Dev no emo (#123)
* Create emo_gen.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update server.py, fix bugs in func get_text() and infer(). (#52) * Extract get_text() and infer() from webui.py. (#53) * Extract get_text() and infer() from webui.py. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * add emo emb * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * init emo gen * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * init emo * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * init emo * Delete bert/bert-base-japanese-v3 directory * Create .gitkeep * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Create add_punc.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix bug in bert_gen.py (#54) * Update README.md * fix bug in models.py (#56) * 更新 models.py * Fix japanese cleaner (#61) * 初步，睡觉明天继续写（ * 好好好放错分支了，熬夜是大忌 * [pre-commit.ci] pre-commit autoupdate (#55) * [pre-commit.ci] pre-commit autoupdate updates: - [github.com/pre-commit/pre-commit-hooks: v4.4.0 → v4.5.0](https://github.com/pre-commit/pre-commit-hooks/compare/v4.4.0...v4.5.0) * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Create tokenizer_config.json * update preprocess_text.py：过滤一个音频匹配多个文本的情况 (#57) * update preprocess_text.py：过滤音频不存在的情况 (#58) * 修复日语cleaner和bert * better * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: Stardust·减 <star_dust_chen@foxmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Sora <atri@suzakuintsubaki.com> * Apply Code Formatter Change * Add config.yml for global configuration. (#62) * Add config.yml for global configuration. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix bug in webui.py. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Rename config.yml to default_config.yml. Add ./config.yml to gitignore. * Add config.py to parse config.yml * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Update webui.py (#65) * Update webui.py: 1. Add auto translation from Chinese to Japanese. 2. Start to use config.py in webui.py to set config instead of using the command line. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Fix (#68) * 加上ー * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Update infer.py and webui.py. Supports loading and inference models of 1.1.1 version. (#66) * Update infer.py and webui.py. Supports loading and inference models of 1.1.1 version. * Update config.json * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Fix bug in translate.py (#69) * Supports loading and inference models of 1.1、1.0.1、1.0 version. (#70) * Supports loading and inference models of 1.1、1.0.1、1.0 version. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Delete useless file in OldVersion --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Update japanese.py (#71) Handling JA long pronunciations * 使用配置文件配置bert_gen.py, preprocess_text.py, resample.py (#72) * Update bert_gen.py, preprocess_text.py, resample.py. Support using config.yml in these files. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update bert_gen.py * Update bert_gen.py, fix bug. --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Delete bert/bert-base-japanese-v3 directory * Create config.json * Create tokenizer_config.json * Create vocab.txt * Update server.py. 支持多版本多模型 (#76) * Update server.py. 支持多版本多模型 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Dev webui (#77) * 申请pr (#75) * 2023/10/11 update 界面优化 * Update webui.py 翻译英文页面为中文 * Update train_ms.py 单卡训练 * 加入图片 * Update extern_subprocess.py * Update asr_transcript.py * Update asr_transcript.py * Update asr_transcript.py * Update extern_subprocess.py * Update asr_transcript.py * Update asr_transcript.py * Update asr_transcript.py * Update all_process.py * Update extern_subprocess.py * Update all_process.py * Update all_process.py * Update asr_transcript.py * Update extern_subprocess.py * Update webui.py * Create re_matching.py * Update webui.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update all_process.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update all_process.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update all_process.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update asr_transcript.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Pack 'update' functions into a module * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update all_process.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update asr_transcript.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update extern_subprocess.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update all_process.py * Update asr_transcript.py * Update webui.py * Add files via upload * Update extern_subprocess.py * Update all_process.py * Update asr_transcript.py * Update bert_gen.py * Update extern_subprocess.py * Update preprocess_text.py * Update re_matching.py * Update resample.py * Update update_status.py * Update update_status.py * Update webui.py * Update all_process.py * Update preprocess_text.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update train_ms.py --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Stardust·减 <star_dust_chen@foxmail.com> Co-authored-by: innnky <67028263+innnky@users.noreply.github.com> * Delete all_process.py * Delete asr_transcript.py * Delete extern_subprocess.py --------- Co-authored-by: spicysama <122108331+AnyaCoder@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: innnky <67028263+innnky@users.noreply.github.com> * Create config.json * Create preprocessor_config.json * Create vocab.json * Delete emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/.gitkeep * Update emo_gen.py * Delete add_punc.py * add emotion_clustering.i * Apply Code Formatter Change * Update models.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update preprocess_text.py (#78) * Update preprocess_text.py. 检测重复以及不存在的音频 (#79) * Handle Janpanese long pronunciations (#80) * Handle Janpanese long pronunciations * Update japanese.py * Update japanese.py * Use unified phonemes for Japanese long vowel (#82) * Use an unified phoneme for Japanese long vowel `symbol.py` has not been updated to ensure compatibility with older version models. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * 增加一个按钮，点击后可以按句子切分，添加“|” (#81) * Update re_matching.py * Update webui.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Fix phonemer bug (#83) * Fix phonemer bug * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Fix long vowel handler bug (#84) * Fix long vowel handler bug * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * 加入整合包管理器的特性：长文本合成可以自定义句间段间停顿 (#85) * Update webui.py * Update re_matching.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Update train_ms.py * fix' * Update cleaner.py * add en * add en * Update english.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add en * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add en * add en * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add en * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 更新 README.md * 更新 README.md * 更新 README.md * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Change phonemer to pyopenjtalk (#86) * Change phonemer to pyopenjtalk * 修改为openjtalk便于安装 --------- Co-authored-by: Stardust·减 <star_dust_chen@foxmail.com> * 更新 english.py * Fix english_bert_mock.py. (#87) * Add punctuation execptions (#88) * Add punctuation execptions * Ellipses exceptions * remove get bert * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix bug in oldVersion. (#89) * Update requirements.txt * change to large * rollback requirements.txt * Feat: Enable 1.1.1 models using fix-ver infer. (#91) * Feat: Enable 1.1.1 models using fix-ver infer. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Add Japanese accent (high-low) (#90) * Add punctuation execptions * Ellipses exceptions * Add Japanese accent * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Do not replace iteration mark (#92) * Add punctuation execptions * Ellipses exceptions * Add Japanese accent * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Do not replace iteration mark --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Fix: fix import error in oldVersion (#93) * Refactor: reusing model loading in webui.py and server.py. (#94) * Feat: Enable using config.yml in train_ms.py (#96) * 更新 emo_gen.py * Change emo_gen.py (#97) * Fix emo_gen bugs * Add multiprocess * Fix queue (#98) * Fix emo_gen bugs * Add multiprocess * Del var * Fix queue * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Fix training bugs (#99) * Updatge cluster notebook * Fix train * Fix filename * Update infer.py (#100) * Update infer.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Add reference audio (#101) * Add reference audio * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update * Update * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Stardust·减 <star_dust_chen@foxmail.com> * Fix: fix 1.1.1-fix (#102) * Fix infer bug (#103) * Feat: Add server_fastapi.py. (#104) * Feat: Add server_fastapi.py. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix: Update requirements.txt. --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Fix: requirements.txt. (#105) * Swith to deberta-v3-large (#106) * Swith to deberta-v3-large * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Feat: Update config.py. (#107) * Feat: Update config.py. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Dev fix (#108) * fix bugs when deploying * fix bugs when deploying * fix bugs when deploying * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Revert "Dev fix (#108)" (#109) This reverts commit 685e18a10498d602b1a9a26079340d11925646f0. * Dev fix (#110) * fix bugs when deploying * fix bugs when deploying * fix bugs when deploying * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix fixed bugs * fix fixed bugs * fix fixed bug 3 * fix fixed bug 4 * fix fixed bug 5 * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Add emo vec quantizer (#111) Co-authored-by: Stardust·减 <star_dust_chen@foxmail.com> * Clean req and gitignore (#112) * Clean req and gitignore * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Switch to deberta-v2-large-japanese (#113) * Switch to deberta-v2-large-japanese * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Fix emo bugs (#114) * Fix english (#115) * Remove emo (#117) * Don't train codebook * Remove emo * Update * Update * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Merge dev into no-emo (#122) * [pre-commit.ci] pre-commit autoupdate (#95) * [pre-commit.ci] pre-commit autoupdate updates: - [github.com/astral-sh/ruff-pre-commit: v0.0.292 → v0.1.1](https://github.com/astral-sh/ruff-pre-commit/compare/v0.0.292...v0.1.1) - [github.com/psf/black: 23.9.1 → 23.10.0](https://github.com/psf/black/compare/23.9.1...23.10.0) * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Don't train codebook (#116) * Update requirements.txt * Update english_bert_mock.py * Fix: server_fastapi.py (#118) * Fix: server_fastapi.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Fix: don't print debug logging. (#119) * Fix: don't print debug logging. * Feat: support emo_gen config * Fix config * Apply Code Formatter Change * 更新，修正bug (#121) * Feat: Update infer.py preprocess_text.py server_fastapi.py. * Fix resample.py. Maintain same directory structure in out_dir as in_dir. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update resample.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Update server_fastapi.py to no-emo ver * Update config.py, no emo config --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: OedoSoldier <31711261+OedoSoldier@users.noreply.github.com> Co-authored-by: Stardust·减 <star_dust_chen@foxmail.com> Co-authored-by: Stardust-minus <Stardust-minus@users.noreply.github.com> * Update train_ms.py * Update latest version info (#124) --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: jiangyuxiaoxiao <atri@suzakuintsubaki.com> Co-authored-by: AkitoLiu <39857739+Akito-UzukiP@users.noreply.github.com> Co-authored-by: Stardust-minus <Stardust-minus@users.noreply.github.com> Co-authored-by: OedoSoldier <31711261+OedoSoldier@users.noreply.github.com> Co-authored-by: spicysama <122108331+AnyaCoder@users.noreply.github.com> Co-authored-by: innnky <67028263+innnky@users.noreply.github.com> Co-authored-by: YYuX-1145 <138500330+YYuX-1145@users.noreply.github.com>
This commit is contained in:
Stardust·减
90
oldVersion/V110/__init__.py
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90
oldVersion/V110/__init__.py
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@@ -0,0 +1,90 @@
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"""
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1.1 版本兼容
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https://github.com/fishaudio/Bert-VITS2/releases/tag/1.1
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"""
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import torch
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import commons
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from .text.cleaner import clean_text
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from .text import cleaned_text_to_sequence
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from oldVersion.V111.text import get_bert
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def get_text(text, language_str, hps, device):
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norm_text, phone, tone, word2ph = clean_text(text, language_str)
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phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
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if hps.data.add_blank:
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phone = commons.intersperse(phone, 0)
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tone = commons.intersperse(tone, 0)
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language = commons.intersperse(language, 0)
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for i in range(len(word2ph)):
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word2ph[i] = word2ph[i] * 2
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word2ph[0] += 1
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bert = get_bert(norm_text, word2ph, language_str, device)
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del word2ph
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assert bert.shape[-1] == len(phone), phone
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if language_str == "ZH":
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bert = bert
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ja_bert = torch.zeros(768, len(phone))
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elif language_str == "JP":
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ja_bert = bert
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bert = torch.zeros(1024, len(phone))
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else:
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bert = torch.zeros(1024, len(phone))
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ja_bert = torch.zeros(768, len(phone))
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assert bert.shape[-1] == len(
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phone
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), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
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phone = torch.LongTensor(phone)
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tone = torch.LongTensor(tone)
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language = torch.LongTensor(language)
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return bert, ja_bert, phone, tone, language
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def infer(
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text,
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sdp_ratio,
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noise_scale,
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noise_scale_w,
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length_scale,
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sid,
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language,
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hps,
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net_g,
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device,
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):
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bert, ja_bert, phones, tones, lang_ids = get_text(text, language, hps, device)
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with torch.no_grad():
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x_tst = phones.to(device).unsqueeze(0)
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tones = tones.to(device).unsqueeze(0)
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lang_ids = lang_ids.to(device).unsqueeze(0)
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bert = bert.to(device).unsqueeze(0)
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ja_bert = ja_bert.to(device).unsqueeze(0)
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x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
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del phones
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speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
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audio = (
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net_g.infer(
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x_tst,
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x_tst_lengths,
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speakers,
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tones,
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lang_ids,
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bert,
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ja_bert,
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sdp_ratio=sdp_ratio,
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noise_scale=noise_scale,
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noise_scale_w=noise_scale_w,
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length_scale=length_scale,
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)[0][0, 0]
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.data.cpu()
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.float()
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.numpy()
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)
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del x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, ja_bert
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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return audio
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986
oldVersion/V110/models.py
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986
oldVersion/V110/models.py
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import math
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import torch
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from torch import nn
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from torch.nn import functional as F
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import commons
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import modules
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import attentions
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import monotonic_align
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from torch.nn import Conv1d, ConvTranspose1d, Conv2d
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from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
||||
|
||||
from commons import init_weights, get_padding
|
||||
from .text import symbols, num_tones, num_languages
|
||||
|
||||
|
||||
class DurationDiscriminator(nn.Module): # vits2
|
||||
def __init__(
|
||||
self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.in_channels = in_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
self.conv_1 = nn.Conv1d(
|
||||
in_channels, filter_channels, kernel_size, padding=kernel_size // 2
|
||||
)
|
||||
self.norm_1 = modules.LayerNorm(filter_channels)
|
||||
self.conv_2 = nn.Conv1d(
|
||||
filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
|
||||
)
|
||||
self.norm_2 = modules.LayerNorm(filter_channels)
|
||||
self.dur_proj = nn.Conv1d(1, filter_channels, 1)
|
||||
|
||||
self.pre_out_conv_1 = nn.Conv1d(
|
||||
2 * filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
|
||||
)
|
||||
self.pre_out_norm_1 = modules.LayerNorm(filter_channels)
|
||||
self.pre_out_conv_2 = nn.Conv1d(
|
||||
filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
|
||||
)
|
||||
self.pre_out_norm_2 = modules.LayerNorm(filter_channels)
|
||||
|
||||
if gin_channels != 0:
|
||||
self.cond = nn.Conv1d(gin_channels, in_channels, 1)
|
||||
|
||||
self.output_layer = nn.Sequential(nn.Linear(filter_channels, 1), nn.Sigmoid())
|
||||
|
||||
def forward_probability(self, x, x_mask, dur, g=None):
|
||||
dur = self.dur_proj(dur)
|
||||
x = torch.cat([x, dur], dim=1)
|
||||
x = self.pre_out_conv_1(x * x_mask)
|
||||
x = torch.relu(x)
|
||||
x = self.pre_out_norm_1(x)
|
||||
x = self.drop(x)
|
||||
x = self.pre_out_conv_2(x * x_mask)
|
||||
x = torch.relu(x)
|
||||
x = self.pre_out_norm_2(x)
|
||||
x = self.drop(x)
|
||||
x = x * x_mask
|
||||
x = x.transpose(1, 2)
|
||||
output_prob = self.output_layer(x)
|
||||
return output_prob
|
||||
|
||||
def forward(self, x, x_mask, dur_r, dur_hat, g=None):
|
||||
x = torch.detach(x)
|
||||
if g is not None:
|
||||
g = torch.detach(g)
|
||||
x = x + self.cond(g)
|
||||
x = self.conv_1(x * x_mask)
|
||||
x = torch.relu(x)
|
||||
x = self.norm_1(x)
|
||||
x = self.drop(x)
|
||||
x = self.conv_2(x * x_mask)
|
||||
x = torch.relu(x)
|
||||
x = self.norm_2(x)
|
||||
x = self.drop(x)
|
||||
|
||||
output_probs = []
|
||||
for dur in [dur_r, dur_hat]:
|
||||
output_prob = self.forward_probability(x, x_mask, dur, g)
|
||||
output_probs.append(output_prob)
|
||||
|
||||
return output_probs
|
||||
|
||||
|
||||
class TransformerCouplingBlock(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
n_flows=4,
|
||||
gin_channels=0,
|
||||
share_parameter=False,
|
||||
):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.n_layers = n_layers
|
||||
self.n_flows = n_flows
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.flows = nn.ModuleList()
|
||||
|
||||
self.wn = (
|
||||
attentions.FFT(
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
isflow=True,
|
||||
gin_channels=self.gin_channels,
|
||||
)
|
||||
if share_parameter
|
||||
else None
|
||||
)
|
||||
|
||||
for i in range(n_flows):
|
||||
self.flows.append(
|
||||
modules.TransformerCouplingLayer(
|
||||
channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
n_layers,
|
||||
n_heads,
|
||||
p_dropout,
|
||||
filter_channels,
|
||||
mean_only=True,
|
||||
wn_sharing_parameter=self.wn,
|
||||
gin_channels=self.gin_channels,
|
||||
)
|
||||
)
|
||||
self.flows.append(modules.Flip())
|
||||
|
||||
def forward(self, x, x_mask, g=None, reverse=False):
|
||||
if not reverse:
|
||||
for flow in self.flows:
|
||||
x, _ = flow(x, x_mask, g=g, reverse=reverse)
|
||||
else:
|
||||
for flow in reversed(self.flows):
|
||||
x = flow(x, x_mask, g=g, reverse=reverse)
|
||||
return x
|
||||
|
||||
|
||||
class StochasticDurationPredictor(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
filter_channels,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
n_flows=4,
|
||||
gin_channels=0,
|
||||
):
|
||||
super().__init__()
|
||||
filter_channels = in_channels # it needs to be removed from future version.
|
||||
self.in_channels = in_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.n_flows = n_flows
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.log_flow = modules.Log()
|
||||
self.flows = nn.ModuleList()
|
||||
self.flows.append(modules.ElementwiseAffine(2))
|
||||
for i in range(n_flows):
|
||||
self.flows.append(
|
||||
modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
|
||||
)
|
||||
self.flows.append(modules.Flip())
|
||||
|
||||
self.post_pre = nn.Conv1d(1, filter_channels, 1)
|
||||
self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
|
||||
self.post_convs = modules.DDSConv(
|
||||
filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
|
||||
)
|
||||
self.post_flows = nn.ModuleList()
|
||||
self.post_flows.append(modules.ElementwiseAffine(2))
|
||||
for i in range(4):
|
||||
self.post_flows.append(
|
||||
modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
|
||||
)
|
||||
self.post_flows.append(modules.Flip())
|
||||
|
||||
self.pre = nn.Conv1d(in_channels, filter_channels, 1)
|
||||
self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
|
||||
self.convs = modules.DDSConv(
|
||||
filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
|
||||
)
|
||||
if gin_channels != 0:
|
||||
self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
|
||||
|
||||
def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
|
||||
x = torch.detach(x)
|
||||
x = self.pre(x)
|
||||
if g is not None:
|
||||
g = torch.detach(g)
|
||||
x = x + self.cond(g)
|
||||
x = self.convs(x, x_mask)
|
||||
x = self.proj(x) * x_mask
|
||||
|
||||
if not reverse:
|
||||
flows = self.flows
|
||||
assert w is not None
|
||||
|
||||
logdet_tot_q = 0
|
||||
h_w = self.post_pre(w)
|
||||
h_w = self.post_convs(h_w, x_mask)
|
||||
h_w = self.post_proj(h_w) * x_mask
|
||||
e_q = (
|
||||
torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype)
|
||||
* x_mask
|
||||
)
|
||||
z_q = e_q
|
||||
for flow in self.post_flows:
|
||||
z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
|
||||
logdet_tot_q += logdet_q
|
||||
z_u, z1 = torch.split(z_q, [1, 1], 1)
|
||||
u = torch.sigmoid(z_u) * x_mask
|
||||
z0 = (w - u) * x_mask
|
||||
logdet_tot_q += torch.sum(
|
||||
(F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2]
|
||||
)
|
||||
logq = (
|
||||
torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q**2)) * x_mask, [1, 2])
|
||||
- logdet_tot_q
|
||||
)
|
||||
|
||||
logdet_tot = 0
|
||||
z0, logdet = self.log_flow(z0, x_mask)
|
||||
logdet_tot += logdet
|
||||
z = torch.cat([z0, z1], 1)
|
||||
for flow in flows:
|
||||
z, logdet = flow(z, x_mask, g=x, reverse=reverse)
|
||||
logdet_tot = logdet_tot + logdet
|
||||
nll = (
|
||||
torch.sum(0.5 * (math.log(2 * math.pi) + (z**2)) * x_mask, [1, 2])
|
||||
- logdet_tot
|
||||
)
|
||||
return nll + logq # [b]
|
||||
else:
|
||||
flows = list(reversed(self.flows))
|
||||
flows = flows[:-2] + [flows[-1]] # remove a useless vflow
|
||||
z = (
|
||||
torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype)
|
||||
* noise_scale
|
||||
)
|
||||
for flow in flows:
|
||||
z = flow(z, x_mask, g=x, reverse=reverse)
|
||||
z0, z1 = torch.split(z, [1, 1], 1)
|
||||
logw = z0
|
||||
return logw
|
||||
|
||||
|
||||
class DurationPredictor(nn.Module):
|
||||
def __init__(
|
||||
self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.in_channels = in_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
self.conv_1 = nn.Conv1d(
|
||||
in_channels, filter_channels, kernel_size, padding=kernel_size // 2
|
||||
)
|
||||
self.norm_1 = modules.LayerNorm(filter_channels)
|
||||
self.conv_2 = nn.Conv1d(
|
||||
filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
|
||||
)
|
||||
self.norm_2 = modules.LayerNorm(filter_channels)
|
||||
self.proj = nn.Conv1d(filter_channels, 1, 1)
|
||||
|
||||
if gin_channels != 0:
|
||||
self.cond = nn.Conv1d(gin_channels, in_channels, 1)
|
||||
|
||||
def forward(self, x, x_mask, g=None):
|
||||
x = torch.detach(x)
|
||||
if g is not None:
|
||||
g = torch.detach(g)
|
||||
x = x + self.cond(g)
|
||||
x = self.conv_1(x * x_mask)
|
||||
x = torch.relu(x)
|
||||
x = self.norm_1(x)
|
||||
x = self.drop(x)
|
||||
x = self.conv_2(x * x_mask)
|
||||
x = torch.relu(x)
|
||||
x = self.norm_2(x)
|
||||
x = self.drop(x)
|
||||
x = self.proj(x * x_mask)
|
||||
return x * x_mask
|
||||
|
||||
|
||||
class TextEncoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
n_vocab,
|
||||
out_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
gin_channels=0,
|
||||
):
|
||||
super().__init__()
|
||||
self.n_vocab = n_vocab
|
||||
self.out_channels = out_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.n_heads = n_heads
|
||||
self.n_layers = n_layers
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.gin_channels = gin_channels
|
||||
self.emb = nn.Embedding(len(symbols), hidden_channels)
|
||||
nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
|
||||
self.tone_emb = nn.Embedding(num_tones, hidden_channels)
|
||||
nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels**-0.5)
|
||||
self.language_emb = nn.Embedding(num_languages, hidden_channels)
|
||||
nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels**-0.5)
|
||||
self.bert_proj = nn.Conv1d(1024, hidden_channels, 1)
|
||||
self.ja_bert_proj = nn.Conv1d(768, hidden_channels, 1)
|
||||
|
||||
self.encoder = attentions.Encoder(
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
gin_channels=self.gin_channels,
|
||||
)
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
||||
|
||||
def forward(self, x, x_lengths, tone, language, bert, ja_bert, g=None):
|
||||
bert_emb = self.bert_proj(bert).transpose(1, 2)
|
||||
ja_bert_emb = self.ja_bert_proj(ja_bert).transpose(1, 2)
|
||||
x = (
|
||||
self.emb(x)
|
||||
+ self.tone_emb(tone)
|
||||
+ self.language_emb(language)
|
||||
+ bert_emb
|
||||
+ ja_bert_emb
|
||||
) * math.sqrt(
|
||||
self.hidden_channels
|
||||
) # [b, t, h]
|
||||
x = torch.transpose(x, 1, -1) # [b, h, t]
|
||||
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
|
||||
x.dtype
|
||||
)
|
||||
|
||||
x = self.encoder(x * x_mask, x_mask, g=g)
|
||||
stats = self.proj(x) * x_mask
|
||||
|
||||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||||
return x, m, logs, x_mask
|
||||
|
||||
|
||||
class ResidualCouplingBlock(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
n_flows=4,
|
||||
gin_channels=0,
|
||||
):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.n_flows = n_flows
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.flows = nn.ModuleList()
|
||||
for i in range(n_flows):
|
||||
self.flows.append(
|
||||
modules.ResidualCouplingLayer(
|
||||
channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
gin_channels=gin_channels,
|
||||
mean_only=True,
|
||||
)
|
||||
)
|
||||
self.flows.append(modules.Flip())
|
||||
|
||||
def forward(self, x, x_mask, g=None, reverse=False):
|
||||
if not reverse:
|
||||
for flow in self.flows:
|
||||
x, _ = flow(x, x_mask, g=g, reverse=reverse)
|
||||
else:
|
||||
for flow in reversed(self.flows):
|
||||
x = flow(x, x_mask, g=g, reverse=reverse)
|
||||
return x
|
||||
|
||||
|
||||
class PosteriorEncoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
gin_channels=0,
|
||||
):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = out_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
|
||||
self.enc = modules.WN(
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
gin_channels=gin_channels,
|
||||
)
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
||||
|
||||
def forward(self, x, x_lengths, g=None):
|
||||
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
|
||||
x.dtype
|
||||
)
|
||||
x = self.pre(x) * x_mask
|
||||
x = self.enc(x, x_mask, g=g)
|
||||
stats = self.proj(x) * x_mask
|
||||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||||
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
|
||||
return z, m, logs, x_mask
|
||||
|
||||
|
||||
class Generator(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
initial_channel,
|
||||
resblock,
|
||||
resblock_kernel_sizes,
|
||||
resblock_dilation_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
upsample_kernel_sizes,
|
||||
gin_channels=0,
|
||||
):
|
||||
super(Generator, self).__init__()
|
||||
self.num_kernels = len(resblock_kernel_sizes)
|
||||
self.num_upsamples = len(upsample_rates)
|
||||
self.conv_pre = Conv1d(
|
||||
initial_channel, upsample_initial_channel, 7, 1, padding=3
|
||||
)
|
||||
resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
|
||||
|
||||
self.ups = nn.ModuleList()
|
||||
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
|
||||
self.ups.append(
|
||||
weight_norm(
|
||||
ConvTranspose1d(
|
||||
upsample_initial_channel // (2**i),
|
||||
upsample_initial_channel // (2 ** (i + 1)),
|
||||
k,
|
||||
u,
|
||||
padding=(k - u) // 2,
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
self.resblocks = nn.ModuleList()
|
||||
for i in range(len(self.ups)):
|
||||
ch = upsample_initial_channel // (2 ** (i + 1))
|
||||
for j, (k, d) in enumerate(
|
||||
zip(resblock_kernel_sizes, resblock_dilation_sizes)
|
||||
):
|
||||
self.resblocks.append(resblock(ch, k, d))
|
||||
|
||||
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
|
||||
self.ups.apply(init_weights)
|
||||
|
||||
if gin_channels != 0:
|
||||
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
|
||||
|
||||
def forward(self, x, g=None):
|
||||
x = self.conv_pre(x)
|
||||
if g is not None:
|
||||
x = x + self.cond(g)
|
||||
|
||||
for i in range(self.num_upsamples):
|
||||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||||
x = self.ups[i](x)
|
||||
xs = None
|
||||
for j in range(self.num_kernels):
|
||||
if xs is None:
|
||||
xs = self.resblocks[i * self.num_kernels + j](x)
|
||||
else:
|
||||
xs += self.resblocks[i * self.num_kernels + j](x)
|
||||
x = xs / self.num_kernels
|
||||
x = F.leaky_relu(x)
|
||||
x = self.conv_post(x)
|
||||
x = torch.tanh(x)
|
||||
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
print("Removing weight norm...")
|
||||
for layer in self.ups:
|
||||
remove_weight_norm(layer)
|
||||
for layer in self.resblocks:
|
||||
layer.remove_weight_norm()
|
||||
|
||||
|
||||
class DiscriminatorP(torch.nn.Module):
|
||||
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
||||
super(DiscriminatorP, self).__init__()
|
||||
self.period = period
|
||||
self.use_spectral_norm = use_spectral_norm
|
||||
norm_f = weight_norm if use_spectral_norm is False else spectral_norm
|
||||
self.convs = nn.ModuleList(
|
||||
[
|
||||
norm_f(
|
||||
Conv2d(
|
||||
1,
|
||||
32,
|
||||
(kernel_size, 1),
|
||||
(stride, 1),
|
||||
padding=(get_padding(kernel_size, 1), 0),
|
||||
)
|
||||
),
|
||||
norm_f(
|
||||
Conv2d(
|
||||
32,
|
||||
128,
|
||||
(kernel_size, 1),
|
||||
(stride, 1),
|
||||
padding=(get_padding(kernel_size, 1), 0),
|
||||
)
|
||||
),
|
||||
norm_f(
|
||||
Conv2d(
|
||||
128,
|
||||
512,
|
||||
(kernel_size, 1),
|
||||
(stride, 1),
|
||||
padding=(get_padding(kernel_size, 1), 0),
|
||||
)
|
||||
),
|
||||
norm_f(
|
||||
Conv2d(
|
||||
512,
|
||||
1024,
|
||||
(kernel_size, 1),
|
||||
(stride, 1),
|
||||
padding=(get_padding(kernel_size, 1), 0),
|
||||
)
|
||||
),
|
||||
norm_f(
|
||||
Conv2d(
|
||||
1024,
|
||||
1024,
|
||||
(kernel_size, 1),
|
||||
1,
|
||||
padding=(get_padding(kernel_size, 1), 0),
|
||||
)
|
||||
),
|
||||
]
|
||||
)
|
||||
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
|
||||
# 1d to 2d
|
||||
b, c, t = x.shape
|
||||
if t % self.period != 0: # pad first
|
||||
n_pad = self.period - (t % self.period)
|
||||
x = F.pad(x, (0, n_pad), "reflect")
|
||||
t = t + n_pad
|
||||
x = x.view(b, c, t // self.period, self.period)
|
||||
|
||||
for layer in self.convs:
|
||||
x = layer(x)
|
||||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
||||
|
||||
|
||||
class DiscriminatorS(torch.nn.Module):
|
||||
def __init__(self, use_spectral_norm=False):
|
||||
super(DiscriminatorS, self).__init__()
|
||||
norm_f = weight_norm if use_spectral_norm is False else spectral_norm
|
||||
self.convs = nn.ModuleList(
|
||||
[
|
||||
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
|
||||
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
|
||||
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
|
||||
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
|
||||
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
|
||||
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
|
||||
]
|
||||
)
|
||||
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
|
||||
for layer in self.convs:
|
||||
x = layer(x)
|
||||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
||||
|
||||
|
||||
class MultiPeriodDiscriminator(torch.nn.Module):
|
||||
def __init__(self, use_spectral_norm=False):
|
||||
super(MultiPeriodDiscriminator, self).__init__()
|
||||
periods = [2, 3, 5, 7, 11]
|
||||
|
||||
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
|
||||
discs = discs + [
|
||||
DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
|
||||
]
|
||||
self.discriminators = nn.ModuleList(discs)
|
||||
|
||||
def forward(self, y, y_hat):
|
||||
y_d_rs = []
|
||||
y_d_gs = []
|
||||
fmap_rs = []
|
||||
fmap_gs = []
|
||||
for i, d in enumerate(self.discriminators):
|
||||
y_d_r, fmap_r = d(y)
|
||||
y_d_g, fmap_g = d(y_hat)
|
||||
y_d_rs.append(y_d_r)
|
||||
y_d_gs.append(y_d_g)
|
||||
fmap_rs.append(fmap_r)
|
||||
fmap_gs.append(fmap_g)
|
||||
|
||||
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
||||
|
||||
|
||||
class ReferenceEncoder(nn.Module):
|
||||
"""
|
||||
inputs --- [N, Ty/r, n_mels*r] mels
|
||||
outputs --- [N, ref_enc_gru_size]
|
||||
"""
|
||||
|
||||
def __init__(self, spec_channels, gin_channels=0):
|
||||
super().__init__()
|
||||
self.spec_channels = spec_channels
|
||||
ref_enc_filters = [32, 32, 64, 64, 128, 128]
|
||||
K = len(ref_enc_filters)
|
||||
filters = [1] + ref_enc_filters
|
||||
convs = [
|
||||
weight_norm(
|
||||
nn.Conv2d(
|
||||
in_channels=filters[i],
|
||||
out_channels=filters[i + 1],
|
||||
kernel_size=(3, 3),
|
||||
stride=(2, 2),
|
||||
padding=(1, 1),
|
||||
)
|
||||
)
|
||||
for i in range(K)
|
||||
]
|
||||
self.convs = nn.ModuleList(convs)
|
||||
# self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)]) # noqa: E501
|
||||
|
||||
out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
|
||||
self.gru = nn.GRU(
|
||||
input_size=ref_enc_filters[-1] * out_channels,
|
||||
hidden_size=256 // 2,
|
||||
batch_first=True,
|
||||
)
|
||||
self.proj = nn.Linear(128, gin_channels)
|
||||
|
||||
def forward(self, inputs, mask=None):
|
||||
N = inputs.size(0)
|
||||
out = inputs.view(N, 1, -1, self.spec_channels) # [N, 1, Ty, n_freqs]
|
||||
for conv in self.convs:
|
||||
out = conv(out)
|
||||
# out = wn(out)
|
||||
out = F.relu(out) # [N, 128, Ty//2^K, n_mels//2^K]
|
||||
|
||||
out = out.transpose(1, 2) # [N, Ty//2^K, 128, n_mels//2^K]
|
||||
T = out.size(1)
|
||||
N = out.size(0)
|
||||
out = out.contiguous().view(N, T, -1) # [N, Ty//2^K, 128*n_mels//2^K]
|
||||
|
||||
self.gru.flatten_parameters()
|
||||
memory, out = self.gru(out) # out --- [1, N, 128]
|
||||
|
||||
return self.proj(out.squeeze(0))
|
||||
|
||||
def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
|
||||
for i in range(n_convs):
|
||||
L = (L - kernel_size + 2 * pad) // stride + 1
|
||||
return L
|
||||
|
||||
|
||||
class SynthesizerTrn(nn.Module):
|
||||
"""
|
||||
Synthesizer for Training
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
n_vocab,
|
||||
spec_channels,
|
||||
segment_size,
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
resblock,
|
||||
resblock_kernel_sizes,
|
||||
resblock_dilation_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
upsample_kernel_sizes,
|
||||
n_speakers=256,
|
||||
gin_channels=256,
|
||||
use_sdp=True,
|
||||
n_flow_layer=4,
|
||||
n_layers_trans_flow=6,
|
||||
flow_share_parameter=False,
|
||||
use_transformer_flow=True,
|
||||
**kwargs
|
||||
):
|
||||
super().__init__()
|
||||
self.n_vocab = n_vocab
|
||||
self.spec_channels = spec_channels
|
||||
self.inter_channels = inter_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.n_heads = n_heads
|
||||
self.n_layers = n_layers
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.resblock = resblock
|
||||
self.resblock_kernel_sizes = resblock_kernel_sizes
|
||||
self.resblock_dilation_sizes = resblock_dilation_sizes
|
||||
self.upsample_rates = upsample_rates
|
||||
self.upsample_initial_channel = upsample_initial_channel
|
||||
self.upsample_kernel_sizes = upsample_kernel_sizes
|
||||
self.segment_size = segment_size
|
||||
self.n_speakers = n_speakers
|
||||
self.gin_channels = gin_channels
|
||||
self.n_layers_trans_flow = n_layers_trans_flow
|
||||
self.use_spk_conditioned_encoder = kwargs.get(
|
||||
"use_spk_conditioned_encoder", True
|
||||
)
|
||||
self.use_sdp = use_sdp
|
||||
self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
|
||||
self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01)
|
||||
self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)
|
||||
self.current_mas_noise_scale = self.mas_noise_scale_initial
|
||||
if self.use_spk_conditioned_encoder and gin_channels > 0:
|
||||
self.enc_gin_channels = gin_channels
|
||||
self.enc_p = TextEncoder(
|
||||
n_vocab,
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
gin_channels=self.enc_gin_channels,
|
||||
)
|
||||
self.dec = Generator(
|
||||
inter_channels,
|
||||
resblock,
|
||||
resblock_kernel_sizes,
|
||||
resblock_dilation_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
upsample_kernel_sizes,
|
||||
gin_channels=gin_channels,
|
||||
)
|
||||
self.enc_q = PosteriorEncoder(
|
||||
spec_channels,
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
5,
|
||||
1,
|
||||
16,
|
||||
gin_channels=gin_channels,
|
||||
)
|
||||
if use_transformer_flow:
|
||||
self.flow = TransformerCouplingBlock(
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers_trans_flow,
|
||||
5,
|
||||
p_dropout,
|
||||
n_flow_layer,
|
||||
gin_channels=gin_channels,
|
||||
share_parameter=flow_share_parameter,
|
||||
)
|
||||
else:
|
||||
self.flow = ResidualCouplingBlock(
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
5,
|
||||
1,
|
||||
n_flow_layer,
|
||||
gin_channels=gin_channels,
|
||||
)
|
||||
self.sdp = StochasticDurationPredictor(
|
||||
hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels
|
||||
)
|
||||
self.dp = DurationPredictor(
|
||||
hidden_channels, 256, 3, 0.5, gin_channels=gin_channels
|
||||
)
|
||||
|
||||
if n_speakers > 0:
|
||||
self.emb_g = nn.Embedding(n_speakers, gin_channels)
|
||||
else:
|
||||
self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
|
||||
|
||||
def forward(self, x, x_lengths, y, y_lengths, sid, tone, language, bert, ja_bert):
|
||||
if self.n_speakers > 0:
|
||||
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
|
||||
else:
|
||||
g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
|
||||
x, m_p, logs_p, x_mask = self.enc_p(
|
||||
x, x_lengths, tone, language, bert, ja_bert, g=g
|
||||
)
|
||||
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
|
||||
z_p = self.flow(z, y_mask, g=g)
|
||||
|
||||
with torch.no_grad():
|
||||
# negative cross-entropy
|
||||
s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
|
||||
neg_cent1 = torch.sum(
|
||||
-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True
|
||||
) # [b, 1, t_s]
|
||||
neg_cent2 = torch.matmul(
|
||||
-0.5 * (z_p**2).transpose(1, 2), s_p_sq_r
|
||||
) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
|
||||
neg_cent3 = torch.matmul(
|
||||
z_p.transpose(1, 2), (m_p * s_p_sq_r)
|
||||
) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
|
||||
neg_cent4 = torch.sum(
|
||||
-0.5 * (m_p**2) * s_p_sq_r, [1], keepdim=True
|
||||
) # [b, 1, t_s]
|
||||
neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
|
||||
if self.use_noise_scaled_mas:
|
||||
epsilon = (
|
||||
torch.std(neg_cent)
|
||||
* torch.randn_like(neg_cent)
|
||||
* self.current_mas_noise_scale
|
||||
)
|
||||
neg_cent = neg_cent + epsilon
|
||||
|
||||
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
|
||||
attn = (
|
||||
monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1))
|
||||
.unsqueeze(1)
|
||||
.detach()
|
||||
)
|
||||
|
||||
w = attn.sum(2)
|
||||
|
||||
l_length_sdp = self.sdp(x, x_mask, w, g=g)
|
||||
l_length_sdp = l_length_sdp / torch.sum(x_mask)
|
||||
|
||||
logw_ = torch.log(w + 1e-6) * x_mask
|
||||
logw = self.dp(x, x_mask, g=g)
|
||||
l_length_dp = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum(
|
||||
x_mask
|
||||
) # for averaging
|
||||
|
||||
l_length = l_length_dp + l_length_sdp
|
||||
|
||||
# expand prior
|
||||
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
|
||||
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
|
||||
|
||||
z_slice, ids_slice = commons.rand_slice_segments(
|
||||
z, y_lengths, self.segment_size
|
||||
)
|
||||
o = self.dec(z_slice, g=g)
|
||||
return (
|
||||
o,
|
||||
l_length,
|
||||
attn,
|
||||
ids_slice,
|
||||
x_mask,
|
||||
y_mask,
|
||||
(z, z_p, m_p, logs_p, m_q, logs_q),
|
||||
(x, logw, logw_),
|
||||
)
|
||||
|
||||
def infer(
|
||||
self,
|
||||
x,
|
||||
x_lengths,
|
||||
sid,
|
||||
tone,
|
||||
language,
|
||||
bert,
|
||||
ja_bert,
|
||||
noise_scale=0.667,
|
||||
length_scale=1,
|
||||
noise_scale_w=0.8,
|
||||
max_len=None,
|
||||
sdp_ratio=0,
|
||||
y=None,
|
||||
):
|
||||
# x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert)
|
||||
# g = self.gst(y)
|
||||
if self.n_speakers > 0:
|
||||
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
|
||||
else:
|
||||
g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
|
||||
x, m_p, logs_p, x_mask = self.enc_p(
|
||||
x, x_lengths, tone, language, bert, ja_bert, g=g
|
||||
)
|
||||
logw = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) * (
|
||||
sdp_ratio
|
||||
) + self.dp(x, x_mask, g=g) * (1 - sdp_ratio)
|
||||
w = torch.exp(logw) * x_mask * length_scale
|
||||
w_ceil = torch.ceil(w)
|
||||
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
|
||||
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(
|
||||
x_mask.dtype
|
||||
)
|
||||
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
|
||||
attn = commons.generate_path(w_ceil, attn_mask)
|
||||
|
||||
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(
|
||||
1, 2
|
||||
) # [b, t', t], [b, t, d] -> [b, d, t']
|
||||
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(
|
||||
1, 2
|
||||
) # [b, t', t], [b, t, d] -> [b, d, t']
|
||||
|
||||
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
|
||||
z = self.flow(z_p, y_mask, g=g, reverse=True)
|
||||
o = self.dec((z * y_mask)[:, :, :max_len], g=g)
|
||||
return o, attn, y_mask, (z, z_p, m_p, logs_p)
|
||||
29
oldVersion/V110/text/__init__.py
Normal file
29
oldVersion/V110/text/__init__.py
Normal file
@@ -0,0 +1,29 @@
|
||||
from .symbols import *
|
||||
|
||||
|
||||
_symbol_to_id = {s: i for i, s in enumerate(symbols)}
|
||||
|
||||
|
||||
def cleaned_text_to_sequence(cleaned_text, tones, language):
|
||||
"""Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
|
||||
Args:
|
||||
text: string to convert to a sequence
|
||||
Returns:
|
||||
List of integers corresponding to the symbols in the text
|
||||
"""
|
||||
phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
|
||||
tone_start = language_tone_start_map[language]
|
||||
tones = [i + tone_start for i in tones]
|
||||
lang_id = language_id_map[language]
|
||||
lang_ids = [lang_id for i in phones]
|
||||
return phones, tones, lang_ids
|
||||
|
||||
|
||||
def get_bert(norm_text, word2ph, language, device):
|
||||
from .chinese_bert import get_bert_feature as zh_bert
|
||||
from .english_bert_mock import get_bert_feature as en_bert
|
||||
from .japanese_bert import get_bert_feature as jp_bert
|
||||
|
||||
lang_bert_func_map = {"ZH": zh_bert, "EN": en_bert, "JP": jp_bert}
|
||||
bert = lang_bert_func_map[language](norm_text, word2ph, device)
|
||||
return bert
|
||||
198
oldVersion/V110/text/chinese.py
Normal file
198
oldVersion/V110/text/chinese.py
Normal file
@@ -0,0 +1,198 @@
|
||||
import os
|
||||
import re
|
||||
|
||||
import cn2an
|
||||
from pypinyin import lazy_pinyin, Style
|
||||
|
||||
from .symbols import punctuation
|
||||
from .tone_sandhi import ToneSandhi
|
||||
|
||||
current_file_path = os.path.dirname(__file__)
|
||||
pinyin_to_symbol_map = {
|
||||
line.split("\t")[0]: line.strip().split("\t")[1]
|
||||
for line in open(os.path.join(current_file_path, "opencpop-strict.txt")).readlines()
|
||||
}
|
||||
|
||||
import jieba.posseg as psg
|
||||
|
||||
|
||||
rep_map = {
|
||||
"：": ",",
|
||||
"；": ",",
|
||||
"，": ",",
|
||||
"。": ".",
|
||||
"！": "!",
|
||||
"？": "?",
|
||||
"\n": ".",
|
||||
"·": ",",
|
||||
"、": ",",
|
||||
"...": "…",
|
||||
"$": ".",
|
||||
"“": "'",
|
||||
"”": "'",
|
||||
"‘": "'",
|
||||
"’": "'",
|
||||
"（": "'",
|
||||
"）": "'",
|
||||
"(": "'",
|
||||
")": "'",
|
||||
"《": "'",
|
||||
"》": "'",
|
||||
"【": "'",
|
||||
"】": "'",
|
||||
"[": "'",
|
||||
"]": "'",
|
||||
"—": "-",
|
||||
"～": "-",
|
||||
"~": "-",
|
||||
"「": "'",
|
||||
"」": "'",
|
||||
}
|
||||
|
||||
tone_modifier = ToneSandhi()
|
||||
|
||||
|
||||
def replace_punctuation(text):
|
||||
text = text.replace("嗯", "恩").replace("呣", "母")
|
||||
pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
|
||||
|
||||
replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
|
||||
|
||||
replaced_text = re.sub(
|
||||
r"[^\u4e00-\u9fa5" + "".join(punctuation) + r"]+", "", replaced_text
|
||||
)
|
||||
|
||||
return replaced_text
|
||||
|
||||
|
||||
def g2p(text):
|
||||
pattern = r"(?<=[{0}])\s*".format("".join(punctuation))
|
||||
sentences = [i for i in re.split(pattern, text) if i.strip() != ""]
|
||||
phones, tones, word2ph = _g2p(sentences)
|
||||
assert sum(word2ph) == len(phones)
|
||||
assert len(word2ph) == len(text) # Sometimes it will crash,you can add a try-catch.
|
||||
phones = ["_"] + phones + ["_"]
|
||||
tones = [0] + tones + [0]
|
||||
word2ph = [1] + word2ph + [1]
|
||||
return phones, tones, word2ph
|
||||
|
||||
|
||||
def _get_initials_finals(word):
|
||||
initials = []
|
||||
finals = []
|
||||
orig_initials = lazy_pinyin(word, neutral_tone_with_five=True, style=Style.INITIALS)
|
||||
orig_finals = lazy_pinyin(
|
||||
word, neutral_tone_with_five=True, style=Style.FINALS_TONE3
|
||||
)
|
||||
for c, v in zip(orig_initials, orig_finals):
|
||||
initials.append(c)
|
||||
finals.append(v)
|
||||
return initials, finals
|
||||
|
||||
|
||||
def _g2p(segments):
|
||||
phones_list = []
|
||||
tones_list = []
|
||||
word2ph = []
|
||||
for seg in segments:
|
||||
# Replace all English words in the sentence
|
||||
seg = re.sub("[a-zA-Z]+", "", seg)
|
||||
seg_cut = psg.lcut(seg)
|
||||
initials = []
|
||||
finals = []
|
||||
seg_cut = tone_modifier.pre_merge_for_modify(seg_cut)
|
||||
for word, pos in seg_cut:
|
||||
if pos == "eng":
|
||||
continue
|
||||
sub_initials, sub_finals = _get_initials_finals(word)
|
||||
sub_finals = tone_modifier.modified_tone(word, pos, sub_finals)
|
||||
initials.append(sub_initials)
|
||||
finals.append(sub_finals)
|
||||
|
||||
# assert len(sub_initials) == len(sub_finals) == len(word)
|
||||
initials = sum(initials, [])
|
||||
finals = sum(finals, [])
|
||||
#
|
||||
for c, v in zip(initials, finals):
|
||||
raw_pinyin = c + v
|
||||
# NOTE: post process for pypinyin outputs
|
||||
# we discriminate i, ii and iii
|
||||
if c == v:
|
||||
assert c in punctuation
|
||||
phone = [c]
|
||||
tone = "0"
|
||||
word2ph.append(1)
|
||||
else:
|
||||
v_without_tone = v[:-1]
|
||||
tone = v[-1]
|
||||
|
||||
pinyin = c + v_without_tone
|
||||
assert tone in "12345"
|
||||
|
||||
if c:
|
||||
# 多音节
|
||||
v_rep_map = {
|
||||
"uei": "ui",
|
||||
"iou": "iu",
|
||||
"uen": "un",
|
||||
}
|
||||
if v_without_tone in v_rep_map.keys():
|
||||
pinyin = c + v_rep_map[v_without_tone]
|
||||
else:
|
||||
# 单音节
|
||||
pinyin_rep_map = {
|
||||
"ing": "ying",
|
||||
"i": "yi",
|
||||
"in": "yin",
|
||||
"u": "wu",
|
||||
}
|
||||
if pinyin in pinyin_rep_map.keys():
|
||||
pinyin = pinyin_rep_map[pinyin]
|
||||
else:
|
||||
single_rep_map = {
|
||||
"v": "yu",
|
||||
"e": "e",
|
||||
"i": "y",
|
||||
"u": "w",
|
||||
}
|
||||
if pinyin[0] in single_rep_map.keys():
|
||||
pinyin = single_rep_map[pinyin[0]] + pinyin[1:]
|
||||
|
||||
assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, seg, raw_pinyin)
|
||||
phone = pinyin_to_symbol_map[pinyin].split(" ")
|
||||
word2ph.append(len(phone))
|
||||
|
||||
phones_list += phone
|
||||
tones_list += [int(tone)] * len(phone)
|
||||
return phones_list, tones_list, word2ph
|
||||
|
||||
|
||||
def text_normalize(text):
|
||||
numbers = re.findall(r"\d+(?:\.?\d+)?", text)
|
||||
for number in numbers:
|
||||
text = text.replace(number, cn2an.an2cn(number), 1)
|
||||
text = replace_punctuation(text)
|
||||
return text
|
||||
|
||||
|
||||
def get_bert_feature(text, word2ph):
|
||||
from text import chinese_bert
|
||||
|
||||
return chinese_bert.get_bert_feature(text, word2ph)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from text.chinese_bert import get_bert_feature
|
||||
|
||||
text = "啊！但是《原神》是由,米哈\游自主， [研发]的一款全.新开放世界.冒险游戏"
|
||||
text = text_normalize(text)
|
||||
print(text)
|
||||
phones, tones, word2ph = g2p(text)
|
||||
bert = get_bert_feature(text, word2ph)
|
||||
|
||||
print(phones, tones, word2ph, bert.shape)
|
||||
|
||||
|
||||
# # 示例用法
|
||||
# text = "这是一个示例文本：,你好！这是一个测试...."
|
||||
# print(g2p_paddle(text)) # 输出: 这是一个示例文本你好这是一个测试
|
||||
97
oldVersion/V110/text/chinese_bert.py
Normal file
97
oldVersion/V110/text/chinese_bert.py
Normal file
@@ -0,0 +1,97 @@
|
||||
import torch
|
||||
import sys
|
||||
from transformers import AutoTokenizer, AutoModelForMaskedLM
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained("./bert/chinese-roberta-wwm-ext-large")
|
||||
|
||||
|
||||
def get_bert_feature(text, word2ph, device=None):
|
||||
if (
|
||||
sys.platform == "darwin"
|
||||
and torch.backends.mps.is_available()
|
||||
and device == "cpu"
|
||||
):
|
||||
device = "mps"
|
||||
if not device:
|
||||
device = "cuda"
|
||||
model = AutoModelForMaskedLM.from_pretrained(
|
||||
"./bert/chinese-roberta-wwm-ext-large"
|
||||
).to(device)
|
||||
with torch.no_grad():
|
||||
inputs = tokenizer(text, return_tensors="pt")
|
||||
for i in inputs:
|
||||
inputs[i] = inputs[i].to(device)
|
||||
res = model(**inputs, output_hidden_states=True)
|
||||
res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
|
||||
|
||||
assert len(word2ph) == len(text) + 2
|
||||
word2phone = word2ph
|
||||
phone_level_feature = []
|
||||
for i in range(len(word2phone)):
|
||||
repeat_feature = res[i].repeat(word2phone[i], 1)
|
||||
phone_level_feature.append(repeat_feature)
|
||||
|
||||
phone_level_feature = torch.cat(phone_level_feature, dim=0)
|
||||
|
||||
return phone_level_feature.T
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import torch
|
||||
|
||||
word_level_feature = torch.rand(38, 1024) # 12个词,每个词1024维特征
|
||||
word2phone = [
|
||||
1,
|
||||
2,
|
||||
1,
|
||||
2,
|
||||
2,
|
||||
1,
|
||||
2,
|
||||
2,
|
||||
1,
|
||||
2,
|
||||
2,
|
||||
1,
|
||||
2,
|
||||
2,
|
||||
2,
|
||||
2,
|
||||
2,
|
||||
1,
|
||||
1,
|
||||
2,
|
||||
2,
|
||||
1,
|
||||
2,
|
||||
2,
|
||||
2,
|
||||
2,
|
||||
1,
|
||||
2,
|
||||
2,
|
||||
2,
|
||||
2,
|
||||
2,
|
||||
1,
|
||||
2,
|
||||
2,
|
||||
2,
|
||||
2,
|
||||
1,
|
||||
]
|
||||
|
||||
# 计算总帧数
|
||||
total_frames = sum(word2phone)
|
||||
print(word_level_feature.shape)
|
||||
print(word2phone)
|
||||
phone_level_feature = []
|
||||
for i in range(len(word2phone)):
|
||||
print(word_level_feature[i].shape)
|
||||
|
||||
# 对每个词重复word2phone[i]次
|
||||
repeat_feature = word_level_feature[i].repeat(word2phone[i], 1)
|
||||
phone_level_feature.append(repeat_feature)
|
||||
|
||||
phone_level_feature = torch.cat(phone_level_feature, dim=0)
|
||||
print(phone_level_feature.shape) # torch.Size([36, 1024])
|
||||
28
oldVersion/V110/text/cleaner.py
Normal file
28
oldVersion/V110/text/cleaner.py
Normal file
@@ -0,0 +1,28 @@
|
||||
from . import chinese, japanese, cleaned_text_to_sequence
|
||||
|
||||
|
||||
language_module_map = {"ZH": chinese, "JP": japanese}
|
||||
|
||||
|
||||
def clean_text(text, language):
|
||||
language_module = language_module_map[language]
|
||||
norm_text = language_module.text_normalize(text)
|
||||
phones, tones, word2ph = language_module.g2p(norm_text)
|
||||
return norm_text, phones, tones, word2ph
|
||||
|
||||
|
||||
def clean_text_bert(text, language):
|
||||
language_module = language_module_map[language]
|
||||
norm_text = language_module.text_normalize(text)
|
||||
phones, tones, word2ph = language_module.g2p(norm_text)
|
||||
bert = language_module.get_bert_feature(norm_text, word2ph)
|
||||
return phones, tones, bert
|
||||
|
||||
|
||||
def text_to_sequence(text, language):
|
||||
norm_text, phones, tones, word2ph = clean_text(text, language)
|
||||
return cleaned_text_to_sequence(phones, tones, language)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
pass
|
||||
214
oldVersion/V110/text/english.py
Normal file
214
oldVersion/V110/text/english.py
Normal file
@@ -0,0 +1,214 @@
|
||||
import pickle
|
||||
import os
|
||||
import re
|
||||
from g2p_en import G2p
|
||||
|
||||
from . import symbols
|
||||
|
||||
current_file_path = os.path.dirname(__file__)
|
||||
CMU_DICT_PATH = os.path.join(current_file_path, "cmudict.rep")
|
||||
CACHE_PATH = os.path.join(current_file_path, "cmudict_cache.pickle")
|
||||
_g2p = G2p()
|
||||
|
||||
arpa = {
|
||||
"AH0",
|
||||
"S",
|
||||
"AH1",
|
||||
"EY2",
|
||||
"AE2",
|
||||
"EH0",
|
||||
"OW2",
|
||||
"UH0",
|
||||
"NG",
|
||||
"B",
|
||||
"G",
|
||||
"AY0",
|
||||
"M",
|
||||
"AA0",
|
||||
"F",
|
||||
"AO0",
|
||||
"ER2",
|
||||
"UH1",
|
||||
"IY1",
|
||||
"AH2",
|
||||
"DH",
|
||||
"IY0",
|
||||
"EY1",
|
||||
"IH0",
|
||||
"K",
|
||||
"N",
|
||||
"W",
|
||||
"IY2",
|
||||
"T",
|
||||
"AA1",
|
||||
"ER1",
|
||||
"EH2",
|
||||
"OY0",
|
||||
"UH2",
|
||||
"UW1",
|
||||
"Z",
|
||||
"AW2",
|
||||
"AW1",
|
||||
"V",
|
||||
"UW2",
|
||||
"AA2",
|
||||
"ER",
|
||||
"AW0",
|
||||
"UW0",
|
||||
"R",
|
||||
"OW1",
|
||||
"EH1",
|
||||
"ZH",
|
||||
"AE0",
|
||||
"IH2",
|
||||
"IH",
|
||||
"Y",
|
||||
"JH",
|
||||
"P",
|
||||
"AY1",
|
||||
"EY0",
|
||||
"OY2",
|
||||
"TH",
|
||||
"HH",
|
||||
"D",
|
||||
"ER0",
|
||||
"CH",
|
||||
"AO1",
|
||||
"AE1",
|
||||
"AO2",
|
||||
"OY1",
|
||||
"AY2",
|
||||
"IH1",
|
||||
"OW0",
|
||||
"L",
|
||||
"SH",
|
||||
}
|
||||
|
||||
|
||||
def post_replace_ph(ph):
|
||||
rep_map = {
|
||||
"：": ",",
|
||||
"；": ",",
|
||||
"，": ",",
|
||||
"。": ".",
|
||||
"！": "!",
|
||||
"？": "?",
|
||||
"\n": ".",
|
||||
"·": ",",
|
||||
"、": ",",
|
||||
"...": "…",
|
||||
"v": "V",
|
||||
}
|
||||
if ph in rep_map.keys():
|
||||
ph = rep_map[ph]
|
||||
if ph in symbols:
|
||||
return ph
|
||||
if ph not in symbols:
|
||||
ph = "UNK"
|
||||
return ph
|
||||
|
||||
|
||||
def read_dict():
|
||||
g2p_dict = {}
|
||||
start_line = 49
|
||||
with open(CMU_DICT_PATH) as f:
|
||||
line = f.readline()
|
||||
line_index = 1
|
||||
while line:
|
||||
if line_index >= start_line:
|
||||
line = line.strip()
|
||||
word_split = line.split(" ")
|
||||
word = word_split[0]
|
||||
|
||||
syllable_split = word_split[1].split(" - ")
|
||||
g2p_dict[word] = []
|
||||
for syllable in syllable_split:
|
||||
phone_split = syllable.split(" ")
|
||||
g2p_dict[word].append(phone_split)
|
||||
|
||||
line_index = line_index + 1
|
||||
line = f.readline()
|
||||
|
||||
return g2p_dict
|
||||
|
||||
|
||||
def cache_dict(g2p_dict, file_path):
|
||||
with open(file_path, "wb") as pickle_file:
|
||||
pickle.dump(g2p_dict, pickle_file)
|
||||
|
||||
|
||||
def get_dict():
|
||||
if os.path.exists(CACHE_PATH):
|
||||
with open(CACHE_PATH, "rb") as pickle_file:
|
||||
g2p_dict = pickle.load(pickle_file)
|
||||
else:
|
||||
g2p_dict = read_dict()
|
||||
cache_dict(g2p_dict, CACHE_PATH)
|
||||
|
||||
return g2p_dict
|
||||
|
||||
|
||||
eng_dict = get_dict()
|
||||
|
||||
|
||||
def refine_ph(phn):
|
||||
tone = 0
|
||||
if re.search(r"\d$", phn):
|
||||
tone = int(phn[-1]) + 1
|
||||
phn = phn[:-1]
|
||||
return phn.lower(), tone
|
||||
|
||||
|
||||
def refine_syllables(syllables):
|
||||
tones = []
|
||||
phonemes = []
|
||||
for phn_list in syllables:
|
||||
for i in range(len(phn_list)):
|
||||
phn = phn_list[i]
|
||||
phn, tone = refine_ph(phn)
|
||||
phonemes.append(phn)
|
||||
tones.append(tone)
|
||||
return phonemes, tones
|
||||
|
||||
|
||||
def text_normalize(text):
|
||||
# todo: eng text normalize
|
||||
return text
|
||||
|
||||
|
||||
def g2p(text):
|
||||
phones = []
|
||||
tones = []
|
||||
words = re.split(r"([,;.\-\?\!\s+])", text)
|
||||
for w in words:
|
||||
if w.upper() in eng_dict:
|
||||
phns, tns = refine_syllables(eng_dict[w.upper()])
|
||||
phones += phns
|
||||
tones += tns
|
||||
else:
|
||||
phone_list = list(filter(lambda p: p != " ", _g2p(w)))
|
||||
for ph in phone_list:
|
||||
if ph in arpa:
|
||||
ph, tn = refine_ph(ph)
|
||||
phones.append(ph)
|
||||
tones.append(tn)
|
||||
else:
|
||||
phones.append(ph)
|
||||
tones.append(0)
|
||||
# todo: implement word2ph
|
||||
word2ph = [1 for i in phones]
|
||||
|
||||
phones = [post_replace_ph(i) for i in phones]
|
||||
return phones, tones, word2ph
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# print(get_dict())
|
||||
# print(eng_word_to_phoneme("hello"))
|
||||
print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder."))
|
||||
# all_phones = set()
|
||||
# for k, syllables in eng_dict.items():
|
||||
# for group in syllables:
|
||||
# for ph in group:
|
||||
# all_phones.add(ph)
|
||||
# print(all_phones)
|
||||
5
oldVersion/V110/text/english_bert_mock.py
Normal file
5
oldVersion/V110/text/english_bert_mock.py
Normal file
@@ -0,0 +1,5 @@
|
||||
import torch
|
||||
|
||||
|
||||
def get_bert_feature(norm_text, word2ph):
|
||||
return torch.zeros(1024, sum(word2ph))
|
||||
586
oldVersion/V110/text/japanese.py
Normal file
586
oldVersion/V110/text/japanese.py
Normal file
@@ -0,0 +1,586 @@
|
||||
# Convert Japanese text to phonemes which is
|
||||
# compatible with Julius https://github.com/julius-speech/segmentation-kit
|
||||
import re
|
||||
import unicodedata
|
||||
|
||||
from transformers import AutoTokenizer
|
||||
|
||||
from . import punctuation, symbols
|
||||
|
||||
try:
|
||||
import MeCab
|
||||
except ImportError as e:
|
||||
raise ImportError("Japanese requires mecab-python3 and unidic-lite.") from e
|
||||
from num2words import num2words
|
||||
|
||||
_CONVRULES = [
|
||||
# Conversion of 2 letters
|
||||
"アァ/ a a",
|
||||
"イィ/ i i",
|
||||
"イェ/ i e",
|
||||
"イャ/ y a",
|
||||
"ウゥ/ u:",
|
||||
"エェ/ e e",
|
||||
"オォ/ o:",
|
||||
"カァ/ k a:",
|
||||
"キィ/ k i:",
|
||||
"クゥ/ k u:",
|
||||
"クャ/ ky a",
|
||||
"クュ/ ky u",
|
||||
"クョ/ ky o",
|
||||
"ケェ/ k e:",
|
||||
"コォ/ k o:",
|
||||
"ガァ/ g a:",
|
||||
"ギィ/ g i:",
|
||||
"グゥ/ g u:",
|
||||
"グャ/ gy a",
|
||||
"グュ/ gy u",
|
||||
"グョ/ gy o",
|
||||
"ゲェ/ g e:",
|
||||
"ゴォ/ g o:",
|
||||
"サァ/ s a:",
|
||||
"シィ/ sh i:",
|
||||
"スゥ/ s u:",
|
||||
"スャ/ sh a",
|
||||
"スュ/ sh u",
|
||||
"スョ/ sh o",
|
||||
"セェ/ s e:",
|
||||
"ソォ/ s o:",
|
||||
"ザァ/ z a:",
|
||||
"ジィ/ j i:",
|
||||
"ズゥ/ z u:",
|
||||
"ズャ/ zy a",
|
||||
"ズュ/ zy u",
|
||||
"ズョ/ zy o",
|
||||
"ゼェ/ z e:",
|
||||
"ゾォ/ z o:",
|
||||
"タァ/ t a:",
|
||||
"チィ/ ch i:",
|
||||
"ツァ/ ts a",
|
||||
"ツィ/ ts i",
|
||||
"ツゥ/ ts u:",
|
||||
"ツャ/ ch a",
|
||||
"ツュ/ ch u",
|
||||
"ツョ/ ch o",
|
||||
"ツェ/ ts e",
|
||||
"ツォ/ ts o",
|
||||
"テェ/ t e:",
|
||||
"トォ/ t o:",
|
||||
"ダァ/ d a:",
|
||||
"ヂィ/ j i:",
|
||||
"ヅゥ/ d u:",
|
||||
"ヅャ/ zy a",
|
||||
"ヅュ/ zy u",
|
||||
"ヅョ/ zy o",
|
||||
"デェ/ d e:",
|
||||
"ドォ/ d o:",
|
||||
"ナァ/ n a:",
|
||||
"ニィ/ n i:",
|
||||
"ヌゥ/ n u:",
|
||||
"ヌャ/ ny a",
|
||||
"ヌュ/ ny u",
|
||||
"ヌョ/ ny o",
|
||||
"ネェ/ n e:",
|
||||
"ノォ/ n o:",
|
||||
"ハァ/ h a:",
|
||||
"ヒィ/ h i:",
|
||||
"フゥ/ f u:",
|
||||
"フャ/ hy a",
|
||||
"フュ/ hy u",
|
||||
"フョ/ hy o",
|
||||
"ヘェ/ h e:",
|
||||
"ホォ/ h o:",
|
||||
"バァ/ b a:",
|
||||
"ビィ/ b i:",
|
||||
"ブゥ/ b u:",
|
||||
"フャ/ hy a",
|
||||
"ブュ/ by u",
|
||||
"フョ/ hy o",
|
||||
"ベェ/ b e:",
|
||||
"ボォ/ b o:",
|
||||
"パァ/ p a:",
|
||||
"ピィ/ p i:",
|
||||
"プゥ/ p u:",
|
||||
"プャ/ py a",
|
||||
"プュ/ py u",
|
||||
"プョ/ py o",
|
||||
"ペェ/ p e:",
|
||||
"ポォ/ p o:",
|
||||
"マァ/ m a:",
|
||||
"ミィ/ m i:",
|
||||
"ムゥ/ m u:",
|
||||
"ムャ/ my a",
|
||||
"ムュ/ my u",
|
||||
"ムョ/ my o",
|
||||
"メェ/ m e:",
|
||||
"モォ/ m o:",
|
||||
"ヤァ/ y a:",
|
||||
"ユゥ/ y u:",
|
||||
"ユャ/ y a:",
|
||||
"ユュ/ y u:",
|
||||
"ユョ/ y o:",
|
||||
"ヨォ/ y o:",
|
||||
"ラァ/ r a:",
|
||||
"リィ/ r i:",
|
||||
"ルゥ/ r u:",
|
||||
"ルャ/ ry a",
|
||||
"ルュ/ ry u",
|
||||
"ルョ/ ry o",
|
||||
"レェ/ r e:",
|
||||
"ロォ/ r o:",
|
||||
"ワァ/ w a:",
|
||||
"ヲォ/ o:",
|
||||
"ディ/ d i",
|
||||
"デェ/ d e:",
|
||||
"デャ/ dy a",
|
||||
"デュ/ dy u",
|
||||
"デョ/ dy o",
|
||||
"ティ/ t i",
|
||||
"テェ/ t e:",
|
||||
"テャ/ ty a",
|
||||
"テュ/ ty u",
|
||||
"テョ/ ty o",
|
||||
"スィ/ s i",
|
||||
"ズァ/ z u a",
|
||||
"ズィ/ z i",
|
||||
"ズゥ/ z u",
|
||||
"ズャ/ zy a",
|
||||
"ズュ/ zy u",
|
||||
"ズョ/ zy o",
|
||||
"ズェ/ z e",
|
||||
"ズォ/ z o",
|
||||
"キャ/ ky a",
|
||||
"キュ/ ky u",
|
||||
"キョ/ ky o",
|
||||
"シャ/ sh a",
|
||||
"シュ/ sh u",
|
||||
"シェ/ sh e",
|
||||
"ショ/ sh o",
|
||||
"チャ/ ch a",
|
||||
"チュ/ ch u",
|
||||
"チェ/ ch e",
|
||||
"チョ/ ch o",
|
||||
"トゥ/ t u",
|
||||
"トャ/ ty a",
|
||||
"トュ/ ty u",
|
||||
"トョ/ ty o",
|
||||
"ドァ/ d o a",
|
||||
"ドゥ/ d u",
|
||||
"ドャ/ dy a",
|
||||
"ドュ/ dy u",
|
||||
"ドョ/ dy o",
|
||||
"ドォ/ d o:",
|
||||
"ニャ/ ny a",
|
||||
"ニュ/ ny u",
|
||||
"ニョ/ ny o",
|
||||
"ヒャ/ hy a",
|
||||
"ヒュ/ hy u",
|
||||
"ヒョ/ hy o",
|
||||
"ミャ/ my a",
|
||||
"ミュ/ my u",
|
||||
"ミョ/ my o",
|
||||
"リャ/ ry a",
|
||||
"リュ/ ry u",
|
||||
"リョ/ ry o",
|
||||
"ギャ/ gy a",
|
||||
"ギュ/ gy u",
|
||||
"ギョ/ gy o",
|
||||
"ヂェ/ j e",
|
||||
"ヂャ/ j a",
|
||||
"ヂュ/ j u",
|
||||
"ヂョ/ j o",
|
||||
"ジェ/ j e",
|
||||
"ジャ/ j a",
|
||||
"ジュ/ j u",
|
||||
"ジョ/ j o",
|
||||
"ビャ/ by a",
|
||||
"ビュ/ by u",
|
||||
"ビョ/ by o",
|
||||
"ピャ/ py a",
|
||||
"ピュ/ py u",
|
||||
"ピョ/ py o",
|
||||
"ウァ/ u a",
|
||||
"ウィ/ w i",
|
||||
"ウェ/ w e",
|
||||
"ウォ/ w o",
|
||||
"ファ/ f a",
|
||||
"フィ/ f i",
|
||||
"フゥ/ f u",
|
||||
"フャ/ hy a",
|
||||
"フュ/ hy u",
|
||||
"フョ/ hy o",
|
||||
"フェ/ f e",
|
||||
"フォ/ f o",
|
||||
"ヴァ/ b a",
|
||||
"ヴィ/ b i",
|
||||
"ヴェ/ b e",
|
||||
"ヴォ/ b o",
|
||||
"ヴュ/ by u",
|
||||
# Conversion of 1 letter
|
||||
"ア/ a",
|
||||
"イ/ i",
|
||||
"ウ/ u",
|
||||
"エ/ e",
|
||||
"オ/ o",
|
||||
"カ/ k a",
|
||||
"キ/ k i",
|
||||
"ク/ k u",
|
||||
"ケ/ k e",
|
||||
"コ/ k o",
|
||||
"サ/ s a",
|
||||
"シ/ sh i",
|
||||
"ス/ s u",
|
||||
"セ/ s e",
|
||||
"ソ/ s o",
|
||||
"タ/ t a",
|
||||
"チ/ ch i",
|
||||
"ツ/ ts u",
|
||||
"テ/ t e",
|
||||
"ト/ t o",
|
||||
"ナ/ n a",
|
||||
"ニ/ n i",
|
||||
"ヌ/ n u",
|
||||
"ネ/ n e",
|
||||
"ノ/ n o",
|
||||
"ハ/ h a",
|
||||
"ヒ/ h i",
|
||||
"フ/ f u",
|
||||
"ヘ/ h e",
|
||||
"ホ/ h o",
|
||||
"マ/ m a",
|
||||
"ミ/ m i",
|
||||
"ム/ m u",
|
||||
"メ/ m e",
|
||||
"モ/ m o",
|
||||
"ラ/ r a",
|
||||
"リ/ r i",
|
||||
"ル/ r u",
|
||||
"レ/ r e",
|
||||
"ロ/ r o",
|
||||
"ガ/ g a",
|
||||
"ギ/ g i",
|
||||
"グ/ g u",
|
||||
"ゲ/ g e",
|
||||
"ゴ/ g o",
|
||||
"ザ/ z a",
|
||||
"ジ/ j i",
|
||||
"ズ/ z u",
|
||||
"ゼ/ z e",
|
||||
"ゾ/ z o",
|
||||
"ダ/ d a",
|
||||
"ヂ/ j i",
|
||||
"ヅ/ z u",
|
||||
"デ/ d e",
|
||||
"ド/ d o",
|
||||
"バ/ b a",
|
||||
"ビ/ b i",
|
||||
"ブ/ b u",
|
||||
"ベ/ b e",
|
||||
"ボ/ b o",
|
||||
"パ/ p a",
|
||||
"ピ/ p i",
|
||||
"プ/ p u",
|
||||
"ペ/ p e",
|
||||
"ポ/ p o",
|
||||
"ヤ/ y a",
|
||||
"ユ/ y u",
|
||||
"ヨ/ y o",
|
||||
"ワ/ w a",
|
||||
"ヰ/ i",
|
||||
"ヱ/ e",
|
||||
"ヲ/ o",
|
||||
"ン/ N",
|
||||
"ッ/ q",
|
||||
"ヴ/ b u",
|
||||
"ー/:",
|
||||
# Try converting broken text
|
||||
"ァ/ a",
|
||||
"ィ/ i",
|
||||
"ゥ/ u",
|
||||
"ェ/ e",
|
||||
"ォ/ o",
|
||||
"ヮ/ w a",
|
||||
"ォ/ o",
|
||||
# Symbols
|
||||
"、/ ,",
|
||||
"。/ .",
|
||||
"！/ !",
|
||||
"？/ ?",
|
||||
"・/ ,",
|
||||
]
|
||||
|
||||
_COLON_RX = re.compile(":+")
|
||||
_REJECT_RX = re.compile("[^ a-zA-Z:,.?]")
|
||||
|
||||
|
||||
def _makerulemap():
|
||||
l = [tuple(x.split("/")) for x in _CONVRULES]
|
||||
return tuple({k: v for k, v in l if len(k) == i} for i in (1, 2))
|
||||
|
||||
|
||||
_RULEMAP1, _RULEMAP2 = _makerulemap()
|
||||
|
||||
|
||||
def kata2phoneme(text: str) -> str:
|
||||
"""Convert katakana text to phonemes."""
|
||||
text = text.strip()
|
||||
res = []
|
||||
while text:
|
||||
if len(text) >= 2:
|
||||
x = _RULEMAP2.get(text[:2])
|
||||
if x is not None:
|
||||
text = text[2:]
|
||||
res += x.split(" ")[1:]
|
||||
continue
|
||||
x = _RULEMAP1.get(text[0])
|
||||
if x is not None:
|
||||
text = text[1:]
|
||||
res += x.split(" ")[1:]
|
||||
continue
|
||||
res.append(text[0])
|
||||
text = text[1:]
|
||||
# res = _COLON_RX.sub(":", res)
|
||||
return res
|
||||
|
||||
|
||||
_KATAKANA = "".join(chr(ch) for ch in range(ord("ァ"), ord("ン") + 1))
|
||||
_HIRAGANA = "".join(chr(ch) for ch in range(ord("ぁ"), ord("ん") + 1))
|
||||
_HIRA2KATATRANS = str.maketrans(_HIRAGANA, _KATAKANA)
|
||||
|
||||
|
||||
def hira2kata(text: str) -> str:
|
||||
text = text.translate(_HIRA2KATATRANS)
|
||||
return text.replace("う゛", "ヴ")
|
||||
|
||||
|
||||
_SYMBOL_TOKENS = set(list("・、。？！"))
|
||||
_NO_YOMI_TOKENS = set(list("「」『』―（）［］[]"))
|
||||
_TAGGER = MeCab.Tagger()
|
||||
|
||||
|
||||
def text2kata(text: str) -> str:
|
||||
parsed = _TAGGER.parse(text)
|
||||
res = []
|
||||
for line in parsed.split("\n"):
|
||||
if line == "EOS":
|
||||
break
|
||||
parts = line.split("\t")
|
||||
|
||||
word, yomi = parts[0], parts[1]
|
||||
if yomi:
|
||||
res.append(yomi)
|
||||
else:
|
||||
if word in _SYMBOL_TOKENS:
|
||||
res.append(word)
|
||||
elif word in ("っ", "ッ"):
|
||||
res.append("ッ")
|
||||
elif word in _NO_YOMI_TOKENS:
|
||||
pass
|
||||
else:
|
||||
res.append(word)
|
||||
return hira2kata("".join(res))
|
||||
|
||||
|
||||
_ALPHASYMBOL_YOMI = {
|
||||
"#": "シャープ",
|
||||
"%": "パーセント",
|
||||
"&": "アンド",
|
||||
"+": "プラス",
|
||||
"-": "マイナス",
|
||||
":": "コロン",
|
||||
";": "セミコロン",
|
||||
"<": "小なり",
|
||||
"=": "イコール",
|
||||
">": "大なり",
|
||||
"@": "アット",
|
||||
"a": "エー",
|
||||
"b": "ビー",
|
||||
"c": "シー",
|
||||
"d": "ディー",
|
||||
"e": "イー",
|
||||
"f": "エフ",
|
||||
"g": "ジー",
|
||||
"h": "エイチ",
|
||||
"i": "アイ",
|
||||
"j": "ジェー",
|
||||
"k": "ケー",
|
||||
"l": "エル",
|
||||
"m": "エム",
|
||||
"n": "エヌ",
|
||||
"o": "オー",
|
||||
"p": "ピー",
|
||||
"q": "キュー",
|
||||
"r": "アール",
|
||||
"s": "エス",
|
||||
"t": "ティー",
|
||||
"u": "ユー",
|
||||
"v": "ブイ",
|
||||
"w": "ダブリュー",
|
||||
"x": "エックス",
|
||||
"y": "ワイ",
|
||||
"z": "ゼット",
|
||||
"α": "アルファ",
|
||||
"β": "ベータ",
|
||||
"γ": "ガンマ",
|
||||
"δ": "デルタ",
|
||||
"ε": "イプシロン",
|
||||
"ζ": "ゼータ",
|
||||
"η": "イータ",
|
||||
"θ": "シータ",
|
||||
"ι": "イオタ",
|
||||
"κ": "カッパ",
|
||||
"λ": "ラムダ",
|
||||
"μ": "ミュー",
|
||||
"ν": "ニュー",
|
||||
"ξ": "クサイ",
|
||||
"ο": "オミクロン",
|
||||
"π": "パイ",
|
||||
"ρ": "ロー",
|
||||
"σ": "シグマ",
|
||||
"τ": "タウ",
|
||||
"υ": "ウプシロン",
|
||||
"φ": "ファイ",
|
||||
"χ": "カイ",
|
||||
"ψ": "プサイ",
|
||||
"ω": "オメガ",
|
||||
}
|
||||
|
||||
|
||||
_NUMBER_WITH_SEPARATOR_RX = re.compile("[0-9]{1,3}(,[0-9]{3})+")
|
||||
_CURRENCY_MAP = {"$": "ドル", "¥": "円", "£": "ポンド", "€": "ユーロ"}
|
||||
_CURRENCY_RX = re.compile(r"([$¥£€])([0-9.]*[0-9])")
|
||||
_NUMBER_RX = re.compile(r"[0-9]+(\.[0-9]+)?")
|
||||
|
||||
|
||||
def japanese_convert_numbers_to_words(text: str) -> str:
|
||||
res = _NUMBER_WITH_SEPARATOR_RX.sub(lambda m: m[0].replace(",", ""), text)
|
||||
res = _CURRENCY_RX.sub(lambda m: m[2] + _CURRENCY_MAP.get(m[1], m[1]), res)
|
||||
res = _NUMBER_RX.sub(lambda m: num2words(m[0], lang="ja"), res)
|
||||
return res
|
||||
|
||||
|
||||
def japanese_convert_alpha_symbols_to_words(text: str) -> str:
|
||||
return "".join([_ALPHASYMBOL_YOMI.get(ch, ch) for ch in text.lower()])
|
||||
|
||||
|
||||
def japanese_text_to_phonemes(text: str) -> str:
|
||||
"""Convert Japanese text to phonemes."""
|
||||
res = unicodedata.normalize("NFKC", text)
|
||||
res = japanese_convert_numbers_to_words(res)
|
||||
# res = japanese_convert_alpha_symbols_to_words(res)
|
||||
res = text2kata(res)
|
||||
res = kata2phoneme(res)
|
||||
return res
|
||||
|
||||
|
||||
def is_japanese_character(char):
|
||||
# 定义日语文字系统的 Unicode 范围
|
||||
japanese_ranges = [
|
||||
(0x3040, 0x309F), # 平假名
|
||||
(0x30A0, 0x30FF), # 片假名
|
||||
(0x4E00, 0x9FFF), # 汉字 (CJK Unified Ideographs)
|
||||
(0x3400, 0x4DBF), # 汉字扩展 A
|
||||
(0x20000, 0x2A6DF), # 汉字扩展 B
|
||||
# 可以根据需要添加其他汉字扩展范围
|
||||
]
|
||||
|
||||
# 将字符的 Unicode 编码转换为整数
|
||||
char_code = ord(char)
|
||||
|
||||
# 检查字符是否在任何一个日语范围内
|
||||
for start, end in japanese_ranges:
|
||||
if start <= char_code <= end:
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
rep_map = {
|
||||
"：": ",",
|
||||
"；": ",",
|
||||
"，": ",",
|
||||
"。": ".",
|
||||
"！": "!",
|
||||
"？": "?",
|
||||
"\n": ".",
|
||||
"·": ",",
|
||||
"、": ",",
|
||||
"...": "…",
|
||||
}
|
||||
|
||||
|
||||
def replace_punctuation(text):
|
||||
pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
|
||||
|
||||
replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
|
||||
|
||||
replaced_text = re.sub(
|
||||
r"[^\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF"
|
||||
+ "".join(punctuation)
|
||||
+ r"]+",
|
||||
"",
|
||||
replaced_text,
|
||||
)
|
||||
|
||||
return replaced_text
|
||||
|
||||
|
||||
def text_normalize(text):
|
||||
res = unicodedata.normalize("NFKC", text)
|
||||
res = japanese_convert_numbers_to_words(res)
|
||||
# res = "".join([i for i in res if is_japanese_character(i)])
|
||||
res = replace_punctuation(res)
|
||||
return res
|
||||
|
||||
|
||||
def distribute_phone(n_phone, n_word):
|
||||
phones_per_word = [0] * n_word
|
||||
for task in range(n_phone):
|
||||
min_tasks = min(phones_per_word)
|
||||
min_index = phones_per_word.index(min_tasks)
|
||||
phones_per_word[min_index] += 1
|
||||
return phones_per_word
|
||||
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained("./bert/bert-base-japanese-v3")
|
||||
|
||||
|
||||
def g2p(norm_text):
|
||||
tokenized = tokenizer.tokenize(norm_text)
|
||||
phs = []
|
||||
ph_groups = []
|
||||
for t in tokenized:
|
||||
if not t.startswith("#"):
|
||||
ph_groups.append([t])
|
||||
else:
|
||||
ph_groups[-1].append(t.replace("#", ""))
|
||||
word2ph = []
|
||||
for group in ph_groups:
|
||||
phonemes = kata2phoneme(text2kata("".join(group)))
|
||||
# phonemes = [i for i in phonemes if i in symbols]
|
||||
for i in phonemes:
|
||||
assert i in symbols, (group, norm_text, tokenized)
|
||||
phone_len = len(phonemes)
|
||||
word_len = len(group)
|
||||
|
||||
aaa = distribute_phone(phone_len, word_len)
|
||||
word2ph += aaa
|
||||
|
||||
phs += phonemes
|
||||
phones = ["_"] + phs + ["_"]
|
||||
tones = [0 for i in phones]
|
||||
word2ph = [1] + word2ph + [1]
|
||||
return phones, tones, word2ph
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
tokenizer = AutoTokenizer.from_pretrained("./bert/bert-base-japanese-v3")
|
||||
text = "hello,こんにちは、世界！……"
|
||||
from text.japanese_bert import get_bert_feature
|
||||
|
||||
text = text_normalize(text)
|
||||
print(text)
|
||||
phones, tones, word2ph = g2p(text)
|
||||
bert = get_bert_feature(text, word2ph)
|
||||
|
||||
print(phones, tones, word2ph, bert.shape)
|
||||
35
oldVersion/V110/text/japanese_bert.py
Normal file
35
oldVersion/V110/text/japanese_bert.py
Normal file
@@ -0,0 +1,35 @@
|
||||
import torch
|
||||
from transformers import AutoTokenizer, AutoModelForMaskedLM
|
||||
import sys
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained("./bert/bert-base-japanese-v3")
|
||||
|
||||
|
||||
def get_bert_feature(text, word2ph, device=None):
|
||||
if (
|
||||
sys.platform == "darwin"
|
||||
and torch.backends.mps.is_available()
|
||||
and device == "cpu"
|
||||
):
|
||||
device = "mps"
|
||||
if not device:
|
||||
device = "cuda"
|
||||
model = AutoModelForMaskedLM.from_pretrained("./bert/bert-base-japanese-v3").to(
|
||||
device
|
||||
)
|
||||
with torch.no_grad():
|
||||
inputs = tokenizer(text, return_tensors="pt")
|
||||
for i in inputs:
|
||||
inputs[i] = inputs[i].to(device)
|
||||
res = model(**inputs, output_hidden_states=True)
|
||||
res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
|
||||
assert inputs["input_ids"].shape[-1] == len(word2ph)
|
||||
word2phone = word2ph
|
||||
phone_level_feature = []
|
||||
for i in range(len(word2phone)):
|
||||
repeat_feature = res[i].repeat(word2phone[i], 1)
|
||||
phone_level_feature.append(repeat_feature)
|
||||
|
||||
phone_level_feature = torch.cat(phone_level_feature, dim=0)
|
||||
|
||||
return phone_level_feature.T
|
||||
429
oldVersion/V110/text/opencpop-strict.txt
Normal file
429
oldVersion/V110/text/opencpop-strict.txt
Normal file
@@ -0,0 +1,429 @@
|
||||
a AA a
|
||||
ai AA ai
|
||||
an AA an
|
||||
ang AA ang
|
||||
ao AA ao
|
||||
ba b a
|
||||
bai b ai
|
||||
ban b an
|
||||
bang b ang
|
||||
bao b ao
|
||||
bei b ei
|
||||
ben b en
|
||||
beng b eng
|
||||
bi b i
|
||||
bian b ian
|
||||
biao b iao
|
||||
bie b ie
|
||||
bin b in
|
||||
bing b ing
|
||||
bo b o
|
||||
bu b u
|
||||
ca c a
|
||||
cai c ai
|
||||
can c an
|
||||
cang c ang
|
||||
cao c ao
|
||||
ce c e
|
||||
cei c ei
|
||||
cen c en
|
||||
ceng c eng
|
||||
cha ch a
|
||||
chai ch ai
|
||||
chan ch an
|
||||
chang ch ang
|
||||
chao ch ao
|
||||
che ch e
|
||||
chen ch en
|
||||
cheng ch eng
|
||||
chi ch ir
|
||||
chong ch ong
|
||||
chou ch ou
|
||||
chu ch u
|
||||
chua ch ua
|
||||
chuai ch uai
|
||||
chuan ch uan
|
||||
chuang ch uang
|
||||
chui ch ui
|
||||
chun ch un
|
||||
chuo ch uo
|
||||
ci c i0
|
||||
cong c ong
|
||||
cou c ou
|
||||
cu c u
|
||||
cuan c uan
|
||||
cui c ui
|
||||
cun c un
|
||||
cuo c uo
|
||||
da d a
|
||||
dai d ai
|
||||
dan d an
|
||||
dang d ang
|
||||
dao d ao
|
||||
de d e
|
||||
dei d ei
|
||||
den d en
|
||||
deng d eng
|
||||
di d i
|
||||
dia d ia
|
||||
dian d ian
|
||||
diao d iao
|
||||
die d ie
|
||||
ding d ing
|
||||
diu d iu
|
||||
dong d ong
|
||||
dou d ou
|
||||
du d u
|
||||
duan d uan
|
||||
dui d ui
|
||||
dun d un
|
||||
duo d uo
|
||||
e EE e
|
||||
ei EE ei
|
||||
en EE en
|
||||
eng EE eng
|
||||
er EE er
|
||||
fa f a
|
||||
fan f an
|
||||
fang f ang
|
||||
fei f ei
|
||||
fen f en
|
||||
feng f eng
|
||||
fo f o
|
||||
fou f ou
|
||||
fu f u
|
||||
ga g a
|
||||
gai g ai
|
||||
gan g an
|
||||
gang g ang
|
||||
gao g ao
|
||||
ge g e
|
||||
gei g ei
|
||||
gen g en
|
||||
geng g eng
|
||||
gong g ong
|
||||
gou g ou
|
||||
gu g u
|
||||
gua g ua
|
||||
guai g uai
|
||||
guan g uan
|
||||
guang g uang
|
||||
gui g ui
|
||||
gun g un
|
||||
guo g uo
|
||||
ha h a
|
||||
hai h ai
|
||||
han h an
|
||||
hang h ang
|
||||
hao h ao
|
||||
he h e
|
||||
hei h ei
|
||||
hen h en
|
||||
heng h eng
|
||||
hong h ong
|
||||
hou h ou
|
||||
hu h u
|
||||
hua h ua
|
||||
huai h uai
|
||||
huan h uan
|
||||
huang h uang
|
||||
hui h ui
|
||||
hun h un
|
||||
huo h uo
|
||||
ji j i
|
||||
jia j ia
|
||||
jian j ian
|
||||
jiang j iang
|
||||
jiao j iao
|
||||
jie j ie
|
||||
jin j in
|
||||
jing j ing
|
||||
jiong j iong
|
||||
jiu j iu
|
||||
ju j v
|
||||
jv j v
|
||||
juan j van
|
||||
jvan j van
|
||||
jue j ve
|
||||
jve j ve
|
||||
jun j vn
|
||||
jvn j vn
|
||||
ka k a
|
||||
kai k ai
|
||||
kan k an
|
||||
kang k ang
|
||||
kao k ao
|
||||
ke k e
|
||||
kei k ei
|
||||
ken k en
|
||||
keng k eng
|
||||
kong k ong
|
||||
kou k ou
|
||||
ku k u
|
||||
kua k ua
|
||||
kuai k uai
|
||||
kuan k uan
|
||||
kuang k uang
|
||||
kui k ui
|
||||
kun k un
|
||||
kuo k uo
|
||||
la l a
|
||||
lai l ai
|
||||
lan l an
|
||||
lang l ang
|
||||
lao l ao
|
||||
le l e
|
||||
lei l ei
|
||||
leng l eng
|
||||
li l i
|
||||
lia l ia
|
||||
lian l ian
|
||||
liang l iang
|
||||
liao l iao
|
||||
lie l ie
|
||||
lin l in
|
||||
ling l ing
|
||||
liu l iu
|
||||
lo l o
|
||||
long l ong
|
||||
lou l ou
|
||||
lu l u
|
||||
luan l uan
|
||||
lun l un
|
||||
luo l uo
|
||||
lv l v
|
||||
lve l ve
|
||||
ma m a
|
||||
mai m ai
|
||||
man m an
|
||||
mang m ang
|
||||
mao m ao
|
||||
me m e
|
||||
mei m ei
|
||||
men m en
|
||||
meng m eng
|
||||
mi m i
|
||||
mian m ian
|
||||
miao m iao
|
||||
mie m ie
|
||||
min m in
|
||||
ming m ing
|
||||
miu m iu
|
||||
mo m o
|
||||
mou m ou
|
||||
mu m u
|
||||
na n a
|
||||
nai n ai
|
||||
nan n an
|
||||
nang n ang
|
||||
nao n ao
|
||||
ne n e
|
||||
nei n ei
|
||||
nen n en
|
||||
neng n eng
|
||||
ni n i
|
||||
nian n ian
|
||||
niang n iang
|
||||
niao n iao
|
||||
nie n ie
|
||||
nin n in
|
||||
ning n ing
|
||||
niu n iu
|
||||
nong n ong
|
||||
nou n ou
|
||||
nu n u
|
||||
nuan n uan
|
||||
nun n un
|
||||
nuo n uo
|
||||
nv n v
|
||||
nve n ve
|
||||
o OO o
|
||||
ou OO ou
|
||||
pa p a
|
||||
pai p ai
|
||||
pan p an
|
||||
pang p ang
|
||||
pao p ao
|
||||
pei p ei
|
||||
pen p en
|
||||
peng p eng
|
||||
pi p i
|
||||
pian p ian
|
||||
piao p iao
|
||||
pie p ie
|
||||
pin p in
|
||||
ping p ing
|
||||
po p o
|
||||
pou p ou
|
||||
pu p u
|
||||
qi q i
|
||||
qia q ia
|
||||
qian q ian
|
||||
qiang q iang
|
||||
qiao q iao
|
||||
qie q ie
|
||||
qin q in
|
||||
qing q ing
|
||||
qiong q iong
|
||||
qiu q iu
|
||||
qu q v
|
||||
qv q v
|
||||
quan q van
|
||||
qvan q van
|
||||
que q ve
|
||||
qve q ve
|
||||
qun q vn
|
||||
qvn q vn
|
||||
ran r an
|
||||
rang r ang
|
||||
rao r ao
|
||||
re r e
|
||||
ren r en
|
||||
reng r eng
|
||||
ri r ir
|
||||
rong r ong
|
||||
rou r ou
|
||||
ru r u
|
||||
rua r ua
|
||||
ruan r uan
|
||||
rui r ui
|
||||
run r un
|
||||
ruo r uo
|
||||
sa s a
|
||||
sai s ai
|
||||
san s an
|
||||
sang s ang
|
||||
sao s ao
|
||||
se s e
|
||||
sen s en
|
||||
seng s eng
|
||||
sha sh a
|
||||
shai sh ai
|
||||
shan sh an
|
||||
shang sh ang
|
||||
shao sh ao
|
||||
she sh e
|
||||
shei sh ei
|
||||
shen sh en
|
||||
sheng sh eng
|
||||
shi sh ir
|
||||
shou sh ou
|
||||
shu sh u
|
||||
shua sh ua
|
||||
shuai sh uai
|
||||
shuan sh uan
|
||||
shuang sh uang
|
||||
shui sh ui
|
||||
shun sh un
|
||||
shuo sh uo
|
||||
si s i0
|
||||
song s ong
|
||||
sou s ou
|
||||
su s u
|
||||
suan s uan
|
||||
sui s ui
|
||||
sun s un
|
||||
suo s uo
|
||||
ta t a
|
||||
tai t ai
|
||||
tan t an
|
||||
tang t ang
|
||||
tao t ao
|
||||
te t e
|
||||
tei t ei
|
||||
teng t eng
|
||||
ti t i
|
||||
tian t ian
|
||||
tiao t iao
|
||||
tie t ie
|
||||
ting t ing
|
||||
tong t ong
|
||||
tou t ou
|
||||
tu t u
|
||||
tuan t uan
|
||||
tui t ui
|
||||
tun t un
|
||||
tuo t uo
|
||||
wa w a
|
||||
wai w ai
|
||||
wan w an
|
||||
wang w ang
|
||||
wei w ei
|
||||
wen w en
|
||||
weng w eng
|
||||
wo w o
|
||||
wu w u
|
||||
xi x i
|
||||
xia x ia
|
||||
xian x ian
|
||||
xiang x iang
|
||||
xiao x iao
|
||||
xie x ie
|
||||
xin x in
|
||||
xing x ing
|
||||
xiong x iong
|
||||
xiu x iu
|
||||
xu x v
|
||||
xv x v
|
||||
xuan x van
|
||||
xvan x van
|
||||
xue x ve
|
||||
xve x ve
|
||||
xun x vn
|
||||
xvn x vn
|
||||
ya y a
|
||||
yan y En
|
||||
yang y ang
|
||||
yao y ao
|
||||
ye y E
|
||||
yi y i
|
||||
yin y in
|
||||
ying y ing
|
||||
yo y o
|
||||
yong y ong
|
||||
you y ou
|
||||
yu y v
|
||||
yv y v
|
||||
yuan y van
|
||||
yvan y van
|
||||
yue y ve
|
||||
yve y ve
|
||||
yun y vn
|
||||
yvn y vn
|
||||
za z a
|
||||
zai z ai
|
||||
zan z an
|
||||
zang z ang
|
||||
zao z ao
|
||||
ze z e
|
||||
zei z ei
|
||||
zen z en
|
||||
zeng z eng
|
||||
zha zh a
|
||||
zhai zh ai
|
||||
zhan zh an
|
||||
zhang zh ang
|
||||
zhao zh ao
|
||||
zhe zh e
|
||||
zhei zh ei
|
||||
zhen zh en
|
||||
zheng zh eng
|
||||
zhi zh ir
|
||||
zhong zh ong
|
||||
zhou zh ou
|
||||
zhu zh u
|
||||
zhua zh ua
|
||||
zhuai zh uai
|
||||
zhuan zh uan
|
||||
zhuang zh uang
|
||||
zhui zh ui
|
||||
zhun zh un
|
||||
zhuo zh uo
|
||||
zi z i0
|
||||
zong z ong
|
||||
zou z ou
|
||||
zu z u
|
||||
zuan z uan
|
||||
zui z ui
|
||||
zun z un
|
||||
zuo z uo
|
||||
187
oldVersion/V110/text/symbols.py
Normal file
187
oldVersion/V110/text/symbols.py
Normal file
@@ -0,0 +1,187 @@
|
||||
punctuation = ["!", "?", "…", ",", ".", "'", "-"]
|
||||
pu_symbols = punctuation + ["SP", "UNK"]
|
||||
pad = "_"
|
||||
|
||||
# chinese
|
||||
zh_symbols = [
|
||||
"E",
|
||||
"En",
|
||||
"a",
|
||||
"ai",
|
||||
"an",
|
||||
"ang",
|
||||
"ao",
|
||||
"b",
|
||||
"c",
|
||||
"ch",
|
||||
"d",
|
||||
"e",
|
||||
"ei",
|
||||
"en",
|
||||
"eng",
|
||||
"er",
|
||||
"f",
|
||||
"g",
|
||||
"h",
|
||||
"i",
|
||||
"i0",
|
||||
"ia",
|
||||
"ian",
|
||||
"iang",
|
||||
"iao",
|
||||
"ie",
|
||||
"in",
|
||||
"ing",
|
||||
"iong",
|
||||
"ir",
|
||||
"iu",
|
||||
"j",
|
||||
"k",
|
||||
"l",
|
||||
"m",
|
||||
"n",
|
||||
"o",
|
||||
"ong",
|
||||
"ou",
|
||||
"p",
|
||||
"q",
|
||||
"r",
|
||||
"s",
|
||||
"sh",
|
||||
"t",
|
||||
"u",
|
||||
"ua",
|
||||
"uai",
|
||||
"uan",
|
||||
"uang",
|
||||
"ui",
|
||||
"un",
|
||||
"uo",
|
||||
"v",
|
||||
"van",
|
||||
"ve",
|
||||
"vn",
|
||||
"w",
|
||||
"x",
|
||||
"y",
|
||||
"z",
|
||||
"zh",
|
||||
"AA",
|
||||
"EE",
|
||||
"OO",
|
||||
]
|
||||
num_zh_tones = 6
|
||||
|
||||
# japanese
|
||||
ja_symbols = [
|
||||
"N",
|
||||
"a",
|
||||
"a:",
|
||||
"b",
|
||||
"by",
|
||||
"ch",
|
||||
"d",
|
||||
"dy",
|
||||
"e",
|
||||
"e:",
|
||||
"f",
|
||||
"g",
|
||||
"gy",
|
||||
"h",
|
||||
"hy",
|
||||
"i",
|
||||
"i:",
|
||||
"j",
|
||||
"k",
|
||||
"ky",
|
||||
"m",
|
||||
"my",
|
||||
"n",
|
||||
"ny",
|
||||
"o",
|
||||
"o:",
|
||||
"p",
|
||||
"py",
|
||||
"q",
|
||||
"r",
|
||||
"ry",
|
||||
"s",
|
||||
"sh",
|
||||
"t",
|
||||
"ts",
|
||||
"ty",
|
||||
"u",
|
||||
"u:",
|
||||
"w",
|
||||
"y",
|
||||
"z",
|
||||
"zy",
|
||||
]
|
||||
num_ja_tones = 1
|
||||
|
||||
# English
|
||||
en_symbols = [
|
||||
"aa",
|
||||
"ae",
|
||||
"ah",
|
||||
"ao",
|
||||
"aw",
|
||||
"ay",
|
||||
"b",
|
||||
"ch",
|
||||
"d",
|
||||
"dh",
|
||||
"eh",
|
||||
"er",
|
||||
"ey",
|
||||
"f",
|
||||
"g",
|
||||
"hh",
|
||||
"ih",
|
||||
"iy",
|
||||
"jh",
|
||||
"k",
|
||||
"l",
|
||||
"m",
|
||||
"n",
|
||||
"ng",
|
||||
"ow",
|
||||
"oy",
|
||||
"p",
|
||||
"r",
|
||||
"s",
|
||||
"sh",
|
||||
"t",
|
||||
"th",
|
||||
"uh",
|
||||
"uw",
|
||||
"V",
|
||||
"w",
|
||||
"y",
|
||||
"z",
|
||||
"zh",
|
||||
]
|
||||
num_en_tones = 4
|
||||
|
||||
# combine all symbols
|
||||
normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
|
||||
symbols = [pad] + normal_symbols + pu_symbols
|
||||
sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
|
||||
|
||||
# combine all tones
|
||||
num_tones = num_zh_tones + num_ja_tones + num_en_tones
|
||||
|
||||
# language maps
|
||||
language_id_map = {"ZH": 0, "JP": 1, "EN": 2}
|
||||
num_languages = len(language_id_map.keys())
|
||||
|
||||
language_tone_start_map = {
|
||||
"ZH": 0,
|
||||
"JP": num_zh_tones,
|
||||
"EN": num_zh_tones + num_ja_tones,
|
||||
}
|
||||
|
||||
if __name__ == "__main__":
|
||||
a = set(zh_symbols)
|
||||
b = set(en_symbols)
|
||||
print(sorted(a & b))
|
||||
769
oldVersion/V110/text/tone_sandhi.py
Normal file
769
oldVersion/V110/text/tone_sandhi.py
Normal file
@@ -0,0 +1,769 @@
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from typing import List
|
||||
from typing import Tuple
|
||||
|
||||
import jieba
|
||||
from pypinyin import lazy_pinyin
|
||||
from pypinyin import Style
|
||||
|
||||
|
||||
class ToneSandhi:
|
||||
def __init__(self):
|
||||
self.must_neural_tone_words = {
|
||||
"麻烦",
|
||||
"麻利",
|
||||
"鸳鸯",
|
||||
"高粱",
|
||||
"骨头",
|
||||
"骆驼",
|
||||
"马虎",
|
||||
"首饰",
|
||||
"馒头",
|
||||
"馄饨",
|
||||
"风筝",
|
||||
"难为",
|
||||
"队伍",
|
||||
"阔气",
|
||||
"闺女",
|
||||
"门道",
|
||||
"锄头",
|
||||
"铺盖",
|
||||
"铃铛",
|
||||
"铁匠",
|
||||
"钥匙",
|
||||
"里脊",
|
||||
"里头",
|
||||
"部分",
|
||||
"那么",
|
||||
"道士",
|
||||
"造化",
|
||||
"迷糊",
|
||||
"连累",
|
||||
"这么",
|
||||
"这个",
|
||||
"运气",
|
||||
"过去",
|
||||
"软和",
|
||||
"转悠",
|
||||
"踏实",
|
||||
"跳蚤",
|
||||
"跟头",
|
||||
"趔趄",
|
||||
"财主",
|
||||
"豆腐",
|
||||
"讲究",
|
||||
"记性",
|
||||
"记号",
|
||||
"认识",
|
||||
"规矩",
|
||||
"见识",
|
||||
"裁缝",
|
||||
"补丁",
|
||||
"衣裳",
|
||||
"衣服",
|
||||
"衙门",
|
||||
"街坊",
|
||||
"行李",
|
||||
"行当",
|
||||
"蛤蟆",
|
||||
"蘑菇",
|
||||
"薄荷",
|
||||
"葫芦",
|
||||
"葡萄",
|
||||
"萝卜",
|
||||
"荸荠",
|
||||
"苗条",
|
||||
"苗头",
|
||||
"苍蝇",
|
||||
"芝麻",
|
||||
"舒服",
|
||||
"舒坦",
|
||||
"舌头",
|
||||
"自在",
|
||||
"膏药",
|
||||
"脾气",
|
||||
"脑袋",
|
||||
"脊梁",
|
||||
"能耐",
|
||||
"胳膊",
|
||||
"胭脂",
|
||||
"胡萝",
|
||||
"胡琴",
|
||||
"胡同",
|
||||
"聪明",
|
||||
"耽误",
|
||||
"耽搁",
|
||||
"耷拉",
|
||||
"耳朵",
|
||||
"老爷",
|
||||
"老实",
|
||||
"老婆",
|
||||
"老头",
|
||||
"老太",
|
||||
"翻腾",
|
||||
"罗嗦",
|
||||
"罐头",
|
||||
"编辑",
|
||||
"结实",
|
||||
"红火",
|
||||
"累赘",
|
||||
"糨糊",
|
||||
"糊涂",
|
||||
"精神",
|
||||
"粮食",
|
||||
"簸箕",
|
||||
"篱笆",
|
||||
"算计",
|
||||
"算盘",
|
||||
"答应",
|
||||
"笤帚",
|
||||
"笑语",
|
||||
"笑话",
|
||||
"窟窿",
|
||||
"窝囊",
|
||||
"窗户",
|
||||
"稳当",
|
||||
"稀罕",
|
||||
"称呼",
|
||||
"秧歌",
|
||||
"秀气",
|
||||
"秀才",
|
||||
"福气",
|
||||
"祖宗",
|
||||
"砚台",
|
||||
"码头",
|
||||
"石榴",
|
||||
"石头",
|
||||
"石匠",
|
||||
"知识",
|
||||
"眼睛",
|
||||
"眯缝",
|
||||
"眨巴",
|
||||
"眉毛",
|
||||
"相声",
|
||||
"盘算",
|
||||
"白净",
|
||||
"痢疾",
|
||||
"痛快",
|
||||
"疟疾",
|
||||
"疙瘩",
|
||||
"疏忽",
|
||||
"畜生",
|
||||
"生意",
|
||||
"甘蔗",
|
||||
"琵琶",
|
||||
"琢磨",
|
||||
"琉璃",
|
||||
"玻璃",
|
||||
"玫瑰",
|
||||
"玄乎",
|
||||
"狐狸",
|
||||
"状元",
|
||||
"特务",
|
||||
"牲口",
|
||||
"牙碜",
|
||||
"牌楼",
|
||||
"爽快",
|
||||
"爱人",
|
||||
"热闹",
|
||||
"烧饼",
|
||||
"烟筒",
|
||||
"烂糊",
|
||||
"点心",
|
||||
"炊帚",
|
||||
"灯笼",
|
||||
"火候",
|
||||
"漂亮",
|
||||
"滑溜",
|
||||
"溜达",
|
||||
"温和",
|
||||
"清楚",
|
||||
"消息",
|
||||
"浪头",
|
||||
"活泼",
|
||||
"比方",
|
||||
"正经",
|
||||
"欺负",
|
||||
"模糊",
|
||||
"槟榔",
|
||||
"棺材",
|
||||
"棒槌",
|
||||
"棉花",
|
||||
"核桃",
|
||||
"栅栏",
|
||||
"柴火",
|
||||
"架势",
|
||||
"枕头",
|
||||
"枇杷",
|
||||
"机灵",
|
||||
"本事",
|
||||
"木头",
|
||||
"木匠",
|
||||
"朋友",
|
||||
"月饼",
|
||||
"月亮",
|
||||
"暖和",
|
||||
"明白",
|
||||
"时候",
|
||||
"新鲜",
|
||||
"故事",
|
||||
"收拾",
|
||||
"收成",
|
||||
"提防",
|
||||
"挖苦",
|
||||
"挑剔",
|
||||
"指甲",
|
||||
"指头",
|
||||
"拾掇",
|
||||
"拳头",
|
||||
"拨弄",
|
||||
"招牌",
|
||||
"招呼",
|
||||
"抬举",
|
||||
"护士",
|
||||
"折腾",
|
||||
"扫帚",
|
||||
"打量",
|
||||
"打算",
|
||||
"打点",
|
||||
"打扮",
|
||||
"打听",
|
||||
"打发",
|
||||
"扎实",
|
||||
"扁担",
|
||||
"戒指",
|
||||
"懒得",
|
||||
"意识",
|
||||
"意思",
|
||||
"情形",
|
||||
"悟性",
|
||||
"怪物",
|
||||
"思量",
|
||||
"怎么",
|
||||
"念头",
|
||||
"念叨",
|
||||
"快活",
|
||||
"忙活",
|
||||
"志气",
|
||||
"心思",
|
||||
"得罪",
|
||||
"张罗",
|
||||
"弟兄",
|
||||
"开通",
|
||||
"应酬",
|
||||
"庄稼",
|
||||
"干事",
|
||||
"帮手",
|
||||
"帐篷",
|
||||
"希罕",
|
||||
"师父",
|
||||
"师傅",
|
||||
"巴结",
|
||||
"巴掌",
|
||||
"差事",
|
||||
"工夫",
|
||||
"岁数",
|
||||
"屁股",
|
||||
"尾巴",
|
||||
"少爷",
|
||||
"小气",
|
||||
"小伙",
|
||||
"将就",
|
||||
"对头",
|
||||
"对付",
|
||||
"寡妇",
|
||||
"家伙",
|
||||
"客气",
|
||||
"实在",
|
||||
"官司",
|
||||
"学问",
|
||||
"学生",
|
||||
"字号",
|
||||
"嫁妆",
|
||||
"媳妇",
|
||||
"媒人",
|
||||
"婆家",
|
||||
"娘家",
|
||||
"委屈",
|
||||
"姑娘",
|
||||
"姐夫",
|
||||
"妯娌",
|
||||
"妥当",
|
||||
"妖精",
|
||||
"奴才",
|
||||
"女婿",
|
||||
"头发",
|
||||
"太阳",
|
||||
"大爷",
|
||||
"大方",
|
||||
"大意",
|
||||
"大夫",
|
||||
"多少",
|
||||
"多么",
|
||||
"外甥",
|
||||
"壮实",
|
||||
"地道",
|
||||
"地方",
|
||||
"在乎",
|
||||
"困难",
|
||||
"嘴巴",
|
||||
"嘱咐",
|
||||
"嘟囔",
|
||||
"嘀咕",
|
||||
"喜欢",
|
||||
"喇嘛",
|
||||
"喇叭",
|
||||
"商量",
|
||||
"唾沫",
|
||||
"哑巴",
|
||||
"哈欠",
|
||||
"哆嗦",
|
||||
"咳嗽",
|
||||
"和尚",
|
||||
"告诉",
|
||||
"告示",
|
||||
"含糊",
|
||||
"吓唬",
|
||||
"后头",
|
||||
"名字",
|
||||
"名堂",
|
||||
"合同",
|
||||
"吆喝",
|
||||
"叫唤",
|
||||
"口袋",
|
||||
"厚道",
|
||||
"厉害",
|
||||
"千斤",
|
||||
"包袱",
|
||||
"包涵",
|
||||
"匀称",
|
||||
"勤快",
|
||||
"动静",
|
||||
"动弹",
|
||||
"功夫",
|
||||
"力气",
|
||||
"前头",
|
||||
"刺猬",
|
||||
"刺激",
|
||||
"别扭",
|
||||
"利落",
|
||||
"利索",
|
||||
"利害",
|
||||
"分析",
|
||||
"出息",
|
||||
"凑合",
|
||||
"凉快",
|
||||
"冷战",
|
||||
"冤枉",
|
||||
"冒失",
|
||||
"养活",
|
||||
"关系",
|
||||
"先生",
|
||||
"兄弟",
|
||||
"便宜",
|
||||
"使唤",
|
||||
"佩服",
|
||||
"作坊",
|
||||
"体面",
|
||||
"位置",
|
||||
"似的",
|
||||
"伙计",
|
||||
"休息",
|
||||
"什么",
|
||||
"人家",
|
||||
"亲戚",
|
||||
"亲家",
|
||||
"交情",
|
||||
"云彩",
|
||||
"事情",
|
||||
"买卖",
|
||||
"主意",
|
||||
"丫头",
|
||||
"丧气",
|
||||
"两口",
|
||||
"东西",
|
||||
"东家",
|
||||
"世故",
|
||||
"不由",
|
||||
"不在",
|
||||
"下水",
|
||||
"下巴",
|
||||
"上头",
|
||||
"上司",
|
||||
"丈夫",
|
||||
"丈人",
|
||||
"一辈",
|
||||
"那个",
|
||||
"菩萨",
|
||||
"父亲",
|
||||
"母亲",
|
||||
"咕噜",
|
||||
"邋遢",
|
||||
"费用",
|
||||
"冤家",
|
||||
"甜头",
|
||||
"介绍",
|
||||
"荒唐",
|
||||
"大人",
|
||||
"泥鳅",
|
||||
"幸福",
|
||||
"熟悉",
|
||||
"计划",
|
||||
"扑腾",
|
||||
"蜡烛",
|
||||
"姥爷",
|
||||
"照顾",
|
||||
"喉咙",
|
||||
"吉他",
|
||||
"弄堂",
|
||||
"蚂蚱",
|
||||
"凤凰",
|
||||
"拖沓",
|
||||
"寒碜",
|
||||
"糟蹋",
|
||||
"倒腾",
|
||||
"报复",
|
||||
"逻辑",
|
||||
"盘缠",
|
||||
"喽啰",
|
||||
"牢骚",
|
||||
"咖喱",
|
||||
"扫把",
|
||||
"惦记",
|
||||
}
|
||||
self.must_not_neural_tone_words = {
|
||||
"男子",
|
||||
"女子",
|
||||
"分子",
|
||||
"原子",
|
||||
"量子",
|
||||
"莲子",
|
||||
"石子",
|
||||
"瓜子",
|
||||
"电子",
|
||||
"人人",
|
||||
"虎虎",
|
||||
}
|
||||
self.punc = "：，；。？！“”‘’':,;.?!"
|
||||
|
||||
# the meaning of jieba pos tag: https://blog.csdn.net/weixin_44174352/article/details/113731041
|
||||
# e.g.
|
||||
# word: "家里"
|
||||
# pos: "s"
|
||||
# finals: ['ia1', 'i3']
|
||||
def _neural_sandhi(self, word: str, pos: str, finals: List[str]) -> List[str]:
|
||||
# reduplication words for n. and v. e.g. 奶奶, 试试, 旺旺
|
||||
for j, item in enumerate(word):
|
||||
if (
|
||||
j - 1 >= 0
|
||||
and item == word[j - 1]
|
||||
and pos[0] in {"n", "v", "a"}
|
||||
and word not in self.must_not_neural_tone_words
|
||||
):
|
||||
finals[j] = finals[j][:-1] + "5"
|
||||
ge_idx = word.find("个")
|
||||
if len(word) >= 1 and word[-1] in "吧呢啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶":
|
||||
finals[-1] = finals[-1][:-1] + "5"
|
||||
elif len(word) >= 1 and word[-1] in "的地得":
|
||||
finals[-1] = finals[-1][:-1] + "5"
|
||||
# e.g. 走了, 看着, 去过
|
||||
# elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}:
|
||||
# finals[-1] = finals[-1][:-1] + "5"
|
||||
elif (
|
||||
len(word) > 1
|
||||
and word[-1] in "们子"
|
||||
and pos in {"r", "n"}
|
||||
and word not in self.must_not_neural_tone_words
|
||||
):
|
||||
finals[-1] = finals[-1][:-1] + "5"
|
||||
# e.g. 桌上, 地下, 家里
|
||||
elif len(word) > 1 and word[-1] in "上下里" and pos in {"s", "l", "f"}:
|
||||
finals[-1] = finals[-1][:-1] + "5"
|
||||
# e.g. 上来, 下去
|
||||
elif len(word) > 1 and word[-1] in "来去" and word[-2] in "上下进出回过起开":
|
||||
finals[-1] = finals[-1][:-1] + "5"
|
||||
# 个做量词
|
||||
elif (
|
||||
ge_idx >= 1
|
||||
and (word[ge_idx - 1].isnumeric() or word[ge_idx - 1] in "几有两半多各整每做是")
|
||||
) or word == "个":
|
||||
finals[ge_idx] = finals[ge_idx][:-1] + "5"
|
||||
else:
|
||||
if (
|
||||
word in self.must_neural_tone_words
|
||||
or word[-2:] in self.must_neural_tone_words
|
||||
):
|
||||
finals[-1] = finals[-1][:-1] + "5"
|
||||
|
||||
word_list = self._split_word(word)
|
||||
finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]]
|
||||
for i, word in enumerate(word_list):
|
||||
# conventional neural in Chinese
|
||||
if (
|
||||
word in self.must_neural_tone_words
|
||||
or word[-2:] in self.must_neural_tone_words
|
||||
):
|
||||
finals_list[i][-1] = finals_list[i][-1][:-1] + "5"
|
||||
finals = sum(finals_list, [])
|
||||
return finals
|
||||
|
||||
def _bu_sandhi(self, word: str, finals: List[str]) -> List[str]:
|
||||
# e.g. 看不懂
|
||||
if len(word) == 3 and word[1] == "不":
|
||||
finals[1] = finals[1][:-1] + "5"
|
||||
else:
|
||||
for i, char in enumerate(word):
|
||||
# "不" before tone4 should be bu2, e.g. 不怕
|
||||
if char == "不" and i + 1 < len(word) and finals[i + 1][-1] == "4":
|
||||
finals[i] = finals[i][:-1] + "2"
|
||||
return finals
|
||||
|
||||
def _yi_sandhi(self, word: str, finals: List[str]) -> List[str]:
|
||||
# "一" in number sequences, e.g. 一零零, 二一零
|
||||
if word.find("一") != -1 and all(
|
||||
[item.isnumeric() for item in word if item != "一"]
|
||||
):
|
||||
return finals
|
||||
# "一" between reduplication words should be yi5, e.g. 看一看
|
||||
elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]:
|
||||
finals[1] = finals[1][:-1] + "5"
|
||||
# when "一" is ordinal word, it should be yi1
|
||||
elif word.startswith("第一"):
|
||||
finals[1] = finals[1][:-1] + "1"
|
||||
else:
|
||||
for i, char in enumerate(word):
|
||||
if char == "一" and i + 1 < len(word):
|
||||
# "一" before tone4 should be yi2, e.g. 一段
|
||||
if finals[i + 1][-1] == "4":
|
||||
finals[i] = finals[i][:-1] + "2"
|
||||
# "一" before non-tone4 should be yi4, e.g. 一天
|
||||
else:
|
||||
# "一" 后面如果是标点，还读一声
|
||||
if word[i + 1] not in self.punc:
|
||||
finals[i] = finals[i][:-1] + "4"
|
||||
return finals
|
||||
|
||||
def _split_word(self, word: str) -> List[str]:
|
||||
word_list = jieba.cut_for_search(word)
|
||||
word_list = sorted(word_list, key=lambda i: len(i), reverse=False)
|
||||
first_subword = word_list[0]
|
||||
first_begin_idx = word.find(first_subword)
|
||||
if first_begin_idx == 0:
|
||||
second_subword = word[len(first_subword) :]
|
||||
new_word_list = [first_subword, second_subword]
|
||||
else:
|
||||
second_subword = word[: -len(first_subword)]
|
||||
new_word_list = [second_subword, first_subword]
|
||||
return new_word_list
|
||||
|
||||
def _three_sandhi(self, word: str, finals: List[str]) -> List[str]:
|
||||
if len(word) == 2 and self._all_tone_three(finals):
|
||||
finals[0] = finals[0][:-1] + "2"
|
||||
elif len(word) == 3:
|
||||
word_list = self._split_word(word)
|
||||
if self._all_tone_three(finals):
|
||||
# disyllabic + monosyllabic, e.g. 蒙古/包
|
||||
if len(word_list[0]) == 2:
|
||||
finals[0] = finals[0][:-1] + "2"
|
||||
finals[1] = finals[1][:-1] + "2"
|
||||
# monosyllabic + disyllabic, e.g. 纸/老虎
|
||||
elif len(word_list[0]) == 1:
|
||||
finals[1] = finals[1][:-1] + "2"
|
||||
else:
|
||||
finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]]
|
||||
if len(finals_list) == 2:
|
||||
for i, sub in enumerate(finals_list):
|
||||
# e.g. 所有/人
|
||||
if self._all_tone_three(sub) and len(sub) == 2:
|
||||
finals_list[i][0] = finals_list[i][0][:-1] + "2"
|
||||
# e.g. 好/喜欢
|
||||
elif (
|
||||
i == 1
|
||||
and not self._all_tone_three(sub)
|
||||
and finals_list[i][0][-1] == "3"
|
||||
and finals_list[0][-1][-1] == "3"
|
||||
):
|
||||
finals_list[0][-1] = finals_list[0][-1][:-1] + "2"
|
||||
finals = sum(finals_list, [])
|
||||
# split idiom into two words who's length is 2
|
||||
elif len(word) == 4:
|
||||
finals_list = [finals[:2], finals[2:]]
|
||||
finals = []
|
||||
for sub in finals_list:
|
||||
if self._all_tone_three(sub):
|
||||
sub[0] = sub[0][:-1] + "2"
|
||||
finals += sub
|
||||
|
||||
return finals
|
||||
|
||||
def _all_tone_three(self, finals: List[str]) -> bool:
|
||||
return all(x[-1] == "3" for x in finals)
|
||||
|
||||
# merge "不" and the word behind it
|
||||
# if don't merge, "不" sometimes appears alone according to jieba, which may occur sandhi error
|
||||
def _merge_bu(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
||||
new_seg = []
|
||||
last_word = ""
|
||||
for word, pos in seg:
|
||||
if last_word == "不":
|
||||
word = last_word + word
|
||||
if word != "不":
|
||||
new_seg.append((word, pos))
|
||||
last_word = word[:]
|
||||
if last_word == "不":
|
||||
new_seg.append((last_word, "d"))
|
||||
last_word = ""
|
||||
return new_seg
|
||||
|
||||
# function 1: merge "一" and reduplication words in it's left and right, e.g. "听","一","听" ->"听一听"
|
||||
# function 2: merge single "一" and the word behind it
|
||||
# if don't merge, "一" sometimes appears alone according to jieba, which may occur sandhi error
|
||||
# e.g.
|
||||
# input seg: [('听', 'v'), ('一', 'm'), ('听', 'v')]
|
||||
# output seg: [['听一听', 'v']]
|
||||
def _merge_yi(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
||||
new_seg = []
|
||||
# function 1
|
||||
for i, (word, pos) in enumerate(seg):
|
||||
if (
|
||||
i - 1 >= 0
|
||||
and word == "一"
|
||||
and i + 1 < len(seg)
|
||||
and seg[i - 1][0] == seg[i + 1][0]
|
||||
and seg[i - 1][1] == "v"
|
||||
):
|
||||
new_seg[i - 1][0] = new_seg[i - 1][0] + "一" + new_seg[i - 1][0]
|
||||
else:
|
||||
if (
|
||||
i - 2 >= 0
|
||||
and seg[i - 1][0] == "一"
|
||||
and seg[i - 2][0] == word
|
||||
and pos == "v"
|
||||
):
|
||||
continue
|
||||
else:
|
||||
new_seg.append([word, pos])
|
||||
seg = new_seg
|
||||
new_seg = []
|
||||
# function 2
|
||||
for i, (word, pos) in enumerate(seg):
|
||||
if new_seg and new_seg[-1][0] == "一":
|
||||
new_seg[-1][0] = new_seg[-1][0] + word
|
||||
else:
|
||||
new_seg.append([word, pos])
|
||||
return new_seg
|
||||
|
||||
# the first and the second words are all_tone_three
|
||||
def _merge_continuous_three_tones(
|
||||
self, seg: List[Tuple[str, str]]
|
||||
) -> List[Tuple[str, str]]:
|
||||
new_seg = []
|
||||
sub_finals_list = [
|
||||
lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
|
||||
for (word, pos) in seg
|
||||
]
|
||||
assert len(sub_finals_list) == len(seg)
|
||||
merge_last = [False] * len(seg)
|
||||
for i, (word, pos) in enumerate(seg):
|
||||
if (
|
||||
i - 1 >= 0
|
||||
and self._all_tone_three(sub_finals_list[i - 1])
|
||||
and self._all_tone_three(sub_finals_list[i])
|
||||
and not merge_last[i - 1]
|
||||
):
|
||||
# if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
|
||||
if (
|
||||
not self._is_reduplication(seg[i - 1][0])
|
||||
and len(seg[i - 1][0]) + len(seg[i][0]) <= 3
|
||||
):
|
||||
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
|
||||
merge_last[i] = True
|
||||
else:
|
||||
new_seg.append([word, pos])
|
||||
else:
|
||||
new_seg.append([word, pos])
|
||||
|
||||
return new_seg
|
||||
|
||||
def _is_reduplication(self, word: str) -> bool:
|
||||
return len(word) == 2 and word[0] == word[1]
|
||||
|
||||
# the last char of first word and the first char of second word is tone_three
|
||||
def _merge_continuous_three_tones_2(
|
||||
self, seg: List[Tuple[str, str]]
|
||||
) -> List[Tuple[str, str]]:
|
||||
new_seg = []
|
||||
sub_finals_list = [
|
||||
lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
|
||||
for (word, pos) in seg
|
||||
]
|
||||
assert len(sub_finals_list) == len(seg)
|
||||
merge_last = [False] * len(seg)
|
||||
for i, (word, pos) in enumerate(seg):
|
||||
if (
|
||||
i - 1 >= 0
|
||||
and sub_finals_list[i - 1][-1][-1] == "3"
|
||||
and sub_finals_list[i][0][-1] == "3"
|
||||
and not merge_last[i - 1]
|
||||
):
|
||||
# if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
|
||||
if (
|
||||
not self._is_reduplication(seg[i - 1][0])
|
||||
and len(seg[i - 1][0]) + len(seg[i][0]) <= 3
|
||||
):
|
||||
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
|
||||
merge_last[i] = True
|
||||
else:
|
||||
new_seg.append([word, pos])
|
||||
else:
|
||||
new_seg.append([word, pos])
|
||||
return new_seg
|
||||
|
||||
def _merge_er(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
||||
new_seg = []
|
||||
for i, (word, pos) in enumerate(seg):
|
||||
if i - 1 >= 0 and word == "儿" and seg[i - 1][0] != "#":
|
||||
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
|
||||
else:
|
||||
new_seg.append([word, pos])
|
||||
return new_seg
|
||||
|
||||
def _merge_reduplication(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
||||
new_seg = []
|
||||
for i, (word, pos) in enumerate(seg):
|
||||
if new_seg and word == new_seg[-1][0]:
|
||||
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
|
||||
else:
|
||||
new_seg.append([word, pos])
|
||||
return new_seg
|
||||
|
||||
def pre_merge_for_modify(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
||||
seg = self._merge_bu(seg)
|
||||
try:
|
||||
seg = self._merge_yi(seg)
|
||||
except:
|
||||
print("_merge_yi failed")
|
||||
seg = self._merge_reduplication(seg)
|
||||
seg = self._merge_continuous_three_tones(seg)
|
||||
seg = self._merge_continuous_three_tones_2(seg)
|
||||
seg = self._merge_er(seg)
|
||||
return seg
|
||||
|
||||
def modified_tone(self, word: str, pos: str, finals: List[str]) -> List[str]:
|
||||
finals = self._bu_sandhi(word, finals)
|
||||
finals = self._yi_sandhi(word, finals)
|
||||
finals = self._neural_sandhi(word, pos, finals)
|
||||
finals = self._three_sandhi(word, finals)
|
||||
return finals
|
||||
Reference in New Issue
Block a user