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humomni2026-track1-empathyeval-team111-final-reproducibility-archive

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Track 1 (EmpathyEval) — code_v5.1 (final, path-generalized copy)

Single, response-aware codebase for HumOmni Track 1: EmpathyEval. A bilingual README — English first (for reviewers), Chinese after (for the maintainer). This is the final v5.1 archive: Task1 uses a pointwise-listwise, 6-prompt-branch, two-stage-fusion pipeline; Task2 uses a single response-aware branch inherited unchanged from v4. All data / model / test paths are resolved from two small JSON files (data_paths.json, test_paths.json) — nothing about "test1" vs "stage2" (or any other round name) is hard-coded anywhere in the scripts.

English

0. Quick Reproduction Guide (Full Pipeline: Data Processing → Training → Testing)

This section is a self-contained, copy-pasteable command list covering the entire pipeline (not just test-time inference). If you only need to reproduce the final submission from the checkpoints already shipped in outputs/models/, you can skip straight to A.3/A.4 below.

A.1 Installation

bash

conda create -n qwen_test python=3.10 -y
conda activate qwen_test
cd /path/to/code_v5.1_filter

python -m pip install --upgrade pip
python -m pip install -r requirements.txt

# Install the vendored Qwen2.5-Omni-compatible transformers build
SITE=$(python -c "import site; print(site.getsitepackages()[0])")
tar -xzf vendor/transformers_qwen_omni_sitepkg.tar.gz -C "$SITE"

Sanity checks:

bash

python - <<PY
import torch
print(torch.__version__, torch.version.cuda,
      torch.cuda.is_available(), torch.cuda.get_device_name(0))
PY
python - <<PY
from transformers import Qwen2_5OmniForConditionalGeneration, Qwen2_5OmniProcessor
print("Qwen2.5-Omni import ok")
PY

A.2 Data Processing & Training (only if retraining from scratch; skip if you just want to reproduce the submission from the shipped checkpoints)

Edit data_paths.json:

json

{
  "data_root": "/path/to/your/dataset_root",
  "model_path": "Qwen/Qwen2.5-Omni-7B"
}

data_root must contain the official dataset unpacked as-is:

text

<data_root>/
├── empatheticDialogue_t_multi-context_flat.jsonl
├── empatheticDialogue_n_multi-emotion_flat.jsonl
├── empatheticDialogue_t_multi-context/{user_audio, response_audio}
└── empatheticDialogue_n_multi-emotion/{user_audio, response_audio}

One-shot (runs everything below automatically):

python prepare_and_train.py

Equivalent step-by-step commands (same thing, for manual/partial reruns):

bash

# 1) official flat jsonl -> finetune_task*.jsonl (adds "response" field)
python 01_prepare_finetune.py --task all

# 2) hold-out val split + intra-group hard negatives + task1 A/B-swap augmentation
python 02_build_augmented.py --task all

# 3) task1 only: binary -> pointwise (per-candidate) train/val
python 02b_build_pointwise.py

# 4) task1: train the 6 independent prompt-branch LoRAs (P0-P5)
python 03b_train_pointwise.py --exp_config exp_configs/task1_p0.yaml
python 03b_train_pointwise.py --exp_config exp_configs/task1_p1.yaml
python 03b_train_pointwise.py --exp_config exp_configs/task1_p2.yaml
python 03b_train_pointwise.py --exp_config exp_configs/task1_p3.yaml
python 03b_train_pointwise.py --exp_config exp_configs/task1_p4.yaml
python 03b_train_pointwise.py --exp_config exp_configs/task1_p5.yaml

# 5) task2: train the single response-aware branch
python 03_train.py --exp_config exp_configs/task2_resp.yaml

(Optional) calibrate fusion weights on val — only needed if branches/prompts changed; config.py's FUSION_CONFIG already holds the real values used for the submission:

bash

python 04_eval_pointwise.py --branch p0   # ... p1 .. p5
python 04_eval_pointwise.py --fuse_only

A.3 Test-Set Path Configuration

Edit test_paths.json:

json

{
  "model_path": "Qwen/Qwen2.5-Omni-7B",
  "task2_lora": "outputs/models/task2_resp/checkpoint-epoch1-step1500",
  "output_submission": "outputs/submission_track1.jsonl",
  "test_sets": {
    "task1_gigaspeech_dir": "/path/to/your/test_batch/task1_gigaspeech_dir",
    "task1_gigaspeech_json": "release.json",
    "task1_meld_dir": "/path/to/your/test_batch/task1_meld_dir",
    "task1_meld_json": "release.json",
    "task2_emovdb_dir": "/path/to/your/test_batch/task2_emovdb_dir",
    "task2_emovdb_json": "release.json"
  }
}

A.4 Reproduction Command

python test_submit.py

Equivalent step-by-step commands:

bash

# Task1: per-candidate scoring for each of the 6 branches (P0-P5)
python 05_test_pointwise.py --exp_config exp_configs/task1_p0.yaml --subset all
python 05_test_pointwise.py --exp_config exp_configs/task1_p1.yaml --subset all
python 05_test_pointwise.py --exp_config exp_configs/task1_p2.yaml --subset all
python 05_test_pointwise.py --exp_config exp_configs/task1_p3.yaml --subset all
python 05_test_pointwise.py --exp_config exp_configs/task1_p4.yaml --subset all
python 05_test_pointwise.py --exp_config exp_configs/task1_p5.yaml --subset all

# Task1: two-stage weighted fusion (fixed production weights, see A.5)
python 07_fuse_task1.py \
  --branches p0 p1 p2 p3 p4 p5 \
  --alpha 0.5 \
  --tau_percentile 0.20 \
  --out outputs/predictions/task1_fused_Bplus_predictions.jsonl

# Task2: single-checkpoint inference
python 05_test.py \
  --exp_config exp_configs/task2_resp.yaml \
  --lora outputs/models/task2_resp/checkpoint-epoch1-step1500

# Merge Task1 (GigaSpeech/MELD) + Task2 (EmoV-DB) -> final submission
python 06_merge.py \
  --task1_giga outputs/predictions/task1_fused_Bplus_predictions_gigaspeech.jsonl \
  --task1_meld outputs/predictions/task1_fused_Bplus_predictions_meld.jsonl \
  --task2_emov outputs/predictions/task2_checkpoint-epoch1-step1500_withresp_emovdb.jsonl \
  --out outputs/submission_track1.jsonl

A.5 Fixed Fusion Settings

Stage	Weights or settings
Task1 stage-1 fusion	`p0=0.23, p1=0.20, p2=0.06, p3=0.15, p4=0.14, p5=0.22`
Task1 stage-2 fusion	`p1=0.25, p3=0.20, p5=0.55`
Hard-sample blend	`alpha=0.5, tau_percentile=0.20`
Task2	single `task2_resp` checkpoint; no multi-branch fusion

A.6 Expected Output

bash

ls -lh outputs/submission_track1.jsonl
wc -l outputs/submission_track1.jsonl
head -3 outputs/submission_track1.jsonl
tail -3 outputs/submission_track1.jsonl

The output file is JSONL, one prediction per line: {"question_id": "...", "answer": "A"}

A.7 Pipeline Summary

01_prepare_finetune (task1+task2)
 -> 02_build_augmented (task1+task2)
 -> 02b_build_pointwise (task1 only)
 -> 03b_train_pointwise x6 (task1 P0-P5) + 03_train (task2)
 -> [optional] 04_eval_pointwise --branch x6 + --fuse_only (fusion-weight calibration)
 -> 05_test_pointwise x6 (task1 P0-P5 scoring)
 -> 07_fuse_task1 (two-stage weighted fusion)
 -> 05_test (task2 inference)
 -> 06_merge (merge GigaSpeech/MELD/EmoV-DB)
 -> outputs/submission_track1.jsonl

1. Overview

This codebase fine-tunes Qwen2.5-Omni-7B with LoRA to choose the most empathetic/appropriate candidate response audio among A/B(/C) options.

Task	Source flat JSONL	Group field	Test subsets	Final method
task1 (multi-context)	`empatheticDialogue_t_multi-context_flat.jsonl`	`conv_id`	gigaspeech, meld	pointwise scoring, 6 prompt branches (P0-P5), two-stage weighted fusion
task2 (multi-emotion)	`empatheticDialogue_n_multi-emotion_flat.jsonl`	`context`	emovdb	single response-aware branch (`task2_resp`), unchanged since v4

Task1's method evolved through several prior versions (pairwise + bidirectional de-biasing in v4.1/v4.2, then pointwise-listwise multi-branch fusion here in v5.1) — see the technical report for the full rationale. This archive only ships the final v5.1 method for both tasks; earlier pairwise/bidirectional scripts are not included here.

2. Two path files — this is the only thing you ever need to edit

File	Used by	Purpose
`data_paths.json`	`prepare_and_train.py`	Where the official training data (+ optionally the base model) lives, for data processing and (re)training from scratch.
`test_paths.json`	`test_submit.py`	Where the test/release data you want to score lives, for inference. Works for any batch/round of release data — just point the paths at it, no code changes needed.

Both are read by a thin Python wrapper that turns their fields into the exact environment variables config.py already knows how to consume (DATA_ROOT, MODEL_PATH, TASK1_GIGASPEECH_DIR, etc.). No script in this codebase branches on a hard-coded "test1"/"stage2" label; whichever data you point test_paths.json at is what gets scored.

3. Layout

code_v5.1/
├── data_paths.json             # EDIT ME to (re)run data processing / training from scratch
├── prepare_and_train.py        # wrapper: data_paths.json -> 01/02/02b + 03b(x6 branches)/03(task2)
├── test_paths.json             # EDIT ME to run inference / produce a submission jsonl
├── test_submit.py              # wrapper: test_paths.json -> 05_test_pointwise(x6)+07_fuse+05_test+06_merge
├── SUBMISSION.md               # short human-readable version of the two workflows above
├── ENVIRONMENT_REPRO.md        # verified Python/CUDA/package versions + vendored transformers install
├── requirements.txt
├── vendor/
│   └── transformers_qwen_omni_sitepkg.tar.gz   # vendored Qwen2.5-Omni-compatible transformers build
│                                                #   (the original GitHub preview commit used to build
│                                                #   this is no longer fetchable from GitHub — see
│                                                #   ENVIRONMENT_REPRO.md; always install from this tarball)
│
├── 01_prepare_finetune.py      # flat jsonl -> finetune_task*.jsonl (adds "response" field)
├── 02_build_augmented.py       # finetune_task*.jsonl -> task*_train.jsonl + task*_val.jsonl
│                                #   (hold-out val split, intra-group hard negatives, task1 A/B-swap aug)
├── 02b_build_pointwise.py      # task1 only: binary train/val -> pointwise (per-candidate) train/val
├── 03_train.py                 # binary LoRA fine-tuning (used for task2; task1's older binary mode)
├── 03b_train_pointwise.py      # task1 pointwise-listwise LoRA fine-tuning (trains ONE branch P0-P5)
├── 04_eval.py                  # binary validation-set evaluation (task2)
├── 04_eval_pointwise.py        # task1 pointwise val scoring + fusion-weight calibration (tau/alpha grid search)
├── 05_test.py                  # test-set prediction, binary pipeline (used for task2)
├── 05_test_pointwise.py        # test-set per-candidate scoring, ONE task1 branch at a time
├── 06_merge.py                 # merge per-subset predictions into one submission jsonl
├── 07_fuse_task1.py            # merge all 6 task1 branch score files -> final task1 predictions
│                                #   (two-stage weighted fusion; see §5 of the technical report)
├── config.py                   # all paths + hyper-params + the 6-branch / fusion registry
├── utils/
│   ├── prompts.py               # binary prompt builder (task2) + pointwise prompt builder (task1 P0-P5)
│   ├── audio_io.py              # load_audio + LRU + optional .npy cache
│   ├── group_sampler.py         # GroupedListwiseSampler (task1 pointwise: whole contrast group together)
│   └── pairwise_voting.py       # MELD 3-choice -> C(N,2) pairwise scoring, optional bidirectional
├── exp_configs/
│   ├── task1_p0.yaml ... task1_p5.yaml   # the 6 production task1 branches (pointwise_listwise mode)
│   ├── task1_baseline.yaml, task1_resp.yaml   # legacy binary-pipeline configs (kept for reference;
│   │                                            no trained checkpoint for these ships in this archive)
│   ├── task2_baseline.yaml, task2_resp.yaml   # task2_resp is the one actually used in the final submission
└── outputs/
    ├── data/               # 01/02/02b products (jsonl)
    ├── models/             # task1_p0..p5/ (6 branches) + task2_resp/ — each keeps ALL intermediate
    │                       #   checkpoints (incl. optimizer.pt/scheduler.pt, so training can resume),
    │                       #   plus best_ckpt.json recording the real val metric per checkpoint
    ├── predictions/        # per-branch score files + fused task1 predictions + task2 predictions
    └── logs/               # real train/eval/fuse logs from the original run (27 files)

4. Quick start — reproduce the submission from the shipped checkpoints

This is the common case: you just want a submission_track1.jsonl from a batch of release data, using the 7 checkpoints already included under outputs/models/.

Step 0 — environment. See ENVIRONMENT_REPRO.md for the exact verified versions and requirements.txt for the pinned packages. In short:

bash

conda create -n track1_repro python=3.10 -y
conda activate track1_repro
pip install --upgrade pip
pip install -r requirements.txt

# Install the vendored Qwen2.5-Omni-compatible transformers build
# (do NOT try to `pip install` the original GitHub preview commit — it is no
# longer fetchable, see ENVIRONMENT_REPRO.md for why):
SITE=$(python -c "import site; print(site.getsitepackages()[0])")
tar -xzf vendor/transformers_qwen_omni_sitepkg.tar.gz -C "$SITE"

Step 1 — edit test_paths.json to point at your batch of release data:

json

{
  "model_path": "Qwen/Qwen2.5-Omni-7B",
  "task2_lora": "outputs/models/task2_resp/checkpoint-epoch1-step1500",
  "output_submission": "outputs/submission_track1.jsonl",
  "test_sets": {
    "task1_gigaspeech_dir": "/path/to/your/test_batch/task1_gigaspeech_dir",
    "task1_gigaspeech_json": "release.json",
    "task1_meld_dir": "/path/to/your/test_batch/task1_meld_dir",
    "task1_meld_json": "release.json",
    "task2_emovdb_dir": "/path/to/your/test_batch/task2_emovdb_dir",
    "task2_emovdb_json": "release.json"
  }
}

Step 2 — run:

python test_submit.py

This runs, in order: 05_test_pointwise.py for each of the 6 task1 branches (P0-P5) → 07_fuse_task1.py (two-stage weighted fusion, fixed production weights, see §6) → 05_test.py for task2 (task2_resp) → 06_merge.py. The final jsonl is written to output_submission. See SUBMISSION.md for the exact fixed fusion weights and flags (--skip_task1_scores, --skip_task2) to re-run only part of the pipeline.

5. Full pipeline (data processing → training → testing)

5.1 Retraining from scratch. Edit data_paths.json (data_root = folder containing the official flat jsonl + user_audio/response_audio; model_path optional), then:

bash

python prepare_and_train.py                 # data (01/02/02b) + all 6 task1 branches + task2
python prepare_and_train.py --skip_data     # reuse existing outputs/data/*.jsonl
python prepare_and_train.py --only task1 --branches p4 p5   # retrain just these branches

5.2 Calibrating the fusion weights on val (optional). After training, run 04_eval_pointwise.py --branch p0 (... p5) to produce per-branch val score files, then 04_eval_pointwise.py --fuse_only to grid-search tau/alpha and print per-branch accuracy for manual weight tuning. The values already in config.py's FUSION_CONFIG are the real calibrated weights used for the final submission (see §6 below), so this step is only needed if you change branches/prompts and want to re-calibrate.

5.3 Testing / merging. See §4 above (test_paths.json + test_submit.py), or call 05_test_pointwise.py / 07_fuse_task1.py / 05_test.py / 06_merge.py directly — each script's own --help / module docstring documents its CLI flags.

6. Task1 method summary (final v5.1)

Each candidate audio is scored independently (score = logit(pos_token) - logit(neg_token)), not compared pairwise. Six independently-trained LoRA branches (P0-P5), each with its own prompt template (see utils/prompts.py and config.py's POINTWISE_BRANCHES), are combined in two stages:

Stage 1: weighted average of all 6 branches — p0=0.23, p1=0.20, p2=0.06, p3=0.15, p4=0.14, p5=0.22 (FUSION_CONFIG.stage1_weights).
Stage 2 (hard samples only, where the stage-1 top1/top2 margin falls below the 20th percentile): re-score with p1=0.25, p3=0.20, p5=0.55 (FUSION_CONFIG.second_stage_weights), then blend final = 0.5*stage1 + 0.5*stage2.

These are the exact production values calibrated on the local val set and used for the real submission — see the technical report (§3.5) for the full derivation and the per-branch checkpoint table.

7. Environment notes

See ENVIRONMENT_REPRO.md for the full verified version table (Python 3.10, torch==2.5.1+cu121, a vendored transformers build with Qwen2.5-Omni support, peft==0.19.1, etc.) and why the vendored tarball — not a GitHub git-commit install — is the supported reproduction path.

8. What was removed from earlier working copies of this codebase

This archive keeps the full data-processing → training → testing pipeline, but drops everything that was only useful during development and is not part of the shipped method: the stage/round-specific --stage2 code paths in config.py/05_test.py/ 06_merge.py/07_fuse_task1.py/05_test_pointwise.py (replaced by the generic test_paths.json mechanism in §2/§4 above), the multi-prompt exploration scripts (09_eval_task1_multiprompt.py, 10_test_task1_multiprompt.py) and their dedicated utils/task1_prompt_variants.py/utils/task1_scoring.py, the post-hoc majority-vote/ weighted ensembling scripts (07_ensemble_task1.py, 08_ensemble_task2_weighted.py) which belonged to the older non-pointwise pipeline, and one-off developer tools (cache_audio_npy.py, convert_wav_to_pcm.py, inspect_pred.py, translate_zh_comments.py, run_all.sh). None of these are needed to reproduce the final v5.1 submission.

9. Troubleshooting

git clone/pip install of the original transformers preview commit fails: expected — see ENVIRONMENT_REPRO.md. Use the vendored tarball in vendor/ instead.
05_test_pointwise.py picks the wrong checkpoint / errors on adapter load without --lora: it auto-resolves from best_ckpt.json, falling back to a local-directory-name match if the recorded absolute path (from the original training machine) doesn't exist here — see the module docstring / _resolve_lora_dir for the exact 4-step priority order. If it still can't find the right checkpoint, pass --lora outputs/models/task1_p*/checkpoint-... explicitly.
[ERROR] Some release json files were not found (from test_submit.py): the paths in test_paths.json are wrong; edit them and re-run.
raw_jsonl 中无任何 response 字段: rerun 01_prepare_finetune.py — the source flat jsonl must contain contexts[i].response (task1) / contexts["<emotion>_response"] (task2).

中文

1. 项目概览

本仓库使用 LoRA 在 Qwen2.5-Omni-7B 上微调，从 A/B(/C) 候选音频里挑选最共情/最贴合的那一个。

任务	源 flat JSONL	分组字段	测试子集	最终方法
task1 (multi-context)	`empatheticDialogue_t_multi-context_flat.jsonl`	`conv_id`	gigaspeech、meld	pointwise 打分，6 个 prompt 分支（P0-P5），两阶段加权融合
task2 (multi-emotion)	`empatheticDialogue_n_multi-emotion_flat.jsonl`	`context`	emovdb	单一 response-aware 分支（`task2_resp`），自 v4 起未变

task1 的方法经过了几版演进（v4.1/v4.2 是 pairwise + 双向去偏，本版 v5.1 换成 pointwise-listwise 多分支融合），完整动机见技术报告。这份归档只保留最终 v5.1 方法，更早的 pairwise/双向脚本不在其中。

2. 两个路径文件——你只需要改这两个

文件	被谁用	作用
`data_paths.json`	`prepare_and_train.py`	官方训练数据（可选还有基座模型）所在位置，用于从零跑数据处理与（重）训练。
`test_paths.json`	`test_submit.py`	要打分的测试/发布数据所在位置，用于推理。适用于任意批次/阶段的发布数据——只需把路径指过去，不用改代码。

两者都由一层很薄的 Python wrapper 读取，转成 config.py 本来就认得的环境变量（DATA_ROOT、 MODEL_PATH、TASK1_GIGASPEECH_DIR 等）。整个代码库里没有任何脚本会按硬编码的 "test1"/"stage2" 标签走不同分支；test_paths.json 指向哪批数据，打分的就是哪批。

3. 目录结构

code_v5.1/
├── data_paths.json             # 改这个：从零跑数据处理/训练
├── prepare_and_train.py        # wrapper：data_paths.json -> 01/02/02b + 03b(x6分支)/03(task2)
├── test_paths.json             # 改这个：跑推理/生成提交文件
├── test_submit.py              # wrapper：test_paths.json -> 05_test_pointwise(x6)+07_fuse+05_test+06_merge
├── SUBMISSION.md                # 上面两套流程的精简版说明
├── ENVIRONMENT_REPRO.md          # 经验证的 Python/CUDA/依赖版本 + vendored transformers 安装方式
├── requirements.txt
├── vendor/
│   └── transformers_qwen_omni_sitepkg.tar.gz   # 打包好的、兼容 Qwen2.5-Omni 的 transformers 构建
│                                                 #（原始 GitHub preview commit 现已无法从 GitHub 拉取，
│                                                 #   详见 ENVIRONMENT_REPRO.md；请始终从这个包安装）
│
├── 01_prepare_finetune.py      # flat jsonl -> finetune_task*.jsonl（新增 response 字段）
├── 02_build_augmented.py       # finetune_task*.jsonl -> task*_train.jsonl + task*_val.jsonl
│                                #  （hold-out val 切分、同组难负、task1 A/B swap 增强）
├── 02b_build_pointwise.py      # 仅 task1：二选一 train/val -> pointwise（逐候选）train/val
├── 03_train.py                  # 二选一 LoRA 训练（task2 用；task1 更早期的二选一模式）
├── 03b_train_pointwise.py       # task1 pointwise-listwise LoRA 训练（训练单个分支 P0-P5）
├── 04_eval.py                   # 二选一验证集评估（task2）
├── 04_eval_pointwise.py         # task1 pointwise val 打分 + 融合权重标定（tau/alpha 网格搜索）
├── 05_test.py                   # 测试集预测，二选一流程（task2 用）
├── 05_test_pointwise.py         # 测试集逐候选打分，一次跑一个 task1 分支
├── 06_merge.py                  # 把各子集预测合并成一份提交 jsonl
├── 07_fuse_task1.py             # 把 6 个 task1 分支的分数文件融合成最终 task1 预测
│                                 #  （两阶段加权融合；见技术报告第5节）
├── config.py                    # 全部路径 + 超参 + 六分支/融合配置表
├── utils/
│   ├── prompts.py                # 二选一 prompt 构造（task2）+ pointwise prompt 构造（task1 P0-P5）
│   ├── audio_io.py               # load_audio + LRU + 可选 .npy 缓存
│   ├── group_sampler.py          # GroupedListwiseSampler（task1 pointwise：整组一起前向）
│   └── pairwise_voting.py        # MELD 三选一 -> C(N,2) 两两打分，可选双向
├── exp_configs/
│   ├── task1_p0.yaml ... task1_p5.yaml   # 6 个生产用 task1 分支（pointwise_listwise 模式）
│   ├── task1_baseline.yaml, task1_resp.yaml   # 旧版二选一流程的配置（留作参考；
│   │                                            本归档没有这两个配置对应的训练权重）
│   ├── task2_baseline.yaml, task2_resp.yaml   # task2_resp 是最终提交实际使用的那个
└── outputs/
    ├── data/               # 01/02/02b 的产物（jsonl）
    ├── models/             # task1_p0..p5/（6分支）+ task2_resp/ —— 每个分支都保留了**全部**中间
    │                       #   checkpoint（含 optimizer.pt/scheduler.pt，可续训），以及记录每个
    │                       #   checkpoint 真实 val 指标的 best_ckpt.json
    ├── predictions/        # 各分支分数文件 + 融合后的 task1 预测 + task2 预测
    └── logs/               # 原始训练/评估/融合的真实日志（27 个文件）

4. 快速开始——用已有 checkpoint 复现提交文件

最常见的场景：拿到一批发布数据，想直接用 outputs/models/ 下已有的 7 个 checkpoint 跑出 submission_track1.jsonl。

Step 0 — 环境。 具体验证过的版本见 ENVIRONMENT_REPRO.md，依赖版本见 requirements.txt。简要步骤：

bash

conda create -n track1_repro python=3.10 -y
conda activate track1_repro
pip install --upgrade pip
pip install -r requirements.txt

# 安装打包好的、兼容 Qwen2.5-Omni 的 transformers 构建
# （不要尝试直接 pip install 原始 GitHub preview commit——它已经拉取不到了，
#   原因见 ENVIRONMENT_REPRO.md）：
SITE=$(python -c "import site; print(site.getsitepackages()[0])")
tar -xzf vendor/transformers_qwen_omni_sitepkg.tar.gz -C "$SITE"

Step 1 — 编辑 test_paths.json，指向你这批发布数据：

json

{
  "model_path": "Qwen/Qwen2.5-Omni-7B",
  "task2_lora": "outputs/models/task2_resp/checkpoint-epoch1-step1500",
  "output_submission": "outputs/submission_track1.jsonl",
  "test_sets": {
    "task1_gigaspeech_dir": "/path/to/your/test_batch/task1_gigaspeech_dir",
    "task1_gigaspeech_json": "release.json",
    "task1_meld_dir": "/path/to/your/test_batch/task1_meld_dir",
    "task1_meld_json": "release.json",
    "task2_emovdb_dir": "/path/to/your/test_batch/task2_emovdb_dir",
    "task2_emovdb_json": "release.json"
  }
}

Step 2 — 运行：

python test_submit.py

依次执行：对 6 个 task1 分支（P0-P5）跑 05_test_pointwise.py → 07_fuse_task1.py（两阶段加权融合，固定生产权重，见第6节）→ 对 task2（task2_resp）跑 05_test.py → 06_merge.py。最终 jsonl 写到 output_submission。只想重跑部分流程的话，test_submit.py 支持 --skip_task1_scores/ --skip_task2，具体见 SUBMISSION.md。

5. 完整流程（数据处理 → 训练 → 测试）

5.1 从零重训。 编辑 data_paths.json（data_root = 包含官方 flat jsonl 和 user_audio/response_audio 的目录；model_path 可选），然后：

bash

python prepare_and_train.py                 # 数据(01/02/02b) + 全部6个task1分支 + task2
python prepare_and_train.py --skip_data     # 复用已有的 outputs/data/*.jsonl
python prepare_and_train.py --only task1 --branches p4 p5   # 只重训这几个分支

5.2 在 val 上标定融合权重（可选）。 训练完后，先跑 04_eval_pointwise.py --branch p0 （...p5）产出各分支的 val 分数文件，再跑 04_eval_pointwise.py --fuse_only 做 tau/alpha 网格搜索并打印各分支准确率供手动调权重。config.py 里 FUSION_CONFIG 现有的数值就是最终提交实际用的真实标定结果（见第6节），只有改了分支/prompt 需要重新标定时才需要这一步。

5.3 测试/合并。 见上方第4节（test_paths.json + test_submit.py），或直接调用 05_test_pointwise.py/07_fuse_task1.py/05_test.py/06_merge.py——每个脚本自己的 --help/模块 docstring 里有完整命令行参数说明。

6. Task1 方法概述（最终 v5.1 版本）

每个候选音频独立打分（score = logit(pos_token) - logit(neg_token)），不是两两比较。六个独立训练的 LoRA 分支（P0-P5），各自配一套 prompt 模板（见 utils/prompts.py 和 config.py 的 POINTWISE_BRANCHES），按两阶段融合：

第一阶段：六分支加权平均——p0=0.23, p1=0.20, p2=0.06, p3=0.15, p4=0.14, p5=0.22 （FUSION_CONFIG.stage1_weights）。
第二阶段（仅对难样本：第一阶段 top1/top2 margin 低于第20百分位的题目）：用 p1=0.25, p3=0.20, p5=0.55 重打分（FUSION_CONFIG.second_stage_weights），再融合 final = 0.5*一阶段 + 0.5*二阶段。

以上都是在本地 val 集上标定、最终提交实际用的真实生产数值——完整推导过程和各分支 checkpoint 对照表见技术报告第3.5节。

7. 环境说明

完整的经验证版本表（Python 3.10、torch==2.5.1+cu121、打包好的兼容 Qwen2.5-Omni 的 transformers 构建、peft==0.19.1 等）以及为什么用 vendored 压缩包而不是 GitHub git-commit 安装，见 ENVIRONMENT_REPRO.md。

8. 相对早期工作副本删掉了什么

这份归档保留了完整的"数据处理→训练→测试"全流程，但删掉了所有只在开发阶段有用、不属于最终提交方法的部分：config.py/05_test.py/06_merge.py/07_fuse_task1.py/ 05_test_pointwise.py 里按阶段/批次区分的 --stage2 专属代码分支（已被上面第2/4节的通用 test_paths.json 机制取代）、多 prompt 探索脚本（09_eval_task1_multiprompt.py、 10_test_task1_multiprompt.py）及其专属的 utils/task1_prompt_variants.py/ utils/task1_scoring.py、属于更早期非-pointwise 流程的事后集成脚本（07_ensemble_task1.py、08_ensemble_task2_weighted.py），以及一次性开发工具（cache_audio_npy.py、convert_wav_to_pcm.py、inspect_pred.py、 translate_zh_comments.py、run_all.sh）。复现最终 v5.1 提交完全不需要这些文件。

9. 故障排查

git clone/pip install 原始 transformers preview commit 失败：预期行为，见 ENVIRONMENT_REPRO.md，改用 vendor/ 下打包好的压缩包。
不传 --lora 时 05_test_pointwise.py 选错 checkpoint / 加载 adapter 报错：脚本会自动从 best_ckpt.json 解析，如果里面记录的绝对路径（原训练机器上的）在本机不存在，会按目录名在本地重新定位——具体4级优先级见模块 docstring / _resolve_lora_dir。如果仍找不到，显式传 --lora outputs/models/task1_p*/checkpoint-...。
test_submit.py 报 [ERROR] Some release json files were not found： test_paths.json 里的路径写错了，改完重跑即可。
raw_jsonl 中无任何 response 字段：重新跑 01_prepare_finetune.py——源 flat jsonl 必须包含 contexts[i].response（task1）/ contexts["<emotion>_response"]（task2）。

zizima/humomni2026-track1-empathyeval-team111-final-reproducibility-archive

Track 1 (EmpathyEval) — code_v5.1 (final, path-generalized copy)

English

0. Quick Reproduction Guide (Full Pipeline: Data Processing → Training → Testing)

1. Overview

2. Two path files — this is the only thing you ever need to edit

3. Layout

4. Quick start — reproduce the submission from the shipped checkpoints

5. Full pipeline (data processing → training → testing)

6. Task1 method summary (final v5.1)

7. Environment notes

8. What was removed from earlier working copies of this codebase

9. Troubleshooting

中文

1. 项目概览

2. 两个路径文件——你只需要改这两个

3. 目录结构

4. 快速开始——用已有 checkpoint 复现提交文件

5. 完整流程（数据处理 → 训练 → 测试）

6. Task1 方法概述（最终 v5.1 版本）

7. 环境说明

8. 相对早期工作副本删掉了什么

9. 故障排查

zizima/
humomni2026-track1-empathyeval-team111-final-reproducibility-archive