Merge branch 'main' of https://github.com/zZxztxZz/GPT-SoVITS-VoiceLoader

Docker/damo.sha256

@@ -0,0 +1,3 @@
+5bba782a5e9196166233b9ab12ba04cadff9ef9212b4ff6153ed9290ff679025 /workspace/tools/damo_asr/models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/model.pb
+b3be75be477f0780277f3bae0fe489f48718f585f3a6e45d7dd1fbb1a4255fc5 /workspace/tools/damo_asr/models/speech_fsmn_vad_zh-cn-16k-common-pytorch/model.pb
+a5818bb9d933805a916eebe41eb41648f7f9caad30b4bd59d56f3ca135421916 /workspace/tools/damo_asr/models/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/model.pb

Docker/download.sh

@@ -0,0 +1,11 @@
+#!/usr/bin/env bash
+
+set -Eeuo pipefail
+
+echo "Downloading models..."
+
+aria2c --disable-ipv6 --input-file /workspace/Docker/links.txt --dir /workspace --continue
+
+echo "Checking SHA256..."
+
+parallel --will-cite -a /workspace/Docker/links.sha256 "echo -n {} | sha256sum -c"

Docker/links.sha256

@@ -0,0 +1,12 @@
+b1c1e17e9c99547a89388f72048cd6e1b41b5a18b170e86a46dfde0324d63eb1 /workspace/GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
+fc579c1db3c1e21b721001cf99d7a584214280df19b002e200b630a34fa06eb8 /workspace/GPT_SoVITS/pretrained_models/s2D488k.pth
+020a014e1e01e550e510f2f61fae5e5f5b6aab40f15c22f1f12f724df507e835 /workspace/GPT_SoVITS/pretrained_models/s2G488k.pth
+24164f129c66499d1346e2aa55f183250c223161ec2770c0da3d3b08cf432d3c /workspace/GPT_SoVITS/pretrained_models/chinese-hubert-base/pytorch_model.bin
+e53a693acc59ace251d143d068096ae0d7b79e4b1b503fa84c9dcf576448c1d8 /workspace/GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large/pytorch_model.bin
+39796caa5db18d7f9382d8ac997ac967bfd85f7761014bb807d2543cc844ef05 /workspace/tools/uvr5/uvr5_weights/HP2_all_vocals.pth
+45e6b65199e781b4a6542002699be9f19cd3d1cb7d1558bc2bfbcd84674dfe28 /workspace/tools/uvr5/uvr5_weights/HP3_all_vocals.pth
+5908891829634926119720241e8573d97cbeb8277110a7512bdb0bd7563258ee /workspace/tools/uvr5/uvr5_weights/HP5_only_main_vocal.pth
+8c8fd1582f9aabc363e47af62ddb88df6cae7e064cae75bbf041a067a5e0aee2 /workspace/tools/uvr5/uvr5_weights/VR-DeEchoAggressive.pth
+01376dd2a571bf3cb9cced680732726d2d732609d09216a610b0d110f133febe /workspace/tools/uvr5/uvr5_weights/VR-DeEchoDeReverb.pth
+56aba59db3bcdd14a14464e62f3129698ecdea62eee0f003b9360923eb3ac79e /workspace/tools/uvr5/uvr5_weights/VR-DeEchoNormal.pth
+233bb5c6aaa365e568659a0a81211746fa881f8f47f82d9e864fce1f7692db80 /workspace/tools/uvr5/uvr5_weights/onnx_dereverb_By_FoxJoy/vocals.onnx

Docker/links.txt

@@ -0,0 +1,34 @@
+# GPT-SoVITS models
+https://huggingface.co/lj1995/GPT-SoVITS/resolve/main/s1bert25hz-2kh-longer-epoch%3D68e-step%3D50232.ckpt
+  out=GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
+https://huggingface.co/lj1995/GPT-SoVITS/resolve/main/s2D488k.pth
+  out=GPT_SoVITS/pretrained_models/s2D488k.pth
+https://huggingface.co/lj1995/GPT-SoVITS/resolve/main/s2G488k.pth
+  out=GPT_SoVITS/pretrained_models/s2G488k.pth
+https://huggingface.co/lj1995/GPT-SoVITS/resolve/main/chinese-hubert-base/config.json
+  out=GPT_SoVITS/pretrained_models/chinese-hubert-base/config.json
+https://huggingface.co/lj1995/GPT-SoVITS/resolve/main/chinese-hubert-base/preprocessor_config.json
+  out=GPT_SoVITS/pretrained_models/chinese-hubert-base/preprocessor_config.json
+https://huggingface.co/lj1995/GPT-SoVITS/resolve/main/chinese-hubert-base/pytorch_model.bin
+  out=GPT_SoVITS/pretrained_models/chinese-hubert-base/pytorch_model.bin
+https://huggingface.co/lj1995/GPT-SoVITS/resolve/main/chinese-roberta-wwm-ext-large/config.json
+  out=GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large/config.json
+https://huggingface.co/lj1995/GPT-SoVITS/resolve/main/chinese-roberta-wwm-ext-large/pytorch_model.bin
+  out=GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large/pytorch_model.bin
+https://huggingface.co/lj1995/GPT-SoVITS/resolve/main/chinese-roberta-wwm-ext-large/tokenizer.json
+  out=GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large/tokenizer.json
+# UVR5
+https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2_all_vocals.pth
+  out=tools/uvr5/uvr5_weights/HP2_all_vocals.pth
+https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP3_all_vocals.pth
+  out=tools/uvr5/uvr5_weights/HP3_all_vocals.pth
+https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5_only_main_vocal.pth
+  out=tools/uvr5/uvr5_weights/HP5_only_main_vocal.pth
+https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoAggressive.pth
+  out=tools/uvr5/uvr5_weights/VR-DeEchoAggressive.pth
+https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoDeReverb.pth
+  out=tools/uvr5/uvr5_weights/VR-DeEchoDeReverb.pth
+https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoNormal.pth
+  out=tools/uvr5/uvr5_weights/VR-DeEchoNormal.pth
+https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/onnx_dereverb_By_FoxJoy/vocals.onnx
+  out=tools/uvr5/uvr5_weights/onnx_dereverb_By_FoxJoy/vocals.onnx

Dockerfile

@@ -0,0 +1,53 @@
+# Base CUDA image
+FROM cnstark/pytorch:2.0.1-py3.9.17-cuda11.8.0-ubuntu20.04
+
+LABEL maintainer="breakstring@hotmail.com"
+LABEL version="dev-20240123.03"
+LABEL description="Docker image for GPT-SoVITS"
+
+
+# Install 3rd party apps
+ENV DEBIAN_FRONTEND=noninteractive
+ENV TZ=Etc/UTC
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends tzdata ffmpeg libsox-dev parallel aria2 git git-lfs && \
+    rm -rf /var/lib/apt/lists/* && \
+    git lfs install
+
+# Copy application
+WORKDIR /workspace
+COPY . /workspace
+
+# Download models
+RUN chmod +x /workspace/Docker/download.sh && /workspace/Docker/download.sh
+
+# 本应该从 requirements.txt 里面安装package，但是由于funasr和modelscope的问题，暂时先在后面手工安装依赖包吧
+RUN pip install --no-cache-dir torch numpy scipy tensorboard librosa==0.9.2 numba==0.56.4 pytorch-lightning gradio==3.14.0 ffmpeg-python onnxruntime tqdm cn2an pypinyin pyopenjtalk g2p_en chardet transformers jieba psutil PyYAML
+# 这里强制指定了modelscope和funasr的版本，后面damo_asr的模型让它们自己下载
+RUN pip install --no-cache-dir modelscope~=1.10.0 torchaudio sentencepiece funasr~=0.8.7
+
+# 先屏蔽掉，让容器里自己下载
+# Clone damo_asr
+#WORKDIR /workspace/tools/damo_asr/models
+#RUN git clone --depth 1 https://www.modelscope.cn/iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch && \
+#    (cd speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch && git lfs pull)
+#RUN git clone --depth 1 https://www.modelscope.cn/iic/speech_fsmn_vad_zh-cn-16k-common-pytorch.git speech_fsmn_vad_zh-cn-16k-common-pytorch && \
+#    (cd speech_fsmn_vad_zh-cn-16k-common-pytorch && git lfs pull)
+#RUN git clone --depth 1 https://www.modelscope.cn/iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch.git punc_ct-transformer_zh-cn-common-vocab272727-pytorch && \
+#    (cd punc_ct-transformer_zh-cn-common-vocab272727-pytorch && git lfs pull)
+
+#RUN parallel --will-cite -a /workspace/Docker/damo.sha256 "echo -n {} | sha256sum -c"
+
+#WORKDIR /workspace
+
+EXPOSE 9870
+EXPOSE 9871
+EXPOSE 9872
+EXPOSE 9873
+EXPOSE 9874
+
+VOLUME /workspace/output
+VOLUME /workspace/logs
+VOLUME /workspace/SoVITS_weights
+
+CMD ["python", "webui.py"]

GPT_SoVITS/inference_webui.py

@@ -1,4 +1,5 @@
-import os
+import os,re
+import pdb
 
 gpt_path = os.environ.get(
     "gpt_path", "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
@@ -42,8 +43,6 @@ if is_half == True:
 else:
     bert_model = bert_model.to(device)
 
-
-# bert_model=bert_model.to(device)
 def get_bert_feature(text, word2ph):
     with torch.no_grad():
         inputs = tokenizer(text, return_tensors="pt")
@@ -57,15 +56,8 @@ def get_bert_feature(text, word2ph):
         repeat_feature = res[i].repeat(word2ph[i], 1)
         phone_level_feature.append(repeat_feature)
     phone_level_feature = torch.cat(phone_level_feature, dim=0)
-    # if(is_half==True):phone_level_feature=phone_level_feature.half()
     return phone_level_feature.T
 
-
-n_semantic = 1024
-
-dict_s2=torch.load(sovits_path,map_location="cpu")
-hps=dict_s2["config"]
-
 class DictToAttrRecursive(dict):
     def __init__(self, input_dict):
         super().__init__(input_dict)
@@ -94,40 +86,48 @@ class DictToAttrRecursive(dict):
             raise AttributeError(f"Attribute {item} not found")
 
 
-hps = DictToAttrRecursive(hps)
-
-hps.model.semantic_frame_rate = "25hz"
-dict_s1 = torch.load(gpt_path, map_location="cpu")
-config = dict_s1["config"]
 ssl_model = cnhubert.get_model()
 if is_half == True:
     ssl_model = ssl_model.half().to(device)
 else:
     ssl_model = ssl_model.to(device)
 
-vq_model = SynthesizerTrn(
+def change_sovits_weights(sovits_path):
-    hps.data.filter_length // 2 + 1,
+    global vq_model,hps
-    hps.train.segment_size // hps.data.hop_length,
+    dict_s2=torch.load(sovits_path,map_location="cpu")
-    n_speakers=hps.data.n_speakers,
+    hps=dict_s2["config"]
-    **hps.model
+    hps = DictToAttrRecursive(hps)
-)
+    hps.model.semantic_frame_rate = "25hz"
-if is_half == True:
+    vq_model = SynthesizerTrn(
-    vq_model = vq_model.half().to(device)
+        hps.data.filter_length // 2 + 1,
-else:
+        hps.train.segment_size // hps.data.hop_length,
-    vq_model = vq_model.to(device)
+        n_speakers=hps.data.n_speakers,
-vq_model.eval()
+        **hps.model
-print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
+    )
-hz = 50
+    del vq_model.enc_q
-max_sec = config["data"]["max_sec"]
+    if is_half == True:
-t2s_model = Text2SemanticLightningModule(config, "ojbk", is_train=False)
+        vq_model = vq_model.half().to(device)
-t2s_model.load_state_dict(dict_s1["weight"])
+    else:
-if is_half == True:
+        vq_model = vq_model.to(device)
-    t2s_model = t2s_model.half()
+    vq_model.eval()
-t2s_model = t2s_model.to(device)
+    print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
-t2s_model.eval()
+change_sovits_weights(sovits_path)
-total = sum([param.nelement() for param in t2s_model.parameters()])
-print("Number of parameter: %.2fM" % (total / 1e6))
 
+def change_gpt_weights(gpt_path):
+    global hz,max_sec,t2s_model,config
+    hz = 50
+    dict_s1 = torch.load(gpt_path, map_location="cpu")
+    config = dict_s1["config"]
+    max_sec = config["data"]["max_sec"]
+    t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
+    t2s_model.load_state_dict(dict_s1["weight"])
+    if is_half == True:
+        t2s_model = t2s_model.half()
+    t2s_model = t2s_model.to(device)
+    t2s_model.eval()
+    total = sum([param.nelement() for param in t2s_model.parameters()])
+    print("Number of parameter: %.2fM" % (total / 1e6))
+change_gpt_weights(gpt_path)
 
 def get_spepc(hps, filename):
     audio = load_audio(filename, int(hps.data.sampling_rate))
@@ -325,14 +325,46 @@ def cut3(inp):
     inp = inp.strip("\n")
     return "\n".join(["%s。" % item for item in inp.strip("。").split("。")])
 
+def custom_sort_key(s):
+    # 使用正则表达式提取字符串中的数字部分和非数字部分
+    parts = re.split('(\d+)', s)
+    # 将数字部分转换为整数，非数字部分保持不变
+    parts = [int(part) if part.isdigit() else part for part in parts]
+    return parts
+
+def change_choices():
+    SoVITS_names, GPT_names = get_weights_names()
+    return {"choices": sorted(SoVITS_names,key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names,key=custom_sort_key), "__type__": "update"}
+
+pretrained_sovits_name="GPT_SoVITS/pretrained_models/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)
+def get_weights_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))
+    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))
+    return SoVITS_names,GPT_names
+SoVITS_names,GPT_names = get_weights_names()
 
 with gr.Blocks(title="GPT-SoVITS WebUI") as app:
     gr.Markdown(
         value=i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>.")
     )
-    # with gr.Tabs():
-    #     with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")):
     with gr.Group():
+        gr.Markdown(value=i18n("模型切换"))
+        with gr.Row():
+            GPT_dropdown = gr.Dropdown(label=i18n("GPT模型列表"), choices=sorted(GPT_names, key=custom_sort_key), value=gpt_path,interactive=True)
+            SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path,interactive=True)
+            refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
+            refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
+            SoVITS_dropdown.change(change_sovits_weights,[SoVITS_dropdown],[])
+            GPT_dropdown.change(change_gpt_weights,[GPT_dropdown],[])
         gr.Markdown(value=i18n("*请上传并填写参考信息"))
         with gr.Row():
             inp_ref = gr.Audio(label=i18n("请上传参考音频"), type="filepath")

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

@@ -49,10 +49,13 @@ maxx=0.95
 alpha=0.5
 device="cuda:0"
 model=cnhubert.get_model()
+# is_half=False
 if(is_half==True):
     model=model.half().to(device)
 else:
     model = model.to(device)
+
+nan_fails=[]
 def name2go(wav_name):
     hubert_path="%s/%s.pt"%(hubert_dir,wav_name)
     if(os.path.exists(hubert_path)):return
@@ -60,25 +63,28 @@ def name2go(wav_name):
     tmp_audio = load_audio(wav_path, 32000)
     tmp_max = np.abs(tmp_audio).max()
     if tmp_max > 2.2:
-        print("%s-%s-%s-filtered" % (idx0, idx1, tmp_max))
+        print("%s-filtered" % (wav_name, tmp_max))
         return
     tmp_audio32 = (tmp_audio / tmp_max * (maxx * alpha*32768)) + ((1 - alpha)*32768) * tmp_audio
+    tmp_audio32b = (tmp_audio / tmp_max * (maxx * alpha*1145.14)) + ((1 - alpha)*1145.14) * tmp_audio
     tmp_audio = librosa.resample(
-        tmp_audio32, orig_sr=32000, target_sr=16000
+        tmp_audio32b, orig_sr=32000, target_sr=16000
-    )
+    )#不是重采样问题
     tensor_wav16 = torch.from_numpy(tmp_audio)
     if (is_half == True):
         tensor_wav16=tensor_wav16.half().to(device)
     else:
         tensor_wav16 = tensor_wav16.to(device)
     ssl=model.model(tensor_wav16.unsqueeze(0))["last_hidden_state"].transpose(1,2).cpu()#torch.Size([1, 768, 215])
-    if np.isnan(ssl.detach().numpy()).sum()!= 0:return
+    if np.isnan(ssl.detach().numpy()).sum()!= 0:
+        nan_fails.append(wav_name)
+        print("nan filtered:%s"%wav_name)
+        return
     wavfile.write(
         "%s/%s"%(wav32dir,wav_name),
         32000,
         tmp_audio32.astype("int16"),
     )
-    # torch.save(ssl,hubert_path )
     my_save(ssl,hubert_path )
 
 with open(inp_text,"r",encoding="utf8")as f:
@@ -92,3 +98,12 @@ for line in lines[int(i_part)::int(all_parts)]:
         name2go(wav_name)
     except:
         print(line,traceback.format_exc())
+
+if(len(nan_fails)>0 and is_half==True):
+    is_half=False
+    model=model.float()
+    for wav_name in nan_fails:
+        try:
+            name2go(wav_name)
+        except:
+            print(wav_name,traceback.format_exc())

GPT_SoVITS/text/chinese.py

@@ -18,7 +18,7 @@ pinyin_to_symbol_map = {
     for line in open(os.path.join(current_file_path, "opencpop-strict.txt")).readlines()
 }
 
-import jieba.posseg as psg
+import jieba_fast.posseg as psg
 
 
 rep_map = {

GPT_SoVITS/text/tone_sandhi.py

@@ -14,7 +14,7 @@
 from typing import List
 from typing import Tuple
 
-import jieba
+import jieba_fast as jieba
 from pypinyin import lazy_pinyin
 from pypinyin import Style
 

GPT_SoVITS/utils.py

@@ -18,7 +18,7 @@ logging.getLogger("matplotlib").setLevel(logging.ERROR)
 
 MATPLOTLIB_FLAG = False
 
-logging.basicConfig(stream=sys.stdout, level=logging.WARNING)
+logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
 logger = logging
 
 
@@ -310,13 +310,13 @@ def check_git_hash(model_dir):
 def get_logger(model_dir, filename="train.log"):
     global logger
     logger = logging.getLogger(os.path.basename(model_dir))
-    logger.setLevel(logging.WARNING)
+    logger.setLevel(logging.DEBUG)
 
     formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
     if not os.path.exists(model_dir):
         os.makedirs(model_dir)
     h = logging.FileHandler(os.path.join(model_dir, filename))
-    h.setLevel(logging.WARNING)
+    h.setLevel(logging.DEBUG)
     h.setFormatter(formatter)
     logger.addHandler(h)
     return logger

README.md

@@ -61,7 +61,7 @@ sudo apt-get install python3.9-distutils
 #### Pip Packages
 
 ```bash
-pip install torch numpy scipy tensorboard librosa==0.9.2 numba==0.56.4 pytorch-lightning gradio==3.14.0 ffmpeg-python onnxruntime tqdm cn2an pypinyin pyopenjtalk g2p_en chardet transformers jieba
+pip install torch numpy scipy tensorboard librosa==0.9.2 numba==0.56.4 pytorch-lightning gradio==3.14.0 ffmpeg-python onnxruntime tqdm cn2an pypinyin pyopenjtalk g2p_en chardet transformers jieba_fast
 ```
 
 #### Additional Requirements
@@ -107,6 +107,31 @@ For Chinese ASR (additionally), download models from [Damo ASR Model](https://mo
 For UVR5 (Vocals/Accompaniment Separation & Reverberation Removal, additionally), download models from [UVR5 Weights](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/uvr5_weights) and place them in `tools/uvr5/uvr5_weights`.
 
 
+### Using Docker
+
+#### docker-compose.yaml configuration
+
+1. Environment Variables：
+  - is_half: Controls half-precision/double-precision. This is typically the cause if the content under the directories 4-cnhubert/5-wav32k is not generated correctly during the "SSL extracting" step. Adjust to True or False based on your actual situation.
+
+2. Volumes Configuration，The application's root directory inside the container is set to /workspace. The default docker-compose.yaml lists some practical examples for uploading/downloading content.
+3. shm_size： The default available memory for Docker Desktop on Windows is too small, which can cause abnormal operations. Adjust according to your own situation.
+4. Under the deploy section, GPU-related settings should be adjusted cautiously according to your system and actual circumstances.
+
+
+#### Running with docker compose
+```
+docker compose -f "docker-compose.yaml" up -d
+```
+
+#### Running with docker command
+
+As above, modify the corresponding parameters based on your actual situation, then run the following command:
+```
+docker run --rm -it --gpus=all --env=is_half=False --volume=G:\GPT-SoVITS-DockerTest\output:/workspace/output --volume=G:\GPT-SoVITS-DockerTest\logs:/workspace/logs --volume=G:\GPT-SoVITS-DockerTest\SoVITS_weights:/workspace/SoVITS_weights --workdir=/workspace -p 9870:9870 -p 9871:9871 -p 9872:9872 -p 9873:9873 -p 9874:9874 --shm-size="16G" -d breakstring/gpt-sovits:dev-20240123.03
+```
+
+
 ## Dataset Format
 
 The TTS annotation .list file format:

config.py

@@ -1,4 +1,4 @@
-import sys
+import sys,os
 
 #model type name
 MODEL_TYPE_GPT = "GPT"
@@ -11,7 +11,7 @@ MODEL_FOLDER_PATH_SOVITS = "SoVITS_weights"
 # 推理用的指定模型
 sovits_path = ""
 gpt_path = ""
-is_half = True
+is_half = eval(os.environ.get("is_half","True"))
 is_share=False
 
 cnhubert_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base"

docker-compose.yaml

@@ -0,0 +1,31 @@
+version: '3.8'
+
+services:
+  gpt-sovits:
+    image: breakstring/gpt-sovits:dev-20240123.03
+    container_name: gpt-sovits-container
+    environment:
+      - is_half=False
+    volumes:
+      - G:/GPT-SoVITS-DockerTest/output:/workspace/output
+      - G:/GPT-SoVITS-DockerTest/logs:/workspace/logs
+      - G:/GPT-SoVITS-DockerTest/SoVITS_weights:/workspace/SoVITS_weights
+      - G:/GPT-SoVITS-DockerTest/reference:/workspace/reference
+    working_dir: /workspace
+    ports:
+      - "9870:9870"
+      - "9871:9871"
+      - "9872:9872"
+      - "9873:9873"
+      - "9874:9874"
+    shm_size: 16G
+    deploy:
+      resources:
+        reservations:
+          devices:
+          - driver: nvidia
+            count: "all"
+            capabilities: [gpu]
+    stdin_open: true
+    tty: true
+    restart: unless-stopped

docs/cn/Changelog_CN.md

@@ -10,7 +10,7 @@
 
 5-清理TEMP文件夹缓存音频等文件
 
-6-在参考音频结尾留空0.3s，削弱合成音频包含参考音频结尾的问题
+6-大幅削弱合成音频包含参考音频结尾的问题
 
 ### 20240122更新
 
@@ -20,5 +20,12 @@
 
 3-音频路径检查。如果尝试读取输入错的路径报错路径不存在，而非ffmpeg错误。
 
-待修复：-hubert提取在half下出现nan概率更高的问题
+### 20240123更新
 
+1-解决hubert提取nan导致SoVITS/GPT训练报错ZeroDivisionError的问题
+
+2-支持推理界面快速切换模型
+
+3-优化模型文件排序逻辑
+
+4-中文分词使用jieba_fast代替jieba

docs/cn/README.md

@@ -87,6 +87,32 @@ brew install ffmpeg
 
 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。
 
+### 在 Docker 中使用
+
+#### docker-compose.yaml 设置
+
+1. 环境变量：
+  - is_half: 半精度/双精度控制。在进行 "SSL extracting" 步骤时如果无法正确生成 4-cnhubert/5-wav32k 目录下的内容时，一般都是它引起的，可以根据实际情况来调整为True或者False。
+
+2. Volume设置，容器内的应用根目录设置为 /workspace。 默认的 docker-compose.yaml 中列出了一些实际的例子，便于上传/下载内容。
+3. shm_size：Windows下的Docker Desktop默认可用内存过小，会导致运行异常，根据自己情况酌情设置。
+4. deploy小节下的gpu相关内容，请根据您的系统和实际情况酌情设置。
+
+
+
+#### 通过 docker compose运行
+```
+docker compose -f "docker-compose.yaml" up -d
+```
+
+#### 通过 docker 命令运行
+
+同上，根据您自己的实际情况修改对应的参数，然后运行如下命令：
+```
+docker run --rm -it --gpus=all --env=is_half=False --volume=G:\GPT-SoVITS-DockerTest\output:/workspace/output --volume=G:\GPT-SoVITS-DockerTest\logs:/workspace/logs --volume=G:\GPT-SoVITS-DockerTest\SoVITS_weights:/workspace/SoVITS_weights --workdir=/workspace -p 9870:9870 -p 9871:9871 -p 9872:9872 -p 9873:9873 -p 9874:9874 --shm-size="16G" -d breakstring/gpt-sovits:dev-20240123.03
+```
+
+
 ### 预训练模型
 
 

docs/ja/README.md

@@ -93,6 +93,30 @@ brew install ffmpeg
 
 [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。
 
+### Dockerの使用
+
+#### docker-compose.yamlの設定
+
+1. 環境変数：
+    - `is_half`：半精度／倍精度の制御。"SSL抽出"ステップ中に`4-cnhubert/5-wav32k`ディレクトリ内の内容が正しく生成されない場合、通常これが原因です。実際の状況に応じてTrueまたはFalseに調整してください。
+
+2. ボリューム設定：コンテナ内のアプリケーションのルートディレクトリは`/workspace`に設定されます。デフォルトの`docker-compose.yaml`には、アップロード／ダウンロードの内容の実例がいくつか記載されています。
+3. `shm_size`：WindowsのDocker Desktopのデフォルトの利用可能メモリが小さすぎるため、異常な動作を引き起こす可能性があります。状況に応じて適宜設定してください。
+4. `deploy`セクションのGPUに関連する内容は、システムと実際の状況に応じて慎重に設定してください。
+
+#### docker composeで実行する
+```markdown
+docker compose -f "docker-compose.yaml" up -d
+```
+
+#### dockerコマンドで実行する
+
+上記と同様に、実際の状況に基づいて対応するパラメータを変更し、次のコマンドを実行します：
+```markdown
+docker run --rm -it --gpus=all --env=is_half=False --volume=G:\GPT-SoVITS-DockerTest\output:/workspace/output --volume=G:\GPT-SoVITS-DockerTest\logs:/workspace/logs --volume=G:\GPT-SoVITS-DockerTest\SoVITS_weights:/workspace/SoVITS_weights --workdir=/workspace -p 9870:9870 -p 9871:9871 -p 9872:9872 -p 9873:9873 -p 9874:9874 --shm-size="16G" -d breakstring/gpt-sovits:dev-20240123.03
+```
+
+
 ### 事前訓練済みモデル
 
 

requirements.txt

@@ -20,4 +20,4 @@ transformers
 chardet
 PyYAML
 psutil
-jieba
+jieba_fast

tools/subfix_webui.py

@@ -110,6 +110,7 @@ def b_submit_change(*text_list):
 
 def b_delete_audio(*checkbox_list):
     global g_data_json, g_index, g_max_json_index
+    b_save_file()
     change = False
     for i, checkbox in reversed(list(enumerate(checkbox_list))):
         if g_index + i < len(g_data_json):
@@ -121,8 +122,8 @@ def b_delete_audio(*checkbox_list):
     if g_index > g_max_json_index:
         g_index = g_max_json_index
         g_index = g_index if g_index >= 0 else 0
-    # if change:
+    if change:
-    #     b_save_file()
+        b_save_file()
     # return gr.Slider(value=g_index, maximum=(g_max_json_index if g_max_json_index>=0 else 0)), *b_change_index(g_index, g_batch)
     return {"value":g_index,"__type__":"update","maximum":(g_max_json_index if g_max_json_index>=0 else 0)},*b_change_index(g_index, g_batch)
 
@@ -172,6 +173,7 @@ def b_audio_split(audio_breakpoint, *checkbox_list):
 
 def b_merge_audio(interval_r, *checkbox_list):
     global g_data_json , g_max_json_index
+    b_save_file()
     checked_index = []
     audios_path = []
     audios_text = []

webui.py

@@ -1,4 +1,4 @@
-import os,shutil,sys,pdb
+import os,shutil,sys,pdb,re
 now_dir = os.getcwd()
 sys.path.append(now_dir)
 import json,yaml,warnings,torch
@@ -85,9 +85,16 @@ os.makedirs(SoVITS_weight_root,exist_ok=True)
 os.makedirs(GPT_weight_root,exist_ok=True)
 SoVITS_names,GPT_names = get_weights_names()
 
+def custom_sort_key(s):
+    # 使用正则表达式提取字符串中的数字部分和非数字部分
+    parts = re.split('(\d+)', s)
+    # 将数字部分转换为整数，非数字部分保持不变
+    parts = [int(part) if part.isdigit() else part for part in parts]
+    return parts
+
 def change_choices():
     SoVITS_names, GPT_names = get_weights_names()
-    return {"choices": sorted(SoVITS_names), "__type__": "update"}, {"choices": sorted(GPT_names), "__type__": "update"}
+    return {"choices": sorted(SoVITS_names,key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names,key=custom_sort_key), "__type__": "update"}
 
 p_label=None
 p_uvr5=None
@@ -733,8 +740,8 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
             with gr.TabItem(i18n("1C-推理")):
                 gr.Markdown(value=i18n("选择训练完存放在SoVITS_weights和GPT_weights下的模型。默认的一个是底模，体验5秒Zero Shot TTS用。"))
                 with gr.Row():
-                    GPT_dropdown = gr.Dropdown(label=i18n("*GPT模型列表"), choices=sorted(GPT_names),value=pretrained_gpt_name)
+                    GPT_dropdown = gr.Dropdown(label=i18n("*GPT模型列表"), choices=sorted(GPT_names,key=custom_sort_key),value=pretrained_gpt_name,interactive=True)
-                    SoVITS_dropdown = gr.Dropdown(label=i18n("*SoVITS模型列表"), choices=sorted(SoVITS_names),value=pretrained_sovits_name)
+                    SoVITS_dropdown = gr.Dropdown(label=i18n("*SoVITS模型列表"), choices=sorted(SoVITS_names,key=custom_sort_key),value=pretrained_sovits_name,interactive=True)
                     gpu_number_1C=gr.Textbox(label=i18n("GPU卡号,只能填1个整数"), value=gpus, interactive=True)
                     refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
                     refresh_button.click(fn=change_choices,inputs=[],outputs=[SoVITS_dropdown,GPT_dropdown])