commit 73dee2d786e5e4452725450d00aeea49e79689b3 Author: Shahovalov MIkhail Date: Sun Oct 18 15:56:52 2026 +0300 Russian narration for the Enshrouded notes: mod 0.4.0, installer, generation pipeline diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..dcc43d4 --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +*.qoa filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..1e424f7 --- /dev/null +++ b/.gitignore @@ -0,0 +1,8 @@ +config.toml +.venv-*/ +tools/ +__pycache__/ +*.pyc +# game-derived data never goes into the repository +work/ +*.wav diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..d9445d2 --- /dev/null +++ b/LICENSE @@ -0,0 +1,24 @@ +MIT License + +Copyright (c) 2026 Mikhail Shahovalov + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +The audio in mod/ is derived from Enshrouded (Keen Games) and is not covered +by this license. diff --git a/README.md b/README.md new file mode 100644 index 0000000..44664aa --- /dev/null +++ b/README.md @@ -0,0 +1,187 @@ +# Enshrouded — русская озвучка записок + +Русская озвучка всех **525 озвученных записок и мини-историй** (Ministory) Enshrouded. Голос — клон +английского диктора игры, текст — официальный русский перевод игры. Мод ставится через EML +(Enshrouded Mod Loader) и не трогает оригинальные файлы игры. + +| | | +|---|---| +| Версия | 0.4.0 (игра 1.0, октябрь 2026) | +| Объём | 525 записок, 425 минут, 467 МБ | +| Синтез | Qwen3-TTS 1.7B, дообучение [`ru-stress-cf`](https://huggingface.co/siriusfreak/qwen3-tts-12hz-1.7b-ru-stress-cf) с ударениями, ICL-клон диктора | +| Контроль | автоматический, без прослушивания: ударения 99,9 % по всем кускам; WER 2,4 % и MOS 3,45 на выборке из 60 записок | + +> Озвучка — синтез, а не запись актёра. Голос повторяет тембр английского диктора, тексты принадлежат +> Keen Games. Это любительский мод: не продавайте его и не выдавайте за официальный. + +## Что в репозитории + +``` +mod/ru_voice_notes/ готовый мод: data/*.qoa (Git LFS), src/*.lua, mod.json +install.py установка и удаление мода (только стандартная библиотека Python) +run.py конвейер генерации по шагам (каждый шаг в своём окружении) +ensvoice/ ключевые скрипты конвейера + extract.py тексты и английский звук из файлов игры + refs.py референсы диктора, его тембр и паузы + segments.py текст для голоса: лексикон, ударения, нарезка по абзацам + lexicon.py ← главное место для правок: имена, ударения, сокращения + render.py, tts/ синтез (локальный GPU или сервер media-node) + qc.py, pick.py автоматическая проверка дублей и отбор лучшего + assemble.py, mod.py склейка записок, QOA, сборка мода + refbench.py сравнение референсов голоса по метрикам +env/ зависимости двух окружений +docs/GENERATION.md как устроена генерация и как её улучшать +config.example.toml пример настроек +``` + +В репозиторий не попадают данные, извлечённые из игры (`work/`), окружения (`.venv-*`), модели, +бинарники EML (`tools/`) и `config.toml`: всё это перечислено в `.gitignore`. + +--- + +## Установка мода (для игрока) + +Нужны Windows, Steam-версия Enshrouded, Python 3.11+, Git с Git LFS. + +```bash +git clone https://git.shahovalov.ru/mikhail/enshrouded-ru-voice.git +cd enshrouded-ru-voice +git lfs pull +python install.py +``` + +В конце должна появиться строка `[ru_voice] replaced 525 notes, 0 failed`, затем `INSTALLED`. +Игру запускать как обычно, из Steam. Язык озвучки в игре оставить **английским**: мод подменяет +английскую дорожку записок, своего русского слота у озвучки в игре нет. + +Удаление: `python install.py --uninstall` (или «Проверить целостность файлов» в Steam). + +--- + +## Инструкция для агента: установка + +Агент выполняет шаги по порядку. После каждого шага есть проверка: если она не прошла, агент не идёт +дальше и разбирает причину по таблице ниже. Команды даны для Git Bash или PowerShell 7 на Windows. + +### A. Только поставить мод (без генерации) + +1. **Проверить окружение.** + ```bash + python --version + git lfs version + ``` + Ожидаемо: Python 3.11 или новее, git-lfs 3.x. Если Python нет, поставить `winget install Python.Python.3.12`; + если нет Git LFS, поставить `winget install GitHub.GitLFS`, затем выполнить `git lfs install`. +2. **Клонировать и скачать звук.** + ```bash + git clone https://git.shahovalov.ru/mikhail/enshrouded-ru-voice.git + cd enshrouded-ru-voice + git lfs pull + ``` + Проверка: в `mod/ru_voice_notes/data` лежат 525 файлов `.qoa`, все больше 1 КБ, вместе около 467 МБ. + Файлы меньше 1 КБ — это LFS-указатели: снова выполнить `git lfs pull`. +3. **Спросить пользователя**, закрыта ли игра и можно ли записать в папку игры `dbghelp.dll` и + `mods/ru_voice_notes`. Без согласия дальше не идти. +4. **Установить.** + ```bash + python install.py + ``` + Если игра стоит не в библиотеке Steam, добавить `--game "<папка с enshrouded.kfc>"`. + Ожидаемо: строки `[ru_voice] replaced 525 notes, 0 failed` и `INSTALLED`. +5. Сообщить пользователю: игру запускать из Steam, язык озвучки оставить английским, записки читаются + по-русски. Откат: `python install.py --uninstall`. + +### B. Поставить конвейер генерации (чтобы дорабатывать) + +Требования: NVIDIA GPU с 12 ГБ VRAM и больше (проверено на RTX 5080 16 ГБ и Tesla V100 32 ГБ), +32 ГБ RAM, около 35 ГБ на диске (модели около 10 ГБ, рабочая папка около 15 ГБ), свежий драйвер NVIDIA +(CUDA 12.8), [uv](https://docs.astral.sh/uv/). Сначала выполнить раздел A до шага 2 включительно. + +1. **Проверить GPU и uv.** + ```bash + nvidia-smi + uv --version + ``` + Если `uv` нет: `winget install astral-sh.uv`. +2. **Создать два окружения.** Их два, потому что `qwen-tts` требует transformers 4.x, а модели контроля + качества требуют 5.x. + ```bash + uv venv -p 3.12 .venv-tts + uv pip install -p .venv-tts/Scripts/python.exe torch torchaudio --index-url https://download.pytorch.org/whl/cu128 + uv pip install -p .venv-tts/Scripts/python.exe -r env/tts.txt + uv venv -p 3.12 .venv-qc + uv pip install -p .venv-qc/Scripts/python.exe torch torchaudio --index-url https://download.pytorch.org/whl/cu128 + uv pip install -p .venv-qc/Scripts/python.exe -r env/qc.txt + ``` + На Linux путь к интерпретатору — `.venv-tts/bin/python`. +3. **Настроить.** Скопировать `config.example.toml` в `config.toml` и заполнить: + - `paths.game` — папка игры (её нашёл `install.py`, путь напечатан в строке `game:`); + - `paths.work` — папка на **том же диске, что и игра** (см. «Ловушки»), с 15 ГБ свободного места; + - `tts.dtype` — `bfloat16` для RTX 30xx и новее, `float16` для RTX 20xx, GTX 16xx, V100. +4. **Инструменты EML.** Если `install.py` ещё не запускался, скачать их через него, ответив пользователю + по шагу A.3, либо взять `kfc-parser.exe` и `emm.exe` вручную из релиза + [Brabb3l/kfc-parser v0.2.0](https://github.com/Brabb3l/kfc-parser/releases/tag/v0.2.0) в `tools/`. +5. **Проверить установку.** + ```bash + python run.py check + ``` + Ожидаемо: две строки с версией torch, `cuda True` и именем GPU, затем `CHECK_OK`. +6. **Пробный прогон на трёх записках**, около 15 минут. Модели (около 10 ГБ) скачиваются с Hugging Face + при первом запуске. + ```bash + export ENSVOICE_NOTES=n016,n238,n363 # PowerShell: $env:ENSVOICE_NOTES="n016,n238,n363" + python run.py extract + python run.py refs + python run.py segments + python run.py render + python run.py qc + python run.py pick + python run.py assemble + ``` + Ожидаемо: после `extract` — `notes 525 EN minutes 488`; после `refs` — + `configured reference ... found`; после `pick` — `'segments': 16, 'picked': 16`; + после `assemble` — `ASSEMBLED 3 notes`. Готовые WAV для проверки лежат в `/wav48/`. + **Не запускать** `python run.py mod` с переменной `ENSVOICE_NOTES`: собранный мод заменит в + `mod/ru_voice_notes` все 525 записок тремя. +7. Снять переменную (`unset ENSVOICE_NOTES`) и сообщить пользователю, что конвейер работает. + Полный прогон (`python run.py all`) на RTX 5080 занимает около суток (см. docs/GENERATION.md), его + запускают по просьбе пользователя. + +### Ловушки и ошибки + +| Симптом | Причина и что делать | +|---|---| +| `extract`: `EN minutes` не 488, а около 425 | В игре применён мод: EML пропатчил `enshrouded.kfc`, и прочиталась русская озвучка. `extract` сам берёт исходный индекс из `enshrouded.kfc.bak` через жёсткие ссылки — для этого `paths.work` должен быть на диске игры. Иначе: `tools/emm.exe restore -g "<игра>"` и повторить `extract`. | +| `install.py`: `close Enshrouded first`, `os error 32` | Игра запущена: закрыть её. | +| `.qoa` по 130 байт, `LFS pointers` | Не скачан LFS: `git lfs install`, затем `git lfs pull`. | +| `render`: `CUDA out of memory` | Уменьшить `tts.batch` до 4. Больше 8 не ставить: на RTX 5080 16 ГБ батч 16 упирается в видеопамять и работает в разы медленнее. | +| `bf16 is not supported` / NaN | GPU старше Ampere: `tts.dtype = "float16"`. | +| `qc` убит, `Killed`, MemoryError | Каждый процесс QC берёт 8–13 ГБ RAM: запускать меньше шардов. | +| `refs`: `configured reference ... NOT FOUND` | Другая версия игры: выбрать id из `/refs.json` через `refbench`. | +| `extract`: `expected 525 voiced notes` | Обновление игры изменило записки: конвейер работает, но мод из репозитория может не подойти — собрать свой. | + +--- + +## Как дорабатывать и делиться + +Порядок работы и все приёмы описаны в [docs/GENERATION.md](docs/GENERATION.md). Коротко: правки идут в +`lexicon.py` или конфиг, затем по цепочке `segments → render → qc → pick → assemble → mod` (заново +считаются только изменённые куски) и `python install.py`. + +Делиться результатом: ветка с изменениями кода, лексикона и обновлённым `mod/ru_voice_notes` плюс +pull request. В описании PR привести числа из `/pick_report.json` до и после (`wer`, `stress`, +`mos`, `weak`): по ним видно, что правка улучшила озвучку, а не просто изменила её. Без доступа на запись +в этот Gitea — форк или patch-файл с `git format-patch`. + +## Благодарности + +[Brabb3l/kfc-parser](https://github.com/Brabb3l/kfc-parser) (распаковка и EML) · +[Qwen3-TTS](https://github.com/QwenLM/Qwen3-TTS) · +[siriusfreak/qwen3-tts-12hz-1.7b-ru-stress-cf](https://huggingface.co/siriusfreak/qwen3-tts-12hz-1.7b-ru-stress-cf) (ударения и meter2) · +[RUAccent](https://github.com/Den4ikAI/ruaccent) · +[GigaAM](https://github.com/salute-developers/GigaAM) · +[UTMOSv2](https://github.com/sarulab-speech/UTMOSv2) · +[WavLM](https://huggingface.co/microsoft/wavlm-base-plus-sv) + +Код — MIT (см. `LICENSE`). Озвучка в `mod/` — производная от материалов игры и не покрывается +лицензией кода. diff --git a/config.example.toml b/config.example.toml new file mode 100644 index 0000000..89ad252 --- /dev/null +++ b/config.example.toml @@ -0,0 +1,37 @@ +# Copy to config.toml (git-ignored) and set the paths. + +[paths] +game = "C:/Program Files (x86)/Steam/steamapps/common/Enshrouded" # contains enshrouded.kfc +work = "D:/ensvoice-work" # everything derived from the game: ~15 GB at 3 seeds (takes are 24 kHz WAV) +tools = "tools" # kfc-parser.exe and emm.exe from Brabb3l/kfc-parser v0.2.0 + +[voice] +# reference clip of the EN narrator for the ICL clone: an id from work/refs.json (step refs); +# this one won the author's bench (MOS 3.43 vs 3.24, WER 3.7% vs 6.6%); refbench compares others +reference = "EN_Grassland_plains_Ministory_05_AlwaysTogether_Page_01_034__p1" +seeds = [1000, 1001, 1002] # takes per segment; the best one is picked automatically +extra_seeds = [1003, 1004] # only for segments with no passing take (render --weak) + +[tts] +backend = "local" # "local" (qwen-tts on this GPU) or "media_node" (MCP server) +model = "siriusfreak/qwen3-tts-12hz-1.7b-ru-stress-cf" # HF id or a local folder +device = "cuda:0" +dtype = "bfloat16" # "float16" on GTX 16xx / RTX 20xx / V100 (no bf16) +batch = 8 # texts per generate call: 4 on an out-of-memory error; 16 is slower on 16 GB + +# [tts.media_node] # only for backend = "media_node" +# url = "http://127.0.0.1:8082/mcp" +# voice = "enshrouded/c238" # a saved voice made from the reference clip + +[qc] +mos = true # UTMOSv2 naturalness (adds ~1 s per take) +wavlm = "microsoft/wavlm-base-plus-sv" + +[assemble] +# workers = 7 # QOA encoding is pure Python: CPU processes (default: cores - 1) + +[mod] +id = "ru_voice_notes" +name = "RU voice: notes" +authors = [] +# dir = "D:/ensvoice-work/mod-test" # build elsewhere (a test); default: /mods/ diff --git a/docs/GENERATION.md b/docs/GENERATION.md new file mode 100644 index 0000000..4a6895f --- /dev/null +++ b/docs/GENERATION.md @@ -0,0 +1,108 @@ +# Генерация озвучки + +Как из файлов игры получается мод, чем меряется качество и что крутить, чтобы стало лучше. +Ни одна записка не прослушивается человеком: все решения принимаются по метрикам, а слушают уже в игре. + +## Конвейер + +``` +игра (enshrouded.kfc + .dat) + │ extract kfc-parser: дескрипторы озвучки, звука, локализации, журнала + │ EN-звук (frameless QOA 48 кГц) → work/en24/*.wav, RU-текст → текст для голоса + ▼ +work/notes.json ── refs ──► work/refs/*.wav референсы диктора (целые короткие записки и первые абзацы) + │ work/centroid.npy тембр диктора (средний x-вектор WavLM) — цель QC + │ work/en_pauses.json паузы диктора + │ segments lexicon.pre → абзацы ≤ 380 знаков → RUAccent (+) → lexicon.post → U+0301 + ▼ +work/segs.json ── render ──► work/takes/__s.wav 3 сида на кусок + │ qc GigaAM-v3 (WER), meter2 (ударения), WavLM-SV (тембр), UTMOSv2 (MOS), темп + │ pick фильтр + счёт; без проходного дубля → render --weak (ещё 2 сида) → qc → pick + │ assemble обрезка, паузы диктора, 24→48 кГц soxr VHQ, громкость по оригиналу, лимитер, QOA + ▼ +work/qoa ── mod ──► mod/ru_voice_notes ── install.py ──► <игра>/mods + dbghelp.dll (EML) +``` + +`python run.py all` выполняет всё по порядку. Каждый шаг можно повторить отдельно: дубли и оценки +кэшируются по хэшу «референс + текст», поэтому после правки лексикона заново синтезируются и +проверяются только затронутые куски. + +## Метрики + +| Метрика | Чем | Что ловит | +|---|---|---| +| `wer`, `hyp` | GigaAM-v3 RNN-T по кускам ≤ 20 с | пропуски, оговорки, «протечку» референса (английский в начале) | +| `stress_ok / stress_n` | meter2 из модели ru-stress-cf | ударение не там, где поставил RUAccent или лексикон | +| `sim` | WavLM-SV, косинус к центроиду диктора | уход тембра | +| `mos` | UTMOSv2 | неестественность, артефакты | +| `cps` | знаков в секунду | затянутость или скороговорку, обрыв | +| `f0` (`qc --f0`) | разброс высоты тона, полутона | монотонность (медленно, на CPU) | + +Отбор (`pick.py`): дубль проходит, если темп 7–19 знаков/с и WER ≤ 0,12 или ошибка не больше одного +слова (у кусков до 3 слов — CER ≤ 0,35: ASR коверкает одиночные имена). Среди прошедших — +максимум `6·stress + 0,5·MOS + 2·SIM − 3·WER`. Ударения весят больше всего: неверное ударение +слушатель слышит сразу. + +Сравнивать версии — по `work/pick_report.json` и по одним и тем же целым запискам (`qc --jobs`). + +## История: от 0.3 к 0.4 (4 итерации) + +0.3.0 — вся записка одним дублем, референс `amethystHermit` (10 с), 3 сида, линейная интерполяция до 48 кГц. + +| Итерация | Что изменено | Чем подтверждено | +|---|---|---| +| 1. Текст | лексикон имён (`Во́ргот`, `Э́мбервэйл`, `Ла́хлан`…), апострофы в именах (`Кра'Тау` → `Кра Тау`), сокращения (`ок.`, `мм`, `в т.ч.`), числа в падеже; ударения переносятся по словам — посимвольный перенос портил 4 % текстов («в го́дод», «))») | сверка текстов: 0 расхождений после правки | +| 2. Тембр | 8 кандидатов-референсов × 16 абзацев × 2 сида | победил `AlwaysTogether_Page_01__p1`: WER 3,7 % против 6,6 %, MOS 3,43 против 3,24, F0 5,30 против 4,52 | +| 3. Синтез | абзацы ≤ 380 знаков вместо целой записки, лучший дубль на каждый абзац, 2 доп. сида для слабых | без проходного дубля: 82 из 2660 (заголовки и имена, ложные) | +| 4. Сведение | паузы диктора (0,45 / 0,7 / 0,9 с вместо 0,3 / 0,7), soxr VHQ, мягкий лимитер | — | + +Итог на 60 случайных записках, целиком: + +| | 0.3.0 | 0.4.0 | +|---|---|---| +| WER | 3,2 % | 2,4 % | +| MOS | 3,27 | 3,45 (лучше в 41 из 60) | +| F0, разброс | 4,80 | 5,69 | +| SIM | 0,838 | 0,840 | + +## Что крутить + +1. **Лексикон** (`ensvoice/lexicon.py`) — самый дешёвый рычаг. `PRE` — замены в обычном тексте + (сокращения, числа, опечатки оригинала), `STRESS` — ударения игровых имён: регулярное выражение + основы и номер ударной гласной. Подсказки, что чинить: поле `bad` в `work/qc/*.json` (слова с + неверным ударением) и `hyp` против текста у дублей с высоким WER. +2. **Референс голоса** (`voice.reference`). `python run.py refbench` озвучивает 16 абзацев каждым из + 9 кандидатов, затем `qc --jobs …` и `refbench --report` дают таблицу. Референс с обрубленным концом + «протекает»: дубли начинаются с английских слов, у таких кандидатов много `bad` по WER. +3. **Сиды** (`voice.seeds`, `extra_seeds`): больше сидов — выше шанс на чистый дубль, время растёт линейно. +4. **Паузы** (`PAUSE` в `assemble.py`) и порог нарезки (`MAXC` в `segments.py`). +5. **Отбор** (`passes` и `score` в `pick.py`). + +Любую правку проверять числами: `pick_report.json` до и после и выборочные записки целиком. + +## Время и ресурсы + +Измерено на RTX 5080 16 ГБ (локальный бэкенд, батч 8): + +| Шаг | Время | +|---|---| +| `extract` | 1–2 мин (CPU) | +| `refs` | 1–2 мин | +| `segments` | 2–5 мин (RUAccent на CPU, в основном загрузка моделей) | +| `render` | около 2 мин на 2500 знаков × сид → около 4 ч на сид, около 12 ч на 3 сида | +| `qc` | около 2,5 с на дубль → около 5 ч на 8000 дублей в один процесс; шарды `--shard i/n` параллельно | +| `assemble` | около 20 мин (QOA-кодер на чистом Python, CPU) | + +Автор генерировал 0.4.0 на Tesla V100 через сервер `media-node` (qwentts.cpp, GGUF Q8_0): там синтез +примерно в 13 раз быстрее (RTF 0,05 против 0,6). Этот бэкенд (`tts.backend = "media_node"`) нужен, только если у вас есть такой сервер. + +## Формат игры (для справки) + +- `enshrouded.kfc`: заголовок KFC4 с 17 таблицами; ключи содержимого `(size, hash0, hash1, hash2)` → + смещение в `enshrouded_NNN.dat`. +- `VoiceoverResource.languages` — фиксированные слоты EN/DE/FR/ES/ZH, русского нет: мод подменяет EN. +- `SoundResource`: `FramelessQOA`, 48 кГц моно, `frameCount`, `duration` в нс, `streamedData.hash`. +- Frameless QOA: срезы QOA без заголовков, u64 little-endian на 20 сэмплов; LMS стартует с весов + `[0, 0, −8192, 16384]` и не сбрасывается. +- Локализация: `LocaTagCollectionResource`, записи по 24 байта (id, смещение строки от своего поля + 4, длина). +- `JournalRegistryResource` связывает озвучку записки с id её названия и текста. diff --git a/ensvoice/__init__.py b/ensvoice/__init__.py new file mode 100644 index 0000000..485b414 --- /dev/null +++ b/ensvoice/__init__.py @@ -0,0 +1,2 @@ +"""Russian narration for the Enshrouded notes: extraction, TTS, automatic QC, EML mod.""" +__version__ = "0.4.0" diff --git a/ensvoice/accent.py b/ensvoice/accent.py new file mode 100644 index 0000000..8184efc --- /dev/null +++ b/ensvoice/accent.py @@ -0,0 +1,59 @@ +"""RUAccent stresses for the text of the voice. + +plus_marks(): '+' before the stressed vowel, carried onto the ORIGINAL text word by word. RUAccent is +fed lowercase (it is worse on Title-case) and drops spaces before "(", so a char-by-char walk drifts and +garbles the tail ("в годод", "))"); here only the n-th Cyrillic word of its output gives its '+' (and ё) +to the n-th word of the source, punctuation and spacing stay the source's. If the word counts differ, +the text is accented clause by clause. +acute_marks(): '+' before a vowel -> the vowel followed by U+0301, the input of the ru-stress model. +""" +import re + +VOWELS = "аеёиоуыэюяАЕЁИОУЫЭЮЯ" +W = re.compile(r"[А-Яа-яЁё]+(?:-[А-Яа-яЁё]+)*") +WP = re.compile(r"[А-Яа-яЁё+]+(?:-[А-Яа-яЁё+]+)*") +_acc = None + + +def accentizer(): + global _acc + if _acc is None: + from ruaccent import RUAccent + _acc = RUAccent() + _acc.load(omograph_model_size="turbo3.1", use_dictionary=True, tiny_mode=False) + return _acc + + +def _transfer(orig, acc): + out, i = [], 0 + for ch in acc: + if ch == "+": + out.append("+"); continue + o = orig[i] + out.append(("Ё" if o == "Е" else "ё") if ch.lower() == "ё" and o in "еЕ" else o) + i += 1 + return "".join(out) + + +def _plus(text): + acc = accentizer().process_all(text.lower()) + src, dst = list(W.finditer(text)), WP.findall(acc) + if len(src) != len(dst) or any(len(a.group(0)) != len(b.replace("+", "")) for a, b in zip(src, dst)): + return None + out, last = [], 0 + for a, b in zip(src, dst): + out += [text[last:a.start()], _transfer(a.group(0), b)] + last = a.end() + return "".join(out) + text[last:] + + +def plus_marks(text: str) -> str: + r = _plus(text) + if r is not None: + return r + parts = re.split(r"([,.;:!?()«»…]+\s*)", text) + return "".join(p if i % 2 or not p.strip() else (_plus(p) or p) for i, p in enumerate(parts)) + + +def acute_marks(plus_text: str) -> str: + return re.sub(r"\+([" + VOWELS + "])", lambda m: m.group(1) + "́", plus_text).replace("+", "") diff --git a/ensvoice/assemble.py b/ensvoice/assemble.py new file mode 100644 index 0000000..74f1a46 --- /dev/null +++ b/ensvoice/assemble.py @@ -0,0 +1,105 @@ +"""Step 7: join every note from its picked takes and encode it for the game (work/qoa, work/wav48). + +- each take trimmed to its speech (top_db 40), 60 ms kept at both ends, 8 ms fades; +- pauses as the EN narrator makes them: PAUSE["s"] inside a paragraph, PAUSE["l"] after a line break, + PAUSE["p"] between paragraphs (EN medians: phrases ~0.6 s, paragraphs ~0.9 s); +- 24 -> 48 kHz with soxr VHQ; loudness: active-speech RMS matched to the EN original, soft limiter + at -1 dBFS; 100 ms of silence at both ends; frameless QOA. +The QOA encoder is pure Python: this step is CPU-bound (assemble.workers processes). +""" +import json +import os +import sys +from multiprocessing import Pool + +import librosa +import numpy as np +import soundfile as sf + +from .config import load +from .qoa import encode + +PAUSE = {"s": 0.45, "l": 0.70, "p": 0.90} +CFG = None + + +def active_rms(x): + v = x[::5] + v = v[np.abs(v) > 200 / 32768] + return float(np.sqrt(np.mean(v * v))) if len(v) else 1e-3 + + +def trim(y, sr): + iv = librosa.effects.split(y, top_db=40, frame_length=1024, hop_length=240) + if not len(iv): + return y + y = y[max(0, iv[0][0] - int(0.06 * sr)): min(len(y), iv[-1][1] + int(0.06 * sr))].copy() + f = int(0.008 * sr) + y[:f] *= np.linspace(0, 1, f) + y[-f:] *= np.linspace(1, 0, f) + return y + + +def job(args): + n, segs, pick = args + cfg = CFG or load() + meta = cfg.w("qoa", f"{n['k']}.json") + if meta.exists(): + return + parts = [] + for s in segs: + y, sr = sf.read(cfg.w("takes", f"{s['take']}_s{pick[s['id']]}.wav"), dtype="float32") + if parts: + parts.append(np.zeros(int(PAUSE.get(s["join"], 0.45) * sr), np.float32)) + parts.append(trim(y, sr)) + y = librosa.resample(np.concatenate(parts), orig_sr=24000, target_sr=48000, res_type="soxr_vhq") + en, _ = sf.read(cfg.w("en24", f"{n['k']}.wav"), dtype="float32") + gain = active_rms(en) / active_rms(y) + y = y * gain + lim = 10 ** (-1 / 20) + knee = 0.7 * lim + over = np.abs(y) > knee + y[over] = np.sign(y[over]) * (knee + (lim - knee) * np.tanh((np.abs(y[over]) - knee) / (lim - knee))) + pad = np.zeros(4800, np.float32) + y = np.concatenate([pad, y, pad]) + sf.write(cfg.w("wav48", f"{n['k']}.wav"), y, 48000, subtype="PCM_16") + pcm = np.clip(np.round(y * 32767), -32768, 32767).astype(np.int32).tolist() + cfg.w("qoa", f"{n['sound']}.qoa").write_bytes(encode(pcm)) + json.dump(dict(k=n["k"], name=n["name"], vo=n["vo"], sound=n["sound"], frames=len(pcm), + seconds=len(pcm) / 48000, gain=round(gain, 3), segments=len(segs)), open(meta, "w")) + + +def main(): + cfg = load() + notes = json.load(open(cfg.w("notes.json"), encoding="utf-8")) + segs = json.load(open(cfg.w("segs.json"), encoding="utf-8")) + pick = json.load(open(cfg.w("pick.json"))) + by = {} + for s in segs: + by.setdefault(s["k"], []).append(s) + notes = [n for n in notes if n["k"] in by] # segments may cover a subset (ENSVOICE_NOTES) + if "--force" in sys.argv: # re-encode every note (after a new pick) + for f in cfg.w("qoa", "x").parent.glob("n*.json"): + f.unlink() + else: # re-encode the notes whose picked takes changed + for n in notes: + m = cfg.w("qoa", f"{n['k']}.json") + if m.exists() and json.load(open(m)).get("takes") != [f"{s['take']}_s{pick.get(s['id'])}" for s in by[n["k"]]]: + m.unlink() + jobs = [(n, by[n["k"]], pick) for n in notes if not cfg.w("qoa", f"{n['k']}.json").exists()] + print("notes to encode", len(jobs), flush=True) + with Pool(int(cfg.get("assemble", "workers", max(1, (os.cpu_count() or 2) - 1)))) as p: + for i, _ in enumerate(p.imap_unordered(job, jobs, chunksize=1)): + if i % 25 == 0: + print(f"{i + 1}/{len(jobs)}", flush=True) + for n in notes: # remember which takes each note was built from + m = cfg.w("qoa", f"{n['k']}.json") + d = json.load(open(m)) + d["takes"] = [f"{s['take']}_s{pick[s['id']]}" for s in by[n["k"]]] + json.dump(d, open(m, "w")) + total = sum(json.load(open(cfg.w("qoa", f"{n['k']}.json")))["seconds"] for n in notes) + print("ASSEMBLED", len(notes), "notes,", round(total / 60, 1), "minutes") + + +if __name__ == "__main__": + main() diff --git a/ensvoice/config.py b/ensvoice/config.py new file mode 100644 index 0000000..9b19224 --- /dev/null +++ b/ensvoice/config.py @@ -0,0 +1,37 @@ +"""Settings from config.toml (or the file named by ENSVOICE_CONFIG) and the work-directory layout.""" +import os +import pathlib +import tomllib + +ROOT = pathlib.Path(__file__).resolve().parent.parent + + +class Config: + def __init__(self, path=None): + path = pathlib.Path(path or os.environ.get("ENSVOICE_CONFIG") or ROOT / "config.toml") + if not path.exists(): + raise SystemExit(f"no config: {path} (copy config.example.toml to config.toml and edit it)") + self.raw = tomllib.loads(path.read_text(encoding="utf-8")) + p = self.raw["paths"] + self.game = pathlib.Path(p["game"]) + self.work = pathlib.Path(p["work"]) + self.tools = pathlib.Path(p.get("tools", ROOT / "tools")) + self.work.mkdir(parents=True, exist_ok=True) + # ENSVOICE_NOTES=n016,n238 limits segments (and so everything after it) to these notes + self.only = {x.strip() for x in os.environ.get("ENSVOICE_NOTES", "").split(",") if x.strip()} + + def __getitem__(self, key): + return self.raw[key] + + def get(self, section, key, default=None): + return self.raw.get(section, {}).get(key, default) + + # work-directory layout (everything derived from the game stays here, never in the repo) + def w(self, *parts) -> pathlib.Path: + p = self.work.joinpath(*parts) + p.parent.mkdir(parents=True, exist_ok=True) + return p + + +def load(path=None) -> Config: + return Config(path) diff --git a/ensvoice/extract.py b/ensvoice/extract.py new file mode 100644 index 0000000..d7f21b0 --- /dev/null +++ b/ensvoice/extract.py @@ -0,0 +1,209 @@ +"""Step 1: from the installed game to work/notes.json + the EN originals as 24 kHz WAV. + +- kfc-parser unpacks the descriptors we need (voiceover, sound, localisation, journal); +- localisation tags of EN and RU are read straight from the .dat content (24-byte tag records); +- every journal entry with a voiceover joins its EN sound and its EN/RU text; +- the RU text is turned into the text for the voice (tags off, dashes to commas, numbers in words); +- the EN audio (frameless QOA, 48 kHz) is decoded to work/en24/.wav with its loudness. +The game files are only read. Keys n000..n524 follow the EN sound names sorted. + +A game with this mod (or any EML mod) applied has a PATCHED enshrouded.kfc: its index then points to the +Russian takes. EML appends new content and keeps the original index as enshrouded.kfc.bak, so the +originals are read through work/orig: a copy of the .bak as enshrouded.kfc plus hard links to the .dat +and .kfc_resources files (the work folder must then be on the game's drive; otherwise run +`tools/emm.exe restore` first — the mod is applied again at the next game launch). +""" +import glob +import json +import os +import re +import shutil +import struct +import subprocess +import sys +import wave +from multiprocessing import Pool + +import numpy as np + +from .config import load +from .kfc import KFC +from .qoa import decode +from .ru_num import normalize + +TYPES = ["VoiceoverResource", "SoundContainerResource", "SoundResource", + "LocaTagCollectionResource", "JournalRegistryResource"] + + +def source(cfg): + """The game folder with the ORIGINAL index (see the module docstring).""" + bak = cfg.game / "enshrouded.kfc.bak" + if not bak.exists(): + return cfg.game + orig = cfg.w("orig", "x").parent + shutil.copyfile(bak, orig / "enshrouded.kfc") + for f in list(cfg.game.glob("enshrouded_*.dat")) + [cfg.game / "enshrouded.kfc_resources"]: + dst = orig / f.name + if dst.exists(): + dst.unlink() + try: + os.link(f, dst) + except OSError: + raise SystemExit(f"the game has EML mods applied and {orig} cannot hard-link {f.name} " + f"(another drive?): put paths.work on the game's drive, or run " + f"'{cfg.tools / 'emm.exe'} restore -g {cfg.game}' and extract again") + print("reading the original index from enshrouded.kfc.bak (the game has EML mods applied)") + return orig + + +def unpack(cfg, src): + out = cfg.w("unpack") + if all(glob.glob(str(out / t / "*.json")) for t in TYPES): + print("unpack: already done"); return out + exe = cfg.tools / "kfc-parser.exe" + flt = ",".join(f"tkeen::{t}" for t in TYPES) + subprocess.run([str(exe), "unpack", "-g", str(src), "-o", str(out), "-f", flt], check=True) + return out + + +def by_guid(d, t): + return {os.path.basename(f).split("_")[0]: f for f in glob.glob(str(d / t / "*.json"))} + + +def loca(cfg, kfc, d): + res = json.load(open(glob.glob(str(d / "LocaTagCollectionResource" / "*.json"))[0], encoding="utf-8")) + langs = {l["language"]: l["data"] for l in res["languages"]} + out = {} + for name in ("En_Us", "Ru_Ru"): + data = kfc.read_hash(langs[name]["hash"]) + off, cnt = struct.unpack_from("]+>", "", t or "") + + +YO = {"еще": "ещё", "все равно": "всё равно", "трясется": "трясётся", "придется": "придётся", "намек": "намёк", + "идет": "идёт", "ее ": "её ", "черный": "чёрный", "темн": "тёмн", "зеленый": "зелёный", "живет": "живёт", + "дает": "даёт", "берет": "берёт"} + + +def voice_text(t): + """The RU screen text -> the text for the voice (the subtitles keep the screen text).""" + t = screen(t) + t = re.sub(r"[ \t]+\n", "\n", t); t = re.sub(r"\n[ \t]+", "\n", t) + t = re.sub(r"^\s*[—–-]\s*", "", t, flags=re.M) # list dashes at a line start + t = re.sub(r"\s+[—–]\s+", ", ", t) # a dash makes a long pause: a comma + t = re.sub(r"(\w)-(то|ка|нибудь|либо)\b", r"\1 \2", t) + for a, b in YO.items(): + t = re.sub(r"\b" + a, b, t) + t = re.sub(r"\n{3,}", "\n\n", t).strip() + t = normalize(t).strip() + return re.sub(r"\.{4,}", "...", t) + + +_FIR = None + + +def _en_job(args): + game, k, r, out = args + global _FIR + if _FIR is None: + n = np.arange(-48, 49) + _FIR = np.sinc(n * 0.46) * np.hamming(len(n)); _FIR /= _FIR.sum() + s = np.asarray(decode(KFC(game).read_hash(r["h"]), int(r["en_sec"] * 48000)), np.float64) + v = s[::5]; v = v[np.abs(v) > 200] + rms = float(np.sqrt(np.mean(v * v))) if len(v) else 1000.0 + y = np.convolve(s, _FIR, mode="same")[::2] + with wave.open(out, "wb") as w: + w.setnchannels(1); w.setsampwidth(2); w.setframerate(24000) + w.writeframes(np.clip(y, -32768, 32767).astype(" Кра Тау); +- STRESS: game names by their English stress, keyed by a stem regex; the value is the index of the + stressed vowel inside the word (0 = first). Existing '+' in a matched word is replaced; +- words with two '+' in one part (Фир+аж+а) keep the mark the lexicon gives or else the last one. +""" +import re + +VOW = "аеёиоуыэюяАЕЁИОУЫЭЮЯ" + +PRE = [ # plain text, before RUAccent + (r"(?<=[А-Яа-яЁё ])c(?=[ А-Яа-яЁё])", "с"), (r"(?<=[А-Яа-яЁё ])C(?=[ а-яё])", "С"), + (r"»>", "»"), (r">", ""), + (r"Пыш'Ка", "Пышка"), (r"Гр[уy]ул", "Грул"), + # abbreviations and the numbers around them (the only cases in the notes) + (r"ок\. одна тысяча двести мм", "около тысячи двухсот миллиметров"), (r"ок\. семьсот мм", "около семисот миллиметров"), + (r"ок\. триста пятьдесят мм", "около трёхсот пятидесяти миллиметров"), (r"ок\. три тысячи мм", "около трёх тысяч миллиметров"), + (r"от пятьсот мм", "от пятисот миллиметров"), (r"до две тысячи мм", "до двух тысяч миллиметров"), + (r"свыше одна тысяча лет", "свыше тысячи лет"), (r"\bиз сто\b", "из ста"), (r"\bот сто\b", "от ста"), + (r"\bмм\b", "миллиметров"), (r"\bв т\.\s?ч\.:?", "в том числе"), (r"\(см\. ", "(смотри "), (r"\bсм\. ниже", "смотри ниже"), + (r"([А-ЯЁа-яё]+)'([А-ЯЁа-яё]+)", lambda m: m.group(1) + " " + m.group(2).lower() + if m.group(1).lower().startswith("драк") else m.group(1) + " " + m.group(2)), +] + +# stem regex (whole word, case-insensitive, may carry endings) -> stressed vowel index +STRESS = { + r"воргот\w*": 0, r"пайкмид\w*": 0, r"эмбервэйл\w*": 0, r"римгард\w*": 0, r"мелдор\w*": 0, + r"морго": 0, r"лэнсхарт\w*": 0, r"гленвудз": 0, r"констанцио": 1, r"мейерс\w*": 0, + r"лэпис\w*": 0, r"хэрриот\w*": 0, r"тау": 0, r"тул": 0, r"раш\w*": 0, r"ках\w*": 0, + r"конвэ\w*": 0, r"герит\w*": 0, r"эндрю": 0, r"лахлан\w*": 0, r"бриттлбуш\w*": 0, + r"лонгкип\w*": 0, r"аркрайт\w*": 0, r"вилли": 0, r"орвил\w*": 0, r"пышка": 0, + r"уайтуинд\w*": 0, r"харпер\w*": 0, r"везт\w*": 0, r"стилуотер": 0, r"голдрок": 0, + r"арлингтон\w*": 0, r"абрахам\w*": 0, r"дрифтвуд": 0, r"мильтон\w*": 0, r"мелвил\w*": 0, + r"итан\w*": 0, r"софи": 0, r"рьель": 1, r"фираж\w*": 1, r"грул": 0, r"рукмор\w*": 0, + r"фэйрберн\w*": 0, r"майлз\w*": 0, r"дэвис\w*": 0, r"келвин\w*": 0, r"честер\w*": 0, + r"сайрус\w*": 0, r"коннор\w*": 0, r"каспер\w*": 0, r"костнер\w*": 0, r"мейнард\w*": 0, + r"эксли": 0, r"пауэлл\w*": 0, r"брайтвич\w*": 0, r"димвельд\w*": 0, r"лорэйн\w*": 1, + r"ирветт\w*": 1, r"иоланд\w*": 1, r"рэттблик\w*": 0, r"кэйден\w*": 0, r"дэниэл\w*": 0, +} +_ST = [(re.compile(r"^" + k + r"$", re.I), v) for k, v in STRESS.items()] + + +def pre(text: str) -> str: + for a, b in PRE: + text = re.sub(a, b, text) + return text + + +def _mark(word: str, k: int) -> str: + out, n = [], 0 + for ch in word: + if ch in VOW: + if n == k: + out.append("+") + n += 1 + out.append(ch) + return "".join(out) + + +def post(plus: str) -> str: + """Fix stresses in RUAccent '+' text: lexicon names, then double marks.""" + def fix_part(p): + clean = p.replace("+", "") + for rx, k in _ST: + if rx.match(clean): + return _mark(clean, k) + if p.count("+") > 1: + return _mark(clean, sum(1 for ch in p[:p.rindex("+")] if ch in VOW)) + return p + + def fix_word(m): + return "-".join(fix_part(p) for p in m.group(0).split("-")) + return re.sub(r"[А-ЯЁа-яё+]+(?:-[А-ЯЁа-яё+]+)*", fix_word, plus) + + +if __name__ == "__main__": + import sys + sys.stdout.reconfigure(encoding="utf-8") + for s in ["Кр+а'Т+ау идёт в Пайкм+идз-Рич", "Фир+аж+а и Л+онгк+ип", "Хэрри+от и Конв+эй", "Стилуот+ер-б+эй"]: + print(s, "->", post(s)) + print(pre("Кра'Тау и драк'дал вернулся c разведки «Брэйлин»>, Пыш'Ка")) diff --git a/ensvoice/mod.py b/ensvoice/mod.py new file mode 100644 index 0000000..dfb89aa --- /dev/null +++ b/ensvoice/mod.py @@ -0,0 +1,64 @@ +"""Step 8: build the EML mod into the repository (mod/: the result that is committed). + +usage: mod then `python install.py` copies it into the game and dry-runs it +The mod replaces each note's EN sound (the Russian slot does not exist in VoiceoverResource: its fixed +languages are EN/DE/FR/ES/ZH): SoundResource.streamedData points to the new QOA content, frameCount and +duration follow it, VoiceoverResource.durations[EN] too (10 ms units). The game files are not touched: +EML (dbghelp.dll proxy) patches enshrouded.kfc at launch and keeps enshrouded.kfc.bak (emm restore). +""" +import json +import pathlib +import shutil + +from . import __version__ +from .config import ROOT, load + +LUA = '''local lines = require("lines") +local ok, fail = 0, 0 +for _, l in ipairs(lines) do + local done, err = pcall(function() + local snd = game.assets.get_resource(l.sound, "keen::SoundResource", 0) + local d = snd.data + local content = game.assets.create_content(io.read("data/" .. l.sound .. ".qoa")) + d.streamedData.hash = content + d.frameCount = l.frames + d.framesPerSecond = 48000 + d.duration.value = math.floor(l.seconds * 1e9) + local vo = game.assets.get_resource(l.vo, "keen::VoiceoverResource", 0) + vo.data.durations[1] = math.floor(l.seconds * 100) + end) + if done then ok = ok + 1 else fail = fail + 1; print("[ru_voice] FAIL " .. l.name .. ": " .. tostring(err)) end +end +print(("[ru_voice] replaced %d notes, %d failed"):format(ok, fail)) +''' + + +def main(): + cfg = load() + notes = json.load(open(cfg.w("notes.json"), encoding="utf-8")) + metas = [json.load(open(cfg.w("qoa", f"{n['k']}.json"))) for n in notes if cfg.w("qoa", f"{n['k']}.json").exists()] + if len(metas) < len(notes): + print(f"WARNING: {len(metas)} of {len(notes)} notes are assembled; the others stay English") + mod = pathlib.Path(cfg.get("mod", "dir") or ROOT / "mod" / cfg.get("mod", "id", "ru_voice_notes")) + if mod.exists(): + shutil.rmtree(mod) + (mod / "src").mkdir(parents=True) + (mod / "data").mkdir() + for m in metas: + shutil.copy(cfg.w("qoa", f"{m['sound']}.qoa"), mod / "data" / f"{m['sound']}.qoa") + lines = ["-- generated by ensvoice: one entry per voiced note", "return {"] + lines += [f' {{ sound = "{m["sound"]}", vo = "{m["vo"]}", frames = {m["frames"]}, seconds = {m["seconds"]:.6f}, ' + f'name = "{m["name"]}" }},' for m in metas] + (mod / "src" / "lines.lua").write_text("\n".join(lines + ["}"]) + "\n", encoding="utf-8") + (mod / "src" / "mod.lua").write_text(LUA, encoding="utf-8") + json.dump(dict(id=cfg.get("mod", "id", "ru_voice_notes"), name=cfg.get("mod", "name", "RU voice: notes"), + version=cfg.get("mod", "version", __version__), capabilities=["patch", "export"], + description="Russian narration for the voiced notes (Qwen3-TTS ru-stress, narrator clone, " + "automatic QC). Replaces the English voiceover slot.", + authors=cfg.get("mod", "authors", [])), open(mod / "mod.json", "w"), indent=2) + mb = sum(f.stat().st_size for f in (mod / "data").iterdir()) / 2 ** 20 + print("mod", mod, len(metas), "notes", round(mb, 1), "MB; install: python install.py") + + +if __name__ == "__main__": + main() diff --git a/ensvoice/pick.py b/ensvoice/pick.py new file mode 100644 index 0000000..519eff4 --- /dev/null +++ b/ensvoice/pick.py @@ -0,0 +1,74 @@ +"""Step 6: pick the best take of every segment (work/pick.json, work/pick_report.json). + +A take passes when it has no error, a pace of 7-19 chars/s and +- segments of up to 3 words (headings, names, signatures): CER <= 0.35 (ASR mangles lone names); +- longer ones: WER <= 0.12 or a single wrong word. +Among passing takes: score = 6*stress + 0.5*MOS + 2*SIM - 3*WER (stress first: a wrong stress is what +a listener hears). No passing take: the lowest WER, and the segment is listed as weak — `render --weak` +speaks it in voice.extra_seeds, then qc and pick again. +""" +import collections +import glob +import json +import re + +import jiwer + +from .config import load + + +def norm(t): + return " ".join(re.sub(r"[^\w\s]", " ", t.lower().replace("ё", "е")).split()) + + +def passes(s, r): + if "error" in r or not 7 <= r["cps"] <= 19: + return False + if len(s["text"].split()) <= 3: + h = norm(r.get("hyp", "")) + return bool(h) and jiwer.cer(norm(s["text"]), h) <= 0.35 + return r["wer"] <= 0.12 or r["wer"] * len(norm(s["text"]).split()) <= 1.01 + + +def score(r): + return 6 * r["stress_ok"] / max(1, r["stress_n"]) + 0.5 * r.get("mos", 3) + 2 * r["sim"] - 3 * r["wer"] + + +def main(): + cfg = load() + segs = json.load(open(cfg.w("segs.json"), encoding="utf-8")) + qc = {} + for f in sorted(glob.glob(str(cfg.w("qc", "x").parent / "*.json"))): + qc.update(json.load(open(f, encoding="utf-8"))) + by = collections.defaultdict(dict) + for stem, r in qc.items(): + take, seed = stem.rsplit("_s", 1) + by[take][int(seed)] = r + pick, weak, missing = {}, [], [] + for s in segs: + takes = by.get(s["take"], {}) + if not takes: + missing.append(s["id"]); continue + ok = {sd: r for sd, r in takes.items() if passes(s, r)} + if ok: + pick[s["id"]] = max(ok, key=lambda sd: score(ok[sd])) + else: + pick[s["id"]] = min(takes, key=lambda sd: takes[sd]["wer"]) + weak.append(s["id"]) + json.dump(pick, open(cfg.w("pick.json"), "w")) + take_of = {s["id"]: s["take"] for s in segs} + P = [by[take_of[i]][sd] for i, sd in pick.items()] + rep = dict(segments=len(segs), picked=len(pick), weak=weak, missing=missing, + wer=round(sum(r["wer"] for r in P) / max(1, len(P)), 4), + mos=round(sum(r.get("mos", 0) for r in P) / max(1, len(P)), 3), + sim=round(sum(r["sim"] for r in P) / max(1, len(P)), 3), + stress=round(sum(r["stress_ok"] for r in P) / max(1, sum(r["stress_n"] for r in P)), 4), + seeds=dict(collections.Counter(pick.values()))) + json.dump(rep, open(cfg.w("pick_report.json"), "w"), indent=1) + print({k: (len(v) if isinstance(v, list) else v) for k, v in rep.items()}) + if missing: + print(f"{len(missing)} segments have no scored take: run render and qc first") + + +if __name__ == "__main__": + main() diff --git a/ensvoice/qc.py b/ensvoice/qc.py new file mode 100644 index 0000000..1057599 --- /dev/null +++ b/ensvoice/qc.py @@ -0,0 +1,161 @@ +"""Step 5: score every take automatically (no listening). + +usage: qc [--shard i/n] [--no-mos] [--f0] [--jobs jobs.json --out out.json] +Default jobs: every work/takes/_s.wav of the current segments; results are cached in +work/qc/of.json by take name, so re-runs and lexicon changes only score new takes. Run n shards +in parallel to use the GPU fully (each process needs ~8-13 GB RAM and ~5 GB VRAM). + +Per take: +- wer, hyp — GigaAM-v3 (RNN-T) on <=20 s pieces cut at silences, against the text; +- stress_ok / stress_n — meter2 (shipped with the ru-stress model) agrees with the RUAccent marks; +- sim — WavLM-SV x-vector cosine to the EN narrator centroid (timbre); +- mos — UTMOSv2 naturalness; cps — pace, chars/s; maxgap — longest inner pause; clip — clipped samples; +- f0 — pitch spread in semitones (only with --f0: pyin is slow and runs on the CPU). +""" +import glob +import json +import os +import re +import sys +import tempfile + +import jiwer +import librosa +import numpy as np +import soundfile as sf +import torch + +from .config import load + +VOW = "аеёиоуыэюя" + + +def norm(t): + t = t.lower().replace("ё", "е").replace("́", "").replace("+", "") + return " ".join(re.sub(r"[^\w\s]", " ", t).split()) + + +def arg(name, default=None): + return sys.argv[sys.argv.index(name) + 1] if name in sys.argv else default + + +def main(): + cfg = load() + sh = tuple(int(x) for x in arg("--shard", "0/1").split("/")) + use_mos, use_f0 = "--no-mos" not in sys.argv and cfg.get("qc", "mos", True), "--f0" in sys.argv + if arg("--jobs"): + jobs, out_path = json.load(open(arg("--jobs"), encoding="utf-8")), arg("--out") + else: + segs = {s["take"]: s for s in json.load(open(cfg.w("segs.json"), encoding="utf-8"))} + jobs = [] + for f in sorted(glob.glob(str(cfg.w("takes", "x").parent / "*_s*.wav"))): + stem = os.path.basename(f)[:-4] + take = stem.rsplit("_s", 1)[0] + if take in segs: + jobs.append(dict(id=stem, text=segs[take]["text"], plus=segs[take]["plus"], wav=f)) + out_path = str(cfg.w("qc", f"{sh[0]}of{sh[1]}.json")) + done = {} + for f in glob.glob(str(cfg.w("qc", "x").parent / "*.json")) + ([out_path] if os.path.exists(out_path) else []): + try: + done.update(json.load(open(f, encoding="utf-8"))) + except Exception: + pass + jobs = [j for j in jobs[sh[0]::sh[1]] if j["id"] not in done] + print("takes to score", len(jobs), flush=True) + if not jobs: + return + res = json.load(open(out_path, encoding="utf-8")) if os.path.exists(out_path) else {} + + from huggingface_hub import snapshot_download + model = cfg.get("tts", "model", "siriusfreak/qwen3-tts-12hz-1.7b-ru-stress-cf") # meter2 ships with it + meter = os.path.join(model if os.path.isdir(model) else snapshot_download(model, allow_patterns=["meter/*"]), + "meter") + os.environ.setdefault("METER2_HEAD", os.path.join(meter, "head.pt")) + os.environ.setdefault("METER2_DEVICE", "cuda") + sys.path.insert(0, meter) + from meter2 import judge, vowel_pos, words_in + + def marks(plus, text): + toks = [m.group(0) for m in re.finditer(r"[А-Яа-яЁё+]+(?:-[А-Яа-яЁё+]+)*", plus)] + if len(toks) != len(words_in(text)): + return {} + return {wi: sum(1 for ch in w[:w.index("+")] if ch.lower() in VOW) for wi, w in enumerate(toks) + if "+" in w and len(vowel_pos(w.replace("+", ""))) > 1} + + import gigaam + asr = gigaam.load_model("v3_rnnt") + tmp = os.path.join(tempfile.gettempdir(), f"ensvoice_qc_{os.getpid()}.wav") # one per process + + def transcribe(y, sr): + iv = librosa.effects.split(y, top_db=35, frame_length=1024, hop_length=256) + pieces, start, cur = [], None, None + for a, b in iv: + if cur is None: + start, cur = a, b + elif (b - start) / sr <= 20: + cur = b + else: + pieces.append((start, cur)); start, cur = a, b + if cur is not None: + pieces.append((start, cur)) + out = [] + for a, b in pieces: + sf.write(tmp, y[max(0, a - 2400):b + 2400], sr) + r = asr.transcribe(tmp) + out.append(r if isinstance(r, str) else getattr(r, "text", str(r))) + return " ".join(out), iv + + from transformers import AutoFeatureExtractor, WavLMForXVector + sv_id = cfg.get("qc", "wavlm", "microsoft/wavlm-base-plus-sv") + fe = AutoFeatureExtractor.from_pretrained(sv_id) + sv = WavLMForXVector.from_pretrained(sv_id).cuda().eval() + cen = torch.tensor(np.load(cfg.w("centroid.npy"))) + + def embed(y16): + if len(y16) < 32000: + y16 = np.pad(y16, (0, 32000 - len(y16))) + x = fe(y16[:16000 * 30], sampling_rate=16000, return_tensors="pt") + with torch.no_grad(): + e = sv(**{k: v.cuda() for k, v in x.items()}).embeddings + return torch.nn.functional.normalize(e, dim=-1)[0].cpu() + + if use_mos: + import utmosv2 + mos_model = utmosv2.create_model(pretrained=True) + + for i, j in enumerate(jobs): + try: + y, sr = sf.read(j["wav"], dtype="float32") + if y.ndim > 1: + y = y.mean(1) + dur = len(y) / sr + hyp, iv = transcribe(y, sr) + wer = jiwer.wer(norm(j["text"]), norm(hyp)) if norm(hyp) else 1.0 + J = judge(y, sr, j["text"]) + items = [(wi, v) for wi, v in marks(j["plus"], j["text"]).items() if wi < len(J) and J[wi]["best"] is not None] + y16 = librosa.resample(y, orig_sr=sr, target_sr=16000) + gaps = [(iv[k + 1][0] - iv[k][1]) / sr for k in range(len(iv) - 1)] + r = dict(dur=round(dur, 2), cps=round(len(j["text"]) / dur, 1), wer=round(wer, 3), hyp=hyp, + stress_ok=sum(1 for wi, v in items if J[wi]["best"] == v), stress_n=len(items), + bad=[J[wi]["word"] for wi, v in items if J[wi]["best"] != v][:10], + sim=round(float(embed(y16) @ cen), 3), maxgap=round(max(gaps), 2) if gaps else 0, + clip=int(np.sum(np.abs(y) > 0.99))) + if use_f0: + f0, vf, _ = librosa.pyin(y16, fmin=60, fmax=400, sr=16000, frame_length=1024) + f0 = f0[vf] + r["f0"] = round(float(np.std(12 * np.log2(f0 / np.median(f0)))) if len(f0) > 10 else 0.0, 2) + if use_mos: + r["mos"] = round(float(mos_model.predict(input_path=j["wav"])), 3) + except Exception as ex: # a broken take simply loses + r = dict(error=str(ex)[:200], wer=1.0, cps=0, stress_ok=0, stress_n=1, sim=0, mos=0) + res[j["id"]] = r + print(f"{i + 1}/{len(jobs)}", j["id"], r.get("wer"), f'{r["stress_ok"]}/{r["stress_n"]}', r.get("sim"), + r.get("mos"), flush=True) + if i % 20 == 0: + json.dump(res, open(out_path, "w", encoding="utf-8"), ensure_ascii=False) + json.dump(res, open(out_path, "w", encoding="utf-8"), ensure_ascii=False) + print("QC_DONE", len(res)) + + +if __name__ == "__main__": + main() diff --git a/ensvoice/qoa.py b/ensvoice/qoa.py new file mode 100644 index 0000000..6a0295c --- /dev/null +++ b/ensvoice/qoa.py @@ -0,0 +1,67 @@ +"""Frameless QOA as Enshrouded stores voiceover (SoundResource format FramelessQOA). + +QOA slices without file or frame headers: one little-endian u64 per 20 samples (4-bit scale factor, +20 x 3-bit residuals), mono 48 kHz. The LMS state starts at weights [0, 0, -8192, 16384], history 0, +and runs on through the whole stream. +""" +import struct + +DQ = [0.75, -0.75, 2.5, -2.5, 4.5, -4.5, 7, -7] +SF = [round((s + 1) ** 2.75) for s in range(16)] +DEQ = [[(1 if sf * d >= 0 else -1) * int(abs(sf * d) + 0.5) for d in DQ] for sf in SF] + + +def _clamp(v): + return -32768 if v < -32768 else 32767 if v > 32767 else v + + +def decode(data: bytes, nsamples: int) -> list: + h = [0, 0, 0, 0] + w = [0, 0, -(1 << 13), 1 << 14] + out, p = [], 0 + for _ in range((nsamples + 19) // 20): + if p + 8 > len(data): + break + sl, = struct.unpack_from("> 60) & 15] + for j in range(20): + rd = dq[(sl >> (57 - 3 * j)) & 7] + s = _clamp(((h[0] * w[0] + h[1] * w[1] + h[2] * w[2] + h[3] * w[3]) >> 13) + rd) + delta = rd >> 4 + for k in range(4): + w[k] += -delta if h[k] < 0 else delta + h[0], h[1], h[2], h[3] = h[1], h[2], h[3], s + out.append(s) + return out[:nsamples] + + +def encode(samples: list) -> bytes: + """mono int16 samples -> frameless QOA; per slice the scale factor with the least squared error.""" + h = [0, 0, 0, 0] + w = [0, 0, -(1 << 13), 1 << 14] + out = bytearray() + pad = list(samples) + [0] * ((-len(samples)) % 20) + for si in range(0, len(pad), 20): + sl, best = pad[si:si + 20], None + for sf in range(16): + dq, hh, ww, err, bits = DEQ[sf], h[:], w[:], 0, sf + for x in sl: + pred = (hh[0] * ww[0] + hh[1] * ww[1] + hh[2] * ww[2] + hh[3] * ww[3]) >> 13 + res = x - pred + q = min(range(8), key=lambda k: abs(res - dq[k])) + rd = dq[q] + s = _clamp(pred + rd) + err += (x - s) ** 2 + if best and err >= best[0]: + break + de = rd >> 4 + ww = [a - de if b < 0 else a + de for a, b in zip(ww, hh)] + hh = [hh[1], hh[2], hh[3], s] + bits = (bits << 3) | q + else: + if best is None or err < best[0]: + best = (err, bits, hh, ww) + _, bits, h, w = best + out += struct.pack(" 0.1: +English at the start), then the highest MOS; put its id into voice.reference. Local backend only. +""" +import collections +import json +import os +import random +import re +import statistics as st +import sys + +from .config import load + + +def report(cfg): + q = json.load(open(cfg.w("refbench", "qc.json"), encoding="utf-8")) + by = collections.defaultdict(list) + for k, r in q.items(): + if "error" not in r: + by[k.split("|")[0]].append(r) + rows = [] + for v, rs in by.items(): + so = sum(r["stress_ok"] for r in rs) / max(1, sum(r["stress_n"] for r in rs)) + rows.append((st.mean(r.get("mos", 0) for r in rs), v, len(rs), st.mean(r["wer"] for r in rs), + sum(r["wer"] > .1 for r in rs), so, st.mean(r["sim"] for r in rs))) + print(f"{'MOS':>5} {'WER':>6} {'bad':>4} {'stress':>7} {'SIM':>6} reference") + for mos, v, n, wer, bad, so, sim in sorted(rows, reverse=True): + print(f"{mos:5.2f} {wer:6.3f} {bad:4d} {so:7.3f} {sim:6.3f} {v}") + + +def main(): + cfg = load() + if "--report" in sys.argv: + return report(cfg) + from .accent import acute_marks, plus_marks + from .lexicon import post, pre + from .render import reference + from .tts.local import LocalTTS + refs = json.load(open(cfg.w("refs.json"), encoding="utf-8")) + ids = [a for a in sys.argv[1:] if not a.startswith("-")] or \ + list(dict.fromkeys([cfg["voice"]["reference"]] + [c["id"] for c in refs[:8]])) + notes = json.load(open(cfg.w("notes.json"), encoding="utf-8")) + tp = cfg.w("refbench", "texts.json") + if tp.exists(): + sel = json.load(open(tp, encoding="utf-8")) + else: + random.seed(7) + paras = [(n["k"], p.strip()) for n in notes for p in re.split(r"\n\s*\n", pre(n["text"])) if 150 <= len(p.strip()) <= 380] + random.shuffle(paras) + seen, sel = set(), [] + for k, p in paras: + if k not in seen and len(sel) < 16: + seen.add(k); sel.append(dict(id=k, text=p, plus=post(plus_marks(p)))) + json.dump(sel, open(tp, "w", encoding="utf-8"), ensure_ascii=False, indent=1) + tts, jobs = None, [] + for rid in ids: + wav, text = reference(cfg, rid) + tts = tts or LocalTTS(cfg, wav, text) + tts.set_voice(wav, text) + for seed in (1000, 1001): + items = [(acute_marks(s["plus"]), str(cfg.w("refbench", "takes", f"{rid}~{s['id']}~{seed}.wav"))) + for s in sel] + todo = [it for it in items if not os.path.exists(it[1])] + if todo: + tts.speak(todo, seed) + jobs += [dict(id=f"{rid}|{s['id']}|{seed}", text=s["text"], plus=s["plus"], wav=it[1]) for s, it in zip(sel, items)] + json.dump(jobs, open(cfg.w("refbench", "jobs.json"), "w", encoding="utf-8"), ensure_ascii=False) + print("REFBENCH_RENDERED", len(jobs), "takes; next: python run.py qc --jobs", cfg.w("refbench", "jobs.json"), + "--out", cfg.w("refbench", "qc.json")) + + +if __name__ == "__main__": + main() diff --git a/ensvoice/refs.py b/ensvoice/refs.py new file mode 100644 index 0000000..fdcf2ac --- /dev/null +++ b/ensvoice/refs.py @@ -0,0 +1,90 @@ +"""Step 2: reference clips of the EN narrator for the voice clone, and the narrator's measurements. + +Candidates: whole notes of 7-16 s and first paragraphs of 7-15 s cut at the pause nearest to where the +paragraph should end (by its share of characters). Each needs its exact EN text (ICL cloning), so a cut +that misses the pause leaks: the clone then starts Russian takes with the English tail ("we were a +harvest...") — refbench catches that by WER. +Writes work/refs/.wav (id = "__all|__p1"), work/refs.json (text, length, similarity +to the narrator centroid, F0 spread), work/centroid.npy (mean WavLM x-vector of the narrator, the QC +target for the timbre) and work/en_pauses.json (pauses between paragraphs and phrases in the EN originals). +""" +import json +import re + +import librosa +import numpy as np +import soundfile as sf +import torch + +from .config import load + + +def clean(s): + return re.sub(r"<[^>]+>", "", s or "") + + +def main(): + cfg = load() + notes = json.load(open(cfg.w("notes.json"), encoding="utf-8")) + from transformers import AutoFeatureExtractor, WavLMForXVector + sv_id = cfg.get("qc", "wavlm", "microsoft/wavlm-base-plus-sv") + fe = AutoFeatureExtractor.from_pretrained(sv_id) + sv = WavLMForXVector.from_pretrained(sv_id).cuda().eval() + + def embed(y24): + y = librosa.resample(y24, orig_sr=24000, target_sr=16000)[:16000 * 30] + x = fe(y, sampling_rate=16000, return_tensors="pt") + with torch.no_grad(): + e = sv(**{k: v.cuda() for k, v in x.items()}).embeddings + return torch.nn.functional.normalize(e, dim=-1)[0].cpu().numpy() + + cands, embs, para_gaps, sent_gaps = [], [], [], [] + for n in notes: + y, sr = sf.read(cfg.w("en24", f"{n['k']}.wav"), dtype="float32") + embs.append(embed(y[: sr * 20])) + iv = librosa.effects.split(y, top_db=40, frame_length=1024, hop_length=240) + if len(iv) < 2: + continue + gaps = [((iv[i][1] + iv[i + 1][0]) / 2 / sr, (iv[i + 1][0] - iv[i][1]) / sr) for i in range(len(iv) - 1)] + a0, a1 = iv[0][0] / sr, iv[-1][1] / sr + paras = [clean(p).strip() for p in re.split(r"\n\s*\n", n["en"] or "") if clean(p).strip()] + full = " ".join(paras) + if not full: + continue + if 7 <= a1 - a0 <= 16: + cands.append(dict(id=f"{n['name']}__all", k=n["k"], a=a0, b=a1, text=re.sub(r"\s+", " ", full))) + if len(paras) >= 2: + pos = a0 + (a1 - a0) * len(paras[0]) / len(full) + g = min(gaps, key=lambda g: abs(g[0] - pos)) + if abs(g[0] - pos) < 0.12 * (a1 - a0) and g[1] > 0.35: + para_gaps.append(g[1]) + if 7 <= g[0] - a0 <= 15: + cands.append(dict(id=f"{n['name']}__p1", k=n["k"], a=a0, b=g[0] - g[1] / 2 + 0.1, + text=re.sub(r"\s+", " ", paras[0]))) + sent_gaps += [g[1] for g in gaps if g[1] > 0.15] + cen = np.mean(embs, 0) + cen /= np.linalg.norm(cen) + np.save(cfg.w("centroid.npy"), cen) + out = [] + for c in cands: + y, sr = sf.read(cfg.w("en24", f"{c['k']}.wav"), dtype="float32") + seg = y[max(0, int((c["a"] - 0.15) * sr)): int((c["b"] + 0.1) * sr)] + sf.write(cfg.w("refs", f"{c['id']}.wav"), seg, sr) + f0, vf, _ = librosa.pyin(librosa.resample(seg, orig_sr=24000, target_sr=16000), fmin=60, fmax=400, sr=16000) + f0 = f0[vf] + c.update(sec=round(len(seg) / sr, 2), sim=round(float(embed(seg) @ cen), 3), + f0=round(float(np.std(12 * np.log2(f0 / np.median(f0)))) if len(f0) > 10 else 0.0, 2)) + out.append(c) + out.sort(key=lambda c: -c["sim"]) + json.dump(out, open(cfg.w("refs.json"), "w", encoding="utf-8"), ensure_ascii=False, indent=1) + pg, sg = np.array(para_gaps), np.array(sent_gaps) + pauses = dict(paragraph_median=float(np.median(pg)), gap_median=float(np.median(sg)), + gap_p75=float(np.percentile(sg, 75))) + json.dump(pauses, open(cfg.w("en_pauses.json"), "w"), indent=1) + print("candidates", len(out), "pauses", pauses) + want = cfg["voice"]["reference"] + print("configured reference", want, "found" if any(c["id"] == want for c in out) else "NOT FOUND") + + +if __name__ == "__main__": + main() diff --git a/ensvoice/render.py b/ensvoice/render.py new file mode 100644 index 0000000..cfd94bb --- /dev/null +++ b/ensvoice/render.py @@ -0,0 +1,43 @@ +"""Step 4: speak every segment in every seed of voice.seeds (work/takes/_s.wav). + +usage: render [--weak] --weak: only the segments pick listed as weak, in voice.extra_seeds +Resumable: existing takes are skipped, so after a lexicon change only the changed segments are spoken. +""" +import json +import sys + +from .config import load +from .tts import backend + + +def reference(cfg, ref_id=None): + ref_id = ref_id or cfg["voice"]["reference"] + refs = {c["id"]: c for c in json.load(open(cfg.w("refs.json"), encoding="utf-8"))} + if ref_id not in refs: + raise SystemExit(f"reference {ref_id!r} is not in work/refs.json (run refs; see README)") + return cfg.w("refs", f"{ref_id}.wav"), refs[ref_id]["text"] + + +def main(): + cfg = load() + segs = json.load(open(cfg.w("segs.json"), encoding="utf-8")) + seeds = cfg["voice"]["seeds"] + if "--weak" in sys.argv: + weak = set(json.load(open(cfg.w("pick_report.json")))["weak"]) + segs = [s for s in segs if s["id"] in weak] + seeds = cfg["voice"].get("extra_seeds", []) + tts = None + for seed in seeds: + todo = [(s["acute"], str(cfg.w("takes", f"{s['take']}_s{seed}.wav"))) for s in segs + if not cfg.w("takes", f"{s['take']}_s{seed}.wav").exists()] + print(f"seed {seed}: {len(todo)} segments to speak", flush=True) + if not todo: + continue + if tts is None: + tts = backend(cfg, *reference(cfg)) + tts.speak(todo, seed, log=lambda m: print(m, flush=True)) + print("RENDER_DONE") + + +if __name__ == "__main__": + main() diff --git a/ensvoice/ru_num.py b/ensvoice/ru_num.py new file mode 100644 index 0000000..98b5a36 --- /dev/null +++ b/ensvoice/ru_num.py @@ -0,0 +1,41 @@ +import re +U=["ноль","один","два","три","четыре","пять","шесть","семь","восемь","девять","десять","одиннадцать","двенадцать","тринадцать","четырнадцать","пятнадцать","шестнадцать","семнадцать","восемнадцать","девятнадцать"] +T=["","","двадцать","тридцать","сорок","пятьдесят","шестьдесят","семьдесят","восемьдесят","девяносто"] +H=["","сто","двести","триста","четыреста","пятьсот","шестьсот","семьсот","восемьсот","девятьсот"] +def card(n): + if n<20: return U[n] + if n<100: return (T[n//10]+(" "+U[n%10] if n%10 else "")) + if n<1000: return (H[n//100]+(" "+card(n%100) if n%100 else "")) + if n<1000000: + th,r=divmod(n,1000); t=th%10; tt=th%100 + w=card(th) if th>2 or th>=20 else "" + if th==1: w="одна тысяча" + elif th==2: w="две тысячи" + else: + w=card(th) + w=re.sub(r"один$","одна",w); w=re.sub(r"два$","две",w) + w+=" "+("тысяча" if t==1 and tt!=11 else "тысячи" if 2<=t<=4 and not 12<=tt<=14 else "тысяч") + return w+(" "+card(r) if r else "") + return str(n) +ORD={"ноль":"нулев","один":"перв","два":"втор","три":"трет","четыре":"четвёрт","пять":"пят","шесть":"шест","семь":"седьм","восемь":"восьм","девять":"девят","десять":"десят","одиннадцать":"одиннадцат","двенадцать":"двенадцат","тринадцать":"тринадцат","четырнадцать":"четырнадцат","пятнадцать":"пятнадцат","шестнадцать":"шестнадцат","семнадцать":"семнадцат","восемнадцать":"восемнадцат","девятнадцать":"девятнадцат","двадцать":"двадцат","тридцать":"тридцат","сорок":"сороков","пятьдесят":"пятидесят","шестьдесят":"шестидесят","семьдесят":"семидесят","восемьдесят":"восьмидесят","девяносто":"девяност","сто":"сот","двести":"двухсот","триста":"трёхсот","четыреста":"четырёхсот","пятьсот":"пятисот","шестьсот":"шестисот","семьсот":"семисот","восемьсот":"восьмисот","девятьсот":"девятисот","тысяча":"тысячн","тысячи":"тысячн","тысяч":"тысячн"} +# ending by case/gender: suffix of written form -> ending (hard stem / трет soft) +END={"й":"ый","го":"ого","му":"ому","м":"ом","е":"ое","я":"ая","ю":"ую","х":"ых","ми":"ыми"} +def ordinal(n,suf): + ws=card(n).split(); last=ws[-1]; stem=ORD.get(last,last) + e=END.get(suf,"ый") + if stem=="трет": e={"ый":"ий","ого":"ьего","ому":"ьему","ом":"ьем","ое":"ье","ая":"ья","ую":"ью","ых":"ьих","ыми":"ьими"}[e] + elif stem in("втор","сороков") or stem.endswith(("тысячн",)): + e=e.replace("ый","ой") if stem=="втор" or stem=="сороков" else e + if e=="ый" and stem in("втор","шест","седьм","восьм","сороков"): e="ой" + return " ".join(ws[:-1]+[stem+e]) +def normalize(t): + t=t.replace("\xa0"," ").replace("•","").replace("*","").replace("_"," ").replace("–",",").replace("№","номер ") + t=re.sub(r"(\d+)%",lambda m:card(int(m[1]))+" процентов",t) + t=re.sub(r"\b(\d+)-(й|го|му|м|е|я|ю|х|ми)\b",lambda m:ordinal(int(m[1]),m[2]),t) + t=re.sub(r"\b(\d+)\s+(году|года|год|годом)\b",lambda m:ordinal(int(m[1]),{"году":"м","года":"го","год":"й","годом":"м"}[m[2]])+" "+m[2],t) + t=re.sub(r"^\s*(\d+)\.\s",lambda m:card(int(m[1]))+". ",t,flags=re.M) + t=re.sub(r"\d+",lambda m:card(int(m[0])),t) + t=re.sub(r"[ \t]{2,}"," ",t) + return t +if __name__=="__main__": + for s in ["в 508 году","3-й ярус","341-го дня","34% урожая","1200 монет","1. Первое","до 507 года","2-ю неделю","3-е место","21 кролик"]: print(s,"->",normalize(s)) diff --git a/ensvoice/segments.py b/ensvoice/segments.py new file mode 100644 index 0000000..f7eabd7 --- /dev/null +++ b/ensvoice/segments.py @@ -0,0 +1,65 @@ +"""Step 3: the text for the voice, cut into segments (work/segs.json). + +Every note: lexicon.pre -> lines (a blank line = paragraph 'p', a single newline = 'l') -> lines over +MAXC characters cut at sentence ends into near-equal pieces ('s') -> RUAccent '+' -> lexicon.post -> +U+0301 text for the ru-stress model. +A segment's takes are named __s.wav, the hash of the reference and the spoken text: +change the lexicon or the reference and only the segments it touches are rendered again. +""" +import hashlib +import json +import math +import re + +from .accent import acute_marks, plus_marks +from .config import load +from .lexicon import post, pre + +MAXC = 380 + + +def split_long(s): + if len(s) <= MAXC: + return [s] + sents = [x.strip() for x in re.findall(r".+?(?:[.!?…]+[»\"')]*(?=\s)|$)\s*", s) if x.strip()] + k = math.ceil(len(s) / 330) + target, out, cur = len(s) / k, [], "" + for x in sents: + if cur and len(cur) + len(x) + 1 > target * 1.15 and len(out) < k - 1: + out.append(cur); cur = x + else: + cur = (cur + " " + x).strip() + out.append(cur) + return out + + +def main(): + cfg = load() + ref = cfg["voice"]["reference"] + notes = json.load(open(cfg.w("notes.json"), encoding="utf-8")) + if cfg.only: # ENSVOICE_NOTES=n016,n238: a quick experiment on a few notes + notes = [n for n in notes if n["k"] in cfg.only] + segs = [] + for n in notes: + units, join = [], "p" + for line in pre(n["text"]).split("\n"): + line = line.strip() + if not line: + join = "p"; continue + for j, piece in enumerate(split_long(line)): + units.append(("s" if j else join, piece)) + join = "l" + for i, (jn, u) in enumerate(units): + plus = post(plus_marks(u)) + acute = acute_marks(plus) + sid = f"{n['k']}_{i:02d}" + h = hashlib.sha1(f"{ref}|{acute}".encode()).hexdigest()[:10] + segs.append(dict(id=sid, take=f"{sid}_{h}", k=n["k"], i=i, join=jn if i else "-", + text=u, plus=plus, acute=acute)) + json.dump(segs, open(cfg.w("segs.json"), "w", encoding="utf-8"), ensure_ascii=False, indent=0) + L = [len(s["text"]) for s in segs] + print("segments", len(segs), "notes", len(notes), "longest", max(L), "chars", sum(L)) + + +if __name__ == "__main__": + main() diff --git a/ensvoice/tts/__init__.py b/ensvoice/tts/__init__.py new file mode 100644 index 0000000..e6ad04b --- /dev/null +++ b/ensvoice/tts/__init__.py @@ -0,0 +1,16 @@ +"""TTS backends: speak(items=[(text, out_wav)], seed) in the cloned narrator voice. + +local — qwen-tts in this process on a CUDA GPU (any machine, the default); +media_node — an MCP server with generate_speech (the author's home GPU node; see media_node.py). +""" + + +def backend(cfg, ref_wav, ref_text): + name = cfg.get("tts", "backend", "local") + if name == "local": + from .local import LocalTTS + return LocalTTS(cfg, ref_wav, ref_text) + if name == "media_node": + from .media_node import MediaNodeTTS + return MediaNodeTTS(cfg) + raise SystemExit(f"unknown tts.backend {name!r}") diff --git a/ensvoice/tts/local.py b/ensvoice/tts/local.py new file mode 100644 index 0000000..c5813af --- /dev/null +++ b/ensvoice/tts/local.py @@ -0,0 +1,33 @@ +"""qwen-tts (Qwen3-TTS 1.7B, fine-tune siriusfreak/qwen3-tts-12hz-1.7b-ru-stress-cf) on a local GPU. + +ICL voice clone: the reference clip and its exact text are encoded once; texts are spoken in batches +of tts.batch with torch.manual_seed(seed + batch start), so a seed is repeatable for the same segments +in the same order. fp16 on Volta/Turing, bfloat16 on Ampere and newer (tts.dtype). +""" +import soundfile as sf +import torch + + +class LocalTTS: + def __init__(self, cfg, ref_wav, ref_text): + from qwen_tts import Qwen3TTSModel + t = cfg["tts"] + self.model = Qwen3TTSModel.from_pretrained(t.get("model", "siriusfreak/qwen3-tts-12hz-1.7b-ru-stress-cf"), + device_map=t.get("device", "cuda:0"), + dtype=getattr(torch, t.get("dtype", "bfloat16"))) + self.batch = int(t.get("batch", 8)) + self.set_voice(ref_wav, ref_text) + + def set_voice(self, ref_wav, ref_text): + self.prompt = self.model.create_voice_clone_prompt(ref_audio=str(ref_wav), ref_text=ref_text) + + def speak(self, items, seed, log=print): + items = sorted(items, key=lambda x: len(x[0])) # similar lengths in one batch: less padding + for i in range(0, len(items), self.batch): + b = items[i:i + self.batch] + torch.manual_seed(seed + i) + wavs, sr = self.model.generate_voice_clone(text=[t for t, _ in b], language=["Russian"] * len(b), + voice_clone_prompt=self.prompt * len(b)) + for (_, out), w in zip(b, wavs): + sf.write(out, w, sr) + log(f"seed {seed}: {min(i + self.batch, len(items))}/{len(items)}") diff --git a/ensvoice/tts/media_node.py b/ensvoice/tts/media_node.py new file mode 100644 index 0000000..caf231d --- /dev/null +++ b/ensvoice/tts/media_node.py @@ -0,0 +1,75 @@ +"""generate_speech over MCP streamable HTTP (media-node, the author's home GPU node with qwentts.cpp). + +Needs a server with the ru-stress model and a saved project voice (tts.media_node.voice, e.g. made with +its save_voice tool from the reference clip). Batches of up to 64 texts / 29000 characters per call. +""" +import itertools +import json +import urllib.request + +_ids = itertools.count(1) + + +class MCP: + def __init__(self, url): + self.url, self.sid = url, None + self._post({"jsonrpc": "2.0", "id": next(_ids), "method": "initialize", + "params": {"protocolVersion": "2025-06-18", "capabilities": {}, + "clientInfo": {"name": "ensvoice", "version": "0.4"}}}) + self._post({"jsonrpc": "2.0", "method": "notifications/initialized"}, expect=False) + + def _post(self, msg, expect=True, timeout=7200): + h = {"Content-Type": "application/json", "Accept": "application/json, text/event-stream"} + if self.sid: + h["Mcp-Session-Id"] = self.sid + with urllib.request.urlopen(urllib.request.Request(self.url, json.dumps(msg).encode(), h), timeout=timeout) as r: + self.sid = r.headers.get("Mcp-Session-Id", self.sid) + body = r.read().decode() + if not expect: + return None + if body.lstrip().startswith("{"): + return json.loads(body) + last = None + for line in body.splitlines(): + if line.startswith("data:"): + d = json.loads(line[5:]) + if d.get("id") == msg.get("id"): + last = d + return last + + def call(self, name, args): + r = self._post({"jsonrpc": "2.0", "id": next(_ids), "method": "tools/call", + "params": {"name": name, "arguments": args}}) + if "error" in r: + raise RuntimeError(r["error"]) + return r["result"] + + +class MediaNodeTTS: + def __init__(self, cfg): + c = cfg["tts"]["media_node"] + self.url, self.voice = c["url"], c["voice"] + self.rewrite = c.get("download_host_rewrite") # ["from", "to"] if file URLs name another host + self.m = MCP(self.url) + + def speak(self, items, seed, log=print): + batches, cur, c = [], [], 0 + for it in items: + if cur and (len(cur) == 64 or c + len(it[0]) > 29000): + batches.append(cur); cur, c = [], 0 + cur.append(it); c += len(it[0]) + if cur: + batches.append(cur) + for bi, b in enumerate(batches): + for attempt in range(3): + try: + r = self.m.call("generate_speech", {"model": "ru-stress", "voice": self.voice, "language": "Russian", + "seed": seed, "texts": [{"text": t} for t, _ in b]}) + break + except Exception as ex: # a backend timeout: once more with a new session + log(f"retry {bi}: {ex}"); self.m = MCP(self.url) + files = sorted(r["structuredContent"]["files"], key=lambda f: f["item"]) + for (_, out), f in zip(b, files): + url = f["url"] if not self.rewrite else f["url"].replace(*self.rewrite) + urllib.request.urlretrieve(url, out) + log(f"seed {seed}: batch {bi + 1}/{len(batches)}") diff --git a/env/qc.txt b/env/qc.txt new file mode 100644 index 0000000..3cc9976 --- /dev/null +++ b/env/qc.txt @@ -0,0 +1,11 @@ +# .venv-qc: automatic quality control and assembly; torch comes first from the CUDA index +transformers>=5.0 +gigaam @ git+https://github.com/salute-developers/GigaAM@7447938d791c4f3e643386ee22c33777004293a5 +utmosv2 @ git+https://github.com/sarulab-speech/UTMOSv2@cc2700db57bb83ee13dc31ebe1b868c254e15d09 +librosa>=0.11 +soxr>=0.5 +soundfile>=0.13 +jiwer>=4.0 +numpy>=2.0 +scipy +huggingface-hub diff --git a/env/tts.txt b/env/tts.txt new file mode 100644 index 0000000..a7bfa9a --- /dev/null +++ b/env/tts.txt @@ -0,0 +1,7 @@ +# .venv-tts: speech (qwen-tts pins transformers 4.x) + RUAccent; torch comes first from the CUDA index +qwen-tts==0.1.1 +transformers==4.57.3 +accelerate==1.12.0 +ruaccent==1.5.8.3 +soundfile>=0.13 +numpy>=2.0 diff --git a/install.py b/install.py new file mode 100644 index 0000000..3c18330 --- /dev/null +++ b/install.py @@ -0,0 +1,128 @@ +"""Install (or remove) the Russian narration mod. Python 3.11+, standard library only, Windows. + +usage: python install.py [--game ] install EML + the mod, dry run + python install.py --uninstall [--game ...] restore the game files, remove EML + the mod + +What it does: +1. finds the game (Steam library folders) unless --game is given; refuses while the game is running; +2. downloads EML from github.com/Brabb3l/kfc-parser release v0.2.0 into tools/ (dbghelp.dll, emm.exe, + kfc-parser.exe) and checks their SHA-256; +3. copies dbghelp.dll into the game folder (the EML proxy: it applies mods when the game starts, keeps + enshrouded.kfc.bak and patches enshrouded.kfc, never the original .dat files) and mod/ru_voice_notes + into /mods/; +4. dry run: emm.exe run --export must print "[ru_voice] replaced 525 notes, 0 failed". +The mod plays the Russian takes in place of the English voiceover of the 525 voiced notes: keep the +game's voice language English. +""" +import hashlib +import os +import pathlib +import re +import shutil +import subprocess +import sys +import urllib.request + +ROOT = pathlib.Path(__file__).resolve().parent +MOD = "ru_voice_notes" +REL = "https://github.com/Brabb3l/kfc-parser/releases/download/v0.2.0/" +SHA256 = { # release v0.2.0 assets + "dbghelp.dll": "038b7a1787026007a68c82a6adeb8e79c2e644f936d4cd00f8b0db206a2349fa", + "emm.exe": "b1133f6b8ea7000596cc7710901bd8307b48165b0e650c0487a611d90ea49305", + "kfc-parser.exe": "b5384a926885a0b59da0a2475ff1fd1c2a0679156e2dbcdb52d1899aabc85b75", +} + + +def find_game(): + roots = [] + try: + import winreg + for hive, key in ((winreg.HKEY_CURRENT_USER, r"Software\Valve\Steam"), + (winreg.HKEY_LOCAL_MACHINE, r"SOFTWARE\WOW6432Node\Valve\Steam")): + try: + with winreg.OpenKey(hive, key) as k: + for name in ("SteamPath", "InstallPath"): + try: + roots.append(pathlib.Path(winreg.QueryValueEx(k, name)[0])) + except OSError: + pass + except OSError: + pass + except ImportError: + pass + libs = set() + for r in roots: + libs.add(r) + vdf = r / "steamapps" / "libraryfolders.vdf" + if vdf.exists(): + libs |= {pathlib.Path(p.replace("\\\\", "\\")) for p in re.findall(r'"path"\s+"([^"]+)"', vdf.read_text("utf-8", "replace"))} + for lib in libs: + g = lib / "steamapps" / "common" / "Enshrouded" + if (g / "enshrouded.kfc").exists(): + return g + raise SystemExit("Enshrouded not found in the Steam libraries: pass --game ") + + +def game_running(): + out = subprocess.run(["tasklist", "/FI", "IMAGENAME eq enshrouded.exe"], capture_output=True, text=True).stdout + return "enshrouded.exe" in out.lower() + + +def fetch_tools(): + tools = ROOT / "tools" + tools.mkdir(exist_ok=True) + for name, sha in SHA256.items(): + f = tools / name + if not f.exists(): + print("download", REL + name) + urllib.request.urlretrieve(REL + name, f) + h = hashlib.sha256(f.read_bytes()).hexdigest() + if sha and h != sha: + f.unlink() + raise SystemExit(f"{name}: SHA-256 {h} does not match the release ({sha}); deleted") + return tools + + +def emm(tools, game, *args): + r = subprocess.run([str(tools / "emm.exe"), *args, "-g", str(game)], capture_output=True, text=True, + encoding="utf-8", errors="replace") + return r.returncode, r.stdout + r.stderr + + +def main(): + a = sys.argv[1:] + game = pathlib.Path(a[a.index("--game") + 1]) if "--game" in a else find_game() + print("game:", game) + if game_running(): + raise SystemExit("close Enshrouded first (EML cannot patch files the game holds open)") + tools = fetch_tools() + if "--uninstall" in a: + if (game / "enshrouded.kfc.bak").exists(): + code, out = emm(tools, game, "restore") + print(out[-500:]) + shutil.rmtree(game / "mods" / MOD, ignore_errors=True) + (game / "dbghelp.dll").unlink(missing_ok=True) + print("UNINSTALLED: the original game files are back; Steam 'Verify integrity' also restores them") + return + src = ROOT / "mod" / MOD + if not (src / "mod.json").exists(): + raise SystemExit(f"{src} is empty: git lfs pull (the takes are in Git LFS), or build it (README)") + small = [f for f in (src / "data").glob("*.qoa") if f.stat().st_size < 1024] + if small: + raise SystemExit(f"{len(small)} .qoa files are LFS pointers, not audio: run 'git lfs pull' first") + shutil.copyfile(tools / "dbghelp.dll", game / "dbghelp.dll") + dst = game / "mods" / MOD + shutil.rmtree(dst, ignore_errors=True) + shutil.copytree(src, dst) + print("mod copied to", dst) + code, out = emm(tools, game, "run", "--export") + line = [l for l in out.splitlines() if "[ru_voice]" in l] + print(re.sub(r"\x1b\[[0-9;]*m", "", line[-1]) if line else out[-1500:]) + if not line or " 0 failed" not in line[-1]: + raise SystemExit("dry run failed: see the output above") + print("INSTALLED: start the game from Steam as usual; notes are read in Russian") + + +if __name__ == "__main__": + os.environ.setdefault("PYTHONUTF8", "1") + main() diff --git a/mod/ru_voice_notes/data/0051d9b9-9aa0-4282-bf4c-4f2ee662a5d0.qoa b/mod/ru_voice_notes/data/0051d9b9-9aa0-4282-bf4c-4f2ee662a5d0.qoa new file mode 100644 index 0000000..ada8a98 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b/mod/ru_voice_notes/mod.json @@ -0,0 +1,13 @@ +{ + "id": "ru_voice_notes", + "name": "RU voice: notes", + "version": "0.4.0", + "capabilities": [ + "patch", + "export" + ], + "description": "Russian narration for the 525 voiced notes (Qwen3-TTS ru-stress-cf, narrator clone, automatic QC). Replaces the English voiceover slot.", + "authors": [ + "mikhail" + ] +} \ No newline at end of file diff --git a/mod/ru_voice_notes/src/lines.lua b/mod/ru_voice_notes/src/lines.lua new file mode 100644 index 0000000..24edcfd --- /dev/null +++ b/mod/ru_voice_notes/src/lines.lua @@ -0,0 +1,528 @@ +-- generated by build_mod.py: one entry per voiced note +return { + { sound = "69e3dda2-b87d-4ac4-97d3-1c33f34f598b", vo = "c55b8765-fef4-4ac8-a298-00418a4279c7", frames = 1700640, seconds = 35.430000, name = "EN_AncientLands_Drak_Ministory_03_bowFishing_Page_01_032" }, + { sound = "0a478bba-80cf-42c3-afe3-4b700da78568", vo = "d3eb2854-d331-434e-9e3c-b739b8a06a78", frames = 1401600, seconds = 29.200000, name = "EN_AncientLands_Drak_Ministory_12_barrierBreach_Page_01_030" }, + { sound = "cda74d75-cb71-43f1-99c4-8b1bbcbfef05", vo = "5d63cb24-c6c5-4cbc-86ff-b5a7c42b84d9", frames = 1414080, seconds = 29.460000, name = "EN_AncientLands_Drak_Ministory_13_risenGods_Page_01_031" }, + { sound = 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local done, err = pcall(function() + local snd = game.assets.get_resource(l.sound, "keen::SoundResource", 0) + local d = snd.data + local content = game.assets.create_content(io.read("data/" .. l.sound .. ".qoa")) + d.streamedData.hash = content + d.frameCount = l.frames + d.framesPerSecond = 48000 + d.duration.value = math.floor(l.seconds * 1e9) + local vo = game.assets.get_resource(l.vo, "keen::VoiceoverResource", 0) + vo.data.durations[1] = math.floor(l.seconds * 100) + end) + if done then ok = ok + 1 else fail = fail + 1; print("[ru_voice] FAIL " .. l.name .. ": " .. tostring(err)) end +end +print(("[ru_voice] replaced %d notes, %d failed"):format(ok, fail)) diff --git a/run.py b/run.py new file mode 100644 index 0000000..ddaf51a --- /dev/null +++ b/run.py @@ -0,0 +1,63 @@ +"""Run a pipeline step in its virtual environment (stdlib only, any Python 3.11+). + +usage: python run.py [args] python run.py all python run.py check +Two environments, because qwen-tts pins transformers 4.x and the QC models need 5.x: + .venv-tts : extract, segments, render, refbench, mod + .venv-qc : refs, qc, pick, assemble +""" +import os +import pathlib +import subprocess +import sys + +ROOT = pathlib.Path(__file__).resolve().parent +ENV = {"extract": "tts", "segments": "tts", "render": "tts", "refbench": "tts", "mod": "tts", + "refs": "qc", "qc": "qc", "pick": "qc", "assemble": "qc"} +ALL = [["extract"], ["refs"], ["segments"], ["render"], ["qc"], ["pick"], + ["render", "--weak"], ["qc"], ["pick"], ["assemble"], ["mod"]] + + +def py(env): + d = ROOT / f".venv-{env}" + exe = d / ("Scripts/python.exe" if os.name == "nt" else "bin/python") + if not exe.exists(): + raise SystemExit(f"{d} is missing: see README, 'Установка'") + return str(exe) + + +def run(step, args): + if step == "refbench" and "--report" in args: + env = "qc" + else: + env = ENV[step] + e = dict(os.environ, PYTHONUTF8="1", PYTHONIOENCODING="utf-8") + print(f"== {step} {' '.join(args)} [{env}]", flush=True) + r = subprocess.run([py(env), "-m", f"ensvoice.{step}", *args], cwd=ROOT, env=e) + if r.returncode: + raise SystemExit(f"step {step} failed ({r.returncode})") + + +def check(): + code = ("import torch,sys;print(sys.version.split()[0],'torch',torch.__version__,'cuda',torch.cuda.is_available()," + "torch.cuda.get_device_name(0) if torch.cuda.is_available() else '-')") + for env, mods in (("tts", "qwen_tts,ruaccent,soundfile"), ("qc", "gigaam,utmosv2,transformers,librosa,soxr,jiwer")): + subprocess.run([py(env), "-c", f"import {mods};" + code], check=True) + from ensvoice.config import load # noqa: E402 (stdlib + tomllib only) + cfg = load() + for f in ("enshrouded.kfc",): + assert (cfg.game / f).exists(), f"game not found: {cfg.game / f}" + assert (cfg.tools / "kfc-parser.exe").exists(), f"missing {cfg.tools / 'kfc-parser.exe'}: python install.py downloads it" + print("CHECK_OK") + + +if __name__ == "__main__": + if len(sys.argv) < 2: + raise SystemExit(__doc__) + step, args = sys.argv[1], sys.argv[2:] + if step == "check": + check() + elif step == "all": + for s in ALL: + run(s[0], s[1:]) + else: + run(step, args)