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d8bfe4a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Build Qwen3-Omni thinker SFT jsonl from aligned Novel query data."""
from __future__ import annotations
import argparse
import collections
import datetime as dt
import json
import random
import re
import shutil
from pathlib import Path
from typing import Any, Dict, Iterable, List, Tuple
from prompt_loader import render_prompt
DEFAULT_INPUT = Path("/workspace/echoloc/Dataset/Novel/query_data/v2_2000/eval_inputs/e2e_input_aligned.jsonl")
DEFAULT_OUT_DIR = Path("/workspace/echoloc/Dataset/Novel/query_data/v2_2000/sft/thinker_emochange")
EMO_TOKEN = "<|EMO_CHANGE|>"
def normalize_emo_change(text: str, language: str) -> str:
text = (text or "").replace("<EMO_CHANGE>", EMO_TOKEN)
if language == "en":
text = re.sub(r"\s*<\|EMO_CHANGE\|>\s*", f" {EMO_TOKEN} ", text)
text = re.sub(r" {2,}", " ", text)
return text.strip()
return re.sub(r"\s*<\|EMO_CHANGE\|>\s*", EMO_TOKEN, text).strip()
def iter_jsonl(path: Path) -> Iterable[Dict[str, Any]]:
with path.open("r", encoding="utf-8") as handle:
for line in handle:
line = line.strip()
if line:
yield json.loads(line)
def write_jsonl(path: Path, rows: Iterable[Dict[str, Any]]) -> int:
path.parent.mkdir(parents=True, exist_ok=True)
count = 0
with path.open("w", encoding="utf-8") as handle:
for row in rows:
handle.write(json.dumps(row, ensure_ascii=False) + "\n")
count += 1
return count
def build_sample(row: Dict[str, Any]) -> Dict[str, Any]:
language = row.get("language", "zh") or "zh"
query_type = row.get("query_type", "unknown") or "unknown"
style = (row.get("oracle_thinker_style") or "").strip()
text = normalize_emo_change(row.get("oracle_thinker_text") or "", language)
answer = render_prompt("thinker_sft.answer_template", "Style: __STYLE__\n\nText: __TEXT__", {"STYLE": style, "TEXT": text})
return {
"id": row["id"],
"task": "novel_query_emochange",
"audio_url": row["query_audio_path"],
"language": language,
"ability": f"novel/{query_type}",
"query_type": query_type,
"answer": answer,
"thinking": "",
"qid": row.get("qid", row.get("id")),
"source_query": row.get("query_text", ""),
"query_audio_control_path": row.get("query_audio_control_path", ""),
"oracle_response_text": row.get("oracle_response_text", ""),
"source_event": row.get("source_event", ""),
"emo_change_spacing_rule": "en:space_around; zh:no_space_around",
}
def split_by_query_type(samples: List[Dict[str, Any]], val_ratio: float, seed: int) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
by_type: Dict[str, List[Dict[str, Any]]] = collections.defaultdict(list)
for sample in samples:
by_type[sample.get("query_type") or "unknown"].append(sample)
rng = random.Random(seed)
train: List[Dict[str, Any]] = []
val: List[Dict[str, Any]] = []
for key in sorted(by_type):
group = by_type[key]
rng.shuffle(group)
n_val = max(1, round(len(group) * val_ratio))
val.extend(group[:n_val])
train.extend(group[n_val:])
rng.shuffle(train)
rng.shuffle(val)
return train, val
def spacing_stats(rows: Iterable[Dict[str, Any]]) -> Dict[str, int]:
counts: collections.Counter[Tuple[str, bool, bool]] = collections.Counter()
for row in rows:
answer = row.get("answer", "")
start = 0
while True:
idx = answer.find(EMO_TOKEN, start)
if idx < 0:
break
left = answer[idx - 1] if idx > 0 else ""
right_pos = idx + len(EMO_TOKEN)
right = answer[right_pos] if right_pos < len(answer) else ""
counts[(row.get("language", ""), left == " ", right == " ")] += 1
start = right_pos
return {str(key): value for key, value in sorted(counts.items())}
def validate_spacing(rows: Iterable[Dict[str, Any]]) -> List[str]:
errors: List[str] = []
for row in rows:
answer = row.get("answer", "")
start = 0
while True:
idx = answer.find(EMO_TOKEN, start)
if idx < 0:
break
left = answer[idx - 1] if idx > 0 else ""
right_pos = idx + len(EMO_TOKEN)
right = answer[right_pos] if right_pos < len(answer) else ""
language = row.get("language", "")
if language == "zh" and (left == " " or right == " "):
errors.append(f"{row.get('id')}: zh token has surrounding space")
if language == "en" and not (left == " " and right == " "):
errors.append(f"{row.get('id')}: en token lacks surrounding spaces")
start = right_pos
return errors
def backup_existing(out_dir: Path) -> str:
existing = [out_dir / name for name in ("train.jsonl", "val.jsonl", "manifest.json") if (out_dir / name).exists()]
if not existing:
return ""
stamp = dt.datetime.now().strftime("%Y%m%d_%H%M%S")
backup_dir = out_dir / f"backup_before_sft_rebuild_{stamp}"
backup_dir.mkdir(parents=True, exist_ok=True)
for path in existing:
shutil.copy2(path, backup_dir / path.name)
return str(backup_dir)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--input", type=Path, default=DEFAULT_INPUT)
parser.add_argument("--out_dir", type=Path, default=DEFAULT_OUT_DIR)
parser.add_argument("--val_ratio", type=float, default=0.05)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--no_backup", action="store_true")
return parser.parse_args()
def main() -> int:
args = parse_args()
samples = [build_sample(row) for row in iter_jsonl(args.input)]
train, val = split_by_query_type(samples, args.val_ratio, args.seed)
errors = validate_spacing(train) + validate_spacing(val)
if errors:
for error in errors[:20]:
print(f"[ERROR] {error}")
raise SystemExit(f"invalid EMO_CHANGE spacing: {len(errors)} errors")
args.out_dir.mkdir(parents=True, exist_ok=True)
(args.out_dir / "logs").mkdir(exist_ok=True)
(args.out_dir / "output").mkdir(exist_ok=True)
backup_dir = "" if args.no_backup else backup_existing(args.out_dir)
write_jsonl(args.out_dir / "train.jsonl", train)
write_jsonl(args.out_dir / "val.jsonl", val)
summary = {
"created_at": dt.datetime.now().isoformat(timespec="seconds"),
"source": str(args.input),
"output_dir": str(args.out_dir),
"backup_before_rebuild": backup_dir,
"split_seed": args.seed,
"split_method": "stratified_by_query_type",
"val_ratio": args.val_ratio,
"emo_change_spacing_rule": "en: add one space before and after token; zh: remove spaces around token",
"total": len(samples),
"train": len(train),
"val": len(val),
"query_type_total": dict(collections.Counter(s.get("query_type") for s in samples)),
"query_type_train": dict(collections.Counter(s.get("query_type") for s in train)),
"query_type_val": dict(collections.Counter(s.get("query_type") for s in val)),
"emo_change_total": sum(EMO_TOKEN in s["answer"] for s in samples),
"emo_change_train": sum(EMO_TOKEN in s["answer"] for s in train),
"emo_change_val": sum(EMO_TOKEN in s["answer"] for s in val),
"spacing_train": spacing_stats(train),
"spacing_val": spacing_stats(val),
}
(args.out_dir / "manifest.json").write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8")
print(json.dumps(summary, ensure_ascii=False, indent=2))
return 0
if __name__ == "__main__":
raise SystemExit(main())
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