diff --git a/experiments/maai-ch-kyoto/Mimi评测说明.md b/experiments/maai-ch-kyoto/Mimi评测说明.md new file mode 100644 index 0000000..ee9572f --- /dev/null +++ b/experiments/maai-ch-kyoto/Mimi评测说明.md @@ -0,0 +1,87 @@ +# MaAI Mimi ch_kyoto：标准版与降噪版双声道评测 + +## 当前主评测口径：流式场景（2026-10-28） + +本场景以 VAP 连续流式推理为主：从录音开始持续维护编码器和 VAP 状态，在每个当前标注 `decision_ms`，取可用时间不晚于它的最后一个分数。判断点不重置模型、不插值、不使用未来音频。分数为 `1 - p_now[user]`，预测规则为严格 `> 0.5`；这是活动预测到可接话的映射，不是原生校准 EOT 概率。 + +前端圆点、悬停分数和准确率统一使用这份连续输出。时间戳包括流式重采样缓冲；没有输出或分数年龄超过两倍标称更新间隔时记为不可用，单独计数，不填 0。保存新增或移动的标注后，用已有真实连续记录重新取点，无需重跑模型： + +```bash +cd experiments/maai-ch-kyoto +uv run --locked python evaluate_streaming.py all +``` + +| 配置 | 当前主结果目录 | 正确数 / 有效点 | 准确率 | FP / FN | +| --- | --- | --- | --- | --- | +| CPC 降噪双声道 | `results/streaming/cpc-stereo/` | 30 / 44 | 68.18% | 7 / 7 | +| CPC 降噪机器人静音 | `results/streaming/cpc-user-only/` | 24 / 44 | 54.55% | 18 / 2 | +| Mimi 标准双声道 | `results/streaming/mimi-standard/` | 31 / 44 | 70.45% | 5 / 8 | +| Mimi 降噪双声道 | `results/streaming/mimi-mc/` | 34 / 44 | 77.27% | 5 / 5 | + +本次 44 点均有可用输出，来自同六通录音、22 个可接话与 22 个不可接话标签。每个目录包含 `predictions.csv`、`summary.json` 和前端 `score-timeline.json`；CSV 记录判断时间、实际评分时间及分数年龄，JSON 保留源记录、标签和评估代码的哈希。总表为 `results/streaming/comparison-summary.json`，文字说明为 `results/streaming/对比分析.txt`。 + +本次复用已有真实连续推理记录，没有新做模型推理或计算延迟实验。它仍是选定判断点上的分类评估，不等同于 LiveKit 的静音区间误抢话与结束延迟策略评估，也不代表完整在线系统的响应延迟。原始独立窗口结果保留作为历史诊断，不能与本表混用。重新运行原推理入口的 `all` 模式（CPC 双声道为 `export_timeline.py`）后会自动刷新对应流式目录；四配置对比统一用上述命令更新。 + +## 历史独立窗口及原始连续推理记录 + +以下记录说明原始实验、权重和独立窗口诊断。所述独立点评分及旧指标不是当前流式场景的主结果；前端已切换为上方流式目录。 + +两版均输入原录音的**用户左声道（索引 0）和机器人右声道（索引 1）**。保留两路音频及静音，不混音、不置零、不改变时间轴。当前人工标签从 `labels/EOT标注/工作数据` 读取，结果另存。 + +## 模型与环境 + +复用本目录现有 uv `.venv` 和 `uv.lock`，不与 Smart Turn 环境混装。本次不修改依赖：Python 3.11.15、MaAI 0.2.22、PyTorch 2.14.1、transformers 5.5.3、ONNX Runtime 1.31.0。执行机器为本机 Apple M4 Mac mini、16 GB 内存；CPU 推理，PyTorch 和 ONNX 各 4 个 intra-op 线程、1 个 inter-op 线程。 + +| 项目 | 标准版 | 降噪版 | +| --- | --- | --- | +| 模型参数 | `model_type="normal-ver2", mc=False` | `model_type="normal-ver2", mc=True` | +| VAP 权重 | `vap_mimi_state_dict_ch_kyoto_12.5hz_20000msec.pt` | `vap_mc_mimi_state_dict_ch_kyoto_12.5hz_20000msec.pt` | +| VAP SHA256 | `2820d2b172a6ddd4696d33edb43933f436a09a3e0d3eb41c7dc390c33b22d5ce` | `61c9e186d765cb6c338cb7fdacb68baf4b2533e72cde7c803e52480a62919219` | +| 频率、上下文 | 12.5 Hz（80 ms）、20 秒 | 相同 | +| 编码器 | 官方 FP32 ONNX Mimi | 与标准版共用同一文件 | + +VAP 来自官方 [vap_ch_kyoto](https://huggingface.co/maai-kyoto/vap_ch_kyoto/tree/main)，固定修订 `c135cdcfa12ff5aa1cce2fdd23df110941a78f38`。编码器来自官方 [continuous-mimi-onnx](https://huggingface.co/maai-kyoto/continuous-mimi-onnx/tree/main)，固定修订 `303ef4ec0c70e4c71b67ea166acd26dd0d96655c`，文件为 `continuous_mimi_fp32.onnx` 和配套 JSON；ONNX SHA256 为 `c86171a48e1bd46aa31a52e7fe00f74acc310e0d15d11881516cdd1df1639b54`。 + +所有文件保存在本目录 `models/mimi/`，固定下载 URL、修订、大小和校验值保存在 `mimi-model-manifest.json`。脚本逐次验证清单，不自动切换或下载其他权重。环境声明为 `mimi-env-spec.json`，实际环境和输入哈希随结果保存。 + +## 复跑 + +从项目根目录执行： + +```bash +cd experiments/maai-ch-kyoto +UV_CACHE_DIR=/private/tmp/xiaobailing-uv-cache uv run --locked --offline python run_mimi.py smoke --variant both +UV_CACHE_DIR=/private/tmp/xiaobailing-uv-cache uv run --locked --offline python run_mimi.py all --variant both +UV_CACHE_DIR=/private/tmp/xiaobailing-uv-cache uv run --locked --offline python compare_mimi.py +``` + +`smoke` 在真实双声道历史上检查权重加载、固定种子重复推理和未来音频不变性。`all` 依次计算两版的判断点和连续曲线；`evaluate` 只计算判断点。`--variant standard` 或 `--variant mc` 可以单独运行一版。已有依赖及本地权重可离线复用。 + +## 判断点与连续评分 + +- 每个判断点独立重置 Mimi 流式状态及 VAP KV cache，使用此前最多 20 秒音频，按 80 ms 帧对齐，最后一帧结束于精确 `decision_ms`。不足整帧的余量从历史开头丢弃。先裁剪收到的双声道音频，再重采样到 16 kHz；不读取未来音频，不使用人工转写。 +- 保留 MaAI 官方启动逻辑：首次 ONNX Mimi 编码输出跳过，后续实际收到评分才使用。当前实现首次有效输出在输入 160 ms 后；不把启动期缺失评分补成 0。 +- 主分数与既有 VAP 前端一致，为 `1 - p_now[user]`，主阈值为严格大于 0.5。它是说话活动预测映射，不是官方校准的 EOT 概率；不加入外部结束信号、VAD 等待或超时接话。 +- 连续曲线在完整录音上持续维护状态，使用流式重采样，时间戳表示输入实际可用时间，包含重采样缓冲。末尾不足一帧不补虚构音频；空心点保留独立历史窗口结果，可能不落在连续曲线上。 +- 模型原生步长是 80 ms，流式重采样可能使更新延后或多帧在同一个输入可用时刻产生，因此页面曲线并非严格每 80 ms 一个点。本批连续评分最早在 240 ms 可用，更新间隔为 80/160 ms；独立窗口不经过流式重采样缓冲，首次模型输出为 160 ms。两种时间均明确记录，不把预测提前显示。 +- 本次比较引用 `results/user-only/predictions.csv` 中已在**相同当前判断点**重跑的 CPC 降噪双声道对照，同时保留 CPC 机器人静音版。复用前严格校验标签、转写、依赖锁和六通原录音的哈希。当前点位变化后，应先重跑 `run_user_only.py all`，不能直接复用旧位置的分数。 + +## 加载审计与比较边界 + +官方本地文件加载路径使用通用 `vap` 架构标识，即使文件是降噪权重，也不会把对象上的 `variant` 自动改为 `vap_mc`。实验因此以指定权重文件及 SHA256 标识标准/降噪版本，并核对全部实际使用的 VAP 参数与对应 checkpoint 完全一致，避免把相同架构误认为相同权重。 + +Mimi 的两个被冻结的帧率转换卷积在原生 12.5 Hz 路径不参与计算；原始 checkpoint 不包含这些卷积。加载审计单独列出这四个未使用的 weight/bias，核验输入输出帧率均为 12.5 Hz；其余参数必须来自 checkpoint。Mimi 编码器本体使用已校验的独立 ONNX 权重。加载器输出中与初始常量相同的参数提示不代表参数缺失，以逐参数相等性检查为准。 + +Mimi 标准与降噪版本使用相同编码器、80 ms 帧、输入和评分规则；两者可作为不同 VAP 权重的直接对照。现有 CPC 为降噪版、100 ms 帧，比较时明确这些配置差异，不把所有变化归因于编码器。这里只评价选定判断点，不代表独立测试集或完整话轮策略指标。记录的历史重放耗时也不等于单次在线更新延迟。 + +## 结果与前端 + +| 目录 | 内容 | +| --- | --- | +| `results/mimi-standard/` | Mimi 标准版的 `predictions.csv`、`errors.csv`（如有）、`thresholds.csv`、`summary.json`、`score-timeline.json`。 | +| `results/mimi-mc/` | Mimi 降噪版的同类结果。 | +| `results/mimi-comparison/` | 由 `compare_mimi.py` 汇总的四配置逐点对照、指标 JSON 和中文对比分析。 | + +逐点 CSV 保留人工真值、原生 `p_now/p_future/vad`、接话候选分数、CPC 同点参考分数及输入窗口。汇总包含分类指标、每通话指标、重复/因果输入验证、完整来源哈希和配置差异说明。 + +工作台展开“模型分数对比”→“选择模型”，可分别勾选 **VAP / MaAI Mimi（标准双声道）**（蓝灰色）和 **VAP / MaAI Mimi（降噪双声道）**（金棕色）。它们支持独立开关、0.11 / 0.5 / 0.8 参考线、悬停分数和判断点定位，与现有 CPC、Smart Turn 等共用录音时间轴。原始录音、标签和其他模型结果保持原样。 diff --git a/experiments/maai-ch-kyoto/compare_mimi.py b/experiments/maai-ch-kyoto/compare_mimi.py new file mode 100644 index 0000000..c983f58 --- /dev/null +++ b/experiments/maai-ch-kyoto/compare_mimi.py @@ -0,0 +1,92 @@ +"""Compare exact current points across Mimi standard/MC and CPC MC controls.""" +from collections import Counter +import csv +import hashlib +import json +from pathlib import Path + +HERE = Path(__file__).resolve().parent +ROOT = HERE.parents[1] +OUT = HERE / "results/mimi-comparison" + + +def sha(path): + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def read(path): + with path.open(encoding="utf-8-sig", newline="") as f: + return list(csv.DictReader(f)) + + +def metrics(rows, field): + c = Counter((int(r['can_reply_now']), int(float(r[field]) > .5)) for r in rows) + tn, fp, fn, tp = c[0,0], c[0,1], c[1,0], c[1,1] + return dict(n=len(rows), correct=tn+tp, tn=tn, fp=fp, fn=fn, tp=tp, + accuracy=(tn+tp)/len(rows), false_reply_rate=fp/(fp+tn), false_hold_rate=fn/(fn+tp)) + + +def main(): + dirs = ['mimi-standard', 'mimi-mc', 'user-only'] + labels = ROOT / 'labels/EOT标注/工作数据/eot_labels.csv' + summaries = {d: json.loads((HERE/'results'/d/'summary.json').read_text()) for d in dirs} + points = {d: {(r['call_id'], int(r['decision_ms'])):r for r in read(HERE/'results'/d/'predictions.csv')} for d in dirs} + current = {(r['call_id'], int(r['decision_ms'])):r['can_reply_now'] for r in read(labels) if r['can_reply_now'] in ('0','1')} + assert current and all(set(p)==set(current) for p in points.values()) + assert all(s['provenance']['label_sha256']==sha(labels) for s in summaries.values()) + assert summaries['mimi-standard']['provenance']['mc'] is False + assert summaries['mimi-mc']['provenance']['mc'] is True + audio_hashes = summaries['mimi-standard']['provenance']['audio_sha256'] + assert all(s['provenance']['audio_sha256']==audio_hashes for s in summaries.values()) + rows = [] + for key, label in sorted(current.items()): + standard, mc, cpc = [points[d][key] for d in dirs] + assert all(r['can_reply_now']==label for r in (standard,mc,cpc)) + assert standard['cpc_mc_stereo_score']==mc['cpc_mc_stereo_score']==cpc['stereo_can_reply_score'] + rows.append(dict(call_id=key[0],decision_ms=key[1],can_reply_now=label, + mimi_standard=float(standard['can_reply_score']),mimi_mc=float(mc['can_reply_score']), + cpc_mc_stereo=float(cpc['stereo_can_reply_score']),cpc_mc_user_only=float(cpc['can_reply_score']))) + models = { + 'cpc_mc_stereo':'CPC 降噪版（双声道）', + 'cpc_mc_user_only':'CPC 降噪版（机器人静音）', + 'mimi_standard':'Mimi 标准版（双声道）', + 'mimi_mc':'Mimi 降噪版（双声道）', + } + result = {key:metrics(rows,key) for key in models} + base = result['cpc_mc_stereo']['accuracy'] + for value in result.values(): + value['accuracy_delta_pp_vs_cpc_mc_stereo'] = (value['accuracy']-base)*100 + value['relative_accuracy_change_vs_cpc_mc_stereo'] = (value['accuracy']-base)/base + paired = {} + for key in ('mimi_standard','mimi_mc'): + paired[key] = dict( + fixed=sum((r['cpc_mc_stereo']>.5)!=bool(int(r['can_reply_now'])) and (r[key]>.5)==bool(int(r['can_reply_now'])) for r in rows), + regressed=sum((r['cpc_mc_stereo']>.5)==bool(int(r['can_reply_now'])) and (r[key]>.5)!=bool(int(r['can_reply_now'])) for r in rows)) + summary = dict(threshold=.5,rule='1 - p_now[user] > 0.5',n=len(rows),positive=sum(r['can_reply_now']=='1' for r in rows), + models=result,paired_vs_cpc_mc_stereo=paired, + per_call={c:{m:metrics([r for r in rows if r['call_id']==c],m) for m in models} for c in sorted(audio_hashes)}, + sources={d:{'summary_sha256':sha(HERE/'results'/d/'summary.json'),'predictions_sha256':sha(HERE/'results'/d/'predictions.csv')} for d in dirs}, + label_sha256=sha(labels),audio_sha256=audio_hashes, + limitations=['Single seed 42 with exact-repeat witness, not multiple-seed statistics.', + 'Only 44 selected points in six calls; descriptive comparison, not a held-out generalization claim.', + 'Mimi standard/MC share the same encoder and framing; CPC uses a different encoder and native 100ms framing.', + 'All scores are activity-projection mappings; no endpointing-policy or full-dialogue cutoff/latency claims.']) + OUT.mkdir(parents=True,exist_ok=True) + with (OUT/'comparison-points.csv').open('w',encoding='utf-8-sig',newline='') as f: + writer=csv.DictWriter(f,fieldnames=list(rows[0]));writer.writeheader();writer.writerows(rows) + (OUT/'comparison-summary.json').write_text(json.dumps(summary,ensure_ascii=False,indent=2)+'\n') + text=['MaAI Mimi 与 CPC 对比（相同当前 44 点，阈值严格 > 0.5）','', + '模型 | 正确数 | 准确率 | 误放行 FP/22 | 误阻止 FN/22 | 相对 CPC 双声道准确率变化'] + for key,name in models.items(): + m=result[key] + text.append(f"{name} | {m['correct']}/{m['n']} | {m['accuracy']:.2%} | {m['fp']}/22 | {m['fn']}/22 | {m['accuracy_delta_pp_vs_cpc_mc_stereo']:+.2f} 个百分点（相对 {m['relative_accuracy_change_vs_cpc_mc_stereo']:+.2%}）") + text+=['','1. 标准版与降噪版的结果差异可直接从上表查看；二者使用相同 Mimi 编码器、双声道输入及 80 ms 帧，变化来自所选 VAP 权重。', + '2. 与 CPC 比较时同时存在编码器、checkpoint 和原生帧率差异；不把结果归结为 Mimi 编码器的单独因果效果。', + '3. 固定种子重复结果一致，但没有多种子误差条；六通录音的 44 个选定点不足以证明泛化优劣。', + '', '建议下一步：扩充并核验按通话隔离的测试集，在独立验证集上选择阈值；若要单独研究编码器，补充标准/降噪与 CPC/Mimi 的完整交叉配置。'] + (OUT/'对比分析.txt').write_text('\n'.join(text)+'\n') + print('\n'.join(text)) + + +if __name__=='__main__': + main() diff --git a/experiments/maai-ch-kyoto/evaluate_streaming.py b/experiments/maai-ch-kyoto/evaluate_streaming.py new file mode 100644 index 0000000..552aaf5 --- /dev/null +++ b/experiments/maai-ch-kyoto/evaluate_streaming.py @@ -0,0 +1,169 @@ +"""Score VAP annotation points from already available continuous stream outputs. + +No model inference, state resets, interpolation, or changes to archived results. +""" +import argparse +from bisect import bisect_right +from collections import Counter +from datetime import datetime, timezone +import csv +import hashlib +import json +import math +from pathlib import Path + +HERE = Path(__file__).resolve().parent +ROOT = HERE.parents[1] +LABELS = ROOT / "labels/EOT标注/工作数据/eot_labels.csv" +SOURCES = { + "maai": ("score-timeline.json", "cpc-stereo", "CPC 降噪双声道"), + "maai_user_only": ("user-only/score-timeline.json", "cpc-user-only", "CPC 降噪机器人静音"), + "maai_mimi_standard": ("mimi-standard/score-timeline.json", "mimi-standard", "Mimi 标准双声道"), + "maai_mimi_mc": ("mimi-mc/score-timeline.json", "mimi-mc", "Mimi 降噪双声道"), +} + + +def sha(path): + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def last_available(points, decision_ms, max_age_ms): + """The same inclusive past-only, bounded-age rule used by Studio scoreAt.""" + index = bisect_right(points, decision_ms, key=lambda p: p[0]) - 1 + if index < 0 or decision_ms - points[index][0] > max_age_ms: + return None + return points[index] + + +def classification(rows, threshold=.5): + labeled = [r for r in rows if str(r['can_reply_now']) in ('0', '1')] + available = [r for r in labeled if r['status'] == 'available'] + counts = Counter((int(r['can_reply_now']), int(float(r['score']) > threshold)) for r in available) + tn, fp, fn, tp = counts[0,0], counts[0,1], counts[1,0], counts[1,1] + n = tn + fp + fn + tp + ratio = lambda a,b: a/b if b else None + return dict(n=n, requested_labeled=len(labeled), unavailable_labeled=len(labeled)-n, + tn=tn, fp=fp, fn=fn, tp=tp, correct=tn+tp, accuracy=ratio(tn+tp,n), + false_reply_rate=ratio(fp,fp+tn), false_hold_rate=ratio(fn,fn+tp), + recall=ratio(tp,tp+fn), precision=ratio(tp,tp+fp), f1=ratio(2*tp,2*tp+fp+fn)) + + +def write_csv(path, rows): + if not rows: + return + with path.open('w', encoding='utf-8-sig', newline='') as f: + writer = csv.DictWriter(f, fieldnames=list(rows[0])) + writer.writeheader() + writer.writerows(rows) + + +def refresh_streaming(model): + source_file, directory, name = SOURCES[model] + source_path = HERE / 'results' / source_file + source_hash, label_hash = sha(source_path), sha(LABELS) + source = json.loads(source_path.read_text()) + if source['model'] != model or source['schema_version'] != 1: + raise ValueError('Unexpected source export') + interval = source['sample_interval_ms'] + assert math.isfinite(interval) and interval > 0 + with LABELS.open(encoding='utf-8-sig', newline='') as f: + labels = list(csv.DictReader(f)) + assert len({(r['call_id'], int(r['decision_ms'])) for r in labels}) == len(labels) + calls = {c['call_id']: c for c in source['calls']} + assert len(calls) == len(source['calls']) + assert {r['call_id'] for r in labels} <= calls.keys(), 'Missing annotated calls' + rows, exported_calls = [], [] + for call_id, call in calls.items(): + audio = ROOT / 'data' / call['audio_path'] + assert audio.resolve().is_relative_to((ROOT/'data').resolve()), 'Audio outside project data' + assert sha(audio) == call['audio_sha256'], 'Source recording changed; rerun inference' + points = call['points'] + assert all(len(p)==2 and math.isfinite(p[0]) and math.isfinite(p[1]) and + 0 <= p[0] <= call['duration_ms'] and 0 <= p[1] <= 1 for p in points) + assert all(a[0] < b[0] for a,b in zip(points,points[1:])) + checkpoints, sources = [], [] + for label in sorted([r for r in labels if r['call_id']==call_id], key=lambda r:int(r['decision_ms'])): + decision = int(label['decision_ms']) + assert 0 <= decision <= call['duration_ms'], 'Annotation outside recording' + point = last_available(points, decision, interval*2) + row = dict(label) + row.update(status='available' if point is not None else 'unavailable', + score_time_ms=point[0] if point is not None else '', + score_age_ms=decision-point[0] if point is not None else '', + score=point[1] if point is not None else '', + prediction_05=int(point[1]>.5) if point is not None else '') + rows.append(row) + if point is not None: + checkpoints.append([decision, point[1]]) + sources.append([decision, point[0]]) + exported_calls.append(dict(call_id=call_id, audio_path=call['audio_path'], audio_sha256=call['audio_sha256'], + duration_ms=call['duration_ms'], points=points, + checkpoints=checkpoints, checkpoint_sources=sources)) + provenance = dict(source_export=str(source_path.relative_to(ROOT)), source_export_sha256=source_hash, + source_inference_provenance=source.get('provenance', {}), + source_model_sha256=source.get('model_sha256', {}), + label_sha256=label_hash, evaluation_script_sha256=sha(Path(__file__))) + method = ('Continuous full-call VAP outputs only. At each exact decision_ms, use the last score with ' + 'availability timestamp <= decision_ms and age <= 2 * sample_interval_ms. ' + 'No state reset, future score, interpolation or independent-window checkpoint. ' + 'Availability includes source resampler buffering; score is the stored activity-projection proxy.') + exported = dict(schema_version=1, model=model, name=source['name'], score=source['score'], + checkpoint_mode='streaming', sample_interval_ms=interval, method=method, + max_score_age_ms=interval*2, provenance=provenance, calls=exported_calls) + summary = dict(model=model, name=name, evaluation_mode='streaming', + created_at=datetime.now(timezone.utc).isoformat(), threshold=.5, + rule='1 - p_now[user] > 0.5', main_metrics=classification(rows), + thresholds=[dict(threshold=t, **classification(rows,t)) for t in (.11,.5,.7,.8,.9,.95,.98)], + per_call={c:classification([r for r in rows if r['call_id']==c]) for c in calls}, + method=method, provenance=provenance, + limitations=['Selected-point classification on six recorded calls, not endpointing-policy cutoff/latency.', + 'Uses stored rounded continuous scores; no new inference or model-speed measurement.', + 'No prior output or stale output is unavailable, not a negative/zero prediction.', + 'Archived independent-window results are a separate evaluation protocol.']) + assert sha(source_path)==source_hash and sha(LABELS)==label_hash, 'Inputs changed during evaluation' + output = HERE/'results/streaming'/directory + output.mkdir(parents=True, exist_ok=True) + write_csv(output/'predictions.csv', rows) + (output/'summary.json').write_text(json.dumps(summary,ensure_ascii=False,indent=2)+'\n') + (output/'score-timeline.json').write_text(json.dumps(exported,ensure_ascii=False,separators=(',',':'))+'\n') + return summary, rows + + +def main(selected): + summaries, rows_by_model = {}, {} + for model in SOURCES if selected=='all' else (selected,): + summary, rows = refresh_streaming(model) + summaries[model], rows_by_model[model] = summary, rows + print(summary['name'], json.dumps(summary['main_metrics'],ensure_ascii=False),flush=True) + if selected != 'all': + return + label_hashes = {s['provenance']['label_sha256'] for s in summaries.values()} + assert len(label_hashes)==1, 'Models use different labels' + base = summaries['maai']['main_metrics']['accuracy'] + for summary in summaries.values(): + accuracy = summary['main_metrics']['accuracy'] + summary['accuracy_delta_pp_vs_cpc_stereo'] = (accuracy-base)*100 if accuracy is not None and base is not None else None + summary['relative_accuracy_change_vs_cpc_stereo'] = (accuracy-base)/base if accuracy is not None and base else None + output = HERE/'results/streaming' + result = dict(evaluation_mode='streaming', label_sha256=next(iter(label_hashes)), models=summaries) + (output/'comparison-summary.json').write_text(json.dumps(result,ensure_ascii=False,indent=2)+'\n') + lines = ['VAP 流式场景判断点对比（阈值严格 > 0.5）', '', + '模型 | 有效点/有标签点 | 正确数 | 准确率 | 误放行 FP | 误阻止 FN | 对 CPC 双声道变化'] + for model, summary in summaries.items(): + m = summary['main_metrics'] + accuracy = f"{m['accuracy']:.2%}" if m['accuracy'] is not None else '不可用' + delta = summary['accuracy_delta_pp_vs_cpc_stereo'] + delta_text = f'{delta:+.2f} 个百分点' if delta is not None else '不可用' + lines.append(f"{summary['name']} | {m['n']}/{m['requested_labeled']} | {m['correct']} | {accuracy} | {m['fp']} | {m['fn']} | {delta_text}") + lines += ['', '1. 本表使用连续推理在判断时刻已经产生的最新分数，与前端 VAP 圆点和悬停分数一致；不使用独立窗口结果。', + '2. 每个有效点记录 decision_ms、score_time_ms、score_age_ms；启动期或超过两倍更新间隔的旧分数不计为 0，而是不可用。', + '3. 数据为六通录音中的选定点，只做描述性比较；下一步宜扩充按通话隔离的测试集，再在验证集上选择阈值。', + '', '复现：在 MaAI 独立 uv 环境运行 python evaluate_streaming.py all。原独立窗口结果保留在原目录，另作历史诊断。'] + (output/'对比分析.txt').write_text('\n'.join(lines)+'\n') + print('\n'.join(lines)) + + +if __name__=='__main__': + parser=argparse.ArgumentParser(description=__doc__) + parser.add_argument('model',choices=('all',*SOURCES),default='all',nargs='?') + main(parser.parse_args().model) diff --git a/experiments/maai-ch-kyoto/export_timeline.py b/experiments/maai-ch-kyoto/export_timeline.py index 78df610..90f0e98 100644 --- a/experiments/maai-ch-kyoto/export_timeline.py +++ b/experiments/maai-ch-kyoto/export_timeline.py @@ -51,6 +51,9 @@ def main(): method='Continuous stereo replay, 16kHz, 10Hz, CPC + ch_kyoto noise-robust VAP, 20s KV cache. Causal streaming resampler; timestamps denote input availability. Checkpoints retain the earlier independent <=20s point evaluation.', model_sha256=EXPECTED,script_sha256=hashlib.sha256((HERE/'export_timeline.py').read_bytes()).hexdigest(),calls=calls) (HERE/'results/score-timeline.json').write_text(json.dumps(output,ensure_ascii=False,separators=(',',':'))+'\n') + from evaluate_streaming import refresh_streaming + summary, _ = refresh_streaming('maai') + print('PRIMARY streaming metrics:', json.dumps(summary['main_metrics']), flush=True) if __name__=='__main__': diff --git a/experiments/maai-ch-kyoto/mimi-env-spec.json b/experiments/maai-ch-kyoto/mimi-env-spec.json new file mode 100644 index 0000000..b060a23 --- /dev/null +++ b/experiments/maai-ch-kyoto/mimi-env-spec.json @@ -0,0 +1,33 @@ +{ + "base": "macOS arm64 CPU + Python 3.11.15", + "environment": "experiments/maai-ch-kyoto/.venv", + "manager": "uv", + "pyproject_sha256": "c780e57c88a01b2ea96875cea94b2fee56e3ecf1a1d95ad1a3c6c302fce7c191", + "uv_lock_sha256": "6863631a5969e0f077d8aefc4335532952fe95486598b609e832e36d5ce2777f", + "resource": { + "device": "cpu", + "torch_threads": 4, + "torch_interop_threads": 1, + "onnx_threads": 4, + "onnx_interop_threads": 1 + }, + "model": { + "type": "normal-ver2", + "frame_rate": 12.5, + "context_seconds": 20, + "input": "recorded user+robot stereo", + "mimi_precision": "fp32", + "variants": [ + "vap (mc=False)", + "vap_mc (mc=True)" + ] + }, + "weights": { + "vap_revision": "c135cdcfa12ff5aa1cce2fdd23df110941a78f38", + "encoder_revision": "303ef4ec0c70e4c71b67ea166acd26dd0d96655c" + }, + "smoke": { + "cmd": "uv run --locked python run_mimi.py smoke --variant both", + "expect": "WITNESS Mimi" + } +} diff --git a/experiments/maai-ch-kyoto/mimi-model-manifest.json b/experiments/maai-ch-kyoto/mimi-model-manifest.json new file mode 100644 index 0000000..24dc689 --- /dev/null +++ b/experiments/maai-ch-kyoto/mimi-model-manifest.json @@ -0,0 +1,40 @@ +{ + "files": [ + { + "filename": 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"accuracy": 0.7777777777777778, + "false_reply_rate": 0.25, + "false_hold_rate": 0.2 + } + }, + "a266b9ef-5e47-4434-a04c-ce4eacd52c19": { + "cpc_mc_stereo": { + "n": 5, + "correct": 3, + "tn": 1, + "fp": 2, + "fn": 0, + "tp": 2, + "accuracy": 0.6, + "false_reply_rate": 0.6666666666666666, + "false_hold_rate": 0.0 + }, + "cpc_mc_user_only": { + "n": 5, + "correct": 3, + "tn": 1, + "fp": 2, + "fn": 0, + "tp": 2, + "accuracy": 0.6, + "false_reply_rate": 0.6666666666666666, + "false_hold_rate": 0.0 + }, + "mimi_standard": { + "n": 5, + "correct": 2, + "tn": 1, + "fp": 2, + "fn": 1, + "tp": 1, + "accuracy": 0.4, + "false_reply_rate": 0.6666666666666666, + "false_hold_rate": 0.5 + }, + "mimi_mc": { + "n": 5, + "correct": 3, + "tn": 1, + "fp": 2, + "fn": 0, + "tp": 2, + "accuracy": 0.6, + "false_reply_rate": 0.6666666666666666, + "false_hold_rate": 0.0 + } + }, + "cb3238dc-3946-42a5-aad8-9b9a54226363": { + "cpc_mc_stereo": { + "n": 9, + "correct": 8, + "tn": 5, + "fp": 1, + "fn": 0, + "tp": 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+ "fn": 0, + "tp": 4, + "accuracy": 0.5714285714285714, + "false_reply_rate": 1.0, + "false_hold_rate": 0.0 + }, + "mimi_standard": { + "n": 7, + "correct": 7, + "tn": 3, + "fp": 0, + "fn": 0, + "tp": 4, + "accuracy": 1.0, + "false_reply_rate": 0.0, + "false_hold_rate": 0.0 + }, + "mimi_mc": { + "n": 7, + "correct": 7, + "tn": 3, + "fp": 0, + "fn": 0, + "tp": 4, + "accuracy": 1.0, + "false_reply_rate": 0.0, + "false_hold_rate": 0.0 + } + } + }, + "sources": { + "mimi-standard": { + "summary_sha256": "b2524af78cd9768a6ee4f3c73e48a6bfc8f5482274acb503bdb0881f70a2ab29", + "predictions_sha256": "1f10ead96648b26ec5bea0cee0ed5dfd7c357886b162a44e8b46f4648f4dfcf8" + }, + "mimi-mc": { + "summary_sha256": "f7f87c19dfd894771896ce798683b7d2f18bcf0bc2e13da185b9f6c71646359f", + "predictions_sha256": "80d850b0044e365cf4554e116f3617af72c7e4a56eb44aaede00004856c20a2b" + }, + "user-only": { + "summary_sha256": "99577db77517e31663256c2489ff2300883485ff85a1d04608e467a0bf7e6f9a", + "predictions_sha256": "266f43c91d232e5fa7e8e9fc10c294c8f7e7df64fac2332a6a7441faf15d30a8" + } + }, + "label_sha256": "780e50f976daf039ab976ea155bdb5d1ca5fffc792f9db7adf8063ec3048ff8e", + "audio_sha256": { + "2c83253a-7977-4e3c-a387-0aa1ea06fbe1": "2b8a67dc76b8975d287df42193647cf040229377537eadaaf3adc13358201fe1", + "40ae686c-8196-4d7b-b7ad-3e12a427ebb6": "130be37286d48f7badca711a068fab19a2686bd83248a71c73c11ce77650d39d", + "872a1e67-873a-4c49-a598-b38a670afcad": "54c5672ea491cc3daedd75e80f97e82cc061ccbf14e521bc22ca55f15fa062a5", + "a266b9ef-5e47-4434-a04c-ce4eacd52c19": "9a7ebe093e66aaaa08ac462f975b7d428df79a38ed5b418265b82c77d8b91f46", + "cb3238dc-3946-42a5-aad8-9b9a54226363": "7a74dbf3dd742d3d14317d069c28b703dd1398d5eb3dc1dba3d7a7c568a18d60", + "e304f740-85d4-427e-9fd6-c5474e9cd737": "44672603d0cf9feac41275e99d59baa4cafbdcdbb172da23de52aa0a2a4961d2" + }, + "limitations": [ + "Single seed 42 with exact-repeat witness, not multiple-seed statistics.", + "Only 44 selected points in six calls; descriptive comparison, not a held-out generalization claim.", + "Mimi standard/MC share the same encoder and framing; CPC uses a different encoder and native 100ms framing.", + "All scores are activity-projection mappings; no endpointing-policy or full-dialogue cutoff/latency claims." + ] +} diff --git a/experiments/maai-ch-kyoto/results/mimi-comparison/对比分析.txt b/experiments/maai-ch-kyoto/results/mimi-comparison/对比分析.txt new file mode 100644 index 0000000..a4aba85 --- /dev/null +++ b/experiments/maai-ch-kyoto/results/mimi-comparison/对比分析.txt @@ -0,0 +1,13 @@ +MaAI Mimi 与 CPC 对比（相同当前 44 点，阈值严格 > 0.5） + +模型 | 正确数 | 准确率 | 误放行 FP/22 | 误阻止 FN/22 | 相对 CPC 双声道准确率变化 +CPC 降噪版（双声道） | 32/44 | 72.73% | 8/22 | 4/22 | +0.00 个百分点（相对 +0.00%） +CPC 降噪版（机器人静音） | 23/44 | 52.27% | 18/22 | 3/22 | -20.45 个百分点（相对 -28.13%） +Mimi 标准版（双声道） | 30/44 | 68.18% | 7/22 | 7/22 | -4.55 个百分点（相对 -6.25%） +Mimi 降噪版（双声道） | 34/44 | 77.27% | 7/22 | 3/22 | +4.55 个百分点（相对 +6.25%） + +1. 标准版与降噪版的结果差异可直接从上表查看；二者使用相同 Mimi 编码器、双声道输入及 80 ms 帧，变化来自所选 VAP 权重。 +2. 与 CPC 比较时同时存在编码器、checkpoint 和原生帧率差异；不把结果归结为 Mimi 编码器的单独因果效果。 +3. 固定种子重复结果一致，但没有多种子误差条；六通录音的 44 个选定点不足以证明泛化优劣。 + +建议下一步：扩充并核验按通话隔离的测试集，在独立验证集上选择阈值；若要单独研究编码器，补充标准/降噪与 CPC/Mimi 的完整交叉配置。 diff --git a/experiments/maai-ch-kyoto/results/mimi-mc/errors.csv b/experiments/maai-ch-kyoto/results/mimi-mc/errors.csv new file mode 100644 index 0000000..d9c5d12 --- /dev/null +++ b/experiments/maai-ch-kyoto/results/mimi-mc/errors.csv @@ -0,0 +1,11 @@ +call_id,decision_ms,user_text_at_decision,can_reply_now,confidence,reason,annotator,can_reply_score,prediction_05,correct_05,p_now_user,p_now_robot,p_future_user,p_future_robot,vad_user,vad_robot,cpc_mc_stereo_score,cpc_mc_user_only_score,audio_start_ms,audio_end_ms,context_ms,model_load_ms,replay_inference_ms,mean_update_ms,p95_update_ms,first_output_ms,frame_count +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,76672,恒泰轮胎前门两条轮胎开裂,0,low,unfinished_expression,xlsx_draft,0.6864748597145081,1,0,0.31352514028549194,0.6864557266235352,0.2911543846130371,0.7088373303413391,0.00031061848858371377,8.592925587436184e-05,0.7066852748394012,0.8640106171369553,56672,76672,20000,44.286083430051804,4691.276874393225,18.7651074975729,27.573074866086234,160.0,250 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,88482,尽快，已经影响行车安全了。,1,low,completed_answer,xlsx_draft,0.2585999369621277,0,0,0.7414000630378723,0.25855928659439087,0.41278982162475586,0.5872008800506592,0.01025086548179388,7.217686652438715e-05,0.11465108394622803,0.16062873601913452,68482,88482,20000,45.3314995393157,3868.7835028395057,15.475134011358023,18.117318768054247,160.0,250 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,70430,轮胎裂纹同意更换新轮胎。但是20,0,low,unfinished_expression,xlsx_draft,0.9186511859297752,1,0,0.08134881407022476,0.9186463356018066,0.17812038958072662,0.8218727111816406,0.00206320290453732,0.9999758005142212,0.9451650716364384,0.03903418779373169,50430,70430,20000,46.39716725796461,3787.041913717985,15.14816765487194,17.745427601039406,160.0,250 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,82941,可以。,1,low,completed_answer,xlsx_draft,0.295051634311676,0,0,0.704948365688324,0.295030802488327,0.6583224534988403,0.3416694402694702,4.905559399048798e-05,0.00015623004583176225,0.27139031887054443,0.9212324172258377,62941,82941,20000,44.78304274380207,3947.4950321018696,15.789980128407478,21.876844344660626,160.0,250 +872a1e67-873a-4c49-a598-b38a670afcad,24846,郑州市，河南郑州市。,1,low,completed_answer,xlsx_draft,0.4162102937698364,0,0,0.5837897062301636,0.41619208455085754,0.5471727848052979,0.45281901955604553,0.0001610368926776573,0.0001012653301586397,0.6936855018138885,0.9015571922063828,4846,24846,20000,42.502458207309246,3819.1620893776417,15.276648357510567,17.85223125480114,160.0,250 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,68313,轮胎质量问题规律，排水槽横向竖向龟裂。,0,low,mid_turn_pause,xlsx_draft,0.8308001905679703,1,0,0.16919980943202972,0.8307783603668213,0.17210285365581512,0.8278889060020447,3.720181484823115e-05,0.00010085354006150737,0.8074509650468826,0.8763869255781174,48313,68313,20000,43.94112527370453,3842.845559120178,15.371382236480713,18.88684444129467,160.0,250 +872a1e67-873a-4c49-a598-b38a670afcad,52326,我的轮胎开裂了。,0,,mid_turn_pause,,0.5535028576850891,1,0,0.4464971423149109,0.5534874200820923,0.32890474796295166,0.6710880398750305,0.0009139698813669384,0.00022386264754459262,0.4240996241569519,0.6631686389446259,32326,52326,20000,57.44829215109348,6204.5572977513075,24.81822919100523,31.657652324065555,160.0,250 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,41219,迈腾2023款330豪华版2.0T,0,,mid_turn_pause,,0.5108224153518677,1,0,0.4891775846481323,0.5108088254928589,0.4531340003013611,0.5468593239784241,0.0001839700562413782,0.00039992082747630775,0.9102829173207283,0.9391313791275024,21219,41219,20000,44.131457805633545,3837.323042564094,15.349292170256376,17.59768151678145,160.0,250 +cb3238dc-3946-42a5-aad8-9b9a54226363,49204,我是2023年、2024年1月份购置的迈腾3302023款轿车,0,,mid_turn_pause,,0.632370263338089,1,0,0.367629736661911,0.6323491334915161,0.298109769821167,0.7018820643424988,0.00010875277075683698,3.1170831789495423e-05,0.8600987195968628,0.9226682409644127,29204,49204,20000,42.79612470418215,4056.103840470314,16.224415361881256,22.201924864202724,160.0,250 +cb3238dc-3946-42a5-aad8-9b9a54226363,73640,轮胎开裂，行驶28000公里，轮胎开裂,0,,mid_turn_pause,,0.6731171607971191,1,0,0.32688283920288086,0.6730939745903015,0.2690482437610626,0.7309429049491882,0.00043130325502716005,4.166780490777455e-05,0.45827627182006836,0.6812975704669952,53640,73640,20000,71.1151659488678,3739.254044368863,14.957016177475452,16.975108766928315,160.0,250 diff --git a/experiments/maai-ch-kyoto/results/mimi-mc/predictions.csv b/experiments/maai-ch-kyoto/results/mimi-mc/predictions.csv new file mode 100644 index 0000000..38b74f2 --- /dev/null +++ b/experiments/maai-ch-kyoto/results/mimi-mc/predictions.csv @@ -0,0 +1,45 @@ +call_id,decision_ms,user_text_at_decision,can_reply_now,confidence,reason,annotator,can_reply_score,prediction_05,correct_05,p_now_user,p_now_robot,p_future_user,p_future_robot,vad_user,vad_robot,cpc_mc_stereo_score,cpc_mc_user_only_score,audio_start_ms,audio_end_ms,context_ms,model_load_ms,replay_inference_ms,mean_update_ms,p95_update_ms,first_output_ms,frame_count +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,21780,贵州省遵义市仁怀市。,1,low,completed_answer,xlsx_draft,0.965790644288063,1,1,0.03420935571193695,0.9657855033874512,0.0819924995303154,0.9180010557174683,3.7903704651398584e-05,0.5870527029037476,0.959835909307003,0.9077615067362785,1780,21780,20000,43.271709233522415,3784.4312572851777,15.13772502914071,17.088055796921253,160.0,250 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,29860,投诉建议。,1,low,completed_answer,xlsx_draft,0.9577881693840027,1,1,0.042211830615997314,0.9577829837799072,0.09785895049571991,0.9021345376968384,7.450370321748778e-05,0.0013776178238913417,0.9285890087485313,0.8796060606837273,9860,29860,20000,42.548040859401226,3926.3197453692555,15.705278981477022,19.16329162195325,160.0,250 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,48125,型号是24年380大众帕萨特星空龙腾版。,0,low,mid_turn_pause,xlsx_draft,0.45545458793640137,0,1,0.5445454120635986,0.4554390013217926,0.43782973289489746,0.5621628165245056,0.0005332710570655763,4.54303044534754e-05,0.5122989416122437,0.712736040353775,28125,48125,20000,42.76270791888237,3810.884552076459,15.243538208305836,17.53009376116097,160.0,250 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,64000,行驶3万公里。,1,low,completed_answer,xlsx_draft,0.8706666529178619,1,1,0.12933334708213806,0.8706606030464172,0.19718800485134125,0.8028054237365723,0.00021450778876896948,0.033923931419849396,0.9369851648807526,0.9320666417479515,44000,64000,20000,42.76237450540066,3786.9309978559613,15.147723991423845,17.638243734836575,160.0,250 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,76672,恒泰轮胎前门两条轮胎开裂,0,low,unfinished_expression,xlsx_draft,0.6864748597145081,1,0,0.31352514028549194,0.6864557266235352,0.2911543846130371,0.7088373303413391,0.00031061848858371377,8.592925587436184e-05,0.7066852748394012,0.8640106171369553,56672,76672,20000,44.286083430051804,4691.276874393225,18.7651074975729,27.573074866086234,160.0,250 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,88482,尽快，已经影响行车安全了。,1,low,completed_answer,xlsx_draft,0.2585999369621277,0,0,0.7414000630378723,0.25855928659439087,0.41278982162475586,0.5872008800506592,0.01025086548179388,7.217686652438715e-05,0.11465108394622803,0.16062873601913452,68482,88482,20000,45.3314995393157,3868.7835028395057,15.475134011358023,18.117318768054247,160.0,250 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,22007,宣传。,1,low,completed_answer,xlsx_draft,0.764378547668457,1,1,0.23562145233154297,0.7643515467643738,0.2577460706233978,0.7422430515289307,2.9067994546494447e-05,0.00020127497555222362,0.69324991106987,0.9323981329798698,2007,22007,20000,42.73808281868696,3839.093502610922,15.356374010443687,17.763662990182638,160.0,250 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,31842,投诉建议。,1,low,completed_answer,xlsx_draft,0.6238378286361694,1,1,0.37616217136383057,0.6238041520118713,0.38932469487190247,0.6106629371643066,7.003927748883143e-05,6.012684025336057e-05,0.7899426519870758,0.9342073872685432,11842,31842,20000,42.99195762723684,3861.5305479615927,15.44612219184637,17.602635454386473,160.0,250 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,52348,爱车型号是大众帕萨特，购车时间2023年10月份，行驶里程24000多公里。,1,low,completed_answer,xlsx_draft,0.8981894701719284,1,1,0.1018105298280716,0.8981728553771973,0.10203441977500916,0.8979586958885193,3.442115121288225e-05,8.422912651440129e-05,0.959786981344223,0.9645285122096539,32348,52348,20000,42.578416876494884,3763.5253248736262,15.054101299494505,16.651683999225497,160.0,250 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,65298,轮胎裂纹。,0,low,mid_turn_pause,xlsx_draft,0.06646430492401123,0,1,0.9335356950759888,0.06645432859659195,0.8953577876091003,0.10463546216487885,0.01178748719394207,0.00037419048021547496,0.16463828086853027,0.8862891793251038,45298,65298,20000,42.96516627073288,3899.2953142151237,15.597181256860495,17.953012511134148,160.0,250 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,67943,轮胎裂纹同意更换新轮胎。但是,0,low,mid_turn_pause,xlsx_draft,0.05880063772201538,0,1,0.9411993622779846,0.05878521874547005,0.9057212471961975,0.09427128732204437,0.0004612920165527612,5.421150490292348e-05,0.1254715919494629,0.6981820166110992,47943,67943,20000,43.21049991995096,4006.958574987948,16.02783429995179,17.564795259386297,160.0,250 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,70430,轮胎裂纹同意更换新轮胎。但是20,0,low,unfinished_expression,xlsx_draft,0.9186511859297752,1,0,0.08134881407022476,0.9186463356018066,0.17812038958072662,0.8218727111816406,0.00206320290453732,0.9999758005142212,0.9451650716364384,0.03903418779373169,50430,70430,20000,46.39716725796461,3787.041913717985,15.14816765487194,17.745427601039406,160.0,250 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,82941,可以。,1,low,completed_answer,xlsx_draft,0.295051634311676,0,0,0.704948365688324,0.295030802488327,0.6583224534988403,0.3416694402694702,4.905559399048798e-05,0.00015623004583176225,0.27139031887054443,0.9212324172258377,62941,82941,20000,44.78304274380207,3947.4950321018696,15.789980128407478,21.876844344660626,160.0,250 +872a1e67-873a-4c49-a598-b38a670afcad,17153,我姓宋。,1,low,completed_answer,xlsx_draft,0.5796335935592651,1,1,0.42036640644073486,0.5796167254447937,0.4285811185836792,0.5714111924171448,0.00010648956958903,0.0003618664632085711,0.4008920192718506,0.8734219521284103,33,17153,17120,44.663624837994576,3583.463209681213,16.745155185426228,23.063037917017933,160.0,214 +872a1e67-873a-4c49-a598-b38a670afcad,24846,郑州市，河南郑州市。,1,low,completed_answer,xlsx_draft,0.4162102937698364,0,0,0.5837897062301636,0.41619208455085754,0.5471727848052979,0.45281901955604553,0.0001610368926776573,0.0001012653301586397,0.6936855018138885,0.9015571922063828,4846,24846,20000,42.502458207309246,3819.1620893776417,15.276648357510567,17.85223125480114,160.0,250 +872a1e67-873a-4c49-a598-b38a670afcad,33471,我的轮胎开裂了。,1,low,completed_answer,xlsx_draft,0.7417943477630615,1,1,0.2582056522369385,0.7417777180671692,0.2864835560321808,0.7135089039802551,4.845396688324399e-05,0.00011398610513424501,0.802086129784584,0.929821141064167,13471,33471,20000,49.57399982959032,3806.8769220262766,15.227507688105106,17.545225331559777,160.0,250 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,18027,天津市。,1,low,completed_answer,xlsx_draft,0.8495909720659256,1,1,0.1504090279340744,0.8495801091194153,0.2183389812707901,0.7816538214683533,1.9667018932523206e-05,0.0006228262209333479,0.5773889422416687,0.9133674055337906,27,18027,18000,43.50012447685003,3392.0775735750794,15.075900327000353,17.92553309351206,160.0,225 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,24768,投诉。,1,low,completed_answer,xlsx_draft,0.8364158272743225,1,1,0.1635841727256775,0.8363915085792542,0.18476350605487823,0.815227210521698,1.982926369237248e-05,7.420095062116161e-05,0.5596272945404053,0.8936084657907486,4768,24768,20000,43.1298753246665,4095.1914144679904,16.38076565787196,20.960564957931638,160.0,250 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,51080,迈腾2023款330豪华版2.0T。出现的问题是轮购车时间是2024年5月份，里程是6851公里。,0,low,unfinished_expression,xlsx_draft,0.007267594337463379,0,1,0.9927324056625366,0.007256271783262491,0.7810314893722534,0.21895655989646912,0.9930852651596069,2.0054849301232025e-05,0.010066509246826172,0.0655754804611206,31080,51080,20000,44.941458851099014,3812.7029556781054,15.250811822712421,17.34905382618308,160.0,250 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,68313,轮胎质量问题规律，排水槽横向竖向龟裂。,0,low,mid_turn_pause,xlsx_draft,0.8308001905679703,1,0,0.16919980943202972,0.8307783603668213,0.17210285365581512,0.8278889060020447,3.720181484823115e-05,0.00010085354006150737,0.8074509650468826,0.8763869255781174,48313,68313,20000,43.94112527370453,3842.845559120178,15.371382236480713,18.88684444129467,160.0,250 +cb3238dc-3946-42a5-aad8-9b9a54226363,13540,辽宁葫芦岛。,1,low,completed_answer,xlsx_draft,0.9350477233529091,1,1,0.06495227664709091,0.9350424408912659,0.12843888998031616,0.8715550303459167,0.0004472145810723305,0.3675979673862457,0.9443961046636105,0.920970730483532,20,13540,13520,42.797333560884,2515.3762940317392,14.88388339663751,17.344791628420353,160.0,169 +cb3238dc-3946-42a5-aad8-9b9a54226363,27520,投诉建议。,1,low,completed_answer,xlsx_draft,0.932467520236969,1,1,0.067532479763031,0.9324623942375183,0.13254563510417938,0.8674479722976685,0.0003318001690786332,0.0020124651491642,0.9681989774107933,0.9300618022680283,7520,27520,20000,45.13741564005613,3790.422670543194,15.161690682172775,17.661491967737664,160.0,250 +cb3238dc-3946-42a5-aad8-9b9a54226363,60790,我的轮胎行驶28000,0,low,unfinished_expression,xlsx_draft,0.012694478034973145,0,1,0.9873055219650269,0.012683030217885971,0.9789314866065979,0.021061856299638748,0.0011321299243718386,2.2103884475654922e-05,0.05659288167953491,0.4327901005744934,40790,60790,20000,42.9811654612422,3819.4279549643397,15.277711819857359,18.20390815846621,160.0,250 +cb3238dc-3946-42a5-aad8-9b9a54226363,70745,轮胎开裂,0,low,mid_turn_pause,xlsx_draft,0.1670762300491333,0,1,0.8329237699508667,0.167057067155838,0.7635847926139832,0.2364073544740677,0.0004951367154717445,2.0481578758335672e-05,0.28872406482696533,0.6845456063747406,50745,70745,20000,43.89862436801195,3801.746037788689,15.206984151154757,17.567271972075105,160.0,250 +e304f740-85d4-427e-9fd6-c5474e9cd737,18842,苏州昆山。,1,low,completed_answer,xlsx_draft,0.7565313577651978,1,1,0.24346864223480225,0.7565113306045532,0.2931012213230133,0.7068899869918823,2.9068718504277058e-05,0.00023237266577780247,0.6192943453788757,0.877316839993,42,18842,18800,43.442459776997566,3587.6654097810388,15.266661318217187,17.902691476047035,160.0,235 +e304f740-85d4-427e-9fd6-c5474e9cd737,29619,投诉。,1,low,completed_answer,xlsx_draft,0.6641051769256592,1,1,0.3358948230743408,0.6640647053718567,0.3357038199901581,0.6642818450927734,0.0001643845607759431,0.00012937521387357265,0.44960129261016846,0.8809438720345497,9619,29619,20000,43.49470976740122,3804.5860696583986,15.218344278633595,17.679024627432224,160.0,250 +e304f740-85d4-427e-9fd6-c5474e9cd737,40701,帕萨特,0,low,mid_turn_pause,xlsx_draft,0.43508362770080566,0,1,0.5649163722991943,0.4350600242614746,0.5692461133003235,0.43074503540992737,0.00017442621174268425,5.979319757898338e-05,0.4185728430747986,0.8340967893600464,20701,40701,20000,43.006375432014465,4302.509634755552,17.210038539022207,25.661859288811684,160.0,250 +e304f740-85d4-427e-9fd6-c5474e9cd737,54600,24年11月份购买的帕萨特，15000公里左右。,1,low,completed_answer,xlsx_draft,0.6717596650123596,1,1,0.3282403349876404,0.671734094619751,0.3497276306152344,0.6502625346183777,6.809142359998077e-05,7.29616149328649e-05,0.7056282758712769,0.8766583725810051,34600,54600,20000,54.418125189840794,6137.44474761188,24.54977899044752,30.092735541984435,160.0,250 +e304f740-85d4-427e-9fd6-c5474e9cd737,65819,四条轮胎，最近我在。,0,low,unfinished_expression,xlsx_draft,0.029637634754180908,0,1,0.9703623652458191,0.029622433707118034,0.9542514085769653,0.04574141651391983,0.0003253452305216342,2.1296966224326752e-05,0.05253005027770996,0.6574011743068695,45819,65819,20000,61.71162519603968,6946.4452574029565,27.785781029611826,43.03545150905847,160.0,250 +872a1e67-873a-4c49-a598-b38a670afcad,47187,帕萨特L2025款 ,0,,mid_turn_pause,,0.14240401983261108,0,1,0.8575959801673889,0.14238691329956055,0.7818540930747986,0.21813781559467316,0.0009181476780213416,3.331053085275926e-05,0.7005376219749451,0.8951215818524361,27187,47187,20000,62.02237494289875,5994.363593868911,23.977454375475645,27.657895861193538,160.0,250 +872a1e67-873a-4c49-a598-b38a670afcad,52326,我的轮胎开裂了。,0,,mid_turn_pause,,0.5535028576850891,1,0,0.4464971423149109,0.5534874200820923,0.32890474796295166,0.6710880398750305,0.0009139698813669384,0.00022386264754459262,0.4240996241569519,0.6631686389446259,32326,52326,20000,57.44829215109348,6204.5572977513075,24.81822919100523,31.657652324065555,160.0,250 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,48536,爱车型号是大众帕萨特，购车时间2023年10月份,0,,mid_turn_pause,,0.43519163131713867,0,1,0.5648083686828613,0.43517354130744934,0.4458739459514618,0.5541183352470398,9.016965486807749e-05,8.782810618868098e-05,0.5186727344989777,0.8199913799762726,28536,48536,20000,44.80900056660175,3908.039120025933,15.632156480103731,17.223906284198165,160.0,250 +872a1e67-873a-4c49-a598-b38a670afcad,44810,帕萨特,0,,mid_turn_pause,,0.3360837697982788,0,1,0.6639162302017212,0.33606526255607605,0.6159429550170898,0.3840493857860565,0.0001647999742999673,5.814846736029722e-05,0.3700411319732666,0.8325859755277634,24810,44810,20000,42.7646255120635,3771.298157982528,15.085192631930113,17.260867170989513,160.0,250 +872a1e67-873a-4c49-a598-b38a670afcad,49500,帕萨特L2025款2025年3月购车,1,,completed_turn,,0.9701901488006115,1,1,0.029809851199388504,0.9701839089393616,0.3111971616744995,0.6887921094894409,0.0010973048629239202,0.9999063014984131,0.9640973135828972,0.4527696967124939,29500,49500,20000,48.071250319480896,3840.5759502202272,15.362303800880909,17.678360408172008,160.0,250 +872a1e67-873a-4c49-a598-b38a670afcad,54318,行驶里程29000公里。,1,,completed_turn,,0.9644164405763149,1,1,0.035583559423685074,0.9644098877906799,0.2226351648569107,0.7773568630218506,0.00023717079602647573,0.9997718930244446,0.9336890131235123,0.48515671491622925,34318,54318,20000,42.80208237469196,3742.3594603314996,14.969437841325998,16.483087604865432,160.0,250 +872a1e67-873a-4c49-a598-b38a670afcad,45581,帕萨特,0,,mid_turn_pause,,0.015885531902313232,0,1,0.9841144680976868,0.015878994017839432,0.9363400936126709,0.06365057826042175,0.41161343455314636,8.217313006753102e-05,0.023214280605316162,0.2830030918121338,25581,45581,20000,43.242957442998886,3812.5865878537297,15.250346351414919,18.323129601776596,160.0,250 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,41219,迈腾2023款330豪华版2.0T,0,,mid_turn_pause,,0.5108224153518677,1,0,0.4891775846481323,0.5108088254928589,0.4531340003013611,0.5468593239784241,0.0001839700562413782,0.00039992082747630775,0.9102829173207283,0.9391313791275024,21219,41219,20000,44.131457805633545,3837.323042564094,15.349292170256376,17.59768151678145,160.0,250 +cb3238dc-3946-42a5-aad8-9b9a54226363,19620,,1,,completed_turn,,0.953842855989933,1,1,0.046157144010066986,0.9538378119468689,0.10356389731168747,0.896429717540741,8.59242572914809e-05,0.6525650024414062,0.9730777312070131,0.9391433596611023,20,19620,19600,43.64204127341509,3706.4446741715074,15.128345608863295,17.202474921941757,160.0,245 +cb3238dc-3946-42a5-aad8-9b9a54226363,41207,我是2023年,0,,mid_turn_pause,,0.014370322227478027,0,1,0.985629677772522,0.014355695806443691,0.974628210067749,0.02536475658416748,0.0008036309154704213,2.3096992663340643e-05,0.12327557802200317,0.7711529731750488,21207,41207,20000,43.93916763365269,3798.0748619884253,15.192299447953701,16.90040798857808,160.0,250 +cb3238dc-3946-42a5-aad8-9b9a54226363,49204,我是2023年、2024年1月份购置的迈腾3302023款轿车,0,,mid_turn_pause,,0.632370263338089,1,0,0.367629736661911,0.6323491334915161,0.298109769821167,0.7018820643424988,0.00010875277075683698,3.1170831789495423e-05,0.8600987195968628,0.9226682409644127,29204,49204,20000,42.79612470418215,4056.103840470314,16.224415361881256,22.201924864202724,160.0,250 +cb3238dc-3946-42a5-aad8-9b9a54226363,58424,我的轮胎,0,,mid_turn_pause,,0.11090946197509766,0,1,0.8890905380249023,0.11089496314525604,0.8546205163002014,0.1453723907470703,0.00026052925386466086,2.9298573281266727e-05,0.19378310441970825,0.7652575969696045,38424,58424,20000,42.649708688259125,3796.476504765451,15.185906019061804,18.03695042617619,160.0,250 +cb3238dc-3946-42a5-aad8-9b9a54226363,73640,轮胎开裂，行驶28000公里，轮胎开裂,0,,mid_turn_pause,,0.6731171607971191,1,0,0.32688283920288086,0.6730939745903015,0.2690482437610626,0.7309429049491882,0.00043130325502716005,4.166780490777455e-05,0.45827627182006836,0.6812975704669952,53640,73640,20000,71.1151659488678,3739.254044368863,14.957016177475452,16.975108766928315,160.0,250 +e304f740-85d4-427e-9fd6-c5474e9cd737,11531,姓赵,1,,completed_turn,,0.7890582382678986,1,1,0.21094176173210144,0.7890357971191406,0.23143410682678223,0.7685575485229492,2.6892485038843006e-05,0.0002254855789942667,0.5200230777263641,0.936267301440239,11,11531,11520,44.782666489481926,2053.215039893985,14.25843777704156,16.578083951026198,160.0,144 +e304f740-85d4-427e-9fd6-c5474e9cd737,62828,四条轮胎,0,,mid_turn_pause,,0.06967586278915405,0,1,0.930324137210846,0.06966244429349899,0.8973567485809326,0.10263627767562866,8.328101830556989e-05,3.070280945394188e-05,0.02205902338027954,0.5078669488430023,42828,62828,20000,43.30666735768318,3870.905957184732,15.483623828738928,17.57947499863803,160.0,250 diff --git a/experiments/maai-ch-kyoto/results/mimi-mc/score-timeline.json b/experiments/maai-ch-kyoto/results/mimi-mc/score-timeline.json new file mode 100644 index 0000000..40c0474 --- /dev/null +++ b/experiments/maai-ch-kyoto/results/mimi-mc/score-timeline.json @@ -0,0 +1 @@ +{"schema_version":1,"model":"maai_mimi_mc","name":"VAP / MaAI Mimi（降噪双声道）","score":"1 - p_now[user]","sample_interval_ms":80,"method":"mc (mc=True) ch_kyoto VAP with official FP32 ONNX Mimi encoder. Recorded user+robot stereo; crop preceding <=20s before 16kHz resampling; 80ms native frames, final frame ends at exact decision. Reset Mimi and VAP state per point, retain official skipped startup encoder output. No future input, annotation text, external end signal, silence gate or forced timeout. Score=1-p_now[user], not calibrated EOT probability.","provenance":{"machine":"macOS-26.4.1-arm64-arm-64bit","hardware":"Apple M4 Mac mini / 16 GB","python":"3.11.15 (main, Mar 25 2026, 02:58:47) [Clang 22.1.1 ]","environment":"experiments/maai-ch-kyoto/.venv (uv.lock)","device":"cpu","torch_threads":4,"torch_interop_threads":1,"onnx_intra_threads":4,"onnx_inter_threads":1,"dependencies":{"maai":"0.2.22","torch":"2.14.1","transformers":"5.5.3","onnxruntime":"1.31.0","numpy":"2.4.6","soundfile":"0.14.0","soxr":"1.1.0"},"model_type":"normal-ver2","variant":"vap_mc","mc":true,"checkpoint_filename":"vap_mc_mimi_state_dict_ch_kyoto_12.5hz_20000msec.pt","encoder":"mimi","encoder_precision":"fp32","channels":{"0":"recorded user","1":"recorded 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local file: /Users/wangx/Code/晓白灵智能打断/experiments/maai-ch-kyoto/models/mimi/vap_mc_mimi_state_dict_ch_kyoto_12.5hz_20000msec.pt\nUsing ONNX Mimi backend (fp32, local): /Users/wangx/Code/晓白灵智能打断/experiments/maai-ch-kyoto/models/mimi/continuous_mimi_fp32.onnx\nFroze EncoderMimiOnnx!\nUsing ONNX Mimi backend (fp32, local): /Users/wangx/Code/晓白灵智能打断/experiments/maai-ch-kyoto/models/mimi/continuous_mimi_fp32.onnx\nFroze EncoderMimiOnnx!\nfreeze encoder\nFroze EncoderMimiOnnx!\nFroze EncoderMimiOnnx!\nWarning: Parameter 'ar_channel.layers.0.mha.m' was not updated from its initial value.\nWarning: Parameter 'ar.layers.0.mha.m' was not updated from its initial value.\nWarning: Parameter 'ar.layers.0.mha_cross.m' was not updated from its initial value.\nWarning: Parameter 'ar.layers.1.mha.m' was not updated from its initial value.\nWarning: Parameter 'ar.layers.1.mha_cross.m' was not updated from its initial value.\nWarning: Parameter 'ar.layers.2.mha.m' was not updated from its initial value.\nWarning: Parameter 'ar.layers.2.mha_cross.m' was not updated from its initial value.\nWarning: Parameter 'objective.codebook.emb.weight' was not updated from its initial value.\n", + "encoder_provider": [ + "CPUExecutionProvider" + ] + } + }, + "method": "mc (mc=True) ch_kyoto VAP with official FP32 ONNX Mimi encoder. Recorded user+robot stereo; crop preceding <=20s before 16kHz resampling; 80ms native frames, final frame ends at exact decision. Reset Mimi and VAP state per point, retain official skipped startup encoder output. No future input, annotation text, external end signal, silence gate or forced timeout. Score=1-p_now[user], not calibrated EOT probability.", + "limitations": [ + "Comparison changes encoder/checkpoints and native framing; no held-out benchmark or claim that differences are caused by the encoder alone.", + "Both models use <=20s histories but native 80ms/100ms framing may differ at recording start by less than one frame.", + "Continuous full-call states differ from independent point replay; checkpoint scores need not lie on the continuous curve.", + "Replay computation includes the entire history; it is not one-step online update latency." + ], + "timeline_points": 4867, + "elapsed_seconds": 278.2951404163614 +} diff --git a/experiments/maai-ch-kyoto/results/mimi-mc/thresholds.csv b/experiments/maai-ch-kyoto/results/mimi-mc/thresholds.csv new file mode 100644 index 0000000..a6cc6eb --- /dev/null +++ b/experiments/maai-ch-kyoto/results/mimi-mc/thresholds.csv @@ -0,0 +1,8 @@ +threshold,n,tn,fp,fn,tp,accuracy,false_reply_rate,false_hold_rate,recall,precision,f1 +0.11,44,8,14,0,22,0.6818181818181818,0.6363636363636364,0.0,1.0,0.6111111111111112,0.7586206896551724 +0.5,44,15,7,3,19,0.7727272727272727,0.3181818181818182,0.13636363636363635,0.8636363636363636,0.7307692307692307,0.7916666666666666 +0.7,44,20,2,7,15,0.7954545454545454,0.09090909090909091,0.3181818181818182,0.6818181818181818,0.8823529411764706,0.7692307692307693 +0.8,44,20,2,11,11,0.7045454545454546,0.09090909090909091,0.5,0.5,0.8461538461538461,0.6285714285714286 +0.9,44,21,1,15,7,0.6363636363636364,0.045454545454545456,0.6818181818181818,0.3181818181818182,0.875,0.4666666666666667 +0.95,44,22,0,17,5,0.6136363636363636,0.0,0.7727272727272727,0.22727272727272727,1.0,0.37037037037037035 +0.98,44,22,0,22,0,0.5,0.0,1.0,0.0,,0.0 diff --git a/experiments/maai-ch-kyoto/results/mimi-run.log b/experiments/maai-ch-kyoto/results/mimi-run.log new file mode 100644 index 0000000..a4a5d17 --- /dev/null +++ b/experiments/maai-ch-kyoto/results/mimi-run.log @@ -0,0 +1,175 @@ +2026-10-27 15:04:31.764 python3[36201:79391758] 2026-10-27 15:04:31.764190 [W:onnxruntime:Default, telemetry_1ds.cc:600 operator()] Failed to persist telemetry device ID; using an in-memory identifier +WITNESS Mimi standard CPU seed=42 score=0.9191182; exact repeat, future invariance, both speakers and loading audit passed +2026-10-27 15:04:40.786 python3[36201:79391796] NetworkStorageDB:_openDBReadConnections: failed to open read connection to DB @ /Users/wangx/Library/Caches/python3/Cache.db. Error=14. Cause=unable to open database file +2026-10-27 15:04:40.786 python3[36201:79391796] The read-connection to the DB=/Users/wangx/Library/Caches/python3/Cache.db is NOT valid. Unable to determine schema version. +2026-10-27 15:04:40.786 python3[36201:79391796] NetworkStorageDB:_openDBWriteConnections: failed to open write connection to DB @ /Users/wangx/Library/Caches/python3/Cache.db. Error=14. Cause=unable to open database file +2026-10-27 15:04:40.786 python3[36201:79391796] DEBUG: there is no SQL cache DB located at /Users/wangx/Library/Caches/python3/Cache.db. +2026-10-27 15:04:40.787 python3[36201:79391796] DEBUG: there is no SQL cache DB located at /Users/wangx/Library/Caches/python3/Cache.db-shm. +2026-10-27 15:04:40.787 python3[36201:79391796] DEBUG: there is no SQL cache DB located at /Users/wangx/Library/Caches/python3/Cache.db-wal. +1/44 2c83253a 21780: label=1, Mimi=0.9191, CPC-mc=0.9598 +2/44 2c83253a 29860: label=1, Mimi=0.9220, CPC-mc=0.9286 +3/44 2c83253a 48125: label=0, Mimi=0.4655, CPC-mc=0.5123 +4/44 2c83253a 64000: label=1, Mimi=0.7919, CPC-mc=0.9370 +5/44 2c83253a 76672: label=0, Mimi=0.5828, CPC-mc=0.7067 +6/44 2c83253a 88482: label=1, Mimi=0.2126, CPC-mc=0.1147 +7/44 40ae686c 22007: label=1, Mimi=0.7385, CPC-mc=0.6932 +8/44 40ae686c 31842: label=1, Mimi=0.4897, CPC-mc=0.7899 +9/44 40ae686c 52348: label=1, Mimi=0.8557, CPC-mc=0.9598 +10/44 40ae686c 65298: label=0, Mimi=0.0211, CPC-mc=0.1646 +11/44 40ae686c 67943: label=0, Mimi=0.0853, CPC-mc=0.1255 +12/44 40ae686c 70430: label=0, Mimi=0.9157, CPC-mc=0.9452 +13/44 40ae686c 82941: label=1, Mimi=0.1312, CPC-mc=0.2714 +14/44 872a1e67 17153: label=1, Mimi=0.3544, CPC-mc=0.4009 +15/44 872a1e67 24846: label=1, Mimi=0.4336, CPC-mc=0.6937 +16/44 872a1e67 33471: label=1, Mimi=0.4404, CPC-mc=0.8021 +17/44 a266b9ef 18027: label=1, Mimi=0.8423, CPC-mc=0.5774 +18/44 a266b9ef 24768: label=1, Mimi=0.4299, CPC-mc=0.5596 +19/44 a266b9ef 51080: label=0, Mimi=0.0061, CPC-mc=0.0101 +20/44 a266b9ef 68313: label=0, Mimi=0.7154, CPC-mc=0.8075 +21/44 cb3238dc 13540: label=1, Mimi=0.8341, CPC-mc=0.9444 +22/44 cb3238dc 27520: label=1, Mimi=0.9249, CPC-mc=0.9682 +23/44 cb3238dc 60790: label=0, Mimi=0.0064, CPC-mc=0.0566 +24/44 cb3238dc 70745: label=0, Mimi=0.1483, CPC-mc=0.2887 +25/44 e304f740 18842: label=1, Mimi=0.7855, CPC-mc=0.6193 +26/44 e304f740 29619: label=1, Mimi=0.6686, CPC-mc=0.4496 +27/44 e304f740 40701: label=0, Mimi=0.2479, CPC-mc=0.4186 +28/44 e304f740 54600: label=1, Mimi=0.6685, CPC-mc=0.7056 +29/44 e304f740 65819: label=0, Mimi=0.0293, CPC-mc=0.0525 +30/44 872a1e67 47187: label=0, Mimi=0.2395, CPC-mc=0.7005 +31/44 872a1e67 52326: label=0, Mimi=0.5609, CPC-mc=0.4241 +32/44 40ae686c 48536: label=0, Mimi=0.2708, CPC-mc=0.5187 +33/44 872a1e67 44810: label=0, Mimi=0.3857, CPC-mc=0.3700 +34/44 872a1e67 49500: label=1, Mimi=0.9565, CPC-mc=0.9641 +35/44 872a1e67 54318: label=1, Mimi=0.9238, CPC-mc=0.9337 +36/44 872a1e67 45581: label=0, Mimi=0.0142, CPC-mc=0.0232 +37/44 a266b9ef 41219: label=0, Mimi=0.5353, CPC-mc=0.9103 +38/44 cb3238dc 19620: label=1, Mimi=0.8876, CPC-mc=0.9731 +39/44 cb3238dc 41207: label=0, Mimi=0.0127, CPC-mc=0.1233 +40/44 cb3238dc 49204: label=0, Mimi=0.7771, CPC-mc=0.8601 +41/44 cb3238dc 58424: label=0, Mimi=0.1450, CPC-mc=0.1938 +42/44 cb3238dc 73640: label=0, Mimi=0.5421, CPC-mc=0.4583 +43/44 e304f740 11531: label=1, Mimi=0.6418, CPC-mc=0.5200 +44/44 e304f740 62828: label=0, Mimi=0.0510, CPC-mc=0.0221 +2c83253a timeline: 932 scores, 6 checkpoints +40ae686c timeline: 834 scores, 8 checkpoints +872a1e67 timeline: 710 scores, 9 checkpoints +a266b9ef timeline: 799 scores, 5 checkpoints +cb3238dc timeline: 902 scores, 9 checkpoints +e304f740 timeline: 690 scores, 7 checkpoints +{ + "main_metrics": { + "n": 44, + "tn": 15, + "fp": 7, + "fn": 7, + "tp": 15, + "accuracy": 0.6818181818181818, + "false_reply_rate": 0.3181818181818182, + "false_hold_rate": 0.3181818181818182, + "recall": 0.6818181818181818, + "precision": 0.6818181818181818, + "f1": 0.6818181818181818 + }, + "cpc_mc_stereo_reference": { + "n": 44, + "tn": 14, + "fp": 8, + "fn": 4, + "tp": 18, + "accuracy": 0.7272727272727273, + "false_reply_rate": 0.36363636363636365, + "false_hold_rate": 0.18181818181818182, + "recall": 0.8181818181818182, + "precision": 0.6923076923076923, + "f1": 0.75 + }, + "changed_vs_cpc_mc_stereo": { + "fixed": 4, + "regressed": 6 + }, + "elapsed_seconds": 267.65075033251196 +} +WITNESS Mimi mc CPU seed=42 score=0.9657906; exact repeat, future invariance, both speakers and loading audit passed +1/44 2c83253a 21780: label=1, Mimi=0.9658, CPC-mc=0.9598 +2/44 2c83253a 29860: label=1, Mimi=0.9578, CPC-mc=0.9286 +3/44 2c83253a 48125: label=0, Mimi=0.4555, CPC-mc=0.5123 +4/44 2c83253a 64000: label=1, Mimi=0.8707, CPC-mc=0.9370 +5/44 2c83253a 76672: label=0, Mimi=0.6865, CPC-mc=0.7067 +6/44 2c83253a 88482: label=1, Mimi=0.2586, CPC-mc=0.1147 +7/44 40ae686c 22007: label=1, Mimi=0.7644, CPC-mc=0.6932 +8/44 40ae686c 31842: label=1, Mimi=0.6238, CPC-mc=0.7899 +9/44 40ae686c 52348: label=1, Mimi=0.8982, CPC-mc=0.9598 +10/44 40ae686c 65298: label=0, Mimi=0.0665, CPC-mc=0.1646 +11/44 40ae686c 67943: label=0, Mimi=0.0588, CPC-mc=0.1255 +12/44 40ae686c 70430: label=0, Mimi=0.9187, CPC-mc=0.9452 +13/44 40ae686c 82941: label=1, Mimi=0.2951, CPC-mc=0.2714 +14/44 872a1e67 17153: label=1, Mimi=0.5796, CPC-mc=0.4009 +15/44 872a1e67 24846: label=1, Mimi=0.4162, CPC-mc=0.6937 +16/44 872a1e67 33471: label=1, Mimi=0.7418, CPC-mc=0.8021 +17/44 a266b9ef 18027: label=1, Mimi=0.8496, CPC-mc=0.5774 +18/44 a266b9ef 24768: label=1, Mimi=0.8364, CPC-mc=0.5596 +19/44 a266b9ef 51080: label=0, Mimi=0.0073, CPC-mc=0.0101 +20/44 a266b9ef 68313: label=0, Mimi=0.8308, CPC-mc=0.8075 +21/44 cb3238dc 13540: label=1, Mimi=0.9350, CPC-mc=0.9444 +22/44 cb3238dc 27520: label=1, Mimi=0.9325, CPC-mc=0.9682 +23/44 cb3238dc 60790: label=0, Mimi=0.0127, CPC-mc=0.0566 +24/44 cb3238dc 70745: label=0, Mimi=0.1671, CPC-mc=0.2887 +25/44 e304f740 18842: label=1, Mimi=0.7565, CPC-mc=0.6193 +26/44 e304f740 29619: label=1, Mimi=0.6641, CPC-mc=0.4496 +27/44 e304f740 40701: label=0, Mimi=0.4351, CPC-mc=0.4186 +28/44 e304f740 54600: label=1, Mimi=0.6718, CPC-mc=0.7056 +29/44 e304f740 65819: label=0, Mimi=0.0296, CPC-mc=0.0525 +30/44 872a1e67 47187: label=0, Mimi=0.1424, CPC-mc=0.7005 +31/44 872a1e67 52326: label=0, Mimi=0.5535, CPC-mc=0.4241 +32/44 40ae686c 48536: label=0, Mimi=0.4352, CPC-mc=0.5187 +33/44 872a1e67 44810: label=0, Mimi=0.3361, CPC-mc=0.3700 +34/44 872a1e67 49500: label=1, Mimi=0.9702, CPC-mc=0.9641 +35/44 872a1e67 54318: label=1, Mimi=0.9644, CPC-mc=0.9337 +36/44 872a1e67 45581: label=0, Mimi=0.0159, CPC-mc=0.0232 +37/44 a266b9ef 41219: label=0, Mimi=0.5108, CPC-mc=0.9103 +38/44 cb3238dc 19620: label=1, Mimi=0.9538, CPC-mc=0.9731 +39/44 cb3238dc 41207: label=0, Mimi=0.0144, CPC-mc=0.1233 +40/44 cb3238dc 49204: label=0, Mimi=0.6324, CPC-mc=0.8601 +41/44 cb3238dc 58424: label=0, Mimi=0.1109, CPC-mc=0.1938 +42/44 cb3238dc 73640: label=0, Mimi=0.6731, CPC-mc=0.4583 +43/44 e304f740 11531: label=1, Mimi=0.7891, CPC-mc=0.5200 +44/44 e304f740 62828: label=0, Mimi=0.0697, CPC-mc=0.0221 +2c83253a timeline: 932 scores, 6 checkpoints +40ae686c timeline: 834 scores, 8 checkpoints +872a1e67 timeline: 710 scores, 9 checkpoints +a266b9ef timeline: 799 scores, 5 checkpoints +cb3238dc timeline: 902 scores, 9 checkpoints +e304f740 timeline: 690 scores, 7 checkpoints +{ + "main_metrics": { + "n": 44, + "tn": 15, + "fp": 7, + "fn": 3, + "tp": 19, + "accuracy": 0.7727272727272727, + "false_reply_rate": 0.3181818181818182, + "false_hold_rate": 0.13636363636363635, + "recall": 0.8636363636363636, + "precision": 0.7307692307692307, + "f1": 0.7916666666666666 + }, + "cpc_mc_stereo_reference": { + "n": 44, + "tn": 14, + "fp": 8, + "fn": 4, + "tp": 18, + "accuracy": 0.7272727272727273, + "false_reply_rate": 0.36363636363636365, + "false_hold_rate": 0.18181818181818182, + "recall": 0.8181818181818182, + "precision": 0.6923076923076923, + "f1": 0.75 + }, + "changed_vs_cpc_mc_stereo": { + "fixed": 5, + "regressed": 3 + }, + "elapsed_seconds": 278.2951404163614 +} diff --git a/experiments/maai-ch-kyoto/results/mimi-smoke.log b/experiments/maai-ch-kyoto/results/mimi-smoke.log new file mode 100644 index 0000000..78e6863 --- /dev/null +++ b/experiments/maai-ch-kyoto/results/mimi-smoke.log @@ -0,0 +1,243 @@ +2026-10-27 15:03:18.929 python3[35872:79390408] 2026-10-27 15:03:18.929469 [W:onnxruntime:Default, telemetry_1ds.cc:600 operator()] Failed to persist telemetry device ID; using an in-memory identifier +WITNESS Mimi standard CPU seed=42 score=0.9191182; exact repeat, future invariance, both speakers and loading audit passed +2026-10-27 15:03:27.958 python3[35872:79390505] NetworkStorageDB:_openDBReadConnections: failed to open read connection to DB @ /Users/wangx/Library/Caches/python3/Cache.db. Error=14. Cause=unable to open database file +2026-10-27 15:03:27.958 python3[35872:79390505] The read-connection to the DB=/Users/wangx/Library/Caches/python3/Cache.db is NOT valid. Unable to determine schema version. +2026-10-27 15:03:27.958 python3[35872:79390505] NetworkStorageDB:_openDBWriteConnections: failed to open write connection to DB @ /Users/wangx/Library/Caches/python3/Cache.db. Error=14. Cause=unable to open database file +2026-10-27 15:03:27.958 python3[35872:79390505] DEBUG: there is no SQL cache DB located at /Users/wangx/Library/Caches/python3/Cache.db. +2026-10-27 15:03:27.959 python3[35872:79390505] DEBUG: there is no SQL cache DB located at /Users/wangx/Library/Caches/python3/Cache.db-shm. +2026-10-27 15:03:27.959 python3[35872:79390505] DEBUG: there is no SQL cache DB located at /Users/wangx/Library/Caches/python3/Cache.db-wal. +{ + "witness": { + "repeat_identical": true, + "future_invariance": true, + "both_channels_nonzero": true, + "score": 0.9191182181239128, + "timing": { + "model_load_ms": 46.263291500508785, + "replay_inference_ms": 3829.8779549077153, + "mean_update_ms": 15.319511819630861, + "p95_update_ms": 17.98368152230978, + "first_output_ms": 160.0, + "frame_count": 250 + }, + "loading_audit": { + "variant": "vap", + "loader_architecture_marker": "vap", + "encoder": "mimi", + "checkpoint_mc": false, + "single_tower": false, + "missing_trainable_parameters": [], + "mismatched_parameters": [], + "unused_frozen_frame_rate_parameters": [ + "encoder1.frame_rate_conv.weight", + "encoder1.frame_rate_conv.bias", + "encoder2.frame_rate_conv.weight", + "encoder2.frame_rate_conv.bias" + ], + "loader_messages": "Loading model from local file: /Users/wangx/Code/晓白灵智能打断/experiments/maai-ch-kyoto/models/mimi/vap_mimi_state_dict_ch_kyoto_12.5hz_20000msec.pt\nUsing ONNX Mimi backend (fp32, local): /Users/wangx/Code/晓白灵智能打断/experiments/maai-ch-kyoto/models/mimi/continuous_mimi_fp32.onnx\nFroze EncoderMimiOnnx!\nUsing ONNX Mimi backend (fp32, local): /Users/wangx/Code/晓白灵智能打断/experiments/maai-ch-kyoto/models/mimi/continuous_mimi_fp32.onnx\nFroze EncoderMimiOnnx!\nfreeze encoder\nFroze EncoderMimiOnnx!\nFroze EncoderMimiOnnx!\nWarning: Parameter 'ar_channel.layers.0.mha.m' was not updated from its initial value.\nWarning: Parameter 'ar.layers.0.mha.m' was not updated from its initial value.\nWarning: Parameter 'ar.layers.0.mha_cross.m' was not updated from its initial value.\nWarning: Parameter 'ar.layers.1.mha.m' was not updated from its initial value.\nWarning: Parameter 'ar.layers.1.mha_cross.m' was not updated from its initial value.\nWarning: Parameter 'ar.layers.2.mha.m' was not updated from its initial value.\nWarning: Parameter 'ar.layers.2.mha_cross.m' was not updated from its initial value.\nWarning: Parameter 'objective.codebook.emb.weight' was not updated from its initial value.\n", + "encoder_provider": [ + "CPUExecutionProvider" + ] + } + }, + "provenance": { + "machine": "macOS-26.4.1-arm64-arm-64bit", + "hardware": "Apple M4 Mac mini / 16 GB", + "python": "3.11.15 (main, Mar 25 2026, 02:58:47) [Clang 22.1.1 ]", + "environment": "experiments/maai-ch-kyoto/.venv (uv.lock)", + "device": "cpu", + "torch_threads": 4, + "torch_interop_threads": 1, + "onnx_intra_threads": 4, + "onnx_inter_threads": 1, + "dependencies": { + "maai": "0.2.22", + "torch": "2.14.1", + "transformers": "5.5.3", + "onnxruntime": "1.31.0", + "numpy": "2.4.6", + "soundfile": "0.14.0", + "soxr": "1.1.0" + }, + "model_type": "normal-ver2", + "variant": "vap", + "mc": false, + "checkpoint_filename": "vap_mimi_state_dict_ch_kyoto_12.5hz_20000msec.pt", + "encoder": "mimi", + "encoder_precision": "fp32", + "channels": { + "0": "recorded user", + "1": "recorded robot" + }, + "sample_rate": 16000, + "frame_rate": 12.5, + "frame_ms": 80, + "context_ms": 20000, + "seed": 42, + "use_kv_cache": true, + "weights": { + "files": [ + { + "filename": "vap_mimi_state_dict_ch_kyoto_12.5hz_20000msec.pt", + "repo": "maai-kyoto/vap_ch_kyoto", + "revision": "c135cdcfa12ff5aa1cce2fdd23df110941a78f38", + "url": "https://huggingface.co/maai-kyoto/vap_ch_kyoto/resolve/c135cdcfa12ff5aa1cce2fdd23df110941a78f38/vap_mimi_state_dict_ch_kyoto_12.5hz_20000msec.pt", + "sha256": "2820d2b172a6ddd4696d33edb43933f436a09a3e0d3eb41c7dc390c33b22d5ce", + "size": 15052341, + "upstream_blob_id": "cd3d8d053652e41ecfba747a193b98a230ffcbca" + }, + { + "filename": "continuous_mimi_fp32.onnx", + "repo": "maai-kyoto/continuous-mimi-onnx", + "revision": "303ef4ec0c70e4c71b67ea166acd26dd0d96655c", + "url": "https://huggingface.co/maai-kyoto/continuous-mimi-onnx/resolve/303ef4ec0c70e4c71b67ea166acd26dd0d96655c/continuous_mimi_fp32.onnx", + "sha256": "c86171a48e1bd46aa31a52e7fe00f74acc310e0d15d11881516cdd1df1639b54", + "size": 155877436, + "upstream_blob_id": "1ade42d5caa75f7567c2f904245cf893584e6ed9" + }, + { + "filename": "continuous_mimi_fp32.json", + "repo": "maai-kyoto/continuous-mimi-onnx", + "revision": "303ef4ec0c70e4c71b67ea166acd26dd0d96655c", + "url": "https://huggingface.co/maai-kyoto/continuous-mimi-onnx/resolve/303ef4ec0c70e4c71b67ea166acd26dd0d96655c/continuous_mimi_fp32.json", + "sha256": "221ab200381209d22ddf0291aff8fd0752633f0ddf7cbdd344cda6b039333c74", + "size": 7411, + "upstream_blob_id": "21be6fa62ec6c92cc9ba003c72ea09c218e9946e" + }, + { + "filename": "vap_mc_mimi_state_dict_ch_kyoto_12.5hz_20000msec.pt", + "repo": "maai-kyoto/vap_ch_kyoto", + "revision": "c135cdcfa12ff5aa1cce2fdd23df110941a78f38", + "url": "https://huggingface.co/maai-kyoto/vap_ch_kyoto/resolve/c135cdcfa12ff5aa1cce2fdd23df110941a78f38/vap_mc_mimi_state_dict_ch_kyoto_12.5hz_20000msec.pt", + "sha256": "61c9e186d765cb6c338cb7fdacb68baf4b2533e72cde7c803e52480a62919219", + "size": 15052584, + "upstream_blob_id": "cd6e6701d6c5fc7a659a82952e92ce2569b7b111" + } + ] + }, + "label_sha256": "780e50f976daf039ab976ea155bdb5d1ca5fffc792f9db7adf8063ec3048ff8e", + "history_sha256": "4c21e20b7323030d590276854f07b454ccbad627e1a7842101a22a99377ffbaa", + "uv_lock_sha256": "6863631a5969e0f077d8aefc4335532952fe95486598b609e832e36d5ce2777f", + "script_sha256": "36b950aa396e6008bbd6e32410902dd2859f68b17b9f8fb5691a97d415e80993", + "maai_encoder_source_sha256": "80f5d928ec48c7839c2e935043df0e1c82b523096834f6cbc968ccb45ddc6fbd", + "maai_model_source_sha256": "824730f564f0da18d0d4c19df1a376b692546732faf8eb7aaf871d74480ff504" + } +} +WITNESS Mimi mc CPU seed=42 score=0.9657906; exact repeat, future invariance, both speakers and loading audit passed +{ + "witness": { + "repeat_identical": true, + "future_invariance": true, + "both_channels_nonzero": true, + "score": 0.965790644288063, + "timing": { + "model_load_ms": 40.622083470225334, + "replay_inference_ms": 3750.3272872418165, + "mean_update_ms": 15.001309148967266, + "p95_update_ms": 17.169949365779757, + "first_output_ms": 160.0, + "frame_count": 250 + }, + "loading_audit": { + "variant": "vap_mc", + "loader_architecture_marker": "vap", + "encoder": "mimi", + "checkpoint_mc": true, + "single_tower": false, + "missing_trainable_parameters": [], + "mismatched_parameters": [], + "unused_frozen_frame_rate_parameters": [ + "encoder1.frame_rate_conv.weight", + "encoder1.frame_rate_conv.bias", + "encoder2.frame_rate_conv.weight", + "encoder2.frame_rate_conv.bias" + ], + "loader_messages": "Loading model from local file: /Users/wangx/Code/晓白灵智能打断/experiments/maai-ch-kyoto/models/mimi/vap_mc_mimi_state_dict_ch_kyoto_12.5hz_20000msec.pt\nUsing ONNX Mimi backend (fp32, local): /Users/wangx/Code/晓白灵智能打断/experiments/maai-ch-kyoto/models/mimi/continuous_mimi_fp32.onnx\nFroze EncoderMimiOnnx!\nUsing ONNX Mimi backend (fp32, local): /Users/wangx/Code/晓白灵智能打断/experiments/maai-ch-kyoto/models/mimi/continuous_mimi_fp32.onnx\nFroze EncoderMimiOnnx!\nfreeze encoder\nFroze EncoderMimiOnnx!\nFroze EncoderMimiOnnx!\nWarning: Parameter 'ar_channel.layers.0.mha.m' was not updated from its initial value.\nWarning: Parameter 'ar.layers.0.mha.m' was not updated from its initial value.\nWarning: Parameter 'ar.layers.0.mha_cross.m' was not updated from its initial value.\nWarning: Parameter 'ar.layers.1.mha.m' was not updated from its initial value.\nWarning: Parameter 'ar.layers.1.mha_cross.m' was not updated from its initial value.\nWarning: Parameter 'ar.layers.2.mha.m' was not updated from its initial value.\nWarning: Parameter 'ar.layers.2.mha_cross.m' was not updated from its initial value.\nWarning: Parameter 'objective.codebook.emb.weight' was not updated from its initial value.\n", + "encoder_provider": [ + "CPUExecutionProvider" + ] + } + }, + "provenance": { + "machine": "macOS-26.4.1-arm64-arm-64bit", + "hardware": "Apple M4 Mac mini / 16 GB", + "python": "3.11.15 (main, Mar 25 2026, 02:58:47) [Clang 22.1.1 ]", + "environment": "experiments/maai-ch-kyoto/.venv (uv.lock)", + "device": "cpu", + "torch_threads": 4, + "torch_interop_threads": 1, + "onnx_intra_threads": 4, + "onnx_inter_threads": 1, + "dependencies": { + "maai": "0.2.22", + "torch": "2.14.1", + "transformers": "5.5.3", + "onnxruntime": "1.31.0", + "numpy": "2.4.6", + "soundfile": "0.14.0", + "soxr": "1.1.0" + }, + "model_type": "normal-ver2", + "variant": "vap_mc", + "mc": true, + "checkpoint_filename": "vap_mc_mimi_state_dict_ch_kyoto_12.5hz_20000msec.pt", + "encoder": "mimi", + "encoder_precision": "fp32", + "channels": { + "0": "recorded user", + "1": "recorded robot" + }, + "sample_rate": 16000, + "frame_rate": 12.5, + "frame_ms": 80, + "context_ms": 20000, + "seed": 42, + "use_kv_cache": true, + "weights": { + "files": [ + { + "filename": "vap_mimi_state_dict_ch_kyoto_12.5hz_20000msec.pt", + "repo": "maai-kyoto/vap_ch_kyoto", + "revision": "c135cdcfa12ff5aa1cce2fdd23df110941a78f38", + "url": "https://huggingface.co/maai-kyoto/vap_ch_kyoto/resolve/c135cdcfa12ff5aa1cce2fdd23df110941a78f38/vap_mimi_state_dict_ch_kyoto_12.5hz_20000msec.pt", + "sha256": "2820d2b172a6ddd4696d33edb43933f436a09a3e0d3eb41c7dc390c33b22d5ce", + "size": 15052341, + "upstream_blob_id": "cd3d8d053652e41ecfba747a193b98a230ffcbca" + }, + { + "filename": "continuous_mimi_fp32.onnx", + "repo": "maai-kyoto/continuous-mimi-onnx", + "revision": "303ef4ec0c70e4c71b67ea166acd26dd0d96655c", + "url": 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"https://huggingface.co/maai-kyoto/vap_ch_kyoto/resolve/c135cdcfa12ff5aa1cce2fdd23df110941a78f38/vap_mc_mimi_state_dict_ch_kyoto_12.5hz_20000msec.pt", + "sha256": "61c9e186d765cb6c338cb7fdacb68baf4b2533e72cde7c803e52480a62919219", + "size": 15052584, + "upstream_blob_id": "cd6e6701d6c5fc7a659a82952e92ce2569b7b111" + } + ] + }, + "label_sha256": "780e50f976daf039ab976ea155bdb5d1ca5fffc792f9db7adf8063ec3048ff8e", + "history_sha256": "4c21e20b7323030d590276854f07b454ccbad627e1a7842101a22a99377ffbaa", + "uv_lock_sha256": "6863631a5969e0f077d8aefc4335532952fe95486598b609e832e36d5ce2777f", + "script_sha256": "36b950aa396e6008bbd6e32410902dd2859f68b17b9f8fb5691a97d415e80993", + "maai_encoder_source_sha256": "80f5d928ec48c7839c2e935043df0e1c82b523096834f6cbc968ccb45ddc6fbd", + "maai_model_source_sha256": "824730f564f0da18d0d4c19df1a376b692546732faf8eb7aaf871d74480ff504" + } +} diff --git a/experiments/maai-ch-kyoto/results/mimi-standard/errors.csv b/experiments/maai-ch-kyoto/results/mimi-standard/errors.csv new file mode 100644 index 0000000..a42b4fa --- /dev/null +++ b/experiments/maai-ch-kyoto/results/mimi-standard/errors.csv @@ -0,0 +1,15 @@ +call_id,decision_ms,user_text_at_decision,can_reply_now,confidence,reason,annotator,can_reply_score,prediction_05,correct_05,p_now_user,p_now_robot,p_future_user,p_future_robot,vad_user,vad_robot,cpc_mc_stereo_score,cpc_mc_user_only_score,audio_start_ms,audio_end_ms,context_ms,model_load_ms,replay_inference_ms,mean_update_ms,p95_update_ms,first_output_ms,frame_count +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,76672,恒泰轮胎前门两条轮胎开裂,0,low,unfinished_expression,xlsx_draft,0.5827796757221222,1,0,0.4172203242778778,0.5827587246894836,0.41673988103866577,0.5832511782646179,0.0002513767685741186,0.0001224606967298314,0.7066852748394012,0.8640106171369553,56672,76672,20000,43.11825055629015,3734.2152446508408,14.936860978603363,17.071229452267286,160.0,250 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,88482,尽快，已经影响行车安全了。,1,low,completed_answer,xlsx_draft,0.2125949263572693,0,0,0.7874050736427307,0.21255195140838623,0.5050087571144104,0.494981050491333,0.01712055504322052,0.0001118458530982025,0.11465108394622803,0.16062873601913452,68482,88482,20000,42.53454227000475,3763.994230888784,15.055976923555136,17.23313126713037,160.0,250 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,31842,投诉建议。,1,low,completed_answer,xlsx_draft,0.48968255519866943,0,0,0.5103174448013306,0.4896518886089325,0.5082431435585022,0.49174630641937256,0.00011276060104137287,5.577295814873651e-05,0.7899426519870758,0.9342073872685432,11842,31842,20000,44.34670880436897,3777.243494056165,15.10897397622466,17.543585738167163,160.0,250 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,70430,轮胎裂纹同意更换新轮胎。但是20,0,low,unfinished_expression,xlsx_draft,0.9156609252095222,1,0,0.08433907479047775,0.9156560301780701,0.2074228972196579,0.7925690412521362,0.005387305747717619,0.9999953508377075,0.9451650716364384,0.03903418779373169,50430,70430,20000,46.39508295804262,3987.742042168975,15.9509681686759,20.263655856251717,160.0,250 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,82941,可以。,1,low,completed_answer,xlsx_draft,0.13118845224380493,0,0,0.8688115477561951,0.13116922974586487,0.8727200031280518,0.12727269530296326,9.329482418252155e-05,0.00039028728497214615,0.27139031887054443,0.9212324172258377,62941,82941,20000,44.22441590577364,3770.675449632108,15.082701798528433,16.84834328480065,160.0,250 +872a1e67-873a-4c49-a598-b38a670afcad,17153,我姓宋。,1,low,completed_answer,xlsx_draft,0.3544103503227234,0,0,0.6455896496772766,0.3543955981731415,0.6671656966209412,0.3328273892402649,0.00037749105831608176,0.0003432980156503618,0.4008920192718506,0.8734219521284103,33,17153,17120,42.86170843988657,3181.1862913891673,14.865356501818539,16.877298150211573,160.0,214 +872a1e67-873a-4c49-a598-b38a670afcad,24846,郑州市，河南郑州市。,1,low,completed_answer,xlsx_draft,0.43358469009399414,0,0,0.5664153099060059,0.43356308341026306,0.5940371751785278,0.4059540331363678,0.00042811373714357615,0.00014359904162120074,0.6936855018138885,0.9015571922063828,4846,24846,20000,43.59066765755415,3757.988958619535,15.03195583447814,17.336932709440582,160.0,250 +872a1e67-873a-4c49-a598-b38a670afcad,33471,我的轮胎开裂了。,1,low,completed_answer,xlsx_draft,0.4404107332229614,0,0,0.5595892667770386,0.4403921663761139,0.5567219257354736,0.4432700574398041,0.00027235213201493025,4.663604704546742e-05,0.802086129784584,0.929821141064167,13471,33471,20000,44.05749961733818,3898.5149217769504,15.594059687107801,16.760270670056343,160.0,250 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,24768,投诉。,1,low,completed_answer,xlsx_draft,0.429895281791687,0,0,0.570104718208313,0.429873526096344,0.6319901347160339,0.36800137162208557,3.379296322236769e-05,0.00017112762725446373,0.5596272945404053,0.8936084657907486,4768,24768,20000,47.33575042337179,3768.2867143303156,15.073146857321262,17.01975828036666,160.0,250 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,68313,轮胎质量问题规律，排水槽横向竖向龟裂。,0,low,mid_turn_pause,xlsx_draft,0.7153808772563934,1,0,0.28461912274360657,0.7153563499450684,0.3098556697368622,0.6901354789733887,0.00015701723168604076,0.00015683767560403794,0.8074509650468826,0.8763869255781174,48313,68313,20000,42.48408321291208,3793.225208297372,15.172900833189487,17.884926451370116,160.0,250 +872a1e67-873a-4c49-a598-b38a670afcad,52326,我的轮胎开裂了。,0,,mid_turn_pause,,0.5608597099781036,1,0,0.43914029002189636,0.5608418583869934,0.32150939106941223,0.6784825921058655,0.00028993270825594664,6.893909448990598e-05,0.4240996241569519,0.6631686389446259,32326,52326,20000,43.711209669709206,3781.8655846640468,15.127462338656187,17.952587874606248,160.0,250 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,41219,迈腾2023款330豪华版2.0T,0,,mid_turn_pause,,0.5353022813796997,1,0,0.4646977186203003,0.53528892993927,0.43114253878593445,0.5688507556915283,0.0004525056283455342,0.0009800773113965988,0.9102829173207283,0.9391313791275024,21219,41219,20000,43.12216676771641,3731.8863114342093,14.927545245736837,17.138534784317017,160.0,250 +cb3238dc-3946-42a5-aad8-9b9a54226363,49204,我是2023年、2024年1月份购置的迈腾3302023款轿车,0,,mid_turn_pause,,0.7771151214838028,1,0,0.2228848785161972,0.7770937085151672,0.1958613097667694,0.8041304349899292,0.0001806421932997182,0.00013914017472416162,0.8600987195968628,0.9226682409644127,29204,49204,20000,42.962207458913326,3849.3550131097436,15.397420052438974,19.366614380851384,160.0,250 +cb3238dc-3946-42a5-aad8-9b9a54226363,73640,轮胎开裂，行驶28000公里，轮胎开裂,0,,mid_turn_pause,,0.5420903265476227,1,0,0.4579096734523773,0.542066752910614,0.33230483531951904,0.667686402797699,0.00046382888103835285,6.0991980717517436e-05,0.45827627182006836,0.6812975704669952,53640,73640,20000,43.076083064079285,3793.9981278032064,15.175992511212826,17.95125198550522,160.0,250 diff --git a/experiments/maai-ch-kyoto/results/mimi-standard/predictions.csv b/experiments/maai-ch-kyoto/results/mimi-standard/predictions.csv new file mode 100644 index 0000000..6730dd0 --- /dev/null +++ b/experiments/maai-ch-kyoto/results/mimi-standard/predictions.csv @@ -0,0 +1,45 @@ +call_id,decision_ms,user_text_at_decision,can_reply_now,confidence,reason,annotator,can_reply_score,prediction_05,correct_05,p_now_user,p_now_robot,p_future_user,p_future_robot,vad_user,vad_robot,cpc_mc_stereo_score,cpc_mc_user_only_score,audio_start_ms,audio_end_ms,context_ms,model_load_ms,replay_inference_ms,mean_update_ms,p95_update_ms,first_output_ms,frame_count +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,21780,贵州省遵义市仁怀市。,1,low,completed_answer,xlsx_draft,0.9191182181239128,1,1,0.08088178187608719,0.9191129207611084,0.178580179810524,0.8214128613471985,4.093406460015103e-05,0.3861665427684784,0.959835909307003,0.9077615067362785,1780,21780,20000,42.778000235557556,3751.8482599407434,15.007393039762974,17.6150916609913,160.0,250 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,29860,投诉建议。,1,low,completed_answer,xlsx_draft,0.9219910651445389,1,1,0.07800893485546112,0.921985924243927,0.1712864339351654,0.8287063837051392,0.0001444009249098599,0.001416263054125011,0.9285890087485313,0.8796060606837273,9860,29860,20000,42.49204229563475,3755.9630842879415,15.023852337151766,17.485685506835576,160.0,250 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,48125,型号是24年380大众帕萨特星空龙腾版。,0,low,mid_turn_pause,xlsx_draft,0.46547502279281616,0,1,0.5345249772071838,0.4654569625854492,0.4123682379722595,0.5876235961914062,0.0002440968673909083,8.326847455464303e-05,0.5122989416122437,0.712736040353775,28125,48125,20000,43.588124215602875,3739.1057750210166,14.956423100084066,17.245206236839294,160.0,250 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,64000,行驶3万公里。,1,low,completed_answer,xlsx_draft,0.7919017523527145,1,1,0.20809824764728546,0.791895866394043,0.3406008780002594,0.6593921780586243,0.000630278023891151,0.03700174018740654,0.9369851648807526,0.9320666417479515,44000,64000,20000,42.840457521378994,3768.6912519857287,15.074765007942915,18.220912292599678,160.0,250 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,76672,恒泰轮胎前门两条轮胎开裂,0,low,unfinished_expression,xlsx_draft,0.5827796757221222,1,0,0.4172203242778778,0.5827587246894836,0.41673988103866577,0.5832511782646179,0.0002513767685741186,0.0001224606967298314,0.7066852748394012,0.8640106171369553,56672,76672,20000,43.11825055629015,3734.2152446508408,14.936860978603363,17.071229452267286,160.0,250 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,88482,尽快，已经影响行车安全了。,1,low,completed_answer,xlsx_draft,0.2125949263572693,0,0,0.7874050736427307,0.21255195140838623,0.5050087571144104,0.494981050491333,0.01712055504322052,0.0001118458530982025,0.11465108394622803,0.16062873601913452,68482,88482,20000,42.53454227000475,3763.994230888784,15.055976923555136,17.23313126713037,160.0,250 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,22007,宣传。,1,low,completed_answer,xlsx_draft,0.7384836673736572,1,1,0.2615163326263428,0.7384558916091919,0.33033156394958496,0.6696574091911316,7.334283873206005e-05,0.0004755387199111283,0.69324991106987,0.9323981329798698,2007,22007,20000,43.30058302730322,3809.3351731076837,15.237340692430735,19.417062727734443,160.0,250 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,31842,投诉建议。,1,low,completed_answer,xlsx_draft,0.48968255519866943,0,0,0.5103174448013306,0.4896518886089325,0.5082431435585022,0.49174630641937256,0.00011276060104137287,5.577295814873651e-05,0.7899426519870758,0.9342073872685432,11842,31842,20000,44.34670880436897,3777.243494056165,15.10897397622466,17.543585738167163,160.0,250 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,52348,爱车型号是大众帕萨特，购车时间2023年10月份，行驶里程24000多公里。,1,low,completed_answer,xlsx_draft,0.8557324260473251,1,1,0.14426757395267487,0.8557127118110657,0.13866016268730164,0.8613322973251343,2.2229069145396352e-05,0.00022814638214185834,0.959786981344223,0.9645285122096539,32348,52348,20000,43.413749895989895,3786.9820557534695,15.147928223013878,18.162129400297996,160.0,250 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,65298,轮胎裂纹。,0,low,mid_turn_pause,xlsx_draft,0.021075308322906494,0,1,0.9789246916770935,0.021063879132270813,0.9602354168891907,0.039757829159498215,0.00917343981564045,0.00019643160339910537,0.16463828086853027,0.8862891793251038,45298,65298,20000,42.82775055617094,3851.261753588915,15.40504701435566,19.156897673383355,160.0,250 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,67943,轮胎裂纹同意更换新轮胎。但是,0,low,mid_turn_pause,xlsx_draft,0.08532929420471191,0,1,0.9146707057952881,0.08531064540147781,0.8706751465797424,0.12931688129901886,0.0002615033008623868,9.120566392084584e-05,0.1254715919494629,0.6981820166110992,47943,67943,20000,43.441167101264,3796.0838358849287,15.184335343539715,18.364259554073215,160.0,250 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,70430,轮胎裂纹同意更换新轮胎。但是20,0,low,unfinished_expression,xlsx_draft,0.9156609252095222,1,0,0.08433907479047775,0.9156560301780701,0.2074228972196579,0.7925690412521362,0.005387305747717619,0.9999953508377075,0.9451650716364384,0.03903418779373169,50430,70430,20000,46.39508295804262,3987.742042168975,15.9509681686759,20.263655856251717,160.0,250 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,82941,可以。,1,low,completed_answer,xlsx_draft,0.13118845224380493,0,0,0.8688115477561951,0.13116922974586487,0.8727200031280518,0.12727269530296326,9.329482418252155e-05,0.00039028728497214615,0.27139031887054443,0.9212324172258377,62941,82941,20000,44.22441590577364,3770.675449632108,15.082701798528433,16.84834328480065,160.0,250 +872a1e67-873a-4c49-a598-b38a670afcad,17153,我姓宋。,1,low,completed_answer,xlsx_draft,0.3544103503227234,0,0,0.6455896496772766,0.3543955981731415,0.6671656966209412,0.3328273892402649,0.00037749105831608176,0.0003432980156503618,0.4008920192718506,0.8734219521284103,33,17153,17120,42.86170843988657,3181.1862913891673,14.865356501818539,16.877298150211573,160.0,214 +872a1e67-873a-4c49-a598-b38a670afcad,24846,郑州市，河南郑州市。,1,low,completed_answer,xlsx_draft,0.43358469009399414,0,0,0.5664153099060059,0.43356308341026306,0.5940371751785278,0.4059540331363678,0.00042811373714357615,0.00014359904162120074,0.6936855018138885,0.9015571922063828,4846,24846,20000,43.59066765755415,3757.988958619535,15.03195583447814,17.336932709440582,160.0,250 +872a1e67-873a-4c49-a598-b38a670afcad,33471,我的轮胎开裂了。,1,low,completed_answer,xlsx_draft,0.4404107332229614,0,0,0.5595892667770386,0.4403921663761139,0.5567219257354736,0.4432700574398041,0.00027235213201493025,4.663604704546742e-05,0.802086129784584,0.929821141064167,13471,33471,20000,44.05749961733818,3898.5149217769504,15.594059687107801,16.760270670056343,160.0,250 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,18027,天津市。,1,low,completed_answer,xlsx_draft,0.8423347920179367,1,1,0.1576652079820633,0.8423238396644592,0.23882931470870972,0.7611636519432068,4.903721492155455e-05,0.0012602335773408413,0.5773889422416687,0.9133674055337906,27,18027,18000,43.84558368474245,3348.231873475015,14.881030548777845,17.06232596188783,160.0,225 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,24768,投诉。,1,low,completed_answer,xlsx_draft,0.429895281791687,0,0,0.570104718208313,0.429873526096344,0.6319901347160339,0.36800137162208557,3.379296322236769e-05,0.00017112762725446373,0.5596272945404053,0.8936084657907486,4768,24768,20000,47.33575042337179,3768.2867143303156,15.073146857321262,17.01975828036666,160.0,250 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,51080,迈腾2023款330豪华版2.0T。出现的问题是轮购车时间是2024年5月份，里程是6851公里。,0,low,unfinished_expression,xlsx_draft,0.006074726581573486,0,1,0.9939252734184265,0.00606428412720561,0.8026061058044434,0.19738179445266724,0.999150276184082,1.9835957573377527e-05,0.010066509246826172,0.0655754804611206,31080,51080,20000,43.78404188901186,3713.4614614769816,14.853845845907927,16.728547774255276,160.0,250 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,68313,轮胎质量问题规律，排水槽横向竖向龟裂。,0,low,mid_turn_pause,xlsx_draft,0.7153808772563934,1,0,0.28461912274360657,0.7153563499450684,0.3098556697368622,0.6901354789733887,0.00015701723168604076,0.00015683767560403794,0.8074509650468826,0.8763869255781174,48313,68313,20000,42.48408321291208,3793.225208297372,15.172900833189487,17.884926451370116,160.0,250 +cb3238dc-3946-42a5-aad8-9b9a54226363,13540,辽宁葫芦岛。,1,low,completed_answer,xlsx_draft,0.834052786231041,1,1,0.16594721376895905,0.8340477347373962,0.2923814058303833,0.7076128125190735,0.0006330053438432515,0.353290319442749,0.9443961046636105,0.920970730483532,20,13540,13520,43.64687483757734,2434.2339858412743,14.403751395510499,16.58752541989088,160.0,169 +cb3238dc-3946-42a5-aad8-9b9a54226363,27520,投诉建议。,1,low,completed_answer,xlsx_draft,0.9249144271016121,1,1,0.07508557289838791,0.9249092936515808,0.17197971045970917,0.8280132412910461,0.0006763077108189464,0.0027803697157651186,0.9681989774107933,0.9300618022680283,7520,27520,20000,43.429833836853504,3751.3313703238964,15.005325481295586,16.984619107097384,160.0,250 +cb3238dc-3946-42a5-aad8-9b9a54226363,60790,我的轮胎行驶28000,0,low,unfinished_expression,xlsx_draft,0.006374657154083252,0,1,0.9936253428459167,0.0063640158623456955,0.98551344871521,0.014480029232800007,0.0009922056924551725,2.334343116672244e-05,0.05659288167953491,0.4327901005744934,40790,60790,20000,43.91112457960844,3768.300282768905,15.07320113107562,17.37064621411264,160.0,250 +cb3238dc-3946-42a5-aad8-9b9a54226363,70745,轮胎开裂,0,low,mid_turn_pause,xlsx_draft,0.14832377433776855,0,1,0.8516762256622314,0.14830422401428223,0.8053054213523865,0.19468680024147034,0.000355003634467721,9.077218419406563e-05,0.28872406482696533,0.6845456063747406,50745,70745,20000,73.68512451648712,3832.994165830314,15.331976663321257,18.693328695371743,160.0,250 +e304f740-85d4-427e-9fd6-c5474e9cd737,18842,苏州昆山。,1,low,completed_answer,xlsx_draft,0.7854653149843216,1,1,0.2145346850156784,0.7854455709457397,0.30133697390556335,0.6986544132232666,2.049054455710575e-05,0.0006262001697905362,0.6192943453788757,0.877316839993,42,18842,18800,43.695250526070595,3559.061948210001,15.14494446046809,18.55476228520274,160.0,235 +e304f740-85d4-427e-9fd6-c5474e9cd737,29619,投诉。,1,low,completed_answer,xlsx_draft,0.6686168611049652,1,1,0.3313831388950348,0.6685822010040283,0.40221649408340454,0.5977713465690613,9.517955186311156e-05,0.000321416009683162,0.44960129261016846,0.8809438720345497,9619,29619,20000,42.906083166599274,3913.635790348053,15.654543161392212,19.339747913181778,160.0,250 +e304f740-85d4-427e-9fd6-c5474e9cd737,40701,帕萨特,0,low,mid_turn_pause,xlsx_draft,0.24793684482574463,0,1,0.7520631551742554,0.24791204929351807,0.7820942997932434,0.2178967446088791,0.0004954806645400822,0.00030564164626412094,0.4185728430747986,0.8340967893600464,20701,40701,20000,43.45991648733616,3842.625617980957,15.370502471923828,17.616989417001605,160.0,250 +e304f740-85d4-427e-9fd6-c5474e9cd737,54600,24年11月份购买的帕萨特，15000公里左右。,1,low,completed_answer,xlsx_draft,0.6685410737991333,1,1,0.3314589262008667,0.6685138940811157,0.3438665270805359,0.6561237573623657,4.7042871301528066e-05,0.00014181240112520754,0.7056282758712769,0.8766583725810051,34600,54600,20000,42.68220718950033,3852.3954609408975,15.40958184376359,18.640206148847938,160.0,250 +e304f740-85d4-427e-9fd6-c5474e9cd737,65819,四条轮胎，最近我在。,0,low,unfinished_expression,xlsx_draft,0.029323875904083252,0,1,0.9706761240959167,0.02930695377290249,0.9603837728500366,0.039609044790267944,0.00038207421312108636,0.00011828791321022436,0.05253005027770996,0.6574011743068695,45819,65819,20000,45.03608401864767,3806.10039178282,15.224401567131281,18.85816054418683,160.0,250 +872a1e67-873a-4c49-a598-b38a670afcad,47187,帕萨特L2025款 ,0,,mid_turn_pause,,0.23951458930969238,0,1,0.7604854106903076,0.23949608206748962,0.7677955627441406,0.2321968376636505,0.0013868125388398767,7.05484562786296e-05,0.7005376219749451,0.8951215818524361,27187,47187,20000,45.17462570220232,3780.3582176566124,15.12143287062645,17.220300249755383,160.0,250 +872a1e67-873a-4c49-a598-b38a670afcad,52326,我的轮胎开裂了。,0,,mid_turn_pause,,0.5608597099781036,1,0,0.43914029002189636,0.5608418583869934,0.32150939106941223,0.6784825921058655,0.00028993270825594664,6.893909448990598e-05,0.4240996241569519,0.6631686389446259,32326,52326,20000,43.711209669709206,3781.8655846640468,15.127462338656187,17.952587874606248,160.0,250 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,48536,爱车型号是大众帕萨特，购车时间2023年10月份,0,,mid_turn_pause,,0.27077269554138184,0,1,0.7292273044586182,0.2707565426826477,0.6061777472496033,0.39381441473960876,6.394299998646602e-05,0.00010518143972149119,0.5186727344989777,0.8199913799762726,28536,48536,20000,43.08662563562393,3740.2610844001174,14.96104433760047,16.695542214438316,160.0,250 +872a1e67-873a-4c49-a598-b38a670afcad,44810,帕萨特,0,,mid_turn_pause,,0.385736346244812,0,1,0.614263653755188,0.3857164680957794,0.6407544016838074,0.3592374920845032,0.0003926920471712947,0.00018302960961591452,0.3700411319732666,0.8325859755277634,24810,44810,20000,44.04187481850386,3746.5439634397626,14.98617585375905,17.028987500816584,160.0,250 +872a1e67-873a-4c49-a598-b38a670afcad,49500,帕萨特L2025款2025年3月购车,1,,completed_turn,,0.9564755298197269,1,1,0.043524470180273056,0.9564691185951233,0.5146688222885132,0.4853191673755646,0.002094415482133627,0.9999518394470215,0.9640973135828972,0.4527696967124939,29500,49500,20000,42.598167434334755,3808.769033290446,15.235076133161783,17.86439842544496,160.0,250 +872a1e67-873a-4c49-a598-b38a670afcad,54318,行驶里程29000公里。,1,,completed_turn,,0.923841305077076,1,1,0.07615869492292404,0.9238342642784119,0.5418813228607178,0.458109050989151,0.006368088535964489,0.9999828338623047,0.9336890131235123,0.48515671491622925,34318,54318,20000,43.789833784103394,3756.494370289147,15.025977481156588,17.149229301139712,160.0,250 +872a1e67-873a-4c49-a598-b38a670afcad,45581,帕萨特,0,,mid_turn_pause,,0.014162182807922363,0,1,0.9858378171920776,0.014155449345707893,0.9286527037620544,0.07133743911981583,0.6578259468078613,0.0001258866977877915,0.023214280605316162,0.2830030918121338,25581,45581,20000,42.84354206174612,3774.24761839211,15.09699047356844,17.4117982853204,160.0,250 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,41219,迈腾2023款330豪华版2.0T,0,,mid_turn_pause,,0.5353022813796997,1,0,0.4646977186203003,0.53528892993927,0.43114253878593445,0.5688507556915283,0.0004525056283455342,0.0009800773113965988,0.9102829173207283,0.9391313791275024,21219,41219,20000,43.12216676771641,3731.8863114342093,14.927545245736837,17.138534784317017,160.0,250 +cb3238dc-3946-42a5-aad8-9b9a54226363,19620,,1,,completed_turn,,0.8876387625932693,1,1,0.11236123740673065,0.8876338005065918,0.24439643323421478,0.7555972933769226,0.00018491022638045251,0.5412195324897766,0.9730777312070131,0.9391433596611023,20,19620,19600,42.84766688942909,3686.933286488056,15.048707291787984,17.619358189404004,160.0,245 +cb3238dc-3946-42a5-aad8-9b9a54226363,41207,我是2023年,0,,mid_turn_pause,,0.012665152549743652,0,1,0.9873348474502563,0.0126510513946414,0.9729987978935242,0.026993975043296814,0.00030199953471310437,3.080569513258524e-05,0.12327557802200317,0.7711529731750488,21207,41207,20000,44.08987518399954,3769.7689663618803,15.079075865447521,17.20036226324737,160.0,250 +cb3238dc-3946-42a5-aad8-9b9a54226363,49204,我是2023年、2024年1月份购置的迈腾3302023款轿车,0,,mid_turn_pause,,0.7771151214838028,1,0,0.2228848785161972,0.7770937085151672,0.1958613097667694,0.8041304349899292,0.0001806421932997182,0.00013914017472416162,0.8600987195968628,0.9226682409644127,29204,49204,20000,42.962207458913326,3849.3550131097436,15.397420052438974,19.366614380851384,160.0,250 +cb3238dc-3946-42a5-aad8-9b9a54226363,58424,我的轮胎,0,,mid_turn_pause,,0.14501017332077026,0,1,0.8549898266792297,0.14499571919441223,0.8255241513252258,0.17446859180927277,6.015930193825625e-05,0.00010232726344838738,0.19378310441970825,0.7652575969696045,38424,58424,20000,43.2002916932106,3876.1507011950016,15.504602804780006,19.361516647040844,160.0,250 +cb3238dc-3946-42a5-aad8-9b9a54226363,73640,轮胎开裂，行驶28000公里，轮胎开裂,0,,mid_turn_pause,,0.5420903265476227,1,0,0.4579096734523773,0.542066752910614,0.33230483531951904,0.667686402797699,0.00046382888103835285,6.0991980717517436e-05,0.45827627182006836,0.6812975704669952,53640,73640,20000,43.076083064079285,3793.9981278032064,15.175992511212826,17.95125198550522,160.0,250 +e304f740-85d4-427e-9fd6-c5474e9cd737,11531,姓赵,1,,completed_turn,,0.6417741775512695,1,1,0.35822582244873047,0.6417521834373474,0.43518248200416565,0.5648091435432434,6.34761672699824e-05,0.0004080769722349942,0.5200230777263641,0.936267301440239,11,11531,11520,43.52133255451918,2102.701776660979,14.602095671256798,17.65278074890375,160.0,144 +e304f740-85d4-427e-9fd6-c5474e9cd737,62828,四条轮胎,0,,mid_turn_pause,,0.051033079624176025,0,1,0.948966920375824,0.05101773142814636,0.9208597540855408,0.07913269847631454,0.00019680036348290741,0.00010214729991275817,0.02205902338027954,0.5078669488430023,42828,62828,20000,42.658708058297634,3953.733755275607,15.814935021102428,18.548191338777535,160.0,250 diff --git a/experiments/maai-ch-kyoto/results/mimi-standard/score-timeline.json b/experiments/maai-ch-kyoto/results/mimi-standard/score-timeline.json new file mode 100644 index 0000000..57c7253 --- /dev/null +++ b/experiments/maai-ch-kyoto/results/mimi-standard/score-timeline.json @@ -0,0 +1 @@ +{"schema_version":1,"model":"maai_mimi_standard","name":"VAP / MaAI Mimi（标准双声道）","score":"1 - p_now[user]","sample_interval_ms":80,"method":"standard (mc=False) ch_kyoto VAP with official FP32 ONNX Mimi encoder. 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on six recorded calls, not endpointing-policy cutoff/latency.", + "Uses stored rounded continuous scores; no new inference or model-speed measurement.", + "No prior output or stale output is unavailable, not a negative/zero prediction.", + "Archived independent-window results are a separate evaluation protocol." + ], + "accuracy_delta_pp_vs_cpc_stereo": 9.090909090909093, + "relative_accuracy_change_vs_cpc_stereo": 0.1333333333333334 + } + } +} diff --git a/experiments/maai-ch-kyoto/results/streaming/cpc-stereo/predictions.csv b/experiments/maai-ch-kyoto/results/streaming/cpc-stereo/predictions.csv new file mode 100644 index 0000000..254d0ef --- /dev/null +++ b/experiments/maai-ch-kyoto/results/streaming/cpc-stereo/predictions.csv @@ -0,0 +1,45 @@ +call_id,decision_ms,user_text_at_decision,can_reply_now,confidence,reason,annotator,status,score_time_ms,score_age_ms,score,prediction_05 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+40ae686c-8196-4d7b-b7ad-3e12a427ebb6,48536,爱车型号是大众帕萨特，购车时间2023年10月份,0,,mid_turn_pause,,available,48500.0,36.0,0.3139373,0 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,52348,爱车型号是大众帕萨特，购车时间2023年10月份，行驶里程24000多公里。,1,low,completed_answer,xlsx_draft,available,52200.0,148.0,0.9582937,1 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,65298,轮胎裂纹。,0,low,mid_turn_pause,xlsx_draft,available,65200.0,98.0,0.2121009,0 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,67943,轮胎裂纹同意更换新轮胎。但是,0,low,mid_turn_pause,xlsx_draft,available,67900.0,43.0,0.0856853,0 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,70430,轮胎裂纹同意更换新轮胎。但是20,0,low,unfinished_expression,xlsx_draft,available,70400.0,30.0,0.9033097,1 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,82941,可以。,1,low,completed_answer,xlsx_draft,available,82900.0,41.0,0.2678279,0 +872a1e67-873a-4c49-a598-b38a670afcad,17153,我姓宋。,1,low,completed_answer,xlsx_draft,available,17100.0,53.0,0.3607031,0 +872a1e67-873a-4c49-a598-b38a670afcad,24846,郑州市，河南郑州市。,1,low,completed_answer,xlsx_draft,available,24800.0,46.0,0.484904,0 +872a1e67-873a-4c49-a598-b38a670afcad,33471,我的轮胎开裂了。,1,low,completed_answer,xlsx_draft,available,33400.0,71.0,0.8286542,1 +872a1e67-873a-4c49-a598-b38a670afcad,44810,帕萨特,0,,mid_turn_pause,,available,44800.0,10.0,0.3072304,0 +872a1e67-873a-4c49-a598-b38a670afcad,45581,帕萨特,0,,mid_turn_pause,,available,45500.0,81.0,0.0926747,0 +872a1e67-873a-4c49-a598-b38a670afcad,47187,帕萨特L2025款 ,0,,mid_turn_pause,,available,47100.0,87.0,0.5402014,1 +872a1e67-873a-4c49-a598-b38a670afcad,49500,帕萨特L2025款2025年3月购车,1,,completed_turn,,available,49400.0,100.0,0.8873855,1 +872a1e67-873a-4c49-a598-b38a670afcad,52326,我的轮胎开裂了。,0,,mid_turn_pause,,available,52200.0,126.0,0.5208828,1 +872a1e67-873a-4c49-a598-b38a670afcad,54318,行驶里程29000公里。,1,,completed_turn,,available,54300.0,18.0,0.9379114,1 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+e304f740-85d4-427e-9fd6-c5474e9cd737,29619,投诉。,1,low,completed_answer,xlsx_draft,available,29600.0,19.0,0.414157,0 +e304f740-85d4-427e-9fd6-c5474e9cd737,40701,帕萨特,0,low,mid_turn_pause,xlsx_draft,available,40700.0,1.0,0.3604563,0 +e304f740-85d4-427e-9fd6-c5474e9cd737,54600,24年11月份购买的帕萨特，15000公里左右。,1,low,completed_answer,xlsx_draft,available,54600.0,0.0,0.7192341,1 +e304f740-85d4-427e-9fd6-c5474e9cd737,62828,四条轮胎,0,,mid_turn_pause,,available,62800.0,28.0,0.0150015,0 +e304f740-85d4-427e-9fd6-c5474e9cd737,65819,四条轮胎，最近我在。,0,low,unfinished_expression,xlsx_draft,available,65800.0,19.0,0.0315222,0 diff --git a/experiments/maai-ch-kyoto/results/streaming/cpc-stereo/score-timeline.json b/experiments/maai-ch-kyoto/results/streaming/cpc-stereo/score-timeline.json new file mode 100644 index 0000000..ff1a2a9 --- /dev/null +++ b/experiments/maai-ch-kyoto/results/streaming/cpc-stereo/score-timeline.json @@ -0,0 +1 @@ +{"schema_version":1,"model":"maai","name":"MaAI","score":"1 - p_now[user]","checkpoint_mode":"streaming","sample_interval_ms":100,"method":"Continuous full-call VAP outputs only. At each exact decision_ms, use the last score with availability timestamp <= decision_ms and age <= 2 * sample_interval_ms. No state reset, future score, interpolation or independent-window checkpoint. Availability includes source resampler buffering; score is the stored activity-projection 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diff --git a/experiments/maai-ch-kyoto/results/streaming/cpc-stereo/summary.json b/experiments/maai-ch-kyoto/results/streaming/cpc-stereo/summary.json new file mode 100644 index 0000000..7625fa1 --- /dev/null +++ b/experiments/maai-ch-kyoto/results/streaming/cpc-stereo/summary.json @@ -0,0 +1,261 @@ +{ + "model": "maai", + "name": "CPC 降噪双声道", + "evaluation_mode": "streaming", + "created_at": "2026-10-28T00:27:46.310011+00:00", + "threshold": 0.5, + "rule": "1 - p_now[user] > 0.5", + "main_metrics": { + "n": 44, + "requested_labeled": 44, + "unavailable_labeled": 0, + "tn": 15, + "fp": 7, + "fn": 7, + "tp": 15, + "correct": 30, + "accuracy": 0.6818181818181818, + "false_reply_rate": 0.3181818181818182, + "false_hold_rate": 0.3181818181818182, + "recall": 0.6818181818181818, + "precision": 0.6818181818181818, + "f1": 0.6818181818181818 + }, + "thresholds": [ + { + "threshold": 0.11, + "n": 44, + "requested_labeled": 44, + "unavailable_labeled": 0, + "tn": 7, + "fp": 15, + "fn": 1, + 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At each exact decision_ms, use the last score with availability timestamp <= decision_ms and age <= 2 * sample_interval_ms. No state reset, future score, interpolation or independent-window checkpoint. Availability includes source resampler buffering; score is the stored activity-projection proxy.", + "provenance": { + "source_export": "experiments/maai-ch-kyoto/results/score-timeline.json", + "source_export_sha256": "492db23873849516eec7449038a861cec2969dc0cc9de4877002d945fcb42d24", + "source_inference_provenance": {}, + "source_model_sha256": { + "vap_mc_state_dict_ch_kyoto_10hz_20000msec.pt": "65b8a51aea6a257b5b9139107f0f0cf22f94523cd4b6a2e7a356b6c43a4596a8", + "60k_epoch4-d0f474de.pt": "d0f474de968c7243c20f43dee1626681618f2f150d3e638e82b79e22563b17f1" + }, + "label_sha256": "780e50f976daf039ab976ea155bdb5d1ca5fffc792f9db7adf8063ec3048ff8e", + "evaluation_script_sha256": "85d0b5f0f8dcf73cc6ee1b651e5a2cfc0b40e95024aacb0bd3ed975079ad9507" + }, + "limitations": [ + "Selected-point classification on six recorded calls, not endpointing-policy cutoff/latency.", + "Uses stored rounded continuous scores; no new inference or model-speed measurement.", + "No prior output or stale output is unavailable, not a negative/zero prediction.", + "Archived independent-window results are a separate evaluation protocol." + ] +} diff --git a/experiments/maai-ch-kyoto/results/streaming/cpc-user-only/predictions.csv b/experiments/maai-ch-kyoto/results/streaming/cpc-user-only/predictions.csv new file mode 100644 index 0000000..e212654 --- /dev/null +++ b/experiments/maai-ch-kyoto/results/streaming/cpc-user-only/predictions.csv @@ -0,0 +1,45 @@ +call_id,decision_ms,user_text_at_decision,can_reply_now,confidence,reason,annotator,status,score_time_ms,score_age_ms,score,prediction_05 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,21780,贵州省遵义市仁怀市。,1,low,completed_answer,xlsx_draft,available,21700.0,80.0,0.9110461,1 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,29860,投诉建议。,1,low,completed_answer,xlsx_draft,available,29800.0,60.0,0.888879,1 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,48125,型号是24年380大众帕萨特星空龙腾版。,0,low,mid_turn_pause,xlsx_draft,available,48100.0,25.0,0.7241996,1 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,64000,行驶3万公里。,1,low,completed_answer,xlsx_draft,available,64000.0,0.0,0.9321202,1 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,76672,恒泰轮胎前门两条轮胎开裂,0,low,unfinished_expression,xlsx_draft,available,76600.0,72.0,0.8603527,1 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,88482,尽快，已经影响行车安全了。,1,low,completed_answer,xlsx_draft,available,88400.0,82.0,0.0225079,0 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,22007,宣传。,1,low,completed_answer,xlsx_draft,available,22000.0,7.0,0.9294046,1 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,31842,投诉建议。,1,low,completed_answer,xlsx_draft,available,31800.0,42.0,0.9358485,1 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,48536,爱车型号是大众帕萨特，购车时间2023年10月份,0,,mid_turn_pause,,available,48500.0,36.0,0.6997409,1 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,52348,爱车型号是大众帕萨特，购车时间2023年10月份，行驶里程24000多公里。,1,low,completed_answer,xlsx_draft,available,52200.0,148.0,0.9736262,1 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,65298,轮胎裂纹。,0,low,mid_turn_pause,xlsx_draft,available,65200.0,98.0,0.855021,1 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,67943,轮胎裂纹同意更换新轮胎。但是,0,low,mid_turn_pause,xlsx_draft,available,67900.0,43.0,0.6292937,1 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,70430,轮胎裂纹同意更换新轮胎。但是20,0,low,unfinished_expression,xlsx_draft,available,70400.0,30.0,0.0464852,0 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,82941,可以。,1,low,completed_answer,xlsx_draft,available,82900.0,41.0,0.8593753,1 +872a1e67-873a-4c49-a598-b38a670afcad,17153,我姓宋。,1,low,completed_answer,xlsx_draft,available,17100.0,53.0,0.885146,1 +872a1e67-873a-4c49-a598-b38a670afcad,24846,郑州市，河南郑州市。,1,low,completed_answer,xlsx_draft,available,24800.0,46.0,0.8653426,1 +872a1e67-873a-4c49-a598-b38a670afcad,33471,我的轮胎开裂了。,1,low,completed_answer,xlsx_draft,available,33400.0,71.0,0.932167,1 +872a1e67-873a-4c49-a598-b38a670afcad,44810,帕萨特,0,,mid_turn_pause,,available,44800.0,10.0,0.7817599,1 +872a1e67-873a-4c49-a598-b38a670afcad,45581,帕萨特,0,,mid_turn_pause,,available,45500.0,81.0,0.5207997,1 +872a1e67-873a-4c49-a598-b38a670afcad,47187,帕萨特L2025款 ,0,,mid_turn_pause,,available,47100.0,87.0,0.8099863,1 +872a1e67-873a-4c49-a598-b38a670afcad,49500,帕萨特L2025款2025年3月购车,1,,completed_turn,,available,49400.0,100.0,0.2200134,0 +872a1e67-873a-4c49-a598-b38a670afcad,52326,我的轮胎开裂了。,0,,mid_turn_pause,,available,52200.0,126.0,0.7189064,1 +872a1e67-873a-4c49-a598-b38a670afcad,54318,行驶里程29000公里。,1,,completed_turn,,available,54300.0,18.0,0.5467448,1 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,18027,天津市。,1,low,completed_answer,xlsx_draft,available,18000.0,27.0,0.9173212,1 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,24768,投诉。,1,low,completed_answer,xlsx_draft,available,24700.0,68.0,0.8803538,1 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,41219,迈腾2023款330豪华版2.0T,0,,mid_turn_pause,,available,41100.0,119.0,0.8995477,1 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,51080,迈腾2023款330豪华版2.0T。出现的问题是轮购车时间是2024年5月份，里程是6851公里。,0,low,unfinished_expression,xlsx_draft,available,51000.0,80.0,0.0312248,0 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,68313,轮胎质量问题规律，排水槽横向竖向龟裂。,0,low,mid_turn_pause,xlsx_draft,available,68300.0,13.0,0.8629259,1 +cb3238dc-3946-42a5-aad8-9b9a54226363,13540,辽宁葫芦岛。,1,low,completed_answer,xlsx_draft,available,13500.0,40.0,0.8859637,1 +cb3238dc-3946-42a5-aad8-9b9a54226363,19620,,1,,completed_turn,,available,19600.0,20.0,0.93896,1 +cb3238dc-3946-42a5-aad8-9b9a54226363,27520,投诉建议。,1,low,completed_answer,xlsx_draft,available,27500.0,20.0,0.9101271,1 +cb3238dc-3946-42a5-aad8-9b9a54226363,41207,我是2023年,0,,mid_turn_pause,,available,41100.0,107.0,0.6600023,1 +cb3238dc-3946-42a5-aad8-9b9a54226363,49204,我是2023年、2024年1月份购置的迈腾3302023款轿车,0,,mid_turn_pause,,available,49200.0,4.0,0.9278291,1 +cb3238dc-3946-42a5-aad8-9b9a54226363,58424,我的轮胎,0,,mid_turn_pause,,available,58400.0,24.0,0.7119515,1 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Availability includes source resampler buffering; score is the stored activity-projection proxy.", + "provenance": { + "source_export": "experiments/maai-ch-kyoto/results/user-only/score-timeline.json", + "source_export_sha256": "dd4959d14883746a13ab382e7a810d37e6d91014198ef53ff1e82e5ac6d61f5a", + "source_inference_provenance": { + "machine": "macOS-26.4.1-arm64-arm-64bit", + "hardware": "Apple M4 Mac mini / 16 GB", + "python": "3.11.15 (main, Mar 25 2026, 02:58:47) [Clang 22.1.1 ]", + "environment": "experiments/maai-ch-kyoto/.venv (uv.lock)", + "device": "cpu", + "threads": 4, + "dependencies": { + "maai": "0.2.22", + "torch": "2.14.1", + "transformers": "5.5.3", + "numpy": "2.4.6", + "soundfile": "0.14.0", + "soxr": "1.1.0" + }, + "sample_rate": 16000, + "frame_ms": 100, + "context_ms": 20000, + "use_kv_cache": true, + "seed": 42, + "channel_mapping": { + "0": "original user", + "1": "all-zero robot input" + }, + "weight_sha256": { + "vap_mc_state_dict_ch_kyoto_10hz_20000msec.pt": 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--git a/experiments/maai-ch-kyoto/results/streaming/frontend-streaming.png b/experiments/maai-ch-kyoto/results/streaming/frontend-streaming.png new file mode 100644 index 0000000..87b5fec Binary files /dev/null and b/experiments/maai-ch-kyoto/results/streaming/frontend-streaming.png differ diff --git a/experiments/maai-ch-kyoto/results/streaming/mimi-mc/predictions.csv b/experiments/maai-ch-kyoto/results/streaming/mimi-mc/predictions.csv new file mode 100644 index 0000000..6788775 --- /dev/null +++ b/experiments/maai-ch-kyoto/results/streaming/mimi-mc/predictions.csv @@ -0,0 +1,45 @@ +call_id,decision_ms,user_text_at_decision,can_reply_now,confidence,reason,annotator,status,score_time_ms,score_age_ms,score,prediction_05 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,21780,贵州省遵义市仁怀市。,1,low,completed_answer,xlsx_draft,available,21760.0,20.0,0.9469772,1 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,29860,投诉建议。,1,low,completed_answer,xlsx_draft,available,29840.0,20.0,0.4063104,0 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+40ae686c-8196-4d7b-b7ad-3e12a427ebb6,52348,爱车型号是大众帕萨特，购车时间2023年10月份，行驶里程24000多公里。,1,low,completed_answer,xlsx_draft,available,52320.0,28.0,0.8785392,1 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,65298,轮胎裂纹。,0,low,mid_turn_pause,xlsx_draft,available,65280.0,18.0,0.0961233,0 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,67943,轮胎裂纹同意更换新轮胎。但是,0,low,mid_turn_pause,xlsx_draft,available,67920.0,23.0,0.1110851,0 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,70430,轮胎裂纹同意更换新轮胎。但是20,0,low,unfinished_expression,xlsx_draft,available,70400.0,30.0,0.9173717,1 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,82941,可以。,1,low,completed_answer,xlsx_draft,available,82880.0,61.0,0.3759804,0 +872a1e67-873a-4c49-a598-b38a670afcad,17153,我姓宋。,1,low,completed_answer,xlsx_draft,available,17040.0,113.0,0.635205,1 +872a1e67-873a-4c49-a598-b38a670afcad,24846,郑州市，河南郑州市。,1,low,completed_answer,xlsx_draft,available,24720.0,126.0,0.6521555,1 +872a1e67-873a-4c49-a598-b38a670afcad,33471,我的轮胎开裂了。,1,low,completed_answer,xlsx_draft,available,33440.0,31.0,0.5060011,1 +872a1e67-873a-4c49-a598-b38a670afcad,44810,帕萨特,0,,mid_turn_pause,,available,44800.0,10.0,0.2956599,0 +872a1e67-873a-4c49-a598-b38a670afcad,45581,帕萨特,0,,mid_turn_pause,,available,45520.0,61.0,0.0647457,0 +872a1e67-873a-4c49-a598-b38a670afcad,47187,帕萨特L2025款 ,0,,mid_turn_pause,,available,47120.0,67.0,0.1469005,0 +872a1e67-873a-4c49-a598-b38a670afcad,49500,帕萨特L2025款2025年3月购车,1,,completed_turn,,available,49440.0,60.0,0.9575438,1 +872a1e67-873a-4c49-a598-b38a670afcad,52326,我的轮胎开裂了。,0,,mid_turn_pause,,available,52320.0,6.0,0.6319908,1 +872a1e67-873a-4c49-a598-b38a670afcad,54318,行驶里程29000公里。,1,,completed_turn,,available,54240.0,78.0,0.9595641,1 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,18027,天津市。,1,low,completed_answer,xlsx_draft,available,18000.0,27.0,0.6629481,1 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,24768,投诉。,1,low,completed_answer,xlsx_draft,available,24720.0,48.0,0.825871,1 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,41219,迈腾2023款330豪华版2.0T,0,,mid_turn_pause,,available,41120.0,99.0,0.4400204,0 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,51080,迈腾2023款330豪华版2.0T。出现的问题是轮购车时间是2024年5月份，里程是6851公里。,0,low,unfinished_expression,xlsx_draft,available,50960.0,120.0,0.0034229,0 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,68313,轮胎质量问题规律，排水槽横向竖向龟裂。,0,low,mid_turn_pause,xlsx_draft,available,68240.0,73.0,0.7901614,1 +cb3238dc-3946-42a5-aad8-9b9a54226363,13540,辽宁葫芦岛。,1,low,completed_answer,xlsx_draft,available,13520.0,20.0,0.9226512,1 +cb3238dc-3946-42a5-aad8-9b9a54226363,19620,,1,,completed_turn,,available,19520.0,100.0,0.7442541,1 +cb3238dc-3946-42a5-aad8-9b9a54226363,27520,投诉建议。,1,low,completed_answer,xlsx_draft,available,27520.0,0.0,0.6371526,1 +cb3238dc-3946-42a5-aad8-9b9a54226363,41207,我是2023年,0,,mid_turn_pause,,available,41120.0,87.0,0.0084641,0 +cb3238dc-3946-42a5-aad8-9b9a54226363,49204,我是2023年、2024年1月份购置的迈腾3302023款轿车,0,,mid_turn_pause,,available,49200.0,4.0,0.6603728,1 +cb3238dc-3946-42a5-aad8-9b9a54226363,58424,我的轮胎,0,,mid_turn_pause,,available,58320.0,104.0,0.0807874,0 +cb3238dc-3946-42a5-aad8-9b9a54226363,60790,我的轮胎行驶28000,0,low,unfinished_expression,xlsx_draft,available,60720.0,70.0,0.0120299,0 +cb3238dc-3946-42a5-aad8-9b9a54226363,70745,轮胎开裂,0,low,mid_turn_pause,xlsx_draft,available,70720.0,25.0,0.1566727,0 +cb3238dc-3946-42a5-aad8-9b9a54226363,73640,轮胎开裂，行驶28000公里，轮胎开裂,0,,mid_turn_pause,,available,73600.0,40.0,0.4763942,0 +e304f740-85d4-427e-9fd6-c5474e9cd737,11531,姓赵,1,,completed_turn,,available,11440.0,91.0,0.8131339,1 +e304f740-85d4-427e-9fd6-c5474e9cd737,18842,苏州昆山。,1,low,completed_answer,xlsx_draft,available,18800.0,42.0,0.681986,1 +e304f740-85d4-427e-9fd6-c5474e9cd737,29619,投诉。,1,low,completed_answer,xlsx_draft,available,29600.0,19.0,0.4093694,0 +e304f740-85d4-427e-9fd6-c5474e9cd737,40701,帕萨特,0,low,mid_turn_pause,xlsx_draft,available,40640.0,61.0,0.3634525,0 +e304f740-85d4-427e-9fd6-c5474e9cd737,54600,24年11月份购买的帕萨特，15000公里左右。,1,low,completed_answer,xlsx_draft,available,54560.0,40.0,0.6911152,1 +e304f740-85d4-427e-9fd6-c5474e9cd737,62828,四条轮胎,0,,mid_turn_pause,,available,62800.0,28.0,0.0803336,0 +e304f740-85d4-427e-9fd6-c5474e9cd737,65819,四条轮胎，最近我在。,0,low,unfinished_expression,xlsx_draft,available,65760.0,59.0,0.0181271,0 diff --git a/experiments/maai-ch-kyoto/results/streaming/mimi-mc/score-timeline.json b/experiments/maai-ch-kyoto/results/streaming/mimi-mc/score-timeline.json new file mode 100644 index 0000000..8ecb69d --- /dev/null +++ b/experiments/maai-ch-kyoto/results/streaming/mimi-mc/score-timeline.json @@ -0,0 +1 @@ +{"schema_version":1,"model":"maai_mimi_mc","name":"VAP / MaAI Mimi（降噪双声道）","score":"1 - p_now[user]","checkpoint_mode":"streaming","sample_interval_ms":80,"method":"Continuous full-call VAP outputs only. At each exact decision_ms, use the last score with availability timestamp <= decision_ms and age <= 2 * sample_interval_ms. No state reset, future score, interpolation or independent-window checkpoint. 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+2c83253a-7977-4e3c-a387-0aa1ea06fbe1,29860,投诉建议。,1,low,completed_answer,xlsx_draft,available,29840.0,20.0,0.3468243,0 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,48125,型号是24年380大众帕萨特星空龙腾版。,0,low,mid_turn_pause,xlsx_draft,available,48080.0,45.0,0.4291775,0 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,64000,行驶3万公里。,1,low,completed_answer,xlsx_draft,available,64000.0,0.0,0.2422283,0 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,76672,恒泰轮胎前门两条轮胎开裂,0,low,unfinished_expression,xlsx_draft,available,76640.0,32.0,0.6647917,1 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,88482,尽快，已经影响行车安全了。,1,low,completed_answer,xlsx_draft,available,88480.0,2.0,0.0857219,0 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,22007,宣传。,1,low,completed_answer,xlsx_draft,available,21920.0,87.0,0.7624916,1 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,31842,投诉建议。,1,low,completed_answer,xlsx_draft,available,31760.0,82.0,0.5455329,1 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,48536,爱车型号是大众帕萨特，购车时间2023年10月份,0,,mid_turn_pause,,available,48480.0,56.0,0.3118085,0 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,52348,爱车型号是大众帕萨特，购车时间2023年10月份，行驶里程24000多公里。,1,low,completed_answer,xlsx_draft,available,52320.0,28.0,0.8394164,1 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,65298,轮胎裂纹。,0,low,mid_turn_pause,xlsx_draft,available,65280.0,18.0,0.0260791,0 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,67943,轮胎裂纹同意更换新轮胎。但是,0,low,mid_turn_pause,xlsx_draft,available,67920.0,23.0,0.0553861,0 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,70430,轮胎裂纹同意更换新轮胎。但是20,0,low,unfinished_expression,xlsx_draft,available,70400.0,30.0,0.8936476,1 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,82941,可以。,1,low,completed_answer,xlsx_draft,available,82880.0,61.0,0.2031001,0 +872a1e67-873a-4c49-a598-b38a670afcad,17153,我姓宋。,1,low,completed_answer,xlsx_draft,available,17040.0,113.0,0.3997135,0 +872a1e67-873a-4c49-a598-b38a670afcad,24846,郑州市，河南郑州市。,1,low,completed_answer,xlsx_draft,available,24720.0,126.0,0.6149294,1 +872a1e67-873a-4c49-a598-b38a670afcad,33471,我的轮胎开裂了。,1,low,completed_answer,xlsx_draft,available,33440.0,31.0,0.4527689,0 +872a1e67-873a-4c49-a598-b38a670afcad,44810,帕萨特,0,,mid_turn_pause,,available,44800.0,10.0,0.3412014,0 +872a1e67-873a-4c49-a598-b38a670afcad,45581,帕萨特,0,,mid_turn_pause,,available,45520.0,61.0,0.0466841,0 +872a1e67-873a-4c49-a598-b38a670afcad,47187,帕萨特L2025款 ,0,,mid_turn_pause,,available,47120.0,67.0,0.18337,0 +872a1e67-873a-4c49-a598-b38a670afcad,49500,帕萨特L2025款2025年3月购车,1,,completed_turn,,available,49440.0,60.0,0.9456378,1 +872a1e67-873a-4c49-a598-b38a670afcad,52326,我的轮胎开裂了。,0,,mid_turn_pause,,available,52320.0,6.0,0.6597835,1 +872a1e67-873a-4c49-a598-b38a670afcad,54318,行驶里程29000公里。,1,,completed_turn,,available,54240.0,78.0,0.9171926,1 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,18027,天津市。,1,low,completed_answer,xlsx_draft,available,18000.0,27.0,0.5814717,1 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on six recorded calls, not endpointing-policy cutoff/latency.", + "Uses stored rounded continuous scores; no new inference or model-speed measurement.", + "No prior output or stale output is unavailable, not a negative/zero prediction.", + "Archived independent-window results are a separate evaluation protocol." + ] +} diff --git a/experiments/maai-ch-kyoto/results/streaming/对比分析.txt b/experiments/maai-ch-kyoto/results/streaming/对比分析.txt new file mode 100644 index 0000000..1a2c1c7 --- /dev/null +++ b/experiments/maai-ch-kyoto/results/streaming/对比分析.txt @@ -0,0 +1,13 @@ +VAP 流式场景判断点对比（阈值严格 > 0.5） + +模型 | 有效点/有标签点 | 正确数 | 准确率 | 误放行 FP | 误阻止 FN | 对 CPC 双声道变化 +CPC 降噪双声道 | 44/44 | 30 | 68.18% | 7 | 7 | +0.00 个百分点 +CPC 降噪机器人静音 | 44/44 | 24 | 54.55% | 18 | 2 | -13.64 个百分点 +Mimi 标准双声道 | 44/44 | 31 | 70.45% | 5 | 8 | +2.27 个百分点 +Mimi 降噪双声道 | 44/44 | 34 | 77.27% | 5 | 5 | +9.09 个百分点 + +1. 本表使用连续推理在判断时刻已经产生的最新分数，与前端 VAP 圆点和悬停分数一致；不使用独立窗口结果。 +2. 每个有效点记录 decision_ms、score_time_ms、score_age_ms；启动期或超过两倍更新间隔的旧分数不计为 0，而是不可用。 +3. 数据为六通录音中的选定点，只做描述性比较；下一步宜扩充按通话隔离的测试集，再在验证集上选择阈值。 + +复现：在 MaAI 独立 uv 环境运行 python evaluate_streaming.py all。原独立窗口结果保留在原目录，另作历史诊断。 diff --git a/experiments/maai-ch-kyoto/results/user-only-run.log b/experiments/maai-ch-kyoto/results/user-only-run.log new file mode 100644 index 0000000..18b152a --- /dev/null +++ b/experiments/maai-ch-kyoto/results/user-only-run.log @@ -0,0 +1,84 @@ +WITNESS CPU seed=42: robot/future invariance and repeat passed; score=0.9077615 +1/44 2c83253a 21780: label=1, user_only=0.9078, stereo=0.9598 +2/44 2c83253a 29860: label=1, user_only=0.8796, stereo=0.9286 +3/44 2c83253a 48125: label=0, user_only=0.7127, stereo=0.5123 +4/44 2c83253a 64000: label=1, user_only=0.9321, stereo=0.9370 +5/44 2c83253a 76672: label=0, user_only=0.8640, stereo=0.7067 +6/44 2c83253a 88482: label=1, user_only=0.1606, stereo=0.1147 +7/44 40ae686c 22007: label=1, user_only=0.9324, stereo=0.6932 +8/44 40ae686c 31842: 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"fn": 4, + "tp": 18, + "accuracy": 0.7272727272727273, + "false_reply_rate": 0.36363636363636365, + "false_hold_rate": 0.18181818181818182, + "recall": 0.8181818181818182, + "precision": 0.6923076923076923, + "f1": 0.75 + }, + "changed_vs_stereo": { + "fixed": 4, + "regressed": 13 + }, + "elapsed_seconds": 124.26453408319503 +} diff --git a/experiments/maai-ch-kyoto/results/user-only/errors.csv b/experiments/maai-ch-kyoto/results/user-only/errors.csv new file mode 100644 index 0000000..f4dd46d --- /dev/null +++ b/experiments/maai-ch-kyoto/results/user-only/errors.csv @@ -0,0 +1,22 @@ +call_id,decision_ms,user_text_at_decision,can_reply_now,confidence,reason,annotator,can_reply_score,stereo_can_reply_score,p_now_user,p_now_robot,p_future_user,p_future_robot,vad_user,vad_robot,prediction_05,correct_05,stereo_prediction_05,audio_start_ms,audio_end_ms,context_ms,robot_input_peak_abs,replay_inference_ms,stereo_replay_inference_ms +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,48125,型号是24年380大众帕萨特星空龙腾版。,0,low,mid_turn_pause,xlsx_draft,0.712736040353775,0.5122989416122437,0.287263959646225,0.7127270102500916,0.30030158162117004,0.699691653251648,0.0005045763682574034,0.2013741433620453,1,0,1,28125,48125,20000,0.0,1064.420540817082,1044.8135826736689 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,76672,恒泰轮胎前门两条轮胎开裂,0,low,unfinished_expression,xlsx_draft,0.8640106171369553,0.7066852748394012,0.13598938286304474,0.8640027642250061,0.2139778882265091,0.7860152125358582,0.0002717908064369112,0.4948178827762604,1,0,1,56672,76672,20000,0.0,1032.4355829507113,1016.6379176080227 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,88482,尽快，已经影响行车安全了。,1,low,completed_answer,xlsx_draft,0.16062873601913452,0.11465108394622803,0.8393712639808655,0.16061516106128693,0.6462375521659851,0.35375407338142395,0.022112371399998665,0.020942039787769318,0,0,0,68482,88482,20000,0.0,1036.321542225778,1105.0837496295571 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,65298,轮胎裂纹。,0,low,mid_turn_pause,xlsx_draft,0.8862891793251038,0.16463828086853027,0.11371082067489624,0.8862825036048889,0.21822746098041534,0.7817663550376892,0.000534569553565234,0.6663311123847961,1,0,0,45298,65298,20000,0.0,1049.0985000506043,1046.0005421191454 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,67943,轮胎裂纹同意更换新轮胎。但是,0,low,mid_turn_pause,xlsx_draft,0.6981820166110992,0.1254715919494629,0.30181798338890076,0.6981750130653381,0.462412565946579,0.5375813841819763,0.00029004551470279694,0.5384137034416199,1,0,0,47943,67943,20000,0.0,1025.8604157716036,1087.0674578472972 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,68313,轮胎质量问题规律，排水槽横向竖向龟裂。,0,low,mid_turn_pause,xlsx_draft,0.8763869255781174,0.8074509650468826,0.12361307442188263,0.8763805031776428,0.21461541950702667,0.7853786945343018,0.00010311136429663748,0.5491448640823364,1,0,1,48313,68313,20000,0.0,1024.2989165708423,1038.7848326936364 +cb3238dc-3946-42a5-aad8-9b9a54226363,70745,轮胎开裂,0,low,mid_turn_pause,xlsx_draft,0.6845456063747406,0.28872406482696533,0.3154543936252594,0.6845382452011108,0.41045159101486206,0.5895419716835022,0.0003880855510942638,0.3676386773586273,1,0,0,50745,70745,20000,0.0,1027.561416849494,1026.4365831390023 +e304f740-85d4-427e-9fd6-c5474e9cd737,40701,帕萨特,0,low,mid_turn_pause,xlsx_draft,0.8340967893600464,0.4185728430747986,0.1659032106399536,0.8340901136398315,0.3302589952945709,0.6697350144386292,5.8670710131991655e-05,0.6218740344047546,1,0,0,20701,40701,20000,0.0,1075.5121251568198,1022.6317914202809 +e304f740-85d4-427e-9fd6-c5474e9cd737,65819,四条轮胎，最近我在。,0,low,unfinished_expression,xlsx_draft,0.6574011743068695,0.05253005027770996,0.3425988256931305,0.6573941111564636,0.5131843686103821,0.48680970072746277,0.00016322250303346664,0.5996755957603455,1,0,0,45819,65819,20000,0.0,1029.8763746395707,1034.4825834035873 +872a1e67-873a-4c49-a598-b38a670afcad,47187,帕萨特L2025款 ,0,,mid_turn_pause,,0.8951215818524361,0.7005376219749451,0.10487841814756393,0.8951144218444824,0.19764570891857147,0.8023478388786316,0.0008713988936506212,0.6476827263832092,1,0,1,27187,47187,20000,0.0,1018.0150838568807,1072.5461663678288 +872a1e67-873a-4c49-a598-b38a670afcad,52326,我的轮胎开裂了。,0,,mid_turn_pause,,0.6631686389446259,0.4240996241569519,0.33683136105537415,0.6631612777709961,0.43475767970085144,0.5652356743812561,0.0027947553899139166,0.4008314907550812,1,0,0,32326,52326,20000,0.0,1034.971167333424,1051.6820829361677 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,48536,爱车型号是大众帕萨特，购车时间2023年10月份,0,,mid_turn_pause,,0.8199913799762726,0.5186727344989777,0.18000862002372742,0.8199833631515503,0.22936034202575684,0.770633339881897,0.00015609677939210087,0.375405877828598,1,0,1,28536,48536,20000,0.0,1050.1775424927473,1044.4884998723865 +872a1e67-873a-4c49-a598-b38a670afcad,44810,帕萨特,0,,mid_turn_pause,,0.8325859755277634,0.3700411319732666,0.16741402447223663,0.832578718662262,0.29413360357284546,0.7058599591255188,0.0006242078961804509,0.6011908054351807,1,0,0,24810,44810,20000,0.0,1040.6767074018717,1023.8962080329657 +872a1e67-873a-4c49-a598-b38a670afcad,49500,帕萨特L2025款2025年3月购车,1,,completed_turn,,0.4527696967124939,0.9640973135828972,0.5472303032875061,0.4527527689933777,0.42345741391181946,0.5765343904495239,0.004929132293909788,0.13983771204948425,0,0,1,29500,49500,20000,0.0,1042.2111246734858,1065.983415581286 +872a1e67-873a-4c49-a598-b38a670afcad,54318,行驶里程29000公里。,1,,completed_turn,,0.48515671491622925,0.9336890131235123,0.5148432850837708,0.4851451814174652,0.4614582359790802,0.5385342240333557,0.0005555779207497835,0.16714082658290863,0,0,1,34318,54318,20000,0.0,1127.1999590098858,1034.5972077921033 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,41219,迈腾2023款330豪华版2.0T,0,,mid_turn_pause,,0.9391313791275024,0.9102829173207283,0.06086862087249756,0.9391252994537354,0.12826824188232422,0.871725857257843,0.00022907710808794945,0.7417447566986084,1,0,1,21219,41219,20000,0.0,1079.2017504572868,1094.9621256440878 +cb3238dc-3946-42a5-aad8-9b9a54226363,41207,我是2023年,0,,mid_turn_pause,,0.7711529731750488,0.12327557802200317,0.22884702682495117,0.7711459398269653,0.35431164503097534,0.6456820964813232,0.0002079915429931134,0.5072486400604248,1,0,0,21207,41207,20000,0.0,1040.657457895577,1037.4416671693325 +cb3238dc-3946-42a5-aad8-9b9a54226363,49204,我是2023年、2024年1月份购置的迈腾3302023款轿车,0,,mid_turn_pause,,0.9226682409644127,0.8600987195968628,0.07733175903558731,0.9226608872413635,0.12602370977401733,0.8739699721336365,0.00014769700646866113,0.5389497876167297,1,0,1,29204,49204,20000,0.0,1047.2399592399597,1084.025208838284 +cb3238dc-3946-42a5-aad8-9b9a54226363,58424,我的轮胎,0,,mid_turn_pause,,0.7652575969696045,0.19378310441970825,0.2347424030303955,0.7652506232261658,0.3740234076976776,0.6259703636169434,0.00014432301395572722,0.49237900972366333,1,0,0,38424,58424,20000,0.0,1054.9904173240066,1021.811000071466 +cb3238dc-3946-42a5-aad8-9b9a54226363,73640,轮胎开裂，行驶28000公里，轮胎开裂,0,,mid_turn_pause,,0.6812975704669952,0.45827627182006836,0.31870242953300476,0.6812891364097595,0.3468272089958191,0.6531659960746765,0.00045241721090860665,0.2853435277938843,1,0,0,53640,73640,20000,0.0,1031.7763751372695,1030.0637912005186 +e304f740-85d4-427e-9fd6-c5474e9cd737,62828,四条轮胎,0,,mid_turn_pause,,0.5078669488430023,0.02205902338027954,0.4921330511569977,0.5078585147857666,0.6710895299911499,0.32890430092811584,0.00022117290063761175,0.3902367055416107,1,0,0,42828,62828,20000,0.0,1024.95929133147,1024.5867501944304 diff --git a/experiments/maai-ch-kyoto/results/user-only/predictions.csv b/experiments/maai-ch-kyoto/results/user-only/predictions.csv new file mode 100644 index 0000000..19be27a --- /dev/null +++ b/experiments/maai-ch-kyoto/results/user-only/predictions.csv @@ -0,0 +1,45 @@ +call_id,decision_ms,user_text_at_decision,can_reply_now,confidence,reason,annotator,can_reply_score,stereo_can_reply_score,p_now_user,p_now_robot,p_future_user,p_future_robot,vad_user,vad_robot,prediction_05,correct_05,stereo_prediction_05,audio_start_ms,audio_end_ms,context_ms,robot_input_peak_abs,replay_inference_ms,stereo_replay_inference_ms +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,21780,贵州省遵义市仁怀市。,1,low,completed_answer,xlsx_draft,0.9077615067362785,0.959835909307003,0.09223849326372147,0.9077540636062622,0.17713485658168793,0.8228583335876465,0.000159550501848571,0.6355426907539368,1,1,1,1780,21780,20000,0.0,1051.0452492162585,1068.465125747025 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,29860,投诉建议。,1,low,completed_answer,xlsx_draft,0.8796060606837273,0.9285890087485313,0.12039393931627274,0.8795983791351318,0.24113894999027252,0.7588542699813843,0.00013205873256083578,0.5916702151298523,1,1,1,9860,29860,20000,0.0,1054.2150000110269,1042.2203745692968 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,48125,型号是24年380大众帕萨特星空龙腾版。,0,low,mid_turn_pause,xlsx_draft,0.712736040353775,0.5122989416122437,0.287263959646225,0.7127270102500916,0.30030158162117004,0.699691653251648,0.0005045763682574034,0.2013741433620453,1,0,1,28125,48125,20000,0.0,1064.420540817082,1044.8135826736689 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,64000,行驶3万公里。,1,low,completed_answer,xlsx_draft,0.9320666417479515,0.9369851648807526,0.06793335825204849,0.9320593476295471,0.13191033899784088,0.8680828809738159,0.000251378194661811,0.6513041257858276,1,1,1,44000,64000,20000,0.0,1052.9709588736296,1054.0036242455244 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,76672,恒泰轮胎前门两条轮胎开裂,0,low,unfinished_expression,xlsx_draft,0.8640106171369553,0.7066852748394012,0.13598938286304474,0.8640027642250061,0.2139778882265091,0.7860152125358582,0.0002717908064369112,0.4948178827762604,1,0,1,56672,76672,20000,0.0,1032.4355829507113,1016.6379176080227 +2c83253a-7977-4e3c-a387-0aa1ea06fbe1,88482,尽快，已经影响行车安全了。,1,low,completed_answer,xlsx_draft,0.16062873601913452,0.11465108394622803,0.8393712639808655,0.16061516106128693,0.6462375521659851,0.35375407338142395,0.022112371399998665,0.020942039787769318,0,0,0,68482,88482,20000,0.0,1036.321542225778,1105.0837496295571 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,22007,宣传。,1,low,completed_answer,xlsx_draft,0.9323981329798698,0.69324991106987,0.06760186702013016,0.9323916435241699,0.15523740649223328,0.8447564244270325,7.546954293502495e-05,0.7427684664726257,1,1,1,2007,22007,20000,0.0,1021.4767083525658,1095.7844154909253 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,31842,投诉建议。,1,low,completed_answer,xlsx_draft,0.9342073872685432,0.7899426519870758,0.06579261273145676,0.9342008829116821,0.12074729800224304,0.8792466521263123,0.00038456678157672286,0.6970545053482056,1,1,1,11842,31842,20000,0.0,1045.9425412118435,1058.4009597077966 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,52348,爱车型号是大众帕萨特，购车时间2023年10月份，行驶里程24000多公里。,1,low,completed_answer,xlsx_draft,0.9645285122096539,0.959786981344223,0.035471487790346146,0.9645218253135681,0.07146214693784714,0.9285316467285156,7.502773951273412e-05,0.5554651618003845,1,1,1,32348,52348,20000,0.0,1025.0394167378545,1030.845332890749 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,65298,轮胎裂纹。,0,low,mid_turn_pause,xlsx_draft,0.8862891793251038,0.16463828086853027,0.11371082067489624,0.8862825036048889,0.21822746098041534,0.7817663550376892,0.000534569553565234,0.6663311123847961,1,0,0,45298,65298,20000,0.0,1049.0985000506043,1046.0005421191454 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,67943,轮胎裂纹同意更换新轮胎。但是,0,low,mid_turn_pause,xlsx_draft,0.6981820166110992,0.1254715919494629,0.30181798338890076,0.6981750130653381,0.462412565946579,0.5375813841819763,0.00029004551470279694,0.5384137034416199,1,0,0,47943,67943,20000,0.0,1025.8604157716036,1087.0674578472972 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,70430,轮胎裂纹同意更换新轮胎。但是20,0,low,unfinished_expression,xlsx_draft,0.03903418779373169,0.9451650716364384,0.9609658122062683,0.03902532905340195,0.9632092118263245,0.036784518510103226,0.0012127686059102416,0.03036552108824253,0,1,1,50430,70430,20000,0.0,1091.4534172043204,1022.8510843589902 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,82941,可以。,1,low,completed_answer,xlsx_draft,0.9212324172258377,0.27139031887054443,0.07876758277416229,0.9212257862091064,0.18721646070480347,0.8127773404121399,0.0001128750664065592,0.7378789782524109,1,1,0,62941,82941,20000,0.0,1056.9429583847523,1043.2109581306577 +872a1e67-873a-4c49-a598-b38a670afcad,17153,我姓宋。,1,low,completed_answer,xlsx_draft,0.8734219521284103,0.4008920192718506,0.12657804787158966,0.8734147548675537,0.2726988196372986,0.7272946238517761,0.0003622843069024384,0.6292691826820374,1,1,0,53,17153,17100,0.0,858.5309581831098,853.220958262682 +872a1e67-873a-4c49-a598-b38a670afcad,24846,郑州市，河南郑州市。,1,low,completed_answer,xlsx_draft,0.9015571922063828,0.6936855018138885,0.09844280779361725,0.9015502333641052,0.21137899160385132,0.7886144518852234,0.0001732363016344607,0.6429467797279358,1,1,1,4846,24846,20000,0.0,1026.8719168379903,1076.7360422760248 +872a1e67-873a-4c49-a598-b38a670afcad,33471,我的轮胎开裂了。,1,low,completed_answer,xlsx_draft,0.929821141064167,0.802086129784584,0.07017885893583298,0.929814338684082,0.17295779287815094,0.8270356059074402,4.714540409622714e-05,0.6263724565505981,1,1,1,13471,33471,20000,0.0,1042.61495731771,1108.0348333343863 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,18027,天津市。,1,low,completed_answer,xlsx_draft,0.9133674055337906,0.5773889422416687,0.08663259446620941,0.9133609533309937,0.18363836407661438,0.8163555264472961,8.530736522516236e-05,0.7179951667785645,1,1,1,27,18027,18000,0.0,905.0040002912283,943.9614582806826 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,24768,投诉。,1,low,completed_answer,xlsx_draft,0.8936084657907486,0.5596272945404053,0.1063915342092514,0.8936018347740173,0.22588488459587097,0.7741089463233948,0.00010253101208945736,0.6374555826187134,1,1,1,4768,24768,20000,0.0,1054.3261673301458,1028.9249997586012 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,51080,迈腾2023款330豪华版2.0T。出现的问题是轮购车时间是2024年5月份，里程是6851公里。,0,low,unfinished_expression,xlsx_draft,0.0655754804611206,0.010066509246826172,0.9344245195388794,0.06556758284568787,0.7127729058265686,0.2872190475463867,0.9905912280082703,0.04207123443484306,0,1,0,31080,51080,20000,0.0,1029.1714994236827,1041.918333619833 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,68313,轮胎质量问题规律，排水槽横向竖向龟裂。,0,low,mid_turn_pause,xlsx_draft,0.8763869255781174,0.8074509650468826,0.12361307442188263,0.8763805031776428,0.21461541950702667,0.7853786945343018,0.00010311136429663748,0.5491448640823364,1,0,1,48313,68313,20000,0.0,1024.2989165708423,1038.7848326936364 +cb3238dc-3946-42a5-aad8-9b9a54226363,13540,辽宁葫芦岛。,1,low,completed_answer,xlsx_draft,0.920970730483532,0.9443961046636105,0.07902926951646805,0.9209638237953186,0.18109828233718872,0.8188952207565308,0.0006419310229830444,0.617168664932251,1,1,1,40,13540,13500,0.0,656.8741258233786,712.0467908680439 +cb3238dc-3946-42a5-aad8-9b9a54226363,27520,投诉建议。,1,low,completed_answer,xlsx_draft,0.9300618022680283,0.9681989774107933,0.06993819773197174,0.9300550222396851,0.17150600254535675,0.8284874558448792,4.362989420769736e-05,0.6488192677497864,1,1,1,7520,27520,20000,0.0,1145.223207771778,1442.9228343069553 +cb3238dc-3946-42a5-aad8-9b9a54226363,60790,我的轮胎行驶28000,0,low,unfinished_expression,xlsx_draft,0.4327901005744934,0.05659288167953491,0.5672098994255066,0.43278226256370544,0.672747790813446,0.3272458016872406,0.00048025097930803895,0.23959499597549438,0,1,0,40790,60790,20000,0.0,1062.5744173303246,1037.0965832844377 +cb3238dc-3946-42a5-aad8-9b9a54226363,70745,轮胎开裂,0,low,mid_turn_pause,xlsx_draft,0.6845456063747406,0.28872406482696533,0.3154543936252594,0.6845382452011108,0.41045159101486206,0.5895419716835022,0.0003880855510942638,0.3676386773586273,1,0,0,50745,70745,20000,0.0,1027.561416849494,1026.4365831390023 +e304f740-85d4-427e-9fd6-c5474e9cd737,18842,苏州昆山。,1,low,completed_answer,xlsx_draft,0.877316839993,0.6192943453788757,0.12268316000699997,0.8773099184036255,0.23173704743385315,0.7682567834854126,8.104179141810164e-05,0.6466754674911499,1,1,1,42,18842,18800,0.0,987.6783341169357,952.5969997048378 +e304f740-85d4-427e-9fd6-c5474e9cd737,29619,投诉。,1,low,completed_answer,xlsx_draft,0.8809438720345497,0.44960129261016846,0.11905612796545029,0.8809370994567871,0.24329762160778046,0.7566962242126465,0.00017530389595776796,0.7010255455970764,1,1,0,9619,29619,20000,0.0,1067.8304582834244,1045.8366246894002 +e304f740-85d4-427e-9fd6-c5474e9cd737,40701,帕萨特,0,low,mid_turn_pause,xlsx_draft,0.8340967893600464,0.4185728430747986,0.1659032106399536,0.8340901136398315,0.3302589952945709,0.6697350144386292,5.8670710131991655e-05,0.6218740344047546,1,0,0,20701,40701,20000,0.0,1075.5121251568198,1022.6317914202809 +e304f740-85d4-427e-9fd6-c5474e9cd737,54600,24年11月份购买的帕萨特，15000公里左右。,1,low,completed_answer,xlsx_draft,0.8766583725810051,0.7056282758712769,0.1233416274189949,0.8766518235206604,0.24074847996234894,0.7592453956604004,5.343570592231117e-05,0.6715101599693298,1,1,1,34600,54600,20000,0.0,1030.4231671616435,1045.8374163135886 +e304f740-85d4-427e-9fd6-c5474e9cd737,65819,四条轮胎，最近我在。,0,low,unfinished_expression,xlsx_draft,0.6574011743068695,0.05253005027770996,0.3425988256931305,0.6573941111564636,0.5131843686103821,0.48680970072746277,0.00016322250303346664,0.5996755957603455,1,0,0,45819,65819,20000,0.0,1029.8763746395707,1034.4825834035873 +872a1e67-873a-4c49-a598-b38a670afcad,47187,帕萨特L2025款 ,0,,mid_turn_pause,,0.8951215818524361,0.7005376219749451,0.10487841814756393,0.8951144218444824,0.19764570891857147,0.8023478388786316,0.0008713988936506212,0.6476827263832092,1,0,1,27187,47187,20000,0.0,1018.0150838568807,1072.5461663678288 +872a1e67-873a-4c49-a598-b38a670afcad,52326,我的轮胎开裂了。,0,,mid_turn_pause,,0.6631686389446259,0.4240996241569519,0.33683136105537415,0.6631612777709961,0.43475767970085144,0.5652356743812561,0.0027947553899139166,0.4008314907550812,1,0,0,32326,52326,20000,0.0,1034.971167333424,1051.6820829361677 +40ae686c-8196-4d7b-b7ad-3e12a427ebb6,48536,爱车型号是大众帕萨特，购车时间2023年10月份,0,,mid_turn_pause,,0.8199913799762726,0.5186727344989777,0.18000862002372742,0.8199833631515503,0.22936034202575684,0.770633339881897,0.00015609677939210087,0.375405877828598,1,0,1,28536,48536,20000,0.0,1050.1775424927473,1044.4884998723865 +872a1e67-873a-4c49-a598-b38a670afcad,44810,帕萨特,0,,mid_turn_pause,,0.8325859755277634,0.3700411319732666,0.16741402447223663,0.832578718662262,0.29413360357284546,0.7058599591255188,0.0006242078961804509,0.6011908054351807,1,0,0,24810,44810,20000,0.0,1040.6767074018717,1023.8962080329657 +872a1e67-873a-4c49-a598-b38a670afcad,49500,帕萨特L2025款2025年3月购车,1,,completed_turn,,0.4527696967124939,0.9640973135828972,0.5472303032875061,0.4527527689933777,0.42345741391181946,0.5765343904495239,0.004929132293909788,0.13983771204948425,0,0,1,29500,49500,20000,0.0,1042.2111246734858,1065.983415581286 +872a1e67-873a-4c49-a598-b38a670afcad,54318,行驶里程29000公里。,1,,completed_turn,,0.48515671491622925,0.9336890131235123,0.5148432850837708,0.4851451814174652,0.4614582359790802,0.5385342240333557,0.0005555779207497835,0.16714082658290863,0,0,1,34318,54318,20000,0.0,1127.1999590098858,1034.5972077921033 +872a1e67-873a-4c49-a598-b38a670afcad,45581,帕萨特,0,,mid_turn_pause,,0.2830030918121338,0.023214280605316162,0.7169969081878662,0.2829975485801697,0.6611828804016113,0.3388102352619171,0.6849336624145508,0.45515570044517517,0,1,0,25581,45581,20000,0.0,1070.103583857417,1049.137583002448 +a266b9ef-5e47-4434-a04c-ce4eacd52c19,41219,迈腾2023款330豪华版2.0T,0,,mid_turn_pause,,0.9391313791275024,0.9102829173207283,0.06086862087249756,0.9391252994537354,0.12826824188232422,0.871725857257843,0.00022907710808794945,0.7417447566986084,1,0,1,21219,41219,20000,0.0,1079.2017504572868,1094.9621256440878 +cb3238dc-3946-42a5-aad8-9b9a54226363,19620,,1,,completed_turn,,0.9391433596611023,0.9730777312070131,0.060856640338897705,0.939136803150177,0.16418568789958954,0.8358079791069031,0.0001521782105555758,0.6308698058128357,1,1,1,20,19620,19600,0.0,1013.4730413556099,995.7905001938343 +cb3238dc-3946-42a5-aad8-9b9a54226363,41207,我是2023年,0,,mid_turn_pause,,0.7711529731750488,0.12327557802200317,0.22884702682495117,0.7711459398269653,0.35431164503097534,0.6456820964813232,0.0002079915429931134,0.5072486400604248,1,0,0,21207,41207,20000,0.0,1040.657457895577,1037.4416671693325 +cb3238dc-3946-42a5-aad8-9b9a54226363,49204,我是2023年、2024年1月份购置的迈腾3302023款轿车,0,,mid_turn_pause,,0.9226682409644127,0.8600987195968628,0.07733175903558731,0.9226608872413635,0.12602370977401733,0.8739699721336365,0.00014769700646866113,0.5389497876167297,1,0,1,29204,49204,20000,0.0,1047.2399592399597,1084.025208838284 +cb3238dc-3946-42a5-aad8-9b9a54226363,58424,我的轮胎,0,,mid_turn_pause,,0.7652575969696045,0.19378310441970825,0.2347424030303955,0.7652506232261658,0.3740234076976776,0.6259703636169434,0.00014432301395572722,0.49237900972366333,1,0,0,38424,58424,20000,0.0,1054.9904173240066,1021.811000071466 +cb3238dc-3946-42a5-aad8-9b9a54226363,73640,轮胎开裂，行驶28000公里，轮胎开裂,0,,mid_turn_pause,,0.6812975704669952,0.45827627182006836,0.31870242953300476,0.6812891364097595,0.3468272089958191,0.6531659960746765,0.00045241721090860665,0.2853435277938843,1,0,0,53640,73640,20000,0.0,1031.7763751372695,1030.0637912005186 +e304f740-85d4-427e-9fd6-c5474e9cd737,11531,姓赵,1,,completed_turn,,0.936267301440239,0.5200230777263641,0.06373269855976105,0.9362608194351196,0.15885427594184875,0.8411394357681274,5.260770922177471e-05,0.7247620820999146,1,1,1,31,11531,11500,0.0,550.2660423517227,542.1248748898506 +e304f740-85d4-427e-9fd6-c5474e9cd737,62828,四条轮胎,0,,mid_turn_pause,,0.5078669488430023,0.02205902338027954,0.4921330511569977,0.5078585147857666,0.6710895299911499,0.32890430092811584,0.00022117290063761175,0.3902367055416107,1,0,0,42828,62828,20000,0.0,1024.95929133147,1024.5867501944304 diff --git a/experiments/maai-ch-kyoto/results/user-only/score-timeline.json b/experiments/maai-ch-kyoto/results/user-only/score-timeline.json new file mode 100644 index 0000000..93baf6b --- /dev/null +++ b/experiments/maai-ch-kyoto/results/user-only/score-timeline.json @@ -0,0 +1 @@ +{"schema_version":1,"model":"maai_user_only","name":"VAP / MaAI（仅用户）","score":"1 - p_now[user]","sample_interval_ms":100,"method":"Continuous user + silent-robot stereo input, 16kHz, 10Hz, 20s KV cache. Robot channel zeroed before causal streaming resampling. Timestamps include resampling buffering. Checkpoints are independent <=20s replays.","provenance":{"machine":"macOS-26.4.1-arm64-arm-64bit","hardware":"Apple M4 Mac mini / 16 GB","python":"3.11.15 (main, Mar 25 2026, 02:58:47) [Clang 22.1.1 ]","environment":"experiments/maai-ch-kyoto/.venv (uv.lock)","device":"cpu","threads":4,"dependencies":{"maai":"0.2.22","torch":"2.14.1","transformers":"5.5.3","numpy":"2.4.6","soundfile":"0.14.0","soxr":"1.1.0"},"sample_rate":16000,"frame_ms":100,"context_ms":20000,"use_kv_cache":true,"seed":42,"channel_mapping":{"0":"original user","1":"all-zero robot 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"environment": "experiments/maai-ch-kyoto/.venv (uv.lock)", + "device": "cpu", + "threads": 4, + "dependencies": { + "maai": "0.2.22", + "torch": "2.14.1", + "transformers": "5.5.3", + "numpy": "2.4.6", + "soundfile": "0.14.0", + "soxr": "1.1.0" + }, + "sample_rate": 16000, + "frame_ms": 100, + "context_ms": 20000, + "use_kv_cache": true, + "seed": 42, + "channel_mapping": { + "0": "original user", + "1": "all-zero robot input" + }, + "weight_sha256": { + "vap_mc_state_dict_ch_kyoto_10hz_20000msec.pt": "65b8a51aea6a257b5b9139107f0f0cf22f94523cd4b6a2e7a356b6c43a4596a8", + "60k_epoch4-d0f474de.pt": "d0f474de968c7243c20f43dee1626681618f2f150d3e638e82b79e22563b17f1" + }, + "label_sha256": "780e50f976daf039ab976ea155bdb5d1ca5fffc792f9db7adf8063ec3048ff8e", + "history_sha256": "4c21e20b7323030d590276854f07b454ccbad627e1a7842101a22a99377ffbaa", + "uv_lock_sha256": "6863631a5969e0f077d8aefc4335532952fe95486598b609e832e36d5ce2777f", + "script_sha256": "93c8a69850cd444c8daa9525eda267cda5f4ca34393df5f175286d875aa19195", + "model_factory_sha256": "ca4d5825bd3d86ddc5f41c5fbf1267849e87cabaaa8da9eabd282cd6bbaac2fe", + "loader_sha256": "4a7b37e6b67e5c55c030bcc399ab58e6575e49592dbda87dc311ec88da38e1d2", + "audio_sha256": { + "2c83253a-7977-4e3c-a387-0aa1ea06fbe1": "2b8a67dc76b8975d287df42193647cf040229377537eadaaf3adc13358201fe1", + "40ae686c-8196-4d7b-b7ad-3e12a427ebb6": "130be37286d48f7badca711a068fab19a2686bd83248a71c73c11ce77650d39d", + "872a1e67-873a-4c49-a598-b38a670afcad": "54c5672ea491cc3daedd75e80f97e82cc061ccbf14e521bc22ca55f15fa062a5", + "a266b9ef-5e47-4434-a04c-ce4eacd52c19": "9a7ebe093e66aaaa08ac462f975b7d428df79a38ed5b418265b82c77d8b91f46", + "cb3238dc-3946-42a5-aad8-9b9a54226363": "7a74dbf3dd742d3d14317d069c28b703dd1398d5eb3dc1dba3d7a7c568a18d60", + "e304f740-85d4-427e-9fd6-c5474e9cd737": "44672603d0cf9feac41275e99d59baa4cafbdcdbb172da23de52aa0a2a4961d2" + } + }, + "method": "At each exact point, independently reset CPC/VAP and replay <=20s in 100ms frames. User channel unchanged; robot channel zero for the full context before resampling. Also rerun the stereo control on identical current points. No annotation transcripts, future audio, end signal, silence gate, or timeout. Score is an activity-projection proxy, not calibrated EOT probability.", + "limitations": [ + "Exploratory selected-point evaluation, not a held-out benchmark or endpointing-policy evaluation.", + "Continuous full-call KV/CPC state differs from independent point replay; curve and checkpoint scores may differ.", + "Zeroing the robot channel cannot remove robot leakage already present in the user channel.", + "Stereo reference here is rerun at current annotation points; existing legacy frontend checkpoints are preserved separately." + ], + "timeline_points": 4873, + "elapsed_seconds": 124.26453408319503 +} diff --git a/experiments/maai-ch-kyoto/results/user-only/thresholds.csv b/experiments/maai-ch-kyoto/results/user-only/thresholds.csv new file mode 100644 index 0000000..20b7a84 --- /dev/null +++ b/experiments/maai-ch-kyoto/results/user-only/thresholds.csv @@ -0,0 +1,8 @@ +threshold,n,tn,fp,fn,tp,accuracy,false_reply_rate,false_hold_rate,recall,precision,f1 +0.11,44,2,20,0,22,0.5454545454545454,0.9090909090909091,0.0,1.0,0.5238095238095238,0.6875 +0.5,44,4,18,3,19,0.5227272727272727,0.8181818181818182,0.13636363636363635,0.8636363636363636,0.5135135135135135,0.6440677966101694 +0.7,44,10,12,3,19,0.6590909090909091,0.5454545454545454,0.13636363636363635,0.8636363636363636,0.6129032258064516,0.7169811320754716 +0.8,44,13,9,3,19,0.7272727272727273,0.4090909090909091,0.13636363636363635,0.8636363636363636,0.6785714285714286,0.76 +0.9,44,20,2,9,13,0.75,0.09090909090909091,0.4090909090909091,0.5909090909090909,0.8666666666666667,0.7027027027027027 +0.95,44,22,0,21,1,0.5227272727272727,0.0,0.9545454545454546,0.045454545454545456,1.0,0.08695652173913043 +0.98,44,22,0,22,0,0.5,0.0,1.0,0.0,,0.0 diff --git a/experiments/maai-ch-kyoto/run_mimi.py b/experiments/maai-ch-kyoto/run_mimi.py new file mode 100644 index 0000000..419a049 --- /dev/null +++ b/experiments/maai-ch-kyoto/run_mimi.py @@ -0,0 +1,295 @@ +"""MaAI ch_kyoto standard/noise-robust Mimi VAP: causal stereo CPU replay.""" +import argparse +from collections import Counter +import contextlib +from datetime import datetime, timezone +import importlib.metadata as metadata +import io +import json +import platform +from pathlib import Path +import sys +import time + +from smoke_run import HERE, ROOT, Maai, MaaiInput, np, sf, soxr, torch +from evaluate_can_reply import metrics, read, write +from run_user_only import LABELS, HISTORY, seed, sha + +OUT = HERE / "results/mimi-standard" +MODELS = HERE / "models/mimi" +VAP = MODELS / "vap_mimi_state_dict_ch_kyoto_12.5hz_20000msec.pt" +MC = False +ONNX = MODELS / "continuous_mimi_fp32.onnx" +META = MODELS / "continuous_mimi_fp32.json" +MANIFEST = HERE / "mimi-model-manifest.json" +FRAME_MS, FRAME_SAMPLES = 80, 1280 + + +def validate_weights(): + manifest = json.loads(MANIFEST.read_text()) + assert {x["filename"] for x in manifest["files"]} == { + "vap_mimi_state_dict_ch_kyoto_12.5hz_20000msec.pt", + "vap_mc_mimi_state_dict_ch_kyoto_12.5hz_20000msec.pt", ONNX.name, META.name} + for item in manifest["files"]: + path = MODELS / item["filename"] + assert sha(path) == item["sha256"] and path.stat().st_size == item["size"], path.name + return manifest + + +def model(): + seed() + messages = io.StringIO() + with contextlib.redirect_stdout(messages): + maai = Maai(mode="vap", lang="ch_kyoto", mc=MC, + audio_ch1=MaaiInput.Chunk(), audio_ch2=MaaiInput.Chunk(), + model_type="normal-ver2", frame_rate=12.5, context_len_sec=20, + device="cpu", use_kv_cache=True, local_model=str(VAP), + use_mimi_onnx=True, mimi_onnx_precision="fp32", + mimi_local_onnx_fp32_path=str(ONNX), mimi_local_onnx_fp32_meta_path=str(META), + mimi_onnx_cpu_intra_threads=4, mimi_onnx_cpu_inter_threads=1, + cache_dir=str(HERE / ".cache/huggingface")) + # Official local_model path uses the common 'vap' architecture marker for + # either checkpoint. The filename/hash and loading audit identify MC weights. + assert maai.variant == "vap" and not maai.is_mono + assert maai.encoder_type == "mimi" and not maai.single_tower + assert maai.audio_samples_per_frame == FRAME_SAMPLES + maai.CALC_PROCESS_TIME_INTERVAL = 10**9 + return maai, messages.getvalue() + + +def loading_audit(maai, messages): + raw = torch.load(VAP, map_location="cpu", weights_only=True) + sd = {**raw.get("encoder_state_dict", {}), **raw["state_dict"]} if "state_dict" in raw else raw + missing, mismatch, bypassed = [], [], [] + for name, parameter in maai.vap.named_parameters(): + key = name + if name.startswith(("encoder1.", "encoder2.")): + encoder = getattr(maai.vap, name.split('.', 1)[0]) + # At native 12.5Hz, _align_frame_rate returns embeddings directly; + # these frozen conversion convolutions are not used or pretrained. + if '.frame_rate_conv.' in name and not parameter.requires_grad: + assert encoder.frame_hz == encoder.frame_hz_mimi == 12.5 + bypassed.append(name) + continue + key = "encoder." + name.split(".", 1)[1] + if key not in sd: + missing.append(name) + elif not torch.equal(parameter.detach().cpu(), sd[key]): + mismatch.append(name) + assert not missing and not mismatch, dict(missing=missing, mismatched=mismatch) + return dict(variant="vap_mc" if MC else "vap", loader_architecture_marker=maai.variant, + encoder=maai.encoder_type, checkpoint_mc=MC, + single_tower=maai.single_tower, missing_trainable_parameters=missing, + mismatched_parameters=mismatch, unused_frozen_frame_rate_parameters=bypassed, loader_messages=messages, + encoder_provider=maai.vap.encoder1._onnx_sess.get_providers()) + + +def prepare(pcm, sr, decision): + duration = min(20000, decision // FRAME_MS * FRAME_MS) + if duration < FRAME_MS * 2: + raise ValueError("Insufficient history for Mimi startup") + origin = decision - duration + start, end = origin * sr // 1000, decision * sr // 1000 + if not 0 <= start < end <= len(pcm): + raise ValueError("Point outside recording") + # Preserve BOTH recorded channels. Crop before resampling to avoid future input. + segment = np.ascontiguousarray(soxr.resample(pcm[start:end], sr, 16000, quality="HQ"), dtype=np.float32) + assert segment.shape == (duration // FRAME_MS * FRAME_SAMPLES, 2) + return segment, origin + + +def consume(maai, frame): + maai.process(np.ascontiguousarray(frame[:, 0]), np.ascontiguousarray(frame[:, 1])) + if maai.result_dict_queue.empty(): + return None # Official ONNX Mimi skips its first encoder output. + result = maai.result_dict_queue.get_nowait() + assert maai.result_dict_queue.empty() + values = {k: np.asarray(result[k], dtype=float).reshape(-1) for k in ("p_now", "p_future", "vad")} + for value in values.values(): + assert value.shape == (2,) and np.isfinite(value).all() + assert ((value >= 0) & (value <= 1)).all() + return values + + +def score(values): + return 1.0 - float(values["p_now"][0]) + + +def replay(segment): + began = time.perf_counter() + maai, _ = model() + load_ms = (time.perf_counter() - began) * 1000 + times, values, first_output_ms = [], None, None + for start in range(0, len(segment), FRAME_SAMPLES): + t = time.perf_counter() + values = consume(maai, segment[start:start+FRAME_SAMPLES]) + times.append((time.perf_counter()-t)*1000) + if values is not None and first_output_ms is None: + first_output_ms = (start+FRAME_SAMPLES)*1000/16000 + if values is None: + raise ValueError("No score at the exact final input frame") + return values, dict(model_load_ms=load_ms, replay_inference_ms=sum(times), + mean_update_ms=float(np.mean(times)), p95_update_ms=float(np.percentile(times, 95)), + first_output_ms=first_output_ms, frame_count=len(times)) + + +def witness(pcm, sr, decision): + maai, messages = model() + audit = loading_audit(maai, messages) + del maai + segment, _ = prepare(pcm, sr, decision) + changed = pcm.copy() + changed[decision*sr//1000:] = np.random.default_rng(42).normal(0, .1, changed[decision*sr//1000:].shape) + other, _ = prepare(changed, sr, decision) + assert np.array_equal(segment, other), "Future audio changed a causal input" + a, timing = replay(segment) + b, _ = replay(other) + assert all(np.array_equal(a[k], b[k]) for k in a), "Seeded inference differs" + assert np.count_nonzero(segment[:, 0]) and np.count_nonzero(segment[:, 1]), "Smoke must exercise both speakers" + print(f"WITNESS Mimi {'mc' if MC else 'standard'} CPU seed=42 score={score(a):.7f}; exact repeat, future invariance, both speakers and loading audit passed", flush=True) + return dict(repeat_identical=True, future_invariance=True, both_channels_nonzero=True, + score=score(a), timing=timing, loading_audit=audit) + + +def continuous(pcm, sr): + maai, _ = model() + resampler = soxr.ResampleStream(sr, 16000, 2, dtype="float32", quality="HQ") + pending = np.empty((0, 2), dtype=np.float32) + points = [] + block_samples = sr * FRAME_MS // 1000 + assert block_samples * 1000 == sr * FRAME_MS + for start in range(0, len(pcm), block_samples): + end = min(len(pcm), start+block_samples) + pending = np.concatenate([pending, resampler.resample_chunk(pcm[start:end], last=end == len(pcm))]) + latest = None + while len(pending) >= FRAME_SAMPLES: + frame, pending = pending[:FRAME_SAMPLES], pending[FRAME_SAMPLES:] + result = consume(maai, frame) + if result is not None: + latest = score(result) + if latest is not None: + points.append([round(end*1000/sr, 3), round(latest, 7)]) + return points + + +def provenance(manifest): + import maai.encoder, maai.model + return dict(machine=platform.platform(), hardware="Apple M4 Mac mini / 16 GB", python=sys.version, + environment="experiments/maai-ch-kyoto/.venv (uv.lock)", device="cpu", + torch_threads=4, torch_interop_threads=1, onnx_intra_threads=4, onnx_inter_threads=1, + dependencies={p: metadata.version(p) for p in ("maai", "torch", "transformers", "onnxruntime", "numpy", "soundfile", "soxr")}, + model_type="normal-ver2", variant="vap_mc" if MC else "vap", mc=MC, + checkpoint_filename=VAP.name, encoder="mimi", encoder_precision="fp32", + channels={"0": "recorded user", "1": "recorded robot"}, sample_rate=16000, + frame_rate=12.5, frame_ms=FRAME_MS, context_ms=20000, seed=42, use_kv_cache=True, + weights=manifest, label_sha256=sha(LABELS), history_sha256=sha(HISTORY), + uv_lock_sha256=sha(HERE/"uv.lock"), script_sha256=sha(HERE/"run_mimi.py"), + maai_encoder_source_sha256=sha(Path(maai.encoder.__file__)), + maai_model_source_sha256=sha(Path(maai.model.__file__))) + + +def references(prov): + directory = HERE/"results/user-only" + summary = json.loads((directory/"summary.json").read_text()) + source = summary["provenance"] + for key in ("label_sha256", "history_sha256", "uv_lock_sha256"): + assert source[key] == prov[key], f"Stale CPC reference: {key}; rerun run_user_only.py all" + return {(r["call_id"], int(r["decision_ms"])): r for r in read(directory/"predictions.csv")}, source, sha(directory/"predictions.csv") + + +def main(mode, variant): + global MC, VAP, OUT + MC = variant == 'mc' + VAP = MODELS / f"vap_{'mc_' if MC else ''}mimi_state_dict_ch_kyoto_12.5hz_20000msec.pt" + OUT = HERE / ('results/mimi-mc' if MC else 'results/mimi-standard') + manifest = validate_weights() + prov = provenance(manifest) + labels = read(LABELS) + annotated = [r for r in labels if r["can_reply_now"] in ("0", "1")] + assert annotated and len({(r['call_id'],r['decision_ms']) for r in labels}) == len(labels) + audio_cache, audio_hashes = {}, {} + for call in sorted({r["call_id"] for r in labels}): + path = ROOT/"data/韩泰机器人抢话/音频"/f"{call}.mp3" + pcm, sr = sf.read(path, dtype="float32", always_2d=True) + assert pcm.shape[1] == 2 + audio_cache[call] = pcm, sr + audio_hashes[call] = sha(path) + began = time.perf_counter() + first = annotated[0] + checks = witness(*audio_cache[first["call_id"]], int(first["decision_ms"])) + if mode == "smoke": + print(json.dumps(dict(witness=checks, provenance=prov), ensure_ascii=False, indent=2)) + return + baseline, source_prov, baseline_hash = references(prov) + assert source_prov["audio_sha256"] == audio_hashes, "CPC reference uses different source recordings" + OUT.mkdir(parents=True, exist_ok=True) + rows = [] + for index, label in enumerate(annotated): + call, decision = label['call_id'], int(label['decision_ms']) + segment, origin = prepare(*audio_cache[call], decision) + values, timing = replay(segment) + value = score(values) + other = baseline[(call, decision)] + assert other['can_reply_now'] == label['can_reply_now'] + row = dict(label) + row.update(can_reply_score=value, prediction_05=int(value > .5), correct_05=int(int(value > .5) == int(label['can_reply_now'])), + p_now_user=float(values['p_now'][0]), p_now_robot=float(values['p_now'][1]), + p_future_user=float(values['p_future'][0]), p_future_robot=float(values['p_future'][1]), + vad_user=float(values['vad'][0]), vad_robot=float(values['vad'][1]), + cpc_mc_stereo_score=float(other['stereo_can_reply_score']), cpc_mc_user_only_score=float(other['can_reply_score']), + audio_start_ms=origin, audio_end_ms=decision, context_ms=decision-origin, **timing) + rows.append(row) + print(f"{index+1}/{len(annotated)} {call[:8]} {decision}: label={label['can_reply_now']}, Mimi={value:.4f}, CPC-mc={float(other['stereo_can_reply_score']):.4f}", flush=True) + thresholds = [dict(threshold=t, **metrics(rows, 'can_reply_score', t)) for t in (.11, .5, .7, .8, .9, .95, .98)] + summary = dict(model=f"VAP / MaAI Mimi {variant} (both speakers)", created_at=datetime.now(timezone.utc).isoformat(), + total_points=len(rows), main_rule="1 - p_now[user] > 0.5", main_metrics=metrics(rows,'can_reply_score'), + cpc_mc_stereo_reference=metrics(rows,'cpc_mc_stereo_score'), cpc_mc_user_only_reference=metrics(rows,'cpc_mc_user_only_score'), + thresholds=thresholds, per_call={c:metrics([r for r in rows if r['call_id']==c],'can_reply_score') for c in audio_cache}, + changed_vs_cpc_mc_stereo=dict(fixed=sum((r['cpc_mc_stereo_score']>.5)!=bool(int(r['can_reply_now'])) and bool(r['correct_05']) for r in rows), + regressed=sum((r['cpc_mc_stereo_score']>.5)==bool(int(r['can_reply_now'])) and not r['correct_05'] for r in rows)), + annotation_sources=dict(Counter(r['annotator'] or 'unspecified' for r in rows)), + provenance={**prov, 'audio_sha256':audio_hashes, 'cpc_reference_predictions_sha256':baseline_hash}, witness=checks, + method=f"{variant} (mc={MC}) ch_kyoto VAP with official FP32 ONNX Mimi encoder. Recorded user+robot stereo; crop preceding <=20s before 16kHz resampling; 80ms native frames, final frame ends at exact decision. Reset Mimi and VAP state per point, retain official skipped startup encoder output. No future input, annotation text, external end signal, silence gate or forced timeout. Score=1-p_now[user], not calibrated EOT probability.", + limitations=["Comparison changes encoder/checkpoints and native framing; no held-out benchmark or claim that differences are caused by the encoder alone.", + "Both models use <=20s histories but native 80ms/100ms framing may differ at recording start by less than one frame.", + "Continuous full-call states differ from independent point replay; checkpoint scores need not lie on the continuous curve.", + "Replay computation includes the entire history; it is not one-step online update latency."]) + if mode == "all": + calls = [] + for call,(pcm,sr) in audio_cache.items(): + points = continuous(pcm,sr) + cs = sorted([r for r in rows if r['call_id']==call],key=lambda r:int(r['decision_ms'])) + calls.append(dict(call_id=call,audio_path=f'韩泰机器人抢话/音频/{call}.mp3',audio_sha256=audio_hashes[call], + duration_ms=round(len(pcm)*1000/sr,3),points=points, + checkpoints=[[int(r['decision_ms']),round(r['can_reply_score'],7)] for r in cs])) + print(f"{call[:8]} timeline: {len(points)} scores, {len(cs)} checkpoints",flush=True) + summary['timeline_points'] = sum(len(c['points']) for c in calls) + exported = dict(schema_version=1,model='maai_mimi_mc' if MC else 'maai_mimi_standard', + name='VAP / MaAI Mimi（降噪双声道）' if MC else 'VAP / MaAI Mimi（标准双声道）', + score='1 - p_now[user]',sample_interval_ms=FRAME_MS,method=summary['method'], + provenance=summary['provenance'],calls=calls) + assert prov['label_sha256']==sha(LABELS) and prov['history_sha256']==sha(HISTORY),'Annotations changed during inference' + write(OUT/'predictions.csv',rows) + errors = [r for r in rows if not r['correct_05']] + if errors:write(OUT/'errors.csv',errors) + else:(OUT/'errors.csv').unlink(missing_ok=True) + write(OUT/'thresholds.csv',thresholds) + summary['elapsed_seconds'] = time.perf_counter()-began + if mode=='all':(OUT/'score-timeline.json').write_text(json.dumps(exported,ensure_ascii=False,separators=(',',':'))+'\n') + (OUT/'summary.json').write_text(json.dumps(summary,ensure_ascii=False,indent=2)+'\n') + print(json.dumps({k:summary[k] for k in ('main_metrics','cpc_mc_stereo_reference','changed_vs_cpc_mc_stereo','elapsed_seconds')},ensure_ascii=False,indent=2)) + if mode=='all': + from evaluate_streaming import refresh_streaming + streaming, _ = refresh_streaming('maai_mimi_mc' if MC else 'maai_mimi_standard') + print('PRIMARY streaming metrics:',json.dumps(streaming['main_metrics']),flush=True) + + +if __name__=='__main__': + parser=argparse.ArgumentParser(description=__doc__) + parser.add_argument('mode',choices=('smoke','evaluate','all')) + parser.add_argument('--variant',choices=('standard','mc','both'),default='standard') + args=parser.parse_args() + torch.set_num_threads(4) + torch.set_num_interop_threads(1) + for selected in (('standard','mc') if args.variant=='both' else (args.variant,)): + main(args.mode,selected) diff --git a/experiments/maai-ch-kyoto/run_user_only.py b/experiments/maai-ch-kyoto/run_user_only.py new file mode 100644 index 0000000..e7502ae --- /dev/null +++ b/experiments/maai-ch-kyoto/run_user_only.py @@ -0,0 +1,247 @@ +"""Evaluate VAP with channel 0=user and channel 1=zeros; export Studio scores. + +Reuse the locked MaAI environment/weights. Independent point replays and the +continuous timeline are separate experiments. Never use annotation text as input. +""" +import argparse +from collections import Counter +from datetime import datetime, timezone +import hashlib +import importlib.metadata as metadata +import json +import platform +import random +import sys +import time + +from smoke_run import HERE, ROOT, EXPECTED, WEIGHTS, CPC, np, sf, soxr, torch +from evaluate_can_reply import model, metrics, read, write + +OUT = HERE / "results/user-only" +LABELS = ROOT / "labels/EOT标注/工作数据/eot_labels.csv" +HISTORY = LABELS.with_name("asr_history.csv") +SEED = 42 +SCORE = "can_reply_score" + + +def sha(path): + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def seed(): + random.seed(SEED) + np.random.seed(SEED) + torch.manual_seed(SEED) + + +def channels(pcm, robot_silent): + if pcm.ndim != 2 or pcm.shape[1] != 2: + raise ValueError("Expected channel 0=user, channel 1=robot") + if robot_silent: + # Do not mix, duplicate user audio, remove pauses, or alter source PCM. + result = np.zeros(pcm.shape, dtype=np.float32) + result[:, 0] = pcm[:, 0] + return result + return np.ascontiguousarray(pcm, dtype=np.float32) + + +def point_audio(pcm, sample_rate, decision_ms, robot_silent): + duration = min(20000, decision_ms // 100 * 100) + if duration <= 0: + raise ValueError("Point has less than 100 ms of history") + origin = decision_ms - duration + start, end = origin * sample_rate // 1000, decision_ms * sample_rate // 1000 + if not 0 <= start < end <= len(pcm): + raise ValueError("Point outside recording") + # Crop received history BEFORE resampling, then mute before any model step. + segment = channels(pcm[start:end], robot_silent) + segment = np.ascontiguousarray(soxr.resample(segment, sample_rate, 16000, quality="HQ"), dtype=np.float32) + assert len(segment) == duration // 100 * 1600 + if robot_silent: + assert np.count_nonzero(segment[:, 1]) == 0 + return segment, origin + + +def replay(segment): + seed() + maai = model() # Reset both the CPC recurrent state and VAP KV cache. + maai.CALC_PROCESS_TIME_INTERVAL = 10**9 + began = time.perf_counter() + for start in range(0, len(segment), 1600): + frame = segment[start:start + 1600] + assert len(frame) == 1600 + maai.process(np.ascontiguousarray(frame[:, 0]), np.ascontiguousarray(frame[:, 1])) + result = maai.result_dict_queue.get_nowait() + assert maai.result_dict_queue.empty() + elapsed = (time.perf_counter() - began) * 1000 + values = {key: np.asarray(result[key], dtype=float).reshape(-1) for key in ("p_now", "p_future", "vad")} + for value in values.values(): + assert value.shape == (2,) and np.isfinite(value).all() + assert ((value >= 0) & (value <= 1)).all() + return values, elapsed + + +def score(values): + # Match the existing Studio score exactly. Speaker outputs have a small + # normalization epsilon, so 1-p_now[user] is not substituted by p_now[robot]. + return 1.0 - float(values["p_now"][0]) + + +def witness(pcm, sr, decision): + segment, _ = point_audio(pcm, sr, decision, True) + original_user = pcm[:, 0].copy() + changed = pcm.copy() + rng = np.random.default_rng(SEED) + changed[:, 1] = rng.normal(0, .1, len(changed)) + changed[decision * sr // 1000:, 0] = rng.normal(0, .1, len(changed) - decision * sr // 1000) + other, _ = point_audio(changed, sr, decision, True) + assert np.array_equal(segment, other), "Robot/future audio leaked into model input" + assert np.array_equal(pcm[:, 0], original_user), "Source user PCM was mutated" + a, _ = replay(segment) + b, _ = replay(other) + assert all(np.array_equal(a[k], b[k]) for k in a), "Seeded inference is not repeatable" + print(f"WITNESS CPU seed={SEED}: robot/future invariance and repeat passed; score={score(a):.7f}", flush=True) + return dict(seed=SEED, robot_and_future_invariance=True, repeat_identical=True, score=score(a)) + + +def timeline(pcm, sr): + seed() + maai = model() + maai.CALC_PROCESS_TIME_INTERVAL = 10**9 + resampler = soxr.ResampleStream(sr, 16000, 2, dtype="float32", quality="HQ") + pending = np.empty((0, 2), dtype=np.float32) + points = [] + for start in range(0, len(pcm), sr // 10): + end = min(len(pcm), start + sr // 10) + block = resampler.resample_chunk(channels(pcm[start:end], True), last=end == len(pcm)) + pending = np.concatenate([pending, block]) + latest = None + while len(pending) >= 1600: + frame, pending = pending[:1600], pending[1600:] + assert np.count_nonzero(frame[:, 1]) == 0 + maai.process(np.ascontiguousarray(frame[:, 0]), np.ascontiguousarray(frame[:, 1])) + result = maai.result_dict_queue.get_nowait() + assert maai.result_dict_queue.empty() + latest = 1.0 - float(result["p_now"][0]) + assert np.isfinite(latest) and 0 <= latest <= 1 + if latest is not None: + # Availability time includes streaming resampler buffering. No + # unreceived audio or trailing synthetic silence is sent to VAP. + points.append([round(end * 1000 / sr, 3), round(latest, 7)]) + return points + + +def provenance(): + return dict(machine=platform.platform(), hardware="Apple M4 Mac mini / 16 GB", python=sys.version, + environment="experiments/maai-ch-kyoto/.venv (uv.lock)", device="cpu", threads=4, + dependencies={p: metadata.version(p) for p in ("maai", "torch", "transformers", "numpy", "soundfile", "soxr")}, + sample_rate=16000, frame_ms=100, context_ms=20000, use_kv_cache=True, seed=SEED, + channel_mapping={"0": "original user", "1": "all-zero robot input"}, + weight_sha256=EXPECTED, label_sha256=sha(LABELS), history_sha256=sha(HISTORY), + uv_lock_sha256=sha(HERE / "uv.lock"), script_sha256=sha(HERE / "run_user_only.py"), + model_factory_sha256=sha(HERE / "evaluate_can_reply.py"), loader_sha256=sha(HERE / "smoke_run.py")) + + +def main(mode): + torch.set_num_threads(4) + torch.set_num_interop_threads(1) + for path in (WEIGHTS, CPC): + if sha(path) != EXPECTED[path.name]: + raise ValueError(f"Weight checksum mismatch: {path}") + labels = read(LABELS) + keys = [(r["call_id"], int(r["decision_ms"])) for r in labels] + assert len(set(keys)) == len(keys), "Duplicate annotation key" + annotated = [r for r in labels if r["can_reply_now"] in ("0", "1")] + if not annotated: + raise ValueError("No binary annotation points") + prov = provenance() + audio_cache, audio_hashes = {}, {} + for call in sorted({r["call_id"] for r in labels}): + path = ROOT / "data/韩泰机器人抢话/音频" / f"{call}.mp3" + pcm, sr = sf.read(path, dtype="float32", always_2d=True) + assert pcm.shape[1] == 2 + audio_cache[call] = pcm, sr + audio_hashes[call] = sha(path) + first = annotated[0] + began = time.perf_counter() + checks = witness(*audio_cache[first["call_id"]], int(first["decision_ms"])) + if mode == "smoke": + print(json.dumps(dict(witness=checks, provenance=prov), ensure_ascii=False, indent=2)) + return + OUT.mkdir(parents=True, exist_ok=True) + rows = [] + for index, label in enumerate(annotated): + call, decision = label["call_id"], int(label["decision_ms"]) + pcm, sr = audio_cache[call] + silent_input, origin = point_audio(pcm, sr, decision, True) + silent, elapsed = replay(silent_input) + stereo_input, stereo_origin = point_audio(pcm, sr, decision, False) + assert origin == stereo_origin and np.array_equal(silent_input[:, 0], stereo_input[:, 0]) + stereo, stereo_elapsed = replay(stereo_input) + value, baseline = score(silent), score(stereo) + row = dict(label) + row.update(can_reply_score=value, stereo_can_reply_score=baseline, + p_now_user=float(silent["p_now"][0]), p_now_robot=float(silent["p_now"][1]), + p_future_user=float(silent["p_future"][0]), p_future_robot=float(silent["p_future"][1]), + vad_user=float(silent["vad"][0]), vad_robot=float(silent["vad"][1]), + prediction_05=int(value > .5), correct_05=int(int(value > .5) == int(label["can_reply_now"])), + stereo_prediction_05=int(baseline > .5), + audio_start_ms=origin, audio_end_ms=decision, context_ms=decision-origin, + robot_input_peak_abs=float(np.max(np.abs(silent_input[:, 1]))), + replay_inference_ms=elapsed, stereo_replay_inference_ms=stereo_elapsed) + rows.append(row) + print(f"{index+1}/{len(annotated)} {call[:8]} {decision}: label={label['can_reply_now']}, user_only={value:.4f}, stereo={baseline:.4f}", flush=True) + # Guard against labels/history being edited during the run. + assert prov["label_sha256"] == sha(LABELS) and prov["history_sha256"] == sha(HISTORY) + write(OUT / "predictions.csv", rows) + # write() intentionally skips empty lists; remove any stale errors instead. + errors = [r for r in rows if not r["correct_05"]] + if errors: + write(OUT / "errors.csv", errors) + else: + (OUT / "errors.csv").unlink(missing_ok=True) + thresholds = [dict(threshold=t, **metrics(rows, SCORE, t)) for t in (.11, .5, .7, .8, .9, .95, .98)] + write(OUT / "thresholds.csv", thresholds) + summary = dict(model="VAP / MaAI (user only, silent robot)", total_points=len(rows), + created_at=datetime.now(timezone.utc).isoformat(), main_rule="1 - p_now[user] > 0.5", + main_metrics=metrics(rows, SCORE), thresholds=thresholds, + same_point_stereo_reference=metrics(rows, "stereo_can_reply_score"), + changed_vs_stereo=dict(fixed=sum(r["stereo_prediction_05"] != int(r["can_reply_now"]) and bool(r["correct_05"]) for r in rows), + regressed=sum(r["stereo_prediction_05"] == int(r["can_reply_now"]) and not r["correct_05"] for r in rows)), + per_call={call: metrics([r for r in rows if r["call_id"] == call], SCORE) for call in audio_cache}, + annotation_sources=dict(Counter(r["annotator"] or "unspecified" for r in rows)), + skipped_unlabeled=len(labels)-len(annotated), witness=checks, + provenance={**prov, "audio_sha256": audio_hashes}, + method="At each exact point, independently reset CPC/VAP and replay <=20s in 100ms frames. User channel unchanged; robot channel zero for the full context before resampling. Also rerun the stereo control on identical current points. No annotation transcripts, future audio, end signal, silence gate, or timeout. Score is an activity-projection proxy, not calibrated EOT probability.", + limitations=["Exploratory selected-point evaluation, not a held-out benchmark or endpointing-policy evaluation.", + "Continuous full-call KV/CPC state differs from independent point replay; curve and checkpoint scores may differ.", + "Zeroing the robot channel cannot remove robot leakage already present in the user channel.", + "Stereo reference here is rerun at current annotation points; existing legacy frontend checkpoints are preserved separately."]) + if mode == "all": + calls = [] + for call, (pcm, sr) in audio_cache.items(): + points = timeline(pcm, sr) + call_rows = sorted([r for r in rows if r["call_id"] == call], key=lambda r: int(r["decision_ms"])) + calls.append(dict(call_id=call, audio_path=f"韩泰机器人抢话/音频/{call}.mp3", audio_sha256=audio_hashes[call], + duration_ms=round(len(pcm)*1000/sr, 3), points=points, + checkpoints=[[int(r["decision_ms"]), round(r[SCORE], 7)] for r in call_rows])) + print(f"{call[:8]} timeline: {len(points)} scores, {len(call_rows)} checkpoints", flush=True) + assert prov["label_sha256"] == sha(LABELS) and prov["history_sha256"] == sha(HISTORY) + exported = dict(schema_version=1, model="maai_user_only", name="VAP / MaAI（仅用户）", score="1 - p_now[user]", + sample_interval_ms=100, method="Continuous user + silent-robot stereo input, 16kHz, 10Hz, 20s KV cache. Robot channel zeroed before causal streaming resampling. Timestamps include resampling buffering. Checkpoints are independent <=20s replays.", + provenance=summary["provenance"], calls=calls) + (OUT / "score-timeline.json").write_text(json.dumps(exported, ensure_ascii=False, separators=(",", ":")) + "\n") + summary["timeline_points"] = sum(len(c["points"]) for c in calls) + summary["elapsed_seconds"] = time.perf_counter()-began + (OUT / "summary.json").write_text(json.dumps(summary, ensure_ascii=False, indent=2) + "\n") + print(json.dumps({k: summary[k] for k in ("main_metrics", "same_point_stereo_reference", "changed_vs_stereo", "elapsed_seconds")}, ensure_ascii=False, indent=2)) + if mode == "all": + from evaluate_streaming import refresh_streaming + streaming, _ = refresh_streaming("maai_user_only") + print('PRIMARY streaming metrics:', json.dumps(streaming['main_metrics']), flush=True) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("mode", choices=("smoke", "evaluate", "all")) + main(parser.parse_args().mode) diff --git a/experiments/maai-ch-kyoto/tests/test_streaming_scores.py b/experiments/maai-ch-kyoto/tests/test_streaming_scores.py new file mode 100644 index 0000000..9a632e3 --- /dev/null +++ b/experiments/maai-ch-kyoto/tests/test_streaming_scores.py @@ -0,0 +1,34 @@ +import unittest + +from evaluate_streaming import classification, last_available + + +class CausalStreamingScores(unittest.TestCase): + def test_startup_future_and_stale_outputs_are_unavailable(self): + points = [[100, .2], [200, .9], [900, .3]] + self.assertIsNone(last_available(points, 99, 200)) + self.assertEqual(last_available(points, 150, 200), [100, .2]) + self.assertEqual(last_available(points, 200, 200), [200, .9]) + self.assertEqual(last_available(points, 400, 200), [200, .9]) + self.assertIsNone(last_available(points, 401, 200)) + self.assertEqual(last_available(points, 1000, 200), [900, .3]) + + def test_missing_outputs_do_not_count_as_correct_negative_predictions(self): + rows = [dict(can_reply_now='0', status='unavailable', score=''), + dict(can_reply_now='0', status='available', score=.7), + dict(can_reply_now='1', status='available', score=.8), + dict(can_reply_now='', status='available', score=.1)] + result = classification(rows) + self.assertEqual((result['n'], result['requested_labeled'], result['unavailable_labeled']), (2, 3, 1)) + self.assertEqual((result['tn'], result['fp'], result['fn'], result['tp']), (0, 1, 0, 1)) + self.assertEqual(result['accuracy'], .5) + + def test_threshold_is_strict_and_empty_scores_have_no_accuracy(self): + self.assertEqual(classification([dict(can_reply_now='1',status='available',score=.5)])['fn'], 1) + result = classification([dict(can_reply_now='0',status='unavailable',score='')]) + self.assertEqual(result['n'], 0) + self.assertIsNone(result['accuracy']) + + +if __name__ == '__main__': + unittest.main() diff --git a/experiments/maai-ch-kyoto/user-only-env-spec.json b/experiments/maai-ch-kyoto/user-only-env-spec.json new file mode 100644 index 0000000..7e103d0 --- /dev/null +++ b/experiments/maai-ch-kyoto/user-only-env-spec.json @@ -0,0 +1,20 @@ +{ + "base": "macOS arm64 CPU + Python 3.11.15", + "environment": "experiments/maai-ch-kyoto/.venv", + "manager": "uv", + "pyproject_sha256": "c780e57c88a01b2ea96875cea94b2fee56e3ecf1a1d95ad1a3c6c302fce7c191", + "uv_lock_sha256": "6863631a5969e0f077d8aefc4335532952fe95486598b609e832e36d5ce2777f", + "resource": { + "device": "cpu", + "threads": 4, + "interop_threads": 1 + }, + "weights": { + "vap_mc_state_dict_ch_kyoto_10hz_20000msec.pt": "65b8a51aea6a257b5b9139107f0f0cf22f94523cd4b6a2e7a356b6c43a4596a8", + "60k_epoch4-d0f474de.pt": "d0f474de968c7243c20f43dee1626681618f2f150d3e638e82b79e22563b17f1" + }, + "smoke": { + "cmd": "uv run --locked python run_user_only.py smoke", + "expect": "WITNESS CPU seed=42" + } +} diff --git a/experiments/maai-ch-kyoto/仅用户评测说明.md b/experiments/maai-ch-kyoto/仅用户评测说明.md new file mode 100644 index 0000000..01fbdf1 --- /dev/null +++ b/experiments/maai-ch-kyoto/仅用户评测说明.md @@ -0,0 +1,67 @@ +# VAP / MaAI：机器人输入静音的用户语音评测 + +## 当前主评测口径：流式场景（2026-10-28） + +本场景以 VAP 连续流式推理为主：从录音开始持续维护编码器和 VAP 状态，在每个当前标注 `decision_ms`，取可用时间不晚于它的最后一个分数。判断点不重置模型、不插值、不使用未来音频。分数为 `1 - p_now[user]`，预测规则为严格 `> 0.5`；这是活动预测到可接话的映射，不是原生校准 EOT 概率。 + +前端圆点、悬停分数和准确率统一使用这份连续输出。时间戳包括流式重采样缓冲；没有输出或分数年龄超过两倍标称更新间隔时记为不可用，单独计数，不填 0。保存新增或移动的标注后，用已有真实连续记录重新取点，无需重跑模型： + +```bash +cd experiments/maai-ch-kyoto +uv run --locked python evaluate_streaming.py all +``` + +| 配置 | 当前主结果目录 | 正确数 / 有效点 | 准确率 | FP / FN | +| --- | --- | --- | --- | --- | +| CPC 降噪双声道 | `results/streaming/cpc-stereo/` | 30 / 44 | 68.18% | 7 / 7 | +| CPC 降噪机器人静音 | `results/streaming/cpc-user-only/` | 24 / 44 | 54.55% | 18 / 2 | +| Mimi 标准双声道 | `results/streaming/mimi-standard/` | 31 / 44 | 70.45% | 5 / 8 | +| Mimi 降噪双声道 | `results/streaming/mimi-mc/` | 34 / 44 | 77.27% | 5 / 5 | + +本次 44 点均有可用输出，来自同六通录音、22 个可接话与 22 个不可接话标签。每个目录包含 `predictions.csv`、`summary.json` 和前端 `score-timeline.json`；CSV 记录判断时间、实际评分时间及分数年龄，JSON 保留源记录、标签和评估代码的哈希。总表为 `results/streaming/comparison-summary.json`，文字说明为 `results/streaming/对比分析.txt`。 + +本次复用已有真实连续推理记录，没有新做模型推理或计算延迟实验。它仍是选定判断点上的分类评估，不等同于 LiveKit 的静音区间误抢话与结束延迟策略评估，也不代表完整在线系统的响应延迟。原始独立窗口结果保留作为历史诊断，不能与本表混用。重新运行原推理入口的 `all` 模式（CPC 双声道为 `export_timeline.py`）后会自动刷新对应流式目录；四配置对比统一用上述命令更新。 + +## 历史独立窗口及原始连续推理记录 + +以下记录说明原始实验、权重和独立窗口诊断。所述独立点评分及旧指标不是当前流式场景的主结果；前端已切换为上方流式目录。 + +本实验复用 MaAI `ch_kyoto` 的独立 uv 环境和同一组 VAP/CPC 权重，新增一个输入对照：保留索引 0 的用户声道，索引 1 的机器人声道全程填零。保留时间轴、停顿及原采样长度，不把用户语音复制到两路、不混音，也不修改源录音或人工标签。用户声道本身已有的机器人串音不会因另一声道置零而消失。 + +## 运行 + +在项目根目录执行： + +```bash +cd experiments/maai-ch-kyoto +uv run --locked python run_user_only.py smoke +uv run --locked python run_user_only.py all +``` + +已有 `.venv` 和权重直接复用，无需重新下载。`smoke` 执行真实判断点两次，验证固定种子下结果一致，并验证机器人声道、未来音频的变化不影响输入和结果；`evaluate` 只计算判断点；`all` 同时生成判断点评测和六通录音连续评分。 + +本次执行机器为本机 Apple M4 Mac mini、16 GB 内存，使用 CPU、4 个计算线程和 1 个 inter-op 线程。Python 3.11.15，MaAI 0.2.22，PyTorch 2.14.1，transformers 5.5.3；其余实际依赖、平台、权重 SHA256、uv.lock 和脚本哈希写入输出的 provenance。Python、NumPy 和 PyTorch 种子均为 42。 + +## 输入与评分 + +- 每个判断点独立重置 CPC/VAP，取判断前最多 20 秒历史，按 100 ms 对齐并保持末帧结束于精确 `decision_ms`；先裁取收到的音频，再置零机器人输入、重采样至 16 kHz。逐帧处理，不读取判断点之后的音频，不使用标注转写作为模型输入。 +- 主分数与现有前端一致，为 `1 - p_now[user]`，默认规则为分数严格大于 `0.5` 可以接话。`p_now` 是说话活动预测，该映射不是官方校准的 EOT 概率；不加入外部结束信号、VAD 静音门槛或超时放行。 +- 同时在**相同的当前判断点**重跑双声道对照，使用相同用户输入、20 秒窗口、分数映射和阈值。原有双声道结果及前端曲线保留，避免因当前标注点有调整而混用旧点评分。 +- 连续曲线按完整录音持续更新 CPC 和 20 秒 KV cache，机器人输入全程为零。使用流式重采样，时间戳包含重采样缓冲；录音尾部不补虚构静音。连续状态与独立窗口不同，因此空心判断点可能不落在连续曲线上。 +- 这是当前选定判断点上的探索性分类评估，不等同于 LiveKit 的区间误抢话和结束延迟策略评估。实际标签来源与音频哈希随结果保存。 + +## 输出与前端 + +输出位于本目录 `results/user-only/`： + +| 文件 | 内容 | +| --- | --- | +| `predictions.csv` | 当前判断点的仅用户分数、同点双声道对照、原生活动预测、窗口和重放计算耗时。 | +| `errors.csv` | 仅用户版本阈值 0.5 下的错误点；没有错误时不生成。 | +| `thresholds.csv` | 不同参考阈值下的分类指标，仅用于分析，不据此声称独立测试集最优效果。 | +| `summary.json` | 汇总、每通话指标、同点双声道对照、种子和输入不变性校验、来源哈希。 | +| `score-timeline.json` | 模型 ID 为 `maai_user_only` 的连续曲线与独立判断点评分。 | + +在工作台展开“模型分数对比”→“选择模型”→勾选 **VAP / MaAI（仅用户）**。橄榄绿色曲线支持独立显示、0.11 / 0.5 / 0.8 参考线、悬停分数和点击判断点。原紫色 VAP / MaAI 仍为双声道版。模型输入静音不改变页面播放的原始录音。 + +本次仅用户判断点来自运行时的“工作数据”；原有双声道曲线的空心点保留此前回测位置。严格同点比较使用本次 `predictions.csv` 的 `stereo_can_reply_score` 和 `summary.json` 的 `same_point_stereo_reference`。 diff --git a/labels/eot-studio/README.md b/labels/eot-studio/README.md index 2ef4285..2c9b6c2 100644 --- a/labels/eot-studio/README.md +++ b/labels/eot-studio/README.md @@ -26,17 +26,30 @@ npm run dev ## 模型分数对比 -点击时间轴上方的“模型分数对比”展开对比，默认收起。VAP / MaAI（紫色）、Smart Turn（青色）、Silero VAD（橙色）、LiveKit v1-mini（蓝色）和 TurnSense 1.0（玫红色）共用录音的时间刻度、缩放、播放头及判断点参考线。点击或拖动曲线区域定位播放，悬停查看当时已产生的分数；点击空心评分点定位到原标注时刻。顶部显示播放位置最近的有效评分，暂无评分时显示“—”，不将缺失数据当作 0。 +点击时间轴上方的“模型分数对比”展开对比，默认收起。VAP / MaAI（紫色）、Smart Turn（青色）、Silero VAD（橙色）、LiveKit v1-mini（蓝色）和 TurnSense 1.0（玫红色）共用录音的时间刻度、缩放、播放头及判断点参考线。点击或拖动曲线区域定位播放，悬停查看当时已产生的分数；点击空心评分点定位到判断时刻。顶部显示播放位置最近的有效评分，暂无评分时显示“—”，不将缺失数据当作 0。 + +新增橄榄绿色 **VAP / MaAI（仅用户）**，在“选择模型”中勾选。它复用相同 MaAI `ch_kyoto` 权重，保留用户声道，将机器人输入全程置零；连续曲线约每 100 ms 更新，空心点取当前判断时刻之前最后可用的连续流评分。分数仍为 `1 - p_now[user]`，可独立切换 0.11 / 0.5 / 0.8 参考线。原双声道版及页面播放录音保留；用户声道内已有的机器人串音不因此消失。当前流式结果位于 `experiments/maai-ch-kyoto/results/streaming/cpc-user-only/`，原始独立窗口结果保留在 `results/user-only/`，复现方式和同点双声道对照见[仅用户评测说明](../../experiments/maai-ch-kyoto/仅用户评测说明.md)。 + +另有 **VAP / MaAI Mimi（标准双声道）**（蓝灰色）和 **VAP / MaAI Mimi（降噪双声道）**（金棕色），均输入用户与机器人原双声道，使用 FP32 Mimi 编码器、12.5 Hz（80 ms）和 20 秒上下文；两版分别使用标准/降噪 VAP 权重。分数映射、独立开关及参考阈值与 CPC 版一致。当前流式结果分别位于 MaAI 目录的 `results/streaming/mimi-standard/` 和 `results/streaming/mimi-mc/`；原独立窗口结果保留在 `results/mimi-standard/` 和 `results/mimi-mc/`；复现、权重校验及比较边界见 [Mimi 评测说明](../../experiments/maai-ch-kyoto/Mimi评测说明.md)。 - 实线阶梯曲线来自对六通完整录音的真实推理，不是用44个点插值。MaAI每100ms输入一块音频，因流式重采样缓冲，部分评分间隔为200ms；Smart Turn每200ms计算一次，并包含原标注的精确时刻。 - MaAI使用双声道连续回放、20秒KV缓存，分数为 `1 - p_now[user]`，是接话候选分数，不是校准的EOT概率。Smart Turn沿用原回测的用户声道、过去ASR定位、500ms前置音频、3秒内续说及最多8秒窗口；没有额外的结束信号、VAD停止门槛或超时放行。 -- 空心点保留原44点的独立窗口回测分数。尤其MaAI的连续状态与独立窗口不同，空心点可能不落在连续曲线上。原始44点指标不会被这次时间序列覆盖。 +- VAP 的四个配置统一按流式场景评估：连续处理完整录音并维护模型状态；判断点、悬停分数和准确率均取判断时刻之前最后可用的输出，不在判断点重置或插值。时间戳包含流式重采样缓冲；尚未输出或分数年龄超过两倍标称更新间隔时显示不可用，并单独计数，不补成 0。空心点应落在阶梯线上。Smart Turn 的原判断点仍保留独立窗口结果。 - Silero VAD 为用户声道逐个完整 32 ms 帧的真实 ONNX 推理，使用 `1 - speech_probability`，仅为非发声基线，不是语义 EOT 概率。时间戳为帧末；空心点取原判断时刻最后可用帧，不读取未来音频。独立环境和复现方法见 [Silero 实验说明](../../experiments/silero-vad/README.md)。 -- 参考阈值只改变虚线位置，不修改人工标签。MaAI可选0.11、0.5、0.8，Smart Turn和Silero可选0.5、0.8。保存的分数不会随标注或转写编辑而重新计算。 +- 参考阈值只改变虚线位置，不修改人工标签。MaAI可选0.11、0.5、0.8，Smart Turn和Silero可选0.5、0.8。编辑不会重跑模型；新增或移动 VAP 判断点时，页面立即从已有连续曲线重新取点。磁盘上的统计需保存标注后运行下述流式评估命令更新。 - “选择模型”打开可搜索、按类型分组的多选面板，支持全选可用 / 清空；无评分模型保留在列表并说明状态。首次显示 VAP、Smart Turn、TurnSense 三条，之后记住显示选择和参考阈值，切换通话、收起和刷新均保留。紧凑图例只显示已选模型与当前分数，悬停或键盘聚焦会突出对应曲线。点击图例打开阈值和模型说明，可单独查看一个模型并恢复原选择。 - 仅在展开时读取当前项目录音的评分，并核对音频SHA256。更换为本地新录音、音频变化或没有对应推理数据时，不显示旧曲线。切换通话会取消旧请求，避免前一段数据覆盖当前段。 -评分数据位于各模型实验目录的 `results/score-timeline.json`。更新数据时，在两个各自的本机uv环境中运行： +VAP 前端读取 MaAI 目录的 `results/streaming/{cpc-stereo,cpc-user-only,mimi-standard,mimi-mc}/score-timeline.json`；其他模型沿用各自的 `results/score-timeline.json`。仅更新当前标注对应的 VAP 流式取点和统计，无需重跑模型： + +```bash +cd experiments/maai-ch-kyoto +uv run --locked python evaluate_streaming.py all +``` + +每点的 `decision_ms`、实际 `score_time_ms` 和 `score_age_ms` 记录在各配置 `predictions.csv`，四版汇总见 `results/streaming/comparison-summary.json` 和 `对比分析.txt`。MaAI 的 `export_timeline.py`、`run_user_only.py all`、`run_mimi.py all` 在重新生成连续推理后也会刷新对应配置的流式结果。原独立窗口文件保留作为另一评估口径的历史记录。 + +如需重新生成连续推理，在两个各自的本机 uv 环境中运行： ```bash cd experiments/maai-ch-kyoto diff --git a/labels/eot-studio/app/api/model-scores/route.ts b/labels/eot-studio/app/api/model-scores/route.ts index 47056f9..1d0d844 100644 --- a/labels/eot-studio/app/api/model-scores/route.ts +++ b/labels/eot-studio/app/api/model-scores/route.ts @@ -23,11 +23,11 @@ export async function GET(request: Request) { for (const source of sources) { try { // Fixed paths only: no request value is used to locate an export. - const file = path.join(root, "experiments", source.directory, "results", "score-timeline.json"); + const file = path.join(root, "experiments", source.directory, "results", source.scoreFile ?? "score-timeline.json"); const data = parseScoreExport(JSON.parse(await fs.readFile(file, "utf8")), source.model); const call = data.calls.find(item => item.call_id === callId && item.audio_path === recordingPath); if (call) payload.series.push({ ...call, model: data.model, name: data.name, - score: data.score, sample_interval_ms: data.sample_interval_ms }); + score: data.score, sample_interval_ms: data.sample_interval_ms, checkpoint_mode: data.checkpoint_mode }); else payload.messages.push(`${source.name} 暂无这段录音的评分。`); } catch (error) { if (!(error instanceof Error && "code" in error && error.code === "ENOENT")) { diff --git a/labels/eot-studio/components/model-score-track.tsx b/labels/eot-studio/components/model-score-track.tsx index 555f748..7916274 100644 --- a/labels/eot-studio/components/model-score-track.tsx +++ b/labels/eot-studio/components/model-score-track.tsx @@ -1,7 +1,7 @@ "use client"; import { useEffect, useMemo, useRef, useState, useId } from "react"; -import { scoreAt, scorePath, type ModelId, type ScoreResponse, type ScoreSeries } from "@/lib/model-scores"; +import { checkpointsForDisplay, scoreAt, scorePath, type ModelId, type ScoreResponse, type ScoreSeries } from "@/lib/model-scores"; import { createPortal } from "react-dom"; import { SCORE_MODELS, SCORE_MODEL_IDS, readScorePreferences } from "@/lib/score-models"; import type { TimelineAnnotation } from "./timeline"; @@ -169,7 +169,8 @@ export default function ModelScoreTrack({ series, width, durationMs, currentMs, }) { const [hoverMs, setHoverMs] = useState(null); const paths = useMemo(() => series.map(row => ({ ...row, - path: scorePath(row.points, durationMs, width, SCORE_HEIGHT, row.sample_interval_ms) })), [series, durationMs, width]); + checkpoints: checkpointsForDisplay(row, annotations.map(annotation => annotation.decision_ms)), + path: scorePath(row.points, durationMs, width, SCORE_HEIGHT, row.sample_interval_ms) })), [series, annotations, durationMs, width]); const y = (p: number) => 12 + (1 - p) * (SCORE_HEIGHT - 24); const x = (ms: number) => ms / durationMs * width; const inspect = hoverMs ?? currentMs; @@ -180,7 +181,7 @@ export default function ModelScoreTrack({ series, width, durationMs, currentMs, }; return
setHoverMs(null)}> { const bounds = event.currentTarget.getBoundingClientRect(); setHoverMs(Math.max(0, Math.min(durationMs, (event.clientX - bounds.left) / bounds.width * durationMs))); @@ -194,12 +195,12 @@ export default function ModelScoreTrack({ series, width, durationMs, currentMs, {row.checkpoints.map(([ms, score]) => event.stopPropagation()} onClick={event => { event.stopPropagation(); selectPoint(ms); }} onKeyDown={event => { if (event.key === "Enter" || event.key === " ") { event.preventDefault(); event.stopPropagation(); selectPoint(ms); } - }}>{row.name} · 原标注点评分 {score.toFixed(3)} · {time(ms)})} + }}>{row.name} · {row.checkpoint_mode === "streaming" ? "流式判断点" : "原标注点评分"} {score.toFixed(3)} · {time(ms)}{row.checkpoint_mode === "streaming" ? ` · 最近更新 ${time(scoreAt(row.points, ms, row.sample_interval_ms * 2)![0])}` : ""})} )} {hoverMs !== null && } diff --git a/labels/eot-studio/components/timeline.tsx b/labels/eot-studio/components/timeline.tsx index 8d6c6b9..8657234 100644 --- a/labels/eot-studio/components/timeline.tsx +++ b/labels/eot-studio/components/timeline.tsx @@ -345,7 +345,7 @@ export default function Timeline({
{scoresOpen &&
{scores.status === "loading" ? "正在读取模型分数…" : scores.status === "error" ? <>{scores.messages.join(" ")} - : <>实线：连续回放评分 · 空心点：原标注点评分 · 虚线：参考阈值。Smart Turn、LiveKit 表示完成倾向，TurnSense 为 P(complete)；VAP 使用 1 − 用户近期发声分数，Silero 使用 1 − 当前发声分数，仅为非发声基线，不是语义 EOT 概率。{scores.messages.join(" ")}} + : <>实线：连续回放评分 · 空心点：判断时评分（VAP 为流式取点） · 虚线：参考阈值。Smart Turn、LiveKit 表示完成倾向，TurnSense 为 P(complete)；VAP 使用 1 − 用户近期发声分数，Silero 使用 1 − 当前发声分数，仅为非发声基线，不是语义 EOT 概率。{scores.messages.join(" ")}}
}
@@ -474,7 +474,7 @@ export default function Timeline({
- {scoresOpen && scores.series.length > 0 &&

点击曲线定位录音；悬停查看分数。VAP 约每 100 ms、Smart Turn 每 200 ms、Silero 每 32 ms、LiveKit、TurnSense 每 200 ms 更新。TurnSense 使用过去 8 秒用户音频；LiveKit 使用过去 1.2 秒用户音频，中文参考阈值 0.355；Silero 空心点取判断前最后完整帧；VAP 和 Smart Turn 原标注点评分使用独立历史窗口，与连续曲线可能不同；TurnSense 的 0.5 参考线不等同于原生三分类取最大值规则；编辑标注或转写不会重算已有分数。

} + {scoresOpen && scores.series.length > 0 &&

点击曲线定位录音；悬停查看分数。VAP CPC 约每 100 ms，Mimi 标准与降噪版约每 80 ms；Smart Turn 每 200 ms、Silero 每 32 ms、LiveKit、TurnSense 每 200 ms 更新。原 VAP 为 CPC 降噪双声道，“仅用户”版将机器人输入全程置为静音；Mimi 两版均输入用户和机器人双声道，20 秒上下文。回放仍为原录音。TurnSense 使用过去 8 秒用户音频；LiveKit 使用过去 1.2 秒用户音频，中文参考阈值 0.355；Silero 空心点取判断前最后完整帧；VAP 各版本圆点与悬停分数均取判断前最后可用的连续流评分，圆点应落在对应阶梯曲线上；Smart Turn 的原判断点仍为独立窗口结果；TurnSense 的 0.5 参考线不等同于原生三分类取最大值规则；编辑标注或转写不会重跑模型；VAP 圆点随当前判断位置从连续曲线重新取点。

}
播放 {timeLabel(current, true)}
diff --git a/labels/eot-studio/lib/model-scores.ts b/labels/eot-studio/lib/model-scores.ts index 4a05897..08a3c38 100644 --- a/labels/eot-studio/lib/model-scores.ts +++ b/labels/eot-studio/lib/model-scores.ts @@ -1,4 +1,4 @@ -export type ModelId = "maai" | "smart_turn" | "silero_vad" | "livekit_mini" | "turnsense"; +export type ModelId = "maai" | "maai_user_only" | "maai_mimi_standard" | "maai_mimi_mc" | "smart_turn" | "silero_vad" | "livekit_mini" | "turnsense"; export type ScorePoint = [timeMs: number, score: number]; export interface ScoreCall { @@ -8,6 +8,7 @@ export interface ScoreCall { duration_ms: number; points: ScorePoint[]; checkpoints: ScorePoint[]; + checkpoint_sources?: [decisionMs: number, sourceTimeMs: number][]; } export interface ScoreExport { @@ -16,6 +17,7 @@ export interface ScoreExport { name: string; score: string; sample_interval_ms: number; + checkpoint_mode?: "streaming" | "independent_window"; calls: ScoreCall[]; } @@ -24,6 +26,7 @@ export interface ScoreSeries extends ScoreCall { name: string; score: string; sample_interval_ms: number; + checkpoint_mode?: "streaming" | "independent_window"; } export interface ScoreResponse { @@ -51,6 +54,7 @@ export function parseScoreExport(value: unknown, model: ModelId): ScoreExport { const data = value as ScoreExport; if (!data || data.schema_version !== 1 || data.model !== model || typeof data.name !== "string" || typeof data.score !== "string" + || (data.checkpoint_mode !== undefined && !["streaming", "independent_window"].includes(data.checkpoint_mode)) || !Number.isFinite(data.sample_interval_ms) || data.sample_interval_ms <= 0 || !Array.isArray(data.calls)) throw new Error("模型评分文件格式不正确。"); const calls = new Set(); @@ -61,11 +65,34 @@ export function parseScoreExport(value: unknown, model: ModelId): ScoreExport { || !validPoints(call.points, call.duration_ms) || !validPoints(call.checkpoints, call.duration_ms)) { throw new Error("模型评分包含重复时间、越界分数或缺失的录音信息。"); } + if (data.checkpoint_mode === "streaming") { + if (!Array.isArray(call.checkpoint_sources) || call.checkpoint_sources.length !== call.checkpoints.length) { + throw new Error("流式判断点缺少实际评分时刻。"); + } + call.checkpoints.forEach(([decision, score], index) => { + const point = scoreAt(call.points, decision, data.sample_interval_ms * 2); + const source = call.checkpoint_sources![index]; + if (!point || !Array.isArray(source) || source.length !== 2 || source[0] !== decision + || source[1] !== point[0] || score !== point[1]) { + throw new Error("流式判断点必须取判断前最后可用的曲线分数。"); + } + }); + } calls.add(call.call_id); } return data; } +/** Streaming markers follow current annotations, using the exact hover rule. */ +export function checkpointsForDisplay(series: ScoreSeries, decisionTimes: readonly number[]): ScorePoint[] { + if (series.checkpoint_mode !== "streaming") return series.checkpoints; + return [...new Set(decisionTimes)].sort((a, b) => a - b).flatMap(ms => { + if (!Number.isFinite(ms) || ms < 0 || ms > series.duration_ms) return []; + const point = scoreAt(series.points, ms, series.sample_interval_ms * 2); + return point ? [[ms, point[1]] as ScorePoint] : []; + }); +} + /** Only an already available sample may describe a playhead/hover position. */ export function scoreAt(points: readonly ScorePoint[], ms: number, maxAgeMs: number): ScorePoint | null { let low = 0, high = points.length - 1, found = -1; @@ -81,11 +108,13 @@ export function scorePath(points: readonly ScorePoint[], duration: number, width height: number, interval: number): string { const x = (t: number) => (t / duration * width).toFixed(2); const y = (p: number) => (12 + (1 - p) * (height - 24)).toFixed(2); - let previous: ScorePoint | null = null; - return points.map(point => { - const step = previous && point[0] - previous[0] <= interval * 2 - ? `H${x(point[0])}V${y(point[1])}` : `M${x(point[0])},${y(point[1])}`; - previous = point; - return step; + return points.map((point, index) => { + const previous = points[index - 1]; + const start = previous && point[0] - previous[0] <= interval * 2 + ? `V${y(point[1])}` : `M${x(point[0])},${y(point[1])}`; + // Match scoreAt's validity period, including the final sample and the + // valid beginning of a gap. Never draw across unavailable/stale time. + const until = Math.min(points[index + 1]?.[0] ?? duration, duration, point[0] + interval * 2); + return start + (until > point[0] ? `H${x(until)}` : ""); }).join(""); } diff --git a/labels/eot-studio/lib/score-models.ts b/labels/eot-studio/lib/score-models.ts index 727c44a..f8182a1 100644 --- a/labels/eot-studio/lib/score-models.ts +++ b/labels/eot-studio/lib/score-models.ts @@ -1,10 +1,13 @@ import type { ModelId } from "./model-scores"; export const SCORE_MODELS: Record = { - maai: { name: "VAP / MaAI", family: "活动预测", color: "#8866b2", directory: "maai-ch-kyoto", thresholds: [.11, .5, .8], defaultThreshold: .5, defaultVisible: true, description: "1 − 用户近期发声分数；约每 100 ms 更新，不是校准的 EOT 概率。" }, + maai: { name: "VAP / MaAI", family: "活动预测", color: "#8866b2", directory: "maai-ch-kyoto", scoreFile: "streaming/cpc-stereo/score-timeline.json", thresholds: [.11, .5, .8], defaultThreshold: .5, defaultVisible: true, description: "CPC 编码器、降噪版，用户与机器人双声道；流式评分，圆点取判断前最后可用分数。1 − 用户近期发声分数，约每 100 ms 更新，不是校准的 EOT 概率。" }, + maai_user_only: { name: "VAP / MaAI（仅用户）", family: "活动预测", color: "#6c8746", directory: "maai-ch-kyoto", scoreFile: "streaming/cpc-user-only/score-timeline.json", thresholds: [.11, .5, .8], defaultThreshold: .5, defaultVisible: false, description: "保留用户语音，机器人输入全程静音；流式评分，圆点取判断前最后可用分数。1 − 用户近期发声分数，约每 100 ms 更新，与双声道版使用相同权重。仅改变模型输入，回放仍为原录音；圆点取判断前最后可用的流式分数，不是校准的 EOT 概率。" }, + maai_mimi_standard: { name: "VAP / MaAI Mimi（标准双声道）", family: "活动预测", color: "#4b859f", directory: "maai-ch-kyoto", scoreFile: "streaming/mimi-standard/score-timeline.json", thresholds: [.11, .5, .8], defaultThreshold: .5, defaultVisible: false, description: "Mimi 编码器、标准版，用户与机器人双声道；流式评分，圆点取判断前最后可用分数。1 − 用户近期发声分数，每 80 ms 更新，20 秒上下文，不是校准的 EOT 概率。" }, + maai_mimi_mc: { name: "VAP / MaAI Mimi（降噪双声道）", family: "活动预测", color: "#a58537", directory: "maai-ch-kyoto", scoreFile: "streaming/mimi-mc/score-timeline.json", thresholds: [.11, .5, .8], defaultThreshold: .5, defaultVisible: false, description: "Mimi 编码器、降噪版，用户与机器人双声道；流式评分，圆点取判断前最后可用分数。1 − 用户近期发声分数，每 80 ms 更新，20 秒上下文，不是校准的 EOT 概率。" }, smart_turn: { name: "Smart Turn", family: "话轮检测", color: "#278d95", directory: "smart-turn-v3.2", thresholds: [.5, .8], defaultThreshold: .5, defaultVisible: true, description: "完成倾向；最近最多 8 秒用户音频，每 200 ms 更新。" }, silero_vad: { name: "Silero VAD", family: "活动预测", color: "#c47b32", directory: "silero-vad", thresholds: [.5, .8], defaultThreshold: .5, defaultVisible: false, description: "1 − 发声概率；每 32 ms 更新，仅为非发声基线，不是语义 EOT 概率。" }, livekit_mini: { name: "LiveKit v1-mini", family: "话轮检测", color: "#5378c4", directory: "livekit-v1-mini", thresholds: [.355, .5, .8], defaultThreshold: .355, defaultVisible: false, description: "P(turn ended)；最近 1.2 秒用户音频，每 200 ms 更新。0.355 为中文参考阈值。" }, diff --git a/labels/eot-studio/tests/model-scores.test.ts b/labels/eot-studio/tests/model-scores.test.ts index af0dbeb..b80a126 100644 --- a/labels/eot-studio/tests/model-scores.test.ts +++ b/labels/eot-studio/tests/model-scores.test.ts @@ -2,7 +2,7 @@ import assert from "node:assert/strict"; import test from "node:test"; import { readFile } from "node:fs/promises"; // @ts-expect-error Node's native TypeScript runner resolves explicit .ts imports. -import { parseScoreExport, scoreAt, scorePath } from "../lib/model-scores.ts"; +import { checkpointsForDisplay, parseScoreExport, scoreAt, scorePath, type ScoreSeries } from "../lib/model-scores.ts"; const fixture = () => ({ schema_version: 1, model: "maai", name: "MaAI", score: "1-p_now[user]", sample_interval_ms: 100, calls: [{ call_id: "demo", audio_path: "demo.mp3", audio_sha256: "a".repeat(64), duration_ms: 1000, @@ -33,20 +33,39 @@ test("playhead values never use a future sample or carry through missing data", test("curve gaps are not connected and steps remain on the audio clock", () => { const path = scorePath([[100, .2], [200, .9], [900, .3]], 1000, 500, 100, 100); - assert.equal(path, "M50.00,72.80H100.00V19.60M450.00,65.20"); + assert.equal(path, "M50.00,72.80H100.00V19.60H200.00M450.00,65.20H500.00"); +}); + +test("streaming markers use current decision times, never future or stale scores", () => { + const data = parseScoreExport(fixture(), "maai"); + const series = { ...data.calls[0], model: data.model, name: data.name, score: data.score, + sample_interval_ms: data.sample_interval_ms, checkpoint_mode: "streaming" as const }; + assert.deepEqual(checkpointsForDisplay(series, [99, 150, 150, 200, 400, 401, 1000]), + [[150, .2], [200, .9], [400, .9], [1000, .3]]); + assert.deepEqual(checkpointsForDisplay({ ...series, checkpoint_mode: undefined }, [150]), [[150, .8]]); + assert.equal(scorePath([[100, .2]], 1000, 1000, 100, 100), "M100.00,72.80H300.00"); +}); + +test("streaming exports reject independent, future or missing checkpoint sources", () => { + const data = { ...fixture(), checkpoint_mode: "streaming" }; + assert.throws(() => parseScoreExport(data, "maai"), /实际评分时刻/); + const valid = { ...data, calls: [{ ...data.calls[0], checkpoints: [[150, .2]], checkpoint_sources: [[150, 100]] }] }; + assert.equal(parseScoreExport(valid, "maai").calls[0].checkpoints[0][1], .2); + assert.throws(() => parseScoreExport({ ...valid, calls: [{ ...valid.calls[0], checkpoints: [[150, .8]] }] }, "maai"), /最后可用/); + assert.throws(() => parseScoreExport({ ...valid, calls: [{ ...valid.calls[0], checkpoint_sources: [[150, 200]] }] }, "maai"), /最后可用/); }); test("recorded inference exports agree on audio identity and retain all 44 checkpoints", async () => { const files = [ - ["maai", "maai-ch-kyoto"], ["smart_turn", "smart-turn-v3.2"], ["silero_vad", "silero-vad"], ["livekit_mini", "livekit-v1-mini"], ["turnsense", "turnsense-1.0"], + ["maai", "maai-ch-kyoto", "streaming/cpc-stereo/score-timeline.json"], ["maai_user_only", "maai-ch-kyoto", "streaming/cpc-user-only/score-timeline.json"], + ["maai_mimi_standard", "maai-ch-kyoto", "streaming/mimi-standard/score-timeline.json"], ["maai_mimi_mc", "maai-ch-kyoto", "streaming/mimi-mc/score-timeline.json"], + ["smart_turn", "smart-turn-v3.2", "score-timeline.json"], ["silero_vad", "silero-vad", "score-timeline.json"], + ["livekit_mini", "livekit-v1-mini", "score-timeline.json"], ["turnsense", "turnsense-1.0", "score-timeline.json"], ] as const; - const exports = await Promise.all(files.map(async ([model, directory]) => parseScoreExport( - JSON.parse(await readFile(new URL(`../../../experiments/${directory}/results/score-timeline.json`, import.meta.url), "utf8")), model))); + const exports = await Promise.all(files.map(async ([model, directory, file]) => parseScoreExport( + JSON.parse(await readFile(new URL(`../../../experiments/${directory}/results/${file}`, import.meta.url), "utf8")), model))); const identity = (data: typeof exports[number]) => data.calls.map(call => [call.call_id, call.audio_path, call.audio_sha256, call.duration_ms]); - assert.deepEqual(identity(exports[0]), identity(exports[1])); - assert.deepEqual(identity(exports[0]), identity(exports[2])); - assert.deepEqual(identity(exports[0]), identity(exports[3])); - assert.deepEqual(identity(exports[0]), identity(exports[4])); + for (const data of exports.slice(1)) assert.deepEqual(identity(exports[0]), identity(data)); for (const data of exports) { assert.equal(data.calls.reduce((n, call) => n + call.checkpoints.length, 0), 44); for (const call of data.calls) { @@ -56,6 +75,54 @@ test("recorded inference exports agree on audio identity and retain all 44 check } }); +test("all four VAP streaming exports, displayed circles and accuracy use the same past-only scores", async () => { + const sources = [["maai", "cpc-stereo"], ["maai_user_only", "cpc-user-only"], + ["maai_mimi_standard", "mimi-standard"], ["maai_mimi_mc", "mimi-mc"]] as const; + const annotationCsv = await readFile(new URL("../../EOT标注/工作数据/eot_labels.csv", import.meta.url), "utf8"); + // @ts-expect-error Node's native TypeScript runner resolves explicit .ts imports. + const { parseAnnotations } = await import("../lib/annotations.ts"); + const annotations = parseAnnotations(annotationCsv); + for (const [model, directory] of sources) { + const url = new URL(`../../../experiments/maai-ch-kyoto/results/streaming/${directory}/`, import.meta.url); + const data = parseScoreExport(JSON.parse(await readFile(new URL("score-timeline.json", url), "utf8")), model); + const summary = JSON.parse(await readFile(new URL("summary.json", url), "utf8")); + assert.equal(data.checkpoint_mode, "streaming"); + let correct = 0, count = 0; + for (const call of data.calls) { + const labels = annotations.filter(row => row.call_id === call.call_id); + const series: ScoreSeries = { ...call, model, name: data.name, score: data.score, + sample_interval_ms: data.sample_interval_ms, checkpoint_mode: data.checkpoint_mode }; + assert.deepEqual(checkpointsForDisplay(series, labels.map(row => row.decision_ms)), call.checkpoints); + for (const [ms, score] of call.checkpoints) { + assert.equal(scoreAt(call.points, ms, data.sample_interval_ms * 2)![1], score); + const label = labels.find(row => row.decision_ms === ms)!; + if (label.can_reply_now !== "") { count += 1; correct += Number(Number(score > .5) === Number(label.can_reply_now)); } + } + } + assert.equal(summary.main_metrics.n, count); + assert.equal(summary.main_metrics.correct, correct); + assert.equal(summary.main_metrics.accuracy, correct / count); + } +}); + +test("user-only VAP checkpoints use the current labels and the silent-robot experiment", async () => { + const load = async (file: string) => JSON.parse(await readFile(new URL( + `../../../experiments/maai-ch-kyoto/results/user-only/${file}`, import.meta.url), "utf8")); + const data = parseScoreExport(await load("score-timeline.json"), "maai_user_only"); + const summary = await load("summary.json"); + const annotationCsv = await readFile(new URL("../../EOT标注/工作数据/eot_labels.csv", import.meta.url), "utf8"); + // @ts-expect-error Node's native TypeScript runner resolves explicit .ts imports. + const { parseAnnotations } = await import("../lib/annotations.ts"); + const annotations = parseAnnotations(annotationCsv).filter(row => row.can_reply_now !== ""); + assert.equal(summary.witness.robot_and_future_invariance, true); + assert.equal(summary.witness.repeat_identical, true); + assert.equal(data.sample_interval_ms, 100); + const checkpoints = data.calls.flatMap(call => call.checkpoints.map(([ms]) => `${call.call_id}:${ms}`)).sort(); + assert.deepEqual(checkpoints, annotations.map(row => `${row.call_id}:${row.decision_ms}`).sort()); + assert.equal(summary.main_metrics.n, annotations.length); + assert.equal(summary.same_point_stereo_reference.n, annotations.length); +}); + test("Silero checkpoints use only the last available frame, and speech is inverted once", async () => { const data = parseScoreExport(JSON.parse(await readFile(new URL( "../../../experiments/silero-vad/results/score-timeline.json", import.meta.url), "utf8")), "silero_vad"); @@ -72,6 +139,31 @@ test("Silero checkpoints use only the last available frame, and speech is invert } }); +test("Mimi standard and MC use verified distinct checkpoints with the same causal stereo inputs", async () => { + const load = async (directory: string, file: string) => JSON.parse(await readFile(new URL( + `../../../experiments/maai-ch-kyoto/results/${directory}/${file}`, import.meta.url), "utf8")); + const standard = await load("mimi-standard", "summary.json"); + const mc = await load("mimi-mc", "summary.json"); + const checkpointKeys = []; + for (const [id, dir, summary] of [["maai_mimi_standard", "mimi-standard", standard], ["maai_mimi_mc", "mimi-mc", mc]] as const) { + const data = parseScoreExport(await load(dir, "score-timeline.json"), id); + assert.equal(data.sample_interval_ms, 80); + assert.equal(summary.witness.repeat_identical, true); + assert.equal(summary.witness.future_invariance, true); + assert.equal(summary.witness.both_channels_nonzero, true); + assert.deepEqual(summary.witness.loading_audit.missing_trainable_parameters, []); + assert.deepEqual(summary.witness.loading_audit.mismatched_parameters, []); + assert.equal(summary.provenance.channels["1"], "recorded robot"); + checkpointKeys.push(data.calls.flatMap(call => call.checkpoints.map(([ms]) => `${call.call_id}:${ms}`)).sort()); + } + assert.equal(standard.provenance.mc, false); + assert.equal(mc.provenance.mc, true); + assert.notEqual(standard.provenance.checkpoint_filename, mc.provenance.checkpoint_filename); + assert.equal(standard.provenance.label_sha256, mc.provenance.label_sha256); + assert.deepEqual(standard.provenance.audio_sha256, mc.provenance.audio_sha256); + assert.deepEqual(checkpointKeys[0], checkpointKeys[1]); +}); + test("LiveKit original checkpoints are real exact-time curve scores", async () => { const data = parseScoreExport(JSON.parse(await readFile(new URL( diff --git a/labels/eot-studio/tests/score-preferences.test.ts b/labels/eot-studio/tests/score-preferences.test.ts index 7eac19a..fe97f79 100644 --- a/labels/eot-studio/tests/score-preferences.test.ts +++ b/labels/eot-studio/tests/score-preferences.test.ts @@ -7,6 +7,8 @@ test("first visit starts with three comparison curves and each model's reference const defaults = readScorePreferences(null); assert.deepEqual(Object.entries(defaults.visibility).filter(([, value]) => value).map(([id]) => id), ["maai", "smart_turn", "turnsense"]); assert.equal(defaults.thresholds.livekit_mini, .355); + assert.equal(defaults.visibility.maai_user_only, false); + assert.equal(defaults.thresholds.maai_user_only, .5); }); test("saved choices preserve clear-all and accept only supported reference thresholds", () => { @@ -26,3 +28,22 @@ test("partial and invalid preferences leave future models on their defaults", () assert.equal("unknown" in saved.visibility, false); assert.deepEqual(readScorePreferences({ version: 999, visibility: { maai: false } }), readScorePreferences(null)); }); + +test("user-only VAP has independent visibility and thresholds alongside stereo VAP", () => { + const saved = readScorePreferences({ version: 1, visibility: { maai: false, maai_user_only: true }, + thresholds: { maai: .8, maai_user_only: .11 } }); + assert.equal(saved.visibility.maai, false); + assert.equal(saved.visibility.maai_user_only, true); + assert.equal(saved.thresholds.maai, .8); + assert.equal(saved.thresholds.maai_user_only, .11); +}); + +test("Mimi standard and noise-robust comparison settings are independent", () => { + const saved = readScorePreferences({ version: 1, + visibility: { maai_mimi_standard: true, maai_mimi_mc: false }, + thresholds: { maai_mimi_standard: .8, maai_mimi_mc: .11 } }); + assert.equal(saved.visibility.maai_mimi_standard, true); + assert.equal(saved.visibility.maai_mimi_mc, false); + assert.equal(saved.thresholds.maai_mimi_standard, .8); + assert.equal(saved.thresholds.maai_mimi_mc, .11); +});