记录三模型话轮检测实验、44点评估及延迟对比
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Xin Wang
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"""Evaluate MaAI activity-projection proxies at exact annotated media times.
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Each point uses a new model state and only preceding stereo audio, up to 20 s.
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Outputs are mapped to can_reply without any end-signal or timeout gating.
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"""
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import argparse
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from collections import Counter
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import contextlib
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import csv
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import hashlib
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import importlib.metadata
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import io
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import json
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from pathlib import Path
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import time
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from smoke_run import HERE, ROOT, WEIGHTS, CPC, EXPECTED, Maai, MaaiInput, np, sf, soxr, torch
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OUT = HERE / "results/work-data-can-reply"
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LABELS = ROOT / "labels/EOT标注/工作数据/eot_labels.csv"
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HISTORY = ROOT / "labels/EOT标注/工作数据/asr_history.csv"
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RULES = ("p_now_robot", "p_future_robot", "both_robot")
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def read(path):
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with path.open(encoding="utf-8-sig", newline="") as f:
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return list(csv.DictReader(f))
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def write(path, rows):
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if not rows:
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return
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with path.open("w", encoding="utf-8-sig", newline="") as f:
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writer = csv.DictWriter(f, fieldnames=list(rows[0]))
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writer.writeheader()
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writer.writerows(rows)
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def metrics(rows, score="p_now_robot", threshold=0.5):
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counts = Counter((int(r["can_reply_now"]), int(float(r[score]) > threshold))
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for r in rows if r["can_reply_now"] in ("0", "1"))
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tn, fp, fn, tp = counts[0, 0], counts[0, 1], counts[1, 0], counts[1, 1]
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total = tn+fp+fn+tp
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ratio = lambda n, d: n/d if d else None
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return dict(n=total, tn=tn, fp=fp, fn=fn, tp=tp,
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accuracy=ratio(tn+tp, total), false_reply_rate=ratio(fp, fp+tn),
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false_hold_rate=ratio(fn, fn+tp), recall=ratio(tp, tp+fn),
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precision=ratio(tp, tp+fp), f1=ratio(2*tp, 2*tp+fp+fn))
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def model():
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# New instance also resets CPC recurrent state, not just VAP's KV cache.
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with contextlib.redirect_stdout(io.StringIO()), torch.serialization.safe_globals([argparse.Namespace]):
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return Maai(mode="vap", lang="ch_kyoto", audio_ch1=MaaiInput.Chunk(),
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audio_ch2=MaaiInput.Chunk(), frame_rate=10, context_len_sec=20,
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device="cpu", model_type="normal", mc=True, use_kv_cache=True,
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cpc_model=str(CPC), local_model=str(WEIGHTS),
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cache_dir=str(HERE / ".cache/huggingface"))
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def main():
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torch.set_num_threads(4)
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torch.set_num_interop_threads(1)
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label_hash = hashlib.sha256(LABELS.read_bytes()).hexdigest()
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for path in (WEIGHTS, CPC):
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assert hashlib.sha256(path.read_bytes()).hexdigest() == EXPECTED[path.name]
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labels, history = read(LABELS), read(HISTORY)
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keys = [(r["call_id"], r["decision_ms"]) for r in labels]
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assert len(set(keys)) == len(keys)
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previous_path = ROOT / "experiments/smart-turn-v3.2/results/work-data-can-reply/predictions.csv"
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previous_summary = json.loads(previous_path.with_name("summary.json").read_text())
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if previous_summary["config"]["label_sha256"] != label_hash:
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raise ValueError("Smart Turn baseline labels differ from current labels; rerun that baseline first")
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previous = {(r["call_id"], r["decision_ms"]): r for r in read(previous_path)}
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audio_cache, rows, audio_hashes = {}, [], {}
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began = time.perf_counter()
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for index, label in enumerate(labels):
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call, decision = label["call_id"], int(label["decision_ms"])
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if call not in audio_cache:
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path = ROOT / "data/韩泰机器人抢话/音频" / f"{call}.mp3"
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audio, sr = sf.read(path, dtype="float32", always_2d=True)
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assert audio.shape[1] == 2
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audio_cache[call] = audio, sr
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audio_hashes[call] = hashlib.sha256(path.read_bytes()).hexdigest()
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audio, sr = audio_cache[call]
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# End aligned at decision_ms, so every point's final 100ms frame ends
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# exactly there. At file beginning drop <100ms from the leading edge.
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duration_ms = min(20000, decision // 100 * 100)
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if duration_ms == 0:
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raise ValueError(f"Insufficient history at {call} {decision}")
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origin = decision-duration_ms
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start, end = origin*sr//1000, decision*sr//1000
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assert 0 <= start < end <= len(audio)
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# Crop BEFORE resampling: resampler cannot observe future audio.
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segment = np.ascontiguousarray(soxr.resample(audio[start:end], sr, 16000, quality="HQ"), dtype=np.float32)
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count = duration_ms//100
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assert len(segment) == count*1600
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maai = model()
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maai.CALC_PROCESS_TIME_INTERVAL = 10**9
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inference_began = time.perf_counter()
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for frame in range(count):
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position = frame*1600
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maai.process(np.ascontiguousarray(segment[position:position+1600, 0]),
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np.ascontiguousarray(segment[position:position+1600, 1]))
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result = maai.result_dict_queue.get_nowait()
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assert maai.result_dict_queue.empty()
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inference_ms = (time.perf_counter()-inference_began)*1000
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values = {}
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for key in ("p_now", "p_future", "vad"):
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values[key] = np.asarray(result[key], dtype=float).reshape(-1)
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assert len(values[key]) == 2
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assert np.isfinite(values[key]).all() and ((values[key] >= 0) & (values[key] <= 1)).all()
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now, future = float(values["p_now"][1]), float(values["p_future"][1])
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expected = int(label["can_reply_now"])
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baseline = previous[(call, label["decision_ms"])]
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call_history = [r for r in history if r["call_id"] == call]
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agent_active = any(r["role"] == "agent" and int(r["start_ms"]) < decision < int(r["end_ms"]) for r in call_history)
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user_active = any(r["role"] == "user" and int(r["start_ms"]) < decision < int(r["end_ms"]) for r in call_history)
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r = dict(label)
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r.update(p_now_user=float(values["p_now"][0]), p_now_robot=now,
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p_future_user=float(values["p_future"][0]), p_future_robot=future,
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both_robot=min(now, future), vad_user=float(values["vad"][0]), vad_robot=float(values["vad"][1]),
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prediction_05=int(now > 0.5), correct_05=int(int(now > 0.5) == expected),
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prediction_08=int(now > 0.8), correct_08=int(int(now > 0.8) == expected),
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future_prediction_05=int(future > 0.5), both_prediction_05=int(min(now, future) > 0.5),
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smart_turn_probability=float(baseline["probability"]),
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smart_turn_prediction_05=int(baseline["prediction"]),
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smart_turn_prediction_08=int(float(baseline["probability"]) > 0.8),
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audio_start_ms=origin, audio_end_ms=decision, context_ms=duration_ms,
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frame_count=count, replay_inference_ms=inference_ms,
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asr_agent_active_at_point=int(agent_active), asr_user_segment_crosses_point=int(user_active),
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label_status="draft" if label["annotator"] == "xlsx_draft" else "unconfirmed_manual")
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rows.append(r)
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print(f"{index+1}/{len(labels)} {call[:8]} {decision}: label={expected}, p_now_robot={now:.4f}, p_future_robot={future:.4f}", flush=True)
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del maai
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OUT.mkdir(parents=True, exist_ok=True)
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write(OUT / "predictions.csv", rows)
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write(OUT / "errors.csv", [r for r in rows if not r["correct_05"]])
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thresholds = [dict(rule=rule, threshold=threshold, **metrics(rows, rule, threshold))
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for rule in RULES for threshold in (0.5, 0.7, 0.8, 0.9, 0.95, 0.98)]
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write(OUT / "thresholds.csv", thresholds)
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groups = {field: {value: metrics([r for r in rows if r[field] == value])
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for value in sorted({r[field] for r in rows})}
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for field in ("call_id", "reason", "label_status", "asr_agent_active_at_point")}
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summary = dict(total_points=len(rows), main_rule="p_now_robot > 0.5", main_metrics=metrics(rows),
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rule_metrics={rule: metrics(rows, rule) for rule in RULES},
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thresholds=thresholds, grouped=groups,
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smart_turn_comparison={str(t): metrics(rows, "smart_turn_probability", t) for t in (0.5, 0.8)},
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changed_vs_smart_turn=dict(
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fixed=sum(int(r["smart_turn_prediction_05"]) != int(r["can_reply_now"]) and r["correct_05"] for r in rows),
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regressed=sum(int(r["smart_turn_prediction_05"]) == int(r["can_reply_now"]) and not r["correct_05"] for r in rows)),
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config=dict(maai_version=importlib.metadata.version("maai"), torch_version=torch.__version__,
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device="cpu", threads=4, frame_rate=10, context_len_sec=20, use_kv_cache=True,
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weight_variant="vap_mc", weight_sha256=EXPECTED, label_sha256=label_hash,
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history_sha256=hashlib.sha256(HISTORY.read_bytes()).hexdigest(), audio_sha256=audio_hashes,
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script_sha256=hashlib.sha256(Path(__file__).read_bytes()).hexdigest()),
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elapsed_seconds=time.perf_counter()-began,
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method="At each exact decision_ms a fresh MaAI/CPC state replays a preceding <=20s stereo window in 100ms frames. Crop before resampling. No future input, no labels as model input, no EOT or VAD gates, no forced timeout replies. Classification is a stated proxy rule, not an official calibrated can_reply output.",
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limitations=["29 xlsx_draft and 15 rows without explicit verification metadata; exploratory selected-point scores.",
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"The proxy p_now[robot] forecasts relative speaker activity over 0–0.6s; p_future forecasts 0.6–2s. Neither directly predicts task completion.",
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"Independent 20s window restart differs from continuous whole-call state, particularly CPC recurrent state and deeply contextualized KV history.",
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"MaAI uses both recorded channels and up to20s; Smart Turn baseline uses user channel and up to8s. Comparison evaluates these input configurations, not identical-input model capability.",
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"Robot speech already present in recording can influence activity forecasts; agent-active checkpoints are separately marked.",
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"ASR alignment issues previously found at cb3238dc 13540 and e304f740 11531 remain; labels unchanged."])
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(OUT / "summary.json").write_text(json.dumps(summary, ensure_ascii=False, indent=2)+"\n")
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print(json.dumps({k:summary[k] for k in ("main_metrics", "rule_metrics", "changed_vs_smart_turn", "elapsed_seconds")}, ensure_ascii=False, indent=2))
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if __name__ == "__main__":
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main()
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