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# -*- coding: utf-8 -*-
"""Export aligned inputs for response TTS, talker inference, and e2e Omni.
The query construction pipeline produces three related artifacts:
1. response_controls.jsonl: response-side Global/Control, used as oracle TTS.
2. thinker_targets.jsonl: constructed thinker output, used as talker input.
3. query audio dirs: synthesized user-query audio, used as full-Omni input.
This script keeps qid as the stable join key and writes compact jsonl files for
the next inference stages.
"""
from __future__ import annotations
import argparse
import glob
import json
import os
from pathlib import Path
from typing import Any, Dict, Iterable, List, Optional, Tuple
BASE_DIR = Path("/workspace/echoloc/Dataset/Novel/query_data/v2_2000")
DEFAULT_RESPONSE_CONTROLS = BASE_DIR / "thinker_targets/response_controls.jsonl"
DEFAULT_THINKER_TARGETS = BASE_DIR / "thinker_targets/thinker_targets.jsonl"
DEFAULT_QUERY_QWEN3_DIR = BASE_DIR / "query_audio/qwen3tts_structured_zh_serena_a800"
DEFAULT_QUERY_INDEXTTS_DIR = BASE_DIR / "query_audio/indextts_structured_zh_serena_a800_floatsave"
DEFAULT_OUT_DIR = BASE_DIR / "eval_inputs"
def iter_jsonl(path: Path) -> Iterable[Dict[str, Any]]:
with path.open("r", encoding="utf-8") as f:
for line in f:
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)
n = 0
with path.open("w", encoding="utf-8") as f:
for row in rows:
f.write(json.dumps(row, ensure_ascii=False) + "\n")
n += 1
return n
def load_by_qid(path: Path) -> Dict[str, Dict[str, Any]]:
out: Dict[str, Dict[str, Any]] = {}
for row in iter_jsonl(path):
qid = row.get("qid")
if qid:
out[str(qid)] = row
return out
def _read_instruct_json(path: str) -> Optional[Dict[str, Any]]:
try:
with open(path, "r", encoding="utf-8") as f:
return json.load(f)
except Exception:
return None
def meta_language(meta: Dict[str, Any], qid: str) -> str:
language = str(meta.get("language") or "").strip().lower()
if language:
return language
qid = str(qid or "")
if qid.startswith("vstyle_en_") or "_en_" in qid:
return "en"
return "zh"
def discover_query_audio(qwen3_dir: Path, indextts_dir: Path) -> Dict[str, Dict[str, Any]]:
"""Map qid -> synthesized query audio paths.
The TTS scripts use line_idx subdirectories, but each Qwen3TTS subdir keeps a
*_instruct.json containing the original qid/instruct_id. We use that to join
against response_controls and thinker_targets.
"""
out: Dict[str, Dict[str, Any]] = {}
for subdir in sorted(qwen3_dir.glob("*")):
if not subdir.is_dir():
continue
line_idx = subdir.name
instruct_paths = list(subdir.glob("*_instruct.json"))
if not instruct_paths:
continue
meta = _read_instruct_json(str(instruct_paths[0])) or {}
qid = meta.get("instruct_id")
if not qid:
continue
language = meta_language(meta, str(qid))
qwen3_vd = subdir / f"{line_idx}_vd_{language}.wav"
qwen3_cv = subdir / f"{line_idx}_cv_serena_{language}.wav"
if language != "zh":
qwen3_vd_zh = subdir / f"{line_idx}_vd_zh.wav"
qwen3_cv_zh = subdir / f"{line_idx}_cv_serena_zh.wav"
else:
qwen3_vd_zh = qwen3_vd
qwen3_cv_zh = qwen3_cv
indextts_wav = indextts_dir / line_idx / f"{line_idx}_indextts_spk-zh_emo-zh_serena.wav"
indextts_control = indextts_dir / line_idx / f"{line_idx}_indextts_spk-zh_emo-zh_serena_control.json"
query_audio_path = ""
query_audio_control_path = ""
if indextts_wav.exists():
query_audio_path = str(indextts_wav)
query_audio_control_path = str(indextts_control) if indextts_control.exists() else ""
elif qwen3_cv.exists():
query_audio_path = str(qwen3_cv)
elif qwen3_vd.exists():
query_audio_path = str(qwen3_vd)
elif language != "zh" and qwen3_cv_zh.exists():
query_audio_path = str(qwen3_cv_zh)
elif language != "zh" and qwen3_vd_zh.exists():
query_audio_path = str(qwen3_vd_zh)
if not query_audio_path:
continue
out[str(qid)] = {
"qid": str(qid),
"language": language,
"query_audio_line_idx": line_idx,
"query_audio_path": query_audio_path,
"query_audio_control_path": query_audio_control_path,
"query_qwen3_vd_path": str(qwen3_vd) if qwen3_vd.exists() else "",
"query_qwen3_cv_path": str(qwen3_cv) if qwen3_cv.exists() else "",
"query_qwen3_vd_zh_path": str(qwen3_vd_zh) if qwen3_vd_zh.exists() else "",
"query_qwen3_cv_zh_path": str(qwen3_cv_zh) if qwen3_cv_zh.exists() else "",
"query_tts_meta": meta,
}
return out
def combined_text(target: Dict[str, Any]) -> Tuple[str, str]:
combined = target.get("combined") or {}
style = str(combined.get("instruct") or "").strip()
text = str(combined.get("txt") or "").strip()
return style, text
def make_talker_row(target: Dict[str, Any], aligned: bool) -> Dict[str, Any]:
style, text = combined_text(target)
qid = str(target["qid"])
return {
"id": qid,
"qid": qid,
"language": target.get("language", "zh"),
"ability": "constructed_thinker_to_talker",
"query_type": target.get("query_type"),
"thinker_style": style,
"thinker_text": text,
"need_emochange": target.get("need_emochange"),
"source_query": target.get("source_query"),
"aligned_with_query_audio": aligned,
}
def make_e2e_row(
target: Dict[str, Any],
response: Dict[str, Any],
audio: Dict[str, Any],
) -> Dict[str, Any]:
style, text = combined_text(target)
qid = str(target["qid"])
visible_query = (
response.get("visible_query")
or (response.get("source_query_candidate") or {}).get("visible_query")
or target.get("source_query")
or {}
)
return {
"id": qid,
"qid": qid,
"query_type": target.get("query_type") or response.get("query_type"),
"language": target.get("language", "zh"),
"query_text": visible_query.get("text", ""),
"query_audio_path": audio["query_audio_path"],
"query_audio_control_path": audio.get("query_audio_control_path", ""),
"oracle_thinker_style": style,
"oracle_thinker_text": text,
"oracle_response_text": response.get("audio_content", text.replace(" <|EMO_CHANGE|> ", "")),
"response_control": response.get("final_generated_control") or response.get("response_generated_control"),
"source_event": response.get("source_event"),
}
def make_manifest_row(
target: Dict[str, Any],
response: Dict[str, Any],
audio: Dict[str, Any],
) -> Dict[str, Any]:
e2e = make_e2e_row(target, response, audio)
return {
**e2e,
"talker_input_id": target["qid"],
"response_tts_input_qid": response["qid"],
}
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--response_controls", type=Path, default=DEFAULT_RESPONSE_CONTROLS)
ap.add_argument("--thinker_targets", type=Path, default=DEFAULT_THINKER_TARGETS)
ap.add_argument("--query_qwen3_dir", type=Path, default=DEFAULT_QUERY_QWEN3_DIR)
ap.add_argument("--query_indextts_dir", type=Path, default=DEFAULT_QUERY_INDEXTTS_DIR)
ap.add_argument("--out_dir", type=Path, default=DEFAULT_OUT_DIR)
args = ap.parse_args()
responses = load_by_qid(args.response_controls)
targets = load_by_qid(args.thinker_targets)
query_audio = discover_query_audio(args.query_qwen3_dir, args.query_indextts_dir)
qids_target = set(targets)
qids_response = set(responses)
qids_audio = set(query_audio)
aligned_qids = sorted(qids_target & qids_response & qids_audio)
args.out_dir.mkdir(parents=True, exist_ok=True)
talker_all = [
make_talker_row(targets[qid], aligned=(qid in aligned_qids))
for qid in sorted(qids_target)
]
talker_aligned = [row for row in talker_all if row["aligned_with_query_audio"]]
e2e_rows = [
make_e2e_row(targets[qid], responses[qid], query_audio[qid])
for qid in aligned_qids
]
manifest_rows = [
make_manifest_row(targets[qid], responses[qid], query_audio[qid])
for qid in aligned_qids
]
response_aligned = [responses[qid] for qid in aligned_qids]
counts = {
"response_controls": len(responses),
"thinker_targets": len(targets),
"query_audio": len(query_audio),
"aligned": len(aligned_qids),
"missing_response_for_target": len(qids_target - qids_response),
"missing_query_audio_for_target": len(qids_target - qids_audio),
}
written = {
"talker_input_all": write_jsonl(args.out_dir / "talker_input_all.jsonl", talker_all),
"talker_input_aligned": write_jsonl(args.out_dir / "talker_input_aligned.jsonl", talker_aligned),
"e2e_input_aligned": write_jsonl(args.out_dir / "e2e_input_aligned.jsonl", e2e_rows),
"aligned_manifest": write_jsonl(args.out_dir / "aligned_manifest.jsonl", manifest_rows),
"response_tts_input_aligned": write_jsonl(args.out_dir / "response_tts_input_aligned.jsonl", response_aligned),
}
with (args.out_dir / "export_summary.json").open("w", encoding="utf-8") as f:
json.dump({"counts": counts, "written": written}, f, ensure_ascii=False, indent=2)
print(json.dumps({"counts": counts, "written": written, "out_dir": str(args.out_dir)}, ensure_ascii=False, indent=2))
if __name__ == "__main__":
main()
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