Upload folder using huggingface_hub

.env

@@ -0,0 +1,49 @@
+OPENBLAS_NUM_THREADS = 1
+no_proxy = localhost, 127.0.0.1, ::1
+
+# You can change the location of the model, etc. by changing here
+weight_root = assets/weights
+weight_uvr5_root = assets/uvr5_weights
+index_root = logs
+outside_index_root = assets/indices
+rmvpe_root = assets/rmvpe
+
+sha256_hubert_base_pt = f54b40fd2802423a5643779c4861af1e9ee9c1564dc9d32f54f20b5ffba7db96
+sha256_rmvpe_pt      = 6d62215f4306e3ca278246188607209f09af3dc77ed4232efdd069798c4ec193
+sha256_rmvpe_onnx    = 5370e71ac80af8b4b7c793d27efd51fd8bf962de3a7ede0766dac0befa3660fd
+
+sha256_v1_D32k_pth   = 2ab20645829460fdad0d3c44254f1ab53c32cae50c22a66c926ae5aa30abda6f
+sha256_v1_D40k_pth   = 547f66dbbcd9023b9051ed244d12ab043ba8a4e854b154cc28761ac7c002909b
+sha256_v1_D48k_pth   = 8cc013fa60ed9c3f902f5bd99f48c7e3b9352d763d4d3cd6bc241c37b0bfd9ad
+sha256_v1_G32k_pth   = 81817645cde7ed2e2d83f23ef883f33dda564924b497e84d792743912eca4c23
+sha256_v1_G40k_pth   = e428573bda1124b0ae0ae843fd8dcded6027d3993444790b3e9b0100938b2113
+sha256_v1_G48k_pth   = 3862a67ea6313e8ffefc05cee6bee656ef3e089442e9ecf4a6618d60721f3e95
+sha256_v1_f0D32k_pth = 294db3087236e2c75260d6179056791c9231245daf5d0485545d9e54c4057c77
+sha256_v1_f0D40k_pth = 7d4f5a441594b470d67579958b2fd4c6b992852ded28ff9e72eda67abcebe423
+sha256_v1_f0D48k_pth = 1b84c8bf347ad1e539c842e8f2a4c36ecd9e7fb23c16041189e4877e9b07925c
+sha256_v1_f0G32k_pth = 285f524bf48bb692c76ad7bd0bc654c12bd9e5edeb784dddf7f61a789a608574
+sha256_v1_f0G40k_pth = 9115654aeef1995f7dd3c6fc4140bebbef0ca9760bed798105a2380a34299831
+sha256_v1_f0G48k_pth = 78bc9cab27e34bcfc194f93029374d871d8b3e663ddedea32a9709e894cc8fe8
+
+sha256_v2_D32k_pth   = d8043378cc6619083d385f5a045de09b83fb3bf8de45c433ca863b71723ac3ca
+sha256_v2_D40k_pth   = 471378e894e7191f89a94eda8288c5947b16bbe0b10c3f1f17efdb7a1d998242
+sha256_v2_D48k_pth   = db01094a93c09868a278e03dafe8bb781bfcc1a5ba8df168c948bf9168c84d82
+sha256_v2_G32k_pth   = 869b26a47f75168d6126f64ac39e6de5247017a8658cfd68aca600f7323efb9f
+sha256_v2_G40k_pth   = a3843da7fde33db1dab176146c70d6c2df06eafe9457f4e3aa10024e9c6a4b69
+sha256_v2_G48k_pth   = 2e2b1581a436d07a76b10b9d38765f64aa02836dc65c7dee1ce4140c11ea158b
+sha256_v2_f0D32k_pth = bd7134e7793674c85474d5145d2d982e3c5d8124fc7bb6c20f710ed65808fa8a
+sha256_v2_f0D40k_pth = 6b6ab091e70801b28e3f41f335f2fc5f3f35c75b39ae2628d419644ec2b0fa09
+sha256_v2_f0D48k_pth = 2269b73c7a4cf34da09aea99274dabf99b2ddb8a42cbfb065fb3c0aa9a2fc748
+sha256_v2_f0G32k_pth = 2332611297b8d88c7436de8f17ef5f07a2119353e962cd93cda5806d59a1133d
+sha256_v2_f0G40k_pth = 3b2c44035e782c4b14ddc0bede9e2f4a724d025cd073f736d4f43708453adfcb
+sha256_v2_f0G48k_pth = b5d51f589cc3632d4eae36a315b4179397695042edc01d15312e1bddc2b764a4
+
+sha256_uvr5_HP2-人声vocals+非人声instrumentals_pth = 39796caa5db18d7f9382d8ac997ac967bfd85f7761014bb807d2543cc844ef05
+sha256_uvr5_HP2_all_vocals_pth =   39796caa5db18d7f9382d8ac997ac967bfd85f7761014bb807d2543cc844ef05
+sha256_uvr5_HP3_all_vocals_pth =   45e6b65199e781b4a6542002699be9f19cd3d1cb7d1558bc2bfbcd84674dfe28
+sha256_uvr5_HP5-主旋律人声vocals+其他instrumentals_pth = 5908891829634926119720241e8573d97cbeb8277110a7512bdb0bd7563258ee   
+sha256_uvr5_HP5_only_main_vocal_pth = 5908891829634926119720241e8573d97cbeb8277110a7512bdb0bd7563258ee
+sha256_uvr5_VR-DeEchoAggressive_pth = 8c8fd1582f9aabc363e47af62ddb88df6cae7e064cae75bbf041a067a5e0aee2
+sha256_uvr5_VR-DeEchoDeReverb_pth = 01376dd2a571bf3cb9cced680732726d2d732609d09216a610b0d110f133febe
+sha256_uvr5_VR-DeEchoNormal_pth =   56aba59db3bcdd14a14464e62f3129698ecdea62eee0f003b9360923eb3ac79e
+sha256_uvr5_vocals_onnx = 233bb5c6aaa365e568659a0a81211746fa881f8f47f82d9e864fce1f7692db80

.gitattributes

@@ -1,60 +1,27 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.avro filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.lz4 filter=lfs diff=lfs merge=lfs -text
-*.mds filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
-# Audio files - uncompressed
-*.pcm filter=lfs diff=lfs merge=lfs -text
-*.sam filter=lfs diff=lfs merge=lfs -text
-*.raw filter=lfs diff=lfs merge=lfs -text
-# Audio files - compressed
-*.aac filter=lfs diff=lfs merge=lfs -text
-*.flac filter=lfs diff=lfs merge=lfs -text
-*.mp3 filter=lfs diff=lfs merge=lfs -text
-*.ogg filter=lfs diff=lfs merge=lfs -text
-*.wav filter=lfs diff=lfs merge=lfs -text
-# Image files - uncompressed
-*.bmp filter=lfs diff=lfs merge=lfs -text
-*.gif filter=lfs diff=lfs merge=lfs -text
-*.png filter=lfs diff=lfs merge=lfs -text
-*.tiff filter=lfs diff=lfs merge=lfs -text
-# Image files - compressed
-*.jpg filter=lfs diff=lfs merge=lfs -text
-*.jpeg filter=lfs diff=lfs merge=lfs -text
-*.webp filter=lfs diff=lfs merge=lfs -text
-# Video files - compressed
-*.mp4 filter=lfs diff=lfs merge=lfs -text
-*.webm filter=lfs diff=lfs merge=lfs -text
+# Auto detect text files and perform LF normalization
+* text=auto
+Dwayne_cut_2.mp3 filter=lfs diff=lfs merge=lfs -text
+a.png filter=lfs diff=lfs merge=lfs -text
+assets/Synthesizer_inputs.pth filter=lfs diff=lfs merge=lfs -text
+assets/hubert/hubert_inputs.pth filter=lfs diff=lfs merge=lfs -text
+assets/pretrained_v2/f0D32k.pth filter=lfs diff=lfs merge=lfs -text
+assets/pretrained_v2/f0G32k.pth filter=lfs diff=lfs merge=lfs -text
+assets/pretrained_v2/f0Ov2Super32kD.pth filter=lfs diff=lfs merge=lfs -text
+assets/pretrained_v2/f0Ov2Super32kG.pth filter=lfs diff=lfs merge=lfs -text
+assets/rmvpe/rmvpe_inputs.pth filter=lfs diff=lfs merge=lfs -text
+audio.mp3 filter=lfs diff=lfs merge=lfs -text
+audios/astronauts.mp3 filter=lfs diff=lfs merge=lfs -text
+audios/output_audio.wav filter=lfs diff=lfs merge=lfs -text
+audios/somegirl.mp3 filter=lfs diff=lfs merge=lfs -text
+audios/someguy.mp3 filter=lfs diff=lfs merge=lfs -text
+audios/unachica.mp3 filter=lfs diff=lfs merge=lfs -text
+audios/unchico.mp3 filter=lfs diff=lfs merge=lfs -text
+logs/mute/0_gt_wavs/mute32k.spec.pt filter=lfs diff=lfs merge=lfs -text
+logs/mute/0_gt_wavs/mute32k.wav filter=lfs diff=lfs merge=lfs -text
+logs/mute/0_gt_wavs/mute40k.wav filter=lfs diff=lfs merge=lfs -text
+logs/mute/0_gt_wavs/mute48k.wav filter=lfs diff=lfs merge=lfs -text
+logs/mute/1_16k_wavs/mute.wav filter=lfs diff=lfs merge=lfs -text
+logs/mute/2a_f0/mute.wav.npy filter=lfs diff=lfs merge=lfs -text
+logs/mute/2b-f0nsf/mute.wav.npy filter=lfs diff=lfs merge=lfs -text
+logs/mute/3_feature256/mute.npy filter=lfs diff=lfs merge=lfs -text
+logs/mute/3_feature768/mute.npy filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,28 @@
+.DS_Store
+__pycache__
+/TEMP
+*.pyd
+.venv
+/opt
+tools/aria2c/
+tools/flag.txt
+
+# Imported from huggingface.co/lj1995/VoiceConversionWebUI
+/pretrained
+/pretrained_v2
+/uvr5_weights
+hubert_base.pt
+rmvpe.onnx
+rmvpe.pt
+
+# Generated by RVC
+/logs
+/weights
+
+# To set a Python version for the project
+.tool-versions
+
+/runtime
+/assets/weights/*
+ffmpeg.*
+ffprobe.*

CONTRIBUTING.md

@@ -0,0 +1,11 @@
+# 贡献规则
+1. 一般来说，作者`@RVC-Boss`将拒绝所有的算法更改，除非它是为了修复某个代码层面的错误或警告
+2. 您可以贡献本仓库的其他位置，如翻译和WebUI，但请尽量作最小更改
+3. 所有更改都需要由`@RVC-Boss`批准，因此您的PR可能会被搁置
+4. 由此带来的不便请您谅解
+
+# Contributing Rules
+1. Generally, the author `@RVC-Boss` will reject all algorithm changes unless what is to fix a code-level error or warning.
+2. You can contribute to other parts of this repo like translations and WebUI, but please minimize your changes as much as possible.
+3. All changes need to be approved by `@RVC-Boss`, so your PR may be put on hold.
+4. Please accept our apologies for any inconvenience caused.

Dockerfile

@@ -0,0 +1,46 @@
+# syntax=docker/dockerfile:1
+
+FROM nvidia/cuda:11.6.2-cudnn8-runtime-ubuntu20.04
+
+EXPOSE 7865
+
+WORKDIR /app
+
+# Install dependenceis to add PPAs
+RUN apt-get update && \
+    apt-get install -y -qq ffmpeg aria2 && apt clean && \
+    apt-get install -y software-properties-common && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
+# Add the deadsnakes PPA to get Python 3.9
+RUN add-apt-repository ppa:deadsnakes/ppa
+
+# Install Python 3.9 and pip
+RUN apt-get update && \
+    apt-get install -y build-essential python-dev python3-dev python3.9-distutils python3.9-dev python3.9 curl && \
+    apt-get clean && \
+    update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 1 && \
+    curl https://bootstrap.pypa.io/get-pip.py | python3.9
+
+# Set Python 3.9 as the default
+RUN update-alternatives --install /usr/bin/python python /usr/bin/python3.9 1
+
+COPY . .
+
+RUN python3 -m pip install --no-cache-dir -r requirements.txt
+
+RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D40k.pth -d assets/pretrained_v2/ -o D40k.pth
+RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G40k.pth -d assets/pretrained_v2/ -o G40k.pth
+RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D40k.pth -d assets/pretrained_v2/ -o f0D40k.pth
+RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G40k.pth -d assets/pretrained_v2/ -o f0G40k.pth
+
+RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2-人声vocals+非人声instrumentals.pth -d assets/uvr5_weights/ -o HP2-人声vocals+非人声instrumentals.pth
+RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5-主旋律人声vocals+其他instrumentals.pth -d assets/uvr5_weights/ -o HP5-主旋律人声vocals+其他instrumentals.pth
+
+RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt -d assets/hubert -o hubert_base.pt
+
+RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/rmvpe.pt -d assets/rmvpe -o rmvpe.pt
+
+VOLUME [ "/app/weights", "/app/opt" ]
+
+CMD ["python3", "infer-web.py"]

Dwayne_cut_2.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:48496f3828fe421c161576358d0e2fe07c40e0cea48676b67a0c1e431e244b27
+size 644310

LICENSE

@@ -0,0 +1,23 @@
+MIT License
+
+Copyright (c) 2023-2024 liujing04
+Copyright (c) 2023-2024 fumiama
+Copyright (c) 2023-2024 Ftps
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

MIT协议暨相关引用库协议

@@ -0,0 +1,45 @@
+本软件及其相关代码以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。
+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。
+
+特此授予任何获得本软件和相关文档文件（以下简称“软件”）副本的人免费使用、复制、修改、合并、出版、分发、再授权和/或销售本软件的权利，以及授予本软件所提供的人使用本软件的权利，但须符合以下条件：
+上述版权声明和本许可声明应包含在软件的所有副本或实质部分中。
+软件是“按原样”提供的，没有任何明示或暗示的保证，包括但不限于适销性、适用于特定目的和不侵权的保证。在任何情况下，作者或版权持有人均不承担因软件或软件的使用或其他交易而产生、产生或与之相关的任何索赔、损害赔偿或其他责任，无论是在合同诉讼、侵权诉讼还是其他诉讼中。
+
+
+The LICENCEs for related libraries are as follows.
+相关引用库协议如下：
+
+ContentVec
+https://github.com/auspicious3000/contentvec/blob/main/LICENSE
+MIT License
+
+VITS
+https://github.com/jaywalnut310/vits/blob/main/LICENSE
+MIT License
+
+HIFIGAN
+https://github.com/jik876/hifi-gan/blob/master/LICENSE
+MIT License
+
+gradio
+https://github.com/gradio-app/gradio/blob/main/LICENSE
+Apache License 2.0
+
+ffmpeg
+https://github.com/FFmpeg/FFmpeg/blob/master/COPYING.LGPLv3
+https://github.com/BtbN/FFmpeg-Builds/releases/download/autobuild-2021-02-28-12-32/ffmpeg-n4.3.2-160-gfbb9368226-win64-lgpl-4.3.zip
+LPGLv3 License
+MIT License
+
+ultimatevocalremovergui
+https://github.com/Anjok07/ultimatevocalremovergui/blob/master/LICENSE
+https://github.com/yang123qwe/vocal_separation_by_uvr5
+MIT License
+
+audio-slicer
+https://github.com/openvpi/audio-slicer/blob/main/LICENSE
+MIT License
+
+FreeSimpleGUI
+https://github.com/spyoungtech/FreeSimpleGUI/blob/master/license.txt
+LPGLv3 License

README.md

@@ -0,0 +1 @@
+RVC

assets/Synthesizer_inputs.pth

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+size 122495

assets/hubert/.gitignore

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+*
+!.gitignore
+!hubert_inputs.pth

assets/hubert/hubert_inputs.pth

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+size 169434

assets/indices/.gitignore

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+*
+!.gitignore

assets/pretrained/.gitignore

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+*
+!.gitignore

assets/pretrained_v2/.gitignore

@@ -0,0 +1,2 @@
+*
+!.gitignore

assets/pretrained_v2/f0D32k.pth

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+size 142875703

assets/pretrained_v2/f0G32k.pth

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+size 73950049

assets/pretrained_v2/f0Ov2Super32kD.pth

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assets/pretrained_v2/f0Ov2Super32kG.pth

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+size 443230526

assets/rmvpe/.gitignore

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+*
+!.gitignore
+!rmvpe_inputs.pth

assets/rmvpe/rmvpe_inputs.pth

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+size 33527

assets/uvr5_weights/.gitignore

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+*
+!.gitignore

audio.mp3

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+size 182637

audios/astronauts.mp3

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+version https://git-lfs.github.com/spec/v1
+oid sha256:638fcd17801db878209c1b5b5b5c0e73a9da842e090d9077fca534c1dd7b6d34
+size 73560

audios/output_audio.wav

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7fb256559e80671ba9e11cedde5bcfeae33f71774533e18e753134a06625320a
+size 481324

audios/somegirl.mp3

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+version https://git-lfs.github.com/spec/v1
+oid sha256:955cc2aa443c588a8eb3d3fbb30bfd31b7503071981076f38c89110d5be9fc28
+size 32182

audios/someguy.mp3

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a30d055d4e0f5f501edc59c66d84fc8b57a052f9906e94041474ee5ad8c5a2e0
+size 24868

audios/unachica.mp3

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+version https://git-lfs.github.com/spec/v1
+oid sha256:131cc78d8cb23cac783bbd0485667dfd59192a0006297d6ec981f3d075dee74e
+size 36362

audios/unchico.mp3

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+version https://git-lfs.github.com/spec/v1
+oid sha256:901d92e7726be546da88416b7e0af251c25a93f5c17c4b0ebb0a70d99f909dfe
+size 35944

configs/config.json

@@ -0,0 +1 @@
+{"pth_path": "assets/weights/kikiV1.pth", "index_path": "logs/kikiV1.index", "sg_hostapi": "MME", "sg_wasapi_exclusive": false, "sg_input_device": "VoiceMeeter Output (VB-Audio Vo", "sg_output_device": "VoiceMeeter Input (VB-Audio Voi", "sr_type": "sr_device", "threhold": -60.0, "pitch": 12.0, "rms_mix_rate": 0.5, "index_rate": 0.0, "block_time": 0.15, "crossfade_length": 0.08, "extra_time": 2.0, "n_cpu": 4.0, "use_jit": false, "use_pv": false, "f0method": "fcpe"}

configs/config.py

@@ -0,0 +1,259 @@
+import argparse
+import os
+import sys
+import json
+import shutil
+from multiprocessing import cpu_count
+
+import torch
+
+try:
+    import intel_extension_for_pytorch as ipex  # pylint: disable=import-error, unused-import
+
+    if torch.xpu.is_available():
+        from infer.modules.ipex import ipex_init
+
+        ipex_init()
+except Exception:  # pylint: disable=broad-exception-caught
+    pass
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+version_config_list = [
+    "v1/32k.json",
+    "v1/40k.json",
+    "v1/48k.json",
+    "v2/48k.json",
+    "v2/32k.json",
+]
+
+
+def singleton_variable(func):
+    def wrapper(*args, **kwargs):
+        if not wrapper.instance:
+            wrapper.instance = func(*args, **kwargs)
+        return wrapper.instance
+
+    wrapper.instance = None
+    return wrapper
+
+
+@singleton_variable
+class Config:
+    def __init__(self):
+        self.device = "cuda:0"
+        self.is_half = True
+        self.use_jit = False
+        self.n_cpu = 0
+        self.gpu_name = None
+        self.json_config = self.load_config_json()
+        self.gpu_mem = None
+        (
+            self.python_cmd,
+            self.listen_port,
+            self.iscolab,
+            self.noparallel,
+            self.noautoopen,
+            self.dml,
+            self.nocheck,
+        ) = self.arg_parse()
+        self.instead = ""
+        self.preprocess_per = 3.7
+        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
+
+    @staticmethod
+    def load_config_json() -> dict:
+        d = {}
+        for config_file in version_config_list:
+            p = f"configs/inuse/{config_file}"
+            if not os.path.exists(p):
+                shutil.copy(f"configs/{config_file}", p)
+            with open(f"configs/inuse/{config_file}", "r") as f:
+                d[config_file] = json.load(f)
+        return d
+
+    @staticmethod
+    def arg_parse() -> tuple:
+        exe = sys.executable or "python"
+        parser = argparse.ArgumentParser()
+        parser.add_argument("--port", type=int, default=7865, help="Listen port")
+        parser.add_argument("--pycmd", type=str, default=exe, help="Python command")
+        parser.add_argument("--colab", action="store_true", help="Launch in colab")
+        parser.add_argument(
+            "--noparallel", action="store_true", help="Disable parallel processing"
+        )
+        parser.add_argument(
+            "--noautoopen",
+            action="store_true",
+            help="Do not open in browser automatically",
+        )
+        parser.add_argument(
+            "--dml",
+            action="store_true",
+            help="torch_dml",
+        )
+        parser.add_argument(
+            "--nocheck", action="store_true", help="Run without checking assets"
+        )
+        cmd_opts = parser.parse_args()
+
+        cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865
+
+        return (
+            cmd_opts.pycmd,
+            cmd_opts.port,
+            cmd_opts.colab,
+            cmd_opts.noparallel,
+            cmd_opts.noautoopen,
+            cmd_opts.dml,
+            cmd_opts.nocheck,
+        )
+
+    # has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
+    # check `getattr` and try it for compatibility
+    @staticmethod
+    def has_mps() -> bool:
+        if not torch.backends.mps.is_available():
+            return False
+        try:
+            torch.zeros(1).to(torch.device("mps"))
+            return True
+        except Exception:
+            return False
+
+    @staticmethod
+    def has_xpu() -> bool:
+        if hasattr(torch, "xpu") and torch.xpu.is_available():
+            return True
+        else:
+            return False
+
+    def use_fp32_config(self):
+        for config_file in version_config_list:
+            self.json_config[config_file]["train"]["fp16_run"] = False
+            with open(f"configs/inuse/{config_file}", "r") as f:
+                strr = f.read().replace("true", "false")
+            with open(f"configs/inuse/{config_file}", "w") as f:
+                f.write(strr)
+            logger.info("overwrite " + config_file)
+        self.preprocess_per = 3.0
+        logger.info("overwrite preprocess_per to %d" % (self.preprocess_per))
+
+    def device_config(self) -> tuple:
+        if torch.cuda.is_available():
+            if self.has_xpu():
+                self.device = self.instead = "xpu:0"
+                self.is_half = True
+            i_device = int(self.device.split(":")[-1])
+            self.gpu_name = torch.cuda.get_device_name(i_device)
+            if (
+                ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
+                or "P40" in self.gpu_name.upper()
+                or "P10" in self.gpu_name.upper()
+                or "1060" in self.gpu_name
+                or "1070" in self.gpu_name
+                or "1080" in self.gpu_name
+            ):
+                logger.info("Found GPU %s, force to fp32", self.gpu_name)
+                self.is_half = False
+                self.use_fp32_config()
+            else:
+                logger.info("Found GPU %s", self.gpu_name)
+            self.gpu_mem = int(
+                torch.cuda.get_device_properties(i_device).total_memory
+                / 1024
+                / 1024
+                / 1024
+                + 0.4
+            )
+            if self.gpu_mem <= 4:
+                self.preprocess_per = 3.0
+        elif self.has_mps():
+            logger.info("No supported Nvidia GPU found")
+            self.device = self.instead = "mps"
+            self.is_half = False
+            self.use_fp32_config()
+        else:
+            logger.info("No supported Nvidia GPU found")
+            self.device = self.instead = "cpu"
+            self.is_half = False
+            self.use_fp32_config()
+
+        if self.n_cpu == 0:
+            self.n_cpu = cpu_count()
+
+        if self.is_half:
+            # 6G显存配置
+            x_pad = 3
+            x_query = 10
+            x_center = 60
+            x_max = 65
+        else:
+            # 5G显存配置
+            x_pad = 1
+            x_query = 6
+            x_center = 38
+            x_max = 41
+
+        if self.gpu_mem is not None and self.gpu_mem <= 4:
+            x_pad = 1
+            x_query = 5
+            x_center = 30
+            x_max = 32
+        if self.dml:
+            logger.info("Use DirectML instead")
+            if (
+                os.path.exists(
+                    "runtime\Lib\site-packages\onnxruntime\capi\DirectML.dll"
+                )
+                == False
+            ):
+                try:
+                    os.rename(
+                        "runtime\Lib\site-packages\onnxruntime",
+                        "runtime\Lib\site-packages\onnxruntime-cuda",
+                    )
+                except:
+                    pass
+                try:
+                    os.rename(
+                        "runtime\Lib\site-packages\onnxruntime-dml",
+                        "runtime\Lib\site-packages\onnxruntime",
+                    )
+                except:
+                    pass
+            # if self.device != "cpu":
+            import torch_directml
+
+            self.device = torch_directml.device(torch_directml.default_device())
+            self.is_half = False
+        else:
+            if self.instead:
+                logger.info(f"Use {self.instead} instead")
+            if (
+                os.path.exists(
+                    "runtime\Lib\site-packages\onnxruntime\capi\onnxruntime_providers_cuda.dll"
+                )
+                == False
+            ):
+                try:
+                    os.rename(
+                        "runtime\Lib\site-packages\onnxruntime",
+                        "runtime\Lib\site-packages\onnxruntime-dml",
+                    )
+                except:
+                    pass
+                try:
+                    os.rename(
+                        "runtime\Lib\site-packages\onnxruntime-cuda",
+                        "runtime\Lib\site-packages\onnxruntime",
+                    )
+                except:
+                    pass
+        logger.info(
+            "Half-precision floating-point: %s, device: %s"
+            % (self.is_half, self.device)
+        )
+        return x_pad, x_query, x_center, x_max

configs/inuse/.gitignore

@@ -0,0 +1,4 @@
+*
+!.gitignore
+!v1
+!v2

configs/inuse/v1/.gitignore

@@ -0,0 +1,2 @@
+*
+!.gitignore

configs/inuse/v1/32k.json

@@ -0,0 +1,46 @@
+{
+  "train": {
+    "log_interval": 200,
+    "seed": 1234,
+    "epochs": 20000,
+    "learning_rate": 1e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 4,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 12800,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "max_wav_value": 32768.0,
+    "sampling_rate": 32000,
+    "filter_length": 1024,
+    "hop_length": 320,
+    "win_length": 1024,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [10,4,2,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4,4],
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "spk_embed_dim": 109
+  }
+}

configs/inuse/v1/40k.json

@@ -0,0 +1,46 @@
+{
+  "train": {
+    "log_interval": 200,
+    "seed": 1234,
+    "epochs": 20000,
+    "learning_rate": 1e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 4,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 12800,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "max_wav_value": 32768.0,
+    "sampling_rate": 40000,
+    "filter_length": 2048,
+    "hop_length": 400,
+    "win_length": 2048,
+    "n_mel_channels": 125,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [10,10,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "spk_embed_dim": 109
+  }
+}

configs/inuse/v1/48k.json

@@ -0,0 +1,46 @@
+{
+  "train": {
+    "log_interval": 200,
+    "seed": 1234,
+    "epochs": 20000,
+    "learning_rate": 1e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 4,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 11520,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "max_wav_value": 32768.0,
+    "sampling_rate": 48000,
+    "filter_length": 2048,
+    "hop_length": 480,
+    "win_length": 2048,
+    "n_mel_channels": 128,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [10,6,2,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4,4],
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "spk_embed_dim": 109
+  }
+}

configs/inuse/v2/.gitignore

@@ -0,0 +1,2 @@
+*
+!.gitignore

configs/inuse/v2/32k.json

@@ -0,0 +1,46 @@
+{
+  "train": {
+    "log_interval": 200,
+    "seed": 1234,
+    "epochs": 20000,
+    "learning_rate": 1e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 4,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 12800,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "max_wav_value": 32768.0,
+    "sampling_rate": 32000,
+    "filter_length": 1024,
+    "hop_length": 320,
+    "win_length": 1024,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [10,8,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [20,16,4,4],
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "spk_embed_dim": 109
+  }
+}

configs/inuse/v2/48k.json

@@ -0,0 +1,46 @@
+{
+  "train": {
+    "log_interval": 200,
+    "seed": 1234,
+    "epochs": 20000,
+    "learning_rate": 1e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 4,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 17280,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "max_wav_value": 32768.0,
+    "sampling_rate": 48000,
+    "filter_length": 2048,
+    "hop_length": 480,
+    "win_length": 2048,
+    "n_mel_channels": 128,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [12,10,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [24,20,4,4],
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "spk_embed_dim": 109
+  }
+}

configs/v1/32k.json

@@ -0,0 +1,46 @@
+{
+  "train": {
+    "log_interval": 200,
+    "seed": 1234,
+    "epochs": 20000,
+    "learning_rate": 1e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 4,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 12800,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "max_wav_value": 32768.0,
+    "sampling_rate": 32000,
+    "filter_length": 1024,
+    "hop_length": 320,
+    "win_length": 1024,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [10,4,2,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4,4],
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "spk_embed_dim": 109
+  }
+}

configs/v1/40k.json

@@ -0,0 +1,46 @@
+{
+  "train": {
+    "log_interval": 200,
+    "seed": 1234,
+    "epochs": 20000,
+    "learning_rate": 1e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 4,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 12800,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "max_wav_value": 32768.0,
+    "sampling_rate": 40000,
+    "filter_length": 2048,
+    "hop_length": 400,
+    "win_length": 2048,
+    "n_mel_channels": 125,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [10,10,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "spk_embed_dim": 109
+  }
+}

configs/v1/48k.json

@@ -0,0 +1,46 @@
+{
+  "train": {
+    "log_interval": 200,
+    "seed": 1234,
+    "epochs": 20000,
+    "learning_rate": 1e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 4,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 11520,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "max_wav_value": 32768.0,
+    "sampling_rate": 48000,
+    "filter_length": 2048,
+    "hop_length": 480,
+    "win_length": 2048,
+    "n_mel_channels": 128,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [10,6,2,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4,4],
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "spk_embed_dim": 109
+  }
+}

configs/v2/32k.json

@@ -0,0 +1,46 @@
+{
+  "train": {
+    "log_interval": 200,
+    "seed": 1234,
+    "epochs": 20000,
+    "learning_rate": 1e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 4,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 12800,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "max_wav_value": 32768.0,
+    "sampling_rate": 32000,
+    "filter_length": 1024,
+    "hop_length": 320,
+    "win_length": 1024,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [10,8,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [20,16,4,4],
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "spk_embed_dim": 109
+  }
+}

configs/v2/48k.json

@@ -0,0 +1,46 @@
+{
+  "train": {
+    "log_interval": 200,
+    "seed": 1234,
+    "epochs": 20000,
+    "learning_rate": 1e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 4,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 17280,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "max_wav_value": 32768.0,
+    "sampling_rate": 48000,
+    "filter_length": 2048,
+    "hop_length": 480,
+    "win_length": 2048,
+    "n_mel_channels": 128,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [12,10,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [24,20,4,4],
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "spk_embed_dim": 109
+  }
+}

demo.py

@@ -0,0 +1,439 @@
+from original import *
+import shutil, glob
+from easyfuncs import download_from_url, CachedModels
+os.makedirs("dataset",exist_ok=True)
+model_library = CachedModels()
+
+with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="rose",neutral_hue="zinc")) as app:
+    with gr.Row():
+        gr.HTML("<img  src='file/a.png' alt='image'>")
+    with gr.Tabs():
+        with gr.TabItem("Inference"):
+            with gr.Row():
+                voice_model = gr.Dropdown(label="Model Voice", choices=sorted(names), value=lambda:sorted(names)[0] if len(sorted(names)) > 0 else '', interactive=True)
+                refresh_button = gr.Button("Refresh", variant="primary")
+                spk_item = gr.Slider(
+                    minimum=0,
+                    maximum=2333,
+                    step=1,
+                    label="Speaker ID",
+                    value=0,
+                    visible=False,
+                    interactive=True,
+                )
+                vc_transform0 = gr.Number(
+                    label="Pitch", 
+                    value=0
+                )
+                but0 = gr.Button(value="Convert", variant="primary")
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        dropbox = gr.File(label="Drop your audio here & hit the Reload button.")
+                    with gr.Row():
+                        record_button=gr.Audio(source="microphone", label="OR Record audio.", type="filepath")
+                    with gr.Row():
+                        paths_for_files = lambda path:[os.path.abspath(os.path.join(path, f)) for f in os.listdir(path) if os.path.splitext(f)[1].lower() in ('.mp3', '.wav', '.flac', '.ogg')]
+                        input_audio0 = gr.Dropdown(
+                            label="Input Path",
+                            value=paths_for_files('audios')[0] if len(paths_for_files('audios')) > 0 else '',
+                            choices=paths_for_files('audios'), # Only show absolute paths for audio files ending in .mp3, .wav, .flac or .ogg
+                            allow_custom_value=True
+                        )
+                    with gr.Row():
+                        audio_player = gr.Audio()
+                        input_audio0.change(
+                            inputs=[input_audio0],
+                            outputs=[audio_player],
+                            fn=lambda path: {"value":path,"__type__":"update"} if os.path.exists(path) else None
+                        )
+                        record_button.stop_recording(
+                            fn=lambda audio:audio, #TODO save wav lambda
+                            inputs=[record_button], 
+                            outputs=[input_audio0])
+                        dropbox.upload(
+                            fn=lambda audio:audio.name,
+                            inputs=[dropbox], 
+                            outputs=[input_audio0])
+                with gr.Column():
+                    with gr.Accordion("Change Index", open=False):
+                        file_index2 = gr.Dropdown(
+                            label="Change Index",
+                            choices=sorted(index_paths),
+                            interactive=True,
+                            value=sorted(index_paths)[0] if len(sorted(index_paths)) > 0 else ''
+                        )
+                        index_rate1 = gr.Slider(
+                            minimum=0,
+                            maximum=1,
+                            label="Index Strength",
+                            value=0.5,
+                            interactive=True,
+                        )
+                    vc_output2 = gr.Audio(label="Output")
+                    with gr.Accordion("General Settings", open=False):
+                        f0method0 = gr.Radio(
+                            label="Method",
+                            choices=["pm", "harvest", "crepe", "rmvpe"]
+                            if config.dml == False
+                            else ["pm", "harvest", "rmvpe"],
+                            value="rmvpe",
+                            interactive=True,
+                        )
+                        filter_radius0 = gr.Slider(
+                            minimum=0,
+                            maximum=7,
+                            label="Breathiness Reduction (Harvest only)",
+                            value=3,
+                            step=1,
+                            interactive=True,
+                        )
+                        resample_sr0 = gr.Slider(
+                            minimum=0,
+                            maximum=48000,
+                            label="Resample",
+                            value=0,
+                            step=1,
+                            interactive=True,
+                            visible=False
+                        )
+                        rms_mix_rate0 = gr.Slider(
+                            minimum=0,
+                            maximum=1,
+                            label="Volume Normalization",
+                            value=0,
+                            interactive=True,
+                        )
+                        protect0 = gr.Slider(
+                            minimum=0,
+                            maximum=0.5,
+                            label="Breathiness Protection (0 is enabled, 0.5 is disabled)",
+                            value=0.33,
+                            step=0.01,
+                            interactive=True,
+                        )
+                        if voice_model != None: vc.get_vc(voice_model.value,protect0,protect0)
+                    file_index1 = gr.Textbox(
+                        label="Index Path",
+                        interactive=True,
+                        visible=False#Not used here
+                    )
+                    refresh_button.click(
+                        fn=change_choices,
+                        inputs=[],
+                        outputs=[voice_model, file_index2],
+                        api_name="infer_refresh",
+                    )
+                    refresh_button.click(
+                        fn=lambda:{"choices":paths_for_files('audios'),"__type__":"update"}, #TODO check if properly returns a sorted list of audio files in the 'audios' folder that have the extensions '.wav', '.mp3', '.ogg', or '.flac'
+                        inputs=[],
+                        outputs = [input_audio0],   
+                    )
+                    refresh_button.click(
+                        fn=lambda:{"value":paths_for_files('audios')[0],"__type__":"update"} if len(paths_for_files('audios')) > 0 else {"value":"","__type__":"update"}, #TODO check if properly returns a sorted list of audio files in the 'audios' folder that have the extensions '.wav', '.mp3', '.ogg', or '.flac'
+                        inputs=[],
+                        outputs = [input_audio0],   
+                    )
+            with gr.Row():
+                f0_file = gr.File(label="F0 Path", visible=False)
+            with gr.Row():
+                vc_output1 = gr.Textbox(label="Information", placeholder="Welcome!",visible=False)
+                but0.click(
+                    vc.vc_single,  
+                    [
+                        spk_item,
+                        input_audio0,
+                        vc_transform0,
+                        f0_file,
+                        f0method0,
+                        file_index1,
+                        file_index2,
+                        index_rate1,
+                        filter_radius0,
+                        resample_sr0,
+                        rms_mix_rate0,
+                        protect0,
+                    ],
+                    [vc_output1, vc_output2],
+                    api_name="infer_convert",
+                )  
+                voice_model.change(
+                    fn=vc.get_vc,
+                    inputs=[voice_model, protect0, protect0],
+                    outputs=[spk_item, protect0, protect0, file_index2, file_index2],
+                    api_name="infer_change_voice",
+                )
+        with gr.TabItem("Download Models"):
+            with gr.Row():
+                url_input = gr.Textbox(label="URL to model", value="",placeholder="https://...", scale=6)
+                name_output = gr.Textbox(label="Save as", value="",placeholder="MyModel",scale=2)
+                url_download = gr.Button(value="Download Model",scale=2)
+                url_download.click(
+                    inputs=[url_input,name_output],
+                    outputs=[url_input],
+                    fn=download_from_url,
+                )
+            with gr.Row():
+                model_browser = gr.Dropdown(choices=list(model_library.models.keys()),label="OR Search Models (Quality UNKNOWN)",scale=5)
+                download_from_browser = gr.Button(value="Get",scale=2)
+                download_from_browser.click(
+                    inputs=[model_browser],
+                    outputs=[model_browser],
+                    fn=lambda model: download_from_url(model_library.models[model],model),
+                )
+        with gr.TabItem("Train"):
+            with gr.Row():
+                with gr.Column():
+                    training_name = gr.Textbox(label="Name your model", value="My-Voice",placeholder="My-Voice")
+                    np7 = gr.Slider(
+                        minimum=0,
+                        maximum=config.n_cpu,
+                        step=1,
+                        label="Number of CPU processes used to extract pitch features",
+                        value=int(np.ceil(config.n_cpu / 1.5)),
+                        interactive=True,
+                    )
+                    sr2 = gr.Radio(
+                        label="Sampling Rate",
+                        choices=["40k", "32k"],
+                        value="32k",
+                        interactive=True,
+                        visible=False
+                    )
+                    if_f0_3 = gr.Radio(
+                        label="Will your model be used for singing? If not, you can ignore this.",
+                        choices=[True, False],
+                        value=True,
+                        interactive=True,
+                        visible=False
+                    )
+                    version19 = gr.Radio(
+                        label="Version",
+                        choices=["v1", "v2"],
+                        value="v2",
+                        interactive=True,
+                        visible=False,
+                    )
+                    dataset_folder = gr.Textbox(
+                        label="dataset folder", value='dataset'
+                    )
+                    easy_uploader = gr.Files(label="Drop your audio files here",file_types=['audio'])
+                    but1 = gr.Button("1. Process", variant="primary")
+                    info1 = gr.Textbox(label="Information", value="",visible=True)
+                    easy_uploader.upload(inputs=[dataset_folder],outputs=[],fn=lambda folder:os.makedirs(folder,exist_ok=True))
+                    easy_uploader.upload(
+                        fn=lambda files,folder: [shutil.copy2(f.name,os.path.join(folder,os.path.split(f.name)[1])) for f in files] if folder != "" else gr.Warning('Please enter a folder name for your dataset'),
+                        inputs=[easy_uploader, dataset_folder], 
+                        outputs=[])
+                    gpus6 = gr.Textbox(
+                        label="Enter the GPU numbers to use separated by -, (e.g. 0-1-2)",
+                        value=gpus,
+                        interactive=True,
+                        visible=F0GPUVisible,
+                    )
+                    gpu_info9 = gr.Textbox(
+                        label="GPU Info", value=gpu_info, visible=F0GPUVisible
+                    )
+                    spk_id5 = gr.Slider(
+                        minimum=0,
+                        maximum=4,
+                        step=1,
+                        label="Speaker ID",
+                        value=0,
+                        interactive=True,
+                        visible=False
+                    )
+                    but1.click(
+                        preprocess_dataset,
+                        [dataset_folder, training_name, sr2, np7],
+                        [info1],
+                        api_name="train_preprocess",
+                    ) 
+                with gr.Column():
+                    f0method8 = gr.Radio(
+                        label="F0 extraction method",
+                        choices=["pm", "harvest", "dio", "rmvpe", "rmvpe_gpu"],
+                        value="rmvpe_gpu",
+                        interactive=True,
+                    )
+                    gpus_rmvpe = gr.Textbox(
+                        label="GPU numbers to use separated by -, (e.g. 0-1-2)",
+                        value="%s-%s" % (gpus, gpus),
+                        interactive=True,
+                        visible=F0GPUVisible,
+                    )
+                    but2 = gr.Button("2. Extract Features", variant="primary")
+                    info2 = gr.Textbox(label="Information", value="", max_lines=8)
+                    f0method8.change(
+                        fn=change_f0_method,
+                        inputs=[f0method8],
+                        outputs=[gpus_rmvpe],
+                    )
+                    but2.click(
+                        extract_f0_feature,
+                        [
+                            gpus6,
+                            np7,
+                            f0method8,
+                            if_f0_3,
+                            training_name,
+                            version19,
+                            gpus_rmvpe,
+                        ],
+                        [info2],
+                        api_name="train_extract_f0_feature",
+                    )
+                with gr.Column():
+                    total_epoch11 = gr.Slider(
+                        minimum=2,
+                        maximum=1000,
+                        step=1,
+                        label="Epochs (more epochs may improve quality but takes longer)",
+                        value=150,
+                        interactive=True,
+                    )
+                    but4 = gr.Button("3. Train Index", variant="primary")
+                    but3 = gr.Button("4. Train Model", variant="primary")
+                    info3 = gr.Textbox(label="Information", value="", max_lines=10)
+                    with gr.Accordion(label="General Settings", open=False):
+                        gpus16 = gr.Textbox(
+                            label="GPUs separated by -, (e.g. 0-1-2)",
+                            value="0",
+                            interactive=True,
+                            visible=True
+                        )
+                        save_epoch10 = gr.Slider(
+                            minimum=1,
+                            maximum=50,
+                            step=1,
+                            label="Weight Saving Frequency",
+                            value=25,
+                            interactive=True,
+                        )
+                        batch_size12 = gr.Slider(
+                            minimum=1,
+                            maximum=40,
+                            step=1,
+                            label="Batch Size",
+                            value=default_batch_size,
+                            interactive=True,
+                        )
+                        if_save_latest13 = gr.Radio(
+                            label="Only save the latest model",
+                            choices=["yes", "no"],
+                            value="yes",
+                            interactive=True,
+                            visible=False
+                        )
+                        if_cache_gpu17 = gr.Radio(
+                            label="If your dataset is UNDER 10 minutes, cache it to train faster",
+                            choices=["yes", "no"],
+                            value="no",
+                            interactive=True,
+                        )
+                        if_save_every_weights18 = gr.Radio(
+                            label="Save small model at every save point",
+                            choices=["yes", "no"],
+                            value="yes",
+                            interactive=True,
+                        )
+                        with gr.Accordion(label="Change pretrains", open=False):
+                            pretrained = lambda sr, letter: [os.path.abspath(os.path.join('assets/pretrained_v2', file)) for file in os.listdir('assets/pretrained_v2') if file.endswith('.pth') and sr in file and letter in file]
+                            pretrained_G14 = gr.Dropdown(
+                                label="pretrained G",
+                                # Get a list of all pretrained G model files in assets/pretrained_v2 that end with .pth
+                                choices = pretrained(sr2.value, 'G'),
+                                value=pretrained(sr2.value, 'G')[0] if len(pretrained(sr2.value, 'G')) > 0 else '',
+                                interactive=True,
+                                visible=True
+                            )
+                            pretrained_D15 = gr.Dropdown(
+                                label="pretrained D",
+                                choices = pretrained(sr2.value, 'D'),
+                                value= pretrained(sr2.value, 'D')[0] if len(pretrained(sr2.value, 'G')) > 0 else '',
+                                visible=True,
+                                interactive=True
+                            )
+                    with gr.Row():
+                        download_model = gr.Button('5.Download Model')
+                    with gr.Row():
+                        model_files = gr.Files(label='Your Model and Index file can be downloaded here:')
+                        download_model.click(
+                            fn=lambda name: os.listdir(f'assets/weights/{name}') + glob.glob(f'logs/{name.split(".")[0]}/added_*.index'),
+                            inputs=[training_name], 
+                            outputs=[model_files, info3])
+                    with gr.Row():
+                        sr2.change(
+                            change_sr2,
+                            [sr2, if_f0_3, version19],
+                            [pretrained_G14, pretrained_D15],
+                        )
+                        version19.change(
+                            change_version19,
+                            [sr2, if_f0_3, version19],
+                            [pretrained_G14, pretrained_D15, sr2],
+                        )
+                        if_f0_3.change(
+                            change_f0,
+                            [if_f0_3, sr2, version19],
+                            [f0method8, pretrained_G14, pretrained_D15],
+                        )
+                    with gr.Row():
+                        but5 = gr.Button("1 Click Training", variant="primary", visible=False)
+                        but3.click(
+                            click_train,
+                            [
+                                training_name,
+                                sr2,
+                                if_f0_3,
+                                spk_id5,
+                                save_epoch10,
+                                total_epoch11,
+                                batch_size12,
+                                if_save_latest13,
+                                pretrained_G14,
+                                pretrained_D15,
+                                gpus16,
+                                if_cache_gpu17,
+                                if_save_every_weights18,
+                                version19,
+                            ],
+                            info3,
+                            api_name="train_start",
+                        )
+                        but4.click(train_index, [training_name, version19], info3)
+                        but5.click(
+                            train1key,
+                            [
+                                training_name,
+                                sr2,
+                                if_f0_3,
+                                dataset_folder,
+                                spk_id5,
+                                np7,
+                                f0method8,
+                                save_epoch10,
+                                total_epoch11,
+                                batch_size12,
+                                if_save_latest13,
+                                pretrained_G14,
+                                pretrained_D15,
+                                gpus16,
+                                if_cache_gpu17,
+                                if_save_every_weights18,
+                                version19,
+                                gpus_rmvpe,
+                            ],
+                            info3,
+                            api_name="train_start_all",
+                        )
+
+    if config.iscolab:
+        app.queue(concurrency_count=511, max_size=1022).launch(share=True)
+    else:
+        app.queue(concurrency_count=511, max_size=1022).launch(
+            server_name="0.0.0.0",
+            inbrowser=not config.noautoopen,
+            server_port=config.listen_port,
+            quiet=True,
+        )

docker-compose.yml

@@ -0,0 +1,20 @@
+version: "3.8"
+services:
+  rvc:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    container_name: rvc
+    volumes:
+      - ./weights:/app/assets/weights
+      - ./opt:/app/opt
+      # - ./dataset:/app/dataset # you can use this folder in order to provide your dataset for model training
+    ports:
+      - 7865:7865
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]

docs/cn/Changelog_CN.md

@@ -0,0 +1,109 @@
+### 20231006更新
+
+我们制作了一个用于实时变声的界面go-realtime-gui.bat/gui_v1.py（事实上早就存在了），本次更新重点也优化了实时变声的性能。对比0813版：
+ - 1、优优化界面操作：参数热更新（调整参数不需要中止再启动），懒加载模型（已加载过的模型不需要重新加载），增加响度因子参数（响度向输入音频靠近）
+ - 2、优化自带降噪效果与速度
+ - 3、大幅优化推理速度
+
+注意输入输出设备应该选择同种类型，例如都选MME类型。
+
+1006版本整体的更新为：
+ - 1、继续提升rmvpe音高提取算法效果，对于男低音有更大的提升
+ - 2、优化推理界面布局
+
+### 20230813更新
+1-常规bug修复
+ - 保存频率总轮数最低改为1 总轮数最低改为2
+ - 修复无pretrain模型训练报错
+ - 增加伴奏人声分离完毕清理显存
+ - faiss保存路径绝对路径改为相对路径
+ - 支持路径包含空格（训练集路径+实验名称均支持，不再会报错）
+ - filelist取消强制utf8编码
+ - 解决实时变声中开启索引导致的CPU极大占用问题
+
+2-重点更新
+ - 训练出当前最强开源人声音高提取模型RMVPE，并用于RVC的训练、离线/实时推理，支持pytorch/onnx/DirectML
+ - 通过pytorch-dml支持A卡和I卡的
+（1）实时变声（2）推理（3）人声伴奏分离（4）训练暂未支持，会切换至CPU训练；通过onnx_dml支持rmvpe_gpu的推理
+
+### 20230618更新
+- v2增加32k和48k两个新预训练模型
+- 修复非f0模型推理报错
+- 对于超过一小时的训练集的索引建立环节，自动kmeans缩小特征处理以加速索引训练、加入和查询
+- 附送一个人声转吉他玩具仓库
+- 数据处理剔除异常值切片
+- onnx导出选项卡
+
+失败的实验：
+- ~~特征检索增加时序维度：寄，没啥效果~~
+- ~~特征检索增加PCAR降维可选项：寄，数据大用kmeans缩小数据量，数据小降维操作耗时比省下的匹配耗时还多~~
+- ~~支持onnx推理（附带仅推理的小压缩包）：寄，生成nsf还是需要pytorch~~
+- ~~训练时在音高、gender、eq、噪声等方面对输入进行随机增强：寄，没啥效果~~
+- ~~接入小型声码器调研:寄，效果变差~~
+
+todolist：
+- ~~训练集音高识别支持crepe：已经被RMVPE取代，不需要~~
+- ~~多进程harvest推理：已经被RMVPE取代，不需要~~
+- ~~crepe的精度支持和RVC-config同步：已经被RMVPE取代，不需要。支持这个还要同步torchcrepe的库，麻烦~~
+- 对接F0编辑器
+  
+  
+### 20230528更新
+- 增加v2的jupyter notebook，韩文changelog，增加一些环境依赖
+- 增加呼吸、清辅音、齿音保护模式
+- 支持crepe-full推理
+- UVR5人声伴奏分离加上3个去延迟模型和MDX-Net去混响模型，增加HP3人声提取模型
+- 索引名称增加版本和实验名称
+- 人声伴奏分离、推理批量导出增加音频导出格式选项
+- 废弃32k模型的训练
+
+### 20230513更新
+- 清除一键包内部老版本runtime内残留的lib.infer_pack和uvr5_pack
+- 修复训练集预处理伪多进程的bug
+- 增加harvest识别音高可选通过中值滤波削弱哑音现象，可调整中值滤波半径
+- 导出音频增加后处理重采样
+- 训练n_cpu进程数从"仅调整f0提取"改为"调整数据预处理和f0提取"
+- 自动检测logs文件夹下的index路径，提供下拉列表功能
+- tab页增加"常见问题解答"（也可参考github-rvc-wiki）
+- 相同路径的输入音频推理增加了音高缓存（用途：使用harvest音高提取，整个pipeline会经历漫长且重复的音高提取过程，如果不使用缓存，实验不同音色、索引、音高中值滤波半径参数的用户在第一次测试后的等待结果会非常痛苦）
+
+### 20230514更新
+- 音量包络对齐输入混合（可以缓解“输入静音输出小幅度噪声”的问题。如果输入音频背景底噪大则不建议开启，默认不开启（值为1可视为不开启））
+- 支持按照指定频率保存提取的小模型（假如你想尝试不同epoch下的推理效果，但是不想保存所有大checkpoint并且每次都要ckpt手工处理提取小模型，这项功能会非常实用）
+- 通过设置环境变量解决服务端开了系统全局代理导致浏览器连接错误的问题
+- 支持v2预训练模型（目前只公开了40k版本进行测试，另外2个采样率还没有训练完全）
+- 推理前限制超过1的过大音量
+- 微调数据预处理参数
+
+
+### 20230409更新
+- 修正训练参数，提升显卡平均利用率，A100最高从25%提升至90%左右，V100:50%->90%左右，2060S:60%->85%左右，P40:25%->95%左右，训练速度显著提升
+- 修正参数：总batch_size改为每张卡的batch_size
+- 修正total_epoch：最大限制100解锁至1000；默认10提升至默认20
+- 修复ckpt提取识别是否带音高错误导致推理异常的问题
+- 修复分布式训练每个rank都保存一次ckpt���问题
+- 特征提取进行nan特征过滤
+- 修复静音输入输出随机辅音or噪声的问题（老版模型需要重做训练集重训）
+
+### 20230416更新
+- 新增本地实时变声迷你GUI，双击go-realtime-gui.bat启动
+- 训练推理均对<50Hz的频段进行滤波过滤
+- 训练推理音高提取pyworld最低音高从默认80下降至50,50-80hz间的男声低音不会哑
+- WebUI支持根据系统区域变更语言（现支持en_US，ja_JP，zh_CN，zh_HK，zh_SG，zh_TW，不支持的默认en_US）
+- 修正部分显卡识别（例如V100-16G识别失败，P4识别失败）
+
+### 20230428更新
+- 升级faiss索引设置，速度更快，质量更高
+- 取消total_npy依赖，后续分享模型不再需要填写total_npy
+- 解锁16系限制。4G显存GPU给到4G的推理设置。
+- 修复部分音频格式下UVR5人声伴奏分离的bug
+- 实时变声迷你gui增加对非40k与不懈怠音高模型的支持
+
+### 后续计划：
+功能：
+- 支持多人训练选项卡（至多4人）
+
+底模：
+- 收集呼吸wav加入训练集修正呼吸变声电音的问题
+- 我们正在训练增加了歌声训练集的底模，未来会公开
+