Add subtitle dictionaries, Polish number normalizer, ComfyUI lifecycle control
Text processing before TTS:
- dictionary.txt rewrites words and phrases (km/h, np., units). Rules compile
into one alternation applied in a single pass, so replacements never chain
and longest-literal wins. Case-sensitive, whole-word.
- A per-queue second dictionary, written by the GUI into the first video's job
folder and merged with the shared one (--dictionary-extra).
- ALL-CAPS blocks become Title Case rather than sentence case: lowercasing
destroyed proper nouns inside captions ("NASA - MARGO MADISON") and invited
the normalizer to "correct" the name. Paired clips in output/clips/all-caps
showed the TTS reads both casings the same.
normalizer/ — digits to spoken Polish, in its own venv because it needs
transformers>=5, which must not touch ComfyUI's pinned versions:
- Two backends. Plain integers go to num2words for the value and a general
Polish LLM for the case; times, dates, decimals and identifiers go to a 0.6B
fine-tune. Measured on 40 real subtitle lines, each is clearly better than
the other on its half. 74% of numbered blocks take the first leg.
- Guards on every answer: runaway length, repetition loops, invalid bytes,
leftover digits, a lost minus sign, a changed number, a lost word. A failure
falls back to num2words, which is nominative but never wrong about the value.
- ComfyUI is asked to unload before normalizing; a normalizer that does not fit
in VRAM is spilled to RAM silently at 5x the cost.
Backend lifecycle:
- ComfyUI is found by port through /proc, so a backend started by hand is
managed too. SIGTERM escalates to SIGKILL, and a suspended process is killed
outright since it never runs its handlers.
- A clip that kills the backend is retried three times, each with a different
seed (the aborts are seed-dependent, not text-dependent), then skipped and
reported so an overnight queue keeps moving.
- start_comfyui.sh execs python, so the script's PID is ComfyUI's.
- GUI gains ComfyUI and Queue dictionary buttons; quitting stops the backend.
--resume-from now reuses the job folder's tts_input.srt instead of reparsing
the subtitles, so block N still pairs with clip_NNNN.flac.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
This commit is contained in:
Claude Opus 5
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#!/usr/bin/env python3
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"""Compare normalizer models on the same sentences.
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The shipped model is a 0.6B task-specific fine-tune that needs no prompt but
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miscounts large numbers and occasionally drops a word. This runs any causal LM
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over the same battery so the trade is measurable rather than argued about:
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python bench.py # the fine-tune
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python bench.py --model models/PLLuM-4B-instruct-2512 --prompted
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python bench.py --model … --prompted --save pllum.json
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python bench.py --compare fine-tune.json pllum.json
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`--prompted` switches from the fine-tune's bare "text in, text out" contract to
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an instruction + few-shot prompt, which a general instruct model needs. Digit
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tokens are banned from the output either way, and decoding is greedy, so a
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difference between two runs is the model and not the sampling.
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Automatic flags are cheap heuristics, not a grade: `digits` means an
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un-normalized digit survived, `lost` means a word from the input has no
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plausible inflected match in the output. Read the text before believing either.
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"""
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import argparse
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import json
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import re
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import sys
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import time
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from pathlib import Path
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HERE = Path(__file__).resolve().parent
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sys.path.insert(0, str(HERE))
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import normalizer as nz # noqa: E402 (reuses its protect/restore and banned list)
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SYSTEM_PROMPTED = (
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"Jesteś narzędziem do normalizacji tekstu dla syntezatora mowy. "
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"Zamieniasz wszystkie liczby zapisane cyframi na słowa, w poprawnej formie "
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"gramatycznej wynikającej z kontekstu zdania. Nie zmieniasz, nie dodajesz "
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"ani nie usuwasz żadnych innych słów. Zwracasz wyłącznie przekształcone "
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"zdanie, bez komentarza."
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)
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# Few-shot examples chosen to pin down the three things a general model gets
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# wrong without them: the case must follow the governing word, the rest of the
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# sentence must survive verbatim, and the answer must be the sentence alone.
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FEWSHOT = [
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("Koncert dla 8250 osób.",
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"Koncert dla ośmiu tysięcy dwustu pięćdziesięciu osób."),
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("Populacja Polski to 38 000 000 mieszkańców.",
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"Populacja Polski to trzydzieści osiem milionów mieszkańców."),
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("Mówimy o 25 psach.",
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"Mówimy o dwudziestu pięciu psach."),
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("Wygrał 1247 złotych.",
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"Wygrał tysiąc dwieście czterdzieści siedem złotych."),
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]
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BATTERY = [
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"Sprawa dotyczy 1 350 000 ludzi.",
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"Koncert dla 8250 osób.",
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"Budżet wynosi 12 500 000 złotych.",
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"Populacja Polski to 38 000 000 mieszkańców.",
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"Zapłacili 1 500 000 000 dolarów.",
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"Zostało 999 999 sztuk.",
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"Numer seryjny 1234567.",
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"Ponad 100 000 widzów na stadionie.",
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"Mówimy o 1 350 000 ludziach.",
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"Z 8250 osobami nie da się dyskutować.",
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"Firma zatrudnia 2 500 pracowników.",
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"Dystans 42 195 metrów.",
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"Cena spadła z 15 700 000 do 8 250 000.",
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"Było tam 25 psów.",
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"Mówimy o 25 psach.",
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"Przyjechał z 25 psami.",
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"Przyglądał się 25 psom.",
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"Nie ma tu 25 psów.",
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"Urodził się 3 maja 1791 roku.",
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"Wzrost o 16 procent w 2026 roku.",
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"Spotkanie o 14:30.",
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"Godzina 23:56.",
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"Radio 95,7 FM.",
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"Około 3,5 metra.",
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"Mieszka pod 12/45.",
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"Wygrał 1247 złotych.",
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"To kosztuje 1999 dolarów i 99 centów.",
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"Mam 3 koty i 25 psów.",
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"Zostało 5 minut.",
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"Temperatura opon w porządku. Przód 85, tył 75.",
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"Louis Chiron miał 57 lat, kiedy wygrał Grand Prix Monako.",
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"Zwykłe zdanie bez żadnych liczb.",
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]
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_WORD_RE = re.compile(r'[^\W\d_]+', re.UNICODE)
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def lost_words(src: str, out: str) -> list:
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"""Input words with no plausible match in the output.
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Polish inflects, so an exact comparison is useless — `ludziach` legitimately
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becomes `ludziach` and nothing else, but `psach`/`psami` share a stem. A
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five-character prefix is a crude stand-in for a lemma and is good enough to
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notice a noun that vanished entirely, which is the failure worth catching.
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"""
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def stems(text):
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return {w.lower()[:5] for w in _WORD_RE.findall(text)}
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missing = stems(src) - stems(out)
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return sorted(w for w in missing if len(w) >= 4)
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def load_prompted(model_dir: str, gpu_gib: float = None,
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system: str = None, fewshot: list = None):
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"""A general instruct model: system prompt, few-shot, greedy, digits banned.
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`gpu_gib` caps GPU use and spills the rest to RAM. Needed for the int4
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"awq" builds: transformers decompresses compressed-tensors weights back to
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bfloat16 at load, so an 11B model wants ~22 GB whatever the file size says.
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Offloading is slow but it is the difference between measuring a model and
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guessing about it.
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"""
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tok = AutoTokenizer.from_pretrained(model_dir)
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kwargs = {"dtype": torch.bfloat16, "device_map": "auto"}
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if gpu_gib == 0:
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# Everything in RAM. Slow, but the only way to run a compressed-tensors
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# int4 build in transformers: its decompression hook expands weights to
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# bfloat16 in place, and it cannot read the meta tensors that accelerate
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# leaves behind when max_memory forces a partial offload.
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kwargs["device_map"] = {"": "cpu"}
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elif gpu_gib:
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kwargs["max_memory"] = {0: f"{gpu_gib}GiB", "cpu": "80GiB"}
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model = AutoModelForCausalLM.from_pretrained(model_dir, **kwargs).eval()
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banned = set()
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for tid in range(len(tok)):
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piece = tok.decode([tid]).strip()
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if piece and piece.isdigit():
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banned.add(tid)
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banned = sorted(banned)
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print(f" {len(banned)} digit tokens banned; device "
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f"{next(model.parameters()).device}", file=sys.stderr)
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def run(text: str) -> str:
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messages = [{"role": "system", "content": system or SYSTEM_PROMPTED}]
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for user, assistant in (fewshot or FEWSHOT):
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messages.append({"role": "user", "content": user})
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messages.append({"role": "assistant", "content": assistant})
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messages.append({"role": "user", "content": text})
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prompt = tok.apply_chat_template(messages, tokenize=False,
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add_generation_prompt=True)
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inputs = tok(prompt, return_tensors="pt").to(model.device)
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n = inputs["input_ids"].shape[1]
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with torch.no_grad():
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out = model.generate(**inputs, max_new_tokens=nz.MAX_NEW_TOKENS,
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do_sample=False,
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pad_token_id=tok.pad_token_id or tok.eos_token_id,
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suppress_tokens=banned)
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answer = tok.decode(out[0][n:], skip_special_tokens=True).strip()
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# Instruct models like to introduce themselves; keep the last non-empty
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# line, which is the sentence in every observed case.
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lines = [l.strip() for l in answer.splitlines() if l.strip()]
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return lines[-1] if lines else answer
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return run
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def main() -> int:
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ap = argparse.ArgumentParser(description=__doc__,
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formatter_class=argparse.RawDescriptionHelpFormatter)
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ap.add_argument("--model", default=None,
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help="Model directory (default: the shipped fine-tune)")
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ap.add_argument("--prompted", action="store_true",
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help="Use an instruction + few-shot prompt (general models)")
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ap.add_argument("--gpu-gib", type=float, default=None, metavar="N",
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help="Cap GPU memory and spill the rest to RAM (slow, but "
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"the only way to run a model that does not fit)")
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ap.add_argument("--no-protect", action="store_true",
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help="Do not shield standalone single digits — normalize the "
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"text exactly as the model card does")
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ap.add_argument("--save", metavar="FILE", help="Write results as JSON")
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ap.add_argument("--compare", nargs=2, metavar=("A", "B"),
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help="Print two saved runs side by side and exit")
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args = ap.parse_args()
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if args.compare:
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a, b = (json.loads(Path(f).read_text(encoding="utf-8")) for f in args.compare)
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agree = 0
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for ra, rb in zip(a["results"], b["results"]):
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if ra["out"] == rb["out"]:
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agree += 1
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continue
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print(f" in : {ra['in']}")
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print(f" A : {ra['out']}")
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print(f" B : {rb['out']}\n")
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print(f"identical: {agree}/{len(a['results'])} "
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f"A={a['model']} B={b['model']}")
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return 0
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if args.prompted:
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if not args.model:
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ap.error("--prompted needs --model")
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run = load_prompted(args.model, args.gpu_gib)
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label = args.model
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else:
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if args.model:
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nz.MODEL_DIR = Path(args.model)
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if not nz.load_model():
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print("model unavailable; refusing to benchmark the fallback",
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file=sys.stderr)
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return 1
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run = nz.normalize
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label = str(nz.MODEL_DIR)
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if args.no_protect:
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nz.KEEP_RE = re.compile(r'(?!x)x') # matches nothing
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label += " [no single-digit protection]"
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results, t0 = [], time.time()
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for text in BATTERY:
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t = time.time()
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out = run(text)
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results.append({"in": text, "out": out, "sec": round(time.time() - t, 2),
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"digits": bool(nz.DIGIT_RE.search(nz.KEEP_RE.sub("", out))),
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"lost": lost_words(text, out)})
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total = time.time() - t0
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for r in results:
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flags = []
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if r["digits"]:
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flags.append("DIGITS LEFT")
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if r["lost"]:
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flags.append("LOST: " + ", ".join(r["lost"]))
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print(f" in : {r['in']}")
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print(f" out: {r['out']}" + (f" [{'; '.join(flags)}]" if flags else ""))
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print()
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n_dig = sum(r["digits"] for r in results)
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n_lost = sum(bool(r["lost"]) for r in results)
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print(f"{label}\n {len(results)} sentences in {total:.1f}s "
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f"({total/len(results):.2f}s each)\n"
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f" {n_dig} with digits left, {n_lost} with a word apparently lost")
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if args.save:
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Path(args.save).write_text(json.dumps(
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{"model": label, "prompted": args.prompted, "seconds": round(total, 1),
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"results": results}, ensure_ascii=False, indent=1), encoding="utf-8")
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print(f" saved to {args.save}")
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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