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Browse files- Dockerfile +22 -0
- LICENSE +21 -0
- Model/Nene_Nanami_Rong_Tang/1374_epochs.pth +3 -0
- Model/Nene_Nanami_Rong_Tang/config.json +35 -0
- Model/Zero_no_tsukaima/1158_epochs.pth +3 -0
- Model/Zero_no_tsukaima/config.json +35 -0
- Model/g/G_953000.pth +3 -0
- Model/g/config.json +55 -0
- README.md +231 -11
- app.py +115 -0
- attentions.py +300 -0
- commons.py +96 -0
- config.py +32 -0
- docker-compose.yaml +12 -0
- hubert_model.py +221 -0
- mel_processing.py +101 -0
- models.py +404 -0
- modules.py +387 -0
- requirements.txt +23 -0
- text/LICENSE +19 -0
- text/__init__.py +32 -0
- text/__pycache__/__init__.cpython-310.pyc +0 -0
- text/__pycache__/cleaners.cpython-310.pyc +0 -0
- text/__pycache__/japanese.cpython-310.pyc +0 -0
- text/__pycache__/mandarin.cpython-310.pyc +0 -0
- text/cantonese.py +59 -0
- text/cleaners.py +145 -0
- text/english.py +188 -0
- text/japanese.py +153 -0
- text/korean.py +210 -0
- text/mandarin.py +330 -0
- text/ngu_dialect.py +30 -0
- text/sanskrit.py +62 -0
- text/shanghainese.py +64 -0
- text/thai.py +44 -0
- transforms.py +193 -0
- utils.py +171 -0
- voice.py +231 -0
Dockerfile
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FROM python:3.9.16-slim-bullseye
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RUN mkdir -p /app
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WORKDIR /app
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ENV DEBIAN_FRONTEND=noninteractive
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RUN apt-get update && \
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apt install build-essential -yq && \
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apt-get clean && \
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apt-get purge -y --auto-remove -o APT::AutoRemove::RecommendsImportant=false && \
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rm -rf /var/lib/apt/lists/*
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COPY requirements.txt /app
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RUN pip install -r requirements.txt
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COPY . /app
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EXPOSE 7860
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CMD [ "uvicorn" , "/app/app.py" , "--host" , "0.0.0.0" , "--port" , "7860" ]
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LICENSE
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MIT License
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Copyright (c) 2022 CjangCjengh
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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Model/Nene_Nanami_Rong_Tang/1374_epochs.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:edfb6b428c398fab83a85b5ae41e13cb5a9f7be12692129e8a880d4553701f7b
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size 158888013
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Model/Nene_Nanami_Rong_Tang/config.json
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{
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"train": {
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"segment_size": 8192
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},
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"data": {
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"text_cleaners":["zh_ja_mixture_cleaners"],
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"max_wav_value": 32768.0,
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"sampling_rate": 22050,
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"filter_length": 1024,
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"hop_length": 256,
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"win_length": 1024,
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"add_blank": true,
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"n_speakers": 5
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},
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"model": {
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"inter_channels": 192,
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"hidden_channels": 192,
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"filter_channels": 768,
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"n_heads": 2,
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"n_layers": 6,
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"kernel_size": 3,
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"p_dropout": 0.1,
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"resblock": "1",
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"resblock_kernel_sizes": [3,7,11],
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"resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
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"upsample_rates": [8,8,2,2],
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"upsample_initial_channel": 512,
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"upsample_kernel_sizes": [16,16,4,4],
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"n_layers_q": 3,
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"use_spectral_norm": false,
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"gin_channels": 256
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},
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"speakers": ["\u7dbe\u5730\u5be7\u3005", "\u5728\u539f\u4e03\u6d77", "\u5c0f\u8338", "\u5510\u4e50\u541f"],
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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", " "]
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}
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Model/Zero_no_tsukaima/1158_epochs.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:da9a595f45f51dee33168c1e32bba38fe402fe3d8857331f5798a2407b6b2a86
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size 158902605
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Model/Zero_no_tsukaima/config.json
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{
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"train": {
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"segment_size": 8192
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},
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"data": {
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"text_cleaners":["japanese_cleaners2"],
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"max_wav_value": 32768.0,
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"sampling_rate": 22050,
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"filter_length": 1024,
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"hop_length": 256,
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"win_length": 1024,
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"add_blank": true,
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"n_speakers": 26
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},
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"model": {
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"inter_channels": 192,
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"hidden_channels": 192,
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"filter_channels": 768,
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"n_heads": 2,
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"n_layers": 6,
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"kernel_size": 3,
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"p_dropout": 0.1,
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"resblock": "1",
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"resblock_kernel_sizes": [3,7,11],
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"resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
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"upsample_rates": [8,8,2,2],
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"upsample_initial_channel": 512,
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"upsample_kernel_sizes": [16,16,4,4],
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"n_layers_q": 3,
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"use_spectral_norm": false,
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"gin_channels": 256
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},
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"speakers": ["\u30eb\u30a4\u30ba", "\u30c6\u30a3\u30d5\u30a1\u30cb\u30a2", "\u30a4\u30eb\u30af\u30af\u30a5", "\u30a2\u30f3\u30ea\u30a8\u30c3\u30bf", "\u30bf\u30d0\u30b5", "\u30b7\u30a8\u30b9\u30bf", "\u30cf\u30eb\u30ca", "\u5c11\u5973\u30ea\u30b7\u30e5", "\u30ea\u30b7\u30e5", "\u30a2\u30ad\u30ca", "\u30af\u30ea\u30b9", "\u30ab\u30c8\u30ec\u30a2", "\u30a8\u30ec\u30aa\u30ce\u30fc\u30eb", "\u30e2\u30f3\u30e2\u30e9\u30f3\u30b7\u30fc", "\u30ea\u30fc\u30f4\u30eb", "\u30ad\u30e5\u30eb\u30b1", "\u30a6\u30a7\u30b6\u30ea\u30fc", "\u30b5\u30a4\u30c8", "\u30ae\u30fc\u30b7\u30e5", "\u30b3\u30eb\u30d9\u30fc\u30eb", "\u30aa\u30b9\u30de\u30f3", "\u30c7\u30eb\u30d5\u30ea\u30f3\u30ac\u30fc", "\u30c6\u30af\u30b9\u30c8", "\u30c0\u30f3\u30d7\u30ea\u30e1", "\u30ac\u30ec\u30c3\u30c8", "\u30b9\u30ab\u30ed\u30f3"],
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"symbols": ["_", ",", ".", "!", "?", "-", "~", "\u2026", "A", "E", "I", "N", "O", "Q", "U", "a", "b", "d", "e", "f", "g", "h", "i", "j", "k", "m", "n", "o", "p", "r", "s", "t", "u", "v", "w", "y", "z", "\u0283", "\u02a7", "\u02a6", "\u2193", "\u2191", " "]
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}
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Model/g/G_953000.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:0c54396a7a9027952e4d72fceb7e1da1f003d108837138927c2054a33eda0292
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size 479276657
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Model/g/config.json
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{
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"train": {
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"log_interval": 200,
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"eval_interval": 1000,
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"seed": 1234,
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"epochs": 10000,
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+
"learning_rate": 2e-4,
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"betas": [0.8, 0.99],
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"eps": 1e-9,
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"batch_size": 64,
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"fp16_run": true,
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"lr_decay": 0.999875,
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"segment_size": 8192,
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"init_lr_ratio": 1,
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"warmup_epochs": 0,
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"c_mel": 45,
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"c_kl": 1.0
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},
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"data": {
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"training_files":"filelists/uma_genshin_genshinjp_bh3_train.txt.cleaned",
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"validation_files":"filelists/uma_genshin_genshinjp_bh3_val.txt.cleaned",
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"text_cleaners":["zh_ja_mixture_cleaners"],
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"max_wav_value": 32768.0,
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"sampling_rate": 22050,
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"filter_length": 1024,
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"hop_length": 256,
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"win_length": 1024,
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"n_mel_channels": 80,
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"mel_fmin": 0.0,
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"mel_fmax": null,
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"add_blank": true,
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"n_speakers": 804,
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"cleaned_text": true
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},
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"model": {
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"inter_channels": 192,
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"hidden_channels": 192,
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"filter_channels": 768,
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"n_heads": 2,
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"n_layers": 6,
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"kernel_size": 3,
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"p_dropout": 0.1,
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"resblock": "1",
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"resblock_kernel_sizes": [3,7,11],
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"resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
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"upsample_rates": [8,8,2,2],
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"upsample_initial_channel": 512,
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48 |
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"upsample_kernel_sizes": [16,16,4,4],
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"n_layers_q": 3,
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"use_spectral_norm": false,
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"gin_channels": 256
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},
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"speakers": ["\u7279\u522b\u5468", "\u65e0\u58f0\u94c3\u9e7f", "\u4e1c\u6d77\u5e1d\u7687\uff08\u5e1d\u5b9d\uff0c\u5e1d\u738b\uff09", "\u4e38\u5584\u65af\u57fa", "\u5bcc\u58eb\u5947\u8ff9", "\u5c0f\u6817\u5e3d", "\u9ec4\u91d1\u8239", "\u4f0f\u7279\u52a0", "\u5927\u548c\u8d64\u9aa5", "\u5927\u6811\u5feb\u8f66", "\u8349\u4e0a\u98de", "\u83f1\u4e9a\u9a6c\u900a", "\u76ee\u767d\u9ea6\u6606", "\u795e\u9e70", "\u597d\u6b4c\u5267", "\u6210\u7530\u767d\u4ec1", "\u9c81\u9053\u592b\u8c61\u5f81\uff08\u7687\u5e1d\uff09", "\u6c14\u69fd", "\u7231\u4e3d\u6570\u7801", "\u661f\u4e91\u5929\u7a7a", "\u7389\u85fb\u5341\u5b57", "\u7f8e\u5999\u59ff\u52bf", "\u7435\u7436\u6668\u5149", "\u6469\u8036\u91cd\u70ae", "\u66fc\u57ce\u8336\u5ea7", "\u7f8e\u6d66\u6ce2\u65c1", "\u76ee\u767d\u8d56\u6069", "\u83f1\u66d9", "\u96ea\u4e2d\u7f8e\u4eba", "\u7c73\u6d74", "\u827e\u5c3c\u65af\u98ce\u795e", "\u7231\u4e3d\u901f\u5b50\uff08\u7231\u4e3d\u5feb\u5b50\uff09", "\u7231\u6155\u7ec7\u59ec", "\u7a3b\u8377\u4e00", 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"\u65e5\u8bed\u963f\u8d1d\u591a\uff08\u91ce\u5c9b\u5065\u513f\uff09", "\u65e5\u8bed\u57c3\u6d1b\u4f0a\uff08\u9ad8\u57a3\u5f69\u9633\uff09", "\u65e5\u8bed\u5b89\u67cf\uff08\u77f3\u89c1\u821e\u83dc\u9999\uff09", "\u65e5\u8bed\u795e\u91cc\u7eeb\u534e\uff08\u65e9\u89c1\u6c99\u7ec7\uff09", "\u65e5\u8bed\u795e\u91cc\u7eeb\u4eba\uff08\u77f3\u7530\u5f70\uff09", "\u65e5\u8bed\u767d\u672f\uff08\u6e38\u4f50\u6d69\u4e8c\uff09", "\u65e5\u8bed\u82ad\u82ad\u62c9\uff08\u9b3c\u5934\u660e\u91cc\uff09", "\u65e5\u8bed\u5317\u6597\uff08\u5c0f\u6e05\u6c34\u4e9a\u7f8e\uff09", "\u65e5\u8bed\u73ed\u5c3c\u7279\uff08\u9022\u5742\u826f\u592a\uff09", "\u65e5\u8bed\u574e\u8482\u4e1d\uff08\u67da\u6728\u51c9\u9999\uff09", "\u65e5\u8bed\u91cd\u4e91\uff08\u9f50\u85e4\u58ee\u9a6c\uff09", "\u65e5\u8bed\u67ef\u83b1\uff08\u524d\u5ddd\u51c9\u5b50\uff09", "\u65e5\u8bed\u8d5b\u8bfa\uff08\u5165\u91ce\u81ea\u7531\uff09", "\u65e5\u8bed\u6234\u56e0\u65af\u96f7\u5e03\uff08\u6d25\u7530\u5065\u6b21\u90ce\uff09", 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"\u5742\u672c", "\u6714\u6b21\u90ce", "\u6b66\u58eba", "\u6b66\u58ebb", "\u6b66\u58ebc", "\u6b66\u58ebd", "\u73ca\u745a", "\u4e09\u7530", "\u838e\u62c9", "\u7b39\u91ce", "\u806a\u7f8e", "\u806a", "\u5c0f\u767e\u5408", "\u6563\u5175", "\u5bb3\u6015\u7684\u5c0f\u5218", "\u8212\u4f2f\u7279", "\u8212\u8328", "\u6d77\u9f99", "\u4e16\u5b50", "\u8c22\u5c14\u76d6", "\u5bb6\u4e01", "\u5546\u534e", "\u6c99\u5bc5", "\u963f\u5347", "\u67f4\u7530", "\u963f\u8302", "\u5f0f\u5927\u5c06", "\u6e05\u6c34", "\u5fd7\u6751\u52d8\u5175\u536b", "\u65b0\u4e4b\u4e1e", "\u5fd7\u7ec7", "\u77f3\u5934", "\u8bd7\u7fbd", "\u8bd7\u7b60", "\u77f3\u58ee", "\u7fd4\u592a", "\u6b63\u4e8c", "\u5468\u5e73", "\u8212\u6768", "\u9f50\u683c\u8299\u4e3d\u96c5", "\u5973\u58eb", "\u601d\u52e4", "\u516d\u6307\u4e54\u745f", "\u611a\u4eba\u4f17\u5c0f\u5175d", "\u611a\u4eba\u4f17\u5c0f\u5175a", "\u611a\u4eba\u4f17\u5c0f\u5175b", "\u611a\u4eba\u4f17\u5c0f\u5175c", "\u5434\u8001\u4e94", "\u5434\u8001\u4e8c", "\u6ed1\u5934\u9b3c", "\u8a00\u7b11", "\u5434\u8001\u4e03", "\u58eb\u5175h", "\u58eb\u5175i", "\u58eb\u5175a", "\u58eb\u5175b", "\u58eb\u5175c", "\u58eb\u5175d", "\u58eb\u5175e", "\u58eb\u5175f", "\u58eb\u5175g", "\u594f\u592a", "\u65af\u5766\u5229", "\u6387\u661f\u652b\u8fb0\u5929\u541b", "\u5c0f\u5934", "\u5927\u6b66", "\u9676\u4e49\u9686", "\u6749\u672c", "\u82cf\u897f", "\u5acc\u7591\u4ebaa", "\u5acc\u7591\u4ebab", "\u5acc\u7591\u4ebac", "\u5acc\u7591\u4ebad", "\u65af\u4e07", "\u5251\u5ba2a", "\u5251\u5ba2b", "\u963f\u4e8c", "\u5fe0\u80dc", "\u5fe0\u592b", "\u963f\u656c", "\u5b5d\u5229", "\u9e70\u53f8\u8fdb", "\u9ad8\u5c71", "\u4e5d\u6761\u5b5d\u884c", "\u6bc5", "\u7af9\u5185", "\u62d3\u771f", "\u5353\u4e5f", "\u592a\u90ce\u4e38", "\u6cf0\u52d2", "\u624b\u5c9b", "\u54f2\u5e73", "\u54f2\u592b", "\u6258\u514b", "\u5927boss", "\u963f\u5f3a", "\u6258\u5c14\u5fb7\u62c9", "\u65c1\u89c2\u8005", "\u5929\u6210", "\u963f\u5927", "\u8482\u739b\u4e4c\u65af", "\u63d0\u7c73", "\u6237\u7530", "\u963f\u4e09", "\u4e00\u8d77\u7684\u4eba", "\u5fb7\u7530", "\u5fb7\u957f", "\u667a\u6811", "\u5229\u5f66", "\u80d6\u4e4e\u4e4e\u7684\u65c5\u884c\u8005", "\u85cf\u5b9d\u4ebaa", "\u85cf\u5b9d\u4ebab", "\u85cf\u5b9d\u4ebac", "\u85cf\u5b9d\u4ebad", "\u963f\u7947", "\u6052\u96c4", "\u9732\u5b50", "\u8bdd\u5267\u56e2\u56e2\u957f", "\u5185\u6751", "\u4e0a\u91ce", "\u4e0a\u6749", "\u8001\u6234", "\u8001\u9ad8", "\u8001\u8d3e", "\u8001\u58a8", "\u8001\u5b59", "\u5929\u67a2\u661f", "\u8001\u4e91", "\u6709\u4e50\u658b", "\u4e11\u96c4", "\u4e4c\u7ef4", "\u74e6\u4eac", "\u83f2\u5c14\u6208\u9edb\u7279", "\u7ef4\u591a\u5229\u4e9a", "\u8587\u5c14", "\u74e6\u683c\u7eb3", "\u963f\u5916", "\u4f8d\u5973", "\u74e6\u62c9", "\u671b\u96c5", "\u5b9b\u70df", "\u742c\u7389", "\u6218\u58eba", "\u6218\u58ebb", "\u6e21\u8fba", "\u6e21\u90e8", "\u963f\u4f1f", "\u6587\u749f", "\u6587\u6e0a", "\u97e6\u5c14\u7eb3", "\u738b\u6273\u624b", "\u6b66\u6c9b", "\u6653\u98de", "\u8f9b\u7a0b", "\u661f\u706b", "\u661f\u7a00", "\u8f9b\u79c0", "\u79c0\u534e", "\u963f\u65ed", "\u5f90\u5218\u5e08", "\u77e2\u90e8", "\u516b\u6728", "\u5c71\u4e0a", "\u963f\u9633", "\u989c\u7b11", "\u5eb7\u660e", "\u6cf0\u4e45", "\u5b89\u6b66", "\u77e2\u7530\u5e78\u559c", "\u77e2\u7530\u8f9b\u559c", "\u4e49\u575a", "\u83ba\u513f", "\u76c8\u4e30", "\u5b9c\u5e74", "\u94f6\u674f", "\u9038\u8f69", "\u6a2a\u5c71", "\u6c38\u8d35", "\u6c38\u4e1a", "\u5609\u4e45", "\u5409\u5ddd", "\u4e49\u9ad8", "\u7528\u9ad8", "\u9633\u592a", "\u5143\u84c9", "\u73a5\u8f89", "\u6bd3\u534e", "\u6709\u9999", "\u5e78\u4e5f", "\u7531\u771f", "\u7ed3\u83dc", "\u97f5\u5b81", "\u767e\u5408", "\u767e\u5408\u534e", "\u5c24\u82cf\u6ce2\u592b", "\u88d5\u5b50", "\u60a0\u7b56", "\u60a0\u4e5f", "\u4e8e\u5ae3", "\u67da\u5b50", "\u8001\u90d1", "\u6b63\u8302", "\u5fd7\u6210", "\u82b7\u5de7", "\u77e5\u6613", "\u652f\u652f", "\u5468\u826f", "\u73e0\u51fd", "\u795d\u660e", "\u795d\u6d9b"],
|
54 |
+
"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", " "]
|
55 |
+
}
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|
|
|
1 |
+
# MoeGoe-Simple-API
|
2 |
+
|
3 |
+
|
4 |
+
|
5 |
+
MoeGoe-Simple-API 是一个易部署的api,可以通过api的方式调用语音合成,可用于聊天机器人等,目前支持的功能有语音合成和语音转换。
|
6 |
+
|
7 |
+
- 可导入模型
|
8 |
+
- 支持加载多模型,可以将多个模型合并为一个新的id对应角色模型的映射表
|
9 |
+
<details><summary>点击预览返回的映射表</summary><pre><code>
|
10 |
+
[
|
11 |
+
{
|
12 |
+
"0": "綾地寧々"
|
13 |
+
},
|
14 |
+
{
|
15 |
+
"1": "在原七海"
|
16 |
+
},
|
17 |
+
{
|
18 |
+
"2": "小茸"
|
19 |
+
},
|
20 |
+
{
|
21 |
+
"3": "唐乐吟"
|
22 |
+
},
|
23 |
+
{
|
24 |
+
"...": "..."
|
25 |
+
},
|
26 |
+
{
|
27 |
+
"196": "ルイズ"
|
28 |
+
},
|
29 |
+
{
|
30 |
+
"197": "ティファニア"
|
31 |
+
}
|
32 |
+
]
|
33 |
+
</code></pre></details>
|
34 |
+
|
35 |
+
# 如何部署
|
36 |
+
|
37 |
+
## Docker部署
|
38 |
+
|
39 |
+
### docker镜像拉取脚本
|
40 |
+
|
41 |
+
```
|
42 |
+
bash -c "$(wget -O- https://gist.githubusercontent.com/Artrajz/b2c02499f91c3a51b8b48f1a3c9a7ead/raw/e3033f1b222868b4b0f1b522e52e18217460ff91/moegoe-simple-api-installer-latest.sh)"
|
43 |
+
```
|
44 |
+
|
45 |
+
镜像大小为5g,所以拉取会比较慢,拉取成功后由于没有导入vits模型所以无法使用,需要按以下步骤导入模型
|
46 |
+
|
47 |
+
### 下载VITS模型
|
48 |
+
|
49 |
+
VITS模型放入`/usr/local/moegoe-simple-api/Model`文件夹中,模型文件夹中要有.pth和config.json文件
|
50 |
+
|
51 |
+
<details><summary>点击查看模型目录结构</summary><pre><code>
|
52 |
+
├─Model
|
53 |
+
│ ├─g
|
54 |
+
│ │ config.json
|
55 |
+
│ │ G_953000.pth
|
56 |
+
│ │
|
57 |
+
│ ├─Nene_Nanami_Rong_Tang
|
58 |
+
│ │ 1374_epochs.pth
|
59 |
+
│ │ config.json
|
60 |
+
│ │
|
61 |
+
│ └─Zero_no_tsukaima
|
62 |
+
│ 1158_epochs.pth
|
63 |
+
│ config.json
|
64 |
+
</code></pre></details>
|
65 |
+
|
66 |
+
### 修改模型路径
|
67 |
+
|
68 |
+
在 `/usr/local/moegoe-simple-api/config.py` 中修改模型路径
|
69 |
+
|
70 |
+
<details><summary>点击查看config.py模型路径填写示例</summary><pre><code>
|
71 |
+
vits模型路径填写方法,MODEL_LIST中的每一行是
|
72 |
+
[ABS_PATH+"/Model/{模型文件夹}/{.pth模型}", ABS_PATH+"/Model/{模型文件夹}/config.json"],
|
73 |
+
也可以写相对路径或绝对路径,由于windows和linux路径写法不同,用上面的写法或绝对路径最稳妥
|
74 |
+
示例:
|
75 |
+
MODEL_LIST = [
|
76 |
+
[ABS_PATH+"/Model/Nene_Nanami_Rong_Tang/1374_epochs.pth", ABS_PATH+"/Model/Nene_Nanami_Rong_Tang/config.json"],
|
77 |
+
[ABS_PATH+"/Model/Zero_no_tsukaima/1158_epochs.pth", ABS_PATH+"/Model/Zero_no_tsukaima/config.json"],
|
78 |
+
[ABS_PATH+"/Model/g/G_953000.pth", ABS_PATH+"/Model/g/config.json"],
|
79 |
+
]
|
80 |
+
</code></pre></details>
|
81 |
+
|
82 |
+
|
83 |
+
### 开始使用!
|
84 |
+
|
85 |
+
终端输入`docker compose up -d`
|
86 |
+
|
87 |
+
或再次执行拉取脚本
|
88 |
+
|
89 |
+
### 镜像更新方法
|
90 |
+
|
91 |
+
重新执行docker镜像拉取脚本即可
|
92 |
+
|
93 |
+
|
94 |
+
## 直接部署
|
95 |
+
|
96 |
+
1. 下载VITS模型并放入`Model`文件夹中
|
97 |
+
2. 在 `config.py` 中修改模型路径
|
98 |
+
3. 安装python依赖(建议用conda虚拟环境) `pip install -r requirements.txt`
|
99 |
+
4. 开始使用!`python app.py`
|
100 |
+
|
101 |
+
# 参数
|
102 |
+
|
103 |
+
## 语音合成voice
|
104 |
+
|
105 |
+
| Name | Parameter | Is must | Default | Value | Instruction |
|
106 |
+
| ------------- | --------- | ------- | ------- | ------------ | ------------------------------------------------ |
|
107 |
+
| 合成文本 | text | true | | text | |
|
108 |
+
| 角色id | id | false | 0 | (number) | |
|
109 |
+
| 音频格式 | format | false | wav | wav,ogg,silk | silk支持tx系语音 |
|
110 |
+
| 文本语言 | lang | false | mix | zh,ja,mix | 当lang=mix时,文本应该用[ZH] 或 [JA] 包裹 |
|
111 |
+
| 语音长度/语速 | speed | false | 1.0 | (number) | 调节语音长度,相当于调节语速,该数值越大语速越慢 |
|
112 |
+
|
113 |
+
## 语音转换voice conversion
|
114 |
+
|
115 |
+
| Name | Parameter | Is must | Default | Value | Instruction |
|
116 |
+
| ---------- | ----------- | ------- | ------- | ---------- | ---------------------- |
|
117 |
+
| 上传音频 | upload | true | | audio file | 只支持wav和ogg |
|
118 |
+
| 源角色id | original_id | true | | (number) | 上传文件所使用的角色id |
|
119 |
+
| 目标角色id | target_id | true | | (number) | 要转换的目标角色id |
|
120 |
+
|
121 |
+
# 调用方法
|
122 |
+
|
123 |
+
## GET
|
124 |
+
|
125 |
+
- GET/POST http://127.0.0.1:23456/voice/speakers
|
126 |
+
|
127 |
+
返回id对应角色的映射表(json格式)
|
128 |
+
|
129 |
+
- GET http://127.0.0.1/voice?text=[JA]text[JA][ZH]text[ZH]&id=0&format=wav&lang=mix
|
130 |
+
|
131 |
+
返回wav音频文件
|
132 |
+
|
133 |
+
- GET http://127.0.0.1/voice?text=[JA]text[JA][ZH]text[ZH]&id=0&format=ogg&lang=mix
|
134 |
+
|
135 |
+
返回ogg音频文件
|
136 |
+
|
137 |
+
- GET http://127.0.0.1/voice?text=text&lang=zh
|
138 |
+
|
139 |
+
设定语言为zh,则文本无需[ZH]包裹
|
140 |
+
|
141 |
+
- GET http://127.0.0.1/voice?text=text&lang=ja
|
142 |
+
|
143 |
+
设定语言为ja,则文本无需[JA]包裹
|
144 |
+
|
145 |
+
- GET http://127.0.0.1/voice?text=text&id=142&format=wav&lang=zh&speed=1.4
|
146 |
+
|
147 |
+
文本为text,角色id为142,音频格式为wav,文本语言为zh,语速为1.4
|
148 |
+
|
149 |
+
## POST
|
150 |
+
|
151 |
+
- python
|
152 |
+
|
153 |
+
```python
|
154 |
+
import re
|
155 |
+
import requests
|
156 |
+
import json
|
157 |
+
import os
|
158 |
+
import random
|
159 |
+
import string
|
160 |
+
from requests_toolbelt.multipart.encoder import MultipartEncoder
|
161 |
+
|
162 |
+
abs_path = os.path.dirname(__file__)
|
163 |
+
|
164 |
+
def voice_speakers():
|
165 |
+
url = "http://127.0.0.1:23456/voice/speakers"
|
166 |
+
|
167 |
+
res = requests.post(url=url)
|
168 |
+
json = res.json()
|
169 |
+
for i in json:
|
170 |
+
print(i)
|
171 |
+
|
172 |
+
def voice():
|
173 |
+
post_json = json.dumps({
|
174 |
+
"text":"需要合成的文本",
|
175 |
+
"id":142,
|
176 |
+
"format":"wav",
|
177 |
+
"lang":"zh",
|
178 |
+
"speed":1.4,
|
179 |
+
})
|
180 |
+
headers={'content-type':'application/json'}
|
181 |
+
url = "http://127.0.0.1:23456/voice"
|
182 |
+
|
183 |
+
res = requests.post(url=url,data=post_json,headers=headers)
|
184 |
+
fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
|
185 |
+
path = f"{abs_path}/{fname}"
|
186 |
+
with open(path, "wb") as f:
|
187 |
+
f.write(res.content)
|
188 |
+
|
189 |
+
def voice_conversion(upload_name):
|
190 |
+
upload_path = f'{abs_path}/{upload_name}'
|
191 |
+
upload_type = f'audio/{upload_name.split(".")[1]}' #wav,ogg
|
192 |
+
|
193 |
+
fields = {
|
194 |
+
"upload": (upload_name, open(upload_path,'rb'),upload_type),
|
195 |
+
"original_id": "142",
|
196 |
+
"target_id": "92",
|
197 |
+
}
|
198 |
+
boundary = '----VoiceConversionFormBoundary' \
|
199 |
+
+ ''.join(random.sample(string.ascii_letters + string.digits, 16))
|
200 |
+
m = MultipartEncoder(fields=fields, boundary=boundary)
|
201 |
+
|
202 |
+
headers = {"Content-Type": m.content_type}
|
203 |
+
url = "http://127.0.0.1:23456/voice/conversion"
|
204 |
+
|
205 |
+
res = requests.post(url=url,data=m,headers=headers)
|
206 |
+
fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
|
207 |
+
path = f"{abs_path}/{fname}"
|
208 |
+
with open(path, "wb") as f:
|
209 |
+
f.write(res.content)
|
210 |
+
```
|
211 |
+
|
212 |
+
# 可能遇到的问题
|
213 |
+
|
214 |
+
~~本人遇到过的问题~~
|
215 |
+
|
216 |
+
### 运行后服务器无响应
|
217 |
+
|
218 |
+
可能是内存不足,可以尝试减少模型加载数量。
|
219 |
+
|
220 |
+
### 模型推理时服务器无响应
|
221 |
+
|
222 |
+
可能是同时处理多个推理任务导致CPU堵塞,可以尝试在*voice.py*中取消以下**两行**代码的注释,意思是让pytorch只使用1个物理CPU核心,防止一个任务抢占过多CPU资源。
|
223 |
+
|
224 |
+
```python
|
225 |
+
import torch
|
226 |
+
torch.set_num_threads(1)
|
227 |
+
```
|
228 |
+
|
229 |
+
# 鸣谢
|
230 |
+
|
231 |
+
该项目基于[CjangCjengh](https://github.com/CjangCjengh)的[MoeGoe](https://github.com/CjangCjengh/MoeGoe)
|
app.py
ADDED
@@ -0,0 +1,115 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
|
3 |
+
import logging
|
4 |
+
import uuid
|
5 |
+
|
6 |
+
from flask import Flask, request, send_file, jsonify
|
7 |
+
from werkzeug.utils import secure_filename
|
8 |
+
from flask_apscheduler import APScheduler
|
9 |
+
|
10 |
+
from utils import clean_folder, merge_model
|
11 |
+
|
12 |
+
app = Flask(__name__)
|
13 |
+
app.config.from_pyfile("config.py")
|
14 |
+
|
15 |
+
scheduler = APScheduler()
|
16 |
+
scheduler.init_app(app)
|
17 |
+
scheduler.start()
|
18 |
+
|
19 |
+
logging.getLogger('numba').setLevel(logging.WARNING)
|
20 |
+
|
21 |
+
voice_obj, voice_speakers = merge_model(app.config["MODEL_LIST"])
|
22 |
+
|
23 |
+
if not os.path.exists(app.config['UPLOAD_FOLDER']):
|
24 |
+
try:
|
25 |
+
os.mkdir(app.config['UPLOAD_FOLDER'])
|
26 |
+
except:
|
27 |
+
pass
|
28 |
+
|
29 |
+
|
30 |
+
@app.route('/')
|
31 |
+
@app.route('/voice/')
|
32 |
+
def index():
|
33 |
+
return "usage:https://github.com/Artrajz/MoeGoe-Simple-API#readme"
|
34 |
+
|
35 |
+
|
36 |
+
@app.route('/voice/speakers', methods=["GET", "POST"])
|
37 |
+
def voice_speakers_api():
|
38 |
+
speakers_list = voice_speakers
|
39 |
+
return jsonify(speakers_list)
|
40 |
+
|
41 |
+
|
42 |
+
@app.route('/voice', methods=["GET", "POST"])
|
43 |
+
def voice_api():
|
44 |
+
if request.method == "GET":
|
45 |
+
text = request.args.get("text")
|
46 |
+
speaker_id = int(request.args.get("id", 0))
|
47 |
+
format = request.args.get("format", "wav")
|
48 |
+
lang = request.args.get("lang", "mix")
|
49 |
+
speed = float(request.args.get("speed", 1.0))
|
50 |
+
elif request.method == "POST":
|
51 |
+
json_data = request.json
|
52 |
+
text = json_data["text"]
|
53 |
+
speaker_id = int(json_data["id"])
|
54 |
+
format = json_data["format"]
|
55 |
+
lang = json_data["lang"]
|
56 |
+
speed = float(json_data["speed"])
|
57 |
+
|
58 |
+
if lang.upper() == "ZH":
|
59 |
+
text = f"[ZH]{text}[ZH]"
|
60 |
+
elif lang.upper() == "JA":
|
61 |
+
text = f"[JA]{text}[JA]"
|
62 |
+
|
63 |
+
real_id = voice_obj[speaker_id][0]
|
64 |
+
real_obj = voice_obj[speaker_id][1]
|
65 |
+
|
66 |
+
output, file_type, fname = real_obj.generate(text, real_id, format, speed)
|
67 |
+
|
68 |
+
return send_file(path_or_file=output, mimetype=file_type, download_name=fname)
|
69 |
+
|
70 |
+
|
71 |
+
@app.route('/voice/conversion', methods=["GET", "POST"])
|
72 |
+
def voice_conversion_api():
|
73 |
+
if request.method == "GET":
|
74 |
+
return jsonify("method should be POST")
|
75 |
+
if request.method == "POST":
|
76 |
+
# json_data = request.json
|
77 |
+
voice = request.files['upload']
|
78 |
+
original_id = int(request.form["original_id"])
|
79 |
+
target_id = int(request.form["target_id"])
|
80 |
+
|
81 |
+
form = {}
|
82 |
+
|
83 |
+
format = voice.filename.split(".")[1]
|
84 |
+
|
85 |
+
fname = secure_filename(str(uuid.uuid1()) + "." + voice.filename.split(".")[1])
|
86 |
+
voice.save(os.path.join(app.config['UPLOAD_FOLDER'], fname))
|
87 |
+
|
88 |
+
real_original_id = int(voice_obj[original_id][0])
|
89 |
+
real_target_id = int(voice_obj[target_id][0])
|
90 |
+
real_obj = voice_obj[original_id][1]
|
91 |
+
real_target_obj = voice_obj[target_id][1]
|
92 |
+
|
93 |
+
if voice_obj[original_id][2] != voice_obj[target_id][2]:
|
94 |
+
form["status"] = "error"
|
95 |
+
form["message"] = "speaker IDs are in diffrent Model!"
|
96 |
+
return form
|
97 |
+
|
98 |
+
output = real_obj.voice_conversion(os.path.join(app.config['UPLOAD_FOLDER'], fname),
|
99 |
+
real_original_id, real_target_id, format)
|
100 |
+
file_type = f"audio/{format}"
|
101 |
+
|
102 |
+
return send_file(path_or_file=output, mimetype=file_type, download_name=fname)
|
103 |
+
# return output
|
104 |
+
|
105 |
+
|
106 |
+
# 定时清理临时文件,每小时清一次
|
107 |
+
@scheduler.task('interval', id='随便写', seconds=3600, misfire_grace_time=900)
|
108 |
+
def clean_task():
|
109 |
+
clean_folder(app.config["UPLOAD_FOLDER"])
|
110 |
+
clean_folder(app.config["SILK_OUT_PATH"])
|
111 |
+
|
112 |
+
|
113 |
+
if __name__ == '__main__':
|
114 |
+
app.run(host='0.0.0.0', port=app.config["PORT"]) # 如果对外开放用这个,docker部署也用这个
|
115 |
+
# app.run(host='127.0.0.1', port=app.config["PORT"], debug=True) # 本地运行、调试
|
attentions.py
ADDED
@@ -0,0 +1,300 @@
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|
|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
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|
|
|
|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import math
|
2 |
+
import torch
|
3 |
+
from torch import nn
|
4 |
+
from torch.nn import functional as F
|
5 |
+
|
6 |
+
import commons
|
7 |
+
from modules import LayerNorm
|
8 |
+
|
9 |
+
|
10 |
+
class Encoder(nn.Module):
|
11 |
+
def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
|
12 |
+
super().__init__()
|
13 |
+
self.hidden_channels = hidden_channels
|
14 |
+
self.filter_channels = filter_channels
|
15 |
+
self.n_heads = n_heads
|
16 |
+
self.n_layers = n_layers
|
17 |
+
self.kernel_size = kernel_size
|
18 |
+
self.p_dropout = p_dropout
|
19 |
+
self.window_size = window_size
|
20 |
+
|
21 |
+
self.drop = nn.Dropout(p_dropout)
|
22 |
+
self.attn_layers = nn.ModuleList()
|
23 |
+
self.norm_layers_1 = nn.ModuleList()
|
24 |
+
self.ffn_layers = nn.ModuleList()
|
25 |
+
self.norm_layers_2 = nn.ModuleList()
|
26 |
+
for i in range(self.n_layers):
|
27 |
+
self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
|
28 |
+
self.norm_layers_1.append(LayerNorm(hidden_channels))
|
29 |
+
self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
|
30 |
+
self.norm_layers_2.append(LayerNorm(hidden_channels))
|
31 |
+
|
32 |
+
def forward(self, x, x_mask):
|
33 |
+
attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
|
34 |
+
x = x * x_mask
|
35 |
+
for i in range(self.n_layers):
|
36 |
+
y = self.attn_layers[i](x, x, attn_mask)
|
37 |
+
y = self.drop(y)
|
38 |
+
x = self.norm_layers_1[i](x + y)
|
39 |
+
|
40 |
+
y = self.ffn_layers[i](x, x_mask)
|
41 |
+
y = self.drop(y)
|
42 |
+
x = self.norm_layers_2[i](x + y)
|
43 |
+
x = x * x_mask
|
44 |
+
return x
|
45 |
+
|
46 |
+
|
47 |
+
class Decoder(nn.Module):
|
48 |
+
def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
|
49 |
+
super().__init__()
|
50 |
+
self.hidden_channels = hidden_channels
|
51 |
+
self.filter_channels = filter_channels
|
52 |
+
self.n_heads = n_heads
|
53 |
+
self.n_layers = n_layers
|
54 |
+
self.kernel_size = kernel_size
|
55 |
+
self.p_dropout = p_dropout
|
56 |
+
self.proximal_bias = proximal_bias
|
57 |
+
self.proximal_init = proximal_init
|
58 |
+
|
59 |
+
self.drop = nn.Dropout(p_dropout)
|
60 |
+
self.self_attn_layers = nn.ModuleList()
|
61 |
+
self.norm_layers_0 = nn.ModuleList()
|
62 |
+
self.encdec_attn_layers = nn.ModuleList()
|
63 |
+
self.norm_layers_1 = nn.ModuleList()
|
64 |
+
self.ffn_layers = nn.ModuleList()
|
65 |
+
self.norm_layers_2 = nn.ModuleList()
|
66 |
+
for i in range(self.n_layers):
|
67 |
+
self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
|
68 |
+
self.norm_layers_0.append(LayerNorm(hidden_channels))
|
69 |
+
self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
|
70 |
+
self.norm_layers_1.append(LayerNorm(hidden_channels))
|
71 |
+
self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
|
72 |
+
self.norm_layers_2.append(LayerNorm(hidden_channels))
|
73 |
+
|
74 |
+
def forward(self, x, x_mask, h, h_mask):
|
75 |
+
"""
|
76 |
+
x: decoder input
|
77 |
+
h: encoder output
|
78 |
+
"""
|
79 |
+
self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
|
80 |
+
encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
|
81 |
+
x = x * x_mask
|
82 |
+
for i in range(self.n_layers):
|
83 |
+
y = self.self_attn_layers[i](x, x, self_attn_mask)
|
84 |
+
y = self.drop(y)
|
85 |
+
x = self.norm_layers_0[i](x + y)
|
86 |
+
|
87 |
+
y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
|
88 |
+
y = self.drop(y)
|
89 |
+
x = self.norm_layers_1[i](x + y)
|
90 |
+
|
91 |
+
y = self.ffn_layers[i](x, x_mask)
|
92 |
+
y = self.drop(y)
|
93 |
+
x = self.norm_layers_2[i](x + y)
|
94 |
+
x = x * x_mask
|
95 |
+
return x
|
96 |
+
|
97 |
+
|
98 |
+
class MultiHeadAttention(nn.Module):
|
99 |
+
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):
|
100 |
+
super().__init__()
|
101 |
+
assert channels % n_heads == 0
|
102 |
+
|
103 |
+
self.channels = channels
|
104 |
+
self.out_channels = out_channels
|
105 |
+
self.n_heads = n_heads
|
106 |
+
self.p_dropout = p_dropout
|
107 |
+
self.window_size = window_size
|
108 |
+
self.heads_share = heads_share
|
109 |
+
self.block_length = block_length
|
110 |
+
self.proximal_bias = proximal_bias
|
111 |
+
self.proximal_init = proximal_init
|
112 |
+
self.attn = None
|
113 |
+
|
114 |
+
self.k_channels = channels // n_heads
|
115 |
+
self.conv_q = nn.Conv1d(channels, channels, 1)
|
116 |
+
self.conv_k = nn.Conv1d(channels, channels, 1)
|
117 |
+
self.conv_v = nn.Conv1d(channels, channels, 1)
|
118 |
+
self.conv_o = nn.Conv1d(channels, out_channels, 1)
|
119 |
+
self.drop = nn.Dropout(p_dropout)
|
120 |
+
|
121 |
+
if window_size is not None:
|
122 |
+
n_heads_rel = 1 if heads_share else n_heads
|
123 |
+
rel_stddev = self.k_channels**-0.5
|
124 |
+
self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
|
125 |
+
self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
|
126 |
+
|
127 |
+
nn.init.xavier_uniform_(self.conv_q.weight)
|
128 |
+
nn.init.xavier_uniform_(self.conv_k.weight)
|
129 |
+
nn.init.xavier_uniform_(self.conv_v.weight)
|
130 |
+
if proximal_init:
|
131 |
+
with torch.no_grad():
|
132 |
+
self.conv_k.weight.copy_(self.conv_q.weight)
|
133 |
+
self.conv_k.bias.copy_(self.conv_q.bias)
|
134 |
+
|
135 |
+
def forward(self, x, c, attn_mask=None):
|
136 |
+
q = self.conv_q(x)
|
137 |
+
k = self.conv_k(c)
|
138 |
+
v = self.conv_v(c)
|
139 |
+
|
140 |
+
x, self.attn = self.attention(q, k, v, mask=attn_mask)
|
141 |
+
|
142 |
+
x = self.conv_o(x)
|
143 |
+
return x
|
144 |
+
|
145 |
+
def attention(self, query, key, value, mask=None):
|
146 |
+
# reshape [b, d, t] -> [b, n_h, t, d_k]
|
147 |
+
b, d, t_s, t_t = (*key.size(), query.size(2))
|
148 |
+
query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
|
149 |
+
key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
150 |
+
value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
151 |
+
|
152 |
+
scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
|
153 |
+
if self.window_size is not None:
|
154 |
+
assert t_s == t_t, "Relative attention is only available for self-attention."
|
155 |
+
key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
|
156 |
+
rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
|
157 |
+
scores_local = self._relative_position_to_absolute_position(rel_logits)
|
158 |
+
scores = scores + scores_local
|
159 |
+
if self.proximal_bias:
|
160 |
+
assert t_s == t_t, "Proximal bias is only available for self-attention."
|
161 |
+
scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
|
162 |
+
if mask is not None:
|
163 |
+
scores = scores.masked_fill(mask == 0, -1e4)
|
164 |
+
if self.block_length is not None:
|
165 |
+
assert t_s == t_t, "Local attention is only available for self-attention."
|
166 |
+
block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
|
167 |
+
scores = scores.masked_fill(block_mask == 0, -1e4)
|
168 |
+
p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
|
169 |
+
p_attn = self.drop(p_attn)
|
170 |
+
output = torch.matmul(p_attn, value)
|
171 |
+
if self.window_size is not None:
|
172 |
+
relative_weights = self._absolute_position_to_relative_position(p_attn)
|
173 |
+
value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
|
174 |
+
output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
|
175 |
+
output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
|
176 |
+
return output, p_attn
|
177 |
+
|
178 |
+
def _matmul_with_relative_values(self, x, y):
|
179 |
+
"""
|
180 |
+
x: [b, h, l, m]
|
181 |
+
y: [h or 1, m, d]
|
182 |
+
ret: [b, h, l, d]
|
183 |
+
"""
|
184 |
+
ret = torch.matmul(x, y.unsqueeze(0))
|
185 |
+
return ret
|
186 |
+
|
187 |
+
def _matmul_with_relative_keys(self, x, y):
|
188 |
+
"""
|
189 |
+
x: [b, h, l, d]
|
190 |
+
y: [h or 1, m, d]
|
191 |
+
ret: [b, h, l, m]
|
192 |
+
"""
|
193 |
+
ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
|
194 |
+
return ret
|
195 |
+
|
196 |
+
def _get_relative_embeddings(self, relative_embeddings, length):
|
197 |
+
max_relative_position = 2 * self.window_size + 1
|
198 |
+
# Pad first before slice to avoid using cond ops.
|
199 |
+
pad_length = max(length - (self.window_size + 1), 0)
|
200 |
+
slice_start_position = max((self.window_size + 1) - length, 0)
|
201 |
+
slice_end_position = slice_start_position + 2 * length - 1
|
202 |
+
if pad_length > 0:
|
203 |
+
padded_relative_embeddings = F.pad(
|
204 |
+
relative_embeddings,
|
205 |
+
commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
|
206 |
+
else:
|
207 |
+
padded_relative_embeddings = relative_embeddings
|
208 |
+
used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
|
209 |
+
return used_relative_embeddings
|
210 |
+
|
211 |
+
def _relative_position_to_absolute_position(self, x):
|
212 |
+
"""
|
213 |
+
x: [b, h, l, 2*l-1]
|
214 |
+
ret: [b, h, l, l]
|
215 |
+
"""
|
216 |
+
batch, heads, length, _ = x.size()
|
217 |
+
# Concat columns of pad to shift from relative to absolute indexing.
|
218 |
+
x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
|
219 |
+
|
220 |
+
# Concat extra elements so to add up to shape (len+1, 2*len-1).
|
221 |
+
x_flat = x.view([batch, heads, length * 2 * length])
|
222 |
+
x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
|
223 |
+
|
224 |
+
# Reshape and slice out the padded elements.
|
225 |
+
x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
|
226 |
+
return x_final
|
227 |
+
|
228 |
+
def _absolute_position_to_relative_position(self, x):
|
229 |
+
"""
|
230 |
+
x: [b, h, l, l]
|
231 |
+
ret: [b, h, l, 2*l-1]
|
232 |
+
"""
|
233 |
+
batch, heads, length, _ = x.size()
|
234 |
+
# padd along column
|
235 |
+
x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
|
236 |
+
x_flat = x.view([batch, heads, length**2 + length*(length -1)])
|
237 |
+
# add 0's in the beginning that will skew the elements after reshape
|
238 |
+
x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
|
239 |
+
x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
|
240 |
+
return x_final
|
241 |
+
|
242 |
+
def _attention_bias_proximal(self, length):
|
243 |
+
"""Bias for self-attention to encourage attention to close positions.
|
244 |
+
Args:
|
245 |
+
length: an integer scalar.
|
246 |
+
Returns:
|
247 |
+
a Tensor with shape [1, 1, length, length]
|
248 |
+
"""
|
249 |
+
r = torch.arange(length, dtype=torch.float32)
|
250 |
+
diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
|
251 |
+
return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
|
252 |
+
|
253 |
+
|
254 |
+
class FFN(nn.Module):
|
255 |
+
def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
|
256 |
+
super().__init__()
|
257 |
+
self.in_channels = in_channels
|
258 |
+
self.out_channels = out_channels
|
259 |
+
self.filter_channels = filter_channels
|
260 |
+
self.kernel_size = kernel_size
|
261 |
+
self.p_dropout = p_dropout
|
262 |
+
self.activation = activation
|
263 |
+
self.causal = causal
|
264 |
+
|
265 |
+
if causal:
|
266 |
+
self.padding = self._causal_padding
|
267 |
+
else:
|
268 |
+
self.padding = self._same_padding
|
269 |
+
|
270 |
+
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
|
271 |
+
self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
|
272 |
+
self.drop = nn.Dropout(p_dropout)
|
273 |
+
|
274 |
+
def forward(self, x, x_mask):
|
275 |
+
x = self.conv_1(self.padding(x * x_mask))
|
276 |
+
if self.activation == "gelu":
|
277 |
+
x = x * torch.sigmoid(1.702 * x)
|
278 |
+
else:
|
279 |
+
x = torch.relu(x)
|
280 |
+
x = self.drop(x)
|
281 |
+
x = self.conv_2(self.padding(x * x_mask))
|
282 |
+
return x * x_mask
|
283 |
+
|
284 |
+
def _causal_padding(self, x):
|
285 |
+
if self.kernel_size == 1:
|
286 |
+
return x
|
287 |
+
pad_l = self.kernel_size - 1
|
288 |
+
pad_r = 0
|
289 |
+
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
290 |
+
x = F.pad(x, commons.convert_pad_shape(padding))
|
291 |
+
return x
|
292 |
+
|
293 |
+
def _same_padding(self, x):
|
294 |
+
if self.kernel_size == 1:
|
295 |
+
return x
|
296 |
+
pad_l = (self.kernel_size - 1) // 2
|
297 |
+
pad_r = self.kernel_size // 2
|
298 |
+
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
299 |
+
x = F.pad(x, commons.convert_pad_shape(padding))
|
300 |
+
return x
|
commons.py
ADDED
@@ -0,0 +1,96 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
from torch.nn import functional as F
|
3 |
+
import torch.jit
|
4 |
+
|
5 |
+
|
6 |
+
def script_method(fn, _rcb=None):
|
7 |
+
return fn
|
8 |
+
|
9 |
+
|
10 |
+
def script(obj, optimize=True, _frames_up=0, _rcb=None):
|
11 |
+
return obj
|
12 |
+
|
13 |
+
|
14 |
+
torch.jit.script_method = script_method
|
15 |
+
torch.jit.script = script
|
16 |
+
|
17 |
+
|
18 |
+
def init_weights(m, mean=0.0, std=0.01):
|
19 |
+
classname = m.__class__.__name__
|
20 |
+
if classname.find("Conv") != -1:
|
21 |
+
m.weight.data.normal_(mean, std)
|
22 |
+
|
23 |
+
|
24 |
+
def get_padding(kernel_size, dilation=1):
|
25 |
+
return int((kernel_size*dilation - dilation)/2)
|
26 |
+
|
27 |
+
|
28 |
+
def intersperse(lst, item):
|
29 |
+
result = [item] * (len(lst) * 2 + 1)
|
30 |
+
result[1::2] = lst
|
31 |
+
return result
|
32 |
+
|
33 |
+
|
34 |
+
def slice_segments(x, ids_str, segment_size=4):
|
35 |
+
ret = torch.zeros_like(x[:, :, :segment_size])
|
36 |
+
for i in range(x.size(0)):
|
37 |
+
idx_str = ids_str[i]
|
38 |
+
idx_end = idx_str + segment_size
|
39 |
+
ret[i] = x[i, :, idx_str:idx_end]
|
40 |
+
return ret
|
41 |
+
|
42 |
+
|
43 |
+
def rand_slice_segments(x, x_lengths=None, segment_size=4):
|
44 |
+
b, d, t = x.size()
|
45 |
+
if x_lengths is None:
|
46 |
+
x_lengths = t
|
47 |
+
ids_str_max = x_lengths - segment_size + 1
|
48 |
+
ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
|
49 |
+
ret = slice_segments(x, ids_str, segment_size)
|
50 |
+
return ret, ids_str
|
51 |
+
|
52 |
+
|
53 |
+
def subsequent_mask(length):
|
54 |
+
mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
|
55 |
+
return mask
|
56 |
+
|
57 |
+
|
58 |
+
@torch.jit.script
|
59 |
+
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
|
60 |
+
n_channels_int = n_channels[0]
|
61 |
+
in_act = input_a + input_b
|
62 |
+
t_act = torch.tanh(in_act[:, :n_channels_int, :])
|
63 |
+
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
|
64 |
+
acts = t_act * s_act
|
65 |
+
return acts
|
66 |
+
|
67 |
+
|
68 |
+
def convert_pad_shape(pad_shape):
|
69 |
+
l = pad_shape[::-1]
|
70 |
+
pad_shape = [item for sublist in l for item in sublist]
|
71 |
+
return pad_shape
|
72 |
+
|
73 |
+
|
74 |
+
def sequence_mask(length, max_length=None):
|
75 |
+
if max_length is None:
|
76 |
+
max_length = length.max()
|
77 |
+
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
|
78 |
+
return x.unsqueeze(0) < length.unsqueeze(1)
|
79 |
+
|
80 |
+
|
81 |
+
def generate_path(duration, mask):
|
82 |
+
"""
|
83 |
+
duration: [b, 1, t_x]
|
84 |
+
mask: [b, 1, t_y, t_x]
|
85 |
+
"""
|
86 |
+
device = duration.device
|
87 |
+
|
88 |
+
b, _, t_y, t_x = mask.shape
|
89 |
+
cum_duration = torch.cumsum(duration, -1)
|
90 |
+
|
91 |
+
cum_duration_flat = cum_duration.view(b * t_x)
|
92 |
+
path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
|
93 |
+
path = path.view(b, t_x, t_y)
|
94 |
+
path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
|
95 |
+
path = path.unsqueeze(1).transpose(2,3) * mask
|
96 |
+
return path
|
config.py
ADDED
@@ -0,0 +1,32 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
import sys
|
3 |
+
|
4 |
+
JSON_AS_ASCII = False
|
5 |
+
MAX_CONTENT_LENGTH = 5242880
|
6 |
+
|
7 |
+
# 端口
|
8 |
+
PORT = 7860
|
9 |
+
# 项目的绝对路径
|
10 |
+
ABS_PATH = os.path.join(os.path.dirname(os.path.realpath(sys.argv[0])))
|
11 |
+
# 上传文件的临时路径,非必要不要动
|
12 |
+
UPLOAD_FOLDER = ABS_PATH + "/upload"
|
13 |
+
# silk文件输出的临时路径,非必要不要动
|
14 |
+
SILK_OUT_PATH = ABS_PATH + "/out_silk"
|
15 |
+
|
16 |
+
'''
|
17 |
+
vits模型路径填写方法,MODEL_LIST中的每一行是
|
18 |
+
[ABS_PATH+"/Model/{模型文件夹}/{.pth模型}", ABS_PATH+"/Model/{模型文件夹}/config.json"],
|
19 |
+
也可以写相对路径或绝对路径,由于windows和linux路径写法不同,用上面的写法或绝对路径最稳妥
|
20 |
+
示例:
|
21 |
+
MODEL_LIST = [
|
22 |
+
[ABS_PATH+"/Model/Nene_Nanami_Rong_Tang/1374_epochs.pth", ABS_PATH+"/Model/Nene_Nanami_Rong_Tang/config.json"],
|
23 |
+
[ABS_PATH+"/Model/Zero_no_tsukaima/1158_epochs.pth", ABS_PATH+"/Model/Zero_no_tsukaima/config.json"],
|
24 |
+
[ABS_PATH+"/Model/g/G_953000.pth", ABS_PATH+"/Model/g/config.json"],
|
25 |
+
]
|
26 |
+
'''
|
27 |
+
# 模型加载列表
|
28 |
+
MODEL_LIST = [
|
29 |
+
[ABS_PATH+"/Model/Nene_Nanami_Rong_Tang/1374_epochs.pth", ABS_PATH+"/Model/Nene_Nanami_Rong_Tang/config.json"],
|
30 |
+
[ABS_PATH+"/Model/Zero_no_tsukaima/1158_epochs.pth", ABS_PATH+"/Model/Zero_no_tsukaima/config.json"],
|
31 |
+
[ABS_PATH+"/Model/g/G_953000.pth", ABS_PATH+"/Model/g/config.json"],
|
32 |
+
]
|
docker-compose.yaml
ADDED
@@ -0,0 +1,12 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
version: '3.4'
|
2 |
+
services:
|
3 |
+
moegoe:
|
4 |
+
image: artrajz/moegoe-simple-api:latest
|
5 |
+
restart: always
|
6 |
+
ports:
|
7 |
+
- 23456:23456
|
8 |
+
environment:
|
9 |
+
LANG: 'C.UTF-8'
|
10 |
+
volumes:
|
11 |
+
- ./Model:/app/Model # 挂载模型文件夹
|
12 |
+
- ./config.py:/app/config.py # 挂载配置文件
|
hubert_model.py
ADDED
@@ -0,0 +1,221 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import copy
|
2 |
+
from typing import Optional, Tuple
|
3 |
+
import random
|
4 |
+
|
5 |
+
import torch
|
6 |
+
import torch.nn as nn
|
7 |
+
import torch.nn.functional as F
|
8 |
+
from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
|
9 |
+
|
10 |
+
class Hubert(nn.Module):
|
11 |
+
def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
|
12 |
+
super().__init__()
|
13 |
+
self._mask = mask
|
14 |
+
self.feature_extractor = FeatureExtractor()
|
15 |
+
self.feature_projection = FeatureProjection()
|
16 |
+
self.positional_embedding = PositionalConvEmbedding()
|
17 |
+
self.norm = nn.LayerNorm(768)
|
18 |
+
self.dropout = nn.Dropout(0.1)
|
19 |
+
self.encoder = TransformerEncoder(
|
20 |
+
nn.TransformerEncoderLayer(
|
21 |
+
768, 12, 3072, activation="gelu", batch_first=True
|
22 |
+
),
|
23 |
+
12,
|
24 |
+
)
|
25 |
+
self.proj = nn.Linear(768, 256)
|
26 |
+
|
27 |
+
self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
|
28 |
+
self.label_embedding = nn.Embedding(num_label_embeddings, 256)
|
29 |
+
|
30 |
+
def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
|
31 |
+
mask = None
|
32 |
+
if self.training and self._mask:
|
33 |
+
mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
|
34 |
+
x[mask] = self.masked_spec_embed.to(x.dtype)
|
35 |
+
return x, mask
|
36 |
+
|
37 |
+
def encode(
|
38 |
+
self, x: torch.Tensor, layer: Optional[int] = None
|
39 |
+
) -> Tuple[torch.Tensor, torch.Tensor]:
|
40 |
+
x = self.feature_extractor(x)
|
41 |
+
x = self.feature_projection(x.transpose(1, 2))
|
42 |
+
x, mask = self.mask(x)
|
43 |
+
x = x + self.positional_embedding(x)
|
44 |
+
x = self.dropout(self.norm(x))
|
45 |
+
x = self.encoder(x, output_layer=layer)
|
46 |
+
return x, mask
|
47 |
+
|
48 |
+
def logits(self, x: torch.Tensor) -> torch.Tensor:
|
49 |
+
logits = torch.cosine_similarity(
|
50 |
+
x.unsqueeze(2),
|
51 |
+
self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
|
52 |
+
dim=-1,
|
53 |
+
)
|
54 |
+
return logits / 0.1
|
55 |
+
|
56 |
+
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
|
57 |
+
x, mask = self.encode(x)
|
58 |
+
x = self.proj(x)
|
59 |
+
logits = self.logits(x)
|
60 |
+
return logits, mask
|
61 |
+
|
62 |
+
|
63 |
+
class HubertSoft(Hubert):
|
64 |
+
def __init__(self):
|
65 |
+
super().__init__()
|
66 |
+
|
67 |
+
@torch.inference_mode()
|
68 |
+
def units(self, wav: torch.Tensor) -> torch.Tensor:
|
69 |
+
wav = F.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
|
70 |
+
x, _ = self.encode(wav)
|
71 |
+
return self.proj(x)
|
72 |
+
|
73 |
+
|
74 |
+
class FeatureExtractor(nn.Module):
|
75 |
+
def __init__(self):
|
76 |
+
super().__init__()
|
77 |
+
self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
|
78 |
+
self.norm0 = nn.GroupNorm(512, 512)
|
79 |
+
self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
80 |
+
self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
81 |
+
self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
82 |
+
self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
83 |
+
self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
|
84 |
+
self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
|
85 |
+
|
86 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
87 |
+
x = F.gelu(self.norm0(self.conv0(x)))
|
88 |
+
x = F.gelu(self.conv1(x))
|
89 |
+
x = F.gelu(self.conv2(x))
|
90 |
+
x = F.gelu(self.conv3(x))
|
91 |
+
x = F.gelu(self.conv4(x))
|
92 |
+
x = F.gelu(self.conv5(x))
|
93 |
+
x = F.gelu(self.conv6(x))
|
94 |
+
return x
|
95 |
+
|
96 |
+
|
97 |
+
class FeatureProjection(nn.Module):
|
98 |
+
def __init__(self):
|
99 |
+
super().__init__()
|
100 |
+
self.norm = nn.LayerNorm(512)
|
101 |
+
self.projection = nn.Linear(512, 768)
|
102 |
+
self.dropout = nn.Dropout(0.1)
|
103 |
+
|
104 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
105 |
+
x = self.norm(x)
|
106 |
+
x = self.projection(x)
|
107 |
+
x = self.dropout(x)
|
108 |
+
return x
|
109 |
+
|
110 |
+
|
111 |
+
class PositionalConvEmbedding(nn.Module):
|
112 |
+
def __init__(self):
|
113 |
+
super().__init__()
|
114 |
+
self.conv = nn.Conv1d(
|
115 |
+
768,
|
116 |
+
768,
|
117 |
+
kernel_size=128,
|
118 |
+
padding=128 // 2,
|
119 |
+
groups=16,
|
120 |
+
)
|
121 |
+
self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
|
122 |
+
|
123 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
124 |
+
x = self.conv(x.transpose(1, 2))
|
125 |
+
x = F.gelu(x[:, :, :-1])
|
126 |
+
return x.transpose(1, 2)
|
127 |
+
|
128 |
+
|
129 |
+
class TransformerEncoder(nn.Module):
|
130 |
+
def __init__(
|
131 |
+
self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
|
132 |
+
) -> None:
|
133 |
+
super(TransformerEncoder, self).__init__()
|
134 |
+
self.layers = nn.ModuleList(
|
135 |
+
[copy.deepcopy(encoder_layer) for _ in range(num_layers)]
|
136 |
+
)
|
137 |
+
self.num_layers = num_layers
|
138 |
+
|
139 |
+
def forward(
|
140 |
+
self,
|
141 |
+
src: torch.Tensor,
|
142 |
+
mask: torch.Tensor = None,
|
143 |
+
src_key_padding_mask: torch.Tensor = None,
|
144 |
+
output_layer: Optional[int] = None,
|
145 |
+
) -> torch.Tensor:
|
146 |
+
output = src
|
147 |
+
for layer in self.layers[:output_layer]:
|
148 |
+
output = layer(
|
149 |
+
output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
|
150 |
+
)
|
151 |
+
return output
|
152 |
+
|
153 |
+
|
154 |
+
def _compute_mask(
|
155 |
+
shape: Tuple[int, int],
|
156 |
+
mask_prob: float,
|
157 |
+
mask_length: int,
|
158 |
+
device: torch.device,
|
159 |
+
min_masks: int = 0,
|
160 |
+
) -> torch.Tensor:
|
161 |
+
batch_size, sequence_length = shape
|
162 |
+
|
163 |
+
if mask_length < 1:
|
164 |
+
raise ValueError("`mask_length` has to be bigger than 0.")
|
165 |
+
|
166 |
+
if mask_length > sequence_length:
|
167 |
+
raise ValueError(
|
168 |
+
f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
|
169 |
+
)
|
170 |
+
|
171 |
+
# compute number of masked spans in batch
|
172 |
+
num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
|
173 |
+
num_masked_spans = max(num_masked_spans, min_masks)
|
174 |
+
|
175 |
+
# make sure num masked indices <= sequence_length
|
176 |
+
if num_masked_spans * mask_length > sequence_length:
|
177 |
+
num_masked_spans = sequence_length // mask_length
|
178 |
+
|
179 |
+
# SpecAugment mask to fill
|
180 |
+
mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
|
181 |
+
|
182 |
+
# uniform distribution to sample from, make sure that offset samples are < sequence_length
|
183 |
+
uniform_dist = torch.ones(
|
184 |
+
(batch_size, sequence_length - (mask_length - 1)), device=device
|
185 |
+
)
|
186 |
+
|
187 |
+
# get random indices to mask
|
188 |
+
mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
|
189 |
+
|
190 |
+
# expand masked indices to masked spans
|
191 |
+
mask_indices = (
|
192 |
+
mask_indices.unsqueeze(dim=-1)
|
193 |
+
.expand((batch_size, num_masked_spans, mask_length))
|
194 |
+
.reshape(batch_size, num_masked_spans * mask_length)
|
195 |
+
)
|
196 |
+
offsets = (
|
197 |
+
torch.arange(mask_length, device=device)[None, None, :]
|
198 |
+
.expand((batch_size, num_masked_spans, mask_length))
|
199 |
+
.reshape(batch_size, num_masked_spans * mask_length)
|
200 |
+
)
|
201 |
+
mask_idxs = mask_indices + offsets
|
202 |
+
|
203 |
+
# scatter indices to mask
|
204 |
+
mask = mask.scatter(1, mask_idxs, True)
|
205 |
+
|
206 |
+
return mask
|
207 |
+
|
208 |
+
|
209 |
+
def hubert_soft(
|
210 |
+
path: str
|
211 |
+
) -> HubertSoft:
|
212 |
+
r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
|
213 |
+
Args:
|
214 |
+
path (str): path of a pretrained model
|
215 |
+
"""
|
216 |
+
hubert = HubertSoft()
|
217 |
+
checkpoint = torch.load(path)
|
218 |
+
consume_prefix_in_state_dict_if_present(checkpoint, "module.")
|
219 |
+
hubert.load_state_dict(checkpoint)
|
220 |
+
hubert.eval()
|
221 |
+
return hubert
|
mel_processing.py
ADDED
@@ -0,0 +1,101 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import torch.utils.data
|
3 |
+
from librosa.filters import mel as librosa_mel_fn
|
4 |
+
|
5 |
+
MAX_WAV_VALUE = 32768.0
|
6 |
+
|
7 |
+
|
8 |
+
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
|
9 |
+
"""
|
10 |
+
PARAMS
|
11 |
+
------
|
12 |
+
C: compression factor
|
13 |
+
"""
|
14 |
+
return torch.log(torch.clamp(x, min=clip_val) * C)
|
15 |
+
|
16 |
+
|
17 |
+
def dynamic_range_decompression_torch(x, C=1):
|
18 |
+
"""
|
19 |
+
PARAMS
|
20 |
+
------
|
21 |
+
C: compression factor used to compress
|
22 |
+
"""
|
23 |
+
return torch.exp(x) / C
|
24 |
+
|
25 |
+
|
26 |
+
def spectral_normalize_torch(magnitudes):
|
27 |
+
output = dynamic_range_compression_torch(magnitudes)
|
28 |
+
return output
|
29 |
+
|
30 |
+
|
31 |
+
def spectral_de_normalize_torch(magnitudes):
|
32 |
+
output = dynamic_range_decompression_torch(magnitudes)
|
33 |
+
return output
|
34 |
+
|
35 |
+
|
36 |
+
mel_basis = {}
|
37 |
+
hann_window = {}
|
38 |
+
|
39 |
+
|
40 |
+
def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
|
41 |
+
if torch.min(y) < -1.:
|
42 |
+
print('min value is ', torch.min(y))
|
43 |
+
if torch.max(y) > 1.:
|
44 |
+
print('max value is ', torch.max(y))
|
45 |
+
|
46 |
+
global hann_window
|
47 |
+
dtype_device = str(y.dtype) + '_' + str(y.device)
|
48 |
+
wnsize_dtype_device = str(win_size) + '_' + dtype_device
|
49 |
+
if wnsize_dtype_device not in hann_window:
|
50 |
+
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
|
51 |
+
|
52 |
+
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
|
53 |
+
y = y.squeeze(1)
|
54 |
+
|
55 |
+
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
|
56 |
+
center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
|
57 |
+
|
58 |
+
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
|
59 |
+
return spec
|
60 |
+
|
61 |
+
|
62 |
+
def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
|
63 |
+
global mel_basis
|
64 |
+
dtype_device = str(spec.dtype) + '_' + str(spec.device)
|
65 |
+
fmax_dtype_device = str(fmax) + '_' + dtype_device
|
66 |
+
if fmax_dtype_device not in mel_basis:
|
67 |
+
mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
|
68 |
+
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
|
69 |
+
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
|
70 |
+
spec = spectral_normalize_torch(spec)
|
71 |
+
return spec
|
72 |
+
|
73 |
+
|
74 |
+
def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
|
75 |
+
if torch.min(y) < -1.:
|
76 |
+
print('min value is ', torch.min(y))
|
77 |
+
if torch.max(y) > 1.:
|
78 |
+
print('max value is ', torch.max(y))
|
79 |
+
|
80 |
+
global mel_basis, hann_window
|
81 |
+
dtype_device = str(y.dtype) + '_' + str(y.device)
|
82 |
+
fmax_dtype_device = str(fmax) + '_' + dtype_device
|
83 |
+
wnsize_dtype_device = str(win_size) + '_' + dtype_device
|
84 |
+
if fmax_dtype_device not in mel_basis:
|
85 |
+
mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
|
86 |
+
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
|
87 |
+
if wnsize_dtype_device not in hann_window:
|
88 |
+
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
|
89 |
+
|
90 |
+
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
|
91 |
+
y = y.squeeze(1)
|
92 |
+
|
93 |
+
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
|
94 |
+
center=center, pad_mode='reflect', normalized=False, onesided=True)
|
95 |
+
|
96 |
+
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
|
97 |
+
|
98 |
+
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
|
99 |
+
spec = spectral_normalize_torch(spec)
|
100 |
+
|
101 |
+
return spec
|
models.py
ADDED
@@ -0,0 +1,404 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
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|
|
|
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|
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|
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|
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|
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|
|
|
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|
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|
|
|
|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
1 |
+
import math
|
2 |
+
import torch
|
3 |
+
from torch import nn
|
4 |
+
from torch.nn import functional as F
|
5 |
+
|
6 |
+
import commons
|
7 |
+
import modules
|
8 |
+
import attentions
|
9 |
+
|
10 |
+
from torch.nn import Conv1d, ConvTranspose1d
|
11 |
+
from torch.nn.utils import weight_norm
|
12 |
+
from commons import init_weights
|
13 |
+
|
14 |
+
|
15 |
+
class StochasticDurationPredictor(nn.Module):
|
16 |
+
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
|
17 |
+
super().__init__()
|
18 |
+
filter_channels = in_channels # it needs to be removed from future version.
|
19 |
+
self.in_channels = in_channels
|
20 |
+
self.filter_channels = filter_channels
|
21 |
+
self.kernel_size = kernel_size
|
22 |
+
self.p_dropout = p_dropout
|
23 |
+
self.n_flows = n_flows
|
24 |
+
self.gin_channels = gin_channels
|
25 |
+
|
26 |
+
self.log_flow = modules.Log()
|
27 |
+
self.flows = nn.ModuleList()
|
28 |
+
self.flows.append(modules.ElementwiseAffine(2))
|
29 |
+
for i in range(n_flows):
|
30 |
+
self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
|
31 |
+
self.flows.append(modules.Flip())
|
32 |
+
|
33 |
+
self.post_pre = nn.Conv1d(1, filter_channels, 1)
|
34 |
+
self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
|
35 |
+
self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
|
36 |
+
self.post_flows = nn.ModuleList()
|
37 |
+
self.post_flows.append(modules.ElementwiseAffine(2))
|
38 |
+
for i in range(4):
|
39 |
+
self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
|
40 |
+
self.post_flows.append(modules.Flip())
|
41 |
+
|
42 |
+
self.pre = nn.Conv1d(in_channels, filter_channels, 1)
|
43 |
+
self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
|
44 |
+
self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
|
45 |
+
if gin_channels != 0:
|
46 |
+
self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
|
47 |
+
|
48 |
+
def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
|
49 |
+
x = torch.detach(x)
|
50 |
+
x = self.pre(x)
|
51 |
+
if g is not None:
|
52 |
+
g = torch.detach(g)
|
53 |
+
x = x + self.cond(g)
|
54 |
+
x = self.convs(x, x_mask)
|
55 |
+
x = self.proj(x) * x_mask
|
56 |
+
|
57 |
+
if not reverse:
|
58 |
+
flows = self.flows
|
59 |
+
assert w is not None
|
60 |
+
|
61 |
+
logdet_tot_q = 0
|
62 |
+
h_w = self.post_pre(w)
|
63 |
+
h_w = self.post_convs(h_w, x_mask)
|
64 |
+
h_w = self.post_proj(h_w) * x_mask
|
65 |
+
e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
|
66 |
+
z_q = e_q
|
67 |
+
for flow in self.post_flows:
|
68 |
+
z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
|
69 |
+
logdet_tot_q += logdet_q
|
70 |
+
z_u, z1 = torch.split(z_q, [1, 1], 1)
|
71 |
+
u = torch.sigmoid(z_u) * x_mask
|
72 |
+
z0 = (w - u) * x_mask
|
73 |
+
logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
|
74 |
+
logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
|
75 |
+
|
76 |
+
logdet_tot = 0
|
77 |
+
z0, logdet = self.log_flow(z0, x_mask)
|
78 |
+
logdet_tot += logdet
|
79 |
+
z = torch.cat([z0, z1], 1)
|
80 |
+
for flow in flows:
|
81 |
+
z, logdet = flow(z, x_mask, g=x, reverse=reverse)
|
82 |
+
logdet_tot = logdet_tot + logdet
|
83 |
+
nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
|
84 |
+
return nll + logq # [b]
|
85 |
+
else:
|
86 |
+
flows = list(reversed(self.flows))
|
87 |
+
flows = flows[:-2] + [flows[-1]] # remove a useless vflow
|
88 |
+
z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
|
89 |
+
for flow in flows:
|
90 |
+
z = flow(z, x_mask, g=x, reverse=reverse)
|
91 |
+
z0, z1 = torch.split(z, [1, 1], 1)
|
92 |
+
logw = z0
|
93 |
+
return logw
|
94 |
+
|
95 |
+
|
96 |
+
class DurationPredictor(nn.Module):
|
97 |
+
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
|
98 |
+
super().__init__()
|
99 |
+
|
100 |
+
self.in_channels = in_channels
|
101 |
+
self.filter_channels = filter_channels
|
102 |
+
self.kernel_size = kernel_size
|
103 |
+
self.p_dropout = p_dropout
|
104 |
+
self.gin_channels = gin_channels
|
105 |
+
|
106 |
+
self.drop = nn.Dropout(p_dropout)
|
107 |
+
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
|
108 |
+
self.norm_1 = modules.LayerNorm(filter_channels)
|
109 |
+
self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
|
110 |
+
self.norm_2 = modules.LayerNorm(filter_channels)
|
111 |
+
self.proj = nn.Conv1d(filter_channels, 1, 1)
|
112 |
+
|
113 |
+
if gin_channels != 0:
|
114 |
+
self.cond = nn.Conv1d(gin_channels, in_channels, 1)
|
115 |
+
|
116 |
+
def forward(self, x, x_mask, g=None):
|
117 |
+
x = torch.detach(x)
|
118 |
+
if g is not None:
|
119 |
+
g = torch.detach(g)
|
120 |
+
x = x + self.cond(g)
|
121 |
+
x = self.conv_1(x * x_mask)
|
122 |
+
x = torch.relu(x)
|
123 |
+
x = self.norm_1(x)
|
124 |
+
x = self.drop(x)
|
125 |
+
x = self.conv_2(x * x_mask)
|
126 |
+
x = torch.relu(x)
|
127 |
+
x = self.norm_2(x)
|
128 |
+
x = self.drop(x)
|
129 |
+
x = self.proj(x * x_mask)
|
130 |
+
return x * x_mask
|
131 |
+
|
132 |
+
|
133 |
+
class TextEncoder(nn.Module):
|
134 |
+
def __init__(self,
|
135 |
+
n_vocab,
|
136 |
+
out_channels,
|
137 |
+
hidden_channels,
|
138 |
+
filter_channels,
|
139 |
+
n_heads,
|
140 |
+
n_layers,
|
141 |
+
kernel_size,
|
142 |
+
p_dropout,
|
143 |
+
emotion_embedding):
|
144 |
+
super().__init__()
|
145 |
+
self.n_vocab = n_vocab
|
146 |
+
self.out_channels = out_channels
|
147 |
+
self.hidden_channels = hidden_channels
|
148 |
+
self.filter_channels = filter_channels
|
149 |
+
self.n_heads = n_heads
|
150 |
+
self.n_layers = n_layers
|
151 |
+
self.kernel_size = kernel_size
|
152 |
+
self.p_dropout = p_dropout
|
153 |
+
self.emotion_embedding = emotion_embedding
|
154 |
+
|
155 |
+
if self.n_vocab!=0:
|
156 |
+
self.emb = nn.Embedding(n_vocab, hidden_channels)
|
157 |
+
if emotion_embedding:
|
158 |
+
self.emo_proj = nn.Linear(1024, hidden_channels)
|
159 |
+
nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
|
160 |
+
|
161 |
+
self.encoder = attentions.Encoder(
|
162 |
+
hidden_channels,
|
163 |
+
filter_channels,
|
164 |
+
n_heads,
|
165 |
+
n_layers,
|
166 |
+
kernel_size,
|
167 |
+
p_dropout)
|
168 |
+
self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
169 |
+
|
170 |
+
def forward(self, x, x_lengths, emotion_embedding=None):
|
171 |
+
if self.n_vocab!=0:
|
172 |
+
x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
|
173 |
+
if emotion_embedding is not None:
|
174 |
+
x = x + self.emo_proj(emotion_embedding.unsqueeze(1))
|
175 |
+
x = torch.transpose(x, 1, -1) # [b, h, t]
|
176 |
+
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
177 |
+
|
178 |
+
x = self.encoder(x * x_mask, x_mask)
|
179 |
+
stats = self.proj(x) * x_mask
|
180 |
+
|
181 |
+
m, logs = torch.split(stats, self.out_channels, dim=1)
|
182 |
+
return x, m, logs, x_mask
|
183 |
+
|
184 |
+
|
185 |
+
class ResidualCouplingBlock(nn.Module):
|
186 |
+
def __init__(self,
|
187 |
+
channels,
|
188 |
+
hidden_channels,
|
189 |
+
kernel_size,
|
190 |
+
dilation_rate,
|
191 |
+
n_layers,
|
192 |
+
n_flows=4,
|
193 |
+
gin_channels=0):
|
194 |
+
super().__init__()
|
195 |
+
self.channels = channels
|
196 |
+
self.hidden_channels = hidden_channels
|
197 |
+
self.kernel_size = kernel_size
|
198 |
+
self.dilation_rate = dilation_rate
|
199 |
+
self.n_layers = n_layers
|
200 |
+
self.n_flows = n_flows
|
201 |
+
self.gin_channels = gin_channels
|
202 |
+
|
203 |
+
self.flows = nn.ModuleList()
|
204 |
+
for i in range(n_flows):
|
205 |
+
self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
|
206 |
+
self.flows.append(modules.Flip())
|
207 |
+
|
208 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
209 |
+
if not reverse:
|
210 |
+
for flow in self.flows:
|
211 |
+
x, _ = flow(x, x_mask, g=g, reverse=reverse)
|
212 |
+
else:
|
213 |
+
for flow in reversed(self.flows):
|
214 |
+
x = flow(x, x_mask, g=g, reverse=reverse)
|
215 |
+
return x
|
216 |
+
|
217 |
+
|
218 |
+
class PosteriorEncoder(nn.Module):
|
219 |
+
def __init__(self,
|
220 |
+
in_channels,
|
221 |
+
out_channels,
|
222 |
+
hidden_channels,
|
223 |
+
kernel_size,
|
224 |
+
dilation_rate,
|
225 |
+
n_layers,
|
226 |
+
gin_channels=0):
|
227 |
+
super().__init__()
|
228 |
+
self.in_channels = in_channels
|
229 |
+
self.out_channels = out_channels
|
230 |
+
self.hidden_channels = hidden_channels
|
231 |
+
self.kernel_size = kernel_size
|
232 |
+
self.dilation_rate = dilation_rate
|
233 |
+
self.n_layers = n_layers
|
234 |
+
self.gin_channels = gin_channels
|
235 |
+
|
236 |
+
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
|
237 |
+
self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
|
238 |
+
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
239 |
+
|
240 |
+
def forward(self, x, x_lengths, g=None):
|
241 |
+
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
242 |
+
x = self.pre(x) * x_mask
|
243 |
+
x = self.enc(x, x_mask, g=g)
|
244 |
+
stats = self.proj(x) * x_mask
|
245 |
+
m, logs = torch.split(stats, self.out_channels, dim=1)
|
246 |
+
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
|
247 |
+
return z, m, logs, x_mask
|
248 |
+
|
249 |
+
|
250 |
+
class Generator(torch.nn.Module):
|
251 |
+
def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
|
252 |
+
super(Generator, self).__init__()
|
253 |
+
self.num_kernels = len(resblock_kernel_sizes)
|
254 |
+
self.num_upsamples = len(upsample_rates)
|
255 |
+
self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
|
256 |
+
resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
|
257 |
+
|
258 |
+
self.ups = nn.ModuleList()
|
259 |
+
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
|
260 |
+
self.ups.append(weight_norm(
|
261 |
+
ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
|
262 |
+
k, u, padding=(k-u)//2)))
|
263 |
+
|
264 |
+
self.resblocks = nn.ModuleList()
|
265 |
+
for i in range(len(self.ups)):
|
266 |
+
ch = upsample_initial_channel//(2**(i+1))
|
267 |
+
for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
|
268 |
+
self.resblocks.append(resblock(ch, k, d))
|
269 |
+
|
270 |
+
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
|
271 |
+
self.ups.apply(init_weights)
|
272 |
+
|
273 |
+
if gin_channels != 0:
|
274 |
+
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
|
275 |
+
|
276 |
+
def forward(self, x, g=None):
|
277 |
+
x = self.conv_pre(x)
|
278 |
+
if g is not None:
|
279 |
+
x = x + self.cond(g)
|
280 |
+
|
281 |
+
for i in range(self.num_upsamples):
|
282 |
+
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
283 |
+
x = self.ups[i](x)
|
284 |
+
xs = None
|
285 |
+
for j in range(self.num_kernels):
|
286 |
+
if xs is None:
|
287 |
+
xs = self.resblocks[i*self.num_kernels+j](x)
|
288 |
+
else:
|
289 |
+
xs += self.resblocks[i*self.num_kernels+j](x)
|
290 |
+
x = xs / self.num_kernels
|
291 |
+
x = F.leaky_relu(x)
|
292 |
+
x = self.conv_post(x)
|
293 |
+
x = torch.tanh(x)
|
294 |
+
|
295 |
+
return x
|
296 |
+
|
297 |
+
|
298 |
+
class SynthesizerTrn(nn.Module):
|
299 |
+
"""
|
300 |
+
Synthesizer for Training
|
301 |
+
"""
|
302 |
+
|
303 |
+
def __init__(self,
|
304 |
+
n_vocab,
|
305 |
+
spec_channels,
|
306 |
+
segment_size,
|
307 |
+
inter_channels,
|
308 |
+
hidden_channels,
|
309 |
+
filter_channels,
|
310 |
+
n_heads,
|
311 |
+
n_layers,
|
312 |
+
kernel_size,
|
313 |
+
p_dropout,
|
314 |
+
resblock,
|
315 |
+
resblock_kernel_sizes,
|
316 |
+
resblock_dilation_sizes,
|
317 |
+
upsample_rates,
|
318 |
+
upsample_initial_channel,
|
319 |
+
upsample_kernel_sizes,
|
320 |
+
n_speakers=0,
|
321 |
+
gin_channels=0,
|
322 |
+
use_sdp=True,
|
323 |
+
emotion_embedding=False,
|
324 |
+
**kwargs):
|
325 |
+
|
326 |
+
super().__init__()
|
327 |
+
self.n_vocab = n_vocab
|
328 |
+
self.spec_channels = spec_channels
|
329 |
+
self.inter_channels = inter_channels
|
330 |
+
self.hidden_channels = hidden_channels
|
331 |
+
self.filter_channels = filter_channels
|
332 |
+
self.n_heads = n_heads
|
333 |
+
self.n_layers = n_layers
|
334 |
+
self.kernel_size = kernel_size
|
335 |
+
self.p_dropout = p_dropout
|
336 |
+
self.resblock = resblock
|
337 |
+
self.resblock_kernel_sizes = resblock_kernel_sizes
|
338 |
+
self.resblock_dilation_sizes = resblock_dilation_sizes
|
339 |
+
self.upsample_rates = upsample_rates
|
340 |
+
self.upsample_initial_channel = upsample_initial_channel
|
341 |
+
self.upsample_kernel_sizes = upsample_kernel_sizes
|
342 |
+
self.segment_size = segment_size
|
343 |
+
self.n_speakers = n_speakers
|
344 |
+
self.gin_channels = gin_channels
|
345 |
+
|
346 |
+
self.use_sdp = use_sdp
|
347 |
+
|
348 |
+
self.enc_p = TextEncoder(n_vocab,
|
349 |
+
inter_channels,
|
350 |
+
hidden_channels,
|
351 |
+
filter_channels,
|
352 |
+
n_heads,
|
353 |
+
n_layers,
|
354 |
+
kernel_size,
|
355 |
+
p_dropout,
|
356 |
+
emotion_embedding)
|
357 |
+
self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
|
358 |
+
self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
|
359 |
+
self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
|
360 |
+
|
361 |
+
if use_sdp:
|
362 |
+
self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
|
363 |
+
else:
|
364 |
+
self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
|
365 |
+
|
366 |
+
if n_speakers > 1:
|
367 |
+
self.emb_g = nn.Embedding(n_speakers, gin_channels)
|
368 |
+
|
369 |
+
def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None, emotion_embedding=None):
|
370 |
+
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, emotion_embedding)
|
371 |
+
if self.n_speakers > 0:
|
372 |
+
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
|
373 |
+
else:
|
374 |
+
g = None
|
375 |
+
|
376 |
+
if self.use_sdp:
|
377 |
+
logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
|
378 |
+
else:
|
379 |
+
logw = self.dp(x, x_mask, g=g)
|
380 |
+
w = torch.exp(logw) * x_mask * length_scale
|
381 |
+
w_ceil = torch.ceil(w)
|
382 |
+
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
|
383 |
+
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
|
384 |
+
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
|
385 |
+
attn = commons.generate_path(w_ceil, attn_mask)
|
386 |
+
|
387 |
+
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
|
388 |
+
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
|
389 |
+
|
390 |
+
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
|
391 |
+
z = self.flow(z_p, y_mask, g=g, reverse=True)
|
392 |
+
o = self.dec((z * y_mask)[:,:,:max_len], g=g)
|
393 |
+
return o, attn, y_mask, (z, z_p, m_p, logs_p)
|
394 |
+
|
395 |
+
def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
|
396 |
+
assert self.n_speakers > 0, "n_speakers have to be larger than 0."
|
397 |
+
g_src = self.emb_g(sid_src).unsqueeze(-1)
|
398 |
+
g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
|
399 |
+
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
|
400 |
+
z_p = self.flow(z, y_mask, g=g_src)
|
401 |
+
z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
|
402 |
+
o_hat = self.dec(z_hat * y_mask, g=g_tgt)
|
403 |
+
return o_hat, y_mask, (z, z_p, z_hat)
|
404 |
+
|
modules.py
ADDED
@@ -0,0 +1,387 @@
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import math
|
2 |
+
import torch
|
3 |
+
from torch import nn
|
4 |
+
from torch.nn import functional as F
|
5 |
+
|
6 |
+
from torch.nn import Conv1d
|
7 |
+
from torch.nn.utils import weight_norm, remove_weight_norm
|
8 |
+
|
9 |
+
import commons
|
10 |
+
from commons import init_weights, get_padding
|
11 |
+
from transforms import piecewise_rational_quadratic_transform
|
12 |
+
|
13 |
+
|
14 |
+
LRELU_SLOPE = 0.1
|
15 |
+
|
16 |
+
|
17 |
+
class LayerNorm(nn.Module):
|
18 |
+
def __init__(self, channels, eps=1e-5):
|
19 |
+
super().__init__()
|
20 |
+
self.channels = channels
|
21 |
+
self.eps = eps
|
22 |
+
|
23 |
+
self.gamma = nn.Parameter(torch.ones(channels))
|
24 |
+
self.beta = nn.Parameter(torch.zeros(channels))
|
25 |
+
|
26 |
+
def forward(self, x):
|
27 |
+
x = x.transpose(1, -1)
|
28 |
+
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
|
29 |
+
return x.transpose(1, -1)
|
30 |
+
|
31 |
+
|
32 |
+
class ConvReluNorm(nn.Module):
|
33 |
+
def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
|
34 |
+
super().__init__()
|
35 |
+
self.in_channels = in_channels
|
36 |
+
self.hidden_channels = hidden_channels
|
37 |
+
self.out_channels = out_channels
|
38 |
+
self.kernel_size = kernel_size
|
39 |
+
self.n_layers = n_layers
|
40 |
+
self.p_dropout = p_dropout
|
41 |
+
assert n_layers > 1, "Number of layers should be larger than 0."
|
42 |
+
|
43 |
+
self.conv_layers = nn.ModuleList()
|
44 |
+
self.norm_layers = nn.ModuleList()
|
45 |
+
self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
|
46 |
+
self.norm_layers.append(LayerNorm(hidden_channels))
|
47 |
+
self.relu_drop = nn.Sequential(
|
48 |
+
nn.ReLU(),
|
49 |
+
nn.Dropout(p_dropout))
|
50 |
+
for _ in range(n_layers-1):
|
51 |
+
self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
|
52 |
+
self.norm_layers.append(LayerNorm(hidden_channels))
|
53 |
+
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
|
54 |
+
self.proj.weight.data.zero_()
|
55 |
+
self.proj.bias.data.zero_()
|
56 |
+
|
57 |
+
def forward(self, x, x_mask):
|
58 |
+
x_org = x
|
59 |
+
for i in range(self.n_layers):
|
60 |
+
x = self.conv_layers[i](x * x_mask)
|
61 |
+
x = self.norm_layers[i](x)
|
62 |
+
x = self.relu_drop(x)
|
63 |
+
x = x_org + self.proj(x)
|
64 |
+
return x * x_mask
|
65 |
+
|
66 |
+
|
67 |
+
class DDSConv(nn.Module):
|
68 |
+
"""
|
69 |
+
Dilated and Depth-Separable Convolution
|
70 |
+
"""
|
71 |
+
def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
|
72 |
+
super().__init__()
|
73 |
+
self.channels = channels
|
74 |
+
self.kernel_size = kernel_size
|
75 |
+
self.n_layers = n_layers
|
76 |
+
self.p_dropout = p_dropout
|
77 |
+
|
78 |
+
self.drop = nn.Dropout(p_dropout)
|
79 |
+
self.convs_sep = nn.ModuleList()
|
80 |
+
self.convs_1x1 = nn.ModuleList()
|
81 |
+
self.norms_1 = nn.ModuleList()
|
82 |
+
self.norms_2 = nn.ModuleList()
|
83 |
+
for i in range(n_layers):
|
84 |
+
dilation = kernel_size ** i
|
85 |
+
padding = (kernel_size * dilation - dilation) // 2
|
86 |
+
self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
|
87 |
+
groups=channels, dilation=dilation, padding=padding
|
88 |
+
))
|
89 |
+
self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
|
90 |
+
self.norms_1.append(LayerNorm(channels))
|
91 |
+
self.norms_2.append(LayerNorm(channels))
|
92 |
+
|
93 |
+
def forward(self, x, x_mask, g=None):
|
94 |
+
if g is not None:
|
95 |
+
x = x + g
|
96 |
+
for i in range(self.n_layers):
|
97 |
+
y = self.convs_sep[i](x * x_mask)
|
98 |
+
y = self.norms_1[i](y)
|
99 |
+
y = F.gelu(y)
|
100 |
+
y = self.convs_1x1[i](y)
|
101 |
+
y = self.norms_2[i](y)
|
102 |
+
y = F.gelu(y)
|
103 |
+
y = self.drop(y)
|
104 |
+
x = x + y
|
105 |
+
return x * x_mask
|
106 |
+
|
107 |
+
|
108 |
+
class WN(torch.nn.Module):
|
109 |
+
def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
|
110 |
+
super(WN, self).__init__()
|
111 |
+
assert(kernel_size % 2 == 1)
|
112 |
+
self.hidden_channels =hidden_channels
|
113 |
+
self.kernel_size = kernel_size,
|
114 |
+
self.dilation_rate = dilation_rate
|
115 |
+
self.n_layers = n_layers
|
116 |
+
self.gin_channels = gin_channels
|
117 |
+
self.p_dropout = p_dropout
|
118 |
+
|
119 |
+
self.in_layers = torch.nn.ModuleList()
|
120 |
+
self.res_skip_layers = torch.nn.ModuleList()
|
121 |
+
self.drop = nn.Dropout(p_dropout)
|
122 |
+
|
123 |
+
if gin_channels != 0:
|
124 |
+
cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
|
125 |
+
self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
|
126 |
+
|
127 |
+
for i in range(n_layers):
|
128 |
+
dilation = dilation_rate ** i
|
129 |
+
padding = int((kernel_size * dilation - dilation) / 2)
|
130 |
+
in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
|
131 |
+
dilation=dilation, padding=padding)
|
132 |
+
in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
|
133 |
+
self.in_layers.append(in_layer)
|
134 |
+
|
135 |
+
# last one is not necessary
|
136 |
+
if i < n_layers - 1:
|
137 |
+
res_skip_channels = 2 * hidden_channels
|
138 |
+
else:
|
139 |
+
res_skip_channels = hidden_channels
|
140 |
+
|
141 |
+
res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
|
142 |
+
res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
|
143 |
+
self.res_skip_layers.append(res_skip_layer)
|
144 |
+
|
145 |
+
def forward(self, x, x_mask, g=None, **kwargs):
|
146 |
+
output = torch.zeros_like(x)
|
147 |
+
n_channels_tensor = torch.IntTensor([self.hidden_channels])
|
148 |
+
|
149 |
+
if g is not None:
|
150 |
+
g = self.cond_layer(g)
|
151 |
+
|
152 |
+
for i in range(self.n_layers):
|
153 |
+
x_in = self.in_layers[i](x)
|
154 |
+
if g is not None:
|
155 |
+
cond_offset = i * 2 * self.hidden_channels
|
156 |
+
g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
|
157 |
+
else:
|
158 |
+
g_l = torch.zeros_like(x_in)
|
159 |
+
|
160 |
+
acts = commons.fused_add_tanh_sigmoid_multiply(
|
161 |
+
x_in,
|
162 |
+
g_l,
|
163 |
+
n_channels_tensor)
|
164 |
+
acts = self.drop(acts)
|
165 |
+
|
166 |
+
res_skip_acts = self.res_skip_layers[i](acts)
|
167 |
+
if i < self.n_layers - 1:
|
168 |
+
res_acts = res_skip_acts[:,:self.hidden_channels,:]
|
169 |
+
x = (x + res_acts) * x_mask
|
170 |
+
output = output + res_skip_acts[:,self.hidden_channels:,:]
|
171 |
+
else:
|
172 |
+
output = output + res_skip_acts
|
173 |
+
return output * x_mask
|
174 |
+
|
175 |
+
def remove_weight_norm(self):
|
176 |
+
if self.gin_channels != 0:
|
177 |
+
torch.nn.utils.remove_weight_norm(self.cond_layer)
|
178 |
+
for l in self.in_layers:
|
179 |
+
torch.nn.utils.remove_weight_norm(l)
|
180 |
+
for l in self.res_skip_layers:
|
181 |
+
torch.nn.utils.remove_weight_norm(l)
|
182 |
+
|
183 |
+
|
184 |
+
class ResBlock1(torch.nn.Module):
|
185 |
+
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
|
186 |
+
super(ResBlock1, self).__init__()
|
187 |
+
self.convs1 = nn.ModuleList([
|
188 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
189 |
+
padding=get_padding(kernel_size, dilation[0]))),
|
190 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
191 |
+
padding=get_padding(kernel_size, dilation[1]))),
|
192 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
|
193 |
+
padding=get_padding(kernel_size, dilation[2])))
|
194 |
+
])
|
195 |
+
self.convs1.apply(init_weights)
|
196 |
+
|
197 |
+
self.convs2 = nn.ModuleList([
|
198 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
199 |
+
padding=get_padding(kernel_size, 1))),
|
200 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
201 |
+
padding=get_padding(kernel_size, 1))),
|
202 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
203 |
+
padding=get_padding(kernel_size, 1)))
|
204 |
+
])
|
205 |
+
self.convs2.apply(init_weights)
|
206 |
+
|
207 |
+
def forward(self, x, x_mask=None):
|
208 |
+
for c1, c2 in zip(self.convs1, self.convs2):
|
209 |
+
xt = F.leaky_relu(x, LRELU_SLOPE)
|
210 |
+
if x_mask is not None:
|
211 |
+
xt = xt * x_mask
|
212 |
+
xt = c1(xt)
|
213 |
+
xt = F.leaky_relu(xt, LRELU_SLOPE)
|
214 |
+
if x_mask is not None:
|
215 |
+
xt = xt * x_mask
|
216 |
+
xt = c2(xt)
|
217 |
+
x = xt + x
|
218 |
+
if x_mask is not None:
|
219 |
+
x = x * x_mask
|
220 |
+
return x
|
221 |
+
|
222 |
+
def remove_weight_norm(self):
|
223 |
+
for l in self.convs1:
|
224 |
+
remove_weight_norm(l)
|
225 |
+
for l in self.convs2:
|
226 |
+
remove_weight_norm(l)
|
227 |
+
|
228 |
+
|
229 |
+
class ResBlock2(torch.nn.Module):
|
230 |
+
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
|
231 |
+
super(ResBlock2, self).__init__()
|
232 |
+
self.convs = nn.ModuleList([
|
233 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
234 |
+
padding=get_padding(kernel_size, dilation[0]))),
|
235 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
236 |
+
padding=get_padding(kernel_size, dilation[1])))
|
237 |
+
])
|
238 |
+
self.convs.apply(init_weights)
|
239 |
+
|
240 |
+
def forward(self, x, x_mask=None):
|
241 |
+
for c in self.convs:
|
242 |
+
xt = F.leaky_relu(x, LRELU_SLOPE)
|
243 |
+
if x_mask is not None:
|
244 |
+
xt = xt * x_mask
|
245 |
+
xt = c(xt)
|
246 |
+
x = xt + x
|
247 |
+
if x_mask is not None:
|
248 |
+
x = x * x_mask
|
249 |
+
return x
|
250 |
+
|
251 |
+
def remove_weight_norm(self):
|
252 |
+
for l in self.convs:
|
253 |
+
remove_weight_norm(l)
|
254 |
+
|
255 |
+
|
256 |
+
class Log(nn.Module):
|
257 |
+
def forward(self, x, x_mask, reverse=False, **kwargs):
|
258 |
+
if not reverse:
|
259 |
+
y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
|
260 |
+
logdet = torch.sum(-y, [1, 2])
|
261 |
+
return y, logdet
|
262 |
+
else:
|
263 |
+
x = torch.exp(x) * x_mask
|
264 |
+
return x
|
265 |
+
|
266 |
+
|
267 |
+
class Flip(nn.Module):
|
268 |
+
def forward(self, x, *args, reverse=False, **kwargs):
|
269 |
+
x = torch.flip(x, [1])
|
270 |
+
if not reverse:
|
271 |
+
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
|
272 |
+
return x, logdet
|
273 |
+
else:
|
274 |
+
return x
|
275 |
+
|
276 |
+
|
277 |
+
class ElementwiseAffine(nn.Module):
|
278 |
+
def __init__(self, channels):
|
279 |
+
super().__init__()
|
280 |
+
self.channels = channels
|
281 |
+
self.m = nn.Parameter(torch.zeros(channels,1))
|
282 |
+
self.logs = nn.Parameter(torch.zeros(channels,1))
|
283 |
+
|
284 |
+
def forward(self, x, x_mask, reverse=False, **kwargs):
|
285 |
+
if not reverse:
|
286 |
+
y = self.m + torch.exp(self.logs) * x
|
287 |
+
y = y * x_mask
|
288 |
+
logdet = torch.sum(self.logs * x_mask, [1,2])
|
289 |
+
return y, logdet
|
290 |
+
else:
|
291 |
+
x = (x - self.m) * torch.exp(-self.logs) * x_mask
|
292 |
+
return x
|
293 |
+
|
294 |
+
|
295 |
+
class ResidualCouplingLayer(nn.Module):
|
296 |
+
def __init__(self,
|
297 |
+
channels,
|
298 |
+
hidden_channels,
|
299 |
+
kernel_size,
|
300 |
+
dilation_rate,
|
301 |
+
n_layers,
|
302 |
+
p_dropout=0,
|
303 |
+
gin_channels=0,
|
304 |
+
mean_only=False):
|
305 |
+
assert channels % 2 == 0, "channels should be divisible by 2"
|
306 |
+
super().__init__()
|
307 |
+
self.channels = channels
|
308 |
+
self.hidden_channels = hidden_channels
|
309 |
+
self.kernel_size = kernel_size
|
310 |
+
self.dilation_rate = dilation_rate
|
311 |
+
self.n_layers = n_layers
|
312 |
+
self.half_channels = channels // 2
|
313 |
+
self.mean_only = mean_only
|
314 |
+
|
315 |
+
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
|
316 |
+
self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
|
317 |
+
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
|
318 |
+
self.post.weight.data.zero_()
|
319 |
+
self.post.bias.data.zero_()
|
320 |
+
|
321 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
322 |
+
x0, x1 = torch.split(x, [self.half_channels]*2, 1)
|
323 |
+
h = self.pre(x0) * x_mask
|
324 |
+
h = self.enc(h, x_mask, g=g)
|
325 |
+
stats = self.post(h) * x_mask
|
326 |
+
if not self.mean_only:
|
327 |
+
m, logs = torch.split(stats, [self.half_channels]*2, 1)
|
328 |
+
else:
|
329 |
+
m = stats
|
330 |
+
logs = torch.zeros_like(m)
|
331 |
+
|
332 |
+
if not reverse:
|
333 |
+
x1 = m + x1 * torch.exp(logs) * x_mask
|
334 |
+
x = torch.cat([x0, x1], 1)
|
335 |
+
logdet = torch.sum(logs, [1,2])
|
336 |
+
return x, logdet
|
337 |
+
else:
|
338 |
+
x1 = (x1 - m) * torch.exp(-logs) * x_mask
|
339 |
+
x = torch.cat([x0, x1], 1)
|
340 |
+
return x
|
341 |
+
|
342 |
+
|
343 |
+
class ConvFlow(nn.Module):
|
344 |
+
def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
|
345 |
+
super().__init__()
|
346 |
+
self.in_channels = in_channels
|
347 |
+
self.filter_channels = filter_channels
|
348 |
+
self.kernel_size = kernel_size
|
349 |
+
self.n_layers = n_layers
|
350 |
+
self.num_bins = num_bins
|
351 |
+
self.tail_bound = tail_bound
|
352 |
+
self.half_channels = in_channels // 2
|
353 |
+
|
354 |
+
self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
|
355 |
+
self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
|
356 |
+
self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
|
357 |
+
self.proj.weight.data.zero_()
|
358 |
+
self.proj.bias.data.zero_()
|
359 |
+
|
360 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
361 |
+
x0, x1 = torch.split(x, [self.half_channels]*2, 1)
|
362 |
+
h = self.pre(x0)
|
363 |
+
h = self.convs(h, x_mask, g=g)
|
364 |
+
h = self.proj(h) * x_mask
|
365 |
+
|
366 |
+
b, c, t = x0.shape
|
367 |
+
h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
|
368 |
+
|
369 |
+
unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
|
370 |
+
unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
|
371 |
+
unnormalized_derivatives = h[..., 2 * self.num_bins:]
|
372 |
+
|
373 |
+
x1, logabsdet = piecewise_rational_quadratic_transform(x1,
|
374 |
+
unnormalized_widths,
|
375 |
+
unnormalized_heights,
|
376 |
+
unnormalized_derivatives,
|
377 |
+
inverse=reverse,
|
378 |
+
tails='linear',
|
379 |
+
tail_bound=self.tail_bound
|
380 |
+
)
|
381 |
+
|
382 |
+
x = torch.cat([x0, x1], 1) * x_mask
|
383 |
+
logdet = torch.sum(logabsdet * x_mask, [1,2])
|
384 |
+
if not reverse:
|
385 |
+
return x, logdet
|
386 |
+
else:
|
387 |
+
return x
|
requirements.txt
ADDED
@@ -0,0 +1,23 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
numba
|
2 |
+
librosa
|
3 |
+
numpy==1.23.3
|
4 |
+
scipy
|
5 |
+
torch
|
6 |
+
unidecode
|
7 |
+
openjtalk>=0.3.0.dev2
|
8 |
+
jamo
|
9 |
+
pypinyin
|
10 |
+
jieba
|
11 |
+
protobuf
|
12 |
+
cn2an
|
13 |
+
inflect
|
14 |
+
eng_to_ipa
|
15 |
+
ko_pron
|
16 |
+
indic_transliteration
|
17 |
+
num_thai
|
18 |
+
opencc
|
19 |
+
audonnx
|
20 |
+
flask
|
21 |
+
av
|
22 |
+
pilk
|
23 |
+
flask_apscheduler
|
text/LICENSE
ADDED
@@ -0,0 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
Copyright (c) 2017 Keith Ito
|
2 |
+
|
3 |
+
Permission is hereby granted, free of charge, to any person obtaining a copy
|
4 |
+
of this software and associated documentation files (the "Software"), to deal
|
5 |
+
in the Software without restriction, including without limitation the rights
|
6 |
+
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
7 |
+
copies of the Software, and to permit persons to whom the Software is
|
8 |
+
furnished to do so, subject to the following conditions:
|
9 |
+
|
10 |
+
The above copyright notice and this permission notice shall be included in
|
11 |
+
all copies or substantial portions of the Software.
|
12 |
+
|
13 |
+
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
14 |
+
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
15 |
+
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
16 |
+
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
17 |
+
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
18 |
+
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
|
19 |
+
THE SOFTWARE.
|
text/__init__.py
ADDED
@@ -0,0 +1,32 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
""" from https://github.com/keithito/tacotron """
|
2 |
+
from text import cleaners
|
3 |
+
|
4 |
+
|
5 |
+
def text_to_sequence(text, symbols, cleaner_names):
|
6 |
+
'''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
|
7 |
+
Args:
|
8 |
+
text: string to convert to a sequence
|
9 |
+
cleaner_names: names of the cleaner functions to run the text through
|
10 |
+
Returns:
|
11 |
+
List of integers corresponding to the symbols in the text
|
12 |
+
'''
|
13 |
+
_symbol_to_id = {s: i for i, s in enumerate(symbols)}
|
14 |
+
|
15 |
+
sequence = []
|
16 |
+
|
17 |
+
clean_text = _clean_text(text, cleaner_names)
|
18 |
+
for symbol in clean_text:
|
19 |
+
if symbol not in _symbol_to_id.keys():
|
20 |
+
continue
|
21 |
+
symbol_id = _symbol_to_id[symbol]
|
22 |
+
sequence += [symbol_id]
|
23 |
+
return sequence
|
24 |
+
|
25 |
+
|
26 |
+
def _clean_text(text, cleaner_names):
|
27 |
+
for name in cleaner_names:
|
28 |
+
cleaner = getattr(cleaners, name)
|
29 |
+
if not cleaner:
|
30 |
+
raise Exception('Unknown cleaner: %s' % name)
|
31 |
+
text = cleaner(text)
|
32 |
+
return text
|
text/__pycache__/__init__.cpython-310.pyc
ADDED
Binary file (1.21 kB). View file
|
|
text/__pycache__/cleaners.cpython-310.pyc
ADDED
Binary file (6.85 kB). View file
|
|
text/__pycache__/japanese.cpython-310.pyc
ADDED
Binary file (4.13 kB). View file
|
|
text/__pycache__/mandarin.cpython-310.pyc
ADDED
Binary file (6.17 kB). View file
|
|
text/cantonese.py
ADDED
@@ -0,0 +1,59 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import re
|
2 |
+
import cn2an
|
3 |
+
import opencc
|
4 |
+
|
5 |
+
|
6 |
+
converter = opencc.OpenCC('jyutjyu')
|
7 |
+
|
8 |
+
# List of (Latin alphabet, ipa) pairs:
|
9 |
+
_latin_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
|
10 |
+
('A', 'ei˥'),
|
11 |
+
('B', 'biː˥'),
|
12 |
+
('C', 'siː˥'),
|
13 |
+
('D', 'tiː˥'),
|
14 |
+
('E', 'iː˥'),
|
15 |
+
('F', 'e˥fuː˨˩'),
|
16 |
+
('G', 'tsiː˥'),
|
17 |
+
('H', 'ɪk̚˥tsʰyː˨˩'),
|
18 |
+
('I', 'ɐi˥'),
|
19 |
+
('J', 'tsei˥'),
|
20 |
+
('K', 'kʰei˥'),
|
21 |
+
('L', 'e˥llou˨˩'),
|
22 |
+
('M', 'ɛːm˥'),
|
23 |
+
('N', 'ɛːn˥'),
|
24 |
+
('O', 'ou˥'),
|
25 |
+
('P', 'pʰiː˥'),
|
26 |
+
('Q', 'kʰiːu˥'),
|
27 |
+
('R', 'aː˥lou˨˩'),
|
28 |
+
('S', 'ɛː˥siː˨˩'),
|
29 |
+
('T', 'tʰiː˥'),
|
30 |
+
('U', 'juː˥'),
|
31 |
+
('V', 'wiː˥'),
|
32 |
+
('W', 'tʊk̚˥piː˥juː˥'),
|
33 |
+
('X', 'ɪk̚˥siː˨˩'),
|
34 |
+
('Y', 'waːi˥'),
|
35 |
+
('Z', 'iː˨sɛːt̚˥')
|
36 |
+
]]
|
37 |
+
|
38 |
+
|
39 |
+
def number_to_cantonese(text):
|
40 |
+
return re.sub(r'\d+(?:\.?\d+)?', lambda x: cn2an.an2cn(x.group()), text)
|
41 |
+
|
42 |
+
|
43 |
+
def latin_to_ipa(text):
|
44 |
+
for regex, replacement in _latin_to_ipa:
|
45 |
+
text = re.sub(regex, replacement, text)
|
46 |
+
return text
|
47 |
+
|
48 |
+
|
49 |
+
def cantonese_to_ipa(text):
|
50 |
+
text = number_to_cantonese(text.upper())
|
51 |
+
text = converter.convert(text).replace('-','').replace('$',' ')
|
52 |
+
text = re.sub(r'[A-Z]', lambda x: latin_to_ipa(x.group())+' ', text)
|
53 |
+
text = re.sub(r'[、;:]', ',', text)
|
54 |
+
text = re.sub(r'\s*,\s*', ', ', text)
|
55 |
+
text = re.sub(r'\s*。\s*', '. ', text)
|
56 |
+
text = re.sub(r'\s*?\s*', '? ', text)
|
57 |
+
text = re.sub(r'\s*!\s*', '! ', text)
|
58 |
+
text = re.sub(r'\s*$', '', text)
|
59 |
+
return text
|
text/cleaners.py
ADDED
@@ -0,0 +1,145 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import re
|
2 |
+
|
3 |
+
|
4 |
+
def japanese_cleaners(text):
|
5 |
+
from text.japanese import japanese_to_romaji_with_accent
|
6 |
+
text = japanese_to_romaji_with_accent(text)
|
7 |
+
text = re.sub(r'([A-Za-z])$', r'\1.', text)
|
8 |
+
return text
|
9 |
+
|
10 |
+
|
11 |
+
def japanese_cleaners2(text):
|
12 |
+
return japanese_cleaners(text).replace('ts', 'ʦ').replace('...', '…')
|
13 |
+
|
14 |
+
|
15 |
+
def korean_cleaners(text):
|
16 |
+
'''Pipeline for Korean text'''
|
17 |
+
from text.korean import latin_to_hangul, number_to_hangul, divide_hangul
|
18 |
+
text = latin_to_hangul(text)
|
19 |
+
text = number_to_hangul(text)
|
20 |
+
text = divide_hangul(text)
|
21 |
+
text = re.sub(r'([\u3131-\u3163])$', r'\1.', text)
|
22 |
+
return text
|
23 |
+
|
24 |
+
|
25 |
+
def chinese_cleaners(text):
|
26 |
+
'''Pipeline for Chinese text'''
|
27 |
+
from text.mandarin import number_to_chinese, chinese_to_bopomofo, latin_to_bopomofo
|
28 |
+
text = number_to_chinese(text)
|
29 |
+
text = chinese_to_bopomofo(text)
|
30 |
+
text = latin_to_bopomofo(text)
|
31 |
+
text = re.sub(r'([ˉˊˇˋ˙])$', r'\1。', text)
|
32 |
+
return text
|
33 |
+
|
34 |
+
|
35 |
+
def zh_ja_mixture_cleaners(text):
|
36 |
+
from text.mandarin import chinese_to_romaji
|
37 |
+
from text.japanese import japanese_to_romaji_with_accent
|
38 |
+
text = re.sub(r'\[ZH\](.*?)\[ZH\]',
|
39 |
+
lambda x: chinese_to_romaji(x.group(1))+' ', text)
|
40 |
+
text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: japanese_to_romaji_with_accent(
|
41 |
+
x.group(1)).replace('ts', 'ʦ').replace('u', 'ɯ').replace('...', '…')+' ', text)
|
42 |
+
text = re.sub(r'\s+$', '', text)
|
43 |
+
text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
|
44 |
+
return text
|
45 |
+
|
46 |
+
|
47 |
+
def sanskrit_cleaners(text):
|
48 |
+
text = text.replace('॥', '।').replace('ॐ', 'ओम्')
|
49 |
+
text = re.sub(r'([^।])$', r'\1।', text)
|
50 |
+
return text
|
51 |
+
|
52 |
+
|
53 |
+
def cjks_cleaners(text):
|
54 |
+
from text.mandarin import chinese_to_lazy_ipa
|
55 |
+
from text.japanese import japanese_to_ipa
|
56 |
+
from text.korean import korean_to_lazy_ipa
|
57 |
+
from text.sanskrit import devanagari_to_ipa
|
58 |
+
from text.english import english_to_lazy_ipa
|
59 |
+
text = re.sub(r'\[ZH\](.*?)\[ZH\]',
|
60 |
+
lambda x: chinese_to_lazy_ipa(x.group(1))+' ', text)
|
61 |
+
text = re.sub(r'\[JA\](.*?)\[JA\]',
|
62 |
+
lambda x: japanese_to_ipa(x.group(1))+' ', text)
|
63 |
+
text = re.sub(r'\[KO\](.*?)\[KO\]',
|
64 |
+
lambda x: korean_to_lazy_ipa(x.group(1))+' ', text)
|
65 |
+
text = re.sub(r'\[SA\](.*?)\[SA\]',
|
66 |
+
lambda x: devanagari_to_ipa(x.group(1))+' ', text)
|
67 |
+
text = re.sub(r'\[EN\](.*?)\[EN\]',
|
68 |
+
lambda x: english_to_lazy_ipa(x.group(1))+' ', text)
|
69 |
+
text = re.sub(r'\s+$', '', text)
|
70 |
+
text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
|
71 |
+
return text
|
72 |
+
|
73 |
+
|
74 |
+
def cjke_cleaners(text):
|
75 |
+
from text.mandarin import chinese_to_lazy_ipa
|
76 |
+
from text.japanese import japanese_to_ipa
|
77 |
+
from text.korean import korean_to_ipa
|
78 |
+
from text.english import english_to_ipa2
|
79 |
+
text = re.sub(r'\[ZH\](.*?)\[ZH\]', lambda x: chinese_to_lazy_ipa(x.group(1)).replace(
|
80 |
+
'ʧ', 'tʃ').replace('ʦ', 'ts').replace('ɥan', 'ɥæn')+' ', text)
|
81 |
+
text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: japanese_to_ipa(x.group(1)).replace('ʧ', 'tʃ').replace(
|
82 |
+
'ʦ', 'ts').replace('ɥan', 'ɥæn').replace('ʥ', 'dz')+' ', text)
|
83 |
+
text = re.sub(r'\[KO\](.*?)\[KO\]',
|
84 |
+
lambda x: korean_to_ipa(x.group(1))+' ', text)
|
85 |
+
text = re.sub(r'\[EN\](.*?)\[EN\]', lambda x: english_to_ipa2(x.group(1)).replace('ɑ', 'a').replace(
|
86 |
+
'ɔ', 'o').replace('ɛ', 'e').replace('ɪ', 'i').replace('ʊ', 'u')+' ', text)
|
87 |
+
text = re.sub(r'\s+$', '', text)
|
88 |
+
text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
|
89 |
+
return text
|
90 |
+
|
91 |
+
|
92 |
+
def cjke_cleaners2(text):
|
93 |
+
from text.mandarin import chinese_to_ipa
|
94 |
+
from text.japanese import japanese_to_ipa2
|
95 |
+
from text.korean import korean_to_ipa
|
96 |
+
from text.english import english_to_ipa2
|
97 |
+
text = re.sub(r'\[ZH\](.*?)\[ZH\]',
|
98 |
+
lambda x: chinese_to_ipa(x.group(1))+' ', text)
|
99 |
+
text = re.sub(r'\[JA\](.*?)\[JA\]',
|
100 |
+
lambda x: japanese_to_ipa2(x.group(1))+' ', text)
|
101 |
+
text = re.sub(r'\[KO\](.*?)\[KO\]',
|
102 |
+
lambda x: korean_to_ipa(x.group(1))+' ', text)
|
103 |
+
text = re.sub(r'\[EN\](.*?)\[EN\]',
|
104 |
+
lambda x: english_to_ipa2(x.group(1))+' ', text)
|
105 |
+
text = re.sub(r'\s+$', '', text)
|
106 |
+
text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
|
107 |
+
return text
|
108 |
+
|
109 |
+
|
110 |
+
def thai_cleaners(text):
|
111 |
+
from text.thai import num_to_thai, latin_to_thai
|
112 |
+
text = num_to_thai(text)
|
113 |
+
text = latin_to_thai(text)
|
114 |
+
return text
|
115 |
+
|
116 |
+
|
117 |
+
def shanghainese_cleaners(text):
|
118 |
+
from text.shanghainese import shanghainese_to_ipa
|
119 |
+
text = shanghainese_to_ipa(text)
|
120 |
+
text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
|
121 |
+
return text
|
122 |
+
|
123 |
+
|
124 |
+
def chinese_dialect_cleaners(text):
|
125 |
+
from text.mandarin import chinese_to_ipa2
|
126 |
+
from text.japanese import japanese_to_ipa3
|
127 |
+
from text.shanghainese import shanghainese_to_ipa
|
128 |
+
from text.cantonese import cantonese_to_ipa
|
129 |
+
from text.english import english_to_lazy_ipa2
|
130 |
+
from text.ngu_dialect import ngu_dialect_to_ipa
|
131 |
+
text = re.sub(r'\[ZH\](.*?)\[ZH\]',
|
132 |
+
lambda x: chinese_to_ipa2(x.group(1))+' ', text)
|
133 |
+
text = re.sub(r'\[JA\](.*?)\[JA\]',
|
134 |
+
lambda x: japanese_to_ipa3(x.group(1)).replace('Q', 'ʔ')+' ', text)
|
135 |
+
text = re.sub(r'\[SH\](.*?)\[SH\]', lambda x: shanghainese_to_ipa(x.group(1)).replace('1', '˥˧').replace('5',
|
136 |
+
'˧˧˦').replace('6', '˩˩˧').replace('7', '˥').replace('8', '˩˨').replace('ᴀ', 'ɐ').replace('ᴇ', 'e')+' ', text)
|
137 |
+
text = re.sub(r'\[GD\](.*?)\[GD\]',
|
138 |
+
lambda x: cantonese_to_ipa(x.group(1))+' ', text)
|
139 |
+
text = re.sub(r'\[EN\](.*?)\[EN\]',
|
140 |
+
lambda x: english_to_lazy_ipa2(x.group(1))+' ', text)
|
141 |
+
text = re.sub(r'\[([A-Z]{2})\](.*?)\[\1\]', lambda x: ngu_dialect_to_ipa(x.group(2), x.group(
|
142 |
+
1)).replace('ʣ', 'dz').replace('ʥ', 'dʑ').replace('ʦ', 'ts').replace('ʨ', 'tɕ')+' ', text)
|
143 |
+
text = re.sub(r'\s+$', '', text)
|
144 |
+
text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
|
145 |
+
return text
|
text/english.py
ADDED
@@ -0,0 +1,188 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
""" from https://github.com/keithito/tacotron """
|
2 |
+
|
3 |
+
'''
|
4 |
+
Cleaners are transformations that run over the input text at both training and eval time.
|
5 |
+
|
6 |
+
Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
|
7 |
+
hyperparameter. Some cleaners are English-specific. You'll typically want to use:
|
8 |
+
1. "english_cleaners" for English text
|
9 |
+
2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
|
10 |
+
the Unidecode library (https://pypi.python.org/pypi/Unidecode)
|
11 |
+
3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
|
12 |
+
the symbols in symbols.py to match your data).
|
13 |
+
'''
|
14 |
+
|
15 |
+
|
16 |
+
# Regular expression matching whitespace:
|
17 |
+
|
18 |
+
|
19 |
+
import re
|
20 |
+
import inflect
|
21 |
+
from unidecode import unidecode
|
22 |
+
import eng_to_ipa as ipa
|
23 |
+
_inflect = inflect.engine()
|
24 |
+
_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
|
25 |
+
_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
|
26 |
+
_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
|
27 |
+
_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
|
28 |
+
_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
|
29 |
+
_number_re = re.compile(r'[0-9]+')
|
30 |
+
|
31 |
+
# List of (regular expression, replacement) pairs for abbreviations:
|
32 |
+
_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
|
33 |
+
('mrs', 'misess'),
|
34 |
+
('mr', 'mister'),
|
35 |
+
('dr', 'doctor'),
|
36 |
+
('st', 'saint'),
|
37 |
+
('co', 'company'),
|
38 |
+
('jr', 'junior'),
|
39 |
+
('maj', 'major'),
|
40 |
+
('gen', 'general'),
|
41 |
+
('drs', 'doctors'),
|
42 |
+
('rev', 'reverend'),
|
43 |
+
('lt', 'lieutenant'),
|
44 |
+
('hon', 'honorable'),
|
45 |
+
('sgt', 'sergeant'),
|
46 |
+
('capt', 'captain'),
|
47 |
+
('esq', 'esquire'),
|
48 |
+
('ltd', 'limited'),
|
49 |
+
('col', 'colonel'),
|
50 |
+
('ft', 'fort'),
|
51 |
+
]]
|
52 |
+
|
53 |
+
|
54 |
+
# List of (ipa, lazy ipa) pairs:
|
55 |
+
_lazy_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
|
56 |
+
('r', 'ɹ'),
|
57 |
+
('æ', 'e'),
|
58 |
+
('ɑ', 'a'),
|
59 |
+
('ɔ', 'o'),
|
60 |
+
('ð', 'z'),
|
61 |
+
('θ', 's'),
|
62 |
+
('ɛ', 'e'),
|
63 |
+
('ɪ', 'i'),
|
64 |
+
('ʊ', 'u'),
|
65 |
+
('ʒ', 'ʥ'),
|
66 |
+
('ʤ', 'ʥ'),
|
67 |
+
('ˈ', '↓'),
|
68 |
+
]]
|
69 |
+
|
70 |
+
# List of (ipa, lazy ipa2) pairs:
|
71 |
+
_lazy_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
|
72 |
+
('r', 'ɹ'),
|
73 |
+
('ð', 'z'),
|
74 |
+
('θ', 's'),
|
75 |
+
('ʒ', 'ʑ'),
|
76 |
+
('ʤ', 'dʑ'),
|
77 |
+
('ˈ', '↓'),
|
78 |
+
]]
|
79 |
+
|
80 |
+
# List of (ipa, ipa2) pairs
|
81 |
+
_ipa_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
|
82 |
+
('r', 'ɹ'),
|
83 |
+
('ʤ', 'dʒ'),
|
84 |
+
('ʧ', 'tʃ')
|
85 |
+
]]
|
86 |
+
|
87 |
+
|
88 |
+
def expand_abbreviations(text):
|
89 |
+
for regex, replacement in _abbreviations:
|
90 |
+
text = re.sub(regex, replacement, text)
|
91 |
+
return text
|
92 |
+
|
93 |
+
|
94 |
+
def collapse_whitespace(text):
|
95 |
+
return re.sub(r'\s+', ' ', text)
|
96 |
+
|
97 |
+
|
98 |
+
def _remove_commas(m):
|
99 |
+
return m.group(1).replace(',', '')
|
100 |
+
|
101 |
+
|
102 |
+
def _expand_decimal_point(m):
|
103 |
+
return m.group(1).replace('.', ' point ')
|
104 |
+
|
105 |
+
|
106 |
+
def _expand_dollars(m):
|
107 |
+
match = m.group(1)
|
108 |
+
parts = match.split('.')
|
109 |
+
if len(parts) > 2:
|
110 |
+
return match + ' dollars' # Unexpected format
|
111 |
+
dollars = int(parts[0]) if parts[0] else 0
|
112 |
+
cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
|
113 |
+
if dollars and cents:
|
114 |
+
dollar_unit = 'dollar' if dollars == 1 else 'dollars'
|
115 |
+
cent_unit = 'cent' if cents == 1 else 'cents'
|
116 |
+
return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
|
117 |
+
elif dollars:
|
118 |
+
dollar_unit = 'dollar' if dollars == 1 else 'dollars'
|
119 |
+
return '%s %s' % (dollars, dollar_unit)
|
120 |
+
elif cents:
|
121 |
+
cent_unit = 'cent' if cents == 1 else 'cents'
|
122 |
+
return '%s %s' % (cents, cent_unit)
|
123 |
+
else:
|
124 |
+
return 'zero dollars'
|
125 |
+
|
126 |
+
|
127 |
+
def _expand_ordinal(m):
|
128 |
+
return _inflect.number_to_words(m.group(0))
|
129 |
+
|
130 |
+
|
131 |
+
def _expand_number(m):
|
132 |
+
num = int(m.group(0))
|
133 |
+
if num > 1000 and num < 3000:
|
134 |
+
if num == 2000:
|
135 |
+
return 'two thousand'
|
136 |
+
elif num > 2000 and num < 2010:
|
137 |
+
return 'two thousand ' + _inflect.number_to_words(num % 100)
|
138 |
+
elif num % 100 == 0:
|
139 |
+
return _inflect.number_to_words(num // 100) + ' hundred'
|
140 |
+
else:
|
141 |
+
return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
|
142 |
+
else:
|
143 |
+
return _inflect.number_to_words(num, andword='')
|
144 |
+
|
145 |
+
|
146 |
+
def normalize_numbers(text):
|
147 |
+
text = re.sub(_comma_number_re, _remove_commas, text)
|
148 |
+
text = re.sub(_pounds_re, r'\1 pounds', text)
|
149 |
+
text = re.sub(_dollars_re, _expand_dollars, text)
|
150 |
+
text = re.sub(_decimal_number_re, _expand_decimal_point, text)
|
151 |
+
text = re.sub(_ordinal_re, _expand_ordinal, text)
|
152 |
+
text = re.sub(_number_re, _expand_number, text)
|
153 |
+
return text
|
154 |
+
|
155 |
+
|
156 |
+
def mark_dark_l(text):
|
157 |
+
return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text)
|
158 |
+
|
159 |
+
|
160 |
+
def english_to_ipa(text):
|
161 |
+
text = unidecode(text).lower()
|
162 |
+
text = expand_abbreviations(text)
|
163 |
+
text = normalize_numbers(text)
|
164 |
+
phonemes = ipa.convert(text)
|
165 |
+
phonemes = collapse_whitespace(phonemes)
|
166 |
+
return phonemes
|
167 |
+
|
168 |
+
|
169 |
+
def english_to_lazy_ipa(text):
|
170 |
+
text = english_to_ipa(text)
|
171 |
+
for regex, replacement in _lazy_ipa:
|
172 |
+
text = re.sub(regex, replacement, text)
|
173 |
+
return text
|
174 |
+
|
175 |
+
|
176 |
+
def english_to_ipa2(text):
|
177 |
+
text = english_to_ipa(text)
|
178 |
+
text = mark_dark_l(text)
|
179 |
+
for regex, replacement in _ipa_to_ipa2:
|
180 |
+
text = re.sub(regex, replacement, text)
|
181 |
+
return text.replace('...', '…')
|
182 |
+
|
183 |
+
|
184 |
+
def english_to_lazy_ipa2(text):
|
185 |
+
text = english_to_ipa(text)
|
186 |
+
for regex, replacement in _lazy_ipa2:
|
187 |
+
text = re.sub(regex, replacement, text)
|
188 |
+
return text
|
text/japanese.py
ADDED
@@ -0,0 +1,153 @@
|
|
|
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|
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|
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|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import re
|
2 |
+
from unidecode import unidecode
|
3 |
+
import pyopenjtalk
|
4 |
+
|
5 |
+
|
6 |
+
# Regular expression matching Japanese without punctuation marks:
|
7 |
+
_japanese_characters = re.compile(
|
8 |
+
r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
|
9 |
+
|
10 |
+
# Regular expression matching non-Japanese characters or punctuation marks:
|
11 |
+
_japanese_marks = re.compile(
|
12 |
+
r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
|
13 |
+
|
14 |
+
# List of (symbol, Japanese) pairs for marks:
|
15 |
+
_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
|
16 |
+
('%', 'パーセント')
|
17 |
+
]]
|
18 |
+
|
19 |
+
# List of (romaji, ipa) pairs for marks:
|
20 |
+
_romaji_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
|
21 |
+
('ts', 'ʦ'),
|
22 |
+
('u', 'ɯ'),
|
23 |
+
('j', 'ʥ'),
|
24 |
+
('y', 'j'),
|
25 |
+
('ni', 'n^i'),
|
26 |
+
('nj', 'n^'),
|
27 |
+
('hi', 'çi'),
|
28 |
+
('hj', 'ç'),
|
29 |
+
('f', 'ɸ'),
|
30 |
+
('I', 'i*'),
|
31 |
+
('U', 'ɯ*'),
|
32 |
+
('r', 'ɾ')
|
33 |
+
]]
|
34 |
+
|
35 |
+
# List of (romaji, ipa2) pairs for marks:
|
36 |
+
_romaji_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
|
37 |
+
('u', 'ɯ'),
|
38 |
+
('ʧ', 'tʃ'),
|
39 |
+
('j', 'dʑ'),
|
40 |
+
('y', 'j'),
|
41 |
+
('ni', 'n^i'),
|
42 |
+
('nj', 'n^'),
|
43 |
+
('hi', 'çi'),
|
44 |
+
('hj', 'ç'),
|
45 |
+
('f', 'ɸ'),
|
46 |
+
('I', 'i*'),
|
47 |
+
('U', 'ɯ*'),
|
48 |
+
('r', 'ɾ')
|
49 |
+
]]
|
50 |
+
|
51 |
+
# List of (consonant, sokuon) pairs:
|
52 |
+
_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
|
53 |
+
(r'Q([↑↓]*[kg])', r'k#\1'),
|
54 |
+
(r'Q([↑↓]*[tdjʧ])', r't#\1'),
|
55 |
+
(r'Q([↑↓]*[sʃ])', r's\1'),
|
56 |
+
(r'Q([↑↓]*[pb])', r'p#\1')
|
57 |
+
]]
|
58 |
+
|
59 |
+
# List of (consonant, hatsuon) pairs:
|
60 |
+
_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
|
61 |
+
(r'N([↑↓]*[pbm])', r'm\1'),
|
62 |
+
(r'N([↑↓]*[ʧʥj])', r'n^\1'),
|
63 |
+
(r'N([↑↓]*[tdn])', r'n\1'),
|
64 |
+
(r'N([↑↓]*[kg])', r'ŋ\1')
|
65 |
+
]]
|
66 |
+
|
67 |
+
|
68 |
+
def symbols_to_japanese(text):
|
69 |
+
for regex, replacement in _symbols_to_japanese:
|
70 |
+
text = re.sub(regex, replacement, text)
|
71 |
+
return text
|
72 |
+
|
73 |
+
|
74 |
+
def japanese_to_romaji_with_accent(text):
|
75 |
+
'''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
|
76 |
+
text = symbols_to_japanese(text)
|
77 |
+
sentences = re.split(_japanese_marks, text)
|
78 |
+
marks = re.findall(_japanese_marks, text)
|
79 |
+
text = ''
|
80 |
+
for i, sentence in enumerate(sentences):
|
81 |
+
if re.match(_japanese_characters, sentence):
|
82 |
+
if text != '':
|
83 |
+
text += ' '
|
84 |
+
labels = pyopenjtalk.extract_fullcontext(sentence)
|
85 |
+
for n, label in enumerate(labels):
|
86 |
+
phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
|
87 |
+
if phoneme not in ['sil', 'pau']:
|
88 |
+
text += phoneme.replace('ch', 'ʧ').replace('sh',
|
89 |
+
'ʃ').replace('cl', 'Q')
|
90 |
+
else:
|
91 |
+
continue
|
92 |
+
# n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
|
93 |
+
a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
|
94 |
+
a2 = int(re.search(r"\+(\d+)\+", label).group(1))
|
95 |
+
a3 = int(re.search(r"\+(\d+)/", label).group(1))
|
96 |
+
if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
|
97 |
+
a2_next = -1
|
98 |
+
else:
|
99 |
+
a2_next = int(
|
100 |
+
re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
|
101 |
+
# Accent phrase boundary
|
102 |
+
if a3 == 1 and a2_next == 1:
|
103 |
+
text += ' '
|
104 |
+
# Falling
|
105 |
+
elif a1 == 0 and a2_next == a2 + 1:
|
106 |
+
text += '↓'
|
107 |
+
# Rising
|
108 |
+
elif a2 == 1 and a2_next == 2:
|
109 |
+
text += '↑'
|
110 |
+
if i < len(marks):
|
111 |
+
text += unidecode(marks[i]).replace(' ', '')
|
112 |
+
return text
|
113 |
+
|
114 |
+
|
115 |
+
def get_real_sokuon(text):
|
116 |
+
for regex, replacement in _real_sokuon:
|
117 |
+
text = re.sub(regex, replacement, text)
|
118 |
+
return text
|
119 |
+
|
120 |
+
|
121 |
+
def get_real_hatsuon(text):
|
122 |
+
for regex, replacement in _real_hatsuon:
|
123 |
+
text = re.sub(regex, replacement, text)
|
124 |
+
return text
|
125 |
+
|
126 |
+
|
127 |
+
def japanese_to_ipa(text):
|
128 |
+
text = japanese_to_romaji_with_accent(text).replace('...', '…')
|
129 |
+
text = re.sub(
|
130 |
+
r'([aiueo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
|
131 |
+
text = get_real_sokuon(text)
|
132 |
+
text = get_real_hatsuon(text)
|
133 |
+
for regex, replacement in _romaji_to_ipa:
|
134 |
+
text = re.sub(regex, replacement, text)
|
135 |
+
return text
|
136 |
+
|
137 |
+
|
138 |
+
def japanese_to_ipa2(text):
|
139 |
+
text = japanese_to_romaji_with_accent(text).replace('...', '…')
|
140 |
+
text = get_real_sokuon(text)
|
141 |
+
text = get_real_hatsuon(text)
|
142 |
+
for regex, replacement in _romaji_to_ipa2:
|
143 |
+
text = re.sub(regex, replacement, text)
|
144 |
+
return text
|
145 |
+
|
146 |
+
|
147 |
+
def japanese_to_ipa3(text):
|
148 |
+
text = japanese_to_ipa2(text).replace('n^', 'ȵ').replace(
|
149 |
+
'ʃ', 'ɕ').replace('*', '\u0325').replace('#', '\u031a')
|
150 |
+
text = re.sub(
|
151 |
+
r'([aiɯeo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
|
152 |
+
text = re.sub(r'((?:^|\s)(?:ts|tɕ|[kpt]))', r'\1ʰ', text)
|
153 |
+
return text
|
text/korean.py
ADDED
@@ -0,0 +1,210 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
|
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|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
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|
|
|
|
|
|
|
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|
|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import re
|
2 |
+
from jamo import h2j, j2hcj
|
3 |
+
import ko_pron
|
4 |
+
|
5 |
+
|
6 |
+
# This is a list of Korean classifiers preceded by pure Korean numerals.
|
7 |
+
_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
|
8 |
+
|
9 |
+
# List of (hangul, hangul divided) pairs:
|
10 |
+
_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [
|
11 |
+
('ㄳ', 'ㄱㅅ'),
|
12 |
+
('ㄵ', 'ㄴㅈ'),
|
13 |
+
('ㄶ', 'ㄴㅎ'),
|
14 |
+
('ㄺ', 'ㄹㄱ'),
|
15 |
+
('ㄻ', 'ㄹㅁ'),
|
16 |
+
('ㄼ', 'ㄹㅂ'),
|
17 |
+
('ㄽ', 'ㄹㅅ'),
|
18 |
+
('ㄾ', 'ㄹㅌ'),
|
19 |
+
('ㄿ', 'ㄹㅍ'),
|
20 |
+
('ㅀ', 'ㄹㅎ'),
|
21 |
+
('ㅄ', 'ㅂㅅ'),
|
22 |
+
('ㅘ', 'ㅗㅏ'),
|
23 |
+
('ㅙ', 'ㅗㅐ'),
|
24 |
+
('ㅚ', 'ㅗㅣ'),
|
25 |
+
('ㅝ', 'ㅜㅓ'),
|
26 |
+
('ㅞ', 'ㅜㅔ'),
|
27 |
+
('ㅟ', 'ㅜㅣ'),
|
28 |
+
('ㅢ', 'ㅡㅣ'),
|
29 |
+
('ㅑ', 'ㅣㅏ'),
|
30 |
+
('ㅒ', 'ㅣㅐ'),
|
31 |
+
('ㅕ', 'ㅣㅓ'),
|
32 |
+
('ㅖ', 'ㅣㅔ'),
|
33 |
+
('ㅛ', 'ㅣㅗ'),
|
34 |
+
('ㅠ', 'ㅣㅜ')
|
35 |
+
]]
|
36 |
+
|
37 |
+
# List of (Latin alphabet, hangul) pairs:
|
38 |
+
_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
|
39 |
+
('a', '에이'),
|
40 |
+
('b', '비'),
|
41 |
+
('c', '시'),
|
42 |
+
('d', '디'),
|
43 |
+
('e', '이'),
|
44 |
+
('f', '에프'),
|
45 |
+
('g', '지'),
|
46 |
+
('h', '에이치'),
|
47 |
+
('i', '아이'),
|
48 |
+
('j', '제이'),
|
49 |
+
('k', '케이'),
|
50 |
+
('l', '엘'),
|
51 |
+
('m', '엠'),
|
52 |
+
('n', '엔'),
|
53 |
+
('o', '오'),
|
54 |
+
('p', '피'),
|
55 |
+
('q', '큐'),
|
56 |
+
('r', '아르'),
|
57 |
+
('s', '에스'),
|
58 |
+
('t', '티'),
|
59 |
+
('u', '유'),
|
60 |
+
('v', '브이'),
|
61 |
+
('w', '더블유'),
|
62 |
+
('x', '엑스'),
|
63 |
+
('y', '와이'),
|
64 |
+
('z', '제트')
|
65 |
+
]]
|
66 |
+
|
67 |
+
# List of (ipa, lazy ipa) pairs:
|
68 |
+
_ipa_to_lazy_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
|
69 |
+
('t͡ɕ','ʧ'),
|
70 |
+
('d͡ʑ','ʥ'),
|
71 |
+
('ɲ','n^'),
|
72 |
+
('ɕ','ʃ'),
|
73 |
+
('ʷ','w'),
|
74 |
+
('ɭ','l`'),
|
75 |
+
('ʎ','ɾ'),
|
76 |
+
('ɣ','ŋ'),
|
77 |
+
('ɰ','ɯ'),
|
78 |
+
('ʝ','j'),
|
79 |
+
('ʌ','ə'),
|
80 |
+
('ɡ','g'),
|
81 |
+
('\u031a','#'),
|
82 |
+
('\u0348','='),
|
83 |
+
('\u031e',''),
|
84 |
+
('\u0320',''),
|
85 |
+
('\u0339','')
|
86 |
+
]]
|
87 |
+
|
88 |
+
|
89 |
+
def latin_to_hangul(text):
|
90 |
+
for regex, replacement in _latin_to_hangul:
|
91 |
+
text = re.sub(regex, replacement, text)
|
92 |
+
return text
|
93 |
+
|
94 |
+
|
95 |
+
def divide_hangul(text):
|
96 |
+
text = j2hcj(h2j(text))
|
97 |
+
for regex, replacement in _hangul_divided:
|
98 |
+
text = re.sub(regex, replacement, text)
|
99 |
+
return text
|
100 |
+
|
101 |
+
|
102 |
+
def hangul_number(num, sino=True):
|
103 |
+
'''Reference https://github.com/Kyubyong/g2pK'''
|
104 |
+
num = re.sub(',', '', num)
|
105 |
+
|
106 |
+
if num == '0':
|
107 |
+
return '영'
|
108 |
+
if not sino and num == '20':
|
109 |
+
return '스무'
|
110 |
+
|
111 |
+
digits = '123456789'
|
112 |
+
names = '일이삼사오육칠팔구'
|
113 |
+
digit2name = {d: n for d, n in zip(digits, names)}
|
114 |
+
|
115 |
+
modifiers = '한 두 세 네 다섯 여섯 일곱 여덟 아홉'
|
116 |
+
decimals = '열 스물 서른 마흔 쉰 예순 일흔 여든 아흔'
|
117 |
+
digit2mod = {d: mod for d, mod in zip(digits, modifiers.split())}
|
118 |
+
digit2dec = {d: dec for d, dec in zip(digits, decimals.split())}
|
119 |
+
|
120 |
+
spelledout = []
|
121 |
+
for i, digit in enumerate(num):
|
122 |
+
i = len(num) - i - 1
|
123 |
+
if sino:
|
124 |
+
if i == 0:
|
125 |
+
name = digit2name.get(digit, '')
|
126 |
+
elif i == 1:
|
127 |
+
name = digit2name.get(digit, '') + '십'
|
128 |
+
name = name.replace('일십', '십')
|
129 |
+
else:
|
130 |
+
if i == 0:
|
131 |
+
name = digit2mod.get(digit, '')
|
132 |
+
elif i == 1:
|
133 |
+
name = digit2dec.get(digit, '')
|
134 |
+
if digit == '0':
|
135 |
+
if i % 4 == 0:
|
136 |
+
last_three = spelledout[-min(3, len(spelledout)):]
|
137 |
+
if ''.join(last_three) == '':
|
138 |
+
spelledout.append('')
|
139 |
+
continue
|
140 |
+
else:
|
141 |
+
spelledout.append('')
|
142 |
+
continue
|
143 |
+
if i == 2:
|
144 |
+
name = digit2name.get(digit, '') + '백'
|
145 |
+
name = name.replace('일백', '백')
|
146 |
+
elif i == 3:
|
147 |
+
name = digit2name.get(digit, '') + '천'
|
148 |
+
name = name.replace('일천', '천')
|
149 |
+
elif i == 4:
|
150 |
+
name = digit2name.get(digit, '') + '만'
|
151 |
+
name = name.replace('일만', '만')
|
152 |
+
elif i == 5:
|
153 |
+
name = digit2name.get(digit, '') + '십'
|
154 |
+
name = name.replace('일십', '십')
|
155 |
+
elif i == 6:
|
156 |
+
name = digit2name.get(digit, '') + '백'
|
157 |
+
name = name.replace('일백', '백')
|
158 |
+
elif i == 7:
|
159 |
+
name = digit2name.get(digit, '') + '천'
|
160 |
+
name = name.replace('일천', '천')
|
161 |
+
elif i == 8:
|
162 |
+
name = digit2name.get(digit, '') + '억'
|
163 |
+
elif i == 9:
|
164 |
+
name = digit2name.get(digit, '') + '십'
|
165 |
+
elif i == 10:
|
166 |
+
name = digit2name.get(digit, '') + '백'
|
167 |
+
elif i == 11:
|
168 |
+
name = digit2name.get(digit, '') + '천'
|
169 |
+
elif i == 12:
|
170 |
+
name = digit2name.get(digit, '') + '조'
|
171 |
+
elif i == 13:
|
172 |
+
name = digit2name.get(digit, '') + '십'
|
173 |
+
elif i == 14:
|
174 |
+
name = digit2name.get(digit, '') + '백'
|
175 |
+
elif i == 15:
|
176 |
+
name = digit2name.get(digit, '') + '천'
|
177 |
+
spelledout.append(name)
|
178 |
+
return ''.join(elem for elem in spelledout)
|
179 |
+
|
180 |
+
|
181 |
+
def number_to_hangul(text):
|
182 |
+
'''Reference https://github.com/Kyubyong/g2pK'''
|
183 |
+
tokens = set(re.findall(r'(\d[\d,]*)([\uac00-\ud71f]+)', text))
|
184 |
+
for token in tokens:
|
185 |
+
num, classifier = token
|
186 |
+
if classifier[:2] in _korean_classifiers or classifier[0] in _korean_classifiers:
|
187 |
+
spelledout = hangul_number(num, sino=False)
|
188 |
+
else:
|
189 |
+
spelledout = hangul_number(num, sino=True)
|
190 |
+
text = text.replace(f'{num}{classifier}', f'{spelledout}{classifier}')
|
191 |
+
# digit by digit for remaining digits
|
192 |
+
digits = '0123456789'
|
193 |
+
names = '영일이삼사오육칠팔구'
|
194 |
+
for d, n in zip(digits, names):
|
195 |
+
text = text.replace(d, n)
|
196 |
+
return text
|
197 |
+
|
198 |
+
|
199 |
+
def korean_to_lazy_ipa(text):
|
200 |
+
text = latin_to_hangul(text)
|
201 |
+
text = number_to_hangul(text)
|
202 |
+
text=re.sub('[\uac00-\ud7af]+',lambda x:ko_pron.romanise(x.group(0),'ipa').split('] ~ [')[0],text)
|
203 |
+
for regex, replacement in _ipa_to_lazy_ipa:
|
204 |
+
text = re.sub(regex, replacement, text)
|
205 |
+
return text
|
206 |
+
|
207 |
+
|
208 |
+
def korean_to_ipa(text):
|
209 |
+
text = korean_to_lazy_ipa(text)
|
210 |
+
return text.replace('ʧ','tʃ').replace('ʥ','dʑ')
|
text/mandarin.py
ADDED
@@ -0,0 +1,330 @@
|
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|
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|
|
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|
|
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|
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|
|
|
|
|
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|
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|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
import sys
|
3 |
+
import re
|
4 |
+
from pypinyin import lazy_pinyin, BOPOMOFO
|
5 |
+
import jieba
|
6 |
+
import cn2an
|
7 |
+
import logging
|
8 |
+
|
9 |
+
logging.getLogger('jieba').setLevel(logging.WARNING)
|
10 |
+
jieba.set_dictionary(os.path.dirname(os.path.realpath(sys.argv[0]))+'/jieba/dict.txt')
|
11 |
+
jieba.initialize()
|
12 |
+
|
13 |
+
|
14 |
+
# List of (Latin alphabet, bopomofo) pairs:
|
15 |
+
_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
|
16 |
+
('a', 'ㄟˉ'),
|
17 |
+
('b', 'ㄅㄧˋ'),
|
18 |
+
('c', 'ㄙㄧˉ'),
|
19 |
+
('d', 'ㄉㄧˋ'),
|
20 |
+
('e', 'ㄧˋ'),
|
21 |
+
('f', 'ㄝˊㄈㄨˋ'),
|
22 |
+
('g', 'ㄐㄧˋ'),
|
23 |
+
('h', 'ㄝˇㄑㄩˋ'),
|
24 |
+
('i', 'ㄞˋ'),
|
25 |
+
('j', 'ㄐㄟˋ'),
|
26 |
+
('k', 'ㄎㄟˋ'),
|
27 |
+
('l', 'ㄝˊㄛˋ'),
|
28 |
+
('m', 'ㄝˊㄇㄨˋ'),
|
29 |
+
('n', 'ㄣˉ'),
|
30 |
+
('o', 'ㄡˉ'),
|
31 |
+
('p', 'ㄆㄧˉ'),
|
32 |
+
('q', 'ㄎㄧㄡˉ'),
|
33 |
+
('r', 'ㄚˋ'),
|
34 |
+
('s', 'ㄝˊㄙˋ'),
|
35 |
+
('t', 'ㄊㄧˋ'),
|
36 |
+
('u', 'ㄧㄡˉ'),
|
37 |
+
('v', 'ㄨㄧˉ'),
|
38 |
+
('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'),
|
39 |
+
('x', 'ㄝˉㄎㄨˋㄙˋ'),
|
40 |
+
('y', 'ㄨㄞˋ'),
|
41 |
+
('z', 'ㄗㄟˋ')
|
42 |
+
]]
|
43 |
+
|
44 |
+
# List of (bopomofo, romaji) pairs:
|
45 |
+
_bopomofo_to_romaji = [(re.compile('%s' % x[0]), x[1]) for x in [
|
46 |
+
('ㄅㄛ', 'p⁼wo'),
|
47 |
+
('ㄆㄛ', 'pʰwo'),
|
48 |
+
('ㄇㄛ', 'mwo'),
|
49 |
+
('ㄈㄛ', 'fwo'),
|
50 |
+
('ㄅ', 'p⁼'),
|
51 |
+
('ㄆ', 'pʰ'),
|
52 |
+
('ㄇ', 'm'),
|
53 |
+
('ㄈ', 'f'),
|
54 |
+
('ㄉ', 't⁼'),
|
55 |
+
('ㄊ', 'tʰ'),
|
56 |
+
('ㄋ', 'n'),
|
57 |
+
('ㄌ', 'l'),
|
58 |
+
('ㄍ', 'k⁼'),
|
59 |
+
('ㄎ', 'kʰ'),
|
60 |
+
('ㄏ', 'h'),
|
61 |
+
('ㄐ', 'ʧ⁼'),
|
62 |
+
('ㄑ', 'ʧʰ'),
|
63 |
+
('ㄒ', 'ʃ'),
|
64 |
+
('ㄓ', 'ʦ`⁼'),
|
65 |
+
('ㄔ', 'ʦ`ʰ'),
|
66 |
+
('ㄕ', 's`'),
|
67 |
+
('ㄖ', 'ɹ`'),
|
68 |
+
('ㄗ', 'ʦ⁼'),
|
69 |
+
('ㄘ', 'ʦʰ'),
|
70 |
+
('ㄙ', 's'),
|
71 |
+
('ㄚ', 'a'),
|
72 |
+
('ㄛ', 'o'),
|
73 |
+
('ㄜ', 'ə'),
|
74 |
+
('ㄝ', 'e'),
|
75 |
+
('ㄞ', 'ai'),
|
76 |
+
('ㄟ', 'ei'),
|
77 |
+
('ㄠ', 'au'),
|
78 |
+
('ㄡ', 'ou'),
|
79 |
+
('ㄧㄢ', 'yeNN'),
|
80 |
+
('ㄢ', 'aNN'),
|
81 |
+
('ㄧㄣ', 'iNN'),
|
82 |
+
('ㄣ', 'əNN'),
|
83 |
+
('ㄤ', 'aNg'),
|
84 |
+
('ㄧㄥ', 'iNg'),
|
85 |
+
('ㄨㄥ', 'uNg'),
|
86 |
+
('ㄩㄥ', 'yuNg'),
|
87 |
+
('ㄥ', 'əNg'),
|
88 |
+
('ㄦ', 'əɻ'),
|
89 |
+
('ㄧ', 'i'),
|
90 |
+
('ㄨ', 'u'),
|
91 |
+
('ㄩ', 'ɥ'),
|
92 |
+
('ˉ', '→'),
|
93 |
+
('ˊ', '↑'),
|
94 |
+
('ˇ', '↓↑'),
|
95 |
+
('ˋ', '↓'),
|
96 |
+
('˙', ''),
|
97 |
+
(',', ','),
|
98 |
+
('。', '.'),
|
99 |
+
('!', '!'),
|
100 |
+
('?', '?'),
|
101 |
+
('—', '-')
|
102 |
+
]]
|
103 |
+
|
104 |
+
# List of (romaji, ipa) pairs:
|
105 |
+
_romaji_to_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
|
106 |
+
('ʃy', 'ʃ'),
|
107 |
+
('ʧʰy', 'ʧʰ'),
|
108 |
+
('ʧ⁼y', 'ʧ⁼'),
|
109 |
+
('NN', 'n'),
|
110 |
+
('Ng', 'ŋ'),
|
111 |
+
('y', 'j'),
|
112 |
+
('h', 'x')
|
113 |
+
]]
|
114 |
+
|
115 |
+
# List of (bopomofo, ipa) pairs:
|
116 |
+
_bopomofo_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
|
117 |
+
('ㄅㄛ', 'p⁼wo'),
|
118 |
+
('ㄆㄛ', 'pʰwo'),
|
119 |
+
('ㄇㄛ', 'mwo'),
|
120 |
+
('ㄈㄛ', 'fwo'),
|
121 |
+
('ㄅ', 'p⁼'),
|
122 |
+
('ㄆ', 'pʰ'),
|
123 |
+
('ㄇ', 'm'),
|
124 |
+
('ㄈ', 'f'),
|
125 |
+
('ㄉ', 't⁼'),
|
126 |
+
('ㄊ', 'tʰ'),
|
127 |
+
('ㄋ', 'n'),
|
128 |
+
('ㄌ', 'l'),
|
129 |
+
('ㄍ', 'k⁼'),
|
130 |
+
('ㄎ', 'kʰ'),
|
131 |
+
('ㄏ', 'x'),
|
132 |
+
('ㄐ', 'tʃ⁼'),
|
133 |
+
('ㄑ', 'tʃʰ'),
|
134 |
+
('ㄒ', 'ʃ'),
|
135 |
+
('ㄓ', 'ts`⁼'),
|
136 |
+
('ㄔ', 'ts`ʰ'),
|
137 |
+
('ㄕ', 's`'),
|
138 |
+
('ㄖ', 'ɹ`'),
|
139 |
+
('ㄗ', 'ts⁼'),
|
140 |
+
('ㄘ', 'tsʰ'),
|
141 |
+
('ㄙ', 's'),
|
142 |
+
('ㄚ', 'a'),
|
143 |
+
('ㄛ', 'o'),
|
144 |
+
('ㄜ', 'ə'),
|
145 |
+
('ㄝ', 'ɛ'),
|
146 |
+
('ㄞ', 'aɪ'),
|
147 |
+
('ㄟ', 'eɪ'),
|
148 |
+
('ㄠ', 'ɑʊ'),
|
149 |
+
('ㄡ', 'oʊ'),
|
150 |
+
('ㄧㄢ', 'jɛn'),
|
151 |
+
('ㄩㄢ', 'ɥæn'),
|
152 |
+
('ㄢ', 'an'),
|
153 |
+
('ㄧㄣ', 'in'),
|
154 |
+
('ㄩㄣ', 'ɥn'),
|
155 |
+
('ㄣ', 'ən'),
|
156 |
+
('ㄤ', 'ɑŋ'),
|
157 |
+
('ㄧㄥ', 'iŋ'),
|
158 |
+
('ㄨㄥ', 'ʊŋ'),
|
159 |
+
('ㄩㄥ', 'jʊŋ'),
|
160 |
+
('ㄥ', 'əŋ'),
|
161 |
+
('ㄦ', 'əɻ'),
|
162 |
+
('ㄧ', 'i'),
|
163 |
+
('ㄨ', 'u'),
|
164 |
+
('ㄩ', 'ɥ'),
|
165 |
+
('ˉ', '→'),
|
166 |
+
('ˊ', '↑'),
|
167 |
+
('ˇ', '↓↑'),
|
168 |
+
('ˋ', '↓'),
|
169 |
+
('˙', ''),
|
170 |
+
(',', ','),
|
171 |
+
('。', '.'),
|
172 |
+
('!', '!'),
|
173 |
+
('?', '?'),
|
174 |
+
('—', '-')
|
175 |
+
]]
|
176 |
+
|
177 |
+
# List of (bopomofo, ipa2) pairs:
|
178 |
+
_bopomofo_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
|
179 |
+
('ㄅㄛ', 'pwo'),
|
180 |
+
('ㄆㄛ', 'pʰwo'),
|
181 |
+
('ㄇㄛ', 'mwo'),
|
182 |
+
('ㄈㄛ', 'fwo'),
|
183 |
+
('ㄅ', 'p'),
|
184 |
+
('ㄆ', 'pʰ'),
|
185 |
+
('ㄇ', 'm'),
|
186 |
+
('ㄈ', 'f'),
|
187 |
+
('ㄉ', 't'),
|
188 |
+
('ㄊ', 'tʰ'),
|
189 |
+
('ㄋ', 'n'),
|
190 |
+
('ㄌ', 'l'),
|
191 |
+
('ㄍ', 'k'),
|
192 |
+
('ㄎ', 'kʰ'),
|
193 |
+
('ㄏ', 'h'),
|
194 |
+
('ㄐ', 'tɕ'),
|
195 |
+
('ㄑ', 'tɕʰ'),
|
196 |
+
('ㄒ', 'ɕ'),
|
197 |
+
('ㄓ', 'tʂ'),
|
198 |
+
('ㄔ', 'tʂʰ'),
|
199 |
+
('ㄕ', 'ʂ'),
|
200 |
+
('ㄖ', 'ɻ'),
|
201 |
+
('ㄗ', 'ts'),
|
202 |
+
('ㄘ', 'tsʰ'),
|
203 |
+
('ㄙ', 's'),
|
204 |
+
('ㄚ', 'a'),
|
205 |
+
('ㄛ', 'o'),
|
206 |
+
('ㄜ', 'ɤ'),
|
207 |
+
('ㄝ', 'ɛ'),
|
208 |
+
('ㄞ', 'aɪ'),
|
209 |
+
('ㄟ', 'eɪ'),
|
210 |
+
('ㄠ', 'ɑʊ'),
|
211 |
+
('ㄡ', 'oʊ'),
|
212 |
+
('ㄧㄢ', 'jɛn'),
|
213 |
+
('ㄩㄢ', 'yæn'),
|
214 |
+
('ㄢ', 'an'),
|
215 |
+
('ㄧㄣ', 'in'),
|
216 |
+
('ㄩㄣ', 'yn'),
|
217 |
+
('ㄣ', 'ən'),
|
218 |
+
('ㄤ', 'ɑŋ'),
|
219 |
+
('ㄧㄥ', 'iŋ'),
|
220 |
+
('ㄨㄥ', 'ʊŋ'),
|
221 |
+
('ㄩㄥ', 'jʊŋ'),
|
222 |
+
('ㄥ', 'ɤŋ'),
|
223 |
+
('ㄦ', 'əɻ'),
|
224 |
+
('ㄧ', 'i'),
|
225 |
+
('ㄨ', 'u'),
|
226 |
+
('ㄩ', 'y'),
|
227 |
+
('ˉ', '˥'),
|
228 |
+
('ˊ', '˧˥'),
|
229 |
+
('ˇ', '˨˩˦'),
|
230 |
+
('ˋ', '˥˩'),
|
231 |
+
('˙', ''),
|
232 |
+
(',', ','),
|
233 |
+
('。', '.'),
|
234 |
+
('!', '!'),
|
235 |
+
('?', '?'),
|
236 |
+
('—', '-')
|
237 |
+
]]
|
238 |
+
|
239 |
+
|
240 |
+
def number_to_chinese(text):
|
241 |
+
numbers = re.findall(r'\d+(?:\.?\d+)?', text)
|
242 |
+
for number in numbers:
|
243 |
+
text = text.replace(number, cn2an.an2cn(number), 1)
|
244 |
+
return text
|
245 |
+
|
246 |
+
|
247 |
+
def chinese_to_bopomofo(text):
|
248 |
+
text = text.replace('、', ',').replace(';', ',').replace(':', ',')
|
249 |
+
words = jieba.lcut(text, cut_all=False)
|
250 |
+
text = ''
|
251 |
+
for word in words:
|
252 |
+
bopomofos = lazy_pinyin(word, BOPOMOFO)
|
253 |
+
if not re.search('[\u4e00-\u9fff]', word):
|
254 |
+
text += word
|
255 |
+
continue
|
256 |
+
for i in range(len(bopomofos)):
|
257 |
+
bopomofos[i] = re.sub(r'([\u3105-\u3129])$', r'\1ˉ', bopomofos[i])
|
258 |
+
if text != '':
|
259 |
+
text += ' '
|
260 |
+
text += ''.join(bopomofos)
|
261 |
+
return text
|
262 |
+
|
263 |
+
|
264 |
+
def latin_to_bopomofo(text):
|
265 |
+
for regex, replacement in _latin_to_bopomofo:
|
266 |
+
text = re.sub(regex, replacement, text)
|
267 |
+
return text
|
268 |
+
|
269 |
+
|
270 |
+
def bopomofo_to_romaji(text):
|
271 |
+
for regex, replacement in _bopomofo_to_romaji:
|
272 |
+
text = re.sub(regex, replacement, text)
|
273 |
+
return text
|
274 |
+
|
275 |
+
|
276 |
+
def bopomofo_to_ipa(text):
|
277 |
+
for regex, replacement in _bopomofo_to_ipa:
|
278 |
+
text = re.sub(regex, replacement, text)
|
279 |
+
return text
|
280 |
+
|
281 |
+
|
282 |
+
def bopomofo_to_ipa2(text):
|
283 |
+
for regex, replacement in _bopomofo_to_ipa2:
|
284 |
+
text = re.sub(regex, replacement, text)
|
285 |
+
return text
|
286 |
+
|
287 |
+
|
288 |
+
def chinese_to_romaji(text):
|
289 |
+
text = number_to_chinese(text)
|
290 |
+
text = chinese_to_bopomofo(text)
|
291 |
+
text = latin_to_bopomofo(text)
|
292 |
+
text = bopomofo_to_romaji(text)
|
293 |
+
text = re.sub('i([aoe])', r'y\1', text)
|
294 |
+
text = re.sub('u([aoəe])', r'w\1', text)
|
295 |
+
text = re.sub('([ʦsɹ]`[⁼ʰ]?)([→↓↑ ]+|$)',
|
296 |
+
r'\1ɹ`\2', text).replace('ɻ', 'ɹ`')
|
297 |
+
text = re.sub('([ʦs][⁼ʰ]?)([→↓↑ ]+|$)', r'\1ɹ\2', text)
|
298 |
+
return text
|
299 |
+
|
300 |
+
|
301 |
+
def chinese_to_lazy_ipa(text):
|
302 |
+
text = chinese_to_romaji(text)
|
303 |
+
for regex, replacement in _romaji_to_ipa:
|
304 |
+
text = re.sub(regex, replacement, text)
|
305 |
+
return text
|
306 |
+
|
307 |
+
|
308 |
+
def chinese_to_ipa(text):
|
309 |
+
text = number_to_chinese(text)
|
310 |
+
text = chinese_to_bopomofo(text)
|
311 |
+
text = latin_to_bopomofo(text)
|
312 |
+
text = bopomofo_to_ipa(text)
|
313 |
+
text = re.sub('i([aoe])', r'j\1', text)
|
314 |
+
text = re.sub('u([aoəe])', r'w\1', text)
|
315 |
+
text = re.sub('([sɹ]`[⁼ʰ]?)([→↓↑ ]+|$)',
|
316 |
+
r'\1ɹ`\2', text).replace('ɻ', 'ɹ`')
|
317 |
+
text = re.sub('([s][⁼ʰ]?)([→↓↑ ]+|$)', r'\1ɹ\2', text)
|
318 |
+
return text
|
319 |
+
|
320 |
+
|
321 |
+
def chinese_to_ipa2(text):
|
322 |
+
text = number_to_chinese(text)
|
323 |
+
text = chinese_to_bopomofo(text)
|
324 |
+
text = latin_to_bopomofo(text)
|
325 |
+
text = bopomofo_to_ipa2(text)
|
326 |
+
text = re.sub(r'i([aoe])', r'j\1', text)
|
327 |
+
text = re.sub(r'u([aoəe])', r'w\1', text)
|
328 |
+
text = re.sub(r'([ʂɹ]ʰ?)([˩˨˧˦˥ ]+|$)', r'\1ʅ\2', text)
|
329 |
+
text = re.sub(r'(sʰ?)([˩˨˧˦˥ ]+|$)', r'\1ɿ\2', text)
|
330 |
+
return text
|
text/ngu_dialect.py
ADDED
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import re
|
2 |
+
import opencc
|
3 |
+
|
4 |
+
|
5 |
+
dialects = {'SZ': 'suzhou', 'WX': 'wuxi', 'CZ': 'changzhou', 'HZ': 'hangzhou',
|
6 |
+
'SX': 'shaoxing', 'NB': 'ningbo', 'JJ': 'jingjiang', 'YX': 'yixing',
|
7 |
+
'JD': 'jiading', 'ZR': 'zhenru', 'PH': 'pinghu', 'TX': 'tongxiang',
|
8 |
+
'JS': 'jiashan', 'HN': 'xiashi', 'LP': 'linping', 'XS': 'xiaoshan',
|
9 |
+
'FY': 'fuyang', 'RA': 'ruao', 'CX': 'cixi', 'SM': 'sanmen',
|
10 |
+
'TT': 'tiantai', 'WZ': 'wenzhou', 'SC': 'suichang', 'YB': 'youbu'}
|
11 |
+
|
12 |
+
converters = {}
|
13 |
+
|
14 |
+
for dialect in dialects.values():
|
15 |
+
try:
|
16 |
+
converters[dialect] = opencc.OpenCC(dialect)
|
17 |
+
except:
|
18 |
+
pass
|
19 |
+
|
20 |
+
|
21 |
+
def ngu_dialect_to_ipa(text, dialect):
|
22 |
+
dialect = dialects[dialect]
|
23 |
+
text = converters[dialect].convert(text).replace('-','').replace('$',' ')
|
24 |
+
text = re.sub(r'[、;:]', ',', text)
|
25 |
+
text = re.sub(r'\s*,\s*', ', ', text)
|
26 |
+
text = re.sub(r'\s*。\s*', '. ', text)
|
27 |
+
text = re.sub(r'\s*?\s*', '? ', text)
|
28 |
+
text = re.sub(r'\s*!\s*', '! ', text)
|
29 |
+
text = re.sub(r'\s*$', '', text)
|
30 |
+
return text
|
text/sanskrit.py
ADDED
@@ -0,0 +1,62 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import re
|
2 |
+
from indic_transliteration import sanscript
|
3 |
+
|
4 |
+
|
5 |
+
# List of (iast, ipa) pairs:
|
6 |
+
_iast_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
|
7 |
+
('a', 'ə'),
|
8 |
+
('ā', 'aː'),
|
9 |
+
('ī', 'iː'),
|
10 |
+
('ū', 'uː'),
|
11 |
+
('ṛ', 'ɹ`'),
|
12 |
+
('ṝ', 'ɹ`ː'),
|
13 |
+
('ḷ', 'l`'),
|
14 |
+
('ḹ', 'l`ː'),
|
15 |
+
('e', 'eː'),
|
16 |
+
('o', 'oː'),
|
17 |
+
('k', 'k⁼'),
|
18 |
+
('k⁼h', 'kʰ'),
|
19 |
+
('g', 'g⁼'),
|
20 |
+
('g⁼h', 'gʰ'),
|
21 |
+
('ṅ', 'ŋ'),
|
22 |
+
('c', 'ʧ⁼'),
|
23 |
+
('ʧ⁼h', 'ʧʰ'),
|
24 |
+
('j', 'ʥ⁼'),
|
25 |
+
('ʥ⁼h', 'ʥʰ'),
|
26 |
+
('ñ', 'n^'),
|
27 |
+
('ṭ', 't`⁼'),
|
28 |
+
('t`⁼h', 't`ʰ'),
|
29 |
+
('ḍ', 'd`⁼'),
|
30 |
+
('d`⁼h', 'd`ʰ'),
|
31 |
+
('ṇ', 'n`'),
|
32 |
+
('t', 't⁼'),
|
33 |
+
('t⁼h', 'tʰ'),
|
34 |
+
('d', 'd⁼'),
|
35 |
+
('d⁼h', 'dʰ'),
|
36 |
+
('p', 'p⁼'),
|
37 |
+
('p⁼h', 'pʰ'),
|
38 |
+
('b', 'b⁼'),
|
39 |
+
('b⁼h', 'bʰ'),
|
40 |
+
('y', 'j'),
|
41 |
+
('ś', 'ʃ'),
|
42 |
+
('ṣ', 's`'),
|
43 |
+
('r', 'ɾ'),
|
44 |
+
('l̤', 'l`'),
|
45 |
+
('h', 'ɦ'),
|
46 |
+
("'", ''),
|
47 |
+
('~', '^'),
|
48 |
+
('ṃ', '^')
|
49 |
+
]]
|
50 |
+
|
51 |
+
|
52 |
+
def devanagari_to_ipa(text):
|
53 |
+
text = text.replace('ॐ', 'ओम्')
|
54 |
+
text = re.sub(r'\s*।\s*$', '.', text)
|
55 |
+
text = re.sub(r'\s*।\s*', ', ', text)
|
56 |
+
text = re.sub(r'\s*॥', '.', text)
|
57 |
+
text = sanscript.transliterate(text, sanscript.DEVANAGARI, sanscript.IAST)
|
58 |
+
for regex, replacement in _iast_to_ipa:
|
59 |
+
text = re.sub(regex, replacement, text)
|
60 |
+
text = re.sub('(.)[`ː]*ḥ', lambda x: x.group(0)
|
61 |
+
[:-1]+'h'+x.group(1)+'*', text)
|
62 |
+
return text
|
text/shanghainese.py
ADDED
@@ -0,0 +1,64 @@
|
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|
|
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|
|
|
|
|
|
|
1 |
+
import re
|
2 |
+
import cn2an
|
3 |
+
import opencc
|
4 |
+
|
5 |
+
|
6 |
+
converter = opencc.OpenCC('zaonhe')
|
7 |
+
|
8 |
+
# List of (Latin alphabet, ipa) pairs:
|
9 |
+
_latin_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
|
10 |
+
('A', 'ᴇ'),
|
11 |
+
('B', 'bi'),
|
12 |
+
('C', 'si'),
|
13 |
+
('D', 'di'),
|
14 |
+
('E', 'i'),
|
15 |
+
('F', 'ᴇf'),
|
16 |
+
('G', 'dʑi'),
|
17 |
+
('H', 'ᴇtɕʰ'),
|
18 |
+
('I', 'ᴀi'),
|
19 |
+
('J', 'dʑᴇ'),
|
20 |
+
('K', 'kʰᴇ'),
|
21 |
+
('L', 'ᴇl'),
|
22 |
+
('M', 'ᴇm'),
|
23 |
+
('N', 'ᴇn'),
|
24 |
+
('O', 'o'),
|
25 |
+
('P', 'pʰi'),
|
26 |
+
('Q', 'kʰiu'),
|
27 |
+
('R', 'ᴀl'),
|
28 |
+
('S', 'ᴇs'),
|
29 |
+
('T', 'tʰi'),
|
30 |
+
('U', 'ɦiu'),
|
31 |
+
('V', 'vi'),
|
32 |
+
('W', 'dᴀbɤliu'),
|
33 |
+
('X', 'ᴇks'),
|
34 |
+
('Y', 'uᴀi'),
|
35 |
+
('Z', 'zᴇ')
|
36 |
+
]]
|
37 |
+
|
38 |
+
|
39 |
+
def _number_to_shanghainese(num):
|
40 |
+
num = cn2an.an2cn(num).replace('一十','十').replace('二十', '廿').replace('二', '两')
|
41 |
+
return re.sub(r'((?:^|[^三四五六七八九])十|廿)两', r'\1二', num)
|
42 |
+
|
43 |
+
|
44 |
+
def number_to_shanghainese(text):
|
45 |
+
return re.sub(r'\d+(?:\.?\d+)?', lambda x: _number_to_shanghainese(x.group()), text)
|
46 |
+
|
47 |
+
|
48 |
+
def latin_to_ipa(text):
|
49 |
+
for regex, replacement in _latin_to_ipa:
|
50 |
+
text = re.sub(regex, replacement, text)
|
51 |
+
return text
|
52 |
+
|
53 |
+
|
54 |
+
def shanghainese_to_ipa(text):
|
55 |
+
text = number_to_shanghainese(text.upper())
|
56 |
+
text = converter.convert(text).replace('-','').replace('$',' ')
|
57 |
+
text = re.sub(r'[A-Z]', lambda x: latin_to_ipa(x.group())+' ', text)
|
58 |
+
text = re.sub(r'[、;:]', ',', text)
|
59 |
+
text = re.sub(r'\s*,\s*', ', ', text)
|
60 |
+
text = re.sub(r'\s*。\s*', '. ', text)
|
61 |
+
text = re.sub(r'\s*?\s*', '? ', text)
|
62 |
+
text = re.sub(r'\s*!\s*', '! ', text)
|
63 |
+
text = re.sub(r'\s*$', '', text)
|
64 |
+
return text
|
text/thai.py
ADDED
@@ -0,0 +1,44 @@
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
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|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import re
|
2 |
+
from num_thai.thainumbers import NumThai
|
3 |
+
|
4 |
+
|
5 |
+
num = NumThai()
|
6 |
+
|
7 |
+
# List of (Latin alphabet, Thai) pairs:
|
8 |
+
_latin_to_thai = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
|
9 |
+
('a', 'เอ'),
|
10 |
+
('b','บี'),
|
11 |
+
('c','ซี'),
|
12 |
+
('d','ดี'),
|
13 |
+
('e','อี'),
|
14 |
+
('f','เอฟ'),
|
15 |
+
('g','จี'),
|
16 |
+
('h','เอช'),
|
17 |
+
('i','ไอ'),
|
18 |
+
('j','เจ'),
|
19 |
+
('k','เค'),
|
20 |
+
('l','แอล'),
|
21 |
+
('m','เอ็ม'),
|
22 |
+
('n','เอ็น'),
|
23 |
+
('o','โอ'),
|
24 |
+
('p','พี'),
|
25 |
+
('q','คิว'),
|
26 |
+
('r','แอร์'),
|
27 |
+
('s','เอส'),
|
28 |
+
('t','ที'),
|
29 |
+
('u','ยู'),
|
30 |
+
('v','วี'),
|
31 |
+
('w','ดับเบิลยู'),
|
32 |
+
('x','เอ็กซ์'),
|
33 |
+
('y','วาย'),
|
34 |
+
('z','ซี')
|
35 |
+
]]
|
36 |
+
|
37 |
+
|
38 |
+
def num_to_thai(text):
|
39 |
+
return re.sub(r'(?:\d+(?:,?\d+)?)+(?:\.\d+(?:,?\d+)?)?', lambda x: ''.join(num.NumberToTextThai(float(x.group(0).replace(',', '')))), text)
|
40 |
+
|
41 |
+
def latin_to_thai(text):
|
42 |
+
for regex, replacement in _latin_to_thai:
|
43 |
+
text = re.sub(regex, replacement, text)
|
44 |
+
return text
|
transforms.py
ADDED
@@ -0,0 +1,193 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
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|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
from torch.nn import functional as F
|
3 |
+
|
4 |
+
import numpy as np
|
5 |
+
|
6 |
+
|
7 |
+
DEFAULT_MIN_BIN_WIDTH = 1e-3
|
8 |
+
DEFAULT_MIN_BIN_HEIGHT = 1e-3
|
9 |
+
DEFAULT_MIN_DERIVATIVE = 1e-3
|
10 |
+
|
11 |
+
|
12 |
+
def piecewise_rational_quadratic_transform(inputs,
|
13 |
+
unnormalized_widths,
|
14 |
+
unnormalized_heights,
|
15 |
+
unnormalized_derivatives,
|
16 |
+
inverse=False,
|
17 |
+
tails=None,
|
18 |
+
tail_bound=1.,
|
19 |
+
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
20 |
+
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
21 |
+
min_derivative=DEFAULT_MIN_DERIVATIVE):
|
22 |
+
|
23 |
+
if tails is None:
|
24 |
+
spline_fn = rational_quadratic_spline
|
25 |
+
spline_kwargs = {}
|
26 |
+
else:
|
27 |
+
spline_fn = unconstrained_rational_quadratic_spline
|
28 |
+
spline_kwargs = {
|
29 |
+
'tails': tails,
|
30 |
+
'tail_bound': tail_bound
|
31 |
+
}
|
32 |
+
|
33 |
+
outputs, logabsdet = spline_fn(
|
34 |
+
inputs=inputs,
|
35 |
+
unnormalized_widths=unnormalized_widths,
|
36 |
+
unnormalized_heights=unnormalized_heights,
|
37 |
+
unnormalized_derivatives=unnormalized_derivatives,
|
38 |
+
inverse=inverse,
|
39 |
+
min_bin_width=min_bin_width,
|
40 |
+
min_bin_height=min_bin_height,
|
41 |
+
min_derivative=min_derivative,
|
42 |
+
**spline_kwargs
|
43 |
+
)
|
44 |
+
return outputs, logabsdet
|
45 |
+
|
46 |
+
|
47 |
+
def searchsorted(bin_locations, inputs, eps=1e-6):
|
48 |
+
bin_locations[..., -1] += eps
|
49 |
+
return torch.sum(
|
50 |
+
inputs[..., None] >= bin_locations,
|
51 |
+
dim=-1
|
52 |
+
) - 1
|
53 |
+
|
54 |
+
|
55 |
+
def unconstrained_rational_quadratic_spline(inputs,
|
56 |
+
unnormalized_widths,
|
57 |
+
unnormalized_heights,
|
58 |
+
unnormalized_derivatives,
|
59 |
+
inverse=False,
|
60 |
+
tails='linear',
|
61 |
+
tail_bound=1.,
|
62 |
+
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
63 |
+
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
64 |
+
min_derivative=DEFAULT_MIN_DERIVATIVE):
|
65 |
+
inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
|
66 |
+
outside_interval_mask = ~inside_interval_mask
|
67 |
+
|
68 |
+
outputs = torch.zeros_like(inputs)
|
69 |
+
logabsdet = torch.zeros_like(inputs)
|
70 |
+
|
71 |
+
if tails == 'linear':
|
72 |
+
unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
|
73 |
+
constant = np.log(np.exp(1 - min_derivative) - 1)
|
74 |
+
unnormalized_derivatives[..., 0] = constant
|
75 |
+
unnormalized_derivatives[..., -1] = constant
|
76 |
+
|
77 |
+
outputs[outside_interval_mask] = inputs[outside_interval_mask]
|
78 |
+
logabsdet[outside_interval_mask] = 0
|
79 |
+
else:
|
80 |
+
raise RuntimeError('{} tails are not implemented.'.format(tails))
|
81 |
+
|
82 |
+
outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
|
83 |
+
inputs=inputs[inside_interval_mask],
|
84 |
+
unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
|
85 |
+
unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
|
86 |
+
unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
|
87 |
+
inverse=inverse,
|
88 |
+
left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
|
89 |
+
min_bin_width=min_bin_width,
|
90 |
+
min_bin_height=min_bin_height,
|
91 |
+
min_derivative=min_derivative
|
92 |
+
)
|
93 |
+
|
94 |
+
return outputs, logabsdet
|
95 |
+
|
96 |
+
def rational_quadratic_spline(inputs,
|
97 |
+
unnormalized_widths,
|
98 |
+
unnormalized_heights,
|
99 |
+
unnormalized_derivatives,
|
100 |
+
inverse=False,
|
101 |
+
left=0., right=1., bottom=0., top=1.,
|
102 |
+
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
103 |
+
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
104 |
+
min_derivative=DEFAULT_MIN_DERIVATIVE):
|
105 |
+
if torch.min(inputs) < left or torch.max(inputs) > right:
|
106 |
+
raise ValueError('Input to a transform is not within its domain')
|
107 |
+
|
108 |
+
num_bins = unnormalized_widths.shape[-1]
|
109 |
+
|
110 |
+
if min_bin_width * num_bins > 1.0:
|
111 |
+
raise ValueError('Minimal bin width too large for the number of bins')
|
112 |
+
if min_bin_height * num_bins > 1.0:
|
113 |
+
raise ValueError('Minimal bin height too large for the number of bins')
|
114 |
+
|
115 |
+
widths = F.softmax(unnormalized_widths, dim=-1)
|
116 |
+
widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
|
117 |
+
cumwidths = torch.cumsum(widths, dim=-1)
|
118 |
+
cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
|
119 |
+
cumwidths = (right - left) * cumwidths + left
|
120 |
+
cumwidths[..., 0] = left
|
121 |
+
cumwidths[..., -1] = right
|
122 |
+
widths = cumwidths[..., 1:] - cumwidths[..., :-1]
|
123 |
+
|
124 |
+
derivatives = min_derivative + F.softplus(unnormalized_derivatives)
|
125 |
+
|
126 |
+
heights = F.softmax(unnormalized_heights, dim=-1)
|
127 |
+
heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
|
128 |
+
cumheights = torch.cumsum(heights, dim=-1)
|
129 |
+
cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
|
130 |
+
cumheights = (top - bottom) * cumheights + bottom
|
131 |
+
cumheights[..., 0] = bottom
|
132 |
+
cumheights[..., -1] = top
|
133 |
+
heights = cumheights[..., 1:] - cumheights[..., :-1]
|
134 |
+
|
135 |
+
if inverse:
|
136 |
+
bin_idx = searchsorted(cumheights, inputs)[..., None]
|
137 |
+
else:
|
138 |
+
bin_idx = searchsorted(cumwidths, inputs)[..., None]
|
139 |
+
|
140 |
+
input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
|
141 |
+
input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
|
142 |
+
|
143 |
+
input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
|
144 |
+
delta = heights / widths
|
145 |
+
input_delta = delta.gather(-1, bin_idx)[..., 0]
|
146 |
+
|
147 |
+
input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
|
148 |
+
input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
|
149 |
+
|
150 |
+
input_heights = heights.gather(-1, bin_idx)[..., 0]
|
151 |
+
|
152 |
+
if inverse:
|
153 |
+
a = (((inputs - input_cumheights) * (input_derivatives
|
154 |
+
+ input_derivatives_plus_one
|
155 |
+
- 2 * input_delta)
|
156 |
+
+ input_heights * (input_delta - input_derivatives)))
|
157 |
+
b = (input_heights * input_derivatives
|
158 |
+
- (inputs - input_cumheights) * (input_derivatives
|
159 |
+
+ input_derivatives_plus_one
|
160 |
+
- 2 * input_delta))
|
161 |
+
c = - input_delta * (inputs - input_cumheights)
|
162 |
+
|
163 |
+
discriminant = b.pow(2) - 4 * a * c
|
164 |
+
assert (discriminant >= 0).all()
|
165 |
+
|
166 |
+
root = (2 * c) / (-b - torch.sqrt(discriminant))
|
167 |
+
outputs = root * input_bin_widths + input_cumwidths
|
168 |
+
|
169 |
+
theta_one_minus_theta = root * (1 - root)
|
170 |
+
denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
|
171 |
+
* theta_one_minus_theta)
|
172 |
+
derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
|
173 |
+
+ 2 * input_delta * theta_one_minus_theta
|
174 |
+
+ input_derivatives * (1 - root).pow(2))
|
175 |
+
logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
|
176 |
+
|
177 |
+
return outputs, -logabsdet
|
178 |
+
else:
|
179 |
+
theta = (inputs - input_cumwidths) / input_bin_widths
|
180 |
+
theta_one_minus_theta = theta * (1 - theta)
|
181 |
+
|
182 |
+
numerator = input_heights * (input_delta * theta.pow(2)
|
183 |
+
+ input_derivatives * theta_one_minus_theta)
|
184 |
+
denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
|
185 |
+
* theta_one_minus_theta)
|
186 |
+
outputs = input_cumheights + numerator / denominator
|
187 |
+
|
188 |
+
derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
|
189 |
+
+ 2 * input_delta * theta_one_minus_theta
|
190 |
+
+ input_derivatives * (1 - theta).pow(2))
|
191 |
+
logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
|
192 |
+
|
193 |
+
return outputs, logabsdet
|
utils.py
ADDED
@@ -0,0 +1,171 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import logging
|
2 |
+
import os
|
3 |
+
import sys
|
4 |
+
from io import BytesIO
|
5 |
+
from json import loads
|
6 |
+
import av
|
7 |
+
import pilk
|
8 |
+
from flask import Flask
|
9 |
+
from torch import load, FloatTensor
|
10 |
+
from numpy import float32
|
11 |
+
import librosa
|
12 |
+
|
13 |
+
from voice import Voice
|
14 |
+
|
15 |
+
app = Flask(__name__)
|
16 |
+
app.config.from_pyfile("config.py")
|
17 |
+
|
18 |
+
|
19 |
+
class HParams():
|
20 |
+
def __init__(self, **kwargs):
|
21 |
+
for k, v in kwargs.items():
|
22 |
+
if type(v) == dict:
|
23 |
+
v = HParams(**v)
|
24 |
+
self[k] = v
|
25 |
+
|
26 |
+
def keys(self):
|
27 |
+
return self.__dict__.keys()
|
28 |
+
|
29 |
+
def items(self):
|
30 |
+
return self.__dict__.items()
|
31 |
+
|
32 |
+
def values(self):
|
33 |
+
return self.__dict__.values()
|
34 |
+
|
35 |
+
def __len__(self):
|
36 |
+
return len(self.__dict__)
|
37 |
+
|
38 |
+
def __getitem__(self, key):
|
39 |
+
return getattr(self, key)
|
40 |
+
|
41 |
+
def __setitem__(self, key, value):
|
42 |
+
return setattr(self, key, value)
|
43 |
+
|
44 |
+
def __contains__(self, key):
|
45 |
+
return key in self.__dict__
|
46 |
+
|
47 |
+
def __repr__(self):
|
48 |
+
return self.__dict__.__repr__()
|
49 |
+
|
50 |
+
|
51 |
+
def load_checkpoint(checkpoint_path, model):
|
52 |
+
checkpoint_dict = load(checkpoint_path, map_location='cpu')
|
53 |
+
iteration = checkpoint_dict['iteration']
|
54 |
+
saved_state_dict = checkpoint_dict['model']
|
55 |
+
if hasattr(model, 'module'):
|
56 |
+
state_dict = model.module.state_dict()
|
57 |
+
else:
|
58 |
+
state_dict = model.state_dict()
|
59 |
+
new_state_dict = {}
|
60 |
+
for k, v in state_dict.items():
|
61 |
+
try:
|
62 |
+
new_state_dict[k] = saved_state_dict[k]
|
63 |
+
except:
|
64 |
+
logging.info("%s is not in the checkpoint" % k)
|
65 |
+
new_state_dict[k] = v
|
66 |
+
if hasattr(model, 'module'):
|
67 |
+
model.module.load_state_dict(new_state_dict)
|
68 |
+
else:
|
69 |
+
model.load_state_dict(new_state_dict)
|
70 |
+
logging.info("Loaded checkpoint '{}' (iteration {})".format(
|
71 |
+
checkpoint_path, iteration))
|
72 |
+
return
|
73 |
+
|
74 |
+
|
75 |
+
def get_hparams_from_file(config_path):
|
76 |
+
with open(config_path, "r") as f:
|
77 |
+
data = f.read()
|
78 |
+
config = loads(data)
|
79 |
+
|
80 |
+
hparams = HParams(**config)
|
81 |
+
return hparams
|
82 |
+
|
83 |
+
|
84 |
+
def load_audio_to_torch(full_path, target_sampling_rate):
|
85 |
+
audio, sampling_rate = librosa.load(full_path, sr=target_sampling_rate, mono=True)
|
86 |
+
return FloatTensor(audio.astype(float32))
|
87 |
+
|
88 |
+
|
89 |
+
def wav2ogg(input, output):
|
90 |
+
with av.open(input, 'rb') as i:
|
91 |
+
with av.open(output, 'wb', format='ogg') as o:
|
92 |
+
out_stream = o.add_stream('libvorbis')
|
93 |
+
for frame in i.decode(audio=0):
|
94 |
+
for p in out_stream.encode(frame):
|
95 |
+
o.mux(p)
|
96 |
+
|
97 |
+
for p in out_stream.encode(None):
|
98 |
+
o.mux(p)
|
99 |
+
|
100 |
+
|
101 |
+
# def wav2silk(input, output):
|
102 |
+
# with av.open(input) as in_wav:
|
103 |
+
# in_stream = in_wav.streams.audio[0]
|
104 |
+
# sample_rate = in_stream.codec_context.sample_rate
|
105 |
+
# with BytesIO() as pcm:
|
106 |
+
# with av.open(pcm, 'w', 's16le') as out_pcm:
|
107 |
+
# out_stream = out_pcm.add_stream(
|
108 |
+
# 'pcm_s16le',
|
109 |
+
# rate=sample_rate,
|
110 |
+
# layout='mono'
|
111 |
+
# )
|
112 |
+
# for frame in in_wav.decode(in_stream):
|
113 |
+
# frame.pts = None
|
114 |
+
# for packet in out_stream.encode(frame):
|
115 |
+
# out_pcm.mux(packet)
|
116 |
+
#
|
117 |
+
# pilk.encode(out_pcm, output, pcm_rate=sample_rate, tencent=True)
|
118 |
+
|
119 |
+
|
120 |
+
def to_pcm(in_path: str) -> tuple[str, int]:
|
121 |
+
out_path = os.path.splitext(in_path)[0] + '.pcm'
|
122 |
+
with av.open(in_path) as in_container:
|
123 |
+
in_stream = in_container.streams.audio[0]
|
124 |
+
sample_rate = in_stream.codec_context.sample_rate
|
125 |
+
with av.open(out_path, 'w', 's16le') as out_container:
|
126 |
+
out_stream = out_container.add_stream(
|
127 |
+
'pcm_s16le',
|
128 |
+
rate=sample_rate,
|
129 |
+
layout='mono'
|
130 |
+
)
|
131 |
+
try:
|
132 |
+
for frame in in_container.decode(in_stream):
|
133 |
+
frame.pts = None
|
134 |
+
for packet in out_stream.encode(frame):
|
135 |
+
out_container.mux(packet)
|
136 |
+
except:
|
137 |
+
pass
|
138 |
+
return out_path, sample_rate
|
139 |
+
|
140 |
+
|
141 |
+
def convert_to_silk(media_path: str) -> str:
|
142 |
+
pcm_path, sample_rate = to_pcm(media_path)
|
143 |
+
silk_path = os.path.splitext(pcm_path)[0] + '.silk'
|
144 |
+
pilk.encode(pcm_path, silk_path, pcm_rate=sample_rate, tencent=True)
|
145 |
+
os.remove(pcm_path)
|
146 |
+
return silk_path
|
147 |
+
|
148 |
+
|
149 |
+
def clean_folder(folder_path):
|
150 |
+
for filename in os.listdir(folder_path):
|
151 |
+
file_path = os.path.join(folder_path, filename)
|
152 |
+
# 如果是文件,则删除文件
|
153 |
+
if os.path.isfile(file_path):
|
154 |
+
os.remove(file_path)
|
155 |
+
|
156 |
+
|
157 |
+
def merge_model(merging_model):
|
158 |
+
voice_obj = []
|
159 |
+
voice_speakers = []
|
160 |
+
new_id = 0
|
161 |
+
out_path = os.path.dirname(os.path.realpath(sys.argv[0])) + "/out_silk"
|
162 |
+
for obj_id, i in enumerate(merging_model):
|
163 |
+
obj = Voice(i[0], i[1], out_path)
|
164 |
+
|
165 |
+
for id, name in enumerate(obj.return_speakers()):
|
166 |
+
voice_obj.append([int(id), obj, obj_id])
|
167 |
+
voice_speakers.append({new_id: name})
|
168 |
+
|
169 |
+
new_id += 1
|
170 |
+
|
171 |
+
return voice_obj, voice_speakers
|
voice.py
ADDED
@@ -0,0 +1,231 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
|
3 |
+
import librosa
|
4 |
+
from scipy.io.wavfile import write
|
5 |
+
from mel_processing import spectrogram_torch
|
6 |
+
from text import text_to_sequence, _clean_text
|
7 |
+
from models import SynthesizerTrn
|
8 |
+
import utils
|
9 |
+
import commons
|
10 |
+
import sys
|
11 |
+
import re
|
12 |
+
import numpy as np
|
13 |
+
# import torch
|
14 |
+
# torch.set_num_threads(1) #设置torch线程为1,防止多任务推理时服务崩溃,但flask仍然会使用多线程
|
15 |
+
from torch import no_grad, LongTensor, inference_mode, FloatTensor
|
16 |
+
import audonnx
|
17 |
+
import uuid
|
18 |
+
from io import BytesIO
|
19 |
+
|
20 |
+
|
21 |
+
class Voice:
|
22 |
+
def __init__(self, model, config, out_path=None):
|
23 |
+
self.out_path = out_path
|
24 |
+
if not os.path.exists(self.out_path):
|
25 |
+
try:
|
26 |
+
os.mkdir(self.out_path)
|
27 |
+
except:
|
28 |
+
pass
|
29 |
+
|
30 |
+
self.hps_ms = utils.get_hparams_from_file(config)
|
31 |
+
self.n_speakers = self.hps_ms.data.n_speakers if 'n_speakers' in self.hps_ms.data.keys() else 0
|
32 |
+
self.n_symbols = len(self.hps_ms.symbols) if 'symbols' in self.hps_ms.keys() else 0
|
33 |
+
self.speakers = self.hps_ms.speakers if 'speakers' in self.hps_ms.keys() else ['0']
|
34 |
+
self.use_f0 = self.hps_ms.data.use_f0 if 'use_f0' in self.hps_ms.data.keys() else False
|
35 |
+
self.emotion_embedding = self.hps_ms.data.emotion_embedding if 'emotion_embedding' in self.hps_ms.data.keys() else False
|
36 |
+
|
37 |
+
self.net_g_ms = SynthesizerTrn(
|
38 |
+
self.n_symbols,
|
39 |
+
self.hps_ms.data.filter_length // 2 + 1,
|
40 |
+
self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
|
41 |
+
n_speakers=self.n_speakers,
|
42 |
+
emotion_embedding=self.emotion_embedding,
|
43 |
+
**self.hps_ms.model)
|
44 |
+
_ = self.net_g_ms.eval()
|
45 |
+
utils.load_checkpoint(model, self.net_g_ms)
|
46 |
+
|
47 |
+
def get_text(self, text, hps, cleaned=False):
|
48 |
+
if cleaned:
|
49 |
+
text_norm = text_to_sequence(text, hps.symbols, [])
|
50 |
+
else:
|
51 |
+
text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
|
52 |
+
if hps.data.add_blank:
|
53 |
+
text_norm = commons.intersperse(text_norm, 0)
|
54 |
+
text_norm = LongTensor(text_norm)
|
55 |
+
return text_norm
|
56 |
+
|
57 |
+
def get_label_value(self, text, label, default, warning_name='value'):
|
58 |
+
value = re.search(rf'\[{label}=(.+?)\]', text)
|
59 |
+
if value:
|
60 |
+
try:
|
61 |
+
text = re.sub(rf'\[{label}=(.+?)\]', '', text, 1)
|
62 |
+
value = float(value.group(1))
|
63 |
+
except:
|
64 |
+
print(f'Invalid {warning_name}!')
|
65 |
+
sys.exit(1)
|
66 |
+
else:
|
67 |
+
value = default
|
68 |
+
return value, text
|
69 |
+
|
70 |
+
def ex_return(self, text, escape=False):
|
71 |
+
if escape:
|
72 |
+
return text.encode('unicode_escape').decode()
|
73 |
+
else:
|
74 |
+
return text
|
75 |
+
|
76 |
+
def return_speakers(self, escape=False):
|
77 |
+
return self.speakers
|
78 |
+
|
79 |
+
def get_label(self, text, label):
|
80 |
+
if f'[{label}]' in text:
|
81 |
+
return True, text.replace(f'[{label}]', '')
|
82 |
+
else:
|
83 |
+
return False, text
|
84 |
+
|
85 |
+
def generate(self, text=None, speaker_id=None, format=None, speed=1, audio_path=None, target_id=None, escape=False,
|
86 |
+
option=None, w2v2_folder=None):
|
87 |
+
if self.n_symbols != 0:
|
88 |
+
if not self.emotion_embedding:
|
89 |
+
length_scale, text = self.get_label_value(text, 'LENGTH', speed, 'length scale')
|
90 |
+
noise_scale, text = self.get_label_value(text, 'NOISE', 0.667, 'noise scale')
|
91 |
+
noise_scale_w, text = self.get_label_value(text, 'NOISEW', 0.8, 'deviation of noise')
|
92 |
+
cleaned, text = self.get_label(text, 'CLEANED')
|
93 |
+
|
94 |
+
stn_tst = self.get_text(text, self.hps_ms, cleaned=cleaned)
|
95 |
+
with no_grad():
|
96 |
+
x_tst = stn_tst.unsqueeze(0)
|
97 |
+
x_tst_lengths = LongTensor([stn_tst.size(0)])
|
98 |
+
sid = LongTensor([speaker_id])
|
99 |
+
audio = self.net_g_ms.infer(x_tst, x_tst_lengths, sid=sid,
|
100 |
+
noise_scale=noise_scale,
|
101 |
+
noise_scale_w=noise_scale_w,
|
102 |
+
length_scale=length_scale)[0][0, 0].data.cpu().float().numpy()
|
103 |
+
|
104 |
+
# else:
|
105 |
+
# w2v2_model = audonnx.load(os.path.dirname(w2v2_folder))
|
106 |
+
#
|
107 |
+
# if option == 'clean':
|
108 |
+
# self.ex_print(_clean_text(
|
109 |
+
# text, self.hps_ms.data.text_cleaners), escape)
|
110 |
+
#
|
111 |
+
# length_scale, text = self.get_label_value(
|
112 |
+
# text, 'LENGTH', 1, 'length scale')
|
113 |
+
# noise_scale, text = self.get_label_value(
|
114 |
+
# text, 'NOISE', 0.667, 'noise scale')
|
115 |
+
# noise_scale_w, text = self.get_label_value(
|
116 |
+
# text, 'NOISEW', 0.8, 'deviation of noise')
|
117 |
+
# cleaned, text = self.get_label(text, 'CLEANED')
|
118 |
+
#
|
119 |
+
# stn_tst = self.get_text(text, self.hps_ms, cleaned=cleaned)
|
120 |
+
#
|
121 |
+
# emotion_reference = input('Path of an emotion reference: ')
|
122 |
+
# if emotion_reference.endswith('.npy'):
|
123 |
+
# emotion = np.load(emotion_reference)
|
124 |
+
# emotion = FloatTensor(emotion).unsqueeze(0)
|
125 |
+
# else:
|
126 |
+
# audio16000, sampling_rate = librosa.load(
|
127 |
+
# emotion_reference, sr=16000, mono=True)
|
128 |
+
# emotion = w2v2_model(audio16000, sampling_rate)[
|
129 |
+
# 'hidden_states']
|
130 |
+
# emotion_reference = re.sub(
|
131 |
+
# r'\..*$', '', emotion_reference)
|
132 |
+
# np.save(emotion_reference, emotion.squeeze(0))
|
133 |
+
# emotion = FloatTensor(emotion)
|
134 |
+
#
|
135 |
+
#
|
136 |
+
# with no_grad():
|
137 |
+
# x_tst = stn_tst.unsqueeze(0)
|
138 |
+
# x_tst_lengths = LongTensor([stn_tst.size(0)])
|
139 |
+
# sid = LongTensor([speaker_id])
|
140 |
+
# audio = self.net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=noise_scale,
|
141 |
+
# noise_scale_w=noise_scale_w,
|
142 |
+
# length_scale=length_scale, emotion_embedding=emotion)[0][
|
143 |
+
# 0, 0].data.cpu().float().numpy()
|
144 |
+
|
145 |
+
# else:
|
146 |
+
# model = input('Path of a hubert-soft Model: ')
|
147 |
+
# from hubert_model import hubert_soft
|
148 |
+
# hubert = hubert_soft(model)
|
149 |
+
|
150 |
+
# if audio_path != '[VC]':
|
151 |
+
# if self.use_f0:
|
152 |
+
# audio, sampling_rate = librosa.load(
|
153 |
+
# audio_path, sr=self.hps_ms.data.sampling_rate, mono=True)
|
154 |
+
# audio16000 = librosa.resample(
|
155 |
+
# audio, orig_sr=sampling_rate, target_sr=16000)
|
156 |
+
# else:
|
157 |
+
# audio16000, sampling_rate = librosa.load(
|
158 |
+
# audio_path, sr=16000, mono=True)
|
159 |
+
#
|
160 |
+
# out_path = "H:/git/MoeGoe-Simple-API/upload/hubert.wav"
|
161 |
+
# length_scale, out_path = self.get_label_value(
|
162 |
+
# out_path, 'LENGTH', 1, 'length scale')
|
163 |
+
# noise_scale, out_path = self.get_label_value(
|
164 |
+
# out_path, 'NOISE', 0.1, 'noise scale')
|
165 |
+
# noise_scale_w, out_path = self.get_label_value(
|
166 |
+
# out_path, 'NOISEW', 0.1, 'deviation of noise')
|
167 |
+
#
|
168 |
+
# with inference_mode():
|
169 |
+
# units = hubert.units(FloatTensor(audio16000).unsqueeze(
|
170 |
+
# 0).unsqueeze(0)).squeeze(0).numpy()
|
171 |
+
# if self.use_f0:
|
172 |
+
# f0_scale, out_path = self.get_label_value(
|
173 |
+
# out_path, 'F0', 1, 'f0 scale')
|
174 |
+
# f0 = librosa.pyin(audio, sr=sampling_rate,
|
175 |
+
# fmin=librosa.note_to_hz('C0'),
|
176 |
+
# fmax=librosa.note_to_hz('C7'),
|
177 |
+
# frame_length=1780)[0]
|
178 |
+
# target_length = len(units[:, 0])
|
179 |
+
# f0 = np.nan_to_num(np.interp(np.arange(0, len(f0) * target_length, len(f0)) / target_length,
|
180 |
+
# np.arange(0, len(f0)), f0)) * f0_scale
|
181 |
+
# units[:, 0] = f0 / 10
|
182 |
+
#
|
183 |
+
# stn_tst = FloatTensor(units)
|
184 |
+
# with no_grad():
|
185 |
+
# x_tst = stn_tst.unsqueeze(0)
|
186 |
+
# x_tst_lengths = LongTensor([stn_tst.size(0)])
|
187 |
+
# sid = LongTensor([target_id])
|
188 |
+
# audio = self.net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=noise_scale,
|
189 |
+
# noise_scale_w=noise_scale_w, length_scale=length_scale)[0][
|
190 |
+
# 0, 0].data.float().numpy()
|
191 |
+
|
192 |
+
with BytesIO() as f:
|
193 |
+
fname = str(uuid.uuid1())
|
194 |
+
|
195 |
+
if format == 'ogg':
|
196 |
+
write(f, self.hps_ms.data.sampling_rate, audio)
|
197 |
+
with BytesIO() as o:
|
198 |
+
utils.wav2ogg(f, o)
|
199 |
+
return BytesIO(o.getvalue()), "audio/ogg", fname + ".ogg"
|
200 |
+
elif format == 'silk':
|
201 |
+
file_path = self.out_path + "/" + fname + ".wav"
|
202 |
+
write(file_path, 24000, audio)
|
203 |
+
silk_path = utils.convert_to_silk(file_path)
|
204 |
+
os.remove(file_path)
|
205 |
+
return silk_path, "audio/silk", fname + ".silk"
|
206 |
+
else:
|
207 |
+
write(f, self.hps_ms.data.sampling_rate, audio)
|
208 |
+
return BytesIO(f.getvalue()), "audio/wav", fname + ".wav"
|
209 |
+
|
210 |
+
def voice_conversion(self, audio_path, original_id, target_id):
|
211 |
+
|
212 |
+
audio = utils.load_audio_to_torch(
|
213 |
+
audio_path, self.hps_ms.data.sampling_rate)
|
214 |
+
|
215 |
+
y = audio.unsqueeze(0)
|
216 |
+
|
217 |
+
spec = spectrogram_torch(y, self.hps_ms.data.filter_length,
|
218 |
+
self.hps_ms.data.sampling_rate, self.hps_ms.data.hop_length,
|
219 |
+
self.hps_ms.data.win_length,
|
220 |
+
center=False)
|
221 |
+
spec_lengths = LongTensor([spec.size(-1)])
|
222 |
+
sid_src = LongTensor([original_id])
|
223 |
+
|
224 |
+
with no_grad():
|
225 |
+
sid_tgt = LongTensor([target_id])
|
226 |
+
audio = self.net_g_ms.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[
|
227 |
+
0][0, 0].data.cpu().float().numpy()
|
228 |
+
|
229 |
+
with BytesIO() as f:
|
230 |
+
write(f, self.hps_ms.data.sampling_rate, audio)
|
231 |
+
return BytesIO(f.getvalue())
|