2026-08-11 22:38:02 +03:00
14 changed files with 552 additions and 380 deletions
@@ -0,0 +1 @@
 3.13.13
@@ -118,8 +118,14 @@ transcribe podcast.wav --model large-v3 --device openvino-gpu
 # Максимальная скорость на CPU (русский)
 transcribe meeting.mp4 --device onnx --model gigaam-v3
-# CPU с пунктуацией (русский, для parakeet-v3 нужен явный язык)
+# CPU с пунктуацией и нормализацией русского текста
-transcribe podcast.wav --device onnx --model parakeet-v3 --language ru
+transcribe podcast.wav --device onnx --model gigaam-v3-e2e-ctc
 # Смешанная русско-английская речь
 transcribe meeting.wav --device onnx --model gigaam-multilingual-ctc
 # Повышенная точность смешанной речи (медленнее, ~590 MB)
 transcribe meeting.wav --device onnx --model gigaam-multilingual-large-ctc
 # Сохранить в конкретный файл
 transcribe interview.m4a --output result.md
@@ -245,14 +251,15 @@ language = "en"
 | Параметр | CUDA | OpenVINO (GPU) | OpenVINO (CPU) | ONNX | CPU |
 |----------|------|----------------|----------------|------|-----|
-| model | medium | medium | medium | gigaam-v3 | medium |
+| model | medium | medium | medium | gigaam-v3-e2e-rnnt | medium |
 | compute_type | float16 | int8 | int8 | int8 | float32 |
 | language | ru | ru | ru | ru | ru |
 ## Модели и GPU
 Рекомендации:
- **По умолчанию:** `medium` — хороший баланс скорости и качества
+- **По умолчанию для ONNX:** `gigaam-v3-e2e-rnnt` — читаемый русский текст с
  пунктуацией почти без потери скорости относительно сырого `gigaam-v3`
 - **Макс. качество (NVIDIA):** `large-v3` + `--compute-type float16`
 - **Макс. качество (Intel GPU):** `large-v3` + `--device openvino-gpu`
 - **Макс. скорость CPU (русский):** `--device onnx --model gigaam-v3` (17-29× RTF, без пунктуации; рекомендуется LLM-нормализация терминов после)
@@ -277,9 +284,34 @@ language = "en"
 | Модель | Размер (int8) | RTFx CPU | Языки | Пунктуация |
 |--------|--------------|----------|-------|-----------|
 | `gigaam-v3` | ~300 MB | 17-29× | ru | ❌ |
-| `parakeet-v3` | ~600 MB | 12-20× | 25 языков | ✅ |
+| `gigaam-multilingual-ctc` | ~300 MB | 10,0×* | ru, en, kk, ky, uz | ❌ |
 | `gigaam-multilingual-large-ctc` | ~590 MB | 4,8×* | ru, en, kk, ky, uz | ❌ |
 | `gigaam-v3-e2e-ctc` | ~300 MB | 11,9×* | ru | ✅ |
 | `gigaam-v3-e2e-rnnt` | ~300 MB | 11,5×* | ru | ✅ |
 | `parakeet-v3` | ~600 MB | 7,6×* | 25 языков | ✅ |
-> **Рекомендация**: для русского — `gigaam-v3` (единственный из onnx-моделей, дающий пригодный для конспекта транскрипт на русских встречах; см. [ADR-006](docs/adr/006-onnx-asr-backend.md)). `parakeet-v3` уместен только для англоязычного / multilingual контента — на русском воспроизводит проблемы из [ADR-005](docs/adr/005-parakeet-evaluation.md) (Mm-hmm-редукция тихих реплик, иноязычные вставки).
+\* Наблюдение на AMD Ryzen 7 8845H, Windows, `int8`, три записи общей
 длительностью 43:37. Это не приёмочный замер для целевого Intel Core i5.
 Методика и качественное сравнение:
 [benchmark GigaAM и Whisper](docs/benchmarks/2026-08-11-gigaam-model-comparison.md).
 > **Рекомендация**: ONNX по умолчанию использует `gigaam-v3-e2e-rnnt` для
 > готового читаемого русского текста. Для последующей машинной обработки можно
 > явно выбрать более точный по словам `gigaam-v3` без пунктуации. Для смешанной
 > речи с приоритетом качества используйте `gigaam-multilingual-large-ctc`: она примерно вдвое
 > медленнее small-варианта, но приблизилась к monolingual GigaAM по WER.
 > `parakeet-v3` в 1,58 раза быстрее Large и ставит пунктуацию, но на тех же
 > трёх записях хуже по WER и вставляет ложные английские фразы в русскую речь;
 > это подтверждает проблемы из [ADR-005](docs/adr/005-parakeet-evaluation.md).
 Обе GigaAM Multilingual сами распознают русский, английский, казахский,
 кыргызский и узбекский внутри одной записи. `onnx-asr` не передаёт этим моделям
 подсказку языка, поэтому `--language` не управляет выбором языка.
 Для моделей из таблицы опубликованы `int8` и `float32`. Если неявный
 device-aware дефолт недоступен для выбранной модели, CLI сообщит о подстановке
 доступного варианта. Явное значение из `--compute-type` или
 `.transcriber.toml` вместо подстановки завершится ошибкой.
 </details>
@@ -291,9 +323,9 @@ language = "en"
 | `float16` | CUDA | ~4.5-5 GB | Отлично | **По умолчанию для CUDA** |
 | `int8_float16` | CUDA | ~4.7 GB | Отлично | GPU от 6 GB, альтернатива float16 |
 | `int8_float32` | CPU | Среднее | Отлично | **Рекомендуется для CPU** — 1.5x быстрее float32 при том же качестве |
-| `int8` | CUDA / OpenVINO | Низкое | Хорошо, но бывают галлюцинации | **По умолчанию для OpenVINO** |
+| `int8` | CUDA / OpenVINO / ONNX | Низкое | Хорошо, но бывают галлюцинации | **По умолчанию для OpenVINO и ONNX** |
 | `fp16` | OpenVINO | Низкое | Отлично | OpenVINO large-v3 (выбирается автоматически) |
-| `float32` | CPU | Среднее | Отлично | **По умолчанию для CPU** |
+| `float32` | CPU / ONNX | Среднее | Отлично | **По умолчанию для CPU** |
 **Важно:** `int8` на длинных записях может давать галлюцинации (повтор фраз, потеря контента).
 `float16`/`fp16` и `float32` значительно стабильнее на записях >20 минут.
@@ -118,7 +118,7 @@ transcribe <путь_к_файлу> [опции]
 ### 4.2. Требования к окружению
- Python ≥ 3.10
+- Python ≥ 3.13
 - ffmpeg в PATH (используется faster-whisper внутри для декодирования любых медиаформатов)
 - Для GPU: Linux/WSL2 — cuBLAS из nvidia-cublas-cu12 (ставится автоматически через `uv sync`); Windows — системный CUDA toolkit (см. ADR-001)
 - Дисковое пространство для моделей: ~3 GB (large-v3)
@@ -134,7 +134,7 @@ transcribe <путь_к_файлу> [опции]
 | Компонент           | Технология                                      |
 |---------------------|-------------------------------------------------|
-| Язык                | Python 3.10+                                    |
+| Язык                | Python 3.13+                                    |
 | Управление проектом | uv (pyproject.toml)                             |
 | Распознавание речи  | faster-whisper (CTranslate2 backend)            |
 | Медиа-декодирование | ffmpeg (системная зависимость, используется faster-whisper внутри) |
@@ -1,7 +1,8 @@
-# ADR-006: onnx-asr бэкенд — GigaAM v3 как CPU-default для русских встреч
+# ADR-006: GigaAM RNN-T как модель по умолчанию для ONNX-пути
 **Статус**: Принято
 **Дата**: 2026-04-25
 **Обновлено**: 2026-08-11
 ## Контекст
@@ -100,19 +101,27 @@ Mm-hmm-редукция и иностранные вставки **не обна
 ## Решение
-**Принять onnx-asr как экспериментальный бэкенд с явным `--device onnx`. GigaAM v3 — рекомендуемая модель для русских встреч на CPU.**
+**Принять onnx-asr как экспериментальный бэкенд с явным `--device onnx`.
 GigaAM v3 E2E RNN-T — модель по умолчанию для русских встреч на CPU.**
 Бэкенд **не в auto-detect** — только при явном указании пользователем (политика experimental backend, как для openvino).
 Модели:
- **`gigaam-v3`** — рекомендуемая для русских встреч на CPU. 17-29× RTF, summary utility 4/5 на всех протестированных файлах. Без пунктуации, без латиницы; ошибки локальны, чинятся LLM-нормализацией.
+- **`gigaam-v3-e2e-rnnt`** — модель по умолчанию: практически равна обычному
  GigaAM по скорости, немного уступает по WER, но выдаёт готовую пунктуацию для
  пользователей, которые читают транскрипт напрямую.
 - **`gigaam-v3`** — явный профиль для последующей машинной обработки. 17-29×
  RTF, summary utility 4/5 на всех протестированных файлах. Без пунктуации и
  латиницы; ошибки локальны, чинятся LLM-нормализацией.
 - **`parakeet-v3`** — multilingual (25 языков), формально доступен. **Не рекомендуется для русских встреч**: Mm-hmm-редукция и иноязычные вставки воспроизводятся систематически (см. выше). Уместен только для англоязычного контента.
-Обе модели в int8-квантизации (~300 MB).
+Все перечисленные модели доступны в int8-квантизации.
 ## Последствия
 - Пользователи CPU-only с русскоязычным контентом получают 3-5× ускорение по сравнению с OpenVINO medium **при превосходящем качестве** (4/5 vs 1-2/5 summary utility на длинных файлах).
 - Пользователи ONNX без явного `--model` получают пунктуацию и нормализацию
  RNN-T; более точный сырой CTC остаётся доступен как `--model gigaam-v3`.
 - Whisper medium (`--device openvino-cpu`) **остаётся допустимым** для коротких (≤30 мин) встреч с равномерной громкостью; на длинных файлах с тихими участками он галлюцинирует целыми блоками — этот риск зафиксирован, но решение не выводит OpenVINO из списка дефолтов (часть пользователей всё ещё нуждается в пунктуации, и для коротких файлов галлюцинации не воспроизводятся).
 - Parakeet-v3 формально доступен, но в README рекомендуется только для англоязычного контента — для русского явно не годится.
 - GPU faster-whisper large-v3 остаётся эталоном по качеству (для пользователей с NVIDIA GPU).
@@ -121,7 +130,6 @@ Mm-hmm-редукция и иностранные вставки **не обна
 ## Открытые вопросы / следующие шаги
 - **Galлюцинации Whisper medium на длинных файлах** — отдельный продуктовый риск, требующий собственного исследования. Возможно, имеет смысл ограничить максимальную длину чанка для openvino-medium, или дать предупреждение пользователю.
 - **GigaAM v3 RNN-T** (вариант `gigaam-v3-rnnt` вместо `gigaam-v3-ctc`) — заявлен как немного качественнее CTC, не тестировался. Может закрыть часть GigaAM-ошибок на латинице.
 - **Canary** (`nemo-canary-1b-v2`) — тяжелее, но multilingual + пунктуация. Кандидат на «лучшее качество за разумную скорость» для тех, кому важна пунктуация.
 - **Auto-detect onnx**: после стабилизации в production-использовании (несколько недель) — рассмотреть включение в auto-detect как первый CPU-бэкенд (ниже CUDA, выше OpenVINO).
@@ -10,6 +10,11 @@
 ### Переоценка CPU-дефолта после обновления `onnx-asr` 0.12
 **Промежуточный статус 2026-08-11:** короткая матрица на трёх записях завершена
 и описана в [benchmark GigaAM и Whisper](benchmarks/2026-08-11-gigaam-model-comparison.md).
 Multilingual Large добавлена как явный качественный профиль; решение о default
 по-прежнему ждёт целевого Intel Core i5 и длинных негативных примеров.
 **Проблема:** текущий CPU-путь через OpenVINO Whisper medium нестабилен на
 длинных записях с тихими участками: модель генерирует правдоподобные повторы и
 несуществующий текст. На файле `2026-07-29 13-58-39.mp4` разговор заканчивается
@@ -28,7 +33,7 @@
   Python 3.10.
   Шаг 1 в части onnx-пути вынесен в отдельную работу —
   [спека «Onnx-каталог, Python 3.13 и границы версий»](specs/2026-08-11-onnx-model-catalog.md): движок
-   обновляется, три модели становятся поддерживаемыми, дефолт и OpenVINO не
+   обновляется, четыре модели становятся поддерживаемыми, дефолт и OpenVINO не
   трогаются намеренно, чтобы сохранить точку отсчёта для будущего сравнения.
 2. После интеграционных тестов провести сравнительный бенчмарк моделей. Не
   менять CPU-дефолт только по model card или результату на одном файле.
@@ -0,0 +1,217 @@
 # Сравнение GigaAM v3, E2E и Whisper на русских встречах
 **Дата:** 2026-08-11
 **Статус:** завершённый benchmark на трёх записях
 **Машина:** AMD Ryzen 7 8845H, Windows, CPU
 **Квантизация ONNX:** int8
 ## Цель
 Выбрать практический CPU-профиль на случай, когда OpenVINO недоступен или
 нестабилен. Сравниваются скорость, близость к внешнему транскрипту, сохранность
 смысла и терминов, пунктуация и пригодность результата для чтения.
 Внешний транскрипт используется как неточный ориентир, а не ground truth: он
 сам содержит ошибки, редактирует разговорную речь и добавляет диаризацию.
 Поэтому WER — сравнительный сигнал внутри одного файла, а не абсолютная оценка.
 ## Набор данных
 Три реальные записи общей длительностью 43:37:
 | Запись | Длительность | Характер речи | Ориентир |
 |---|---:|---|---|
 | Уточнение задачи BDMA | 14:51 | 2 говорящих, русский с IT-терминами | внешний транскрипт |
 | RSQM, внутренний дейлик | 14:04 | 5 говорящих, короткие реплики и задачи | внешний транскрипт с диаризацией |
 | DataCat, обсуждение архитектуры | 14:42 | 3 говорящих, высокая плотность английских терминов | внешний транскрипт с диаризацией |
 Сырые записи и внешние транскрипты не коммитятся в репозиторий.
 ### Манифест файлов
 На тестовом ноутбуке все файлы находятся в `C:\Users\ddmitry\Videos\OBS`.
 Размер и SHA-256 позволяют проверить, что в повторном прогоне используется та
 же версия записи или ориентира.
 | Набор | Роль | Имя файла | Размер, байт | SHA-256 |
 |---|---|---|---:|---|
 | BDMA | видео | `2026-07-29 T2 BDMA уточнение задачи от Ильи.mp4` | 19 216 865 | `51866D247FE3EDA134CDD884F707B1F1DB8855B492E8BD14D2B56B62476255ED` |
 | BDMA | ориентир | `2026-07-29 T2 BDMA уточнение задачи от Ильи.md` | 18 000 | `9098548743A86F868DFCDD906BCDE34ED1CE5A935602CBE399F4BCEDA8685B3C` |
 | RSQM | видео | `2026-02-18 RSQM дейлик внутренний.mp4` | 19 694 785 | `E72000BE43A5E04BFB670B0A16D85D8AEA51A02A285ED778FF5726767E97E9B0` |
 | RSQM | ориентир | `2026-02-18 RSQM дейлик внутренний_transcript.md` | 16 128 | `260DF5D6964817434092005936127C0DC2C22682B7CB20FF07D14C5D81B8D287` |
 | DataCat | видео | `2026-03-23 T2 DataCat - архитектура в контуре заказчика - первое обсуждение с Машей.mp4` | 22 584 108 | `ACFF960A68AAA6A305F7E7053BB0642B5DA265B68865ABD018E7C9A8980AC3F5` |
 | DataCat | ориентир | `2026-03-23 T2 DataCat - архитектура в контуре заказчика - первое обсуждение с Машей-transcript-whsper.md` | 24 874 | `7943944A0A046EAA3A1384CAC47D36B381FBEF2FCFDF18089411619F00725456` |
 ## Запись 1: уточнение задачи BDMA
 Файл `2026-07-29 T2 BDMA уточнение задачи от Ильи.mp4`, длительность 14:51,
 преимущественно русская речь с IT- и продуктовыми терминами.
 | Модель | Время | RTFx | WER к внешнему тексту | Пунктуация / 1000 слов |
 |---|---:|---:|---:|---:|
 | FasterWhisper medium CPU | 1133,9 с* | 0,79×* | **20,8%** | 313 |
 | OpenVINO medium | 190,6 с | 4,67× | **23,9%** | 187 |
 | GigaAM v3 | **72,6 с** | **12,27×** | 26,5% | 0 |
 | GigaAM Multilingual CTC | 84,8 с | 10,51× | 27,9% | 0 |
 | GigaAM Multilingual Large CTC | 192,0 с | 4,64× | 27,5% | 0 |
 | Parakeet v3 | 119,2 с | 7,47× | 28,2% | 338 |
 | GigaAM v3 E2E RNN-T | 76,1 с | 11,71× | 29,0% | 326 |
 | GigaAM v3 E2E CTC | **72,6 с** | **12,27×** | 31,8% | 338 |
 \* FasterWhisper включает около 263 секунд первой загрузки модели. Оценка
 самого распознавания — около 871 секунды, то есть 1,02× RTFx. Нужен отдельный
 cached-прогон для точного замера.
 Все модели дошли до конца записи без предупреждений о потере хвоста.
 ## Расширенный прогон
 | Запись | Модель | Время | RTFx | WER |
 |---|---|---:|---:|---:|
 | RSQM | GigaAM v3 | 75,1 с | 11,25× | **23,1%** |
 | RSQM | Multilingual CTC | 82,7 с | 10,21× | 24,4% |
 | RSQM | Multilingual Large CTC | 163,4 с | 5,17× | 23,8% |
 | RSQM | Parakeet v3 | 103,7 с | 8,14× | 29,1% |
 | RSQM | E2E RNN-T | 72,8 с | 11,60× | 25,4% |
 | RSQM | E2E CTC | **71,1 с** | **11,88×** | 28,0% |
 | RSQM | FasterWhisper medium | 686,1 с | 1,23× | **19,7%** |
 | DataCat | GigaAM v3 | **74,8 с** | **11,79×** | 27,5% |
 | DataCat | Multilingual CTC | 94,2 с | 9,36× | 27,9% |
 | DataCat | Multilingual Large CTC | 189,6 с | 4,65× | 26,7% |
 | DataCat | Parakeet v3 | 122,7 с | 7,19× | 27,9% |
 | DataCat | E2E RNN-T | 78,0 с | 11,31× | **26,4%** |
 | DataCat | E2E CTC | 76,0 с | 11,60× | 28,3% |
 | DataCat | FasterWhisper medium | 954,5 с | 0,92× | **21,9%** |
 Все модели обработали записи до конца. Разрыв между длительностью файла и
 последним сегментом составил 0,3–5,9 секунды, предупреждений о потере хвоста или
 повторах не было.
 ### Сводка по трём записям
 WER ниже посчитан как micro-average: сумма ошибок по трём файлам делится на
 суммарное число слов во внешних транскриптах.
 | Модель | Суммарное время | RTFx | WER | Пунктуация / 1000 слов |
 |---|---:|---:|---:|---:|
 | FasterWhisper medium CPU | 2511,6 с* | 1,04× | **20,9%** | 269 |
 | GigaAM v3 | 222,5 с | 11,76× | **26,0%** | 0 |
 | GigaAM Multilingual Large CTC | 545,0 с | 4,80× | **26,2%** | 0 |
 | GigaAM v3 E2E RNN-T | 226,9 с | 11,54× | 26,9% | 290 |
 | GigaAM Multilingual CTC | 261,7 с | 10,00× | 26,9% | 0 |
 | Parakeet v3 | 345,6 с | 7,57× | 28,3% | 301 |
 | GigaAM v3 E2E CTC | **219,7 с** | **11,91×** | 29,3% | 305 |
 \* Для BDMA использована оценка 871 секунд чистого inference: первый запуск
 FasterWhisper включал ещё около 263 секунд загрузки. RSQM и DataCat измерены на
 прогретом кеше — 686,1 и 954,5 секунды соответственно.
 RNN-T оказалась лишь на 2% медленнее обычного GigaAM по суммарному времени.
 Multilingual Small медленнее обычного GigaAM на 18%, а выигрыш E2E CTC
 относительно RNN-T составляет только 3% и не компенсирует ухудшение текста.
 Multilingual Large в 2,08 раза медленнее Small и в 2,45 раза медленнее обычного
 GigaAM, но всё ещё в 4,6 раза быстрее FasterWhisper.
 ### GigaAM Multilingual Large против Parakeet v3
 Large точнее Parakeet на каждой записи: 27,5% против 28,2% WER на BDMA,
 23,8% против 29,1% на RSQM и 26,7% против 27,9% на DataCat. В сумме Large
 получила 26,2% WER против 28,3%. Особенно заметна разница в пропусках:
 103 удаления у Large против 264 у Parakeet.
 Parakeet обработала набор за 345,6 секунды против 545,0 секунды у Large, то
 есть была в 1,58 раза быстрее, и сразу расставила пунктуацию. Однако ручное
 чтение выявило частые ложные переключения языка. В русских репликах RSQM
 появляются `Yeah`, `No`, `Just die`, `What foo`; в DataCat — `Don't`,
 `Skid of all`, `Talk no`, `Boy`. Это не единичная орфографическая ошибка, а
 искажение обычной русской речи. Large в тех же местах сохраняет русский смысл,
 хотя передаёт английские термины фонетически и не ставит пунктуацию.
 На этом массиве Large предпочтительнее по качеству и сохранности содержания.
 Parakeet интересна как более быстрый вариант с пунктуацией, но для русских
 встреч требует обязательной ручной проверки и не рекомендуется как основной
 профиль.
 ## Качественное чтение
 Независимый reviewer сначала прочитал полные тексты без таблицы метрик, затем
 сверил вывод с WER:
 - FasterWhisper лучше сохраняет логику разговора и техническую лексику:
  `Teradata`, `ClickHouse`, `ETL`, `legacy`, `MPP`, `SAP/BW/HANA`, `GUI`,
  `Apex`. Значимых пропусков целых смысловых фрагментов не обнаружено.
 - OpenVINO близок к FasterWhisper, но чаще склеивает слова и иногда локально
  меняет смысл: около 00:59 «расчёт маржинальности называется» превращается в
  «расчёт маржинальности не бывает».
 - Обычный GigaAM v3 сохраняет почти весь смысл, числа и структуру обсуждения,
  но фонетические ошибки и отсутствие пунктуации ухудшают чтение и поиск.
 - E2E RNN-T читается лучше обычного GigaAM и устойчивее E2E CTC, но агрессивно
  дробит живую речь. На DataCat она лучше обычного GigaAM по WER, однако на
  BDMA и RSQM хуже на 2,3–2,5 процентного пункта.
 - E2E CTC даёт больше обрывков, ложных границ и смешанных артефактов:
  `иnженter`, `мetднные`, `оrкl`, `линейдgами`.
 - Multilingual CTC не улучшила даже терминологически насыщенный DataCat и
  оказалась на 15–26% медленнее обычного GigaAM на новых записях.
 - Multilingual Large CTC улучшила Small на всех трёх записях и почти сравнялась
  с monolingual GigaAM по совокупному WER: 26,2% против 26,0%. При ручном
  чтении скрытой деградации не найдено, но пунктуации по-прежнему нет, а термины
  передаются преимущественно фонетически (`файнбиай`, `найфай`, `терадата`).
 - Parakeet v3 быстрее Large и выдаёт пунктуацию, но хуже по WER на всех трёх
  записях, чаще пропускает слова и вставляет ложные английские фразы в русскую
  речь. Для этого массива преимущество в читаемости не компенсирует искажения.
 Длинных автономных галлюцинаций не найдено. Есть локальные потенциально
 опасные искажения имён и терминов: `Иришка`, `Lutriness`, «расчёт
 маржинальности не бывает», «расчёты по губам», «понюхаю дело».
 На RSQM FasterWhisper сохраняет `edge-кейсы`, `Confluence` и `Backlog`; GigaAM
 передаёт их фонетически и нестабильно (`еджкейсы`, `ичкейсы`). На DataCat
 FasterWhisper лучше сохраняет `API`, `ETL`, `Open Platform`; обычный GigaAM
 хорошо удерживает сам смысл архитектурного обсуждения и большинство названий
 фонетически. RNN-T делает текст визуально аккуратнее, но название одного и того
 же продукта может плавать между `Fine BI`, `Fine Bia`, `FNB`, а `API` — между
 `API`, `AP`, `пи`.
 ## Рекомендация
 - Для чтения человеком без дополнительного постпроцессинга —
  `gigaam-v3-e2e-rnnt`: по трём записям всего на 2% медленнее обычного GigaAM,
  но уже содержит пунктуацию и нормализацию. Это выбор цена/качество, а не
  максимум дословной точности.
 - Для последующей машинной обработки, поиска или собственной расстановки
  пунктуации — `gigaam-v3`: лучший совокупный WER среди быстрых ONNX-вариантов
  и более стабильный сырой текст.
 - Для смешанной речи с приоритетом качества сырого текста —
  `gigaam-multilingual-large-ctc`. Она устойчиво лучше Small и почти равна
  monolingual GigaAM по WER, но требует 545 секунд против 262 секунд на том же
  наборе. Small остаётся вариантом с приоритетом скорости.
 - `parakeet-v3` в 1,58 раза быстрее Large и содержит пунктуацию, однако хуже
  по WER на всех трёх записях и нестабилен на русской речи. Использовать его
  стоит только там, где скорость и готовая пунктуация важнее сохранности текста.
 - `gigaam-v3-e2e-ctc` преимуществ на этом наборе не показала.
 - FasterWhisper medium остаётся выбором для важных записей с плотной
  терминологией, если допустима обработка около realtime. По трём файлам она в
  11 раз медленнее GigaAM ради снижения WER примерно на 5–6 процентных пунктов.
  На CUDA соотношение необходимо измерять отдельно.
 По итогам сравнения и обсуждения двух основных сценариев RNN-T выбрана моделью
 по умолчанию для явного `--device onnx`: небольшая потеря дословной точности
 принята ради читаемого результата с пунктуацией. Для LLM-пайплайнов и поиска,
 где важнее сырой текст, `gigaam-v3` остаётся явным профилем.
 ## Воспроизводимость
 Для каждого ONNX-профиля использовалась команда вида:
 ```powershell
 uv run transcribe <recording.mp4> --device onnx --model <model> --compute-type int8
 ```
 Для всех прогонов явно передавался язык `--language ru`.
 Время измерялось на прогретом кеше модели. WER считался после удаления
 метаданных и таймкодов, приведения к нижнему регистру, замены `ё` на `е` и
 удаления пунктуации.
@@ -14,9 +14,11 @@
 ## Что делаем
 Снимаем ограничение версии, поднимаем `onnx-asr` до 0.12 и делаем
-поддерживаемыми три модели:
+поддерживаемыми четыре модели:
- `gigaam-multilingual-ctc` — смешанная речь с англоязычными терминами;
+- `gigaam-multilingual-ctc` и `gigaam-multilingual-large-ctc` — смешанная речь
  с англоязычными терминами; large добавлена после сравнительного benchmark по
  явному решению пользователя;
 - `gigaam-v3-e2e-ctc` и `gigaam-v3-e2e-rnnt` — пунктуация и нормализация текста.
 Каталог моделей в бэкенде хранит, помимо имени, опубликованные для модели
@@ -81,11 +83,18 @@ CTranslate2, а onnx-путь ставится в CPU-варианте.
 Конкретные номера берутся из `uv.lock` при реализации.
 ## Последующее решение о модели по умолчанию
 После сравнительного прогона на трёх реальных записях и обсуждения двух
 основных потребителей транскрипта модель `gigaam-v3-e2e-rnnt` выбрана моделью
 по умолчанию для явного `--device onnx`. Она всего на 2% медленнее сырого
 `gigaam-v3`, но выдаёт пунктуацию для чтения человеком; `gigaam-v3` остаётся
 явным профилем с приоритетом дословной точности и последующей обработки LLM.
 Это решение не включает ONNX в auto-detect.
 ## Чего не делаем
- Не меняем модель по умолчанию и auto-detect: `gigaam-v3` остаётся дефолтом
+- Не меняем auto-detect: ONNX остаётся только явным выбором пользователя.
  `--device onnx`. Решение о CPU-дефолте требует замеров на целевом Intel Core
  i5, которого сейчас нет в доступе.
 - Не обновляем OpenVINO, OpenVINO GenAI и CTranslate2. Whisper medium на
  OpenVINO — то, с чем сравниваются новые модели; менять его движок
  одновременно значит потерять точку отсчёта. Верхние границы версий им при
@@ -142,9 +151,10 @@ Python и зависимости при неизменных моделях —
 - **скорость на Intel Core i5 не измерялась.** Замеры делаются на Ryzen 7
  8845H и записываются как наблюдение с указанием CPU — для решения о
  CPU-дефолте этого недостаточно;
- **качество моделей друг относительно друга не измерялось.** Ручная проверка
+- **качество моделей измерено предварительно на трёх 14–15-минутных записях.**
-  отвечает на вопрос «работает ли», а не «лучше ли»; сравнение — отдельная
+  Результат фиксирует WER, скорость и ручное чтение, но не заменяет приёмку на
-  работа, см. [backlog](../backlog.md).
+  целевом Intel Core i5 и длинных записях; см.
  [benchmark](../benchmarks/2026-08-11-gigaam-model-comparison.md).
 Пошаговый чеклист и практика прогона нужны только на время работ и живут в
 задаче трекера
@@ -153,8 +163,10 @@ Python и зависимости при неизменных моделях —
 ## Документация
-README: три модели в таблицу ONNX-моделей. Рекомендация остаётся прежней —
+README: поддерживаемые модели добавляются в таблицу ONNX-моделей. Для готового
-`gigaam-v3` для русского — пока нет данных, чтобы её менять; у новых моделей
+читаемого русского текста и как модель по умолчанию используется E2E RNN-T,
-стоит оговорка, что скорость на слабых CPU не измерялась. Требование Python
+для русского без пунктуации и LLM-пайплайнов остаётся явный `gigaam-v3`, для
 смешанной речи с приоритетом качества — multilingual large. Скорость на слабых
 CPU не измерялась. Требование Python
 правится в [`docs/PRD.md`](../PRD.md) — раздел «Требования» и таблица
 технологий; в README версии Python не упоминаются.
@@ -3,16 +3,16 @@ name = "local-transcriber"
 version = "0.1.0"
 description = "Local CLI tool for audio/video transcription using faster-whisper"
 license = "MIT"
-requires-python = ">=3.10"
+requires-python = ">=3.13"
 dependencies = [
-    "typer",
+    "typer>=0.24.1,<1",
-    "rich",
+    "rich>=14.3.3,<15",
-    "faster-whisper>=1.2.1",
+    "faster-whisper>=1.2.1,<2",
-    "socksio>=1.0.0",
+    "socksio>=1.0.0,<2",
-    "nvidia-cublas-cu12>=12.4; sys_platform == 'linux' and platform_machine == 'x86_64'",
+    "nvidia-cublas-cu12>=12.4,<13; sys_platform == 'linux' and platform_machine == 'x86_64'",
-    "openvino-genai>=2025.0; sys_platform != 'darwin' and (platform_machine == 'x86_64' or platform_machine == 'AMD64')",
+    "openvino-genai>=2026.0.0.0,<2026.1; sys_platform != 'darwin' and (platform_machine == 'x86_64' or platform_machine == 'AMD64')",
-    "tomli>=2.0; python_version < '3.11'",
+    "onnx-asr[cpu,hub]>=0.12,<0.13",
-    "onnx-asr[cpu,hub]>=0.11.0,<0.12.0",
+    "onnxruntime>=1.28,<2",
 ]
 [project.scripts]
@@ -32,7 +32,7 @@ def get_backend(device: str, *, compute_type_explicit: bool = True) -> Backend:
            raise ValueError(
                "onnx-asr бэкенд недоступен. Установите: pip install onnx-asr[cpu,hub]"
            ) from None
-        return OnnxAsrBackend()
+        return OnnxAsrBackend(compute_type_explicit=compute_type_explicit)
    # cuda, cpu и всё остальное → faster-whisper
    from .faster_whisper import FasterWhisperBackend
@@ -3,14 +3,50 @@
 from __future__ import annotations
 from collections.abc import Callable
 from dataclasses import dataclass
 from pathlib import Path
 from typing import Any
 from local_transcriber.types import Segment, TranscribeResult
@dataclass(frozen=True)
 class OnnxModelSpec:
    """Имя onnx-asr и опубликованные варианты квантизации модели."""
    model_id: str
    quantizations: frozenset[str | None]
 _INT8_AND_FLOAT32 = frozenset({"int8", None})
 MODEL_CATALOG: dict[str, OnnxModelSpec] = {
    "gigaam-v3": OnnxModelSpec("gigaam-v3-ctc", _INT8_AND_FLOAT32),
    "parakeet-v3": OnnxModelSpec(
        "nemo-parakeet-tdt-0.6b-v3",
        _INT8_AND_FLOAT32,
    ),
    "gigaam-multilingual-ctc": OnnxModelSpec(
        "gigaam-multilingual-ctc",
        _INT8_AND_FLOAT32,
    ),
    "gigaam-multilingual-large-ctc": OnnxModelSpec(
        "gigaam-multilingual-large-ctc",
        _INT8_AND_FLOAT32,
    ),
    "gigaam-v3-e2e-ctc": OnnxModelSpec(
        "gigaam-v3-e2e-ctc",
        _INT8_AND_FLOAT32,
    ),
    "gigaam-v3-e2e-rnnt": OnnxModelSpec(
        "gigaam-v3-e2e-rnnt",
        _INT8_AND_FLOAT32,
    ),
 }
 # Оставлено как совместимое представление публичного каталога алиасов.
 MODEL_ALIASES: dict[str, str] = {
-    "gigaam-v3": "gigaam-v3-ctc",
+    alias: spec.model_id for alias, spec in MODEL_CATALOG.items()
    "parakeet-v3": "nemo-parakeet-tdt-0.6b-v3",
 }
 SUPPORTED_ALIASES = ", ".join(MODEL_ALIASES)
@@ -45,7 +81,8 @@ def _normalize_quantization(compute_type: str) -> str | None:
 class OnnxAsrBackend:
    """Бэкенд транскрипции через onnx-asr (ONNX Runtime)."""
-    def __init__(self):
+    def __init__(self, compute_type_explicit: bool = True):
        self._compute_type_explicit = compute_type_explicit
        self.actual_compute_type: str | None = None
        self._resolved_model_id: str | None = None
        self._vad: Any = None
@@ -61,7 +98,26 @@ class OnnxAsrBackend:
        onnx-asr downloads models automatically via load_model(),
        so this just validates the alias and returns the identifier string.
        """
-        self.actual_compute_type = compute_type
+        spec = MODEL_CATALOG.get(model_name)
        quantization = _normalize_quantization(compute_type)
        if spec is not None and quantization not in spec.quantizations:
            if self._compute_type_explicit:
                available = _format_compute_types(spec.quantizations)
                raise ValueError(
                    f"Модель '{model_name}' недоступна с compute_type='{compute_type}' "
                    f"для onnx-asr. Доступные варианты: {available}"
                )
            resolved_compute_type = _preferred_compute_type(spec.quantizations)
            _notify(
                on_status,
                f"Модель {model_name} недоступна с compute_type={compute_type}; "
                f"использую {resolved_compute_type}.",
            )
        else:
            resolved_compute_type = _compute_type_for_quantization(quantization)
        self.actual_compute_type = resolved_compute_type
        self._resolved_model_id = self._resolve_model(model_name)
        return self._resolved_model_id
@@ -79,7 +135,8 @@ class OnnxAsrBackend:
        """
        import onnx_asr
-        quantization = _normalize_quantization(compute_type)
+        actual_compute_type = self.actual_compute_type or compute_type
        quantization = _normalize_quantization(actual_compute_type)
        model = onnx_asr.load_model(
            model=model_path,
@@ -113,10 +170,16 @@ class OnnxAsrBackend:
        segments: list[Segment] = []
        detected_language = language or "unknown"
-        for vad_seg in model.recognize(audio_array, sample_rate=16000, language=language):
+        for vad_seg in model.recognize(
            audio_array, sample_rate=16000, language=language
        ):
            start = max(0.0, vad_seg.start)
            end = max(0.0, vad_seg.end)
            if end <= start:
                continue
            seg = Segment(
-                start=max(0.0, vad_seg.start),
+                start=start,
-                end=max(0.0, vad_seg.end),
+                end=end,
                text=vad_seg.text,
            )
            if on_segment is not None:
@@ -149,6 +212,22 @@ class OnnxAsrBackend:
        )
 def _format_compute_types(quantizations: frozenset[str | None]) -> str:
    values = [_compute_type_for_quantization(value) for value in quantizations]
    return ", ".join(sorted(values))
 def _compute_type_for_quantization(quantization: str | None) -> str:
    return "float32" if quantization is None else quantization
 def _preferred_compute_type(quantizations: frozenset[str | None]) -> str:
    for quantization in ("int8", None, "fp16"):
        if quantization in quantizations:
            return _compute_type_for_quantization(quantization)
    raise ValueError("Для ONNX-модели не указаны доступные квантизации")
 def _notify(on_status: Callable[[str], None] | None, message: str) -> None:
    if on_status is not None:
        on_status(message)
@@ -1,14 +1,9 @@
 """Загрузка конфигурации из ``.transcriber.toml`` и каскад приоритетов."""
-import sys
+import tomllib
 import warnings
 from pathlib import Path
 if sys.version_info >= (3, 11):
    import tomllib
 else:
    import tomli as tomllib
 HARDCODED_DEFAULTS: dict[str, str] = {
    "model": "medium",
    "language": "ru",
@@ -22,12 +17,20 @@ DEVICE_DEFAULTS: dict[str, dict[str, str]] = {
    "openvino": {"model": "medium", "compute_type": "int8"},
    "openvino-gpu": {"model": "medium", "compute_type": "int8"},
    "openvino-cpu": {"model": "medium", "compute_type": "int8"},
-    "onnx": {"model": "gigaam-v3", "compute_type": "int8"},
+    "onnx": {"model": "gigaam-v3-e2e-rnnt", "compute_type": "int8"},
 }
 # Одно место правды для допустимых ключей конфига
 _VALID_KEYS = set(HARDCODED_DEFAULTS)
-_VALID_DEVICES = {"auto", "cpu", "cuda", "openvino", "openvino-gpu", "openvino-cpu", "onnx"}
+_VALID_DEVICES = {
    "auto",
    "cpu",
    "cuda",
    "openvino",
    "openvino-gpu",
    "openvino-cpu",
    "onnx",
 }
 def find_config_file() -> Path | None:
@@ -113,6 +113,21 @@ def test_apply_device_defaults_cpu():
    assert result["compute_type"] == "float32"
 def test_apply_device_defaults_onnx_uses_readable_model():
    defaults = {
        "model": "medium",
        "language": "ru",
        "device": "auto",
        "compute_type": "float32",
    }
    cli = {"model": None, "language": None, "device": None, "compute_type": None}
    result = apply_device_defaults(defaults, "onnx", cli, {})
    assert result["model"] == "gigaam-v3-e2e-rnnt"
    assert result["compute_type"] == "int8"
 def test_apply_device_defaults_cli_overrides():
    defaults = {"model": "large-v3", "language": "ru", "device": "auto", "compute_type": "int8"}
    cli = {"model": "large-v3", "language": None, "device": None, "compute_type": "int8"}
@@ -1,9 +1,8 @@
 """Tests for onnx-asr backend."""
 import pytest
 from pathlib import Path
-from local_transcriber.backends.onnx_asr import OnnxAsrBackend, MODEL_ALIASES
+from local_transcriber.backends.onnx_asr import OnnxAsrBackend
 from local_transcriber.types import Segment, TranscribeResult
@@ -24,7 +23,7 @@ class TestEnsureModelAvailable:
    def test_returns_model_id_for_parakeet(self):
        backend = OnnxAsrBackend()
-        result = backend.ensure_model_available("parakeet-v3", "fp16")
+        result = backend.ensure_model_available("parakeet-v3", "int8")
        assert result == "nemo-parakeet-tdt-0.6b-v3"
    def test_stores_compute_type(self):
@@ -33,6 +32,52 @@ class TestEnsureModelAvailable:
        assert backend._resolved_model_id == "gigaam-v3-ctc"
        assert backend.actual_compute_type == "float32"
    @pytest.mark.parametrize(
        "model_name",
        [
            "gigaam-multilingual-ctc",
            "gigaam-multilingual-large-ctc",
            "gigaam-v3-e2e-ctc",
            "gigaam-v3-e2e-rnnt",
        ],
    )
    def test_explicit_unavailable_compute_type_is_rejected(self, model_name):
        backend = OnnxAsrBackend(compute_type_explicit=True)
        with pytest.raises(ValueError, match="недоступна с compute_type='fp16'"):
            backend.ensure_model_available(model_name, "fp16")
    def test_implicit_unavailable_compute_type_falls_back_and_reports(
        self, monkeypatch
    ):
        quantizations = []
        statuses = []
        class FakeAsrAdapter:
            def with_vad(self, vad):
                return self
        def fake_load_model(*, model, quantization):
            quantizations.append(quantization)
            return FakeAsrAdapter()
        monkeypatch.setattr("onnx_asr.load_model", fake_load_model)
        monkeypatch.setattr("onnx_asr.load_vad", lambda model: None)
        backend = OnnxAsrBackend(compute_type_explicit=False)
        model_id = backend.ensure_model_available(
            "gigaam-v3-e2e-ctc",
            "fp16",
            on_status=statuses.append,
        )
        backend.create_model(model_id, "onnx", "fp16")
        assert backend.actual_compute_type == "int8"
        assert quantizations == ["int8"]
        assert statuses == [
            "Модель gigaam-v3-e2e-ctc недоступна с compute_type=fp16; использую int8."
        ]
 class TestCreateModel:
    def test_calls_load_model_with_correct_args(self, monkeypatch):
@@ -80,7 +125,7 @@ class TestCreateModel:
        monkeypatch.setattr("onnx_asr.load_vad", fake_load_vad)
        backend = OnnxAsrBackend()
-        model = backend.create_model("gigaam-v3-ctc", "onnx", "int8")
+        backend.create_model("gigaam-v3-ctc", "onnx", "int8")
        assert vad_calls == ["silero"]
@@ -228,7 +273,7 @@ class TestTranscribe:
        monkeypatch.setattr("faster_whisper.decode_audio", fake_decode_audio)
        backend = OnnxAsrBackend()
-        result = backend.transcribe(
+        backend.transcribe(
            FakeModel(), wav_file, language=None,
            on_segment=lambda s: segments_captured.append(s),
        )
@@ -284,6 +329,27 @@ class TestTranscribe:
        assert result.language == "unknown"
        assert result.duration == 1.0
    def test_transcribe_skips_zero_length_vad_segments(self, monkeypatch, tmp_path):
        wav_file = tmp_path / "test.wav"
        wav_file.write_bytes(b"fake audio")
        def fake_decode_audio(path, sampling_rate=16000):
            import numpy as np
            return np.array([0.0] * 16000, dtype=np.float32)
        class FakeModel:
            def recognize(self, waveform, sample_rate, language=None):
                yield FakeVadSegment(0.5, 0.5, "нулевой")
                yield FakeVadSegment(0.75, 0.5, "обратный")
                yield FakeVadSegment(0.5, 1.0, "валидный")
        monkeypatch.setattr("faster_whisper.decode_audio", fake_decode_audio)
        result = OnnxAsrBackend().transcribe(FakeModel(), wav_file, language=None)
        assert result.segments == [Segment(start=0.5, end=1.0, text="валидный")]
 class TestBackendRegistration:
    def test_get_backend_returns_onnx_backend(self):
@@ -291,6 +357,14 @@ class TestBackendRegistration:
        backend = get_backend("onnx")
        assert isinstance(backend, OnnxAsrBackend)
    def test_get_backend_preserves_implicit_compute_type(self):
        from local_transcriber.backends import get_backend
        backend = get_backend("onnx", compute_type_explicit=False)
        backend.ensure_model_available("gigaam-v3-e2e-rnnt", "fp16")
        assert backend.actual_compute_type == "int8"
 class TestModelAliases:
    def test_gigaam_v3_resolves(self):
@@ -1,10 +1,6 @@
 version = 1
 revision = 3
-requires-python = ">=3.10"
+requires-python = ">=3.13"
 resolution-markers = [
    "python_full_version >= '3.11'",
    "python_full_version < '3.11'",
 ]
 [[package]]
 name = "annotated-doc"
@@ -20,9 +16,7 @@ name = "anyio"
 version = "4.12.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
    { name = "idna" },
    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
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+    { name = "numpy" },
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