e87f6cd215
- таблица jobs + очередь с воркером: POST /jobs сразу отвечает, элементы
плейлиста обрабатываются последовательно, есть отмена и живой прогресс
(скачивание %, минуты распознанного Vosk-аудио)
- expand_url: плейлист раскрывается в список видео
- keep_video=false теперь качает только аудио (для 4-5ч лекций)
- настройки (LLM-ключ, модель, длина выжимки, хранение видео, путь Vosk)
хранятся в Postgres: переживают перезапуск и перезагрузку страницы
- длина выжимки прокидывается в LLM-промпт ({n} предложений)
- UI: вкладки «Главная»/«Настройки», карточка задач с автообновлением
каждые 3 сек, прогресс-бар, отмена, библиотека обновляется по мере
готовности элементов
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
134 lines
3.8 KiB
Python
134 lines
3.8 KiB
Python
"""Vosk-based fallback transcription: stream PCM from ffmpeg into a Vosk recognizer."""
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from __future__ import annotations
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import json
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import logging
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import os
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import shutil
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import subprocess
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import sys
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import threading
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from pathlib import Path
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try:
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from vosk import KaldiRecognizer, Model
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except Exception:
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Model = None
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KaldiRecognizer = None
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logger = logging.getLogger("transcribe")
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class VoskModelManager:
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"""Loads a Vosk model. Captures the model's stderr to Python logs while loading."""
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def __init__(self, model_path: Path):
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if Model is None or KaldiRecognizer is None:
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raise RuntimeError("vosk is not installed in runtime")
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self.model = self._load_with_stderr_capture(model_path)
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@staticmethod
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def _load_with_stderr_capture(path: Path):
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log = logging.getLogger("vosk.loader")
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r_fd, w_fd = os.pipe()
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saved_stderr = os.dup(sys.stderr.fileno())
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os.dup2(w_fd, sys.stderr.fileno())
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os.close(w_fd)
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def reader(fd: int) -> None:
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with os.fdopen(fd, "rb") as fh:
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for raw in iter(fh.readline, b""):
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log.info(raw.decode(errors="ignore").rstrip())
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t = threading.Thread(target=reader, args=(r_fd,), daemon=True)
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t.start()
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try:
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return Model(str(path))
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finally:
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try:
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os.dup2(saved_stderr, sys.stderr.fileno())
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finally:
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os.close(saved_stderr)
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t.join(timeout=2)
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def transcribe_via_ffmpeg(
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media_path: Path,
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model_path: Path,
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sample_rate: int = 16000,
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on_progress=None,
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) -> str:
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"""Decode the source media to PCM s16le mono via ffmpeg and feed it to Vosk in chunks.
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on_progress: callback(minutes: int) — вызывается каждые ~5 минут распознанного аудио.
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"""
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if Model is None or KaldiRecognizer is None:
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raise RuntimeError("vosk is not installed in runtime")
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if shutil.which("ffmpeg") is None:
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raise RuntimeError("ffmpeg is not available in PATH")
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if not media_path.exists():
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raise FileNotFoundError(media_path)
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mgr = VoskModelManager(model_path)
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recognizer = KaldiRecognizer(mgr.model, sample_rate)
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try:
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recognizer.SetWords(True)
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except Exception:
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pass
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cmd = [
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"ffmpeg",
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"-hide_banner",
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"-loglevel",
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"error",
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"-i",
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str(media_path),
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"-f",
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"s16le",
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"-acodec",
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"pcm_s16le",
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"-ac",
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"1",
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"-ar",
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str(sample_rate),
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"-vn",
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"-",
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]
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logger.info("Spawning ffmpeg streaming decode for %s", media_path.name)
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proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
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if proc.stdout is None:
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proc.kill()
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raise RuntimeError("ffmpeg did not provide stdout")
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try:
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bytes_read = 0
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last_logged_mb = 0
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while True:
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chunk = proc.stdout.read(4000)
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if not chunk:
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break
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recognizer.AcceptWaveform(chunk)
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bytes_read += len(chunk)
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# PCM s16le mono: sample_rate * 2 байта в секунду
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minutes = bytes_read // (sample_rate * 2 * 60)
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if minutes >= last_logged_mb + 5:
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last_logged_mb = minutes
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logger.info("Transcribed %d minutes of audio so far", minutes)
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if on_progress is not None:
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try:
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on_progress(minutes)
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except Exception:
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pass
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result = json.loads(recognizer.FinalResult())
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text = result.get("text", "")
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logger.info("Transcription finished, length=%d", len(text))
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return text
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finally:
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try:
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proc.kill()
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except Exception:
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pass
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