colgora
ba1813c583
Remplace la voicebank générée par Kokoro (timbre anglais sur français phonémisé -> accent que Qwen3 clonait) par 41 vraies voix FR issues de CML-TTS (livres audio studio) : 1 narrateur dédié, 18F/14M nommées, 4F/4M anonymes réservées. - scripts/import_voices.py : import multi-shards parquet, 1 clip/locuteur (le plus propre via levenshtein), genre estimé par F0 (YIN, anti-octave), filtre débit de parole (ref_text aligné sur l'audio). - VoiceEntry.anonymous + assign_voices : les figurants « anonyme (...) » tirent dans un pool réservé, jamais mélangé avec les voix nommées ; narrateur dédié (fr_narrator remplace fr_f_siwis). - dedup._anon_attrs : genre/âge déduits du nom anonyme (bon genre de voix). - tts/qwen3.py : garde-fou anti-dérive (rejette/réessaie les sorties en boucle ou coupées en estimant la durée plausible du chunk). Limite connue : Qwen3 ne sait pas synthétiser les fragments d'1-2 mots (incises, titres) -> trous ; à traiter (repli Kokoro ou fusion des incises). Inclut aussi du travail en cours antérieur (refacto backend LLM pluggable mlx/lmstudio, benchmark, ajustements frontend/API). Claude-Session: https://claude.ai/code/session_01XSVvcy1mfb4k1xDgib9vVU
365 lines
15 KiB
Python
365 lines
15 KiB
Python
"""Orchestrateur : execute les etapes du pipeline en tache de fond, piste l'etat
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et diffuse l'etat complet a l'UI a chaque changement.
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- Un seul worker thread execute les jobs en serie (un Mac = une charge MLX a la
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fois). Les jobs sont enfiles et rendent la main immediatement a l'API.
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- L'etat (ProjectState) est persiste dans data/<slug>/state.json -> reprenable.
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- La diffusion passe par un `broadcaster` injecte par la couche API (pour rester
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independant de FastAPI). Il recoit (slug, dict_etat).
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"""
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from __future__ import annotations
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import queue
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import threading
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import traceback
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from pathlib import Path
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from typing import Callable, Optional
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from ..config import book_data_dir, book_output_dir
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from ..epub.parser import load_book, load_chapter_text
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from ..models import ChapterRenderState, ProjectState, StageStatus
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from ..store import artifacts
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Broadcaster = Callable[[str, dict], None]
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def state_path(slug: str) -> Path:
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return book_data_dir(slug) / "state.json"
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def load_state(slug: str) -> ProjectState:
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path = state_path(slug)
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if path.exists():
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state = ProjectState.model_validate_json(path.read_text(encoding="utf-8"))
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else:
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book = load_book(slug)
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state = ProjectState(slug=slug, title=book.title,
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stages={"parse": StageStatus.DONE})
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return _reconcile(slug, state)
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def _reconcile(slug: str, state: ProjectState) -> ProjectState:
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"""Aligne l'etat sur les artefacts presents sur disque (reprise robuste).
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Permet a l'UI de refleter ce qui a deja ete fait, meme via la CLI ou apres
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un redemarrage, sans rejouer les etapes.
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"""
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book = load_book(slug)
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state.stages.setdefault("parse", StageStatus.DONE)
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# Analyse : chapitres possedant un artefact d'analyse.
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analyzed = [c.index for c in book.render_chapters
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if artifacts.analysis_path(slug, c.index).exists()]
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if analyzed:
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for idx in analyzed:
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if idx not in state.analyzed_chapters:
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state.analyzed_chapters.append(idx)
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if state.stage("analyze") == StageStatus.PENDING:
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state.stages["analyze"] = (
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StageStatus.DONE if len(analyzed) == len(book.render_chapters)
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else StageStatus.RUNNING)
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# Casting : au moins une voix attribuee.
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cast = artifacts.load_cast(slug)
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if cast.narrator_voice_id or any(c.voice_id for c in cast.characters):
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state.stages.setdefault("cast", StageStatus.DONE)
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# Prononciation : au moins une entree.
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if artifacts.load_pronunciation(slug).entries:
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state.stages.setdefault("pronounce", StageStatus.DONE)
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# Rendu : mp3 presents en sortie.
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out_dir = book_output_dir(book.title)
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for ch in book.render_chapters:
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existing = state.render.get(ch.index)
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if existing and existing.mp3:
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continue
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if ch.output_name and (out_dir / ch.output_name).exists():
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state.render[ch.index] = ChapterRenderState(
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index=ch.index, status=StageStatus.DONE, progress=1.0,
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mp3=ch.output_name)
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return state
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class Orchestrator:
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def __init__(self) -> None:
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self._q: "queue.Queue[tuple[str, Callable[[], None]]]" = queue.Queue()
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self._worker: Optional[threading.Thread] = None
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self._broadcaster: Optional[Broadcaster] = None
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self._lock = threading.Lock()
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self.busy_slug: Optional[str] = None
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# --- infra ---------------------------------------------------------------
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def set_broadcaster(self, fn: Broadcaster) -> None:
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self._broadcaster = fn
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def _ensure_worker(self) -> None:
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if self._worker is None or not self._worker.is_alive():
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self._worker = threading.Thread(target=self._loop, daemon=True)
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self._worker.start()
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def _loop(self) -> None:
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while True:
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slug, job = self._q.get()
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self.busy_slug = slug
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try:
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job()
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except Exception: # noqa: BLE001
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traceback.print_exc()
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finally:
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self.busy_slug = None
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self._q.task_done()
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def _save_and_emit(self, state: ProjectState) -> None:
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path = state_path(state.slug)
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path.parent.mkdir(parents=True, exist_ok=True)
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path.write_text(state.model_dump_json(indent=2), encoding="utf-8")
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if self._broadcaster:
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self._broadcaster(state.slug, state.model_dump(mode="json"))
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def enqueue(self, slug: str, job: Callable[[], None]) -> None:
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self._ensure_worker()
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self._q.put((slug, job))
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# --- etapes --------------------------------------------------------------
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def run_analyze(self, slug: str, chapter_indexes: Optional[list[int]] = None) -> None:
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def job() -> None:
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from ..analysis.llm.client import LLM
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from ..analysis.segmenter import analyze_chapter
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from ..models import Cast
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from ..settings import get_settings
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state = load_state(slug)
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book = load_book(slug)
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targets = [c for c in book.render_chapters
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if chapter_indexes is None or c.index in chapter_indexes]
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state.stages["analyze"] = StageStatus.RUNNING
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state.active_stage = "analyze"
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self._save_and_emit(state)
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gemma = LLM()
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dedup_gemma = gemma if get_settings().dedup_use_gemma else None
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cast = artifacts.load_cast(slug)
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chars = list(cast.characters)
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total = len(targets)
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for i, ch in enumerate(targets):
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state.active_detail = f"Analyse {ch.title}"
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state.active_progress = i / max(total, 1)
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self._save_and_emit(state)
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ct = load_chapter_text(slug, ch)
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try:
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# La dedup est faite dans analyze_chapter : `chars` recoit le
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# cast cumule reconcilie.
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analysis, chars = analyze_chapter(
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ch, ct, gemma, book_chars=chars, dedup_gemma=dedup_gemma)
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except Exception: # noqa: BLE001 — chapitre ignore, on continue
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traceback.print_exc()
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continue
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artifacts.save_analysis(slug, analysis)
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if ch.index not in state.analyzed_chapters:
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state.analyzed_chapters.append(ch.index)
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self._save_and_emit(state)
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artifacts.save_cast(slug, Cast(
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narrator_voice_id=cast.narrator_voice_id, characters=chars))
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state.stages["analyze"] = StageStatus.DONE
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self._finish(state)
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self.enqueue(slug, job)
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def run_cast(self, slug: str) -> None:
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def job() -> None:
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from ..casting.assign import assign_voices
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from ..casting.voicebank import build_voicebank, load_voicebank
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state = load_state(slug)
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state.stages["cast"] = StageStatus.RUNNING
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state.active_stage = "cast"
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state.active_detail = "Preparation de la voicebank"
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self._save_and_emit(state)
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vb = load_voicebank()
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if not vb.entries or not any(e.ref_audio for e in vb.entries):
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vb = build_voicebank()
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cast = artifacts.load_cast(slug)
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cast = assign_voices(cast.characters, vb,
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narrator_voice_id=cast.narrator_voice_id)
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artifacts.save_cast(slug, cast)
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state.stages["cast"] = StageStatus.DONE
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self._finish(state)
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self.enqueue(slug, job)
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def run_cast_analyze(self, slug: str, chapter_indexes: Optional[list[int]] = None) -> None:
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"""(Re)extrait les personnages d'un/des chapitre(s) et les reconcilie.
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Plus leger que `run_analyze` : ne re-segmente pas (les artefacts d'analyse
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existants restent intacts). Sert le casting "a l'echelle d'un chapitre"
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tout en maintenant la coherence du livre (deduplication).
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"""
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def job() -> None:
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from ..analysis.llm.client import LLM
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from ..analysis.segmenter import extract_characters
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from ..casting.dedup import reconcile_characters
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from ..models import Cast
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from ..settings import get_settings
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state = load_state(slug)
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book = load_book(slug)
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targets = [c for c in book.render_chapters
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if chapter_indexes is None or c.index in chapter_indexes]
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state.active_stage = "cast"
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self._save_and_emit(state)
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gemma = LLM()
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dedup_gemma = gemma if get_settings().dedup_use_gemma else None
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cast = artifacts.load_cast(slug)
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chars = list(cast.characters)
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total = len(targets)
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for i, ch in enumerate(targets):
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state.active_detail = f"Casting — {ch.title}"
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state.active_progress = i / max(total, 1)
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self._save_and_emit(state)
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ct = load_chapter_text(slug, ch)
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try:
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found = extract_characters("\n".join(ct.paragraphs), gemma)
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speakers: list[str] = []
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if artifacts.analysis_path(slug, ch.index).exists():
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analysis = artifacts.load_analysis(slug, ch.index)
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speakers = [s.speaker for s in analysis.segments]
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chars, _ = reconcile_characters(
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chars, found, dedup_gemma, speaker_names=speakers)
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except Exception: # noqa: BLE001 — chapitre ignore, on continue
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traceback.print_exc()
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continue
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artifacts.save_cast(slug, Cast(
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narrator_voice_id=cast.narrator_voice_id, characters=chars))
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self._save_and_emit(state)
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self._finish(state)
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self.enqueue(slug, job)
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def run_dedup_cast(self, slug: str) -> None:
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"""Replie les doublons d'un casting deja constitue (Holden/James Holden...)."""
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def job() -> None:
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from ..analysis.llm.client import LLM
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from ..casting.dedup import dedup_cast
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from ..models import Cast
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from ..settings import get_settings
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state = load_state(slug)
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state.active_stage = "cast"
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state.active_detail = "Deduplication du casting"
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self._save_and_emit(state)
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cast = artifacts.load_cast(slug)
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gemma = LLM() if get_settings().dedup_use_gemma else None
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chars = dedup_cast(cast.characters, gemma)
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artifacts.save_cast(slug, Cast(
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narrator_voice_id=cast.narrator_voice_id, characters=chars))
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self._finish(state)
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self.enqueue(slug, job)
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def run_pronounce(self, slug: str) -> None:
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def job() -> None:
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from ..analysis.llm.client import LLM
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from ..analysis.pronunciation import (
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merge_pronunciations,
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propose_pronunciations,
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)
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state = load_state(slug)
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book = load_book(slug)
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state.stages["pronounce"] = StageStatus.RUNNING
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state.active_stage = "pronounce"
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self._save_and_emit(state)
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gemma = LLM()
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pron = artifacts.load_pronunciation(slug)
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targets = book.render_chapters[:3] # echantillon de chapitres
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for i, ch in enumerate(targets):
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state.active_detail = f"Mots a risque — {ch.title}"
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state.active_progress = i / max(len(targets), 1)
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self._save_and_emit(state)
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ct = load_chapter_text(slug, ch)
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pron = merge_pronunciations(
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pron, propose_pronunciations("\n".join(ct.paragraphs), gemma))
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artifacts.save_pronunciation(slug, pron)
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state.stages["pronounce"] = StageStatus.DONE
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self._finish(state)
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self.enqueue(slug, job)
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def run_render(self, slug: str, chapter_indexes: list[int],
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backend: Optional[str] = None, mono: bool = False) -> None:
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from ..settings import get_settings
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backend = backend or get_settings().default_backend
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def job() -> None:
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from ..casting.voicebank import load_voicebank, voice_spec_for
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from ..pipeline.render import (
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build_units_mono,
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build_units_multi,
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make_voice_resolver,
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render_chapter_to_mp3,
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)
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from ..tts.factory import get_backend
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state = load_state(slug)
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book = load_book(slug)
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state.stages["render"] = StageStatus.RUNNING
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state.active_stage = "render"
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self._save_and_emit(state)
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tts = get_backend(backend)
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pron = artifacts.load_pronunciation(slug)
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cast = artifacts.load_cast(slug)
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vb = load_voicebank()
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render_list = [c for c in book.render_chapters if c.index in chapter_indexes]
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for ch in render_list:
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rs = state.render.get(ch.index) or ChapterRenderState(index=ch.index)
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rs.status = StageStatus.RUNNING
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rs.progress = 0.0
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rs.backend = backend
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state.render[ch.index] = rs
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state.active_detail = f"Synthese — {ch.title}"
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self._save_and_emit(state)
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try:
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ct = load_chapter_text(slug, ch)
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if mono or ch.index not in state.analyzed_chapters:
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units = build_units_mono(ct, tts.default_voice())
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else:
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analysis = artifacts.load_analysis(slug, ch.index)
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narr = vb.by_id(cast.narrator_voice_id) if cast.narrator_voice_id else None
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default_voice = (voice_spec_for(narr, backend)
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if narr else tts.default_voice())
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resolver = make_voice_resolver(cast, vb, backend)
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units = build_units_multi(analysis, resolver, default_voice)
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def _p(done: int, total: int, _rs=rs, _state=state) -> None:
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_rs.progress = done / max(total, 1)
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_state.active_progress = _rs.progress
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self._save_and_emit(_state)
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track = book.render_chapters.index(ch) + 1
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mp3 = render_chapter_to_mp3(book, ch, units, tts, pron=pron,
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track=track, progress=_p)
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rs.status = StageStatus.DONE
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rs.progress = 1.0
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rs.mp3 = mp3.name
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except Exception as exc: # noqa: BLE001
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rs.status = StageStatus.ERROR
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rs.error = str(exc)
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self._save_and_emit(state)
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state.stages["render"] = StageStatus.DONE
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self._finish(state)
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self.enqueue(slug, job)
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def _finish(self, state: ProjectState) -> None:
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state.active_stage = None
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state.active_detail = None
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state.active_progress = 0.0
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self._save_and_emit(state)
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# Singleton partage par l'API.
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orchestrator = Orchestrator()
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