a172610b0e
- Add cross-project voice/name library (library.py + webUI library tabs) - chapter_builder: embedding-mode generation + auto-load TTS base model in preflight - qwen_tts: vmatrix capture, embedding/voice-prompt refinements - registry: vmatrix_path column + migration for approved voices - webUI: speakers list, chapter audio detail, proper names updates - docs: update voice generation and tts_server README
726 lines
27 KiB
Python
726 lines
27 KiB
Python
"""Self-healing chapter audio builder pipeline.
|
|
|
|
When triggered, runs a 6-phase pipeline:
|
|
1. Preflight — verify TTS server, model, voice refs, DB
|
|
2. Validate — identify missing/stale utterances
|
|
3. Generate — produce audio with TTS health monitoring + retry
|
|
4. Stitch — concatenate utterance WAVs into chapter WAV
|
|
5. Export — convert chapter WAV to MP3 via ffmpeg
|
|
6. Verify — run quality checks, report issues
|
|
|
|
Progress is written to ``ch{id}_build_status.json`` so the web UI can
|
|
poll ``/api/chapter/{id}/build-status`` for real-time updates.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
import json
|
|
import logging
|
|
import os
|
|
import sqlite3
|
|
import subprocess
|
|
import threading
|
|
import time
|
|
from dataclasses import dataclass, field
|
|
from datetime import UTC, datetime
|
|
from typing import TYPE_CHECKING, Any
|
|
|
|
if TYPE_CHECKING:
|
|
from pathlib import Path
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Status model
|
|
# ---------------------------------------------------------------------------
|
|
|
|
PHASES = (
|
|
"preflight",
|
|
"validating",
|
|
"generating",
|
|
"stitching",
|
|
"exporting",
|
|
"verifying",
|
|
"complete",
|
|
"error",
|
|
"warning",
|
|
)
|
|
|
|
|
|
@dataclass
|
|
class BuildResult:
|
|
phase: str = "idle"
|
|
detail: str = ""
|
|
progress: int = 0
|
|
total_utterances: int = 0
|
|
generated: int = 0
|
|
skipped: int = 0
|
|
failed: list[int] = field(default_factory=list)
|
|
errors: list[str] = field(default_factory=list)
|
|
chapter_wav: str | None = None
|
|
chapter_mp3: str | None = None
|
|
started_at: str = ""
|
|
elapsed: str = ""
|
|
quality_issues: list[dict] = field(default_factory=list)
|
|
|
|
def to_dict(self) -> dict:
|
|
return {
|
|
"phase": self.phase,
|
|
"detail": self.detail,
|
|
"progress": self.progress,
|
|
"total_utterances": self.total_utterances,
|
|
"generated": self.generated,
|
|
"skipped": self.skipped,
|
|
"failed": self.failed,
|
|
"errors": self.errors,
|
|
"chapter_wav": self.chapter_wav,
|
|
"chapter_mp3": self.chapter_mp3,
|
|
"started_at": self.started_at,
|
|
"elapsed": self.elapsed,
|
|
"quality_issues": self.quality_issues,
|
|
}
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Helpers
|
|
# ---------------------------------------------------------------------------
|
|
|
|
def _load_speakers(data_dir: Path) -> dict[str, dict]:
|
|
path = data_dir / "speakers.json"
|
|
if not path.exists():
|
|
return {}
|
|
with open(path) as f:
|
|
data = json.load(f)
|
|
return {s["primary_name"]: s for s in data.get("speakers", [])}
|
|
|
|
|
|
def _build_alias_map(speakers: dict[str, dict]) -> dict[str, dict]:
|
|
alias_map: dict[str, dict] = {}
|
|
for name, info in speakers.items():
|
|
alias_map[name] = info
|
|
for a in info.get("aliases", []):
|
|
if a:
|
|
alias_map[a] = info
|
|
return alias_map
|
|
|
|
|
|
def _resolve_voice_ref(
|
|
speaker: str,
|
|
alias_map: dict[str, dict],
|
|
voices_dir: Path,
|
|
) -> Path | None:
|
|
spk_info = alias_map.get(speaker)
|
|
candidates: list[str] = [speaker]
|
|
if spk_info and spk_info.get("primary_name"):
|
|
candidates.append(spk_info["primary_name"])
|
|
for name in candidates:
|
|
ref_path = voices_dir / (name.lower().replace(" ", "_") + "_ref.wav")
|
|
if ref_path.exists():
|
|
return ref_path
|
|
for name in candidates:
|
|
needle = name.lower().replace(" ", "_")
|
|
for vf in voices_dir.glob("*_ref.wav"):
|
|
if needle in vf.stem.lower():
|
|
return vf
|
|
return None
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# ChapterBuilder
|
|
# ---------------------------------------------------------------------------
|
|
|
|
class ChapterBuilder:
|
|
"""Self-healing chapter audio generation pipeline.
|
|
|
|
Usage::
|
|
|
|
builder = ChapterBuilder(config, chapter_id=6)
|
|
result = builder.run()
|
|
print(result.phase, result.detail)
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
config: Any,
|
|
chapter_id: int,
|
|
temperature: float = 0.5,
|
|
use_seed: bool = True,
|
|
max_retries: int = 3,
|
|
tts_health_interval: float = 30.0,
|
|
) -> None:
|
|
self.config = config
|
|
self.chapter_id = chapter_id
|
|
self.temperature = temperature
|
|
self.use_seed = use_seed
|
|
self.max_retries = max_retries
|
|
self.tts_health_interval = tts_health_interval
|
|
# Generation mode for cached voice prompts. Full-vmatrix ICL generation
|
|
# (prompt_slug -> cached ref_code) is measured at >15 min/utterance on
|
|
# the GPU box; embedding-mode generation (speaker embedding extracted
|
|
# from the current approved reference wav) is ~17s/utterance with the
|
|
# same voice identity. Default ON; disable via env to force slug mode.
|
|
self.use_embedding_mode = os.environ.get("UMBRA_TTS_EMBEDDING_MODE", "1") != "0"
|
|
|
|
self.data_dir: Path = config.data_dir
|
|
self.status_path: Path = (
|
|
self.data_dir / "chapter_audio" / f"ch{chapter_id}_build_status.json"
|
|
)
|
|
self.result = BuildResult()
|
|
self._start_time: float = 0.0
|
|
self._tts_healthy = True
|
|
self._health_stop = threading.Event()
|
|
self._health_thread: threading.Thread | None = None
|
|
|
|
# ------------------------------------------------------------------
|
|
# Status writing
|
|
# ------------------------------------------------------------------
|
|
|
|
def _write_status(self) -> None:
|
|
elapsed_secs = time.time() - self._start_time
|
|
mins = int(elapsed_secs // 60)
|
|
secs = int(elapsed_secs % 60)
|
|
self.result.elapsed = f"{mins}m {secs:02d}s"
|
|
self.status_path.parent.mkdir(parents=True, exist_ok=True)
|
|
with open(self.status_path, "w") as f:
|
|
json.dump(self.result.to_dict(), f)
|
|
|
|
def _set_phase(self, phase: str, detail: str, progress: int) -> None:
|
|
self.result.phase = phase
|
|
self.result.detail = detail
|
|
self.result.progress = progress
|
|
self._write_status()
|
|
|
|
# ------------------------------------------------------------------
|
|
# TTS health monitor
|
|
# ------------------------------------------------------------------
|
|
|
|
def _monitor_tts_health(self) -> None:
|
|
import httpx
|
|
|
|
url = f"{self.config.tts.qwen_tts_url}/health"
|
|
consecutive_failures = 0
|
|
while not self._health_stop.is_set():
|
|
try:
|
|
r = httpx.get(url, timeout=5)
|
|
healthy = r.status_code < 500
|
|
if healthy:
|
|
data = r.json()
|
|
healthy = data.get("base_loaded", False)
|
|
except Exception:
|
|
healthy = False
|
|
|
|
if healthy:
|
|
consecutive_failures = 0
|
|
if not self._tts_healthy:
|
|
logger.info("TTS server recovered")
|
|
self._tts_healthy = True
|
|
else:
|
|
consecutive_failures += 1
|
|
logger.warning(
|
|
"TTS health check failed (%d consecutive)", consecutive_failures
|
|
)
|
|
if consecutive_failures >= 5:
|
|
self._tts_healthy = False
|
|
self.result.errors.append(
|
|
f"TTS server unreachable after {consecutive_failures} checks"
|
|
)
|
|
self._write_status()
|
|
break
|
|
self._tts_healthy = False
|
|
|
|
self._health_stop.wait(self.tts_health_interval)
|
|
|
|
def _start_health_monitor(self) -> None:
|
|
self._health_stop.clear()
|
|
self._tts_healthy = True
|
|
self._health_thread = threading.Thread(
|
|
target=self._monitor_tts_health, daemon=True
|
|
)
|
|
self._health_thread.start()
|
|
|
|
def _stop_health_monitor(self) -> None:
|
|
self._health_stop.set()
|
|
if self._health_thread:
|
|
self._health_thread.join(timeout=5)
|
|
|
|
# ------------------------------------------------------------------
|
|
# Phase 1: Preflight
|
|
# ------------------------------------------------------------------
|
|
|
|
def _check_prerequisites(self) -> None:
|
|
self._set_phase("preflight", "Checking prerequisites...", 2)
|
|
|
|
errors: list[str] = []
|
|
|
|
# TTS server reachable
|
|
import httpx
|
|
|
|
url = f"{self.config.tts.qwen_tts_url}/health"
|
|
health = None
|
|
try:
|
|
r = httpx.get(url, timeout=10)
|
|
if r.status_code >= 500:
|
|
errors.append(f"TTS server returned HTTP {r.status_code}")
|
|
else:
|
|
health = r.json()
|
|
except Exception as e:
|
|
errors.append(f"TTS server unreachable: {e}")
|
|
|
|
# Auto-load the TTS base model if it isn't already loaded so that the
|
|
# webUI chapter build is self-sufficient (no manual pre-load step).
|
|
if not errors and health is not None and not health.get("base_loaded"):
|
|
load_url = f"{self.config.tts.qwen_tts_url}/load"
|
|
try:
|
|
self._set_phase("preflight", "Loading TTS base model...", 3)
|
|
rr = httpx.post(load_url, json={"model_type": "base"}, timeout=600)
|
|
if rr.status_code >= 500:
|
|
errors.append(f"TTS server failed to load base model (HTTP {rr.status_code})")
|
|
else:
|
|
hr = httpx.get(url, timeout=10)
|
|
health = hr.json() if hr.status_code < 500 else {}
|
|
except Exception as e:
|
|
errors.append(f"Failed to load TTS base model: {e}")
|
|
if not errors and not health.get("base_loaded"):
|
|
errors.append("TTS base model not loaded")
|
|
|
|
if not errors and not health.get("base_loaded"):
|
|
errors.append("TTS base model not loaded")
|
|
|
|
# Chapter has utterances
|
|
db = sqlite3.connect(str(self.data_dir / "project.db"))
|
|
db.row_factory = sqlite3.Row
|
|
count = db.execute(
|
|
"SELECT COUNT(*) as n FROM utterances WHERE chapter_id = ?",
|
|
(self.chapter_id,),
|
|
).fetchone()["n"]
|
|
db.close()
|
|
if count == 0:
|
|
errors.append(f"No utterances found for chapter {self.chapter_id}")
|
|
|
|
# Voice refs exist for speakers in chapter
|
|
speakers = _load_speakers(self.data_dir)
|
|
alias_map = _build_alias_map(speakers)
|
|
voices_dir = self.data_dir / "voices"
|
|
|
|
db = sqlite3.connect(str(self.data_dir / "project.db"))
|
|
db.row_factory = sqlite3.Row
|
|
rows = db.execute(
|
|
"SELECT DISTINCT speaker_name FROM utterances "
|
|
"WHERE chapter_id = ? AND type = 'dialogue' AND speaker_name IS NOT NULL",
|
|
(self.chapter_id,),
|
|
).fetchall()
|
|
db.close()
|
|
|
|
missing_refs: list[str] = []
|
|
for row in rows:
|
|
spk = row["speaker_name"].strip()
|
|
if spk.lower() == "narrator":
|
|
continue
|
|
ref = _resolve_voice_ref(spk, alias_map, voices_dir)
|
|
if ref is None:
|
|
missing_refs.append(spk)
|
|
if missing_refs:
|
|
errors.append(f"Missing voice refs for: {', '.join(missing_refs)}")
|
|
|
|
# Output directory writable
|
|
audio_dir = self.data_dir / "chapter_audio" / f"ch{self.chapter_id}"
|
|
try:
|
|
audio_dir.mkdir(parents=True, exist_ok=True)
|
|
except OSError as e:
|
|
errors.append(f"Cannot create output directory: {e}")
|
|
|
|
if errors:
|
|
self.result.errors = errors
|
|
self._set_phase("error", f"Preflight failed: {'; '.join(errors)}", 0)
|
|
raise RuntimeError(f"Preflight failed: {errors}")
|
|
|
|
self._set_phase("preflight", "All prerequisites met", 5)
|
|
|
|
# ------------------------------------------------------------------
|
|
# Phase 2: Validate
|
|
# ------------------------------------------------------------------
|
|
|
|
def _validate_utterances(self) -> list[dict]:
|
|
self._set_phase("validating", "Checking utterance staleness...", 8)
|
|
|
|
from umbraaudio.generation.staleness import is_current
|
|
from umbraaudio.pronunciation import apply_phonetics, load_phonetic_names
|
|
|
|
phonetic = load_phonetic_names(self.data_dir)
|
|
speakers = _load_speakers(self.data_dir)
|
|
alias_map = _build_alias_map(speakers)
|
|
voices_dir = self.data_dir / "voices"
|
|
|
|
db = sqlite3.connect(str(self.data_dir / "project.db"))
|
|
db.row_factory = sqlite3.Row
|
|
utterances = db.execute(
|
|
"SELECT id, utterance_index, text, speaker_name, emotion, pace, instruct, type "
|
|
"FROM utterances WHERE chapter_id = ? ORDER BY utterance_index",
|
|
(self.chapter_id,),
|
|
).fetchall()
|
|
db.close()
|
|
|
|
if not utterances:
|
|
self._set_phase("error", "No utterances found", 0)
|
|
raise RuntimeError("No utterances found")
|
|
|
|
self.result.total_utterances = len(utterances)
|
|
audio_dir = self.data_dir / "chapter_audio" / f"ch{self.chapter_id}"
|
|
audio_dir.mkdir(parents=True, exist_ok=True)
|
|
|
|
missing: list[dict] = []
|
|
for u in utterances:
|
|
idx = u["utterance_index"]
|
|
text = u["text"] or ""
|
|
utt_type = u["type"] or "narration"
|
|
|
|
if utt_type == "scene_break" or text.strip() == "<<<<<<>>>>>":
|
|
continue
|
|
if not text.strip():
|
|
continue
|
|
|
|
fname = f"utt_{idx:04d}.wav"
|
|
spoken_text = apply_phonetics(text, phonetic)
|
|
spk = (u["speaker_name"] or "narrator").strip()
|
|
vref = _resolve_voice_ref(spk, alias_map, voices_dir)
|
|
|
|
if not is_current(
|
|
spoken_text, audio_dir / fname,
|
|
speaker=spk, voice_ref=vref, voice_mode="icl",
|
|
):
|
|
missing.append(dict(u))
|
|
|
|
self.result.skipped = self.result.total_utterances - len(missing)
|
|
detail = f"{len(missing)} utterances need generation"
|
|
self._set_phase("validating", detail, 12)
|
|
return missing
|
|
|
|
# ------------------------------------------------------------------
|
|
# Phase 3: Generate
|
|
# ------------------------------------------------------------------
|
|
|
|
def _generate_utterances(self, missing: list[dict]) -> None:
|
|
if not missing:
|
|
self._set_phase("generating", "All utterances current", 70)
|
|
return
|
|
|
|
from umbraaudio.generation.audio_effects import (
|
|
apply_effects_to_file,
|
|
apply_effects_to_file_by_name,
|
|
load_speaker_distortion_effects,
|
|
load_speaker_traits,
|
|
)
|
|
from umbraaudio.generation.chapter import EMOTION_INSTRUCT_MAP
|
|
from umbraaudio.generation.staleness import write_marker
|
|
from umbraaudio.pronunciation import apply_phonetics, load_phonetic_names
|
|
from umbraaudio.voices.qwen_tts import (
|
|
VOICE_REFERENCE_TEXT,
|
|
QwenTTSEngine,
|
|
QwenTTSUnavailableError,
|
|
)
|
|
|
|
self._set_phase("generating", f"Generating {len(missing)} utterances...", 15)
|
|
|
|
phonetic = load_phonetic_names(self.data_dir)
|
|
speakers = _load_speakers(self.data_dir)
|
|
alias_map = _build_alias_map(speakers)
|
|
voices_dir = self.data_dir / "voices"
|
|
audio_dir = self.data_dir / "chapter_audio" / f"ch{self.chapter_id}"
|
|
|
|
speaker_traits = load_speaker_traits(self.data_dir / "speakers.json")
|
|
distortion_effects = load_speaker_distortion_effects(self.data_dir / "speakers.json")
|
|
|
|
engine = QwenTTSEngine(qwen_tts_url=self.config.tts.qwen_tts_url)
|
|
|
|
# Capture the full vmatrix (the "8k matrix" ref_code + speaker embedding)
|
|
# once per distinct speaker, cached server-side as .pt under PROMPT_DIR.
|
|
# x_vector_only=False (explicit, and the engine default) captures the FULL
|
|
# vmatrix so the approved voice's exact numbers are persisted as the
|
|
# canonical record. Generation reuses that identity via embedding-mode
|
|
# (~17s/utt); full-vmatrix ICL generation is >15 min/utt, see
|
|
# use_embedding_mode.
|
|
prompt_map: dict[str, str] = {}
|
|
for u in missing:
|
|
spk = (u.get("speaker_name") or "narrator").strip()
|
|
if spk in prompt_map:
|
|
continue
|
|
vref = _resolve_voice_ref(spk, alias_map, voices_dir)
|
|
if not vref:
|
|
continue
|
|
prompt_map[spk] = engine.build_voice_prompt(
|
|
vref,
|
|
ref_text=VOICE_REFERENCE_TEXT,
|
|
x_vector_only=False,
|
|
)
|
|
|
|
self._start_health_monitor()
|
|
|
|
try:
|
|
for i, u in enumerate(missing):
|
|
idx = u["utterance_index"]
|
|
text = u["text"] or ""
|
|
speaker = (u["speaker_name"] or "narrator").strip()
|
|
emotion = u["emotion"] or "neutral"
|
|
instruct = u["instruct"] or ""
|
|
|
|
pct = int(15 + (i / max(len(missing), 1)) * 55)
|
|
self._set_phase(
|
|
"generating",
|
|
f"Generating {i + 1}/{len(missing)}: {speaker}",
|
|
pct,
|
|
)
|
|
|
|
if not text.strip() or text.strip() == "<<<<<<>>>>>":
|
|
continue
|
|
|
|
spoken_text = apply_phonetics(text, phonetic)
|
|
out = audio_dir / f"utt_{idx:04d}.wav"
|
|
voice_ref = _resolve_voice_ref(speaker, alias_map, voices_dir)
|
|
|
|
tts_instruct = instruct
|
|
if not tts_instruct and emotion in EMOTION_INSTRUCT_MAP:
|
|
tts_instruct = EMOTION_INSTRUCT_MAP[emotion]
|
|
|
|
seed = (self.chapter_id * 10000 + idx) if self.use_seed else None
|
|
success = False
|
|
|
|
for attempt in range(self.max_retries):
|
|
# Wait for TTS health
|
|
wait_count = 0
|
|
while not self._tts_healthy and wait_count < 60:
|
|
self._set_phase(
|
|
"generating",
|
|
f"Waiting for TTS server... (utt {idx}, attempt {attempt + 1})",
|
|
pct,
|
|
)
|
|
time.sleep(5)
|
|
wait_count += 1
|
|
|
|
if not self._tts_healthy:
|
|
self.result.errors.append(
|
|
f"TTS server down, skipping utt_{idx:04d}"
|
|
)
|
|
break
|
|
|
|
try:
|
|
prompt_slug = prompt_map.get(speaker)
|
|
voice_mode = None
|
|
if voice_ref and self.use_embedding_mode:
|
|
engine.generate(
|
|
text=spoken_text,
|
|
output_path=out,
|
|
voice_ref=voice_ref,
|
|
ref_text=VOICE_REFERENCE_TEXT,
|
|
temperature=self.temperature,
|
|
seed=seed,
|
|
)
|
|
voice_mode = "embedding"
|
|
elif prompt_slug:
|
|
engine.generate(
|
|
text=spoken_text,
|
|
output_path=out,
|
|
prompt_slug=prompt_slug,
|
|
temperature=self.temperature,
|
|
seed=seed,
|
|
)
|
|
voice_mode = "icl"
|
|
elif voice_ref:
|
|
engine.generate(
|
|
text=spoken_text,
|
|
output_path=out,
|
|
voice_ref=voice_ref,
|
|
ref_text=VOICE_REFERENCE_TEXT,
|
|
temperature=self.temperature,
|
|
seed=seed,
|
|
)
|
|
voice_mode = "embedding"
|
|
else:
|
|
engine.generate(
|
|
text=spoken_text,
|
|
output_path=out,
|
|
instruct=tts_instruct or "A calm, neutral voice speaking clearly.",
|
|
temperature=self.temperature,
|
|
seed=seed,
|
|
)
|
|
|
|
if out.exists():
|
|
explicit = distortion_effects.get(speaker, [])
|
|
if explicit:
|
|
apply_effects_to_file_by_name(out, explicit)
|
|
else:
|
|
traits = speaker_traits.get(speaker, [])
|
|
if traits:
|
|
apply_effects_to_file(out, traits)
|
|
|
|
write_marker(
|
|
spoken_text, out,
|
|
speaker=speaker, voice_ref=voice_ref,
|
|
voice_mode=voice_mode,
|
|
)
|
|
success = True
|
|
break
|
|
|
|
except QwenTTSUnavailableError as e:
|
|
logger.warning(
|
|
"TTS unavailable for ch%d utt%d (attempt %d): %s",
|
|
self.chapter_id, idx, attempt + 1, e,
|
|
)
|
|
self._tts_healthy = False
|
|
time.sleep(5 * (attempt + 1))
|
|
except Exception as e:
|
|
logger.error(
|
|
"Generation failed for ch%d utt%d (attempt %d): %s",
|
|
self.chapter_id, idx, attempt + 1, e,
|
|
)
|
|
time.sleep(2 * (attempt + 1))
|
|
|
|
if not success:
|
|
self.result.failed.append(idx)
|
|
logger.error("All attempts exhausted for utt_%04d", idx)
|
|
|
|
finally:
|
|
self._stop_health_monitor()
|
|
|
|
if self.result.failed:
|
|
self.result.errors.append(
|
|
f"Failed utterances: {self.result.failed}"
|
|
)
|
|
self._set_phase(
|
|
"error",
|
|
f"Generation incomplete: {len(self.result.failed)} failures",
|
|
0,
|
|
)
|
|
raise RuntimeError(f"Generation failed for {len(self.result.failed)} utterances")
|
|
|
|
self._set_phase("generating", "All utterances generated", 70)
|
|
|
|
# ------------------------------------------------------------------
|
|
# Phase 4: Stitch
|
|
# ------------------------------------------------------------------
|
|
|
|
def _stitch_chapter(self) -> None:
|
|
self._set_phase("stitching", "Stitching chapter audio...", 78)
|
|
|
|
from umbraaudio.assembly import AudioStitcher
|
|
|
|
audio_dir = self.data_dir / "chapter_audio" / f"ch{self.chapter_id}"
|
|
audio_files = sorted(audio_dir.glob("utt_*.wav"))
|
|
|
|
if not audio_files:
|
|
self._set_phase("error", "No audio files to stitch", 0)
|
|
raise RuntimeError("No audio files to stitch")
|
|
|
|
stitcher = AudioStitcher()
|
|
output_dir = self.data_dir / "chapter_audio"
|
|
output_path = output_dir / f"ch{self.chapter_id}_full.wav"
|
|
stitcher.stitch_utterances(audio_files, output_path)
|
|
|
|
self.result.chapter_wav = output_path.name
|
|
self._set_phase("stitching", f"Stitched {len(audio_files)} files → {output_path.name}", 85)
|
|
|
|
# ------------------------------------------------------------------
|
|
# Phase 5: Export MP3
|
|
# ------------------------------------------------------------------
|
|
|
|
def _export_mp3(self) -> None:
|
|
self._set_phase("exporting", "Converting to MP3...", 88)
|
|
|
|
wav_path = (
|
|
self.data_dir / "chapter_audio" / f"ch{self.chapter_id}_full.wav"
|
|
)
|
|
if not wav_path.exists():
|
|
self._set_phase("exporting", "WAV not found, skipping MP3", 88)
|
|
return
|
|
|
|
mp3_path = wav_path.with_suffix(".mp3")
|
|
try:
|
|
result = subprocess.run(
|
|
[
|
|
"ffmpeg", "-y",
|
|
"-i", str(wav_path),
|
|
"-codec:a", "libmp3lame",
|
|
"-b:a", "192k",
|
|
str(mp3_path),
|
|
],
|
|
capture_output=True,
|
|
text=True,
|
|
timeout=300,
|
|
)
|
|
if result.returncode == 0 and mp3_path.exists():
|
|
self.result.chapter_mp3 = mp3_path.name
|
|
self._set_phase("exporting", f"MP3 exported: {mp3_path.name}", 92)
|
|
else:
|
|
logger.warning("ffmpeg MP3 conversion failed: %s", result.stderr[:300])
|
|
self._set_phase("exporting", "MP3 conversion failed (non-fatal)", 92)
|
|
except FileNotFoundError:
|
|
logger.warning("ffmpeg not found, skipping MP3 export")
|
|
self._set_phase("exporting", "ffmpeg not available, skipping MP3", 92)
|
|
except subprocess.TimeoutExpired:
|
|
logger.warning("ffmpeg MP3 conversion timed out")
|
|
self._set_phase("exporting", "MP3 conversion timed out (non-fatal)", 92)
|
|
|
|
# ------------------------------------------------------------------
|
|
# Phase 6: Verify
|
|
# ------------------------------------------------------------------
|
|
|
|
def _verify_quality(self) -> None:
|
|
self._set_phase("verifying", "Running quality checks...", 94)
|
|
|
|
from umbraaudio.assembly import ChapterQualityChecker
|
|
|
|
speakers_path = self.data_dir / "speakers.json"
|
|
known_speakers: list[str] = []
|
|
if speakers_path.exists():
|
|
with open(speakers_path) as f:
|
|
data = json.load(f)
|
|
known_speakers = [s["primary_name"] for s in data.get("speakers", [])]
|
|
|
|
checker = ChapterQualityChecker(self.data_dir, known_speakers)
|
|
issues = checker.check_chapter(self.chapter_id)
|
|
self.result.quality_issues = [i.to_dict() for i in issues]
|
|
|
|
errors = sum(1 for i in issues if i.severity == "error")
|
|
warnings = sum(1 for i in issues if i.severity == "warning")
|
|
|
|
if errors:
|
|
self.result.phase = "warning"
|
|
self.result.detail = f"Completed with {errors} errors, {warnings} warnings"
|
|
self.result.progress = 100
|
|
elif warnings:
|
|
self.result.detail = f"Completed with {warnings} warnings"
|
|
else:
|
|
self.result.detail = "All quality checks passed"
|
|
|
|
self._set_phase(
|
|
self.result.phase if self.result.phase == "warning" else "complete",
|
|
self.result.detail,
|
|
100,
|
|
)
|
|
|
|
# ------------------------------------------------------------------
|
|
# Main entry point
|
|
# ------------------------------------------------------------------
|
|
|
|
def run(self) -> BuildResult:
|
|
"""Run the full chapter build pipeline.
|
|
|
|
Returns the :class:`BuildResult` with final status.
|
|
"""
|
|
self._start_time = time.time()
|
|
self.result.started_at = datetime.now(UTC).isoformat()
|
|
|
|
try:
|
|
self._check_prerequisites()
|
|
missing = self._validate_utterances()
|
|
self._generate_utterances(missing)
|
|
self._stitch_chapter()
|
|
self._export_mp3()
|
|
self._verify_quality()
|
|
except Exception as e:
|
|
if self.result.phase != "error":
|
|
self._set_phase("error", str(e), 0)
|
|
logger.error("Chapter %d build failed: %s", self.chapter_id, e)
|
|
|
|
self._write_status()
|
|
return self.result
|