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
+vits/text/chinese_dialect_lexicons/jyutjyu_2.ocd2 filter=lfs diff=lfs merge=lfs -text
+vits/text/chinese_dialect_lexicons/zaonhe_2.ocd2 filter=lfs diff=lfs merge=lfs -text
+vits/text/chinese_dialect_lexicons/zaonhe.ocd2 filter=lfs diff=lfs merge=lfs -text

.gitignore

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+**/__pycache__
+/Model/
+/bert_vits2/monotonic_align/monotonic_align.egg-info/
+/bert_vits2/monotonic_align/dist/
+/bert_vits2/monotonic_align/build/

Dockerfile

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+FROM python:3.10.11-slim-bullseye
+
+RUN mkdir -p /app
+WORKDIR /app
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+COPY . /app
+
+RUN apt-get update && \
+    apt-get install -yq build-essential espeak-ng cmake wget && \
+    apt-get clean && \
+    apt-get purge -y --auto-remove -o APT::AutoRemove::RecommendsImportant=false && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN pip install --upgrade pip --no-cache-dir && \
+    pip install MarkupSafe==2.1.2 numpy==1.23.3 cython six==1.16.0 safetensors==0.3.2 --no-cache-dir
+
+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 --no-cache-dir
+
+RUN pip install -r requirements.txt --no-cache-dir
+
+RUN pip install gunicorn --no-cache-dir
+
+EXPOSE 23456
+
+CMD ["gunicorn", "-c", "gunicorn_config.py", "app:app"]

Dockerfile_GPU

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+FROM python:3.10.11-slim-bullseye
+
+RUN mkdir -p /app
+WORKDIR /app
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+COPY . /app
+
+RUN apt-get update && \
+    apt-get install -yq build-essential espeak-ng cmake wget && \
+    apt-get clean && \
+    apt-get purge -y --auto-remove -o APT::AutoRemove::RecommendsImportant=false && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN pip install --upgrade pip --no-cache-dir && \
+    pip install MarkupSafe==2.1.2 numpy==1.23.3 cython six==1.16.0  safetensors==0.3.2 --no-cache-dir
+
+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/cu117 --no-cache-dir
+
+RUN pip install -r requirements.txt --no-cache-dir
+
+RUN pip install gunicorn --no-cache-dir
+
+EXPOSE 23456
+
+CMD ["gunicorn", "-c", "gunicorn_config.py", "app:app"]

LICENSE

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
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+    it under the terms of the GNU Affero General Public License as published
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+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
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+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.

LICENSE-MoeGoe

@@ -0,0 +1,21 @@
+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
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+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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+{
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+    },
+    "data": {
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+      "sampling_rate": 22050,
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+      "n_speakers": 6
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+      "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],
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+  }
+  

Model/Bishojo_Mangekyo/generator_mangekyo.pth

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Model/Cantonese/config.json

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+{
+  "train": {
+    "segment_size": 8192
+  },
+  "data": {
+    "text_cleaners":["chinese_dialect_cleaners"],
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+    "sampling_rate": 22050,
+    "filter_length": 1024,
+    "hop_length": 256,
+    "win_length": 1024,
+    "add_blank": true,
+    "n_speakers": 50
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+    "kernel_size": 3,
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+    "resblock": "1",
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+    "upsample_kernel_sizes": [16,16,4,4],
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+    "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,
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+    "add_blank": true,
+    "n_speakers": 5
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+    "n_layers": 6,
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+    "upsample_kernel_sizes": [16,16,4,4],
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+}

Model/genshin/G_953000.pth

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+size 159716737

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],
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+    "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"],
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+    "add_blank": true,
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+    "cleaned_text": true
+  },
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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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+version https://git-lfs.github.com/spec/v1
+oid sha256:e82e7d079df05fe3aa535f6f7d42d309bdae1d2a53324e2b2386c56721f4f649
+size 378435957

Model/louise/360_epochs.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:20b38cc55191ec02c2809e80d758ff0d56bd44760841704feb9921aa58a4d9de
+size 203264375

Model/louise/config.json

@@ -0,0 +1,32 @@
+{
+  "train": {
+    "segment_size": 8192
+  },
+  "data": {
+    "max_wav_value": 32768.0,
+    "sampling_rate": 22050,
+    "filter_length": 1024,
+    "hop_length": 256,
+    "win_length": 1024,
+    "add_blank": true,
+    "n_speakers": 0
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 256,
+    "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
+  },
+  "speakers": ["\u30eb\u30a4\u30ba"]
+}

Model/model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9b6e449686c0db86f5c607b8c9fa1d87468c27198a1f0a20280c4e258239763d
+size 661423381

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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+    "training_files":"filelists/paimon_6k_train_chs.txt.cleaned",
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+}

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+    "text_cleaners":["shanghainese_cleaners"],
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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", " "]
+}

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+    "validation_files":"filelists/vctk_audio_sid_text_val_filelist.txt.cleaned",
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+}

Model/vits_chinese/bert_vits.json

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+{
+  "train": {
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+    "eval_interval": 10000,
+    "seed": 1234,
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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,
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+    "mel_fmin": 0.0,
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+    "add_blank": false,
+    "n_speakers": 0
+  },
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+    "hidden_channels": 192,
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+    "n_heads": 2,
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+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
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+    "upsample_rates": [8,8,2,2],
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+    "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 +1,9 @@
+---
+license: mit
+title: vits-simple-api
+sdk: gradio
+python_version: 3.10.11
+emoji: 👀
+app_file: app.py
+duplicated_from: Artrajz/vits-simple-api
+---

README_zh.md

@@ -0,0 +1,618 @@
+<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，语音转换
+- [x] HuBert-soft VITS模型
+- [x] W2V2 VITS / emotional-vits维度情感模型
+- [x] [vits_chinese](https://github.com/PlayVoice/vits_chinese)
+- [x] [Bert-VITS2](https://github.com/Stardust-minus/Bert-VITS2)
+- [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...）
+
+
+## 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=我觉得1%2B1≠3&id=164&lang=zh`（get中一些字符需要转义不然会被过滤掉）
+- `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   | 需要合成语音的文本。                                         |
+| 角色id        | id        | false   | 从`config.py`中获取 | int   | 即说话人id。                                                 |
+| 音频格式      | format    | false   | 从`config.py`中获取 | str   | 支持wav,ogg,silk,mp3,flac                                    |
+| 文本语言      | lang      | false   | 从`config.py`中获取 | 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   | 从`config.py`中获取 | 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   | 从`config.py`中获取 | float | 样本噪声，控制合成的随机性。                                 |
+| sdp噪声       | noisew    | false   | 从`config.py`中获取 | float | 随机时长预测器噪声，控制音素发音长度。                       |
+| 分段阈值      | max       | false   | 从`config.py`中获取 | 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。 |
+| 流式响应      | streaming | false   | false               | bool  | 流式合成语音，更快的首包响应。                               |
+
+## 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  | 需要转换说话人的音频文件。                       |
+| 目标角色id    | id        | true    |         | int   | 目标说话人id。                                   |
+| 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 | 样本噪声，控制合成的随机性。                     |
+| sdp噪声       | noisew    | true    |         | float | 随机时长预测器噪声，控制音素发音长度。           |
+
+## W2V2-VITS
+
+| Name          | Parameter | Is must | Default             | Type  | Instruction                                                  |
+| ------------- | --------- | ------- | ------------------- | ----- | ------------------------------------------------------------ |
+| 合成文本      | text      | true    |                     | str   | 需要合成语音的文本。                                         |
+| 角色id        | id        | false   | 从`config.py`中获取 | int   | 即说话人id。                                                 |
+| 音频格式      | format    | false   | 从`config.py`中获取 | str   | 支持wav,ogg,silk,mp3,flac                                    |
+| 文本语言      | lang      | false   | 从`config.py`中获取 | 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   | 从`config.py`中获取 | 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   | 从`config.py`中获取 | float | 样本噪声，控制合成的随机性。                                 |
+| sdp噪声       | noisew    | false   | 从`config.py`中获取 | float | 随机时长预测器噪声，控制音素发音长度。                       |
+| 分段阈值      | max       | false   | 从`config.py`中获取 | 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 |
+
+## 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 |
+
+## Bert-VITS2语音合成
+
+| Name          | Parameter | Is must | Default             | Type  | Instruction                                                  |
+| ------------- | --------- | ------- | ------------------- | ----- | ------------------------------------------------------------ |
+| 合成文本      | text      | true    |                     | str   | 需要合成语音的文本。                                         |
+| 角色id        | id        | false   | 从`config.py`中获取 | int   | 即说话人id。                                                 |
+| 音频格式      | format    | false   | 从`config.py`中获取 | str   | 支持wav,ogg,silk,mp3,flac                                    |
+| 文本语言      | lang      | false   | 从`config.py`中获取 | str   | 目前只有中文。                                               |
+| 语音长度/语速 | length    | false   | 从`config.py`中获取 | 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   | 从`config.py`中获取 | float | 样本噪声，控制合成的随机性。                                 |
+| sdp噪声       | noisew    | false   | 从`config.py`中获取 | float | 随机时长预测器噪声，控制音素发音长度。                       |
+| 分段阈值      | max       | false   | 从`config.py`中获取 | 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。 |
+| SDP/DP混合比  | sdp_ratio | false   | 从`config.py`中获取 | int   | SDP在合成时的占比，理论上此比率越高，合成的语音语调方差越大。 |
+
+## 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">白天里一定要做的事，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
+- vits_chinese:https://github.com/PlayVoice/vits_chinese
+

app.py

@@ -0,0 +1,546 @@
+import os
+import time
+import uuid
+from logger import logger
+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()
+
+for path in (app.config['LOGS_PATH'], app.config['UPLOAD_FOLDER'], app.config['CACHE_PATH']):
+    try:
+        os.makedirs(path, exist_ok=True)
+    except Exception as e:
+        logger.error(f"Unable to create directory {path}: {str(e)}")
+
+# load model
+tts = merge_model(app.config["MODEL_LIST"])
+
+
+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,
+        "vits_speakers_count": tts.vits_speakers_count,
+        "w2v2_speakers_count": tts.w2v2_speakers_count,
+        "w2v2_emotion_count": tts.w2v2_emotion_count,
+        "bert_vits2_speakers_count": tts.bert_vits2_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)
+    voice_tasks, format = tts.parse_ssml(ssml)
+    fname = f"{str(uuid.uuid1())}.{format}"
+    file_type = f"audio/{format}"
+
+    t1 = time.time()
+    audio = tts.create_ssml_infer_task(voice_tasks, format, 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")
+
+    return send_file(path_or_file=audio, mimetype=file_type, download_name=fname)
+
+
+@app.route('/voice/dimension-emotion', methods=["POST"])
+@require_api_key
+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/bert-vits2', methods=["GET", "POST"])
+@require_api_key
+def voice_bert_vits2_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"))
+            lang = "ZH"
+            length = float(request.args.get("length", app.config.get("LENGTH", 1)))
+            noise = float(request.args.get("noise", app.config.get("NOISE", 0.5)))
+            noisew = float(request.args.get("noisew", app.config.get("NOISEW", 0.6)))
+            sdp_ratio = float(request.args.get("sdp_ratio", 0.2))
+            max = int(request.args.get("max", app.config.get("MAX", 50)))
+        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"))
+            lang = "ZH"
+            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)))
+            sdp_ratio = float(data.get("noisew", app.config.get("SDP_RATIO", 0.2)))
+            max = int(data.get("max", app.config.get("MAX", 50)))
+    except Exception as e:
+        logger.error(f"[Bert-VITS2] {e}")
+        return make_response("parameter error", 400)
+
+    logger.info(f"[Bert-VITS2] id:{id} format:{format} lang:{lang} length:{length} noise:{noise} noisew:{noisew} sdp_ratio:{sdp_ratio}")
+    logger.info(f"[Bert-VITS2] len:{len(text)} text：{text}")
+
+    if check_is_none(text):
+        logger.info(f"[Bert-VITS2] text is empty")
+        return make_response(jsonify({"status": "error", "message": "text is empty"}), 400)
+
+    if check_is_none(id):
+        logger.info(f"[Bert-VITS2] speaker id is empty")
+        return make_response(jsonify({"status": "error", "message": "speaker id is empty"}), 400)
+
+    if id < 0 or id >= tts.bert_vits2_speakers_count:
+        logger.info(f"[Bert-VITS2] 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["BERT-VITS2"][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"[Bert-VITS2] 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")
+
+    fname = f"{str(uuid.uuid1())}.{format}"
+    file_type = f"audio/{format}"
+    task = {"text": text,
+            "id": id,
+            "format": format,
+            "length": length,
+            "noise": noise,
+            "noisew": noisew,
+            "sdp_ratio": sdp_ratio,
+            "max": max,
+            "lang": lang,
+            "speaker_lang": speaker_lang}
+
+    if app.config.get("SAVE_AUDIO", False):
+        logger.debug(f"[Bert-VITS2] {fname}")
+
+
+    t1 = time.time()
+    audio = tts.bert_vits2_infer(task, fname)
+    t2 = time.time()
+    logger.info(f"[Bert-VITS2] finish in {(t2 - t1):.2f}s")
+    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

bert_vits2/LICENSE

@@ -0,0 +1,674 @@
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+<https://www.gnu.org/licenses/why-not-lgpl.html>.

bert_vits2/README.md

@@ -0,0 +1,5 @@
+# Bert-VITS2
+
+VITS2 Backbone with bert
+## 成熟的旅行者/开拓者/舰长/博士/sensei/猎魔人/喵喵露/V应该参阅代码自己学习如何训练。
+### 严禁将此项目用于一切违反《中华人民共和国宪法》，《中华人民共和国刑法》，《中华人民共和国治安管理处罚法》和《中华人民共和国民法典》之用途。

bert_vits2/__init__.py

@@ -0,0 +1,2 @@
+from bert_vits2.bert_vits2 import Bert_VITS2
+from bert_vits2 import text

bert_vits2/attentions.py

@@ -0,0 +1,352 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+from bert_vits2 import commons
+from torch.nn.utils import weight_norm, remove_weight_norm
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-5):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = nn.Parameter(torch.ones(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        x = x.transpose(1, -1)
+        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+        return x.transpose(1, -1)
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+class Encoder(nn.Module):
+    def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4,
+                 isflow=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.window_size = window_size
+        if isflow:
+            cond_layer = torch.nn.Conv1d(256, 2 * hidden_channels * n_layers, 1)
+            self.cond_pre = torch.nn.Conv1d(hidden_channels, 2 * hidden_channels, 1)
+            self.cond_layer = weight_norm(cond_layer, name='weight')
+            self.gin_channels = 256
+        self.cond_layer_idx = self.n_layers
+        if 'gin_channels' in kwargs:
+            self.gin_channels = kwargs['gin_channels']
+            if self.gin_channels != 0:
+                self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
+                # vits2 says 3rd block, so idx is 2 by default
+                self.cond_layer_idx = kwargs['cond_layer_idx'] if 'cond_layer_idx' in kwargs else 2
+                # print(self.gin_channels, self.cond_layer_idx)
+                assert self.cond_layer_idx < self.n_layers, 'cond_layer_idx should be less than n_layers'
+        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, g=None):
+        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            if i == self.cond_layer_idx and g is not None:
+                g = self.spk_emb_linear(g.transpose(1, 2))
+                g = g.transpose(1, 2)
+                x = x + g
+                x = x * x_mask
+            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_vits2/bert/chinese-roberta-wwm-ext-large/.gitattributes

@@ -0,0 +1,9 @@
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text

bert_vits2/bert/chinese-roberta-wwm-ext-large/README.md

@@ -0,0 +1,57 @@
+---
+language: 
+- zh
+tags:
+- bert
+license: "apache-2.0"
+---
+
+# Please use 'Bert' related functions to load this model!
+
+## Chinese BERT with Whole Word Masking
+For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**. 
+
+**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**  
+Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
+
+This repository is developed based on：https://github.com/google-research/bert
+
+You may also interested in,
+- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
+- Chinese MacBERT: https://github.com/ymcui/MacBERT
+- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
+- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
+- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
+
+More resources by HFL: https://github.com/ymcui/HFL-Anthology
+
+## Citation
+If you find the technical report or resource is useful, please cite the following technical report in your paper.
+- Primary: https://arxiv.org/abs/2004.13922
+```
+@inproceedings{cui-etal-2020-revisiting,
+    title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
+    author = "Cui, Yiming  and
+      Che, Wanxiang  and
+      Liu, Ting  and
+      Qin, Bing  and
+      Wang, Shijin  and
+      Hu, Guoping",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
+    pages = "657--668",
+}
+```
+- Secondary: https://arxiv.org/abs/1906.08101  
+```
+@article{chinese-bert-wwm,
+  title={Pre-Training with Whole Word Masking for Chinese BERT},
+  author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
+  journal={arXiv preprint arXiv:1906.08101},
+  year={2019}
+ }
+```

bert_vits2/bert/chinese-roberta-wwm-ext-large/added_tokens.json

@@ -0,0 +1 @@
+{}

bert_vits2/bert/chinese-roberta-wwm-ext-large/config.json

@@ -0,0 +1,28 @@
+{
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "directionality": "bidi",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "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_vits2/bert/chinese-roberta-wwm-ext-large/flax_model.msgpack

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+size 1302196529

bert_vits2/bert/chinese-roberta-wwm-ext-large/pytorch_model.bin

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+oid sha256:4ac62d49144d770c5ca9a5d1d3039c4995665a080febe63198189857c6bd11cd
+size 1306484351

bert_vits2/bert/chinese-roberta-wwm-ext-large/special_tokens_map.json

@@ -0,0 +1 @@
+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

bert_vits2/bert/chinese-roberta-wwm-ext-large/tf_model.h5

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+size 1302594480

bert_vits2/bert/chinese-roberta-wwm-ext-large/tokenizer_config.json

@@ -0,0 +1 @@
+{"init_inputs": []}

bert_vits2/bert_vits2.py

@@ -0,0 +1,86 @@
+import numpy as np
+import torch
+
+from bert_vits2 import utils, commons
+from bert_vits2.models import SynthesizerTrn
+from bert_vits2.text import symbols, cleaned_text_to_sequence, get_bert
+from bert_vits2.text.cleaner import clean_text
+from utils.nlp import sentence_split, cut
+
+
+class Bert_VITS2:
+    def __init__(self, model, config, device=torch.device("cpu")):
+        self.hps_ms = utils.get_hparams_from_file(config)
+        self.n_speakers = getattr(self.hps_ms.data, 'n_speakers', 0)
+        self.speakers = [item[0] for item in
+                         sorted(list(getattr(self.hps_ms.data, 'spk2id', {'0': 0}).items()), key=lambda x: x[1])]
+        self.net_g = SynthesizerTrn(
+            len(symbols),
+            self.hps_ms.data.filter_length // 2 + 1,
+            self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
+            n_speakers=self.hps_ms.data.n_speakers,
+            **self.hps_ms.model).to(device)
+        _ = self.net_g.eval()
+        self.device = device
+        self.load_model(model)
+
+    def load_model(self, model):
+        utils.load_checkpoint(model, self.net_g, None, skip_optimizer=True)
+
+    def get_speakers(self):
+        return self.speakers
+
+    def get_text(self, text, language_str, hps):
+        norm_text, phone, tone, word2ph = clean_text(text, language_str)
+        # print([f"{p}{t}" for p, t in zip(phone, tone)])
+        phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
+
+        if hps.data.add_blank:
+            phone = commons.intersperse(phone, 0)
+            tone = commons.intersperse(tone, 0)
+            language = commons.intersperse(language, 0)
+            for i in range(len(word2ph)):
+                word2ph[i] = word2ph[i] * 2
+            word2ph[0] += 1
+        bert = get_bert(norm_text, word2ph, language_str)
+
+        assert bert.shape[-1] == len(phone)
+
+        phone = torch.LongTensor(phone)
+        tone = torch.LongTensor(tone)
+        language = torch.LongTensor(language)
+
+        return bert, phone, tone, language
+
+    def infer(self, text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid):
+        bert, phones, tones, lang_ids = self.get_text(text, "ZH", self.hps_ms)
+        with torch.no_grad():
+            x_tst = phones.to(self.device).unsqueeze(0)
+            tones = tones.to(self.device).unsqueeze(0)
+            lang_ids = lang_ids.to(self.device).unsqueeze(0)
+            bert = bert.to(self.device).unsqueeze(0)
+            x_tst_lengths = torch.LongTensor([phones.size(0)]).to(self.device)
+            speakers = torch.LongTensor([int(sid)]).to(self.device)
+            audio = self.net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio
+                                     , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[
+                0][0, 0].data.cpu().float().numpy()
+
+        torch.cuda.empty_cache()
+        return audio
+
+    def get_audio(self, voice, auto_break=False):
+        text = voice.get("text", None)
+        sdp_ratio = voice.get("sdp_ratio", 0.2)
+        noise_scale = voice.get("noise", 0.5)
+        noise_scale_w = voice.get("noisew", 0.6)
+        length_scale = voice.get("length", 1)
+        sid = voice.get("id", 0)
+        max = voice.get("max", 50)
+        # sentence_list = sentence_split(text, max, "ZH", ["zh"])
+        sentence_list = cut(text, max)
+        audios = []
+        for sentence in sentence_list:
+            audio = self.infer(sentence, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid)
+            audios.append(audio)
+        audio = np.concatenate(audios)
+        return audio

bert_vits2/commons.py

@@ -0,0 +1,161 @@
+import math
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+
+def init_weights(m, mean=0.0, std=0.01):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        m.weight.data.normal_(mean, std)
+
+
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size * dilation - dilation) / 2)
+
+
+def convert_pad_shape(pad_shape):
+    l = pad_shape[::-1]
+    pad_shape = [item for sublist in l for item in sublist]
+    return pad_shape
+
+
+def intersperse(lst, item):
+    result = [item] * (len(lst) * 2 + 1)
+    result[1::2] = lst
+    return result
+
+
+def kl_divergence(m_p, logs_p, m_q, logs_q):
+    """KL(P||Q)"""
+    kl = (logs_q - logs_p) - 0.5
+    kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2. * logs_q)
+    return kl
+
+
+def rand_gumbel(shape):
+    """Sample from the Gumbel distribution, protect from overflows."""
+    uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
+    return -torch.log(-torch.log(uniform_samples))
+
+
+def rand_gumbel_like(x):
+    g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
+    return g
+
+
+def slice_segments(x, ids_str, segment_size=4):
+    ret = torch.zeros_like(x[:, :, :segment_size])
+    for i in range(x.size(0)):
+        idx_str = ids_str[i]
+        idx_end = idx_str + segment_size
+        ret[i] = x[i, :, idx_str:idx_end]
+    return ret
+
+
+def rand_slice_segments(x, x_lengths=None, segment_size=4):
+    b, d, t = x.size()
+    if x_lengths is None:
+        x_lengths = t
+    ids_str_max = x_lengths - segment_size + 1
+    ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
+    ret = slice_segments(x, ids_str, segment_size)
+    return ret, ids_str
+
+
+def get_timing_signal_1d(
+        length, channels, min_timescale=1.0, max_timescale=1.0e4):
+    position = torch.arange(length, dtype=torch.float)
+    num_timescales = channels // 2
+    log_timescale_increment = (
+            math.log(float(max_timescale) / float(min_timescale)) /
+            (num_timescales - 1))
+    inv_timescales = min_timescale * torch.exp(
+        torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
+    scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
+    signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
+    signal = F.pad(signal, [0, 0, 0, channels % 2])
+    signal = signal.view(1, channels, length)
+    return signal
+
+
+def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
+    b, channels, length = x.size()
+    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+    return x + signal.to(dtype=x.dtype, device=x.device)
+
+
+def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
+    b, channels, length = x.size()
+    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+    return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
+
+
+def subsequent_mask(length):
+    mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
+    return mask
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+def convert_pad_shape(pad_shape):
+    l = pad_shape[::-1]
+    pad_shape = [item for sublist in l for item in sublist]
+    return pad_shape
+
+
+def shift_1d(x):
+    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
+    return x
+
+
+def sequence_mask(length, max_length=None):
+    if max_length is None:
+        max_length = length.max()
+    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
+    return x.unsqueeze(0) < length.unsqueeze(1)
+
+
+def generate_path(duration, mask):
+    """
+    duration: [b, 1, t_x]
+    mask: [b, 1, t_y, t_x]
+    """
+    device = duration.device
+
+    b, _, t_y, t_x = mask.shape
+    cum_duration = torch.cumsum(duration, -1)
+
+    cum_duration_flat = cum_duration.view(b * t_x)
+    path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
+    path = path.view(b, t_x, t_y)
+    path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
+    path = path.unsqueeze(1).transpose(2, 3) * mask
+    return path
+
+
+def clip_grad_value_(parameters, clip_value, norm_type=2):
+    if isinstance(parameters, torch.Tensor):
+        parameters = [parameters]
+    parameters = list(filter(lambda p: p.grad is not None, parameters))
+    norm_type = float(norm_type)
+    if clip_value is not None:
+        clip_value = float(clip_value)
+
+    total_norm = 0
+    for p in parameters:
+        param_norm = p.grad.data.norm(norm_type)
+        total_norm += param_norm.item() ** norm_type
+        if clip_value is not None:
+            p.grad.data.clamp_(min=-clip_value, max=clip_value)
+    total_norm = total_norm ** (1. / norm_type)
+    return total_norm