| """Director stage: turn a topic into a script the TTS front end can consume. |
| |
| The 0.8B model is good at prose and bad at JSON punctuation: zero-shot it wrote |
| the array as one object with repeated keys, and in 3 of 6 samples it closed the |
| bracket after a single line. Few-shot fixed the structure but not the counts. |
| |
| So the director no longer writes JSON. It writes six prefixed lines, which is |
| pattern completion - the thing a small LM is actually reliable at - and the host |
| does the parsing. `parse_script` still accepts JSON if a model insists. |
| |
| Runs on transformers 5.x. Ming pins 4.46.3, so this is a separate process and |
| leaves a file behind rather than calling TTS directly. |
| |
| Modes (`MODE`): |
| probe - N samples with the prompt as written, report pass rate |
| collect - keep accepted scripts (repairing lengths) into sft.jsonl |
| eval - score the current model on the fixed topic list |
| """ |
| import json |
| import os |
| import re |
| import time |
| from pathlib import Path |
|
|
| ROOT = Path(os.environ.get("WORK_ROOT", "/kaggle/working")) |
| MODEL_ID = os.environ.get("DIRECTOR_MODEL", "Qwen/Qwen3.5-0.8B") |
| ADAPTER = os.environ.get("ADAPTER", "") |
| MODE = os.environ.get("MODE", "probe") |
| N_SAMPLES = int(os.environ.get("N_SAMPLES", "8")) |
| OUT = ROOT / "director" |
|
|
| SLOTS = ["开场", "叙述", "故事", "叙述", "耳语", "耳语"] |
| MIN_CHARS, MAX_CHARS = 8, 40 |
| |
| |
| |
| SLOT_MAX = [40, 40, 40, 40, 15, 15] |
| LATIN_OR_DIGIT = re.compile(r"[A-Za-z0-9]") |
| ALLOWED = re.compile(r"^[\u4e00-\u9fff，。？！、；：…——“”‘’「」《》]+$") |
|
|
| TOPICS = [ |
| "雨夜的末班公交", "一家很老的唱片店", "海边的灯塔", "凌晨的便利店", |
| "搬家时翻出的旧照片", "楼下那棵一直没有开花的树", "深夜的收音机", |
| "一场没有下完的雨", "旧公寓的电梯", "凌晨四点的菜市场", "山顶的观景台", |
| "一个人的火锅店", "医院走廊的灯", "江边的长椅", "落了灰的钢琴", |
| "雨天的出租车", "深夜的洗衣房", "开在二楼的旧书店", "废弃的火车站", |
| "冬天的第一个霜降", "阳台上的那盆绿萝", "深夜的加油站", "清晨的面包房", |
| "海边的小旅馆", "老街的修表摊", "天台上的风", "没有寄出的信", |
| "一只总在窗边的猫", "雨停之后的马路", "深夜的游泳池", "断电的夜晚", |
| "巷子尽头的路灯", "夏天最后一场雨", "老式闹钟的指针", "凌晨的公交站", |
| "一条很长的隧道", "停电的电梯间", "小城的电影院", "合上的日记本", |
| "厨房里烧开的水", |
| ] |
|
|
| SYSTEM = "你是深夜电台的编剧。按给定格式写串词，不解释，不加标题。" |
|
|
| RULES = """写一段约 60 秒的深夜电台串词。一共六行，每行以标签开头，格式是「标签：正文」。 |
| |
| 六行的标签依次是：开场、叙述、故事、叙述、耳语、耳语。 |
| |
| 正文规则：每行 8 到 40 个汉字；只能用汉字和，。？！这几种标点；不要数字、英文、emoji、引号、括号。""" |
|
|
| SHOT = """示例： |
| 开场：晚上好，这里是深夜频道，我是今晚陪你的人。 |
| 叙述：窗外的雨下了一整晚，路灯把水汽照成一片模糊的黄。 |
| 故事：很多年前这条街上有一家很小的唱片店，老板总在打烊前放同一首歌。 |
| 叙述：如果你还醒着，就把灯关小一点，让声音替你把今天收好。 |
| 耳语：后来店关了，歌还在。 |
| 耳语：晚安，我们明天见。""" |
|
|
|
|
| def log(msg: str) -> None: |
| print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True) |
|
|
|
|
| def build_prompt(topic: str, few_shot: bool = True) -> str: |
| head = f"{RULES}\n\n{SHOT}" if few_shot else RULES |
| return f"{head}\n\n现在写「{topic}」，只输出这六行。" |
|
|
|
|
| def parse_script(raw: str) -> list[dict]: |
| """Read either six prefixed lines or (if a model insists) a JSON array.""" |
| out = [] |
| for line in raw.splitlines(): |
| line = line.strip().strip("`").strip() |
| if not line: |
| continue |
| m = re.match(r"^(开场|叙述|故事|耳语)\s*[：:]\s*(.+)$", line) |
| if m: |
| out.append({"style": m.group(1), "text": m.group(2).strip()}) |
| continue |
| m = re.search(r'"style"\s*:\s*"([^"]*)"\s*,\s*"text"\s*:\s*"([^"]*)"', line) |
| if m: |
| out.append({"style": m.group(1), "text": m.group(2)}) |
| if not out: |
| pairs = re.findall(r'"style"\s*:\s*"([^"]*)"\s*,\s*"text"\s*:\s*"([^"]*)"', raw) |
| out = [{"style": s, "text": t} for s, t in pairs if t.strip()] |
| return out |
|
|
|
|
| def trim(text: str, limit: int = MAX_CHARS) -> str: |
| """Cut an over-long line at its last clause boundary under the limit.""" |
| if len(text) <= limit: |
| return text |
| cut = text[:limit] |
| for i in range(len(cut) - 1, MIN_CHARS - 1, -1): |
| if cut[i] in "，。？！": |
| return cut[:i] + "。" |
| return cut[: limit - 1] + "。" |
|
|
|
|
| def check(lines: list[dict]) -> dict: |
| bad = [] |
| if len(lines) != len(SLOTS): |
| bad.append(f"count={len(lines)}") |
| for i, l in enumerate(lines): |
| t = l.get("text", "") |
| if i < len(SLOTS) and l.get("style") != SLOTS[i]: |
| bad.append(f"L{i}:style={l.get('style')!r}") |
| if not (MIN_CHARS <= len(t) <= SLOT_MAX[min(i, len(SLOT_MAX) - 1)]): |
| bad.append(f"L{i}:len={len(t)}") |
| if LATIN_OR_DIGIT.search(t) or not ALLOWED.match(t): |
| bad.append(f"L{i}:chars") |
| return {"ok": not bad, "violations": bad} |
|
|
|
|
| def load_model(): |
| import torch |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
|
|
| tok = AutoTokenizer.from_pretrained(MODEL_ID) |
| model = AutoModelForCausalLM.from_pretrained( |
| MODEL_ID, dtype=torch.float16, device_map="cuda:0").eval() |
| if ADAPTER and Path(ADAPTER).exists(): |
| from peft import PeftModel |
| model = PeftModel.from_pretrained(model, ADAPTER).eval() |
| log(f"adapter {ADAPTER}") |
| log(f"loaded {MODEL_ID} -> {sum(p.numel() for p in model.parameters())/1e6:.0f}M params") |
| return tok, model |
|
|
|
|
| def sample(tok, model, topic: str, few_shot: bool = True) -> dict: |
| import torch |
|
|
| msgs = [{"role": "system", "content": SYSTEM}, |
| {"role": "user", "content": build_prompt(topic, few_shot)}] |
| text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True) |
| ids = tok([text], return_tensors="pt").to("cuda") |
| t0 = time.time() |
| with torch.no_grad(): |
| gen = model.generate(**ids, max_new_tokens=512, do_sample=True, |
| temperature=0.8, top_p=0.9, top_k=20, |
| repetition_penalty=1.05, |
| pad_token_id=tok.eos_token_id) |
| raw = tok.decode(gen[0][ids["input_ids"].shape[1]:], skip_special_tokens=True) |
| lines = parse_script(raw) |
| return {"topic": topic, "seconds": round(time.time() - t0, 1), |
| "n_tokens": int(gen.shape[1] - ids["input_ids"].shape[1]), |
| "lines": lines, "raw": raw, **check(lines)} |
|
|
|
|
| def main() -> None: |
| OUT.mkdir(parents=True, exist_ok=True) |
| tok, model = load_model() |
|
|
| if MODE in ("probe", "eval"): |
| rows = [sample(tok, model, TOPICS[i % len(TOPICS)]) for i in range(N_SAMPLES)] |
| for r in rows: |
| log(f"{r['topic'][:8]:<10} {r['seconds']:>4}s n={len(r['lines'])} ok={r['ok']} {r['violations'][:3]}") |
| for l in r["lines"]: |
| log(f" [{l.get('style')}] {len(l.get('text',''))}字 {l.get('text')}") |
| ok = sum(r["ok"] for r in rows) |
| log(f"pass {ok}/{len(rows)}") |
| (OUT / f"{MODE}.json").write_text(json.dumps(rows, ensure_ascii=False, indent=1)) |
|
|
| elif MODE == "collect": |
| target = int(os.environ.get("TARGET", "160")) |
| max_tries = int(os.environ.get("MAX_TRIES", "600")) |
| kept, tries = [], 0 |
| while len(kept) < target and tries < max_tries: |
| topic = TOPICS[tries % len(TOPICS)] |
| r = sample(tok, model, topic) |
| tries += 1 |
| if len(r["lines"]) != len(SLOTS): |
| continue |
| |
| |
| |
| lines = [{"style": SLOTS[i], "text": trim(l["text"], SLOT_MAX[i])} |
| for i, l in enumerate(r["lines"])] |
| if any(LATIN_OR_DIGIT.search(l["text"]) or len(l["text"]) < MIN_CHARS for l in lines): |
| continue |
| kept.append({"topic": topic, "lines": lines}) |
| if len(kept) % 10 == 0: |
| log(f"{tries} tried, {len(kept)} kept ({len(kept)/tries:.0%})") |
| with open(OUT / "sft.jsonl", "w") as f: |
| for g in kept: |
| f.write(json.dumps(g, ensure_ascii=False) + "\n") |
| log(f"kept {len(kept)} of {tries}") |
|
|
|
|
| elif MODE == "script": |
| |
| |
| topic = os.environ.get("TOPIC") or TOPICS[0] |
| best = None |
| for attempt in range(int(os.environ.get("TRIES", "6"))): |
| r = sample(tok, model, topic) |
| log(f"try {attempt}: n={len(r['lines'])} ok={r['ok']} {r['violations'][:3]}") |
| if r["ok"]: |
| best = r |
| break |
| if best is None and len(r["lines"]) >= len(SLOTS) - 1: |
| best = r |
| if best is None: |
| raise SystemExit("no script generated") |
| lines = [{"style": SLOTS[i], "text": trim(l["text"], SLOT_MAX[i])} |
| for i, l in enumerate(best["lines"][:len(SLOTS)])] |
| doc = {"model": MODEL_ID, "topic": topic, "tries": attempt + 1, |
| "lines": lines} |
| (OUT / "script.json").write_text(json.dumps(doc, ensure_ascii=False, indent=1)) |
| log(f"wrote {OUT/'script.json'} ({len(lines)} lines, {best.get('seconds')}s)") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
