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[ "I just run into the same problem.\r\n\r\nin se_extractor.py line 22 I had to change the parameters, see below. Would be nice if this could be made configurable for Apple Silicon users.\r\n\r\n model = WhisperModel(model_size, device=\"cpu\", compute_type=\"float32\")" ]

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[ "change compute type to int8" ]

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[ "It's the network error.", "However, i can visit the file via web browser, just like visit google.com.", "已经解决了。通过降级urllibs和requests包即可。后面还是会下载1-2个G的模型包，主要是语言类的，最好还是有vpn支持。" ]

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[ "确实是这样，而且我感觉怎么有一股陕西话的口味，用普通话复刻后", "在那个网站上试了下，和自己的声音不能说一模一样，只能说毫不相干。。。", "好奇，其他语言是不是也这样，毕竟不是母语，听不出其他语言的太多差异", "> 好奇，其他语言是不是也这样，毕竟不是母语，听不出其他语言的太多差异\n\n我试了英语，但不是我的声音，感觉比中文好很多" ]

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[ "同问，How to simulate pauses in sound" ]

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[ "pip install git+https://github.com/myshell-ai/MeloTTS.git\r\npython -m unidic download", "so much gradio package, should I install all of those?\r\n`$ pip install git+https://github.com/myshell-ai/MeloTTS.git\r\nLooking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\r\nCollecting git+https://github.com/myshell-ai/MeloTTS.git\r\n Cloning https://github.com/myshell-ai/MeloTTS.git to /tmp/pip-req-build-syz29wrf\r\n Running command git clone --filter=blob:none --quiet https://github.com/myshell-ai/MeloTTS.git /tmp/pip-req-build-syz29wrf\r\n Resolved https://github.com/myshell-ai/MeloTTS.git to commit abf885acf7c8322af07e6584099748cba09e3909\r\n Installing build dependencies ... done\r\n Getting requirements to build wheel ... done\r\n Installing backend dependencies ... done\r\n Preparing metadata (pyproject.toml) ... done\r\nCollecting txtsplit (from melotts==0.1.2)\r\n Using cached txtsplit-1.0.0-py3-none-any.whl\r\nRequirement already satisfied: torch in ./.venv/lib/python3.11/site-packages (from melotts==0.1.2) (2.3.0)\r\nRequirement already satisfied: torchaudio in ./.venv/lib/python3.11/site-packages (from melotts==0.1.2) (2.3.0)\r\nCollecting cached-path (from melotts==0.1.2)\r\n Using cached https://pypi.tuna.tsinghua.edu.cn/packages/eb/7b/b793dccfceb3d0de9e3f376f1a8e3a1e4015361ced5f96bb78c279d552be/cached_path-1.6.2-py3-none-any.whl (35 kB)\r\nCollecting transformers==4.27.4 (from melotts==0.1.2)\r\n Using cached https://pypi.tuna.tsinghua.edu.cn/packages/87/f0/2a152ed10ab8601431e87a606d397f7473c5fa4f8162f4ec5bda6ddb2df4/transformers-4.27.4-py3-none-any.whl (6.8 MB)\r\nCollecting mecab-python3==1.0.5 (from melotts==0.1.2)\r\n Using cached https://pypi.tuna.tsinghua.edu.cn/packages/c0/64/269b32bc82cf88d6885aa38afcbbb15b5e86f9b58d298223c9b8f29ffc51/mecab-python3-1.0.5.tar.gz (77 kB)\r\n Installing build dependencies ... done\r\n Getting requirements to build wheel ... done\r\n Installing backend dependencies ... done\r\n Preparing metadata (pyproject.toml) ... done\r\nCollecting num2words==0.5.12 (from melotts==0.1.2)\r\n Using cached https://pypi.tuna.tsinghua.edu.cn/packages/eb/09/b14d798bc02411b1e5a9896d680f8f417cadc53232bbf7ae9d30263dcf45/num2words-0.5.12-py3-none-any.whl (125 kB)\r\nCollecting unidic-lite==1.0.8 (from melotts==0.1.2)\r\n Using cached unidic_lite-1.0.8-py3-none-any.whl\r\nCollecting unidic==1.1.0 (from melotts==0.1.2)\r\n Using cached unidic-1.1.0-py3-none-any.whl\r\nCollecting pykakasi==2.2.1 (from melotts==0.1.2)\r\n Using cached https://pypi.tuna.tsinghua.edu.cn/packages/82/15/8d2fb3ede5477821704070859e63fefb16596b56339b68460770db75f5ea/pykakasi-2.2.1-py3-none-any.whl (2.4 MB)\r\nCollecting fugashi==1.3.0 (from melotts==0.1.2)\r\n Using cached https://pypi.tuna.tsinghua.edu.cn/packages/f3/27/939cf07c4b4fbeb52404d30244ce9166ae9da123d1987640b1dfc839beea/fugashi-1.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (606 kB)\r\nCollecting g2p-en==2.1.0 (from melotts==0.1.2)\r\n Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d7/d9/b77dc634a7a0c0c97716ba97dd0a28cbfa6267c96f359c4f27ed71cbd284/g2p_en-2.1.0-py3-none-any.whl (3.1 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 3.1/3.1 MB 566.8 kB/s eta 0:00:00\r\nRequirement already satisfied: anyascii==0.3.2 in ./.venv/lib/python3.11/site-packages (from melotts==0.1.2) (0.3.2)\r\nRequirement already satisfied: jamo==0.4.1 in ./.venv/lib/python3.11/site-packages (from melotts==0.1.2) (0.4.1)\r\nRequirement already satisfied: gruut==2.2.3 in ./.venv/lib/python3.11/site-packages (from gruut[de,es,fr]==2.2.3->melotts==0.1.2) (2.2.3)\r\nRequirement already satisfied: g2pkk>=0.1.1 in ./.venv/lib/python3.11/site-packages (from melotts==0.1.2) (0.1.2)\r\nCollecting librosa==0.9.1 (from melotts==0.1.2)\r\n Using cached https://pypi.tuna.tsinghua.edu.cn/packages/5b/da/bd63187b2ca1b97c04c270df90c934a97cbe512c8238ab65c89c1b043ae2/librosa-0.9.1-py3-none-any.whl (213 kB)\r\nRequirement already satisfied: pydub==0.25.1 in ./.venv/lib/python3.11/site-packages (from melotts==0.1.2) (0.25.1)\r\nRequirement already satisfied: eng-to-ipa==0.0.2 in ./.venv/lib/python3.11/site-packages (from melotts==0.1.2) (0.0.2)\r\nCollecting inflect==7.0.0 (from melotts==0.1.2)\r\n Using cached https://pypi.tuna.tsinghua.edu.cn/packages/fb/c6/d9feb758be584f729424390af24687d3a4363d968164f94079f83cd536b4/inflect-7.0.0-py3-none-any.whl (34 kB)\r\nCollecting unidecode==1.3.7 (from melotts==0.1.2)\r\n Using cached https://pypi.tuna.tsinghua.edu.cn/packages/e4/63/7685ef40c65aba621ccd2524a24181bf11f0535ab1fdba47e40738eacff6/Unidecode-1.3.7-py3-none-any.whl (235 kB)\r\nCollecting pypinyin==0.50.0 (from melotts==0.1.2)\r\n Using cached https://pypi.tuna.tsinghua.edu.cn/packages/f6/a2/13adff7046a0913917a30cf5a8d8524f1e49b039aa0e6ab6826ad263b176/pypinyin-0.50.0-py2.py3-none-any.whl (1.4 MB)\r\nRequirement already satisfied: cn2an==0.5.22 in ./.venv/lib/python3.11/site-packages (from melotts==0.1.2) (0.5.22)\r\nRequirement already satisfied: jieba==0.42.1 in ./.venv/lib/python3.11/site-packages (from melotts==0.1.2) (0.42.1)\r\nCollecting gradio (from melotts==0.1.2)\r\n Downloading https://pypi.tuna.tsinghua.edu.cn/packages/39/db/b8addf4b194838330e12a887a4d8e765259d55d9946a806a46a684f660a1/gradio-4.31.2-py3-none-any.whl (12.3 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 12.3/12.3 MB 719.2 kB/s eta 0:00:00\r\nRequirement already satisfied: langid==1.1.6 in ./.venv/lib/python3.11/site-packages (from melotts==0.1.2) (1.1.6)\r\nRequirement already satisfied: tqdm in ./.venv/lib/python3.11/site-packages (from melotts==0.1.2) (4.66.4)\r\nRequirement already satisfied: tensorboard==2.16.2 in ./.venv/lib/python3.11/site-packages (from melotts==0.1.2) (2.16.2)\r\nCollecting loguru==0.7.2 (from melotts==0.1.2)\r\n Using cached https://pypi.tuna.tsinghua.edu.cn/packages/03/0a/4f6fed21aa246c6b49b561ca55facacc2a44b87d65b8b92362a8e99ba202/loguru-0.7.2-py3-none-any.whl (62 kB)\r\nRequirement already satisfied: setuptools>=47.3.1 in ./.venv/lib/python3.11/site-packages (from cn2an==0.5.22->melotts==0.1.2) (69.1.1)\r\nRequirement already satisfied: proces>=0.1.3 in ./.venv/lib/python3.11/site-packages (from cn2an==0.5.22->melotts==0.1.2) (0.1.7)\r\nRequirement already satisfied: numpy>=1.13.1 in ./.venv/lib/python3.11/site-packages (from g2p-en==2.1.0->melotts==0.1.2) (1.26.4)\r\nRequirement already satisfied: nltk>=3.2.4 in ./.venv/lib/python3.11/site-packages (from g2p-en==2.1.0->melotts==0.1.2) (3.8.1)\r\nCollecting distance>=0.1.3 (from g2p-en==2.1.0->melotts==0.1.2)\r\n Using cached Distance-0.1.3-py3-none-any.whl\r\nRequirement already satisfied: Babel<3.0.0,>=2.8.0 in ./.venv/lib/python3.11/site-packages (from gruut==2.2.3->gruut[de,es,fr]==2.2.3->melotts==0.1.2) (2.15.0)\r\nRequirement already satisfied: dateparser~=1.1.0 in ./.venv/lib/python3.11/site-packages (from gruut==2.2.3->gruut[de,es,fr]==2.2.3->melotts==0.1.2) (1.1.8)\r\nRequirement already satisfied: gruut-ipa<1.0,>=0.12.0 in ./.venv/lib/python3.11/site-packages (from gruut==2.2.3->gruut[de,es,fr]==2.2.3->melotts==0.1.2) (0.13.0)\r\nRequirement already satisfied: gruut-lang-en~=2.0.0 in ./.venv/lib/python3.11/site-packages (from gruut==2.2.3->gruut[de,es,fr]==2.2.3->melotts==0.1.2) (2.0.0)\r\nRequirement already satisfied: jsonlines~=1.2.0 in ./.venv/lib/python3.11/site-packages (from gruut==2.2.3->gruut[de,es,fr]==2.2.3->melotts==0.1.2) (1.2.0)\r\nRequirement already satisfied: networkx<3.0.0,>=2.5.0 in ./.venv/lib/python3.11/site-packages (from gruut==2.2.3->gruut[de,es,fr]==2.2.3->melotts==0.1.2) (2.8.8)\r\nRequirement already satisfied: python-crfsuite~=0.9.7 in ./.venv/lib/python3.11/site-packages (from gruut==2.2.3->gruut[de,es,fr]==2.2.3->melotts==0.1.2) (0.9.10)\r\nRequirement already satisfied: gruut-lang-de~=2.0.0 in ./.venv/lib/python3.11/site-packages (from gruut[de,es,fr]==2.2.3->melotts==0.1.2) (2.0.0)\r\nRequirement already satisfied: gruut-lang-es~=2.0.0 in ./.venv/lib/python3.11/site-packages (from gruut[de,es,fr]==2.2.3->melotts==0.1.2) (2.0.0)\r\nRequirement already satisfied: gruut-lang-fr~=2.0.0 in ./.venv/lib/python3.11/site-packages (from gruut[de,es,fr]==2.2.3->melotts==0.1.2) (2.0.2)\r\nRequirement already satisfied: pydantic>=1.9.1 in ./.venv/lib/python3.11/site-packages (from inflect==7.0.0->melotts==0.1.2) (2.7.1)\r\nRequirement already satisfied: typing-extensions in ./.venv/lib/python3.11/site-packages (from inflect==7.0.0->melotts==0.1.2) (4.11.0)\r\nRequirement already satisfied: audioread>=2.1.5 in ./.venv/lib/python3.11/site-packages (from librosa==0.9.1->melotts==0.1.2) (3.0.1)\r\nRequirement already satisfied: scipy>=1.2.0 in ./.venv/lib/python3.11/site-packages (from librosa==0.9.1->melotts==0.1.2) (1.13.0)\r\nRequirement already satisfied: scikit-learn>=0.19.1 in ./.venv/lib/python3.11/site-packages (from librosa==0.9.1->melotts==0.1.2) (1.4.2)\r\nRequirement already satisfied: joblib>=0.14 in ./.venv/lib/python3.11/site-packages (from librosa==0.9.1->melotts==0.1.2) (1.4.2)\r\nRequirement already satisfied: decorator>=4.0.10 in ./.venv/lib/python3.11/site-packages (from librosa==0.9.1->melotts==0.1.2) (5.1.1)\r\nCollecting resampy>=0.2.2 (from librosa==0.9.1->melotts==0.1.2)\r\n Using cached https://pypi.tuna.tsinghua.edu.cn/packages/4d/b9/3b00ac340a1aab3389ebcc52c779914a44aadf7b0cb7a3bf053195735607/resampy-0.4.3-py3-none-any.whl (3.1 MB)\r\nRequirement already satisfied: numba>=0.45.1 in ./.venv/lib/python3.11/site-packages (from librosa==0.9.1->melotts==0.1.2) (0.59.1)\r\nRequirement already satisfied: soundfile>=0.10.2 in ./.venv/lib/python3.11/site-packages (from librosa==0.9.1->melotts==0.1.2) (0.12.1)\r\nRequirement already satisfied: pooch>=1.0 in ./.venv/lib/python3.11/site-packages (from librosa==0.9.1->melotts==0.1.2) (1.8.1)\r\nRequirement already satisfied: packaging>=20.0 in ./.venv/lib/python3.11/site-packages (from librosa==0.9.1->melotts==0.1.2) 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./.venv/lib/python3.11/site-packages (from tensorboard==2.16.2->melotts==0.1.2) (5.26.1)\r\nRequirement already satisfied: six>1.9 in ./.venv/lib/python3.11/site-packages (from tensorboard==2.16.2->melotts==0.1.2) (1.16.0)\r\nRequirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in ./.venv/lib/python3.11/site-packages (from tensorboard==2.16.2->melotts==0.1.2) (0.7.2)\r\nRequirement already satisfied: werkzeug>=1.0.1 in ./.venv/lib/python3.11/site-packages (from tensorboard==2.16.2->melotts==0.1.2) (3.0.3)\r\nRequirement already satisfied: filelock in ./.venv/lib/python3.11/site-packages (from transformers==4.27.4->melotts==0.1.2) (3.14.0)\r\nRequirement already satisfied: huggingface-hub<1.0,>=0.11.0 in ./.venv/lib/python3.11/site-packages (from transformers==4.27.4->melotts==0.1.2) (0.23.0)\r\nRequirement already satisfied: pyyaml>=5.1 in ./.venv/lib/python3.11/site-packages (from transformers==4.27.4->melotts==0.1.2) (6.0.1)\r\nRequirement already satisfied: regex!=2019.12.17 in ./.venv/lib/python3.11/site-packages (from transformers==4.27.4->melotts==0.1.2) (2024.4.28)\r\nRequirement already satisfied: requests in ./.venv/lib/python3.11/site-packages (from transformers==4.27.4->melotts==0.1.2) (2.31.0)\r\nCollecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers==4.27.4->melotts==0.1.2)\r\n Using cached https://pypi.tuna.tsinghua.edu.cn/packages/e9/d9/660f08ecd88bed54df9f85cab44e01184733c8e42d79b583fb985f1dc412/tokenizers-0.13.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\r\nCollecting wasabi<1.0.0,>=0.6.0 (from unidic==1.1.0->melotts==0.1.2)\r\n Using cached https://pypi.tuna.tsinghua.edu.cn/packages/34/74/bd566f876c2de097e75d525c2696fb9829009987a0d93a4fb3576778a0a8/wasabi-0.10.1-py3-none-any.whl (26 kB)\r\nCollecting plac<2.0.0,>=1.1.3 (from unidic==1.1.0->melotts==0.1.2)\r\n Using cached https://pypi.tuna.tsinghua.edu.cn/packages/8f/af/4c61d2ac0d589719f548f5a1ba919738e44bac7b0c723ce147de5556d233/plac-1.4.3-py2.py3-none-any.whl (22 kB)\r\nRequirement already satisfied: rich<14.0,>=12.1 in ./.venv/lib/python3.11/site-packages (from cached-path->melotts==0.1.2) (13.7.1)\r\nCollecting filelock (from transformers==4.27.4->melotts==0.1.2)\r\n Using cached https://pypi.tuna.tsinghua.edu.cn/packages/6e/b5/15b3b36f298bcbc0be82a371ac744f4f5a10309ade0b8bbde286598dd612/filelock-3.13.4-py3-none-any.whl (11 kB)\r\nCollecting boto3<2.0,>=1.0 (from cached-path->melotts==0.1.2)\r\n Downloading https://pypi.tuna.tsinghua.edu.cn/packages/34/54/6038f386e64cd4555461124524aa3b687afbedfddb44490bf070d35535e8/boto3-1.34.105-py3-none-any.whl (139 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 139.3/139.3 kB 1.2 MB/s eta 0:00:00\r\nCollecting google-cloud-storage<3.0,>=1.32.0 (from cached-path->melotts==0.1.2)\r\n Using cached 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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 304.8/304.8 kB 1.2 MB/s eta 0:00:00\r\nCollecting gradio (from melotts==0.1.2)\r\n Downloading https://pypi.tuna.tsinghua.edu.cn/packages/16/9b/46c017d6bbc60ccb13ef51af4b07dbb288b9080ea3170b8d47beee7842b6/gradio-4.10.0-py3-none-any.whl (16.6 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 16.6/16.6 MB 1.1 MB/s eta 0:00:00\r\n Downloading https://pypi.tuna.tsinghua.edu.cn/packages/f3/ef/da7601f3ec07cca00f535dc1bce93397fed30705d77e41df233db327c9c8/gradio-4.9.1-py3-none-any.whl (16.6 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 16.6/16.6 MB 1.0 MB/s eta 0:00:00\r\n Downloading https://pypi.tuna.tsinghua.edu.cn/packages/1e/f6/93fe8ff39a7387ac56b555547f3d6f154eeccd1b6fb87198b96f007d939c/gradio-4.9.0-py3-none-any.whl (16.6 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 16.6/16.6 MB 782.0 kB/s eta 0:00:00`", "@miaohf maybe yes, but most of packages overlap with openvoice.\r\nMy problem was solved by answer of @chldong \r\nThanks!" ]

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[ "I had the same problem (in COLAB and Vscode) this is what i did \r\n1. Install CUDA ToolKiT NETWORK : https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=11&target_type=exe_network\r\n2. Install Pytorch Cuda using the command in this page : https://pytorch.org/get-started/locally/ , be sure pytorch use cuda correctly , with torch.cuda.is_available() get in true giving to you true\r\n3. Use a env Python 3.9.XX (I use 3.9.19) ", "@Dan-H-Muniz-Sanchez Thanks!!!!" ]

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[ "conda install -c cuda icc_rt >>> for nvidiaGPU\r\nconda install -s intel icc_rt >>> for intelCPU" ]

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[ "Try to do the following:\r\n\r\n```\r\nexport LD_LIBRARY_PATH=`python3 -c 'import os; import nvidia.cublas.lib; import nvidia.cudnn.lib; print(os.path.dirname(nvidia.cublas.lib.__file__) + \":\" + os.path.dirname(nvidia.cudnn.lib.__file__))'`\r\n```", "It worked, though I also had to install libcublass. I'll check the documentation again to see if maybe I missed some kind of step.\r\n\r\n```\r\n(openvoice) ╭─arthur at aquarelle in ~/dev/ai/OpenVoice on main✘✘✘ 24-05-10 - 13:52:06\r\n╰─(openvoice) ⠠⠵ export LD_LIBRARY_PATH=`python3 -c 'import os; import nvidia.cublas.lib; import nvidia.cudnn.lib; print(os.path.dirname(nvidia.cublas.lib.__file__) + \":\" + os.path.dirname(nvidia.cudnn.lib.__file__))'`\r\n(openvoice) ╭─arthur at aquarelle in ~/dev/ai/OpenVoice on main✘✘✘ 24-05-10 - 13:52:43\r\n╰─(openvoice) ⠠⠵ python tts.py on main|…8\r\n/home/arthur/.anaconda3/envs/openvoice/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\r\n warnings.warn(\"torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\")\r\nLoaded checkpoint 'checkpoints_v2/converter/checkpoint.pth'\r\nmissing/unexpected keys: [] []\r\nOpenVoice version: v2\r\nTraceback (most recent call last):\r\n File \"/home/arthur/dev/ai/OpenVoice/tts.py\", line 23, in <module>\r\n target_se, audio_name = se_extractor.get_se(reference_speaker, tone_color_converter, vad=False)\r\n File \"/home/arthur/dev/ai/OpenVoice/openvoice/se_extractor.py\", line 146, in get_se\r\n wavs_folder = split_audio_whisper(audio_path, target_dir=target_dir, audio_name=audio_name)\r\n File \"/home/arthur/dev/ai/OpenVoice/openvoice/se_extractor.py\", line 28, in split_audio_whisper\r\n segments, info = model.transcribe(audio_path, beam_size=5, word_timestamps=True)\r\n File \"/home/arthur/.anaconda3/envs/openvoice/lib/python3.9/site-packages/faster_whisper/transcribe.py\", line 308, in transcribe\r\n encoder_output = self.encode(segment)\r\n File \"/home/arthur/.anaconda3/envs/openvoice/lib/python3.9/site-packages/faster_whisper/transcribe.py\", line 610, in encode\r\n return self.model.encode(features, to_cpu=to_cpu)\r\nRuntimeError: Library libcublas.so.11 is not found or cannot be loaded\r\n(openvoice) ╭─arthur at aquarelle in ~/dev/ai/OpenVoice on main✘✘✘ 24-05-10 - 13:52:59\r\n╰─(openvoice) ⠠⠵ sudo apt install libcublas11 on main|…8\r\n[sudo] password for arthur: \r\nReading package lists... Done\r\nBuilding dependency tree... Done\r\nReading state information... Done\r\nThe following additional packages will be installed:\r\n libcublaslt11\r\nThe following NEW packages will be installed:\r\n libcublas11 libcublaslt11\r\n0 upgraded, 2 newly installed, 0 to remove and 11 not upgraded.\r\nNeed to get 259 MB of archives.\r\nAfter this operation, 670 MB of additional disk space will be used.\r\nDo you want to continue? [Y/n] \r\nGet:1 http://fr.archive.ubuntu.com/ubuntu lunar/multiverse amd64 libcublaslt11 amd64 11.11.3.6~11.8.0-3 [212 MB]\r\nGet:2 http://fr.archive.ubuntu.com/ubuntu lunar/multiverse amd64 libcublas11 amd64 11.11.3.6~11.8.0-3 [46,7 MB]\r\nFetched 259 MB in 7s (39,1 MB/s) \r\nSelecting previously unselected package libcublaslt11:amd64.\r\n(Reading database ... 721648 files and directories currently installed.)\r\nPreparing to unpack .../libcublaslt11_11.11.3.6~11.8.0-3_amd64.deb ...\r\nUnpacking libcublaslt11:amd64 (11.11.3.6~11.8.0-3) ...\r\nSelecting previously unselected package libcublas11:amd64.\r\nPreparing to unpack .../libcublas11_11.11.3.6~11.8.0-3_amd64.deb ...\r\nUnpacking libcublas11:amd64 (11.11.3.6~11.8.0-3) ...\r\nSetting up libcublaslt11:amd64 (11.11.3.6~11.8.0-3) ...\r\nSetting up libcublas11:amd64 (11.11.3.6~11.8.0-3) ...\r\nProcessing triggers for libc-bin (2.37-0ubuntu2.2) ...\r\n(openvoice) ╭─arthur at aquarelle in ~/dev/ai/OpenVoice on main✘✘✘ 24-05-10 - 13:53:51\r\n╰─(openvoice) ⠠⠵ python tts.py on main|…8\r\n/home/arthur/.anaconda3/envs/openvoice/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\r\n warnings.warn(\"torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\")\r\nLoaded checkpoint 'checkpoints_v2/converter/checkpoint.pth'\r\nmissing/unexpected keys: [] []\r\nOpenVoice version: v2\r\n/home/arthur/.anaconda3/envs/openvoice/lib/python3.9/site-packages/torch/functional.py:665: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.\r\nNote: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:873.)\r\n return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]\r\n/home/arthur/.anaconda3/envs/openvoice/lib/python3.9/site-packages/torch/nn/modules/conv.py:456: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\r\n return F.conv2d(input, weight, bias, self.stride,\r\nProcessing TTS...\r\nProcessing EN_NEWEST...\r\nDownloading config.json: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.41k/3.41k [00:00<00:00, 1.64MB/s]\r\n/home/arthur/.anaconda3/envs/openvoice/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\r\n warnings.warn(\"torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\")\r\nDownloading checkpoint.pth: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 208M/208M [00:01<00:00, 111MB/s]\r\n > Text split to sentences.\r\nDid you ever hear a folk tale about a giant turtle?\r\n > ===========================\r\n 0%| | 0/1 [00:00<?, ?it/s]Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias']\r\n- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\r\n- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\r\n/home/arthur/.anaconda3/envs/openvoice/lib/python3.9/site-packages/torch/nn/modules/conv.py:306: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\r\n return F.conv1d(input, weight, bias, self.stride,\r\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:06<00:00, 6.09s/it]\r\n/home/arthur/.anaconda3/envs/openvoice/lib/python3.9/site-packages/torch/nn/modules/conv.py:456: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\r\n return F.conv2d(input, weight, bias, self.stride,\r\nProcessing EN...\r\n/home/arthur/.anaconda3/envs/openvoice/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\r\n warnings.warn(\"torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\")\r\n > Text split to sentences.\r\nDid you ever hear a folk tale about a giant turtle?\r\n > ===========================\r\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 6.28it/s]\r\n > Text split to sentences.\r\nDid you ever hear a folk tale about a giant turtle?\r\n > ===========================\r\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 6.72it/s]\r\n > Text split to sentences.\r\nDid you ever hear a folk tale about a giant turtle?\r\n > ===========================\r\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 10.68it/s]\r\n > Text split to sentences.\r\nDid you ever hear a folk tale about a giant turtle?\r\n > ===========================\r\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 7.00it/s]\r\n > Text split to sentences.\r\nDid you ever hear a folk tale about a giant turtle?\r\n > ===========================\r\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 7.32it/s]\r\nProcessing ES...\r\nDownloading config.json: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.43k/3.43k [00:00<00:00, 4.08MB/s]\r\nDownloading checkpoint.pth: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 208M/208M [00:02<00:00, 102MB/s]\r\n > Text split to sentences.\r\nEl resplandor del sol acaricia las olas, pintando el cielo con una paleta deslumbrante.\r\n > ===========================\r\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.17s/it]\r\nProcessing FR...\r\nDownloading config.json: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.40k/3.40k [00:00<00:00, 3.82MB/s]\r\nDownloading checkpoint.pth: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 208M/208M [00:01<00:00, 108MB/s]\r\n > Text split to sentences.\r\nLa lueur dorée du soleil caresse les vagues, peignant le ciel d'une palette éblouissante.\r\n > ===========================\r\nDownloading pytorch_model.bin: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 445M/445M [00:03<00:00, 112MB/s]\r\nSome weights of the model checkpoint at dbmdz/bert-base-french-europeana-cased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias']MB/s]\r\n- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\r\n- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\r\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:06<00:00, 6.34s/it]\r\nProcessing ZH...\r\nDownloading config.json: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.30k/2.30k [00:00<00:00, 5.02MB/s]\r\nDownloading checkpoint.pth: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 208M/208M [00:02<00:00, 102MB/s]\r\n > Text split to sentences.\r\n在这次vacation中,\r\n我们计划去Paris欣赏埃菲尔铁塔和卢浮宫的美景.\r\n > ===========================\r\n 0%| | 0/2 [00:00<?, ?it/s]Building prefix dict from the default dictionary ...\r\nDumping model to file cache /tmp/jieba.cache\r\nLoading model cost 0.477 seconds.\r\nPrefix dict has been built successfully.\r\nDownloading pytorch_model.bin: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 672M/672M [00:06<00:00, 108MB/s]\r\nSome weights of the model checkpoint at bert-base-multilingual-uncased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias']:00, 108MB/s]\r\n- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\r\n- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\r\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:10<00:00, 5.01s/it]\r\nProcessing JP...\r\nDownloading config.json: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.43k/3.43k [00:00<00:00, 5.00MB/s]\r\nDownloading checkpoint.pth: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 208M/208M [00:02<00:00, 102MB/s]\r\n > Text split to sentences.\r\n彼は毎朝ジョギングをして体を健康に保っています.\r\n > ===========================\r\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.84it/s]\r\nProcessing KR...\r\nDownloading config.json: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.40k/3.40k [00:00<00:00, 1.33MB/s]\r\nDownloading checkpoint.pth: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 208M/208M [00:01<00:00, 113MB/s]\r\n > Text split to sentences.\r\n안녕하세요! 오늘은 날씨가 정말 좋네요.\r\n > ===========================\r\n 0%| | 0/1 [00:00<?, ?it/s]you have to install python-mecab-ko. install it...\r\nhuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\r\nTo disable this warning, you can either:\r\n - Avoid using `tokenizers` before the fork if possible\r\n - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\r\nCollecting python-mecab-ko\r\n Downloading python_mecab_ko-1.3.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.4 kB)\r\nCollecting python-mecab-ko-dic (from python-mecab-ko)\r\n Downloading python_mecab_ko_dic-2.1.1.post2-py3-none-any.whl.metadata (1.4 kB)\r\nDownloading python_mecab_ko-1.3.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (578 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 578.4/578.4 kB 9.6 MB/s eta 0:00:00\r\nDownloading python_mecab_ko_dic-2.1.1.post2-py3-none-any.whl (34.5 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 34.5/34.5 MB 9.9 MB/s eta 0:00:00\r\nInstalling collected packages: python-mecab-ko-dic, python-mecab-ko\r\nSuccessfully installed python-mecab-ko-1.3.5 python-mecab-ko-dic-2.1.1.post2\r\nDownloading pytorch_model.bin: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 476M/476M [00:04<00:00, 109MB/s]\r\nSome weights of the model checkpoint at kykim/bert-kor-base were not used when initializing BertForMaskedLM: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias']M [00:04<00:00, 114MB/s]\r\n- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\r\n- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\r\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:24<00:00, 24.42s/it]\r\nTTS processing complete. Check the outputs in: outputs_v2\r\n(openvoice) ╭─arthur at aquarelle in ~/dev/ai/OpenVoice on main✘✘✘ 24-05-10 - 13:55:22\r\n╰─(openvoice) ⠠⠵ ls \r\n``` \r\n\r\nthanks a lot !", "Updating the library path to include the python virtual env works but speaker embeddings depend on nvidia-cublas-cu11 and will break with version 12. Error output is\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/lee/src/OpenVoice/example.py\", line 16, in <module>\r\n target_se, audio_name = se_extractor.get_se(reference_speaker, tone_color_converter, vad=False)\r\n File \"/home/lee/src/OpenVoice/openvoice/se_extractor.py\", line 146, in get_se\r\n wavs_folder = split_audio_whisper(audio_path, target_dir=target_dir, audio_name=audio_name)\r\n File \"/home/lee/src/OpenVoice/openvoice/se_extractor.py\", line 28, in split_audio_whisper\r\n segments, info = model.transcribe(audio_path, beam_size=5, word_timestamps=True)\r\n File \"/home/lee/src/OpenVoice/.venv/lib/python3.10/site-packages/faster_whisper/transcribe.py\", line 308, in transcribe\r\n encoder_output = self.encode(segment)\r\n File \"/home/lee/src/OpenVoice/.venv/lib/python3.10/site-packages/faster_whisper/transcribe.py\", line 610, in encode\r\n return self.model.encode(features, to_cpu=to_cpu)\r\nRuntimeError: Library libcublas.so.11 is not found or cannot be loaded\r\n```\r\n\r\nI was lucky enough to have a copy of that shared library elsewhere and manually added it to the LD_LIBRARY_PATH.", "> Updating the library path to include the python virtual env works but speaker embeddings depend on nvidia-cublas-cu11 and will break with version 12. Error output is\r\n> \r\n> ```\r\n> Traceback (most recent call last):\r\n> File \"/home/lee/src/OpenVoice/example.py\", line 16, in <module>\r\n> target_se, audio_name = se_extractor.get_se(reference_speaker, tone_color_converter, vad=False)\r\n> File \"/home/lee/src/OpenVoice/openvoice/se_extractor.py\", line 146, in get_se\r\n> wavs_folder = split_audio_whisper(audio_path, target_dir=target_dir, audio_name=audio_name)\r\n> File \"/home/lee/src/OpenVoice/openvoice/se_extractor.py\", line 28, in split_audio_whisper\r\n> segments, info = model.transcribe(audio_path, beam_size=5, word_timestamps=True)\r\n> File \"/home/lee/src/OpenVoice/.venv/lib/python3.10/site-packages/faster_whisper/transcribe.py\", line 308, in transcribe\r\n> encoder_output = self.encode(segment)\r\n> File \"/home/lee/src/OpenVoice/.venv/lib/python3.10/site-packages/faster_whisper/transcribe.py\", line 610, in encode\r\n> return self.model.encode(features, to_cpu=to_cpu)\r\n> RuntimeError: Library libcublas.so.11 is not found or cannot be loaded\r\n> ```\r\n> \r\n> I was lucky enough to have a copy of that shared library elsewhere and manually added it to the LD_LIBRARY_PATH.\r\n\r\nI have encountered this problem. Can you tell me how to handle it? Thank you!" ]

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[ "@IdoWSC 'target_se' was already defined.\r\n`reference_speaker = 'resources/example_reference.mp3' # This is the voice you want to clone`\r\n`target_se, audio_name = se_extractor.get_se(reference_speaker, tone_color_converter, vad=False)`" ]

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[ "-fix-\r\npip install --upgrade gradio==3.48.0" ]

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[ "Have you found a solution to the question?" ]

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[ "Dose source_se need to be from audio of the same person's voice as source audio to inference to get close or better clone quality? ", "I got the following warnings, could any of those warnings make the clone similarity to drastically degrade ?\r\n1. UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\r\n2. UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.\r\n3. UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED\r\n\r\n\r\n", "> Dose source_se need to be from audio of the same person's voice as source audio to inference to get close or better clone quality?\r\n\r\nI tried to use same (base-speaker) person's voice/mp3 for getting \"source_se/tone color embedding\" and \"source audio to inference\" , and a third male voice/mp3 as reference speaker. The resulting cloned audio, which sometime is female with a bit noise, is still far from the reference male audio. Very Bizarred !\r\n\r\nso, to my conclusion from the experiment, the source_se and source audio to inference don't have to be from same person, or at least, it doesn't matter towards affecting/improving clone similarity.\r\n\r\njust a couplel of sents to share ... have fun\r\n\r\nSean " ]

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[ "@ahmetkca Have you solved it yet?", "same problem here with v2" ]

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[ "I am also interested in Persian/ Farsi" ]

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[ "Same issue. openVoise v1 is OK, but v2:\r\n```bash\r\nLoaded checkpoint 'checkpoints_v2/converter/checkpoint.pth'\r\nmissing/unexpected keys: [] []\r\nOpenVoice version: v2\r\nCould not load library libcudnn_cnn_infer.so.8. Error: libcudnn_cnn_infer.so.8: cannot open shared object file: No such file or directory\r\nPlease make sure libcudnn_cnn_infer.so.8 is in your library path!\r\n```", "Same issue here with demopart3 on Windows", "I just ran into the same issue. Here's how I resolved it (on Ubuntu 20.04 w Python 3.10 using a python virtual environment (venv)):\r\n- context: PyTorch already comes bundled with cuDNN. One option to resolving this error is to ensure PyTorch can find the bundled cuDNN. The error above indicates that your LD_LIBRARY_PATH doesn't point to the location that contains the bundled cuDNN library.\r\n- locate the file: e.g., via `$ find ~ -name \"libcudnn_cnn_infer.so.8\"`, which pointed me to `/home/xxx/dev/openvoicev2_venv/lib/python3.10/site-packages/nvidia/cudnn/lib/libcudnn_cnn_infer.so.8`\r\n- set LD_LIBRARY_PATH: e.g., via `$ export LD_LIBRARY_PATH=/home/xxx/dev/openvoicev2_venv/lib/python3.10/site-packages/nvidia/cudnn/lib:$LD_LIBRARY_PATH` \r\n- afterwards, I can run the demo script on the command line as well as in jupyter-lab w/o errors.\r\n\r\n", "Same issue here on Ubuntu\r\n\r\n```\r\nLoaded checkpoint 'checkpoints_v2/converter/checkpoint.pth'\r\nmissing/unexpected keys: [] []\r\n```\r\n\r\nSetting LD_LIBRARY_PATH did not to help", "I'm experiencing the same thing on my WSL2 Ubuntu environment. When I run the code below, it restarts the kernel. I'm currently using an RTX 4090 with 24GB Vram, so I don't think it's a hardware issue.\r\n\r\n\r\nreference_speaker = 'resources/example_reference.mp3' # This is the voice you want to clone\r\ntarget_se, audio_name = se_extractor.get_se(reference_speaker, tone_color_converter, vad=False)", "I don't know if this might help anyone here, but I kept getting the error \"Could not load library cudnn_ops_infer64_8.dll. Error code 126\" when trying to run the code in the demopart3 jupyter notebook as a .py-file. If anyone has that Problem too, I was able to solve the error by doing the following:\r\n\r\n- I downloaded the zip-file for cuDNN v8.9.7 from https://developer.nvidia.com/rdp/cudnn-archive#a-collapse897-120\r\n- I just extracted the files \"cudnn_cnn_infer64_8.dll\" and \"cudnn_ops_infer64_8.dll\" from the \\bin folder of that zip into the torch-folder of my venv for the project (\\.venv\\Lib\\site-packages\\torch\\lib).\r\n\r\nSeems like this fixed it for me (using it on cpu) and thought, I'll post it here, if anyone runs into the same issue.", "To fix this on windows:\r\n\r\n1. Go to the [NVIDIA cuDNN download page](https://developer.nvidia.com/rdp/cudnn-archive)\r\n2. Select the appropriate version of cuDNN for your cuda version (cuDNN v8.9.7 for CUDA 12.x)\r\n3. Download the cuDNN installer for win\r\n4. Extract the contents of the downloaded file\r\n5. Copy Files to cuda toolkit directory\r\n6. Navigate to the extracted directory\r\n7. Copy the following files to your cuda installation directory:\r\n\r\n- Include files: Copy cudnn*.h files from cudnn/include to C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.x\\include\r\n\r\n- Library files: Copy cudnn*.lib files from cudnn/lib to C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.x\\lib\\x64\r\n\r\n- DLL files: Copy cudnn*.dll files from cudnn/bin to C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.x\\bin\r\n\r\n9. Set Environment Variables\r\n10. Open the start menu and search for \"environment variables\"\r\n11. Click on \"Edit the system environment variables\"\r\n12. In the system properties window, click on the \"environment variables\" button\r\n13. In the env vars window, find the path var under \"system variables\" and select it, then click \"edit\"\r\n14. Add the following paths to the Path var (if they are not already there):\r\n\r\n```\r\nC:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.x\\bin\r\nC:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.x\\libnvvp\r\n```\r\n15. Verify Installation\r\n16. Open a command prompt and run the following command to verify that CUDA is installed correctly:\r\n\r\n```shell\r\nnvcc --version\r\n```\r\nYou should see information about the cuda compiler\r\n\r\nTo verify cuDNN installation, you can write and compile a small cuda program that uses cuDNN, or you can use pytorch to check if it detects cuDNN.\r\n\r\n`pip install torch`\r\n\r\nRun a simple script:\r\n\r\n```python\r\npip install torch\r\nprint(\"Is CUDA available: \", torch.cuda.is_available())\r\nprint(\"CUDA version: \", torch.version.cuda)\r\nprint(\"cuDNN version: \", torch.backends.cudnn.version())\r\nprint(\"Number of GPUs: \", torch.cuda.device_count())\r\n```\r\nIf it detects your gpu and lists it, then your cuDNN installation is successful", "@vladlearns \r\nThank you very much for the detailed instructions!", "edit:\r\nDon't forget: cuda 12.0 and cuDNN 8.9.7 with pytorch, you need to install a version of pytorch that is built for cuda 12.0. pytorch provides binaries for specific CUDA versions, so you need to specify the correct version when installing.\r\n\r\n`pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu121`\r\n\r\nIf you want to run in the current session. You need to manually update the Path variable for the current session to prioritize new cuda paths\r\n\r\n```\r\n$currentPath = [System.Environment]::GetEnvironmentVariable(\"Path\", [System.EnvironmentVariableTarget]::Machine)\r\n\r\n$newCudaPaths = \"C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.0\\bin;C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.0\\libnvvp;\"\r\n\r\n# Remove old (if any)\r\n$currentPath = $currentPath -replace \"C:\\\\Program Files\\\\NVIDIA GPU Computing Toolkit\\\\CUDA\\\\v\\d+\\.\\d+\\\\bin;\",\"\"\r\n$currentPath = $currentPath -replace \"C:\\\\Program Files\\\\NVIDIA GPU Computing Toolkit\\\\CUDA\\\\v\\d+\\.\\d+\\\\libnvvp;\",\"\"\r\n\r\n# Combine the new paths with the current\r\n$updatedPath = $newCudaPaths + $currentPath\r\n\r\n# Update\r\n[System.Environment]::SetEnvironmentVariable(\"Path\", $updatedPath, [System.EnvironmentVariableTarget]::Process)\r\n\r\necho $env:Path\r\n```\r\n\r\n\r\n" ]

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[ "> <! #214 \r\n", "> <! #214 ", "> <! #214 " ]

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[ "Same problem with version 2. I get pass that part but runs into another problem. (idk how to put in code segment)\r\n\r\n****\\env\\lib\\site-packages\\torch\\nn\\modules\\conv.py:456: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ..\\aten\\src\\ATen\\native\\cudnn\\Conv_v8.cpp:919.)\r\n return F.conv2d(input, weight, bias, self.stride,\r\nHello\r\n\r\n 0%| | 0/1 [00:00<?, ?it/s]Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']\r\n- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\r\n- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\r\n 0%| | 0/1 [00:01<?, ?it/s]\r\nError during audio generation: too many dimensions 'str'", "I had to add a bunch of CUDA shared libraries to my laptop's search path. No idea why this didn't happen automatically like it does for every other project. I'll probably have to debug this for real eventually.\r\n\r\nI'm using python through pyenv with the venv module for a virtual environment.\r\n\r\n```\r\n(.venv) lee@suzanne:~/src/OpenVoice$ echo $LD_LIBRARY_PATH \r\n/home/lee/src/OpenVoice/.venv/lib/python3.10/site-packages/nvidia/cublas/lib:/home/lee/src/OpenVoice/.venv/lib/python3.10/site-packages/nvidia/cudnn/lib:/home/lee/.pyenv/versions/3.10.13/lib/python3.10/site-packages/nvidia/cublas/lib/\r\n```" ]

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[ "Please update the README.md file and give more information regarding the code . please connect.....", "You can reach out to me via email vatsal.gamit@mindinventory.com", "Is there any way to use hinddi on it?\r\n" ]

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[ "\r\nhttps://github.com/myshell-ai/OpenVoice/assets/119748743/7e8dc67f-6d07-4729-97b8-af4bd5f4f680\r\n\r\n", "### \r\n\r\n> @`****`", "hh" ]

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[ "Also encountered this problem, please ask how to solve", "Got this error also when upgrade to V2\r\nfrom melo.api import TTS\r\nOSError: libtorch_cuda_cpp.so: cannot open shared object file: No such file or directory", "I have the sam question, waiting solve...", "We solved the confict on MeloTTS side. Now MeloTTS will no longer require downgrading pytorch" ]

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[ "Same error :\r\n---------------------------------------------------------------------------\r\nFileNotFoundError Traceback (most recent call last)\r\nCell In[15], line 2\r\n 1 reference_speaker = 'resources/example_reference.mp3' # This is the voice you want to clone\r\n----> 2 target_se, audio_name = se_extractor.get_se(reference_speaker, tone_color_converter, target_dir='processed', vad=True)\r\n\r\nFile c:\\Users\\MAT1212\\Documents\\OpenVoiceAI\\OpenVoice\\openvoice\\se_extractor.py:144, in get_se(audio_path, vc_model, target_dir, vad)\r\n 137 # if os.path.isfile(se_path):\r\n 138 # se = torch.load(se_path).to(device)\r\n 139 # return se, audio_name\r\n 140 # if os.path.isdir(audio_path):\r\n 141 # wavs_folder = audio_path\r\n 143 if vad:\r\n--> 144 wavs_folder = split_audio_vad(audio_path, target_dir=target_dir, audio_name=audio_name)\r\n 145 else:\r\n 146 wavs_folder = split_audio_whisper(audio_path, target_dir=target_dir, audio_name=audio_name)\r\n\r\nFile c:\\Users\\MAT1212\\Documents\\OpenVoiceAI\\OpenVoice\\openvoice\\se_extractor.py:79, in split_audio_vad(audio_path, audio_name, target_dir, split_seconds)\r\n 77 def split_audio_vad(audio_path, audio_name, target_dir, split_seconds=10.0):\r\n 78 SAMPLE_RATE = 16000\r\n---> 79 audio_vad = get_audio_tensor(audio_path)\r\n 80 segments = get_vad_segments(\r\n 81 audio_vad,\r\n 82 output_sample=True,\r\n (...)\r\n...\r\n 1553 self._close_pipe_fds(p2cread, p2cwrite,\r\n 1554 c2pread, c2pwrite,\r\n 1555 errread, errwrite)\r\n\r\nFileNotFoundError: [WinError 2] Le fichier spécifié est introuvable\r\nOutput is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...", "Your environment may lack ffmpeg, which whisper relies on", "> FileNotFoundError Traceback (most recent call last) Cell In[18], line 2 1 reference_speaker = 'resources/example_reference.mp3' # This is the voice you want to clone\r\n> FileNotFoundError: [WinError 2] The system cannot find the file specified\r\n\r\nLiterally the comment tells you that you have to change the path to the audio path of the voice you want to clone, there's no issue here. I tried the code today and it works fine on colab.\r\nJust replace the path.\r\n", "The environment needs ffmpeg, if you're using conda: ```conda install ffmpeg```.\r\nBut if you are using a pip-only environment, extract the contents of the bin folder from a pre-compiled version of ffmpeg such as ```ffmpeg-master-latest-win64-gpl-shared.zip``` @ https://github.com/BtbN/FFmpeg-Builds/releases into the following folder: ```.venv\\Scripts```.", "> Literally the comment tells you that you have to change the path to the audio path of the voice you want to clone, there's no issue here. I tried the code today and it works fine on colab. Just replace the path.\r\n\r\nThe path as nothing to do with it, I `git clone` the repo and it should work fine.\r\n\r\nTo clarify, I used Conda because I noticed in another GitHub Issue that this was the answer.\r\n\r\nAs for ffmpeg I already installed it but VS Code caused some problems because of the Windows PATH (the PATH does not update, etc...) but I digress (although if you know how to make the PATH of VS Code and the Windows PATH are updated at the same time I am interested, demo 2 does not work with me because of that)." ]

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[ "I think this needs to be implemented urgently " ]

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[ "Hi Christoph! Welcome collaboration. I've sent you an email. If you didn't receive, just email me at qinzy@mit.edu" ]

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[ "we clone every voice \r\n", "or trained model for voice\r\n", "nice" ]

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