Files
kami bec9411af3 Put every S3 URI in one place, and add a lint gate
Five workers built output URIs with inline f-strings, so the bucket-per-artifact
layout was spread across worker_tts, worker_identity, worker_crop, worker_layers
and worker_render. Moving a class between buckets meant a grep. They are now
templates in transport.py, formatted at each call site.

Three of those workers also each reimplemented the same parse to recover
manga_id and chapter_id from an input uri, because the orchestrator does not
send them. That is transport.ids_from_uri now, and it raises on a uri too short
to carry the ids rather than returning a wrong pair.

ruff.toml makes `ruff check .` exit 0, so CI can gate on it and a new finding
means a new defect. Fixed: an implicit Optional in 8 signatures, an unparenthesized
implicit concatenation in the ASS filter list, 5 subprocess.run calls now saying
check=False out loud, an unused import, a duplicate exception handler and a
non-executable shebang. Every rule left off carries its reason in ruff.toml.

The ASYNC rules are off because ffmpeg on the event loop is real and already
recorded at caveats/audit-open.md#blocking-event-loop. It needs a refactor per
handler, not a lint fix.

Checked: transport, collage, bubble_detect, test_vision_parse, worker_crop,
worker_scene, worker_script, worker_identity, worker_tts, session_manager,
worker_vision and worker_render self-checks all pass. worker_layers still fails
on a missing legacy/qwen_layered_workflow.json, which predates this branch.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-13 23:02:06 +04:00

130 lines
4.2 KiB
Python
Executable File

#!/usr/bin/env python3
"""Interactively generate and choose a dots.tts narrator reference."""
import argparse
import os
from pathlib import Path
import shutil
import subprocess
import sys
import time
DEFAULT_TEXT = "The story continues as our hero steps forward into the unknown."
DEFAULT_MODEL = "rednote-hilab/dots.tts-base"
DEFAULT_VOICE_DIR = Path("~/.cache/manga-tts").expanduser()
def load_model(model_name: str):
# Match worker_tts.py's workaround for the tokenizer bundled with dots.tts.
import transformers
original = transformers.AutoTokenizer.from_pretrained.__func__
transformers.AutoTokenizer.from_pretrained = classmethod(
lambda cls, *args, **kwargs: original(
cls, *args, **{"fix_mistral_regex": True, **kwargs}
)
)
# Match worker_tts.py's torch/torchaudio minor-mismatch bypass too.
import torch
from importlib import metadata
real_version = metadata.version
metadata.version = (
lambda name: torch.__version__ if name == "torchaudio" else real_version(name)
)
try:
from dots_tts.runtime import DotsTtsRuntime
finally:
metadata.version = real_version
return DotsTtsRuntime.from_pretrained(model_name, precision="bfloat16")
def write_wav(result, path: Path) -> None:
import numpy as np
import soundfile as sf
audio = result["audio"]
if hasattr(audio, "detach"):
audio = audio.detach().cpu().numpy()
sf.write(path, np.asarray(audio, dtype="float32").squeeze(),
result["sample_rate"], subtype="PCM_16")
def play(path: Path) -> None:
players = (
("ffplay", "-nodisp", "-autoexit", "-loglevel", "error"),
("aplay", "-q"),
("paplay",),
)
for command in players:
if shutil.which(command[0]):
subprocess.run([*command, str(path)], check=False)
return
print(f"No ffplay, aplay, or paplay found; listen manually: {path}")
def choice() -> str:
while True:
answer = input("[p]ick / p[a]rk / [s]kip: ").strip().lower()
aliases = {"p": "pick", "pick": "pick", "a": "park", "park": "park",
"s": "skip", "skip": "skip"}
if answer in aliases:
return aliases[answer]
print("Enter p, a, or s.")
def main() -> int:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--text", default=os.environ.get("VOICE_REF_TEXT", DEFAULT_TEXT),
help="sample text to speak")
parser.add_argument("--model", default=DEFAULT_MODEL)
parser.add_argument("--voice-dir", type=Path, default=DEFAULT_VOICE_DIR)
args = parser.parse_args()
voice_dir = args.voice_dir.expanduser()
parked_dir = voice_dir / "parked"
voice_dir.mkdir(parents=True, exist_ok=True)
parked_dir.mkdir(parents=True, exist_ok=True)
candidate = voice_dir / "voice_candidate.wav"
selected = voice_dir / "narrator_ref.wav"
print(f"Loading {args.model} ...")
model = load_model(args.model)
print(f"Reference text: {args.text!r}")
try:
while True:
print("\nGenerating a new voice ...")
write_wav(model.generate(text=args.text), candidate)
play(candidate)
action = choice()
if action == "pick":
os.replace(candidate, selected)
(voice_dir / "narrator_ref.txt").write_text(args.text + "\n")
print(f"Selected: {selected}")
return 0
if action == "park":
stamp = time.strftime("%Y%m%d-%H%M%S")
parked = parked_dir / f"voice-{stamp}.wav"
suffix = 2
while parked.exists():
parked = parked_dir / f"voice-{stamp}-{suffix}.wav"
suffix += 1
os.replace(candidate, parked)
parked.with_suffix(".txt").write_text(args.text + "\n")
print(f"Parked: {parked}")
else:
candidate.unlink(missing_ok=True)
print("Skipped.")
except (KeyboardInterrupt, EOFError):
candidate.unlink(missing_ok=True)
print("\nStopped without changing the selected voice.")
return 130
if __name__ == "__main__":
sys.exit(main())