diff --git a/Docker/download.py b/Docker/download.py new file mode 100644 index 00000000..46becacd --- /dev/null +++ b/Docker/download.py @@ -0,0 +1,7 @@ +# Download moda ASR related models +from modelscope import snapshot_download +model_dir = snapshot_download('damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch') +model_dir = snapshot_download('damo/speech_fsmn_vad_zh-cn-16k-common-pytorch') +model_dir = snapshot_download('damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch') + + diff --git a/Dockerfile b/Dockerfile index cbf92cb5..ac85a4bf 100644 --- a/Dockerfile +++ b/Dockerfile @@ -2,7 +2,7 @@ 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 version="dev-20240127" LABEL description="Docker image for GPT-SoVITS" @@ -18,27 +18,19 @@ RUN apt-get update && \ WORKDIR /workspace COPY . /workspace +# install python packages +RUN pip install -r requirements.txt + # 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 +# Download moda ASR related +RUN python /workspace/Docker/download.py -# 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) +# Download nltk realted +RUN python -m nltk.downloader averaged_perceptron_tagger +RUN python -m nltk.downloader cmudict -#RUN parallel --will-cite -a /workspace/Docker/damo.sha256 "echo -n {} | sha256sum -c" - -#WORKDIR /workspace EXPOSE 9870 EXPOSE 9871 diff --git a/GPT_SoVITS/AR/data/data_module.py b/GPT_SoVITS/AR/data/data_module.py index f3d895a0..037484a9 100644 --- a/GPT_SoVITS/AR/data/data_module.py +++ b/GPT_SoVITS/AR/data/data_module.py @@ -41,7 +41,7 @@ class Text2SemanticDataModule(LightningDataModule): # pad_val=self.config['data']['pad_val']) def train_dataloader(self): - batch_size = self.config["train"]["batch_size"] + batch_size = max(min(self.config["train"]["batch_size"],len(self._train_dataset)//4),1)#防止不保存 sampler = DistributedBucketSampler(self._train_dataset, batch_size=batch_size) return DataLoader( self._train_dataset, diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py index 5d2f98f9..21ff0875 100644 --- a/GPT_SoVITS/inference_webui.py +++ b/GPT_SoVITS/inference_webui.py @@ -283,17 +283,26 @@ def nonen_get_bert_inf(text, language): return bert -#i18n("不切"),i18n("凑五句一切"),i18n("凑50字一切"),i18n("按中文句号。切"),i18n("按英文句号.切") +splits = {"，","。","？","！",",",".","?","!","~",":","：","—","…",} +def get_first(text): + pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]" + text = re.split(pattern, text)[0].strip() + return text + def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,how_to_cut=i18n("不切")): t0 = ttime() prompt_text = prompt_text.strip("\n") - prompt_language, text = prompt_language, text.strip("\n") + if(prompt_text[-1]not in splits):prompt_text+="。"if prompt_text!="en"else "." + text = text.strip("\n") + if(len(get_first(text))<4):text+="。"if text!="en"else "." zero_wav = np.zeros( int(hps.data.sampling_rate * 0.3), dtype=np.float16 if is_half == True else np.float32, ) with torch.no_grad(): wav16k, sr = librosa.load(ref_wav_path, sr=16000) + if(wav16k.shape[0]>160000 or wav16k.shape[0]<48000): + raise OSError(i18n("参考音频在3~10秒范围外，请更换！")) wav16k = torch.from_numpy(wav16k) zero_wav_torch = torch.from_numpy(zero_wav) if is_half == True: @@ -318,7 +327,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, phones1, word2ph1, norm_text1 = clean_text_inf(prompt_text, prompt_language) else: phones1, word2ph1, norm_text1 = nonen_clean_text_inf(prompt_text, prompt_language) - if(how_to_cut==i18n("凑五句一切")):text=cut1(text) + if(how_to_cut==i18n("凑四句一切")):text=cut1(text) elif(how_to_cut==i18n("凑50字一切")):text=cut2(text) elif(how_to_cut==i18n("按中文句号。切")):text=cut3(text) elif(how_to_cut==i18n("按英文句号.切")):text=cut4(text) @@ -390,24 +399,6 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, np.int16 ) - -splits = { - "，", - "。", - "？", - "！", - ",", - ".", - "?", - "!", - "~", - ":", - "：", - "—", - "…", -} # 不考虑省略号 - - def split(todo_text): todo_text = todo_text.replace("……", "。").replace("——", "，") if todo_text[-1] not in splits: @@ -430,7 +421,7 @@ def split(todo_text): def cut1(inp): inp = inp.strip("\n") inps = split(inp) - split_idx = list(range(0, len(inps), 5)) + split_idx = list(range(0, len(inps), 4)) split_idx[-1] = None if len(split_idx) > 1: opts = [] @@ -444,7 +435,6 @@ def cut1(inp): def cut2(inp): inp = inp.strip("\n") inps = split(inp) - # print(inps) if len(inps) < 2: return inp opts = [] @@ -517,6 +507,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: with gr.Row(): inp_ref = gr.Audio(label=i18n("请上传参考音频"), type="filepath") whisper_button = gr.Button(i18n("faster_whisper转写音频内容到文本")) + inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath") prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="") prompt_language = gr.Dropdown( label=i18n("参考音频的语种"),choices=[i18n("中文"),i18n("英文"),i18n("日文")],value=i18n("中文") @@ -530,7 +521,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: ) how_to_cut = gr.Radio( label=i18n("怎么切"), - choices=[i18n("不切"),i18n("凑五句一切"),i18n("凑50字一切"),i18n("按中文句号。切"),i18n("按英文句号.切"),], + choices=[i18n("不切"),i18n("凑四句一切"),i18n("凑50字一切"),i18n("按中文句号。切"),i18n("按英文句号.切"),], value=i18n("凑50字一切"), interactive=True, ) @@ -546,7 +537,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: gr.Markdown(value=i18n("文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。")) with gr.Row(): text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"),value="") - button1 = gr.Button(i18n("凑五句一切"), variant="primary") + button1 = gr.Button(i18n("凑四句一切"), variant="primary") button2 = gr.Button(i18n("凑50字一切"), variant="primary") button3 = gr.Button(i18n("按中文句号。切"), variant="primary") button4 = gr.Button(i18n("按英文句号.切"), variant="primary") diff --git a/GPT_SoVITS/s1_train.py b/GPT_SoVITS/s1_train.py index 30c167e5..3bbfdfb3 100644 --- a/GPT_SoVITS/s1_train.py +++ b/GPT_SoVITS/s1_train.py @@ -44,9 +44,8 @@ class my_model_ckpt(ModelCheckpoint): self.config = config def on_train_epoch_end(self, trainer, pl_module): - if not self._should_skip_saving_checkpoint( - trainer - ) and self._should_save_on_train_epoch_end(trainer): + # if not self._should_skip_saving_checkpoint(trainer) and self._should_save_on_train_epoch_end(trainer): + if self._should_save_on_train_epoch_end(trainer): monitor_candidates = self._monitor_candidates(trainer) if ( self._every_n_epochs >= 1 diff --git a/README.md b/README.md index 86138161..6d5dd220 100644 --- a/README.md +++ b/README.md @@ -121,8 +121,9 @@ For UVR5 (Vocals/Accompaniment Separation & Reverberation Removal, additionally) ### Using Docker -#### docker-compose.yaml configuration +#### docker-compose.yaml configuration +0. Regarding image tags: Due to rapid updates in the codebase and the slow process of packaging and testing images, please check [Docker Hub](https://hub.docker.com/r/breakstring/gpt-sovits) for the currently packaged latest images and select as per your situation, or alternatively, build locally using a Dockerfile according to your own needs. 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. @@ -140,7 +141,7 @@ docker compose -f "docker-compose.yaml" up -d 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 +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:xxxxx ``` diff --git a/docker-compose.yaml b/docker-compose.yaml index ed6f82a5..874824e0 100644 --- a/docker-compose.yaml +++ b/docker-compose.yaml @@ -2,7 +2,7 @@ version: '3.8' services: gpt-sovits: - image: breakstring/gpt-sovits:dev-20240123.03 + image: breakstring/gpt-sovits:xxxxx # please change the image name and tag base your environment container_name: gpt-sovits-container environment: - is_half=False diff --git a/docs/cn/README.md b/docs/cn/README.md index 3b077534..d66b2a59 100644 --- a/docs/cn/README.md +++ b/docs/cn/README.md @@ -110,7 +110,7 @@ brew install ffmpeg ### 在 Docker 中使用 #### docker-compose.yaml 设置 - +0. image的标签：由于代码库更新很快，镜像的打包和测试又很慢，所以请自行在 [Docker Hub](https://hub.docker.com/r/breakstring/gpt-sovits) 查看当前打包好的最新的镜像并根据自己的情况选用，或者在本地根据您自己的需求通过Dockerfile进行构建。 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。 @@ -129,7 +129,7 @@ docker compose -f "docker-compose.yaml" up -d 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 +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:xxxxx ``` diff --git a/docs/ja/README.md b/docs/ja/README.md index af5be46d..94867523 100644 --- a/docs/ja/README.md +++ b/docs/ja/README.md @@ -106,8 +106,9 @@ brew install ffmpeg ### Dockerの使用 -#### docker-compose.yamlの設定 +#### docker-compose.yamlの設定 +0. イメージのタグについて：コードベースの更新が速く、イメージのパッケージングとテストが遅いため、[Docker Hub](https://hub.docker.com/r/breakstring/gpt-sovits) で現在パッケージされている最新のイメージをご覧になり、ご自身の状況に応じて選択するか、またはご自身のニーズに応じてDockerfileを使用してローカルで構築してください。 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。 @@ -124,7 +125,7 @@ docker compose -f "docker-compose.yaml" up -d 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 +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:xxxxx ``` diff --git a/webui.py b/webui.py index 885aecbf..6577705a 100644 --- a/webui.py +++ b/webui.py @@ -16,7 +16,11 @@ if(os.path.exists(tmp)): if(name=="jieba.cache"):continue path="%s/%s"%(tmp,name) delete=os.remove if os.path.isfile(path) else shutil.rmtree - delete(path) + try: + delete(path) + except Exception as e: + print(str(e)) + pass import site site_packages_roots = [] for path in site.getsitepackages(): @@ -26,7 +30,6 @@ if(site_packages_roots==[]):site_packages_roots=["%s/runtime/Lib/site-packages" #os.environ["OPENBLAS_NUM_THREADS"] = "4" os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1" os.environ["all_proxy"] = "" - for site_packages_root in site_packages_roots: if os.path.exists(site_packages_root): try: @@ -38,7 +41,6 @@ for site_packages_root in site_packages_roots: break except PermissionError: pass - from tools import my_utils import traceback import shutil @@ -662,7 +664,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: with gr.Row(): if_label = gr.Checkbox(label=i18n("是否开启打标WebUI"),show_label=True) path_list = gr.Textbox( - label=i18n("打标数据标注文件路径"), + label=i18n(".list标注文件的路径"), value="D:\\RVC1006\\GPT-SoVITS\\raw\\xxx.list", interactive=True, ) @@ -688,7 +690,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: label=i18n("*训练集音频文件目录"), # value=r"D:\RVC1006\GPT-SoVITS\raw\xxx", interactive=True, - placeholder=i18n("训练集音频文件目录-拼接-list文件里波形对应的文件名（不是全路径）。") + placeholder=i18n("填切割后音频所在目录！读取的音频文件完整路径=该目录-拼接-list文件里波形对应的文件名（不是全路径）。") ) gr.Markdown(value=i18n("1Aa-文本内容")) with gr.Row():