director: Qwen3.5-0.8B probe against the TTS contract

kaggle/director_probe.py

@@ -0,0 +1,118 @@
+"""Probe: can Qwen3.5-0.8B write a script that the TTS front end can consume?
+
+The failure in v2/v3 was not the schema alone. A line that is *valid JSON* can
+still be unusable for Ming: five characters is long enough for the whisper voice
+to collapse, digits and Latin words bypass the normaliser we turned off, and
+emoji break the text encoder. So this measures the contract, not just the syntax.
+
+Runs on transformers 5.x, which cannot share a process with Ming (pinned at
+4.46.3). That is why the director is a separate stage with its own JSON file.
+"""
+import json
+import os
+import re
+import time
+from pathlib import Path
+
+import torch
+
+ROOT = Path(os.environ.get("WORK_ROOT", "/kaggle/working"))
+MODEL_ID = os.environ.get("DIRECTOR_MODEL", "Qwen/Qwen3.5-0.8B")
+OUT = ROOT / "director"
+N_SAMPLES = int(os.environ.get("N_SAMPLES", "6"))
+
+STYLES = {"开场", "叙述", "故事", "耳语"}
+MIN_CHARS, MAX_CHARS = 8, 30
+# Anything outside this set has to be handled by a normaliser we deliberately
+# turned off, so the director is not allowed to emit it.
+ALLOWED = re.compile(r"^[\u4e00-\u9fff，。？！、；：…—]+$")
+
+PROMPT = """你是一档深夜电台的编剧。写一段约 60 秒的节目串词，一共 6 句。
+
+硬性规则：
+1. 正好 6 句，顺序是：开场、叙述、故事、叙述、耳语、耳语。
+2. 每句 8 到 30 个汉字。
+3. 只能用汉字和中文标点（，。？！），不要数字、英文、emoji、括号、引号。
+4. 内容要连贯：讲一件小事，最后两句压低声音收尾。
+
+只输出一个 JSON 数组，不要解释，不要代码块。格式：
+[{"style":"开场","text":"..."},{"style":"叙述","text":"..."},{"style":"故事","text":"..."},{"style":"叙述","text":"..."},{"style":"耳语","text":"..."},{"style":"耳语","text":"..."}]"""
+
+
+def log(msg: str) -> None:
+    print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True)
+
+
+def parse(raw: str) -> list[dict]:
+    """Same tolerant extraction the runtime uses: pair-scan, not json.loads."""
+    pairs = re.findall(r'"style"\s*:\s*"([^"]*)"\s*,\s*"text"\s*:\s*"([^"]*)"', raw)
+    if not pairs:
+        pairs = [(s, t) for t, s in re.findall(
+            r'"text"\s*:\s*"([^"]*)"\s*,\s*"style"\s*:\s*"([^"]*)"', raw)]
+    return [{"style": s, "text": t} for s, t in pairs if t.strip()]
+
+
+def check(lines: list[dict]) -> dict:
+    """Every reason the TTS front end would have to reject this script."""
+    bad = []
+    if len(lines) != 6:
+        bad.append(f"count={len(lines)}")
+    if lines and lines[0].get("style") != "开场":
+        bad.append("first!=开场")
+    for i, l in enumerate(lines):
+        t = l.get("text", "")
+        if l.get("style") not in STYLES:
+            bad.append(f"L{i}:style={l.get('style')!r}")
+        if not (MIN_CHARS <= len(t) <= MAX_CHARS):
+            bad.append(f"L{i}:len={len(t)}")
+        if not ALLOWED.match(t):
+            bad.append(f"L{i}:chars")
+    whisper_at = [i for i, l in enumerate(lines) if l.get("style") == "耳语"]
+    if whisper_at and whisper_at[-1] < len(lines) - 2:
+        bad.append("whisper not at the end")
+    return {"ok": not bad, "violations": bad}
+
+
+def main() -> None:
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+
+    OUT.mkdir(parents=True, exist_ok=True)
+    log(f"loading {MODEL_ID}")
+    tok = AutoTokenizer.from_pretrained(MODEL_ID)
+    model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype=torch.float16).cuda().eval()
+    log(f"loaded, {sum(p.numel() for p in model.parameters())/1e6:.0f}M params")
+
+    msgs = [{"role": "user", "content": PROMPT}]
+    text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
+    ids = tok([text], return_tensors="pt").to("cuda")
+
+    results = []
+    for k in range(N_SAMPLES):
+        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,
+                                 presence_penalty=1.0, 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)
+        dt = time.time() - t0
+        lines = parse(raw)
+        v = check(lines)
+        results.append({"sample": k, "seconds": round(dt, 1),
+                        "n_tokens": int(gen.shape[1] - ids["input_ids"].shape[1]),
+                        "lines": lines, "raw": raw, **v})
+        log(f"#{k} {dt:.0f}s parsed={len(lines)} ok={v['ok']} {v['violations'][:4]}")
+        for l in lines:
+            log(f"     [{l.get('style')}] {len(l.get('text',''))}字 {l.get('text')}")
+
+    ok = sum(1 for r in results if r["ok"])
+    log(f"pass {ok}/{len(results)}")
+    lens = [len(l["text"]) for r in results for l in r["lines"]]
+    if lens:
+        lens.sort()
+        log(f"line length: min {lens[0]} median {lens[len(lens)//2]} max {lens[-1]}")
+    (OUT / "probe.json").write_text(json.dumps(results, ensure_ascii=False, indent=1))
+
+
+if __name__ == "__main__":
+    main()