", "--device", type=str, default=None, choices=['cpu','cuda'],
help="CPU or CUDA")
parser.add_argument("-p", "--precision", type=str, default='float32', choices=['float32'],
help="fp16 or fp32")
parser.add_argument("-s", "--model_size", type=str, default='small',
choices=['small'],
help="Model Size of Faster Whisper")
cmd = parser.parse_args()
output_file_path = execute_asr(

99
webui.py
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@ -194,25 +194,28 @@ def change_tts_inference(if_tts,bert_path,cnhubert_base_path,gpu_number,gpt_path
p_tts_inference=None
yield i18n("TTS推理进程已关闭")
def open_asr(asr_inp_dir, asr_opt_dir, asr_model, asr_model_size, asr_lang):
from tools.asr.config import asr_dict
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)
cmd = f'"{python_exec}" tools/asr/sensevoice.py'
cmd = f'"{python_exec}" tools/asr/{asr_dict[asr_model]["path"]}'
cmd += f' -i "{asr_inp_dir}"'
cmd += f' -o "{asr_opt_dir}"'
cmd += f' -s {asr_model_size}'
cmd += f' -l {asr_lang}'
cmd += f" -p {asr_precision}"
output_file_name = os.path.basename(asr_inp_dir)
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"}
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},{"__type__":"update","value":output_file_path}
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},{"__type__":"update"}
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():
@ -220,7 +223,7 @@ def close_asr():
if(p_asr!=None):
kill_process(p_asr.pid)
p_asr=None
return "已终止ASR进程",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
return "已终止ASR进程", {"__type__":"update","visible":True}, {"__type__":"update","visible":False}
def open_denoise(denoise_inp_dir, denoise_opt_dir):
global p_denoise
if(p_denoise==None):
@ -228,14 +231,14 @@ 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():
@ -243,7 +246,7 @@ def close_denoise():
if(p_denoise!=None):
kill_process(p_denoise.pid)
p_denoise=None
return "已终止语音降噪进程",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
return "已终止语音降噪进程", {"__type__":"update","visible":True}, {"__type__":"update","visible":False}
p_train_SoVITS=None
def open1Ba(batch_size,total_epoch,exp_name,text_low_lr_rate,if_save_latest,if_save_every_weights,save_every_epoch,gpu_numbers1Ba,pretrained_s2G,pretrained_s2D):
@ -273,21 +276,21 @@ def open1Ba(batch_size,total_epoch,exp_name,text_low_lr_rate,if_save_latest,if_s
with open(tmp_config_path,"w")as f:f.write(json.dumps(data))
cmd = '"%s" GPT_SoVITS/s2_train.py --config "%s"'%(python_exec,tmp_config_path)
yield "SoVITS训练开始：%s"%cmd,{"__type__":"update","visible":False},{"__type__":"update","visible":True}
yield "SoVITS训练开始：%s"%cmd, {"__type__":"update","visible":False}, {"__type__":"update","visible":True}
print(cmd)
p_train_SoVITS = Popen(cmd, shell=True)
p_train_SoVITS.wait()
p_train_SoVITS=None
yield "SoVITS训练完成",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
yield "SoVITS训练完成", {"__type__":"update","visible":True}, {"__type__":"update","visible":False}
else:
yield "已有正在进行的SoVITS训练任务，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SoVITS训练任务，需先终止才能开启下一次任务", {"__type__":"update","visible":False}, {"__type__":"update","visible":True}
def close1Ba():
global p_train_SoVITS
if(p_train_SoVITS!=None):
kill_process(p_train_SoVITS.pid)
p_train_SoVITS=None
return "已终止SoVITS训练",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
return "已终止SoVITS训练", {"__type__":"update","visible":True}, {"__type__":"update","visible":False}
p_train_GPT=None
def open1Bb(batch_size,total_epoch,exp_name,if_dpo,if_save_latest,if_save_every_weights,save_every_epoch,gpu_numbers,pretrained_s1):
@ -320,21 +323,21 @@ def open1Bb(batch_size,total_epoch,exp_name,if_dpo,if_save_latest,if_save_every_
with open(tmp_config_path, "w") as f:f.write(yaml.dump(data, default_flow_style=False))
# cmd = '"%s" GPT_SoVITS/s1_train.py --config_file "%s" --train_semantic_path "%s/6-name2semantic.tsv" --train_phoneme_path "%s/2-name2text.txt" --output_dir "%s/logs_s1"'%(python_exec,tmp_config_path,s1_dir,s1_dir,s1_dir)
cmd = '"%s" GPT_SoVITS/s1_train.py --config_file "%s" '%(python_exec,tmp_config_path)
yield "GPT训练开始：%s"%cmd,{"__type__":"update","visible":False},{"__type__":"update","visible":True}
yield "GPT训练开始：%s"%cmd, {"__type__":"update","visible":False}, {"__type__":"update","visible":True}
print(cmd)
p_train_GPT = Popen(cmd, shell=True)
p_train_GPT.wait()
p_train_GPT=None
yield "GPT训练完成",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
yield "GPT训练完成", {"__type__":"update","visible":True}, {"__type__":"update","visible":False}
else:
yield "已有正在进行的GPT训练任务，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GPT训练任务，需先终止才能开启下一次任务", {"__type__":"update","visible":False}, {"__type__":"update","visible":True}
def close1Bb():
global p_train_GPT
if(p_train_GPT!=None):
kill_process(p_train_GPT.pid)
p_train_GPT=None
return "已终止GPT训练",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
return "已终止GPT训练", {"__type__":"update","visible":True}, {"__type__":"update","visible":False}
ps_slice=[]
def open_slice(inp,opt_root,threshold,min_length,min_interval,hop_size,max_sil_kept,_max,alpha,n_parts):
@ -342,12 +345,12 @@ 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):
@ -355,13 +358,13 @@ 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
@ -468,7 +471,7 @@ def open1b(inp_text,inp_wav_dir,exp_name,gpu_numbers,ssl_pretrained_dir):
for p in ps1b:
p.wait()
ps1b=[]
yield "SSL提取进程结束",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
yield "SSL提取进程结束", {"__type__":"update","visible":True}, {"__type__":"update","visible":False}
else:
yield "已有正在进行的SSL提取任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
@ -525,7 +528,7 @@ def open1c(inp_text,exp_name,gpu_numbers,pretrained_s2G_path):
with open(path_semantic, "w", encoding="utf8") as f:
f.write("\n".join(opt) + "\n")
ps1c=[]
yield "语义token提取进程结束",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
yield "语义token提取进程结束", {"__type__":"update","visible":True}, {"__type__":"update","visible":False}
else:
yield "已有正在进行的语义token提取任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
@ -731,25 +734,53 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
with gr.Row():
asr_model = gr.Dropdown(
label = i18n("ASR 模型"),
choices = ['SenseVoice'],
choices = list(asr_dict.keys()),
interactive = True,
value="SenseVoice"
value="达摩 ASR (中文)"
)
asr_size = gr.Dropdown(
label = i18n("ASR 模型尺寸"),
choices = ["small"],
choices = ["large"],
interactive = True,
value="small"
value="large"
)
asr_lang = gr.Dropdown(
label = i18n("ASR 语言设置"),
choices = ["auto","zh","en","ja"],
choices = ["zh"],
interactive = True,
value="auto"
value="zh"
)
asr_precision = gr.Dropdown(
label = i18n("ASR 语言设置"),
choices = ["zh"],
interactive = True,
value="zh"
)
with gr.Row():
asr_info = gr.Textbox(label=i18n("ASR进程输出信息"))
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'],"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]['lang'])
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_size_choices, [asr_model], [asr_precision])
gr.Markdown(value=i18n("0d-语音文本校对标注工具"))
with gr.Row():
@ -762,11 +793,11 @@ 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,path_list])
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")):