Duplicate from Artrajz/vits-simple-api

Co-authored-by: Artrajz <Artrajz@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow 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
+*.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
+*.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
+chinese_dialect_lexicons/jyutjyu_2.ocd2 filter=lfs diff=lfs merge=lfs -text
+chinese_dialect_lexicons/zaonhe_2.ocd2 filter=lfs diff=lfs merge=lfs -text
+chinese_dialect_lexicons/zaonhe.ocd2 filter=lfs diff=lfs merge=lfs -text

Dockerfile

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+FROM python:3.10.11-slim-bullseye
+
+RUN mkdir -p /app
+WORKDIR /app
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get update && \
+    apt install build-essential -yq && \
+    apt install espeak-ng -yq && \
+    apt install cmake -yq && \
+    apt install -y wget -yq && \
+    apt-get clean && \
+    apt-get purge -y --auto-remove -o APT::AutoRemove::RecommendsImportant=false && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN pip install MarkupSafe==2.1.2 numpy==1.23.3 cython six==1.16.0
+
+RUN wget https://raw.githubusercontent.com/Artrajz/archived/main/openjtalk/openjtalk-0.3.0.dev2.tar.gz && \
+    tar -zxvf openjtalk-0.3.0.dev2.tar.gz && \
+    cd openjtalk-0.3.0.dev2 && \
+    rm -rf ./pyopenjtalk/open_jtalk_dic_utf_8-1.11 && \
+    python setup.py install && \
+    cd ../ && \
+    rm -f openjtalk-0.3.0.dev2.tar.gz && \
+    rm -rf openjtalk-0.3.0.dev2
+
+RUN pip install torch --index-url https://download.pytorch.org/whl/cpu
+
+COPY requirements.txt /app
+RUN pip install -r requirements.txt
+
+COPY . /app
+
+EXPOSE 23456
+
+CMD ["python", "/app/app.py"]

LICENSE

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+MIT License
+
+Copyright (c) 2023 Artrajz
+
+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.

LICENSE-MoeGoe

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+MIT License
+
+Copyright (c) 2022 CjangCjengh
+
+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.

Model/Bishojo_Mangekyo/config_mangekyo.json

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+{
+    "train": {
+      "segment_size": 8192
+    },
+    "data": {
+      "text_cleaners":["japanese_cleaners"],
+      "max_wav_value": 32768.0,
+      "sampling_rate": 22050,
+      "filter_length": 1024,
+      "hop_length": 256,
+      "win_length": 1024,
+      "add_blank": true,
+      "n_speakers": 6
+    },
+    "model": {
+      "inter_channels": 192,
+      "hidden_channels": 192,
+      "filter_channels": 768,
+      "n_heads": 2,
+      "n_layers": 6,
+      "kernel_size": 3,
+      "p_dropout": 0.1,
+      "resblock": "1",
+      "resblock_kernel_sizes": [3,7,11],
+      "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+      "upsample_rates": [8,8,2,2],
+      "upsample_initial_channel": 512,
+      "upsample_kernel_sizes": [16,16,4,4],
+      "n_layers_q": 3,
+      "use_spectral_norm": false,
+      "gin_channels": 256
+    },
+    "speakers": ["\u84ee\u83ef", "\u7bdd\u30ce\u9727\u679d", "\u6ca2\u6e21\u96eb", "\u4e9c\u7483\u5b50", "\u706f\u9732\u690e", "\u89a1\u5915\u8389"],
+    "symbols": ["_", ",", ".", "!", "?", "-", "~","A", "E", "I", "N", "O", "Q", "U", "a", "b", "d", "e", "f", "g", "h", "i", "j", "k", "m", "n", "o", "p", "r", "s", "t", "u", "v", "w", "y", "z", "\u0283", "\u02a7", "\u2193", "\u2191", " "]
+  }
+  

Model/Bishojo_Mangekyo/generator_mangekyo.pth

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+oid sha256:0acae1546bdda967fff35eba4e001c9d4d854d57c83678eb1575160a91a9b6fd
+size 158893717

Model/Cantonese/config.json

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+{
+  "train": {
+    "segment_size": 8192
+  },
+  "data": {
+    "text_cleaners":["chinese_dialect_cleaners"],
+    "max_wav_value": 32768.0,
+    "sampling_rate": 22050,
+    "filter_length": 1024,
+    "hop_length": 256,
+    "win_length": 1024,
+    "add_blank": true,
+    "n_speakers": 50
+  },
+  "model": {
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+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [8,8,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 256
+  },
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+}

Model/Cantonese/model.pth

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Model/Nene_Nanami_Rong_Tang/1374_epochs.pth

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Model/Nene_Nanami_Rong_Tang/config.json

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+{
+  "train": {
+    "segment_size": 8192
+  },
+  "data": {
+    "text_cleaners":["zh_ja_mixture_cleaners"],
+    "max_wav_value": 32768.0,
+    "sampling_rate": 22050,
+    "filter_length": 1024,
+    "hop_length": 256,
+    "win_length": 1024,
+    "add_blank": true,
+    "n_speakers": 5
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [8,8,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 256
+  },
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+  "symbols": ["_", ",", ".", "!", "?", "-", "~", "\u2026", "A", "E", "I", "N", "O", "Q", "U", "a", "b", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "r", "s", "t", "u", "v", "w", "y", "z", "\u0283", "\u02a7", "\u02a6", "\u026f", "\u0279", "\u0259", "\u0265", "\u207c", "\u02b0", "`", "\u2192", "\u2193", "\u2191", " "]
+}

Model/genshin/G_953000.pth

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Model/genshin/config.json

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+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 1000,
+    "seed": 1234,
+    "epochs": 10000,
+    "learning_rate": 2e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 64,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 8192,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "training_files":"filelists/uma_genshin_genshinjp_bh3_train.txt.cleaned",
+    "validation_files":"filelists/uma_genshin_genshinjp_bh3_val.txt.cleaned",
+    "text_cleaners":["zh_ja_mixture_cleaners"],
+    "max_wav_value": 32768.0,
+    "sampling_rate": 22050,
+    "filter_length": 1024,
+    "hop_length": 256,
+    "win_length": 1024,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null,
+    "add_blank": true,
+    "n_speakers": 804,
+    "cleaned_text": true
+  },
+  "model": {
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+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [8,8,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "n_layers_q": 3,
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+    "gin_channels": 256
+  },
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"\u7a7a\uff08\u7a7a\u54e5\uff09", "\u4e3d\u838e", "\u8367\uff08\u8367\u59b9\uff09", "\u82ad\u82ad\u62c9", "\u51ef\u4e9a", "\u8fea\u5362\u514b", "\u96f7\u6cfd", "\u5b89\u67cf", "\u6e29\u8fea", "\u9999\u83f1", "\u5317\u6597", "\u884c\u79cb", "\u9b48", "\u51dd\u5149", "\u53ef\u8389", "\u949f\u79bb", "\u83f2\u8c22\u5c14\uff08\u7687\u5973\uff09", "\u73ed\u5c3c\u7279", "\u8fbe\u8fbe\u5229\u4e9a\uff08\u516c\u5b50\uff09", "\u8bfa\u827e\u5c14\uff08\u5973\u4ec6\uff09", "\u4e03\u4e03", "\u91cd\u4e91", "\u7518\u96e8\uff08\u6930\u7f8a\uff09", "\u963f\u8d1d\u591a", "\u8fea\u5965\u5a1c\uff08\u732b\u732b\uff09", "\u83ab\u5a1c", "\u523b\u6674", "\u7802\u7cd6", "\u8f9b\u7131", "\u7f57\u838e\u8389\u4e9a", "\u80e1\u6843", "\u67ab\u539f\u4e07\u53f6\uff08\u4e07\u53f6\uff09", "\u70df\u7eef", "\u5bb5\u5bab", "\u6258\u9a6c", "\u4f18\u83c8", "\u96f7\u7535\u5c06\u519b\uff08\u96f7\u795e\uff09", "\u65e9\u67da", "\u73ca\u745a\u5bab\u5fc3\u6d77\uff08\u5fc3\u6d77\uff0c\u6263\u6263\u7c73\uff09", "\u4e94\u90ce", "\u4e5d\u6761\u88df\u7f57", "\u8352\u6cf7\u4e00\u6597\uff08\u4e00\u6597\uff09", "\u57c3\u6d1b\u4f0a", "\u7533\u9e64", "\u516b\u91cd\u795e\u5b50\uff08\u795e\u5b50\uff09", "\u795e\u91cc\u7eeb\u4eba\uff08\u7eeb\u4eba\uff09", "\u591c\u5170", "\u4e45\u5c90\u5fcd", "\u9e7f\u91ce\u82d1\u5e73\u85cf", "\u63d0\u7eb3\u91cc", "\u67ef\u83b1", "\u591a\u8389", "\u4e91\u5807", "\u7eb3\u897f\u59b2\uff08\u8349\u795e\uff09", "\u6df1\u6e0a\u4f7f\u5f92", "\u59ae\u9732", "\u8d5b\u8bfa", "\u503a\u52a1\u5904\u7406\u4eba", "\u574e\u8482\u4e1d", "\u771f\u5f13\u5feb\u8f66", "\u79cb\u4eba", "\u671b\u65cf", "\u827e\u5c14\u83f2", "\u827e\u8389\u4e1d", "\u827e\u4f26", "\u963f\u6d1b\u74e6", "\u5929\u91ce", "\u5929\u76ee\u5341\u4e94", "\u611a\u4eba\u4f17-\u5b89\u5fb7\u70c8", "\u5b89\u987a", "\u5b89\u897f", "\u8475", "\u9752\u6728", "\u8352\u5ddd\u5e78\u6b21", "\u8352\u8c37", "\u6709\u6cfd", "\u6d45\u5ddd", "\u9ebb\u7f8e", "\u51dd\u5149\u52a9\u624b", "\u963f\u6258", "\u7afa\u5b50", "\u767e\u8bc6", "\u767e\u95fb", "\u767e\u6653", "\u767d\u672f", "\u8d1d\u96c5\u7279\u4e3d\u5947", "\u4e3d\u5854", "\u5931\u843d\u8ff7\u8fed", "\u7f2d\u4e71\u661f\u68d8", "\u4f0a\u7538", "\u4f0f\u7279\u52a0\u5973\u5b69", "\u72c2\u70ed\u84dd\u8c03", "\u8389\u8389\u5a05", "\u841d\u838e\u8389\u5a05", "\u516b\u91cd\u6a31", "\u516b\u91cd\u971e", "\u5361\u83b2", "\u7b2c\u516d\u591c\u60f3\u66f2", "\u5361\u841d\u5c14", "\u59ec\u5b50", "\u6781\u5730\u6218\u5203", "\u5e03\u6d1b\u59ae\u5a05", "\u6b21\u751f\u94f6\u7ffc", "\u7406\u4e4b\u5f8b\u8005%26\u5e0c\u513f", "\u7406\u4e4b\u5f8b\u8005", "\u8ff7\u57ce\u9a87\u5154", "\u5e0c\u513f", "\u9b47\u591c\u661f\u6e0a", "\u9ed1\u5e0c\u513f", "\u5e15\u6735\u83f2\u8389\u4e1d", "\u4e0d\u706d\u661f\u951a", "\u5929\u5143\u9a91\u82f1", "\u5e7d\u5170\u9edb\u5c14", "\u6d3e\u8499bh3", "\u7231\u9171", "\u7eef\u7389\u4e38", "\u5fb7\u4e3d\u838e", "\u6708\u4e0b\u521d\u62e5", "\u6714\u591c\u89c2\u661f", "\u66ae\u5149\u9a91\u58eb", "\u683c\u857e\u4fee", "\u7559\u4e91\u501f\u98ce\u771f\u541b", "\u6885\u6bd4\u4e4c\u65af", "\u4eff\u72b9\u5927", "\u514b\u83b1\u56e0", "\u5723\u5251\u5e7d\u5170\u9edb\u5c14", "\u5996\u7cbe\u7231\u8389", "\u7279\u65af\u62c9zero", "\u82cd\u7384", "\u82e5\u6c34", "\u897f\u7433", "\u6234\u56e0\u65af\u96f7\u5e03", "\u8d1d\u62c9", "\u8d64\u9e22", "\u9547\u9b42\u6b4c", "\u6e21\u9e26", "\u4eba\u4e4b\u5f8b\u8005", "\u7231\u8389\u5e0c\u96c5", "\u5929\u7a79\u6e38\u4fa0", "\u742a\u4e9a\u5a1c", "\u7a7a\u4e4b\u5f8b\u8005", "\u85aa\u708e\u4e4b\u5f8b\u8005", "\u4e91\u58a8\u4e39\u5fc3", "\u7b26\u534e", "\u8bc6\u4e4b\u5f8b\u8005", "\u7279\u74e6\u6797", "\u7ef4\u5c14\u8587", "\u82bd\u8863", "\u96f7\u4e4b\u5f8b\u8005", "\u65ad\u7f6a\u5f71\u821e", "\u963f\u6ce2\u5c3c\u4e9a", "\u698e\u672c", "\u5384\u5c3c\u65af\u7279", "\u6076\u9f99", "\u8303\u4e8c\u7237", "\u6cd5\u62c9", "\u611a\u4eba\u4f17\u58eb\u5175", "\u611a\u4eba\u4f17\u58eb\u5175a", "\u611a\u4eba\u4f17\u58eb\u5175b", "\u611a\u4eba\u4f17\u58eb\u5175c", "\u611a\u4eba\u4f17a", "\u611a\u4eba\u4f17b", "\u98de\u98de", "\u83f2\u5229\u514b\u65af", "\u5973\u6027\u8ddf\u968f\u8005", "\u9022\u5ca9", "\u6446\u6e21\u4eba", "\u72c2\u8e81\u7684\u7537\u4eba", "\u5965\u5179", "\u8299\u841d\u62c9", "\u8ddf\u968f\u8005", "\u871c\u6c41\u751f\u7269", "\u9ec4\u9ebb\u5b50", "\u6e0a\u4e0a", "\u85e4\u6728", "\u6df1\u89c1", "\u798f\u672c", "\u8299\u84c9", "\u53e4\u6cfd", "\u53e4\u7530", "\u53e4\u5c71", "\u53e4\u8c37\u6607", "\u5085\u4e09\u513f", "\u9ad8\u8001\u516d", "\u77ff\u5de5\u5192", "\u5143\u592a", "\u5fb7\u5b89\u516c", "\u8302\u624d\u516c", "\u6770\u62c9\u5fb7", "\u845b\u7f57\u4e3d", "\u91d1\u5ffd\u5f8b", "\u516c\u4fca", "\u9505\u5df4", "\u6b4c\u5fb7", "\u963f\u8c6a", "\u72d7\u4e09\u513f", "\u845b\u745e\u4e1d", "\u82e5\u5fc3", "\u963f\u5c71\u5a46", "\u602a\u9e1f", "\u5e7f\u7af9", "\u89c2\u6d77", "\u5173\u5b8f", "\u871c\u6c41\u536b\u5175", "\u5b88\u536b1", "\u50b2\u6162\u7684\u5b88\u536b", "\u5bb3\u6015\u7684\u5b88\u536b", "\u8d35\u5b89", "\u76d6\u4f0a", "\u963f\u521b", "\u54c8\u592b\u4e39", "\u65e5\u8bed\u963f\u8d1d\u591a\uff08\u91ce\u5c9b\u5065\u513f\uff09", "\u65e5\u8bed\u57c3\u6d1b\u4f0a\uff08\u9ad8\u57a3\u5f69\u9633\uff09", "\u65e5\u8bed\u5b89\u67cf\uff08\u77f3\u89c1\u821e\u83dc\u9999\uff09", "\u65e5\u8bed\u795e\u91cc\u7eeb\u534e\uff08\u65e9\u89c1\u6c99\u7ec7\uff09", "\u65e5\u8bed\u795e\u91cc\u7eeb\u4eba\uff08\u77f3\u7530\u5f70\uff09", "\u65e5\u8bed\u767d\u672f\uff08\u6e38\u4f50\u6d69\u4e8c\uff09", "\u65e5\u8bed\u82ad\u82ad\u62c9\uff08\u9b3c\u5934\u660e\u91cc\uff09", "\u65e5\u8bed\u5317\u6597\uff08\u5c0f\u6e05\u6c34\u4e9a\u7f8e\uff09", "\u65e5\u8bed\u73ed\u5c3c\u7279\uff08\u9022\u5742\u826f\u592a\uff09", "\u65e5\u8bed\u574e\u8482\u4e1d\uff08\u67da\u6728\u51c9\u9999\uff09", "\u65e5\u8bed\u91cd\u4e91\uff08\u9f50\u85e4\u58ee\u9a6c\uff09", "\u65e5\u8bed\u67ef\u83b1\uff08\u524d\u5ddd\u51c9\u5b50\uff09", "\u65e5\u8bed\u8d5b\u8bfa\uff08\u5165\u91ce\u81ea\u7531\uff09", "\u65e5\u8bed\u6234\u56e0\u65af\u96f7\u5e03\uff08\u6d25\u7530\u5065\u6b21\u90ce\uff09", "\u65e5\u8bed\u8fea\u5362\u514b\uff08\u5c0f\u91ce\u8d24\u7ae0\uff09", "\u65e5\u8bed\u8fea\u5965\u5a1c\uff08\u4e95\u6cfd\u8bd7\u7ec7\uff09", "\u65e5\u8bed\u591a\u8389\uff08\u91d1\u7530\u670b\u5b50\uff09", "\u65e5\u8bed\u4f18\u83c8\uff08\u4f50\u85e4\u5229\u5948\uff09", "\u65e5\u8bed\u83f2\u8c22\u5c14\uff08\u5185\u7530\u771f\u793c\uff09", "\u65e5\u8bed\u7518\u96e8\uff08\u4e0a\u7530\u4e3d\u5948\uff09", "\u65e5\u8bed\uff08\u7560\u4e2d\u7950\uff09", "\u65e5\u8bed\u9e7f\u91ce\u9662\u5e73\u85cf\uff08\u4e95\u53e3\u7950\u4e00\uff09", "\u65e5\u8bed\u7a7a\uff08\u5800\u6c5f\u77ac\uff09", "\u65e5\u8bed\u8367\uff08\u60a0\u6728\u78a7\uff09", "\u65e5\u8bed\u80e1\u6843\uff08\u9ad8\u6865\u674e\u4f9d\uff09", "\u65e5\u8bed\u4e00\u6597\uff08\u897f\u5ddd\u8d35\u6559\uff09", "\u65e5\u8bed\u51ef\u4e9a\uff08\u9e1f\u6d77\u6d69\u8f85\uff09", "\u65e5\u8bed\u4e07\u53f6\uff08\u5c9b\u5d0e\u4fe1\u957f\uff09", "\u65e5\u8bed\u523b\u6674\uff08\u559c\u591a\u6751\u82f1\u68a8\uff09", "\u65e5\u8bed\u53ef\u8389\uff08\u4e45\u91ce\u7f8e\u54b2\uff09", "\u65e5\u8bed\u5fc3\u6d77\uff08\u4e09\u68ee\u94c3\u5b50\uff09", "\u65e5\u8bed\u4e5d\u6761\u88df\u7f57\uff08\u6fd1\u6237\u9ebb\u6c99\u7f8e\uff09", "\u65e5\u8bed\u4e3d\u838e\uff08\u7530\u4e2d\u7406\u60e0\uff09", "\u65e5\u8bed\u83ab\u5a1c\uff08\u5c0f\u539f\u597d\u7f8e\uff09", "\u65e5\u8bed\u7eb3\u897f\u59b2\uff08\u7530\u6751\u7531\u52a0\u8389\uff09", "\u65e5\u8bed\u59ae\u9732\uff08\u91d1\u5143\u5bff\u5b50\uff09", "\u65e5\u8bed\u51dd\u5149\uff08\u5927\u539f\u6c99\u8036\u9999\uff09", "\u65e5\u8bed\u8bfa\u827e\u5c14\uff08\u9ad8\u5c3e\u594f\u97f3\uff09", "\u65e5\u8bed\u5965\u5179\uff08\u589e\u8c37\u5eb7\u7eaa\uff09", "\u65e5\u8bed\u6d3e\u8499\uff08\u53e4\u8d3a\u8475\uff09", "\u65e5\u8bed\u7434\uff08\u658b\u85e4\u5343\u548c\uff09", "\u65e5\u8bed\u4e03\u4e03\uff08\u7530\u6751\u7531\u52a0\u8389\uff09", "\u65e5\u8bed\u96f7\u7535\u5c06\u519b\uff08\u6cfd\u57ce\u7f8e\u96ea\uff09", 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+  "symbols": ["_", ",", ".", "!", "?", "-", "~", "\u2026", "A", "E", "I", "N", "O", "Q", "U", "a", "b", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "r", "s", "t", "u", "v", "w", "y", "z", "\u0283", "\u02a7", "\u02a6", "\u026f", "\u0279", "\u0259", "\u0265", "\u207c", "\u02b0", "`", "\u2192", "\u2193", "\u2191", " "]
+}

Model/hubert-soft-0d54a1f4.pt

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Model/louise/360_epochs.pth

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+size 203264375

Model/louise/config.json

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+{
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+}

Model/model.onnx

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Model/model.yaml

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+$audonnx.core.model.Model==0.3.2:
+  path: model.onnx
+  labels:
+    logits:
+    - arousal
+    - dominance
+    - valence
+  transform: null

Model/npy/all_emotions.npy

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Model/paimon/paimon6k.json

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+    "seed": 1234,
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+    "validation_files":"filelists/paimon_6k_val_chs.txt.cleaned",
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+    "upsample_kernel_sizes": [16,16,4,4],
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+    "use_spectral_norm": false,
+    "gin_channels": 256
+  },
+  "speakers": ["paimon"],
+  "symbols": ["_", "\uff0c", "\u3002", "\uff01", "\uff1f", "\u2014", "\u2026", "\u300c", "\u300d", "\u3105", "\u3106", "\u3107", "\u3108", "\u3109", "\u310a", "\u310b", "\u310c", "\u310d", "\u310e", "\u310f", "\u3110", "\u3111", "\u3112", "\u3113", "\u3114", "\u3115", "\u3116", "\u3117", "\u3118", "\u3119", "\u311a", "\u311b", "\u311c", "\u311d", "\u311e", "\u311f", "\u3120", "\u3121", "\u3122", "\u3123", "\u3124", "\u3125", "\u3126", "\u3127", "\u3128", "\u3129", "\u02c9", "\u02ca", "\u02c7", "\u02cb", "\u02d9", " "]
+}

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+  "train": {
+    "segment_size": 8192
+  },
+  "data": {
+    "text_cleaners":["shanghainese_cleaners"],
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+    "add_blank": true,
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+    "upsample_kernel_sizes": [16,16,4,4],
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+    "use_spectral_norm": false,
+    "gin_channels": 256
+  },
+  "speakers": ["\u4e0a\u6d77\u8bdd(shanghainese)"],
+  "symbols": ["_", ",", ".", "!", "?", "\u2026", "a", "b", "d", "f", "g", "h", "i", "k", "l", "m", "n", "o", "p", "s", "t", "u", "v", "y", "z", "\u00f8", "\u014b", "\u0235", "\u0251", "\u0254", "\u0255", "\u0259", "\u0264", "\u0266", "\u026a", "\u027f", "\u0291", "\u0294", "\u02b0", "\u0303", "\u0329", "\u1d00", "\u1d07", "1", "5", "6", "7", "8", " "]
+}

Model/vctk/pretrained_vctk.pth

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Model/vctk/vctk_base.json

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+  "data": {
+    "training_files":"filelists/vctk_audio_sid_text_train_filelist.txt.cleaned",
+    "validation_files":"filelists/vctk_audio_sid_text_val_filelist.txt.cleaned",
+    "text_cleaners":["english_cleaners2"],
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+    "add_blank": true,
+    "n_speakers": 109,
+    "cleaned_text": true
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+    "gin_channels": 256
+  },
+  "speakers": ["vctk0(english)","vctk1(english)","vctk2(english)","vctk3(english)","vctk4(english)","vctk5(english)","vctk6(english)","vctk7(english)","vctk8(english)","vctk9(english)","vctk10(english)","vctk11(english)","vctk12(english)","vctk13(english)","vctk14(english)","vctk15(english)","vctk16(english)","vctk17(english)","vctk18(english)","vctk19(english)"],
+  "symbols":  ["_", ";", ":", ",", ".", "!", "?", "¡", "¿", "—", "…", "\"", "«", "»", "“", "”", " ", "A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "ɑ", "ɐ", "ɒ", "æ", "ɓ", "ʙ", "β", "ɔ", "ɕ", "ç", "ɗ", "ɖ", "ð", "ʤ", "ə", "ɘ", "ɚ", "ɛ", "ɜ", "ɝ", "ɞ", "ɟ", "ʄ", "ɡ", "ɠ", "ɢ", "ʛ", "ɦ", "ɧ", "ħ", "ɥ", "ʜ", "ɨ", "ɪ", "ʝ", "ɭ", "ɬ", "ɫ", "ɮ", "ʟ", "ɱ", "ɯ", "ɰ", "ŋ", "ɳ", "ɲ", "ɴ", "ø", "ɵ", "ɸ", "θ", "œ", "ɶ", "ʘ", "ɹ", "ɺ", "ɾ", "ɻ", "ʀ", "ʁ", "ɽ", "ʂ", "ʃ", "ʈ", "ʧ", "ʉ", "ʊ", "ʋ", "ⱱ", "ʌ", "ɣ", "ɤ", "ʍ", "χ", "ʎ", "ʏ", "ʑ", "ʐ", "ʒ", "ʔ", "ʡ", "ʕ", "ʢ", "ǀ", "ǁ", "ǂ", "ǃ", "ˈ", "ˌ", "ː", "ˑ", "ʼ", "ʴ", "ʰ", "ʱ", "ʲ", "ʷ", "ˠ", "ˤ", "˞", "↓", "↑", "→", "↗", "↘", "'", "̩", "'", "ᵻ"]
+}

Model/vits_chinese/bert_vits.json

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+{
+  "train": {
+    "log_interval": 100,
+    "eval_interval": 10000,
+    "seed": 1234,
+    "epochs": 20000,
+    "learning_rate": 1e-4,
+    "betas": [0.8, 0.99],
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+    "batch_size": 8,
+    "fp16_run": false,
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+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "training_files":"filelists/train.txt",
+    "validation_files":"filelists/valid.txt",
+    "text_cleaners":["bert_chinese_cleaners"],
+    "max_wav_value": 32768.0,
+    "sampling_rate": 16000,
+    "filter_length": 1024,
+    "hop_length": 256,
+    "win_length": 1024,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null,
+    "add_blank": false,
+    "n_speakers": 0
+  },
+  "model": {
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+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
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+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [8,8,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "use_sdp": false,
+    "bert_embedding": true
+  },
+  "speakers": ["bert"],
+  "symbols": ["sil", "eos", "sp", "#0", "#1", "#2", "#3", "^", "b", "c", "ch", "d", "f", "g", "h", "j", "k", "l", "m", "n", "p", "q", "r", "s", "sh", "t", "x", "z", "zh", "a1", "a2", "a3", "a4", "a5", "ai1", "ai2", "ai3", "ai4", "ai5", "an1", "an2", "an3", "an4", "an5", "ang1", "ang2", "ang3", "ang4", "ang5", "ao1", "ao2", "ao3", "ao4", "ao5", "e1", "e2", "e3", "e4", "e5", "ei1", "ei2", "ei3", "ei4", "ei5", "en1", "en2", "en3", "en4", "en5", "eng1", "eng2", "eng3", "eng4", "eng5", "er1", "er2", "er3", "er4", "er5", "i1", "i2", "i3", "i4", "i5", "ia1", "ia2", "ia3", "ia4", "ia5", "ian1", "ian2", "ian3", "ian4", "ian5", "iang1", "iang2", "iang3", "iang4", "iang5", "iao1", "iao2", "iao3", "iao4", "iao5", "ie1", "ie2", "ie3", "ie4", "ie5", "ii1", "ii2", "ii3", "ii4", "ii5", "iii1", "iii2", "iii3", "iii4", "iii5", "in1", "in2", "in3", "in4", "in5", "ing1", "ing2", "ing3", "ing4", "ing5", "iong1", "iong2", "iong3", "iong4", "iong5", "iou1", "iou2", "iou3", "iou4", "iou5", "o1", "o2", "o3", "o4", "o5", "ong1", "ong2", "ong3", "ong4", "ong5", "ou1", "ou2", "ou3", "ou4", "ou5", "u1", "u2", "u3", "u4", "u5", "ua1", "ua2", "ua3", "ua4", "ua5", "uai1", "uai2", "uai3", "uai4", "uai5", "uan1", "uan2", "uan3", "uan4", "uan5", "uang1", "uang2", "uang3", "uang4", "uang5", "uei1", "uei2", "uei3", "uei4", "uei5", "uen1", "uen2", "uen3", "uen4", "uen5", "ueng1", "ueng2", "ueng3", "ueng4", "ueng5", "uo1", "uo2", "uo3", "uo4", "uo5", "v1", "v2", "v3", "v4", "v5", "van1", "van2", "van3", "van4", "van5", "ve1", "ve2", "ve3", "ve4", "ve5", "vn1", "vn2", "vn3", "vn4", "vn5"]
+}

Model/vits_chinese/vits_bert_model.pth

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Model/w2v2-vits/1026_epochs.pth

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Model/w2v2-vits/config.json

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+{
+  "train": {
+    "segment_size": 8192
+  },
+  "data": {
+    "text_cleaners":["zh_ja_mixture_cleaners"],
+    "max_wav_value": 32768.0,
+    "sampling_rate": 22050,
+    "filter_length": 1024,
+    "hop_length": 256,
+    "win_length": 1024,
+    "add_blank": true,
+    "n_speakers": 5,
+    "emotion_embedding": true
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [8,8,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 256
+  },
+  "speakers": ["\u7dbe\u5730\u5be7\u3005", "\u5728\u539f\u4e03\u6d77", "\u5c0f\u8338", "\u5510\u4e50\u541f"],
+  "symbols": ["_", ",", ".", "!", "?", "-", "~", "\u2026", "A", "E", "I", "N", "O", "Q", "U", "a", "b", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "r", "s", "t", "u", "v", "w", "y", "z", "\u0283", "\u02a7", "\u02a6", "\u026f", "\u0279", "\u0259", "\u0265", "\u207c", "\u02b0", "`", "\u2192", "\u2193", "\u2191", " "]
+}

README.md

@@ -0,0 +1,10 @@
+---
+license: mit
+title: vits-simple-api
+sdk: gradio
+pinned: true
+python_version: 3.10.11
+emoji: 👀
+app_file: app.py
+duplicated_from: Artrajz/vits-simple-api
+---

README_zh.md

@@ -0,0 +1,626 @@
+<div class="title" align=center>
+    <h1>vits-simple-api</h1>
+	<div>Simply call the vits api</div>
+    <br/>
+    <br/>
+    <p>
+        <img src="https://img.shields.io/github/license/Artrajz/vits-simple-api">
+    	<img src="https://img.shields.io/badge/python-3.9%7C3.10-green">
+        <a href="https://hub.docker.com/r/artrajz/vits-simple-api">
+            <img src="https://img.shields.io/docker/pulls/artrajz/vits-simple-api"></a>
+    </p>
+        <a href="https://github.com/Artrajz/vits-simple-api/blob/main/README.md">English</a>|<a href="https://github.com/Artrajz/vits-simple-api/blob/main/README_zh.md">中文文档</a>
+    <br/>
+</div>
+
+
+
+
+# Feature
+
+- [x] VITS语音合成
+- [x] VITS语音转换
+- [x] HuBert-soft VITS模型
+- [x] W2V2 VITS / emotional-vits维度情感模型
+- [x] 加载多模型
+- [x] 自动识别语言并处理，根据模型的cleaner设置语言类型识别的范围，支持自定义语言类型范围
+- [x] 自定义默认参数
+- [x] 长文本批处理
+- [x] GPU加速推理
+- [x] SSML语音合成标记语言（完善中...）
+
+<details><summary>Update Logs</summary><pre><code>
+<h2>2023.6.5</h2>
+<p>更换音频编码使用的库，增加flac格式，增加中文对读简单数学公式的支持</p>
+<h2>2023.5.24</h2>
+<p>添加dimensional_emotion api,从文件夹加载多个npy文件,Docker添加了Linux/ARM64和Linux/ARM64/v8平台</p>
+<h2>2023.5.15</h2>
+<p>增加english_cleaner，需要额外安装espeak才能使用</p>
+<h2>2023.5.12</h2>
+<p>增加ssml支持，但仍需完善。重构部分功能，hubert_vits中的speaker_id改为id</p>
+<h2>2023.5.2</h2>
+<p>增加w2v2-vits/emotional-vits模型支持，修改了speakers映射表并添加了对应模型支持的语言</p>
+<h2>2023.4.23</h2>
+<p>增加api key鉴权，默认禁用，需要在config.py中启用</p>
+<h2>2023.4.17</h2>
+<p>修改单语言的cleaner需要标注才会clean，增加GPU加速推理，但需要手动安装gpu推理环境</p>
+<h2>2023.4.12</h2>
+<p>项目由MoeGoe-Simple-API更名为vits-simple-api，支持长文本批处理，增加长文本分段阈值max</p>
+<h2>2023.4.7</h2>
+<p>增加配置文件可自定义默认参数，本次更新需要手动更新config.py，具体使用方法见config.py</p>
+<h2>2023.4.6</h2>
+<p>加入自动识别语种选项auto，lang参数默认修改为auto，自动识别仍有一定缺陷，请自行选择</p>
+<p>统一POST请求类型为multipart/form-data</p>
+</code></pre></details>
+
+
+
+## demo
+
+[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Artrajz/vits-simple-api)
+
+注意不同的id支持的语言可能有所不同。[speakers](https://artrajz-vits-simple-api.hf.space/voice/speakers)
+
+
+- `https://artrajz-vits-simple-api.hf.space/voice/vits?text=你好,こんにちは&id=164`
+- `https://artrajz-vits-simple-api.hf.space/voice/vits?text=你知道1+1=几吗？我觉得1+1≠3&id=164&lang=zh`
+- `https://artrajz-vits-simple-api.hf.space/voice/vits?text=Difficult the first time, easy the second.&id=4`
+- 激动：`https://artrajz-vits-simple-api.hf.space/voice/w2v2-vits?text=こんにちは&id=3&emotion=111`
+- 小声：`https://artrajz-vits-simple-api.hf.space/voice/w2v2-vits?text=こんにちは&id=3&emotion=2077`
+
+https://user-images.githubusercontent.com/73542220/237995061-c1f25b4e-dd86-438a-9363-4bb1fe65b425.mov
+
+# 部署
+
+## Docker部署
+
+### 镜像拉取脚本
+
+```
+bash -c "$(wget -O- https://raw.githubusercontent.com/Artrajz/vits-simple-api/main/vits-simple-api-installer-latest.sh)"
+```
+
+- 目前docker镜像支持的平台`linux/amd64,linux/arm64`
+- 在拉取完成后，需要导入VITS模型才能使用，请根据以下步骤导入模型。
+
+### 下载VITS模型
+
+将模型放入`/usr/local/vits-simple-api/Model`
+
+<details><summary>Folder structure</summary><pre><code>
+│  hubert-soft-0d54a1f4.pt
+│  model.onnx
+│  model.yaml
+├─g
+│      config.json
+│      G_953000.pth
+│
+├─louise
+│      360_epochs.pth
+│      config.json
+│
+├─Nene_Nanami_Rong_Tang
+│      1374_epochs.pth
+│      config.json
+│
+├─Zero_no_tsukaima
+│       1158_epochs.pth
+│       config.json
+│
+└─npy
+       25ecb3f6-f968-11ed-b094-e0d4e84af078.npy
+       all_emotions.npy
+</code></pre></details>
+
+
+
+### 修改模型路径
+
+Modify in  `/usr/local/vits-simple-api/config.py` 
+
+<details><summary>config.py</summary><pre><code>
+# 在此填写模型路径
+MODEL_LIST = [
+    # VITS
+    [ABS_PATH + "/Model/Nene_Nanami_Rong_Tang/1374_epochs.pth", ABS_PATH + "/Model/Nene_Nanami_Rong_Tang/config.json"],
+    [ABS_PATH + "/Model/Zero_no_tsukaima/1158_epochs.pth", ABS_PATH + "/Model/Zero_no_tsukaima/config.json"],
+    [ABS_PATH + "/Model/g/G_953000.pth", ABS_PATH + "/Model/g/config.json"],
+    # HuBert-VITS (Need to configure HUBERT_SOFT_MODEL)
+    [ABS_PATH + "/Model/louise/360_epochs.pth", ABS_PATH + "/Model/louise/config.json"],
+    # W2V2-VITS (Need to configure DIMENSIONAL_EMOTION_NPY)
+    [ABS_PATH + "/Model/w2v2-vits/1026_epochs.pth", ABS_PATH + "/Model/w2v2-vits/config.json"],
+]
+# hubert-vits: hubert soft 编码器
+HUBERT_SOFT_MODEL = ABS_PATH + "/Model/hubert-soft-0d54a1f4.pt"
+# w2v2-vits: Dimensional emotion npy file
+# 加载单独的npy: ABS_PATH+"/all_emotions.npy
+# 加载多个npy: [ABS_PATH + "/emotions1.npy", ABS_PATH + "/emotions2.npy"]
+# 从文件夹里加载npy: ABS_PATH + "/Model/npy"
+DIMENSIONAL_EMOTION_NPY = ABS_PATH + "/Model/npy"
+# w2v2-vits: 需要在同一路径下有model.onnx和model.yaml
+DIMENSIONAL_EMOTION_MODEL = ABS_PATH + "/Model/model.yaml"
+</code></pre></details>
+
+
+
+### 启动
+
+`docker compose up -d`
+
+或者重新执行拉取脚本
+
+### 镜像更新
+
+重新执行docker镜像拉取脚本即可
+
+## 虚拟环境部署
+
+### Clone
+
+`git clone https://github.com/Artrajz/vits-simple-api.git`
+
+###  下载python依赖
+
+推荐使用python的虚拟环境，python版本 >= 3.9
+
+`pip install -r requirements.txt`
+
+windows下可能安装不了fasttext,可以用以下命令安装，附[wheels下载地址](https://www.lfd.uci.edu/~gohlke/pythonlibs/#fasttext)
+
+```
+#python3.10 win_amd64
+pip install https://github.com/Artrajz/archived/raw/main/fasttext/fasttext-0.9.2-cp310-cp310-win_amd64.whl
+#python3.9 win_amd64
+pip install https://github.com/Artrajz/archived/raw/main/fasttext/fasttext-0.9.2-cp39-cp39-win_amd64.whl
+```
+
+### 下载VITS模型
+
+将模型放入 `/path/to/vits-simple-api/Model`
+
+<details><summary>文件夹结构</summary><pre><code>
+├─g
+│      config.json
+│      G_953000.pth
+│
+├─louise
+│      360_epochs.pth
+│      config.json
+│      hubert-soft-0d54a1f4.pt
+│
+├─Nene_Nanami_Rong_Tang
+│      1374_epochs.pth
+│      config.json
+│
+└─Zero_no_tsukaima
+        1158_epochs.pth
+        config.json
+</code></pre></details>
+
+### 修改模型路径
+
+在 `/path/to/vits-simple-api/config.py` 修改
+
+<details><summary>config.py</summary><pre><code>
+# 在此填写模型路径
+MODEL_LIST = [
+    # VITS
+    [ABS_PATH + "/Model/Nene_Nanami_Rong_Tang/1374_epochs.pth", ABS_PATH + "/Model/Nene_Nanami_Rong_Tang/config.json"],
+    [ABS_PATH + "/Model/Zero_no_tsukaima/1158_epochs.pth", ABS_PATH + "/Model/Zero_no_tsukaima/config.json"],
+    [ABS_PATH + "/Model/g/G_953000.pth", ABS_PATH + "/Model/g/config.json"],
+    # HuBert-VITS (Need to configure HUBERT_SOFT_MODEL)
+    [ABS_PATH + "/Model/louise/360_epochs.pth", ABS_PATH + "/Model/louise/config.json"],
+    # W2V2-VITS (Need to configure DIMENSIONAL_EMOTION_NPY)
+    [ABS_PATH + "/Model/w2v2-vits/1026_epochs.pth", ABS_PATH + "/Model/w2v2-vits/config.json"],
+]
+# hubert-vits: hubert soft 编码器
+HUBERT_SOFT_MODEL = ABS_PATH + "/Model/hubert-soft-0d54a1f4.pt"
+# w2v2-vits: Dimensional emotion npy file
+# 加载单独的npy: ABS_PATH+"/all_emotions.npy
+# 加载多个npy: [ABS_PATH + "/emotions1.npy", ABS_PATH + "/emotions2.npy"]
+# 从文件夹里加载npy: ABS_PATH + "/Model/npy"
+DIMENSIONAL_EMOTION_NPY = ABS_PATH + "/Model/npy"
+# w2v2-vits: 需要在同一路径下有model.onnx和model.yaml
+DIMENSIONAL_EMOTION_MODEL = ABS_PATH + "/Model/model.yaml"
+</code></pre></details>
+
+
+
+### 启动
+
+`python app.py`
+
+# GPU 加速
+
+## windows
+
+### 安装CUDA
+
+查看显卡最高支持CUDA的版本
+
+```
+nvidia-smi
+```
+
+以CUDA11.7为例，[官网](https://developer.nvidia.com/cuda-11-7-0-download-archive?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exe_local)
+
+### 安装GPU版pytorch
+
+CUDA11.7对应的pytorch是用这个命令安装
+
+```
+pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
+```
+
+对应版本的命令可以在[官网](https://pytorch.org/get-started/locally/)找到
+
+## Linux
+
+安装过程类似，但我没有相应的环境所以没办法测试
+
+# Openjtalk安装问题
+
+如果你是arm64架构的平台，由于pypi官网上没有arm64对应的whl，可能安装会出现一些问题，你可以使用我构建的whl来安装
+
+```
+pip install openjtalk==0.3.0.dev2 --index-url https://pypi.artrajz.cn/simple
+```
+
+或者是自己手动构建一个whl，可以根据[教程](https://artrajz.cn/index.php/archives/167/)来构建
+
+# API
+
+## GET
+
+#### speakers list 
+
+- GET http://127.0.0.1:23456/voice/speakers
+
+  返回id对应角色的映射表
+
+#### voice vits
+
+- GET http://127.0.0.1:23456/voice/vits?text=text
+
+  其他参数不指定时均为默认值
+
+- GET http://127.0.0.1:23456/voice/vits?text=[ZH]text[ZH][JA]text[JA]&lang=mix
+
+  lang=mix时文本要标注
+
+- GET http://127.0.0.1:23456/voice/vits?text=text&id=142&format=wav&lang=zh&length=1.4
+
+  文本为text，角色id为142，音频格式为wav，文本语言为zh，语音长度为1.4，其余参数默认
+
+#### check
+
+- GET http://127.0.0.1:23456/voice/check?id=0&model=vits
+
+## POST
+
+- python
+
+```python
+import re
+import requests
+import os
+import random
+import string
+from requests_toolbelt.multipart.encoder import MultipartEncoder
+
+abs_path = os.path.dirname(__file__)
+base = "http://127.0.0.1:23456"
+
+
+# 映射表
+def voice_speakers():
+    url = f"{base}/voice/speakers"
+
+    res = requests.post(url=url)
+    json = res.json()
+    for i in json:
+        print(i)
+        for j in json[i]:
+            print(j)
+    return json
+
+
+# 语音合成 voice vits
+def voice_vits(text, id=0, format="wav", lang="auto", length=1, noise=0.667, noisew=0.8, max=50):
+    fields = {
+        "text": text,
+        "id": str(id),
+        "format": format,
+        "lang": lang,
+        "length": str(length),
+        "noise": str(noise),
+        "noisew": str(noisew),
+        "max": str(max)
+    }
+    boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16))
+
+    m = MultipartEncoder(fields=fields, boundary=boundary)
+    headers = {"Content-Type": m.content_type}
+    url = f"{base}/voice"
+
+    res = requests.post(url=url, data=m, headers=headers)
+    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
+    path = f"{abs_path}/{fname}"
+
+    with open(path, "wb") as f:
+        f.write(res.content)
+    print(path)
+    return path
+
+
+# 语音转换 hubert-vits
+def voice_hubert_vits(upload_path, id, format="wav", length=1, noise=0.667, noisew=0.8):
+    upload_name = os.path.basename(upload_path)
+    upload_type = f'audio/{upload_name.split(".")[1]}'  # wav,ogg
+
+    with open(upload_path, 'rb') as upload_file:
+        fields = {
+            "upload": (upload_name, upload_file, upload_type),
+            "id": str(id),
+            "format": format,
+            "length": str(length),
+            "noise": str(noise),
+            "noisew": str(noisew),
+        }
+        boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16))
+
+        m = MultipartEncoder(fields=fields, boundary=boundary)
+        headers = {"Content-Type": m.content_type}
+        url = f"{base}/voice/hubert-vits"
+
+        res = requests.post(url=url, data=m, headers=headers)
+    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
+    path = f"{abs_path}/{fname}"
+
+    with open(path, "wb") as f:
+        f.write(res.content)
+    print(path)
+    return path
+
+
+# 维度情感模型 w2v2-vits
+def voice_w2v2_vits(text, id=0, format="wav", lang="auto", length=1, noise=0.667, noisew=0.8, max=50, emotion=0):
+    fields = {
+        "text": text,
+        "id": str(id),
+        "format": format,
+        "lang": lang,
+        "length": str(length),
+        "noise": str(noise),
+        "noisew": str(noisew),
+        "max": str(max),
+        "emotion": str(emotion)
+    }
+    boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16))
+
+    m = MultipartEncoder(fields=fields, boundary=boundary)
+    headers = {"Content-Type": m.content_type}
+    url = f"{base}/voice/w2v2-vits"
+
+    res = requests.post(url=url, data=m, headers=headers)
+    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
+    path = f"{abs_path}/{fname}"
+
+    with open(path, "wb") as f:
+        f.write(res.content)
+    print(path)
+    return path
+
+
+# 语音转换 同VITS模型内角色之间的音色转换
+def voice_conversion(upload_path, original_id, target_id):
+    upload_name = os.path.basename(upload_path)
+    upload_type = f'audio/{upload_name.split(".")[1]}'  # wav,ogg
+
+    with open(upload_path, 'rb') as upload_file:
+        fields = {
+            "upload": (upload_name, upload_file, upload_type),
+            "original_id": str(original_id),
+            "target_id": str(target_id),
+        }
+        boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16))
+        m = MultipartEncoder(fields=fields, boundary=boundary)
+
+        headers = {"Content-Type": m.content_type}
+        url = f"{base}/voice/conversion"
+
+        res = requests.post(url=url, data=m, headers=headers)
+
+    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
+    path = f"{abs_path}/{fname}"
+
+    with open(path, "wb") as f:
+        f.write(res.content)
+    print(path)
+    return path
+
+
+def voice_ssml(ssml):
+    fields = {
+        "ssml": ssml,
+    }
+    boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16))
+
+    m = MultipartEncoder(fields=fields, boundary=boundary)
+    headers = {"Content-Type": m.content_type}
+    url = f"{base}/voice/ssml"
+
+    res = requests.post(url=url, data=m, headers=headers)
+    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
+    path = f"{abs_path}/{fname}"
+
+    with open(path, "wb") as f:
+        f.write(res.content)
+    print(path)
+    return path
+
+def voice_dimensional_emotion(upload_path):
+    upload_name = os.path.basename(upload_path)
+    upload_type = f'audio/{upload_name.split(".")[1]}'  # wav,ogg
+
+    with open(upload_path, 'rb') as upload_file:
+        fields = {
+            "upload": (upload_name, upload_file, upload_type),
+        }
+        boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16))
+
+        m = MultipartEncoder(fields=fields, boundary=boundary)
+        headers = {"Content-Type": m.content_type}
+        url = f"{base}/voice/dimension-emotion"
+
+        res = requests.post(url=url, data=m, headers=headers)
+    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
+    path = f"{abs_path}/{fname}"
+
+    with open(path, "wb") as f:
+        f.write(res.content)
+    print(path)
+    return path
+```
+
+## API KEY
+
+在config.py中设置`API_KEY_ENABLED = True`以启用，api key填写：`API_KEY = "api-key"`。
+
+启用后，GET请求中使用需要增加参数api_key，POST请求中使用需要在header中添加参数`X-API-KEY`。
+
+# Parameter
+
+## VITS语音合成
+
+| Name          | Parameter | Is must | Default | Type  | Instruction                                                  |
+| ------------- | --------- | ------- | ------- | ----- | ------------------------------------------------------------ |
+| 合成文本      | text      | true    |         | str   |                                                              |
+| 角色id        | id        | false   | 0       | int   |                                                              |
+| 音频格式      | format    | false   | wav     | str   | 支持wav,ogg,silk,mp3,flac                                    |
+| 文本语言      | lang      | false   | auto    | str   | auto为自动识别语言模式，也是默认模式。lang=mix时，文本应该用[ZH] 或 [JA] 包裹。方言无法自动识别。 |
+| 语音长度/语速 | length    | false   | 1.0     | float | 调节语音长度，相当于调节语速，该数值越大语速越慢             |
+| 噪声          | noise     | false   | 0.667   | float |                                                              |
+| 噪声偏差      | noisew    | false   | 0.8     | float |                                                              |
+| 分段阈值      | max       | false   | 50      | int   | 按标点符号分段，加起来大于max时为一段文本。max<=0表示不分段。 |
+
+## VITS 语音转换
+
+| Name       | Parameter   | Is must | Default | Type | Instruction            |
+| ---------- | ----------- | ------- | ------- | ---- | ---------------------- |
+| 上传音频   | upload      | true    |         | file | wav or ogg             |
+| 源角色id   | original_id | true    |         | int  | 上传文件所使用的角色id |
+| 目标角色id | target_id   | true    |         | int  | 要转换的目标角色id     |
+
+## HuBert-VITS 语音转换
+
+| Name          | Parameter | Is must | Default | Type  | Instruction                                      |
+| ------------- | --------- | ------- | ------- | ----- | ------------------------------------------------ |
+| 上传音频      | upload    | true    |         | file  |                                                  |
+| 目标角色id    | id        | true    |         | int   |                                                  |
+| 音频格式      | format    | true    |         | str   | wav,ogg,silk                                     |
+| 语音长度/语速 | length    | true    |         | float | 调节语音长度，相当于调节语速，该数值越大语速越慢 |
+| 噪声          | noise     | true    |         | float |                                                  |
+| 噪声偏差      | noisew    | true    |         | float |                                                  |
+
+## Dimensional emotion
+
+| Name     | Parameter | Is must | Default | Type | Instruction                   |
+| -------- | --------- | ------- | ------- | ---- | ----------------------------- |
+| 上传音频 | upload    | true    |         | file | 返回存储维度情感向量的npy文件 |
+
+## W2V2-VITS
+
+| Name          | Parameter | Is must | Default | Type  | Instruction                                                  |
+| ------------- | --------- | ------- | ------- | ----- | ------------------------------------------------------------ |
+| 合成文本      | text      | true    |         | str   |                                                              |
+| 角色id        | id        | false   | 0       | int   |                                                              |
+| 音频格式      | format    | false   | wav     | str   | 支持wav,ogg,silk,mp3,flac                                    |
+| 文本语言      | lang      | false   | auto    | str   | auto为自动识别语言模式，也是默认模式。lang=mix时，文本应该用[ZH] 或 [JA] 包裹。方言无法自动识别。 |
+| 语音长度/语速 | length    | false   | 1.0     | float | 调节语音长度，相当于调节语速，该数值越大语速越慢             |
+| 噪声          | noise     | false   | 0.667   | float |                                                              |
+| 噪声偏差      | noisew    | false   | 0.8     | float |                                                              |
+| 分段阈值      | max       | false   | 50      | int   | 按标点符号分段，加起来大于max时为一段文本。max<=0表示不分段。 |
+| 维度情感      | emotion   | false   | 0       | int   | 范围取决于npy情感参考文件，如[innnky](https://huggingface.co/spaces/innnky/nene-emotion/tree/main)的all_emotions.npy模型范围是0-5457 |
+
+## SSML语音合成标记语言
+目前支持的元素与属性
+
+`speak`元素
+
+| Attribute | Description                                                  | Is must |
+| --------- | ------------------------------------------------------------ | ------- |
+| id        | 默认值从`config.py`中读取                                    | false   |
+| lang      | 默认值从`config.py`中读取                                    | false   |
+| length    | 默认值从`config.py`中读取                                    | false   |
+| noise     | 默认值从`config.py`中读取                                    | false   |
+| noisew    | 默认值从`config.py`中读取                                    | false   |
+| max       | 按标点符号分段，加起来大于max时为一段文本。max<=0表示不分段，这里默认为0。 | false   |
+| model     | 默认为vits，可选`w2v2-vits`，`emotion-vits`                  | false   |
+| emotion   | 只有用`w2v2-vits`或`emotion-vits`时`emotion`才生效，范围取决于npy情感参考文件 | false   |
+
+`voice`元素
+
+优先级大于`speak`
+
+| Attribute | Description                                                  | Is must |
+| --------- | ------------------------------------------------------------ | ------- |
+| id        | 默认值从`config.py`中读取                                    | false   |
+| lang      | 默认值从`config.py`中读取                                    | false   |
+| length    | 默认值从`config.py`中读取                                    | false   |
+| noise     | 默认值从`config.py`中读取                                    | false   |
+| noisew    | 默认值从`config.py`中读取                                    | false   |
+| max       | 按标点符号分段，加起来大于max时为一段文本。max<=0表示不分段，这里默认为0。 | false   |
+| model     | 默认为vits，可选`w2v2-vits`，`emotion-vits`                  | false   |
+| emotion   | 只有用`w2v2-vits`或`emotion-vits`时`emotion`才会生效         | false   |
+
+`break`元素
+
+| Attribute | Description                                                  | Is must |
+| --------- | ------------------------------------------------------------ | ------- |
+| strength  | x-weak,weak,medium（默认值）,strong,x-strong                 | false   |
+| time      | 暂停的绝对持续时间，以秒为单位（例如 `2s`）或以毫秒为单位（例如 `500ms`）。 有效值的范围为 0 到 5000 毫秒。 如果设置的值大于支持的最大值，则服务将使用 `5000ms`。 如果设置了 `time` 属性，则会忽略 `strength` 属性。 | false   |
+
+| Strength | Relative Duration |
+| :------- | :---------------- |
+| x-weak   | 250 毫秒          |
+| weak     | 500 毫秒          |
+| Medium   | 750 毫秒          |
+| Strong   | 1000 毫秒         |
+| x-strong | 1250 毫秒         |
+
+示例
+
+```xml
+<speak lang="zh" format="mp3" length="1.2">
+    <voice id="92" >这几天心里颇不宁静。</voice>
+    <voice id="125">今晚在院子里坐着乘凉，忽然想起日日走过的荷塘，在这满月的光里，总该另有一番样子吧。</voice>
+    <voice id="142">月亮渐渐地升高了，墙外马路上孩子们的欢笑，已经听不见了；</voice>
+    <voice id="98">妻在屋里拍着闰儿，迷迷糊糊地哼着眠歌。</voice>
+    <voice id="120">我悄悄地披了大衫，带上门出去。</voice><break time="2s"/>
+    <voice id="121">沿着荷塘，是一条曲折的小煤屑路。</voice>
+    <voice id="122">这是一条幽僻的路；白天也少人走，夜晚更加寂寞。</voice>
+    <voice id="123">荷塘四面，长着许多树，蓊蓊郁郁的。</voice>
+    <voice id="124">路的一旁，是些杨柳，和一些不知道名字的树。</voice>
+    <voice id="125">没有月光的晚上，这路上阴森森的，有些怕人。</voice>
+    <voice id="126">今晚却很好，虽然月光也还是淡淡的。</voice><break time="2s"/>
+    <voice id="127">路上只我一个人，背着手踱着。</voice>
+    <voice id="128">这一片天地好像是我的；我也像超出了平常的自己，到了另一个世界里。</voice>
+    <voice id="129">我爱热闹，也爱冷静；<break strength="x-weak"/>爱群居，也爱独处。</voice>
+    <voice id="130">像今晚上，一个人在这苍茫的月下，什么都可以想，什么都可以不想，便觉是个自由的人。</voice>
+    <voice id="131">白天里��定要做的事，一定要说的话，现在都可不理。</voice>
+    <voice id="132">这是独处的妙处，我且受用这无边的荷香月色好了。</voice>
+</speak>
+```
+
+# 交流平台
+
+现在只有 [Q群](https://qm.qq.com/cgi-bin/qm/qr?k=-1GknIe4uXrkmbDKBGKa1aAUteq40qs_&jump_from=webapi&authKey=x5YYt6Dggs1ZqWxvZqvj3fV8VUnxRyXm5S5Kzntc78+Nv3iXOIawplGip9LWuNR/)
+
+# 鸣谢
+
+- vits:https://github.com/jaywalnut310/vits
+- MoeGoe:https://github.com/CjangCjengh/MoeGoe
+- emotional-vits:https://github.com/innnky/emotional-vits
+- vits-uma-genshin-honkai:https://huggingface.co/spaces/zomehwh/vits-uma-genshin-honkai
+

app.py

@@ -0,0 +1,474 @@
+import os
+import logging
+import time
+import logzero
+import uuid
+from flask import Flask, request, send_file, jsonify, make_response, render_template
+from werkzeug.utils import secure_filename
+from flask_apscheduler import APScheduler
+from functools import wraps
+from utils.utils import clean_folder, check_is_none
+from utils.merge import merge_model
+from io import BytesIO
+
+app = Flask(__name__)
+app.config.from_pyfile("config.py")
+
+scheduler = APScheduler()
+scheduler.init_app(app)
+if app.config.get("CLEAN_INTERVAL_SECONDS", 3600) > 0:
+    scheduler.start()
+
+logzero.loglevel(logging.WARNING)
+logger = logging.getLogger("vits-simple-api")
+level = app.config.get("LOGGING_LEVEL", "DEBUG")
+level_dict = {'DEBUG': logging.DEBUG, 'INFO': logging.INFO, 'WARNING': logging.WARNING, 'ERROR': logging.ERROR,
+              'CRITICAL': logging.CRITICAL}
+logging.basicConfig(level=level_dict[level])
+logging.getLogger('numba').setLevel(logging.WARNING)
+logging.getLogger("langid.langid").setLevel(logging.INFO)
+logging.getLogger("apscheduler.scheduler").setLevel(logging.INFO)
+
+tts = merge_model(app.config["MODEL_LIST"])
+
+if not os.path.exists(app.config['UPLOAD_FOLDER']):
+    os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
+
+if not os.path.exists(app.config['CACHE_PATH']):
+    os.makedirs(app.config['CACHE_PATH'], exist_ok=True)
+
+
+def require_api_key(func):
+    @wraps(func)
+    def check_api_key(*args, **kwargs):
+        if not app.config.get('API_KEY_ENABLED', False):
+            return func(*args, **kwargs)
+        else:
+            api_key = request.args.get('api_key') or request.headers.get('X-API-KEY')
+            if api_key and api_key == app.config['API_KEY']:
+                return func(*args, **kwargs)
+            else:
+                return make_response(jsonify({"status": "error", "message": "Invalid API Key"}), 401)
+
+    return check_api_key
+
+
+@app.route('/', methods=["GET", "POST"])
+def index():
+    kwargs = {
+        "speakers": tts.voice_speakers,
+        "speakers_count": tts.speakers_count
+    }
+    return render_template("index.html", **kwargs)
+
+
+@app.route('/voice/speakers', methods=["GET", "POST"])
+def voice_speakers_api():
+    return jsonify(tts.voice_speakers)
+
+
+@app.route('/voice', methods=["GET", "POST"])
+@app.route('/voice/vits', methods=["GET", "POST"])
+@require_api_key
+def voice_vits_api():
+    try:
+        if request.method == "GET":
+            text = request.args.get("text", "")
+            id = int(request.args.get("id", app.config.get("ID", 0)))
+            format = request.args.get("format", app.config.get("FORMAT", "wav"))
+            lang = request.args.get("lang", app.config.get("LANG", "auto"))
+            length = float(request.args.get("length", app.config.get("LENGTH", 1)))
+            noise = float(request.args.get("noise", app.config.get("NOISE", 0.667)))
+            noisew = float(request.args.get("noisew", app.config.get("NOISEW", 0.8)))
+            max = int(request.args.get("max", app.config.get("MAX", 50)))
+            use_streaming = request.args.get('streaming', False, type=bool)
+        elif request.method == "POST":
+            content_type = request.headers.get('Content-Type')
+            if content_type == 'application/json':
+                data = request.get_json()
+            else:
+                data = request.form
+            text = data.get("text", "")
+            id = int(data.get("id", app.config.get("ID", 0)))
+            format = data.get("format", app.config.get("FORMAT", "wav"))
+            lang = data.get("lang", app.config.get("LANG", "auto"))
+            length = float(data.get("length", app.config.get("LENGTH", 1)))
+            noise = float(data.get("noise", app.config.get("NOISE", 0.667)))
+            noisew = float(data.get("noisew", app.config.get("NOISEW", 0.8)))
+            max = int(data.get("max", app.config.get("MAX", 50)))
+            use_streaming = request.form.get('streaming', False, type=bool)
+    except Exception as e:
+        logger.error(f"[VITS] {e}")
+        return make_response("parameter error", 400)
+
+    logger.info(f"[VITS] id:{id} format:{format} lang:{lang} length:{length} noise:{noise} noisew:{noisew}")
+    logger.info(f"[VITS] len:{len(text)} text：{text}")
+
+    if check_is_none(text):
+        logger.info(f"[VITS] text is empty")
+        return make_response(jsonify({"status": "error", "message": "text is empty"}), 400)
+
+    if check_is_none(id):
+        logger.info(f"[VITS] speaker id is empty")
+        return make_response(jsonify({"status": "error", "message": "speaker id is empty"}), 400)
+
+    if id < 0 or id >= tts.vits_speakers_count:
+        logger.info(f"[VITS] speaker id {id} does not exist")
+        return make_response(jsonify({"status": "error", "message": f"id {id} does not exist"}), 400)
+
+    # 校验模型是否支持输入的语言
+    speaker_lang = tts.voice_speakers["VITS"][id].get('lang')
+    if lang.upper() != "AUTO" and lang.upper() != "MIX" and len(speaker_lang) != 1 and lang not in speaker_lang:
+        logger.info(f"[VITS] lang \"{lang}\" is not in {speaker_lang}")
+        return make_response(jsonify({"status": "error", "message": f"lang '{lang}' is not in {speaker_lang}"}), 400)
+
+    # 如果配置文件中设置了LANGUAGE_AUTOMATIC_DETECT则强制将speaker_lang设置为LANGUAGE_AUTOMATIC_DETECT
+    if app.config.get("LANGUAGE_AUTOMATIC_DETECT", []) != []:
+        speaker_lang = app.config.get("LANGUAGE_AUTOMATIC_DETECT")
+
+    if use_streaming and format.upper() != "MP3":
+        format = "mp3"
+        logger.warning("Streaming response only supports MP3 format.")
+
+    fname = f"{str(uuid.uuid1())}.{format}"
+    file_type = f"audio/{format}"
+    task = {"text": text,
+            "id": id,
+            "format": format,
+            "length": length,
+            "noise": noise,
+            "noisew": noisew,
+            "max": max,
+            "lang": lang,
+            "speaker_lang": speaker_lang}
+
+    if app.config.get("SAVE_AUDIO", False):
+        logger.debug(f"[VITS] {fname}")
+
+    if use_streaming:
+        audio = tts.stream_vits_infer(task, fname)
+        response = make_response(audio)
+        response.headers['Content-Disposition'] = f'attachment; filename={fname}'
+        response.headers['Content-Type'] = file_type
+        return response
+    else:
+        t1 = time.time()
+        audio = tts.vits_infer(task, fname)
+        t2 = time.time()
+        logger.info(f"[VITS] finish in {(t2 - t1):.2f}s")
+        return send_file(path_or_file=audio, mimetype=file_type, download_name=fname)
+
+
+@app.route('/voice/hubert-vits', methods=["POST"])
+@require_api_key
+def voice_hubert_api():
+    if request.method == "POST":
+        try:
+            voice = request.files['upload']
+            id = int(request.form.get("id"))
+            format = request.form.get("format", app.config.get("LANG", "auto"))
+            length = float(request.form.get("length", app.config.get("LENGTH", 1)))
+            noise = float(request.form.get("noise", app.config.get("NOISE", 0.667)))
+            noisew = float(request.form.get("noisew", app.config.get("NOISEW", 0.8)))
+            use_streaming = request.form.get('streaming', False, type=bool)
+        except Exception as e:
+            logger.error(f"[hubert] {e}")
+            return make_response("parameter error", 400)
+
+    logger.info(f"[hubert] id:{id} format:{format} length:{length} noise:{noise} noisew:{noisew}")
+
+    fname = secure_filename(str(uuid.uuid1()) + "." + voice.filename.split(".")[1])
+    voice.save(os.path.join(app.config['UPLOAD_FOLDER'], fname))
+
+    if check_is_none(id):
+        logger.info(f"[hubert] speaker id is empty")
+        return make_response(jsonify({"status": "error", "message": "speaker id is empty"}), 400)
+
+    if id < 0 or id >= tts.hubert_speakers_count:
+        logger.info(f"[hubert] speaker id {id} does not exist")
+        return make_response(jsonify({"status": "error", "message": f"id {id} does not exist"}), 400)
+
+    file_type = f"audio/{format}"
+    task = {"id": id,
+            "format": format,
+            "length": length,
+            "noise": noise,
+            "noisew": noisew,
+            "audio_path": os.path.join(app.config['UPLOAD_FOLDER'], fname)}
+
+    t1 = time.time()
+    audio = tts.hubert_vits_infer(task, fname)
+    t2 = time.time()
+    if app.config.get("SAVE_AUDIO", False):
+        logger.debug(f"[hubert] {fname}")
+    logger.info(f"[hubert] finish in {(t2 - t1):.2f}s")
+    if use_streaming:
+        audio = tts.generate_audio_chunks(audio)
+        response = make_response(audio)
+        response.headers['Content-Disposition'] = f'attachment; filename={fname}'
+        response.headers['Content-Type'] = file_type
+        return response
+    else:
+        return send_file(path_or_file=audio, mimetype=file_type, download_name=fname)
+
+
+@app.route('/voice/w2v2-vits', methods=["GET", "POST"])
+@require_api_key
+def voice_w2v2_api():
+    try:
+        if request.method == "GET":
+            text = request.args.get("text", "")
+            id = int(request.args.get("id", app.config.get("ID", 0)))
+            format = request.args.get("format", app.config.get("FORMAT", "wav"))
+            lang = request.args.get("lang", app.config.get("LANG", "auto"))
+            length = float(request.args.get("length", app.config.get("LENGTH", 1)))
+            noise = float(request.args.get("noise", app.config.get("NOISE", 0.667)))
+            noisew = float(request.args.get("noisew", app.config.get("NOISEW", 0.8)))
+            max = int(request.args.get("max", app.config.get("MAX", 50)))
+            emotion = int(request.args.get("emotion", app.config.get("EMOTION", 0)))
+            use_streaming = request.args.get('streaming', False, type=bool)
+        elif request.method == "POST":
+            content_type = request.headers.get('Content-Type')
+            if content_type == 'application/json':
+                data = request.get_json()
+            else:
+                data = request.form
+            text = data.get("text", "")
+            id = int(data.get("id", app.config.get("ID", 0)))
+            format = data.get("format", app.config.get("FORMAT", "wav"))
+            lang = data.get("lang", app.config.get("LANG", "auto"))
+            length = float(data.get("length"))
+            noise = float(data.get("noise", app.config.get("NOISE", 0.667)))
+            noisew = float(data.get("noisew", app.config.get("NOISEW", 0.8)))
+            max = int(data.get("max", app.config.get("MAX", 50)))
+            emotion = int(data.get("emotion", app.config.get("EMOTION", 0)))
+            use_streaming = request.form.get('streaming', False, type=bool)
+    except Exception as e:
+        logger.error(f"[w2v2] {e}")
+        return make_response(f"parameter error", 400)
+
+    logger.info(f"[w2v2] id:{id} format:{format} lang:{lang} "
+                f"length:{length} noise:{noise} noisew:{noisew} emotion:{emotion}")
+    logger.info(f"[w2v2] len:{len(text)} text：{text}")
+
+    if check_is_none(text):
+        logger.info(f"[w2v2] text is empty")
+        return make_response(jsonify({"status": "error", "message": "text is empty"}), 400)
+
+    if check_is_none(id):
+        logger.info(f"[w2v2] speaker id is empty")
+        return make_response(jsonify({"status": "error", "message": "speaker id is empty"}), 400)
+
+    if id < 0 or id >= tts.w2v2_speakers_count:
+        logger.info(f"[w2v2] speaker id {id} does not exist")
+        return make_response(jsonify({"status": "error", "message": f"id {id} does not exist"}), 400)
+
+    # 校验模型是否支持输入的语言
+    speaker_lang = tts.voice_speakers["W2V2-VITS"][id].get('lang')
+    if lang.upper() != "AUTO" and lang.upper() != "MIX" and len(speaker_lang) != 1 and lang not in speaker_lang:
+        logger.info(f"[w2v2] lang \"{lang}\" is not in {speaker_lang}")
+        return make_response(jsonify({"status": "error", "message": f"lang '{lang}' is not in {speaker_lang}"}), 400)
+
+    # 如果配置文件中设置了LANGUAGE_AUTOMATIC_DETECT则强制将speaker_lang设置为LANGUAGE_AUTOMATIC_DETECT
+    if app.config.get("LANGUAGE_AUTOMATIC_DETECT", []) != []:
+        speaker_lang = app.config.get("LANGUAGE_AUTOMATIC_DETECT")
+
+    if use_streaming and format.upper() != "MP3":
+        format = "mp3"
+        logger.warning("Streaming response only supports MP3 format.")
+
+    fname = f"{str(uuid.uuid1())}.{format}"
+    file_type = f"audio/{format}"
+    task = {"text": text,
+            "id": id,
+            "format": format,
+            "length": length,
+            "noise": noise,
+            "noisew": noisew,
+            "max": max,
+            "lang": lang,
+            "emotion": emotion,
+            "speaker_lang": speaker_lang}
+
+    t1 = time.time()
+    audio = tts.w2v2_vits_infer(task, fname)
+    t2 = time.time()
+    if app.config.get("SAVE_AUDIO", False):
+        logger.debug(f"[W2V2] {fname}")
+    if use_streaming:
+        audio = tts.generate_audio_chunks(audio)
+        response = make_response(audio)
+        response.headers['Content-Disposition'] = f'attachment; filename={fname}'
+        response.headers['Content-Type'] = file_type
+        return response
+    else:
+        logger.info(f"[w2v2] finish in {(t2 - t1):.2f}s")
+        return send_file(path_or_file=audio, mimetype=file_type, download_name=fname)
+
+
+@app.route('/voice/conversion', methods=["POST"])
+@app.route('/voice/vits/conversion', methods=["POST"])
+@require_api_key
+def vits_voice_conversion_api():
+    if request.method == "POST":
+        try:
+            voice = request.files['upload']
+            original_id = int(request.form["original_id"])
+            target_id = int(request.form["target_id"])
+            format = request.form.get("format", voice.filename.split(".")[1])
+            use_streaming = request.form.get('streaming', False, type=bool)
+        except Exception as e:
+            logger.error(f"[vits_voice_convertsion] {e}")
+            return make_response("parameter error", 400)
+
+        logger.info(f"[vits_voice_convertsion] orginal_id:{original_id} target_id:{target_id}")
+        fname = secure_filename(str(uuid.uuid1()) + "." + voice.filename.split(".")[1])
+        audio_path = os.path.join(app.config['UPLOAD_FOLDER'], fname)
+        voice.save(audio_path)
+        file_type = f"audio/{format}"
+        task = {"audio_path": audio_path,
+                "original_id": original_id,
+                "target_id": target_id,
+                "format": format}
+
+        t1 = time.time()
+        audio = tts.vits_voice_conversion(task, fname)
+        t2 = time.time()
+        if app.config.get("SAVE_AUDIO", False):
+            logger.debug(f"[Voice conversion] {fname}")
+        logger.info(f"[Voice conversion] finish in {(t2 - t1):.2f}s")
+        if use_streaming:
+            audio = tts.generate_audio_chunks(audio)
+            response = make_response(audio)
+            response.headers['Content-Disposition'] = f'attachment; filename={fname}'
+            response.headers['Content-Type'] = file_type
+            return response
+        else:
+            return send_file(path_or_file=audio, mimetype=file_type, download_name=fname)
+
+
+@app.route('/voice/ssml', methods=["POST"])
+@require_api_key
+def ssml():
+    try:
+        content_type = request.headers.get('Content-Type')
+        if content_type == 'application/json':
+            data = request.get_json()
+        else:
+            data = request.form
+        ssml = data.get("ssml")
+    except Exception as e:
+        logger.info(f"[ssml] {e}")
+        return make_response(jsonify({"status": "error", "message": f"parameter error"}), 400)
+
+    logger.debug(ssml)
+
+    fname = f"{str(uuid.uuid1())}.{format}"
+    file_type = f"audio/{format}"
+
+    t1 = time.time()
+    audio, format = tts.create_ssml_infer_task(ssml, fname)
+    t2 = time.time()
+    if app.config.get("SAVE_AUDIO", False):
+        logger.debug(f"[ssml] {fname}")
+    logger.info(f"[ssml] finish in {(t2 - t1):.2f}s")
+
+    if eval(ssml.get('streaming', False)):
+        audio = tts.generate_audio_chunks(audio)
+        response = make_response(audio)
+        response.headers['Content-Disposition'] = f'attachment; filename={fname}'
+        response.headers['Content-Type'] = file_type
+        return response
+    else:
+        return send_file(path_or_file=audio, mimetype=file_type, download_name=fname)
+
+
+@app.route('/voice/dimension-emotion', methods=["POST"])
+def dimensional_emotion():
+    if request.method == "POST":
+        try:
+            audio = request.files['upload']
+            use_streaming = request.form.get('streaming', False, type=bool)
+        except Exception as e:
+            logger.error(f"[dimensional_emotion] {e}")
+            return make_response("parameter error", 400)
+
+    content = BytesIO(audio.read())
+
+    file_type = "application/octet-stream; charset=ascii"
+    fname = os.path.splitext(audio.filename)[0] + ".npy"
+    audio = tts.get_dimensional_emotion_npy(content)
+    if use_streaming:
+        audio = tts.generate_audio_chunks(audio)
+        response = make_response(audio)
+        response.headers['Content-Disposition'] = f'attachment; filename={fname}'
+        response.headers['Content-Type'] = file_type
+        return response
+    else:
+        return send_file(path_or_file=audio, mimetype=file_type, download_name=fname)
+
+
+@app.route('/voice/check', methods=["GET", "POST"])
+def check():
+    try:
+        if request.method == "GET":
+            model = request.args.get("model")
+            id = int(request.args.get("id"))
+        elif request.method == "POST":
+            content_type = request.headers.get('Content-Type')
+            if content_type == 'application/json':
+                data = request.get_json()
+            else:
+                data = request.form
+            model = data.get("model")
+            id = int(data.get("id"))
+    except Exception as e:
+        logger.info(f"[check] {e}")
+        return make_response(jsonify({"status": "error", "message": "parameter error"}), 400)
+
+    if check_is_none(model):
+        logger.info(f"[check] model {model} is empty")
+        return make_response(jsonify({"status": "error", "message": "model is empty"}), 400)
+
+    if model.upper() not in ("VITS", "HUBERT", "W2V2"):
+        res = make_response(jsonify({"status": "error", "message": f"model {model} does not exist"}))
+        res.status = 404
+        logger.info(f"[check] speaker id {id} error")
+        return res
+
+    if check_is_none(id):
+        logger.info(f"[check] speaker id is empty")
+        return make_response(jsonify({"status": "error", "message": "speaker id is empty"}), 400)
+
+    if model.upper() == "VITS":
+        speaker_list = tts.voice_speakers["VITS"]
+    elif model.upper() == "HUBERT":
+        speaker_list = tts.voice_speakers["HUBERT-VITS"]
+    elif model.upper() == "W2V2":
+        speaker_list = tts.voice_speakers["W2V2-VITS"]
+
+    if len(speaker_list) == 0:
+        logger.info(f"[check] {model} not loaded")
+        return make_response(jsonify({"status": "error", "message": f"{model} not loaded"}), 400)
+
+    if id < 0 or id >= len(speaker_list):
+        logger.info(f"[check] speaker id {id} does not exist")
+        return make_response(jsonify({"status": "error", "message": f"id {id} does not exist"}), 400)
+    name = str(speaker_list[id]["name"])
+    lang = speaker_list[id]["lang"]
+    logger.info(f"[check] check id:{id} name:{name} lang:{lang}")
+
+    return make_response(jsonify({"status": "success", "id": id, "name": name, "lang": lang}), 200)
+
+
+# regular cleaning
+@scheduler.task('interval', id='clean_task', seconds=app.config.get("CLEAN_INTERVAL_SECONDS", 3600),
+                misfire_grace_time=900)
+def clean_task():
+    clean_folder(app.config["UPLOAD_FOLDER"])
+    clean_folder(app.config["CACHE_PATH"])
+
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=app.config.get("PORT", 23456), debug=app.config.get("DEBUG", False))  # 对外开放
+    # app.run(host='127.0.0.1', port=app.config.get("PORT",23456), debug=True)  # 本地运行、调试

attentions.py

@@ -0,0 +1,300 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+from modules import LayerNorm
+   
+
+class Encoder(nn.Module):
+  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
+    super().__init__()
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.window_size = window_size
+
+    self.drop = nn.Dropout(p_dropout)
+    self.attn_layers = nn.ModuleList()
+    self.norm_layers_1 = nn.ModuleList()
+    self.ffn_layers = nn.ModuleList()
+    self.norm_layers_2 = nn.ModuleList()
+    for i in range(self.n_layers):
+      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
+      self.norm_layers_1.append(LayerNorm(hidden_channels))
+      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
+      self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+  def forward(self, x, x_mask):
+    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+    x = x * x_mask
+    for i in range(self.n_layers):
+      y = self.attn_layers[i](x, x, attn_mask)
+      y = self.drop(y)
+      x = self.norm_layers_1[i](x + y)
+
+      y = self.ffn_layers[i](x, x_mask)
+      y = self.drop(y)
+      x = self.norm_layers_2[i](x + y)
+    x = x * x_mask
+    return x
+
+
+class Decoder(nn.Module):
+  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
+    super().__init__()
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.proximal_bias = proximal_bias
+    self.proximal_init = proximal_init
+
+    self.drop = nn.Dropout(p_dropout)
+    self.self_attn_layers = nn.ModuleList()
+    self.norm_layers_0 = nn.ModuleList()
+    self.encdec_attn_layers = nn.ModuleList()
+    self.norm_layers_1 = nn.ModuleList()
+    self.ffn_layers = nn.ModuleList()
+    self.norm_layers_2 = nn.ModuleList()
+    for i in range(self.n_layers):
+      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
+      self.norm_layers_0.append(LayerNorm(hidden_channels))
+      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
+      self.norm_layers_1.append(LayerNorm(hidden_channels))
+      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
+      self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+  def forward(self, x, x_mask, h, h_mask):
+    """
+    x: decoder input
+    h: encoder output
+    """
+    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
+    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+    x = x * x_mask
+    for i in range(self.n_layers):
+      y = self.self_attn_layers[i](x, x, self_attn_mask)
+      y = self.drop(y)
+      x = self.norm_layers_0[i](x + y)
+
+      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
+      y = self.drop(y)
+      x = self.norm_layers_1[i](x + y)
+      
+      y = self.ffn_layers[i](x, x_mask)
+      y = self.drop(y)
+      x = self.norm_layers_2[i](x + y)
+    x = x * x_mask
+    return x
+
+
+class MultiHeadAttention(nn.Module):
+  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
+    super().__init__()
+    assert channels % n_heads == 0
+
+    self.channels = channels
+    self.out_channels = out_channels
+    self.n_heads = n_heads
+    self.p_dropout = p_dropout
+    self.window_size = window_size
+    self.heads_share = heads_share
+    self.block_length = block_length
+    self.proximal_bias = proximal_bias
+    self.proximal_init = proximal_init
+    self.attn = None
+
+    self.k_channels = channels // n_heads
+    self.conv_q = nn.Conv1d(channels, channels, 1)
+    self.conv_k = nn.Conv1d(channels, channels, 1)
+    self.conv_v = nn.Conv1d(channels, channels, 1)
+    self.conv_o = nn.Conv1d(channels, out_channels, 1)
+    self.drop = nn.Dropout(p_dropout)
+
+    if window_size is not None:
+      n_heads_rel = 1 if heads_share else n_heads
+      rel_stddev = self.k_channels**-0.5
+      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
+      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
+
+    nn.init.xavier_uniform_(self.conv_q.weight)
+    nn.init.xavier_uniform_(self.conv_k.weight)
+    nn.init.xavier_uniform_(self.conv_v.weight)
+    if proximal_init:
+      with torch.no_grad():
+        self.conv_k.weight.copy_(self.conv_q.weight)
+        self.conv_k.bias.copy_(self.conv_q.bias)
+      
+  def forward(self, x, c, attn_mask=None):
+    q = self.conv_q(x)
+    k = self.conv_k(c)
+    v = self.conv_v(c)
+    
+    x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+    x = self.conv_o(x)
+    return x
+
+  def attention(self, query, key, value, mask=None):
+    # reshape [b, d, t] -> [b, n_h, t, d_k]
+    b, d, t_s, t_t = (*key.size(), query.size(2))
+    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+
+    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
+    if self.window_size is not None:
+      assert t_s == t_t, "Relative attention is only available for self-attention."
+      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
+      scores_local = self._relative_position_to_absolute_position(rel_logits)
+      scores = scores + scores_local
+    if self.proximal_bias:
+      assert t_s == t_t, "Proximal bias is only available for self-attention."
+      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
+    if mask is not None:
+      scores = scores.masked_fill(mask == 0, -1e4)
+      if self.block_length is not None:
+        assert t_s == t_t, "Local attention is only available for self-attention."
+        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
+        scores = scores.masked_fill(block_mask == 0, -1e4)
+    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
+    p_attn = self.drop(p_attn)
+    output = torch.matmul(p_attn, value)
+    if self.window_size is not None:
+      relative_weights = self._absolute_position_to_relative_position(p_attn)
+      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
+      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
+    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
+    return output, p_attn
+
+  def _matmul_with_relative_values(self, x, y):
+    """
+    x: [b, h, l, m]
+    y: [h or 1, m, d]
+    ret: [b, h, l, d]
+    """
+    ret = torch.matmul(x, y.unsqueeze(0))
+    return ret
+
+  def _matmul_with_relative_keys(self, x, y):
+    """
+    x: [b, h, l, d]
+    y: [h or 1, m, d]
+    ret: [b, h, l, m]
+    """
+    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
+    return ret
+
+  def _get_relative_embeddings(self, relative_embeddings, length):
+    max_relative_position = 2 * self.window_size + 1
+    # Pad first before slice to avoid using cond ops.
+    pad_length = max(length - (self.window_size + 1), 0)
+    slice_start_position = max((self.window_size + 1) - length, 0)
+    slice_end_position = slice_start_position + 2 * length - 1
+    if pad_length > 0:
+      padded_relative_embeddings = F.pad(
+          relative_embeddings,
+          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
+    else:
+      padded_relative_embeddings = relative_embeddings
+    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
+    return used_relative_embeddings
+
+  def _relative_position_to_absolute_position(self, x):
+    """
+    x: [b, h, l, 2*l-1]
+    ret: [b, h, l, l]
+    """
+    batch, heads, length, _ = x.size()
+    # Concat columns of pad to shift from relative to absolute indexing.
+    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
+
+    # Concat extra elements so to add up to shape (len+1, 2*len-1).
+    x_flat = x.view([batch, heads, length * 2 * length])
+    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
+
+    # Reshape and slice out the padded elements.
+    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
+    return x_final
+
+  def _absolute_position_to_relative_position(self, x):
+    """
+    x: [b, h, l, l]
+    ret: [b, h, l, 2*l-1]
+    """
+    batch, heads, length, _ = x.size()
+    # padd along column
+    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
+    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
+    # add 0's in the beginning that will skew the elements after reshape
+    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
+    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
+    return x_final
+
+  def _attention_bias_proximal(self, length):
+    """Bias for self-attention to encourage attention to close positions.
+    Args:
+      length: an integer scalar.
+    Returns:
+      a Tensor with shape [1, 1, length, length]
+    """
+    r = torch.arange(length, dtype=torch.float32)
+    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
+    super().__init__()
+    self.in_channels = in_channels
+    self.out_channels = out_channels
+    self.filter_channels = filter_channels
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.activation = activation
+    self.causal = causal
+
+    if causal:
+      self.padding = self._causal_padding
+    else:
+      self.padding = self._same_padding
+
+    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+    self.drop = nn.Dropout(p_dropout)
+
+  def forward(self, x, x_mask):
+    x = self.conv_1(self.padding(x * x_mask))
+    if self.activation == "gelu":
+      x = x * torch.sigmoid(1.702 * x)
+    else:
+      x = torch.relu(x)
+    x = self.drop(x)
+    x = self.conv_2(self.padding(x * x_mask))
+    return x * x_mask
+  
+  def _causal_padding(self, x):
+    if self.kernel_size == 1:
+      return x
+    pad_l = self.kernel_size - 1
+    pad_r = 0
+    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+    x = F.pad(x, commons.convert_pad_shape(padding))
+    return x
+
+  def _same_padding(self, x):
+    if self.kernel_size == 1:
+      return x
+    pad_l = (self.kernel_size - 1) // 2
+    pad_r = self.kernel_size // 2
+    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+    x = F.pad(x, commons.convert_pad_shape(padding))
+    return x

bert/ProsodyModel.py

@@ -0,0 +1,75 @@
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from transformers import BertModel, BertConfig, BertTokenizer
+
+
+class CharEmbedding(nn.Module):
+    def __init__(self, model_dir):
+        super().__init__()
+        self.tokenizer = BertTokenizer.from_pretrained(model_dir)
+        self.bert_config = BertConfig.from_pretrained(model_dir)
+        self.hidden_size = self.bert_config.hidden_size
+        self.bert = BertModel(self.bert_config)
+        self.proj = nn.Linear(self.hidden_size, 256)
+        self.linear = nn.Linear(256, 3)
+
+    def text2Token(self, text):
+        token = self.tokenizer.tokenize(text)
+        txtid = self.tokenizer.convert_tokens_to_ids(token)
+        return txtid
+
+    def forward(self, inputs_ids, inputs_masks, tokens_type_ids):
+        out_seq = self.bert(input_ids=inputs_ids,
+                            attention_mask=inputs_masks,
+                            token_type_ids=tokens_type_ids)[0]
+        out_seq = self.proj(out_seq)
+        return out_seq
+
+
+class TTSProsody(object):
+    def __init__(self, path, device):
+        self.device = device
+        self.char_model = CharEmbedding(path)
+        self.char_model.load_state_dict(
+            torch.load(
+                os.path.join(path, 'prosody_model.pt'),
+                map_location="cpu"
+            ),
+            strict=False
+        )
+        self.char_model.eval()
+        self.char_model.to(self.device)
+
+    def get_char_embeds(self, text):
+        input_ids = self.char_model.text2Token(text)
+        input_masks = [1] * len(input_ids)
+        type_ids = [0] * len(input_ids)
+        input_ids = torch.LongTensor([input_ids]).to(self.device)
+        input_masks = torch.LongTensor([input_masks]).to(self.device)
+        type_ids = torch.LongTensor([type_ids]).to(self.device)
+
+        with torch.no_grad():
+            char_embeds = self.char_model(
+                input_ids, input_masks, type_ids).squeeze(0).cpu()
+        return char_embeds
+
+    def expand_for_phone(self, char_embeds, length):  # length of phones for char
+        assert char_embeds.size(0) == len(length)
+        expand_vecs = list()
+        for vec, leng in zip(char_embeds, length):
+            vec = vec.expand(leng, -1)
+            expand_vecs.append(vec)
+        expand_embeds = torch.cat(expand_vecs, 0)
+        assert expand_embeds.size(0) == sum(length)
+        return expand_embeds.numpy()
+
+
+if __name__ == "__main__":
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    prosody = TTSProsody('./bert/', device)
+    while True:
+        text = input("请输入文本：")
+        prosody.get_char_embeds(text)

bert/__init__.py

@@ -0,0 +1,2 @@
+""" from https://github.com/PlayVoice/vits_chinese """
+from .ProsodyModel import TTSProsody

bert/config.json

@@ -0,0 +1,19 @@
+{
+  "attention_probs_dropout_prob": 0.1, 
+  "directionality": "bidi", 
+  "hidden_act": "gelu", 
+  "hidden_dropout_prob": 0.1, 
+  "hidden_size": 768, 
+  "initializer_range": 0.02, 
+  "intermediate_size": 3072, 
+  "max_position_embeddings": 512, 
+  "num_attention_heads": 12, 
+  "num_hidden_layers": 12, 
+  "pooler_fc_size": 768, 
+  "pooler_num_attention_heads": 12, 
+  "pooler_num_fc_layers": 3, 
+  "pooler_size_per_head": 128, 
+  "pooler_type": "first_token_transform", 
+  "type_vocab_size": 2, 
+  "vocab_size": 21128
+}

bert/prosody_model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3accec7a0d5cbfccaa8a42b96374a91d442a69801c6a01402baae3bf06b8c015
+size 409941419

bert/prosody_tool.py

@@ -0,0 +1,426 @@
+def is_chinese(uchar):
+    if uchar >= u'\u4e00' and uchar <= u'\u9fa5':
+        return True
+    else:
+        return False
+
+
+pinyin_dict = {
+    "a": ("^", "a"),
+    "ai": ("^", "ai"),
+    "an": ("^", "an"),
+    "ang": ("^", "ang"),
+    "ao": ("^", "ao"),
+    "ba": ("b", "a"),
+    "bai": ("b", "ai"),
+    "ban": ("b", "an"),
+    "bang": ("b", "ang"),
+    "bao": ("b", "ao"),
+    "be": ("b", "e"),
+    "bei": ("b", "ei"),
+    "ben": ("b", "en"),
+    "beng": ("b", "eng"),
+    "bi": ("b", "i"),
+    "bian": ("b", "ian"),
+    "biao": ("b", "iao"),
+    "bie": ("b", "ie"),
+    "bin": ("b", "in"),
+    "bing": ("b", "ing"),
+    "bo": ("b", "o"),
+    "bu": ("b", "u"),
+    "ca": ("c", "a"),
+    "cai": ("c", "ai"),
+    "can": ("c", "an"),
+    "cang": ("c", "ang"),
+    "cao": ("c", "ao"),
+    "ce": ("c", "e"),
+    "cen": ("c", "en"),
+    "ceng": ("c", "eng"),
+    "cha": ("ch", "a"),
+    "chai": ("ch", "ai"),
+    "chan": ("ch", "an"),
+    "chang": ("ch", "ang"),
+    "chao": ("ch", "ao"),
+    "che": ("ch", "e"),
+    "chen": ("ch", "en"),
+    "cheng": ("ch", "eng"),
+    "chi": ("ch", "iii"),
+    "chong": ("ch", "ong"),
+    "chou": ("ch", "ou"),
+    "chu": ("ch", "u"),
+    "chua": ("ch", "ua"),
+    "chuai": ("ch", "uai"),
+    "chuan": ("ch", "uan"),
+    "chuang": ("ch", "uang"),
+    "chui": ("ch", "uei"),
+    "chun": ("ch", "uen"),
+    "chuo": ("ch", "uo"),
+    "ci": ("c", "ii"),
+    "cong": ("c", "ong"),
+    "cou": ("c", "ou"),
+    "cu": ("c", "u"),
+    "cuan": ("c", "uan"),
+    "cui": ("c", "uei"),
+    "cun": ("c", "uen"),
+    "cuo": ("c", "uo"),
+    "da": ("d", "a"),
+    "dai": ("d", "ai"),
+    "dan": ("d", "an"),
+    "dang": ("d", "ang"),
+    "dao": ("d", "ao"),
+    "de": ("d", "e"),
+    "dei": ("d", "ei"),
+    "den": ("d", "en"),
+    "deng": ("d", "eng"),
+    "di": ("d", "i"),
+    "dia": ("d", "ia"),
+    "dian": ("d", "ian"),
+    "diao": ("d", "iao"),
+    "die": ("d", "ie"),
+    "ding": ("d", "ing"),
+    "diu": ("d", "iou"),
+    "dong": ("d", "ong"),
+    "dou": ("d", "ou"),
+    "du": ("d", "u"),
+    "duan": ("d", "uan"),
+    "dui": ("d", "uei"),
+    "dun": ("d", "uen"),
+    "duo": ("d", "uo"),
+    "e": ("^", "e"),
+    "ei": ("^", "ei"),
+    "en": ("^", "en"),
+    "ng": ("^", "en"),
+    "eng": ("^", "eng"),
+    "er": ("^", "er"),
+    "fa": ("f", "a"),
+    "fan": ("f", "an"),
+    "fang": ("f", "ang"),
+    "fei": ("f", "ei"),
+    "fen": ("f", "en"),
+    "feng": ("f", "eng"),
+    "fo": ("f", "o"),
+    "fou": ("f", "ou"),
+    "fu": ("f", "u"),
+    "ga": ("g", "a"),
+    "gai": ("g", "ai"),
+    "gan": ("g", "an"),
+    "gang": ("g", "ang"),
+    "gao": ("g", "ao"),
+    "ge": ("g", "e"),
+    "gei": ("g", "ei"),
+    "gen": ("g", "en"),
+    "geng": ("g", "eng"),
+    "gong": ("g", "ong"),
+    "gou": ("g", "ou"),
+    "gu": ("g", "u"),
+    "gua": ("g", "ua"),
+    "guai": ("g", "uai"),
+    "guan": ("g", "uan"),
+    "guang": ("g", "uang"),
+    "gui": ("g", "uei"),
+    "gun": ("g", "uen"),
+    "guo": ("g", "uo"),
+    "ha": ("h", "a"),
+    "hai": ("h", "ai"),
+    "han": ("h", "an"),
+    "hang": ("h", "ang"),
+    "hao": ("h", "ao"),
+    "he": ("h", "e"),
+    "hei": ("h", "ei"),
+    "hen": ("h", "en"),
+    "heng": ("h", "eng"),
+    "hong": ("h", "ong"),
+    "hou": ("h", "ou"),
+    "hu": ("h", "u"),
+    "hua": ("h", "ua"),
+    "huai": ("h", "uai"),
+    "huan": ("h", "uan"),
+    "huang": ("h", "uang"),
+    "hui": ("h", "uei"),
+    "hun": ("h", "uen"),
+    "huo": ("h", "uo"),
+    "ji": ("j", "i"),
+    "jia": ("j", "ia"),
+    "jian": ("j", "ian"),
+    "jiang": ("j", "iang"),
+    "jiao": ("j", "iao"),
+    "jie": ("j", "ie"),
+    "jin": ("j", "in"),
+    "jing": ("j", "ing"),
+    "jiong": ("j", "iong"),
+    "jiu": ("j", "iou"),
+    "ju": ("j", "v"),
+    "juan": ("j", "van"),
+    "jue": ("j", "ve"),
+    "jun": ("j", "vn"),
+    "ka": ("k", "a"),
+    "kai": ("k", "ai"),
+    "kan": ("k", "an"),
+    "kang": ("k", "ang"),
+    "kao": ("k", "ao"),
+    "ke": ("k", "e"),
+    "kei": ("k", "ei"),
+    "ken": ("k", "en"),
+    "keng": ("k", "eng"),
+    "kong": ("k", "ong"),
+    "kou": ("k", "ou"),
+    "ku": ("k", "u"),
+    "kua": ("k", "ua"),
+    "kuai": ("k", "uai"),
+    "kuan": ("k", "uan"),
+    "kuang": ("k", "uang"),
+    "kui": ("k", "uei"),
+    "kun": ("k", "uen"),
+    "kuo": ("k", "uo"),
+    "la": ("l", "a"),
+    "lai": ("l", "ai"),
+    "lan": ("l", "an"),
+    "lang": ("l", "ang"),
+    "lao": ("l", "ao"),
+    "le": ("l", "e"),
+    "lei": ("l", "ei"),
+    "leng": ("l", "eng"),
+    "li": ("l", "i"),
+    "lia": ("l", "ia"),
+    "lian": ("l", "ian"),
+    "liang": ("l", "iang"),
+    "liao": ("l", "iao"),
+    "lie": ("l", "ie"),
+    "lin": ("l", "in"),
+    "ling": ("l", "ing"),
+    "liu": ("l", "iou"),
+    "lo": ("l", "o"),
+    "long": ("l", "ong"),
+    "lou": ("l", "ou"),
+    "lu": ("l", "u"),
+    "lv": ("l", "v"),
+    "luan": ("l", "uan"),
+    "lve": ("l", "ve"),
+    "lue": ("l", "ve"),
+    "lun": ("l", "uen"),
+    "luo": ("l", "uo"),
+    "ma": ("m", "a"),
+    "mai": ("m", "ai"),
+    "man": ("m", "an"),
+    "mang": ("m", "ang"),
+    "mao": ("m", "ao"),
+    "me": ("m", "e"),
+    "mei": ("m", "ei"),
+    "men": ("m", "en"),
+    "meng": ("m", "eng"),
+    "mi": ("m", "i"),
+    "mian": ("m", "ian"),
+    "miao": ("m", "iao"),
+    "mie": ("m", "ie"),
+    "min": ("m", "in"),
+    "ming": ("m", "ing"),
+    "miu": ("m", "iou"),
+    "mo": ("m", "o"),
+    "mou": ("m", "ou"),
+    "mu": ("m", "u"),
+    "na": ("n", "a"),
+    "nai": ("n", "ai"),
+    "nan": ("n", "an"),
+    "nang": ("n", "ang"),
+    "nao": ("n", "ao"),
+    "ne": ("n", "e"),
+    "nei": ("n", "ei"),
+    "nen": ("n", "en"),
+    "neng": ("n", "eng"),
+    "ni": ("n", "i"),
+    "nia": ("n", "ia"),
+    "nian": ("n", "ian"),
+    "niang": ("n", "iang"),
+    "niao": ("n", "iao"),
+    "nie": ("n", "ie"),
+    "nin": ("n", "in"),
+    "ning": ("n", "ing"),
+    "niu": ("n", "iou"),
+    "nong": ("n", "ong"),
+    "nou": ("n", "ou"),
+    "nu": ("n", "u"),
+    "nv": ("n", "v"),
+    "nuan": ("n", "uan"),
+    "nve": ("n", "ve"),
+    "nue": ("n", "ve"),
+    "nuo": ("n", "uo"),
+    "o": ("^", "o"),
+    "ou": ("^", "ou"),
+    "pa": ("p", "a"),
+    "pai": ("p", "ai"),
+    "pan": ("p", "an"),
+    "pang": ("p", "ang"),
+    "pao": ("p", "ao"),
+    "pe": ("p", "e"),
+    "pei": ("p", "ei"),
+    "pen": ("p", "en"),
+    "peng": ("p", "eng"),
+    "pi": ("p", "i"),
+    "pian": ("p", "ian"),
+    "piao": ("p", "iao"),
+    "pie": ("p", "ie"),
+    "pin": ("p", "in"),
+    "ping": ("p", "ing"),
+    "po": ("p", "o"),
+    "pou": ("p", "ou"),
+    "pu": ("p", "u"),
+    "qi": ("q", "i"),
+    "qia": ("q", "ia"),
+    "qian": ("q", "ian"),
+    "qiang": ("q", "iang"),
+    "qiao": ("q", "iao"),
+    "qie": ("q", "ie"),
+    "qin": ("q", "in"),
+    "qing": ("q", "ing"),
+    "qiong": ("q", "iong"),
+    "qiu": ("q", "iou"),
+    "qu": ("q", "v"),
+    "quan": ("q", "van"),
+    "que": ("q", "ve"),
+    "qun": ("q", "vn"),
+    "ran": ("r", "an"),
+    "rang": ("r", "ang"),
+    "rao": ("r", "ao"),
+    "re": ("r", "e"),
+    "ren": ("r", "en"),
+    "reng": ("r", "eng"),
+    "ri": ("r", "iii"),
+    "rong": ("r", "ong"),
+    "rou": ("r", "ou"),
+    "ru": ("r", "u"),
+    "rua": ("r", "ua"),
+    "ruan": ("r", "uan"),
+    "rui": ("r", "uei"),
+    "run": ("r", "uen"),
+    "ruo": ("r", "uo"),
+    "sa": ("s", "a"),
+    "sai": ("s", "ai"),
+    "san": ("s", "an"),
+    "sang": ("s", "ang"),
+    "sao": ("s", "ao"),
+    "se": ("s", "e"),
+    "sen": ("s", "en"),
+    "seng": ("s", "eng"),
+    "sha": ("sh", "a"),
+    "shai": ("sh", "ai"),
+    "shan": ("sh", "an"),
+    "shang": ("sh", "ang"),
+    "shao": ("sh", "ao"),
+    "she": ("sh", "e"),
+    "shei": ("sh", "ei"),
+    "shen": ("sh", "en"),
+    "sheng": ("sh", "eng"),
+    "shi": ("sh", "iii"),
+    "shou": ("sh", "ou"),
+    "shu": ("sh", "u"),
+    "shua": ("sh", "ua"),
+    "shuai": ("sh", "uai"),
+    "shuan": ("sh", "uan"),
+    "shuang": ("sh", "uang"),
+    "shui": ("sh", "uei"),
+    "shun": ("sh", "uen"),
+    "shuo": ("sh", "uo"),
+    "si": ("s", "ii"),
+    "song": ("s", "ong"),
+    "sou": ("s", "ou"),
+    "su": ("s", "u"),
+    "suan": ("s", "uan"),
+    "sui": ("s", "uei"),
+    "sun": ("s", "uen"),
+    "suo": ("s", "uo"),
+    "ta": ("t", "a"),
+    "tai": ("t", "ai"),
+    "tan": ("t", "an"),
+    "tang": ("t", "ang"),
+    "tao": ("t", "ao"),
+    "te": ("t", "e"),
+    "tei": ("t", "ei"),
+    "teng": ("t", "eng"),
+    "ti": ("t", "i"),
+    "tian": ("t", "ian"),
+    "tiao": ("t", "iao"),
+    "tie": ("t", "ie"),
+    "ting": ("t", "ing"),
+    "tong": ("t", "ong"),
+    "tou": ("t", "ou"),
+    "tu": ("t", "u"),
+    "tuan": ("t", "uan"),
+    "tui": ("t", "uei"),
+    "tun": ("t", "uen"),
+    "tuo": ("t", "uo"),
+    "wa": ("^", "ua"),
+    "wai": ("^", "uai"),
+    "wan": ("^", "uan"),
+    "wang": ("^", "uang"),
+    "wei": ("^", "uei"),
+    "wen": ("^", "uen"),
+    "weng": ("^", "ueng"),
+    "wo": ("^", "uo"),
+    "wu": ("^", "u"),
+    "xi": ("x", "i"),
+    "xia": ("x", "ia"),
+    "xian": ("x", "ian"),
+    "xiang": ("x", "iang"),
+    "xiao": ("x", "iao"),
+    "xie": ("x", "ie"),
+    "xin": ("x", "in"),
+    "xing": ("x", "ing"),
+    "xiong": ("x", "iong"),
+    "xiu": ("x", "iou"),
+    "xu": ("x", "v"),
+    "xuan": ("x", "van"),
+    "xue": ("x", "ve"),
+    "xun": ("x", "vn"),
+    "ya": ("^", "ia"),
+    "yan": ("^", "ian"),
+    "yang": ("^", "iang"),
+    "yao": ("^", "iao"),
+    "ye": ("^", "ie"),
+    "yi": ("^", "i"),
+    "yin": ("^", "in"),
+    "ying": ("^", "ing"),
+    "yo": ("^", "iou"),
+    "yong": ("^", "iong"),
+    "you": ("^", "iou"),
+    "yu": ("^", "v"),
+    "yuan": ("^", "van"),
+    "yue": ("^", "ve"),
+    "yun": ("^", "vn"),
+    "za": ("z", "a"),
+    "zai": ("z", "ai"),
+    "zan": ("z", "an"),
+    "zang": ("z", "ang"),
+    "zao": ("z", "ao"),
+    "ze": ("z", "e"),
+    "zei": ("z", "ei"),
+    "zen": ("z", "en"),
+    "zeng": ("z", "eng"),
+    "zha": ("zh", "a"),
+    "zhai": ("zh", "ai"),
+    "zhan": ("zh", "an"),
+    "zhang": ("zh", "ang"),
+    "zhao": ("zh", "ao"),
+    "zhe": ("zh", "e"),
+    "zhei": ("zh", "ei"),
+    "zhen": ("zh", "en"),
+    "zheng": ("zh", "eng"),
+    "zhi": ("zh", "iii"),
+    "zhong": ("zh", "ong"),
+    "zhou": ("zh", "ou"),
+    "zhu": ("zh", "u"),
+    "zhua": ("zh", "ua"),
+    "zhuai": ("zh", "uai"),
+    "zhuan": ("zh", "uan"),
+    "zhuang": ("zh", "uang"),
+    "zhui": ("zh", "uei"),
+    "zhun": ("zh", "uen"),
+    "zhuo": ("zh", "uo"),
+    "zi": ("z", "ii"),
+    "zong": ("z", "ong"),
+    "zou": ("z", "ou"),
+    "zu": ("z", "u"),
+    "zuan": ("z", "uan"),
+    "zui": ("z", "uei"),
+    "zun": ("z", "uen"),
+    "zuo": ("z", "uo"),
+}

chinese_dialect_lexicons/changzhou.json

@@ -0,0 +1,23 @@
+{
+  "name": "Changzhou dialect to IPA",
+  "segmentation": {
+    "type": "mmseg",
+    "dict": {
+      "type": "ocd2",
+      "file": "changzhou.ocd2"
+    }
+  },
+  "conversion_chain": [
+    {
+      "dict": {
+        "type": "group",
+        "dicts": [
+          {
+            "type": "ocd2",
+            "file": "changzhou.ocd2"
+          }
+        ]
+      }
+    }
+  ]
+}

chinese_dialect_lexicons/changzhou_3.json

@@ -0,0 +1,23 @@
+{
+  "name": "Changzhou dialect to IPA",
+  "segmentation": {
+    "type": "mmseg",
+    "dict": {
+      "type": "ocd2",
+      "file": "changzhou.ocd2"
+    }
+  },
+  "conversion_chain": [
+    {
+      "dict": {
+        "type": "group",
+        "dicts": [
+          {
+            "type": "ocd2",
+            "file": "changzhou.ocd2"
+          }
+        ]
+      }
+    }
+  ]
+}

chinese_dialect_lexicons/cixi_2.json

@@ -0,0 +1,23 @@
+{
+  "name": "Cixi dialect to IPA",
+  "segmentation": {
+    "type": "mmseg",
+    "dict": {
+      "type": "ocd2",
+      "file": "cixi.ocd2"
+    }
+  },
+  "conversion_chain": [
+    {
+      "dict": {
+        "type": "group",
+        "dicts": [
+          {
+            "type": "ocd2",
+            "file": "cixi.ocd2"
+          }
+        ]
+      }
+    }
+  ]
+}

chinese_dialect_lexicons/fuyang_2.json

@@ -0,0 +1,23 @@
+{
+  "name": "Fuyang dialect to IPA",
+  "segmentation": {
+    "type": "mmseg",
+    "dict": {
+      "type": "ocd2",
+      "file": "fuyang.ocd2"
+    }
+  },
+  "conversion_chain": [
+    {
+      "dict": {
+        "type": "group",
+        "dicts": [
+          {
+            "type": "ocd2",
+            "file": "fuyang.ocd2"
+          }
+        ]
+      }
+    }
+  ]
+}

chinese_dialect_lexicons/hangzhou_2.json

@@ -0,0 +1,19 @@
+{
+  "name": "Hangzhounese to IPA",
+  "segmentation": {
+    "type": "mmseg",
+    "dict": {
+      "type": "ocd2",
+      "file": "hangzhou.ocd2"
+    }
+  },
+  "conversion_chain": [{
+    "dict": {
+      "type": "group",
+      "dicts": [{
+        "type": "ocd2",
+        "file": "hangzhou.ocd2"
+      }] 
+    }
+  }]
+}

chinese_dialect_lexicons/jiading_2.json

@@ -0,0 +1,23 @@
+{
+  "name": "Jiading dialect to IPA",
+  "segmentation": {
+    "type": "mmseg",
+    "dict": {
+      "type": "ocd2",
+      "file": "jiading.ocd2"
+    }
+  },
+  "conversion_chain": [
+    {
+      "dict": {
+        "type": "group",
+        "dicts": [
+          {
+            "type": "ocd2",
+            "file": "jiading.ocd2"
+          }
+        ]
+      }
+    }
+  ]
+}