diff --git a/deliverables/model-curve-selector.png b/deliverables/model-curve-selector.png new file mode 100644 index 0000000..88dfdfa Binary files /dev/null and b/deliverables/model-curve-selector.png differ diff --git a/experiments/livekit-v1-mini/.gitignore b/experiments/livekit-v1-mini/.gitignore new file mode 100644 index 0000000..5a27dc3 --- /dev/null +++ b/experiments/livekit-v1-mini/.gitignore @@ -0,0 +1,3 @@ +.venv/ +__pycache__/ +cache/ diff --git a/experiments/livekit-v1-mini/.python-version b/experiments/livekit-v1-mini/.python-version new file mode 100644 index 0000000..2c07333 --- /dev/null +++ b/experiments/livekit-v1-mini/.python-version @@ -0,0 +1 @@ +3.11 diff --git a/experiments/livekit-v1-mini/README.md b/experiments/livekit-v1-mini/README.md new file mode 100644 index 0000000..b5cd9bf --- /dev/null +++ b/experiments/livekit-v1-mini/README.md @@ -0,0 +1,42 @@ +# LiveKit Turn Detector v1-mini 韩泰评估 + +执行机器：本机 Apple M4 Mac mini，CPU，16 GB。Python 3.11.15；独立 uv 环境。官方 PyPI `livekit-local-inference==0.2.7`（内嵌模型）与 `livekit-agents==1.8.8`，完整依赖见 uv.lock。 + +## 复现 + +从项目根目录执行： + +```bash +cd experiments/livekit-v1-mini +uv sync --locked +uv run --locked python run_experiment.py smoke +uv run --locked python run_experiment.py all +``` + +`smoke` 使用种子 42 生成固定输入，重复推理必须一致，输出 `WITNESS`；`all` 生成 44 点 predictions.csv、summary.json 和六通录音的 score-timeline.json。`evaluate` 只跑标注点。所有输出位于 results，不修改人工标注。 + +## 输入与评分 + +- 只用用户左声道 channel 0；先裁取判断时间之前的 int16 音频，除以 32768，再用 scipy resample_poly 重采样到 16 kHz，取最近 19200 样本（1.2 秒），乘以 32767 转为 int16。短窗口由原生模型左补零，不使用未来音频或人工转写。 +- 使用 Agents `_LocalTransport._predict` 入口，严格锁定 SDK 版本；原生异常或框架默认回退分数会导致实验失败，不计成真实预测。 +- 每 200 ms 及每个原标注精确时刻计算原始分数；不加 VAD 停止门槛、静音等待、超时放行、会话结束信号或前一预测状态。 +- 默认阈值为锁定 SDK 的中文阈值 0.355，同时报告 0.5，未在本批数据上搜索最优阈值。 +- 每个判断点重复推理并验证分数一致；将该点之后的音频改为零，验证输入不变。该方法的前缀重采样与官方 benchmark 的 librosa 或在线重采样存在实现差异，因此不称为完整官方在线策略复现。 +- 单个评估请求依次执行。脚本请求 EOT_PARALLELISM=1；当前原生库实际报告 parallelism=2，记录于结果，不声称原生单线程。OMP/MKL/OPENBLAS 请求线程数均为 1。 + +## 环境与模型校验 + +macOS arm64 CPython 3.11 官方 wheel SHA256：`fa4cb2d495c80be6121642dba4def3a88b22a68d52c1b0affccd66c563de6622`。 +原生二进制 SHA256：`7afa5899367957328c9916153b5d672392e191c0bfadf1f49325af2e4f3055b4`。模型嵌于二进制，未暴露独立权重文件，不虚构独立权重哈希。结果还记录 Python、依赖、脚本、标注及各录音哈希。环境规范见 env-spec.json。 + +## 限制与许可 + +这 44 个手选判断点仅作探索性对比，不是独立大样本泛化评估。点级误接话率不等于整段对话的抢话率，用户声道也可能存在机器人回声。模型分数和 SDK 完整终点策略须分开讨论。 + +当前 wheel 附带的 MODEL_LICENSE.md 要求模型配合 LiveKit Agents 使用，并限制用其输出改进其他模型；本实验通过 Agents 官方本地传输入口执行。网页仅查看分数；不将其用作其他模型的训练标签。不要将该模型描述为 Apache-2.0 权重。 + +官方参考： +- https://docs.livekit.io/agents/logic/turns/turn-detector/ +- https://github.com/livekit/eot-bench/blob/main/eot_harness/livekit_turn_detector_mini_adapter.py + +结果解读见 results/summary.json 和 results/评估说明.md。 diff --git a/experiments/livekit-v1-mini/env-spec.json b/experiments/livekit-v1-mini/env-spec.json new file mode 100644 index 0000000..2f44ecc --- /dev/null +++ b/experiments/livekit-v1-mini/env-spec.json @@ -0,0 +1,34 @@ +{ + "base": "macOS arm64 CPU + Python 3.11", + "system_pkgs": [], + "pip_phases": [ + [ + "livekit-local-inference==0.2.7", + "numpy==2.2.6", + "soundfile==0.13.1", + "scipy==1.15.3", + "livekit-agents==1.8.8" + ] + ], + "env": { + "OMP_NUM_THREADS": "1", + "OPENBLAS_NUM_THREADS": "1", + "MKL_NUM_THREADS": "1", + "EOT_PARALLELISM": "1" + }, + "resource": { + "machine": "local Mac mini M4", + "gpu": false, + "model_workers": 1 + }, + "weight_dirs": { + "eot": { + "source": "official PyPI livekit-local-inference wheel", + "gated": false + } + }, + "smoke": { + "cmd": "uv run --locked python run_experiment.py smoke", + "expect": "WITNESS" + } +} diff --git a/experiments/livekit-v1-mini/pyproject.toml b/experiments/livekit-v1-mini/pyproject.toml new file mode 100644 index 0000000..5cc16ce --- /dev/null +++ b/experiments/livekit-v1-mini/pyproject.toml @@ -0,0 +1,5 @@ +[project] +name = "livekit-v1-mini-evaluation" +version = "0.1.0" +requires-python = ">=3.11,<3.12" +dependencies = ["livekit-local-inference==0.2.7", 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a/experiments/livekit-v1-mini/results/run.log b/experiments/livekit-v1-mini/results/run.log new file mode 100644 index 0000000..a3d5f94 --- /dev/null +++ b/experiments/livekit-v1-mini/results/run.log @@ -0,0 +1,8 @@ +WITNESS CPU seeded probability=0.580093086, repeat=0.580093086, parallelism=2 +2c83253a: 489 scores, 6 checkpoints; repeat/future-suffix checks passed +40ae686c: 441 scores, 8 checkpoints; repeat/future-suffix checks passed +872a1e67: 379 scores, 9 checkpoints; repeat/future-suffix checks passed +a266b9ef: 421 scores, 5 checkpoints; repeat/future-suffix checks passed +cb3238dc: 478 scores, 9 checkpoints; repeat/future-suffix checks passed +e304f740: 365 scores, 7 checkpoints; repeat/future-suffix checks passed +{"threshold": 0.355, "n": 44, "tn": 6, "fp": 16, "fn": 2, "tp": 20, "accuracy": 0.5909090909090909, "false_reply_rate": 0.7272727272727273, "false_hold_rate": 0.09090909090909091} diff --git a/experiments/livekit-v1-mini/results/score-timeline.json b/experiments/livekit-v1-mini/results/score-timeline.json new file mode 100644 index 0000000..5eb733c --- /dev/null +++ b/experiments/livekit-v1-mini/results/score-timeline.json @@ -0,0 +1 @@ +{"schema_version":1,"model":"livekit_mini","name":"LiveKit v1-mini","score":"P(turn ended)","sample_interval_ms":200,"method":"Agents 1.8.8 local transport, native v1-mini 0.2.7. User channel 0; received int16 prefix /32768 cropped BEFORE scipy resample_poly to 16kHz; last 19200 samples converted with *32767. Native left-padding below 1.2s. No ASR, VAD gate, silence timeout or fallback scores. EOT_PARALLELISM=1. Prefix resampling differs from official librosa and online streaming resampling; not a full service replay.","provenance":{"machine":"macOS-26.4.1-arm64-arm-64bit","hardware":"Apple M4 Mac mini CPU, 16 GB","python":"3.11.15 (main, Mar 25 2026, 02:58:47) [Clang 22.1.1 ]","dependencies":{"livekit-local-inference":"0.2.7","livekit-agents":"1.8.8","numpy":"2.2.6","scipy":"1.15.3","soundfile":"0.13.1"},"native_binary_sha256":"7afa5899367957328c9916153b5d672392e191c0bfadf1f49325af2e4f3055b4","weight_identity":"Weights embedded in pinned official binary; separate hash not 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diff --git a/experiments/livekit-v1-mini/results/smoke.log b/experiments/livekit-v1-mini/results/smoke.log new file mode 100644 index 0000000..0cc2219 --- /dev/null +++ b/experiments/livekit-v1-mini/results/smoke.log @@ -0,0 +1,2 @@ +WITNESS CPU seeded probability=0.580093086, repeat=0.580093086, parallelism=2 +{"load_ms": 123.47654160112143, "provenance": {"machine": "macOS-26.4.1-arm64-arm-64bit", "hardware": "Apple M4 Mac mini CPU, 16 GB", "python": "3.11.15 (main, Mar 25 2026, 02:58:47) [Clang 22.1.1 ]", "dependencies": {"livekit-local-inference": "0.2.7", "livekit-agents": "1.8.8", "numpy": "2.2.6", "scipy": "1.15.3", "soundfile": "0.13.1"}, "native_binary_sha256": "7afa5899367957328c9916153b5d672392e191c0bfadf1f49325af2e4f3055b4", "weight_identity": "Weights embedded in pinned official binary; separate hash not exposed", "wheel_sha256": "fa4cb2d495c80be6121642dba4def3a88b22a68d52c1b0affccd66c563de6622", "native_parallelism": 2, "script_sha256": "e05a0408fcce2a8194966bf594d7324e17cd8025d0b7eb06152e58eb9b46f892", "annotation_sha256": "780e50f976daf039ab976ea155bdb5d1ca5fffc792f9db7adf8063ec3048ff8e"}} diff --git a/experiments/livekit-v1-mini/results/summary.json b/experiments/livekit-v1-mini/results/summary.json new file mode 100644 index 0000000..ac24401 --- /dev/null +++ b/experiments/livekit-v1-mini/results/summary.json @@ -0,0 +1,60 @@ +{ + "metrics": { + "threshold": 0.355, + "n": 44, + "tn": 6, + "fp": 16, + "fn": 2, + "tp": 20, + "accuracy": 0.5909090909090909, + "false_reply_rate": 0.7272727272727273, + "false_hold_rate": 0.09090909090909091 + }, + "reference_threshold_05": { + "threshold": 0.5, + "n": 44, + "tn": 11, + "fp": 11, + "fn": 4, + "tp": 18, + "accuracy": 0.6590909090909091, + "false_reply_rate": 0.5, + "false_hold_rate": 0.18181818181818182 + }, + "model": "LiveKit Turn Detector v1-mini", + "official_zh_threshold": 0.355, + "load_ms": 119.18720882385969, + "method": "Agents 1.8.8 local transport, native v1-mini 0.2.7. User channel 0; received int16 prefix /32768 cropped BEFORE scipy resample_poly to 16kHz; last 19200 samples converted with *32767. Native left-padding below 1.2s. No ASR, VAD gate, silence timeout or fallback scores. EOT_PARALLELISM=1. Prefix resampling differs from official librosa and online streaming resampling; not a full service replay.", + "provenance": { + "machine": "macOS-26.4.1-arm64-arm-64bit", + "hardware": "Apple M4 Mac mini CPU, 16 GB", + "python": "3.11.15 (main, Mar 25 2026, 02:58:47) [Clang 22.1.1 ]", + "dependencies": { + "livekit-local-inference": "0.2.7", + "livekit-agents": "1.8.8", + "numpy": "2.2.6", + "scipy": "1.15.3", + "soundfile": "0.13.1" + }, + "native_binary_sha256": "7afa5899367957328c9916153b5d672392e191c0bfadf1f49325af2e4f3055b4", + "weight_identity": "Weights embedded in pinned official binary; separate hash not exposed", + "wheel_sha256": "fa4cb2d495c80be6121642dba4def3a88b22a68d52c1b0affccd66c563de6622", + "native_parallelism": 2, + "script_sha256": "e05a0408fcce2a8194966bf594d7324e17cd8025d0b7eb06152e58eb9b46f892", + "annotation_sha256": "780e50f976daf039ab976ea155bdb5d1ca5fffc792f9db7adf8063ec3048ff8e" + }, + "preparation_ms": { + "p50": 5.839478690177202, + "p95": 9.578727371990682 + }, + "inference_ms": { + "p50": 6.118791177868843, + "p95": 7.3341685347259045 + }, + "limitations": [ + "44 selected points: exploratory, not held-out generalization.", + "Point false reply rate is not interval interruption rate; user channel may contain robot echo.", + "Raw zh threshold without production VAD/endpointing gates.", + "Wheel license restricts use to LiveKit Agents; do not use outputs to improve other models." + ] +} diff --git a/experiments/livekit-v1-mini/results/评估说明.md b/experiments/livekit-v1-mini/results/评估说明.md new file mode 100644 index 0000000..0276bbb --- /dev/null +++ b/experiments/livekit-v1-mini/results/评估说明.md @@ -0,0 +1,16 @@ +# LiveKit v1-mini 韩泰评估结果 + +本机 M4 CPU；6 通录音、44 个判断点，正负各 22。官方中文阈值来自锁定 SDK 的 LOCAL_LANGUAGES[zh]，未对本批数据搜索最优阈值。 + +| 阈值 | 正确 / 总数 | 准确率 | 误接话（应等待却接话） | 漏接话（应接话却等待） | +| --- | --- | --- | --- | --- | +| 0.355 | 26 / 44 | 59.09% | 16 / 22（72.73%） | 2 / 22（9.09%） | +| 0.5 | 29 / 44 | 65.91% | 11 / 22（50.00%） | 4 / 22（18.18%） | + +模型已加载后，框架本地推理调用（包含异步派发）中位数 6.12 ms、P95 7.33 ms；输入准备/前缀重采样中位数 5.84 ms、P95 9.58 ms。模型构造耗时 119.19 ms，不含 Python/Agents 导入，不是完整进程冷启动时间。原生库报告 parallelism=2，不能称为单线程。耗时不等于端点等待时间。 + +连续曲线每 200 ms 独立计算最近 1.2 秒窗口，并包含精确判断点，非插值生成。全部 44 点验证了重复结果一致，以及修改未来音频不改变输入。本实验读取当前标注；已有其他模型结果来自之前的标注快照，已发现第二通录音有一个判断点从旧结果的 48348 ms 移至当前 48536 ms，其余 43 点时间和标签一致。因此不能直接拿旧 44 点汇总作严格横向排名；旧曲线空心点仍保留原快照时刻。 + +中文默认阈值下，16 个应等待点误判，仍不能认为解决了韩泰抢话问题。0.5 是预先选定参考值，不是独立验证的最优阈值。小样本手选点仅为探索性回测，点级误接话率不等于连续对话打断率，用户声道可能有机器人回声。 + +只评估原始模型分数，不含 LiveKit 会话静音、VAD、超时或故障放行策略。预处理采用判断前裁取再重采样，具体差异见上级 README。模型许可限制见上级 README，结果不用于其他模型训练。 diff --git a/experiments/livekit-v1-mini/run_experiment.py b/experiments/livekit-v1-mini/run_experiment.py new file mode 100644 index 0000000..c87ba33 --- /dev/null +++ b/experiments/livekit-v1-mini/run_experiment.py @@ -0,0 +1,111 @@ +"""Causal v1-mini evaluation using LiveKit Agents' official local transport.""" +import os +for k in ('OMP_NUM_THREADS','OPENBLAS_NUM_THREADS','MKL_NUM_THREADS','EOT_PARALLELISM'): os.environ[k]='1' +import argparse,asyncio,csv,hashlib,json,math,platform,sys,time,weakref +import importlib.metadata as metadata +from collections import Counter +from pathlib import Path +import numpy as np +import soundfile as sf +from scipy.signal import resample_poly +from livekit.agents.inference import TurnDetector +from livekit.agents.inference.eot.transports import _LocalTransport +from livekit.agents.inference.eot.languages import LOCAL_LANGUAGES +from livekit.local_inference import _native +HERE=Path(__file__).resolve().parent +ROOT=HERE.parents[1] +OUT=HERE/'results' +LABELS=ROOT/'labels/EOT标注/工作数据/eot_labels.csv' +THRESHOLD=LOCAL_LANGUAGES['zh'] +def sha(p): return hashlib.sha256(p.read_bytes()).hexdigest() +def pcm_at(user,sr,ms): + end=min(len(user),ms*sr//1000) + if end<=0: raise ValueError('No received audio') + prefix=user[:end].astype(np.float32)/32768. + g=math.gcd(sr,16000) + audio=resample_poly(prefix,16000//g,sr//g) if sr!=16000 else prefix + return np.ascontiguousarray((np.clip(audio[-19200:],-1,1)*32767).astype(np.int16)) +class Receiver: + def _resolve_prediction(self,request_id,prob,**kwargs): self.value=prob +class CheckedNative: + """Reject the framework's error fallback instead of counting it as a model score.""" + def __init__(self,native): self.native=native;self.success=False + def predict(self,pcm): + self.success=False + self.value=float(self.native.predict(pcm)) + if not np.isfinite(self.value) or not 0<=self.value<=1: raise ValueError('Invalid score') + self.success=True + return self.value +class Scorer: + def __init__(self): + self.detector=TurnDetector(version='v1-mini') + self.transport=_LocalTransport(opts=self.detector._opts) + self.checked=CheckedNative(self.transport._eot) + self.transport._eot=self.checked + self.receiver=Receiver() + self.transport._stream_ref=weakref.ref(self.receiver) + async def predict(self,pcm): + self.checked.success=False;self.receiver.value=None + await self.transport._predict('offline-evaluation',pcm) + if not self.checked.success or self.receiver.value!=self.checked.value: raise RuntimeError('Inference fallback rejected') + return self.receiver.value + +def metrics(rows,threshold): + c=Counter((int(r['can_reply_now']),int(r['probability']>=threshold)) for r in rows if r['can_reply_now'] in ('0','1')) + tn,fp,fn,tp=c[0,0],c[0,1],c[1,0],c[1,1];n=tn+fp+fn+tp + return dict(threshold=threshold,n=n,tn=tn,fp=fp,fn=fn,tp=tp,accuracy=(tn+tp)/n, + false_reply_rate=fp/(tn+fp) if tn+fp else None,false_hold_rate=fn/(tp+fn) if tp+fn else None) +def provenance(): + return dict(machine=platform.platform(),hardware='Apple M4 Mac mini CPU, 16 GB',python=sys.version, + dependencies={p:metadata.version(p) for p in ['livekit-local-inference','livekit-agents','numpy','scipy','soundfile']}, + native_binary_sha256=sha(Path(_native.__file__)),weight_identity='Weights embedded in pinned official binary; separate hash not exposed', + wheel_sha256='fa4cb2d495c80be6121642dba4def3a88b22a68d52c1b0affccd66c563de6622', + native_parallelism=_native._parallelism(),script_sha256=sha(Path(__file__)),annotation_sha256=sha(LABELS)) +async def main(mode): + OUT.mkdir(exist_ok=True) + t=time.perf_counter();model=Scorer();load_ms=(time.perf_counter()-t)*1000 + rng=np.random.default_rng(42);x=(rng.normal(0,.005,19200)*32767).astype(np.int16) + p=await model.predict(x);q=await model.predict(x);assert p==q + print(f'WITNESS CPU seeded probability={p:.9f}, repeat={q:.9f}, parallelism={_native._parallelism()}',flush=True) + if mode=='smoke': + print(json.dumps(dict(load_ms=load_ms,provenance=provenance()),ensure_ascii=False));return + with LABELS.open(encoding='utf-8-sig',newline='') as f: labels=list(csv.DictReader(f)) + calls,rows=[],[] + 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='int16',always_2d=True);user=pcm[:,0];duration=round(len(user)*1000/sr,3) + exact={int(r['decision_ms']):r for r in labels if r['call_id']==call} + if any(t>duration for t in exact): raise ValueError('Annotation exceeds audio') + times=sorted(set(range(200,int(duration)+1,200))|set(exact)|{int(duration)}) if mode=='all' else sorted(exact) + points=[];checkpoints=[] + for ms in times: + t=time.perf_counter();audio=pcm_at(user,sr,ms);prep=(time.perf_counter()-t)*1000 + t=time.perf_counter();score=await model.predict(audio);latency=(time.perf_counter()-t)*1000 + points.append([ms,round(score,7)]) + if ms in exact: + assert score==await model.predict(audio) + modified=user.copy();modified[ms*sr//1000:]=0 + assert np.array_equal(audio,pcm_at(modified,sr,ms)) + checkpoints.append([ms,round(score,7)]);row=exact[ms] + rows.append(dict(call_id=call,decision_ms=ms,can_reply_now=row['can_reply_now'],probability=score, + prediction=int(score>=THRESHOLD),threshold=THRESHOLD,input_samples=len(audio), + available_end_ms=(ms*sr//1000)*1000/sr,preparation_ms=prep,inference_ms=latency)) + calls.append(dict(call_id=call,audio_path=f'韩泰机器人抢话/音频/{call}.mp3',audio_sha256=sha(path), + duration_ms=duration,points=points,checkpoints=checkpoints)) + print(f'{call[:8]}: {len(points)} scores, {len(checkpoints)} checkpoints; repeat/future-suffix checks passed',flush=True) + with (OUT/'predictions.csv').open('w',encoding='utf-8',newline='') as f: + w=csv.DictWriter(f,fieldnames=list(rows[0]));w.writeheader();w.writerows(rows) + method='Agents 1.8.8 local transport, native v1-mini 0.2.7. User channel 0; received int16 prefix /32768 cropped BEFORE scipy resample_poly to 16kHz; last 19200 samples converted with *32767. Native left-padding below 1.2s. No ASR, VAD gate, silence timeout or fallback scores. EOT_PARALLELISM=1. Prefix resampling differs from official librosa and online streaming resampling; not a full service replay.' + summary=dict(metrics=metrics(rows,THRESHOLD),reference_threshold_05=metrics(rows,.5),model='LiveKit Turn Detector v1-mini', + official_zh_threshold=THRESHOLD,load_ms=load_ms,method=method,provenance=provenance(), + preparation_ms={k:float(np.percentile([r['preparation_ms'] for r in rows],v)) for k,v in [('p50',50),('p95',95)]}, + inference_ms={k:float(np.percentile([r['inference_ms'] for r in rows],v)) for k,v in [('p50',50),('p95',95)]}, + limitations=['44 selected points: exploratory, not held-out generalization.', 'Point false reply rate is not interval interruption rate; user channel may contain robot echo.', 'Raw zh threshold without production VAD/endpointing gates.', 'Wheel license restricts use to LiveKit Agents; do not use outputs to improve other models.']) + (OUT/'summary.json').write_text(json.dumps(summary,ensure_ascii=False,indent=2)+'\n') + if mode=='all': + out=dict(schema_version=1,model='livekit_mini',name='LiveKit v1-mini',score='P(turn ended)',sample_interval_ms=200, + method=method,provenance=provenance(),calls=calls) + (OUT/'score-timeline.json').write_text(json.dumps(out,ensure_ascii=False,separators=(',',':'))+'\n') + print(json.dumps(summary['metrics']),flush=True) +if __name__=='__main__': + p=argparse.ArgumentParser();p.add_argument('mode',choices=['smoke','evaluate','all']);asyncio.run(main(p.parse_args().mode)) diff --git a/experiments/livekit-v1-mini/uv.lock b/experiments/livekit-v1-mini/uv.lock new file mode 100644 index 0000000..95cdecb --- /dev/null +++ b/experiments/livekit-v1-mini/uv.lock @@ -0,0 +1,1141 @@ +version = 1 +revision = 3 +requires-python = "==3.11.*" + +[[package]] +name = "aiofiles" +version = "25.1.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/41/c3/534eac40372d8ee36ef40df62ec129bee4fdb5ad9706e58a29be53b2c970/aiofiles-25.1.0.tar.gz", hash = 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b/experiments/turnsense-1.0/.python-version @@ -0,0 +1 @@ +3.11 diff --git a/experiments/turnsense-1.0/README.md b/experiments/turnsense-1.0/README.md new file mode 100644 index 0000000..74c20c6 --- /dev/null +++ b/experiments/turnsense-1.0/README.md @@ -0,0 +1,39 @@ +# TurnSense 1.0 FP32 韩泰测试 + +本机 Apple M4 Mac mini、CPU、16 GB，独立 Python 3.11 uv 环境。锁定依赖见 pyproject.toml / uv.lock，声明规范见 env-spec.json。ONNX Runtime CPU，intra/inter-op 各 1 线程。官方代码与权重版本、SHA256 见 model-manifest.json。 + +## 复现 + +从项目根目录运行： + +```bash +cd experiments/turnsense-1.0 +uv sync --locked +uv run --locked python setup_assets.py +uv run --locked python run_experiment.py smoke +uv run --locked python run_experiment.py all +``` + +本机 Hugging Face 直连失败时，使用已配置代理（示例为本机现有 7897 端口）下载公开权重： + +```bash +HTTPS_PROXY=http://127.0.0.1:7897 uv run --locked python setup_assets.py +``` + +已有固定 vendor/ 与 models/ 时，可直接执行 smoke，不需联网。`smoke` 应输出 WITNESS、三项概率之和为 1、repeat identical；固定 seed=42 噪声输入应主要判为 invalid。`all` 跑全部六通录音的 200 ms 网格及当前 44 个精确判断点；`evaluate` 仅跑判断点。输出只写 results，不修改人工标注。 + +## 输入与规则 + +- 原录音用户左声道 channel 0，int16 / 32768；先裁取 decision_ms 之前已收到的前缀，再用官方依赖 librosa 重采样到 16 kHz，取末尾最多 8 秒。不用转写、未来音频、超时放行或 VAD 停止门槛。 +- 官方前端：kaldi-native-fbank，80 mel，25 ms Hamming 窗、10 ms 帧移，snip_edges=true，dither=0，LFR m=7/n=6，官方 am.mvn。LFR 边界重复的是已收到音频的最后特征，不读真实未来帧。官方完整前端未改动。 +- 保留 complete/incomplete/invalid 三项概率和原生 argmax 类别。主要二类映射为原生 complete=>can_reply=1，其余两类=>0；另报告 P(complete)>=0.5。网页曲线显示 P(complete)，0.5 参考线不等同原生三分类 argmax。 +- 每个精确判断点重复推理须完全一致；将之后的音频置零，准备出的输入须保持一致。固定 200 ms 网格是逐次真实独立推理，非插值，也不是原生流式模型。前缀重采样与特征抽取均可重算，因此计算耗时不能声称为在线增量开销。 +- 实际环境、输入文件/脚本/前端/CMVN/权重哈希写入 summary.json。分开记录重采样、特征提取、ONNX 推理耗时；模型构造不含 Python 进程启动和 import。 + +官方来源： +- https://github.com/Bairong-Xdynamics/TurnSense +- https://huggingface.co/brgroup/TurnSense/tree/main/TurnSense1.0 + +供应代码 LICENSE 为 Apache-2.0，保留在 vendor/LICENSE；README 还提到附加条件，产品部署前应以所用版本实际许可为准。本目录只做公开模型的本地比较，不重分发权重。 + +44 个选定点仅为探索性测试，点级误接话率不等于连续对话抢话率，用户声道可能含机器人回声。旧模型若标注时间不同，不能直接用旧汇总排名。结果解读见 results/评估说明.md。 diff --git a/experiments/turnsense-1.0/env-spec.json b/experiments/turnsense-1.0/env-spec.json new file mode 100644 index 0000000..6c6a339 --- /dev/null +++ b/experiments/turnsense-1.0/env-spec.json @@ -0,0 +1,38 @@ +{ + "base": "macOS arm64 CPU, Python 3.11", + "pip_phases": [ + [ + "numpy==2.2.6", + "onnxruntime==1.24.4", + "soundfile==0.13.1", + "librosa==0.11.0", + "kaldi-native-fbank==1.22.3" + ] + ], + "env": { + "OMP_NUM_THREADS": "1", + "OPENBLAS_NUM_THREADS": "1", + "MKL_NUM_THREADS": "1" + }, + "resource": { + "machine": "Apple M4 Mac mini / 16 GB", + "device": "CPUExecutionProvider", + "inter_op_threads": 1, + "intra_op_threads": 1 + }, + "code_revision": "c6d83e5b0d1355125b05eaf1b4b5c6c1acabb338", + "weights": { + "repo": "brgroup/TurnSense", + "directory": "TurnSense1.0", + "variant": "FP32", + "revision": "341dd68c9df87d74d35b9cee63a1dcf0fff24966", + "sha256": { + "model_fp32.onnx": "82256247dd38f0d9f2f213beec7ede1507347f84422fa48f29d565b613d89976", + "am.mvn": "29b3c740a2c0cfc6b308126d31d7f265fa2be74f3bb095cd2f143ea970896ae5" + } + }, + "smoke": { + "cmd": "uv run --locked python run_experiment.py smoke", + "expect": "WITNESS" + } +} diff --git a/experiments/turnsense-1.0/model-manifest.json b/experiments/turnsense-1.0/model-manifest.json new file mode 100644 index 0000000..9c5a716 --- /dev/null +++ b/experiments/turnsense-1.0/model-manifest.json @@ -0,0 +1,12 @@ +{ + "repo": "brgroup/TurnSense", + "revision": "341dd68c9df87d74d35b9cee63a1dcf0fff24966", + "version": "TurnSense1.0 FP32", + "code_repo": "Bairong-Xdynamics/TurnSense", + "code_revision": "c6d83e5b0d1355125b05eaf1b4b5c6c1acabb338", + "files": { + "model_fp32.onnx": "82256247dd38f0d9f2f213beec7ede1507347f84422fa48f29d565b613d89976", + "am.mvn": "29b3c740a2c0cfc6b308126d31d7f265fa2be74f3bb095cd2f143ea970896ae5" + }, + "verified_against": "Hugging Face fixed revision LFS SHA256 / Git blob SHA1" +} diff --git a/experiments/turnsense-1.0/pyproject.toml b/experiments/turnsense-1.0/pyproject.toml new file mode 100644 index 0000000..8ddc531 --- /dev/null +++ b/experiments/turnsense-1.0/pyproject.toml @@ -0,0 +1,5 @@ +[project] +name = "turnsense-1-0-evaluation" +version = "0.1.0" +requires-python = ">=3.11,<3.12" +dependencies = ["numpy==2.2.6", "onnxruntime==1.24.4", "soundfile==0.13.1", "librosa==0.11.0", "kaldi-native-fbank==1.22.3"] diff --git a/experiments/turnsense-1.0/results/predictions.csv b/experiments/turnsense-1.0/results/predictions.csv new file mode 100644 index 0000000..363e894 --- /dev/null +++ b/experiments/turnsense-1.0/results/predictions.csv @@ -0,0 +1,45 @@ +call_id,decision_ms,can_reply_now,p_complete,p_incomplete,p_invalid,native_class,native_can_reply,prediction_05,input_samples,available_end_ms,preparation_ms,frontend_ms,inference_ms 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seed=42 [2.4581515390309505e-05, 7.737393025308847e-05, 0.9998980760574341] repeat identical +2c83253a: 100/489 scores +2c83253a: 200/489 scores +2c83253a: 300/489 scores +2c83253a: 400/489 scores +2c83253a DONE: 489 scores, 6 checkpoints; repeat/future-suffix checks passed +40ae686c: 100/441 scores +40ae686c: 200/441 scores +40ae686c: 300/441 scores +40ae686c: 400/441 scores +40ae686c DONE: 441 scores, 8 checkpoints; repeat/future-suffix checks passed +872a1e67: 100/379 scores +872a1e67: 200/379 scores +872a1e67: 300/379 scores +872a1e67 DONE: 379 scores, 9 checkpoints; repeat/future-suffix checks passed +a266b9ef: 100/421 scores +a266b9ef: 200/421 scores +a266b9ef: 300/421 scores +a266b9ef: 400/421 scores +a266b9ef DONE: 421 scores, 5 checkpoints; repeat/future-suffix checks passed +cb3238dc: 100/478 scores +cb3238dc: 200/478 scores +cb3238dc: 300/478 scores +cb3238dc: 400/478 scores +cb3238dc DONE: 478 scores, 9 checkpoints; repeat/future-suffix checks passed +e304f740: 100/365 scores +e304f740: 200/365 scores +e304f740: 300/365 scores +e304f740 DONE: 365 scores, 7 checkpoints; repeat/future-suffix checks passed +{"native_argmax": {"n": 44, "tn": 17, "fp": 5, "fn": 9, "tp": 13, "accuracy": 0.6818181818181818, "false_reply_rate": 0.22727272727272727, "false_hold_rate": 0.4090909090909091}, "threshold_05": {"n": 44, "tn": 17, "fp": 5, "fn": 9, "tp": 13, "accuracy": 0.6818181818181818, "false_reply_rate": 0.22727272727272727, "false_hold_rate": 0.4090909090909091}, "class_counts": {"complete": 18, "incomplete": 26}} diff --git a/experiments/turnsense-1.0/results/score-timeline.json b/experiments/turnsense-1.0/results/score-timeline.json new file mode 100644 index 0000000..423be70 --- /dev/null +++ b/experiments/turnsense-1.0/results/score-timeline.json @@ -0,0 +1 @@ +{"schema_version":1,"model":"turnsense","name":"TurnSense 1.0","score":"P(complete), 3-class output","sample_interval_ms":200,"method":"TurnSense1.0 FP32, official code/frontend/CMVN and 3-class 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CPU one intra/inter-op thread. User channel 0 int16/32768, crop received prefix BEFORE librosa resample to 16kHz, last 8s, no future ASR/audio or silence/timeout gates. Score=P(complete), native argmax complete=>1 else0; separately report threshold0.5. Full-prefix resampling is offline causal-window evaluation, not an incremental frontend benchmark.","provenance":{"machine":"macOS-26.4.1-arm64-arm-64bit","hardware":"Apple M4 Mac mini CPU / 16 GB","python":"3.11.15 (main, Mar 25 2026, 02:58:47) [Clang 22.1.1 ]","dependencies":{"numpy":"2.2.6","onnxruntime":"1.24.4","librosa":"0.11.0","kaldi-native-fbank":"1.22.3","soundfile":"0.13.1"},"providers":["CPUExecutionProvider"],"intra_op_threads":1,"inter_op_threads":1,"model_manifest":{"repo":"brgroup/TurnSense","revision":"341dd68c9df87d74d35b9cee63a1dcf0fff24966","version":"TurnSense1.0 FP32","code_repo":"Bairong-Xdynamics/TurnSense","code_revision":"c6d83e5b0d1355125b05eaf1b4b5c6c1acabb338","files":{"model_fp32.onnx":"82256247dd38f0d9f2f213beec7ede1507347f84422fa48f29d565b613d89976","am.mvn":"29b3c740a2c0cfc6b308126d31d7f265fa2be74f3bb095cd2f143ea970896ae5"},"verified_against":"Hugging Face fixed revision LFS SHA256 / Git blob 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diff --git a/experiments/turnsense-1.0/results/smoke.log b/experiments/turnsense-1.0/results/smoke.log new file mode 100644 index 0000000..d51eb57 --- /dev/null +++ b/experiments/turnsense-1.0/results/smoke.log @@ -0,0 +1,2 @@ +WITNESS CPU seed=42 [2.4581515390309505e-05, 7.737393025308847e-05, 0.9998980760574341] repeat identical +{"load_ms": 576.1605408042669, "provenance": {"machine": "macOS-26.4.1-arm64-arm-64bit", "hardware": "Apple M4 Mac mini CPU / 16 GB", "python": "3.11.15 (main, Mar 25 2026, 02:58:47) [Clang 22.1.1 ]", "dependencies": {"numpy": "2.2.6", "onnxruntime": "1.24.4", "librosa": "0.11.0", "kaldi-native-fbank": "1.22.3", "soundfile": "0.13.1"}, "providers": ["CPUExecutionProvider"], "intra_op_threads": 1, "inter_op_threads": 1, "model_manifest": {"repo": "brgroup/TurnSense", "revision": "341dd68c9df87d74d35b9cee63a1dcf0fff24966", "version": "TurnSense1.0 FP32", "code_repo": "Bairong-Xdynamics/TurnSense", "code_revision": "c6d83e5b0d1355125b05eaf1b4b5c6c1acabb338", "files": {"model_fp32.onnx": "82256247dd38f0d9f2f213beec7ede1507347f84422fa48f29d565b613d89976", "am.mvn": "29b3c740a2c0cfc6b308126d31d7f265fa2be74f3bb095cd2f143ea970896ae5"}, "verified_against": "Hugging Face fixed revision LFS SHA256 / Git blob SHA1"}, "script_sha256": "23215a6db3c39f6819761adf1e6cd46a8f7c0c7767d1a3640bc928ecd548b2fb", "annotation_sha256": "780e50f976daf039ab976ea155bdb5d1ca5fffc792f9db7adf8063ec3048ff8e", "frontend_sha256": "22808c254ea2150e290e6bdcf115322a723b67fc4e53b1ef02b8f3f42697187d", "official_infer_sha256": "40295fb42a36ae4ecca30093d6bc37acbca9c75304b1a722dba73fd3b30498df"}} diff --git a/experiments/turnsense-1.0/results/summary.json b/experiments/turnsense-1.0/results/summary.json new file mode 100644 index 0000000..0f49dd4 --- /dev/null +++ b/experiments/turnsense-1.0/results/summary.json @@ -0,0 +1,76 @@ +{ + "model": "TurnSense 1.0 FP32", + "native_argmax": { + "n": 44, + "tn": 17, + "fp": 5, + "fn": 9, + "tp": 13, + "accuracy": 0.6818181818181818, + "false_reply_rate": 0.22727272727272727, + "false_hold_rate": 0.4090909090909091 + }, + "threshold_05": { + "n": 44, + "tn": 17, + "fp": 5, + "fn": 9, + "tp": 13, + "accuracy": 0.6818181818181818, + "false_reply_rate": 0.22727272727272727, + "false_hold_rate": 0.4090909090909091 + }, + "class_counts": { + "complete": 18, + "incomplete": 26 + }, + "load_ms": 657.7007500454783, + "method": "TurnSense1.0 FP32, official code/frontend/CMVN and 3-class postprocessing. CPU one intra/inter-op thread. User channel 0 int16/32768, crop received prefix BEFORE librosa resample to 16kHz, last 8s, no future ASR/audio or silence/timeout gates. Score=P(complete), native argmax complete=>1 else0; separately report threshold0.5. Full-prefix resampling is offline causal-window evaluation, not an incremental frontend benchmark.", + "provenance": { + "machine": "macOS-26.4.1-arm64-arm-64bit", + "hardware": "Apple M4 Mac mini CPU / 16 GB", + "python": "3.11.15 (main, Mar 25 2026, 02:58:47) [Clang 22.1.1 ]", + "dependencies": { + "numpy": "2.2.6", + "onnxruntime": "1.24.4", + "librosa": "0.11.0", + "kaldi-native-fbank": "1.22.3", + "soundfile": "0.13.1" + }, + "providers": [ + "CPUExecutionProvider" + ], + "intra_op_threads": 1, + "inter_op_threads": 1, + "model_manifest": { + "repo": "brgroup/TurnSense", + "revision": "341dd68c9df87d74d35b9cee63a1dcf0fff24966", + "version": "TurnSense1.0 FP32", + "code_repo": "Bairong-Xdynamics/TurnSense", + "code_revision": "c6d83e5b0d1355125b05eaf1b4b5c6c1acabb338", + "files": { + "model_fp32.onnx": "82256247dd38f0d9f2f213beec7ede1507347f84422fa48f29d565b613d89976", + "am.mvn": "29b3c740a2c0cfc6b308126d31d7f265fa2be74f3bb095cd2f143ea970896ae5" + }, + "verified_against": "Hugging Face fixed revision LFS SHA256 / Git blob SHA1" + }, + "script_sha256": "23215a6db3c39f6819761adf1e6cd46a8f7c0c7767d1a3640bc928ecd548b2fb", + "annotation_sha256": "780e50f976daf039ab976ea155bdb5d1ca5fffc792f9db7adf8063ec3048ff8e", + "frontend_sha256": "22808c254ea2150e290e6bdcf115322a723b67fc4e53b1ef02b8f3f42697187d", + "official_infer_sha256": "40295fb42a36ae4ecca30093d6bc37acbca9c75304b1a722dba73fd3b30498df" + }, + "latency_ms": { + "preparation_ms": { + "p50": 1.7886869609355927, + "p95": 3.0542627442628145 + }, + "frontend_ms": { + "p50": 4.848978947848082, + "p95": 5.63460667617619 + }, + "inference_ms": { + "p50": 31.06177132576704, + "p95": 35.107012651860714 + } + } +} diff --git a/experiments/turnsense-1.0/results/评估说明.md b/experiments/turnsense-1.0/results/评估说明.md new file mode 100644 index 0000000..c6f2ce2 --- /dev/null +++ b/experiments/turnsense-1.0/results/评估说明.md @@ -0,0 +1,36 @@ +# TurnSense 1.0 FP32 韩泰评估 + +本机 Apple M4 Mac mini、CPU；六通录音，当前 44 个判断点，正负各 22。只使用判断时已收到的用户左声道音频、末尾最多 8 秒，使用官方特征前端与 CMVN，未输入标注转写。 + +| 判断规则 | 正确 / 总数 | 准确率 | 误接话（应等待却接话） | 漏接话（应接话却等待） | +| --- | --- | --- | --- | --- | +| 原生 argmax：complete=>1，其余=>0 | 30 / 44 | 68.18% | 5 / 22（22.73%） | 9 / 22（40.91%） | +| P(complete) ≥ 0.5 | 30 / 44 | 68.18% | 5 / 22（22.73%） | 9 / 22（40.91%） | + +本批两种规则得到相同二分类结果，但它们在一般情况下并不等价。原生类别：complete 18 点、incomplete 26 点、invalid 0 点。invalid 输出仍保留，不能据此评估噪声识别能力，因为本批没有独立 invalid 真值。 + +## 计算耗时 + +| 阶段 | p50 | p95 | +| --- | --- | --- | +| 裁取/前缀重采样 | 1.79 ms | 3.05 ms | +| 官方特征前端 | 4.85 ms | 5.63 ms | +| ONNX 模型推理 | 31.06 ms | 35.11 ms | + +模型构造 657.70 ms，不含 Python 启动/import，不能称为完整冷启动。CPU intra/inter-op 各 1 线程，已预热；前缀重采样和特征重算不等于在线增量开销。阶段分位数不应相加成总耗时分位数。 + +## 解读与比较边界 + +同一当前标注哈希的 LiveKit v1-mini 在 0.5 下为 29/44，误接话 11、漏接话 4；TurnSense 在 0.5 下为 30/44，误接话 5、漏接话 9。可见这批点上更倾向等待：少误接话，但更多应接话时未接话。样本小、上下文长度不同（8 秒 vs 1.2 秒），不能把这 1 点正确率差异当成稳定优势，更不能宣布已解决抢话。 + +当前标注第二通一个点已由旧 VAP / Smart Turn 快照的 48348 ms 改为 48536 ms，不能直接用其旧 44 点汇总严格排名。原曲线空心点保留各自评分时刻。 + +## 可核验性 + +- 全部判断点重复模型推理结果完全一致。 +- 将判断点之后的音频置零，准备出的模型输入不变。 +- 权重 SHA256 与固定 Hugging Face 修订的 LFS SHA256 一致；CMVN 用 Git blob SHA1 核对，并另记 SHA256。 +- 模型、CMVN、脚本、前端、标注、录音哈希与环境版本均写入结果，独立环境文档已由另一代理按说明复现 smoke。 +- 连续曲线为每 200 ms 实际推理及每个判断点的精确分数，共 2573 个采样点，非插值生成。 + +44 个手选判断点仅为探索性评估；点级误接话率不等于整段对话抢话率，用户声道可能有机器人回声。不加 VAD 门槛、静音延迟或超时策略。模型为 1.0 FP32，本次没有测试 INT8 或 1.1。 diff --git a/experiments/turnsense-1.0/run_experiment.py b/experiments/turnsense-1.0/run_experiment.py new file mode 100644 index 0000000..00530d1 --- /dev/null +++ b/experiments/turnsense-1.0/run_experiment.py @@ -0,0 +1,105 @@ +"""TurnSense 1.0 FP32: real causal-prefix inference with the official frontend.""" +import os +for k in ('OMP_NUM_THREADS','MKL_NUM_THREADS','OPENBLAS_NUM_THREADS'): os.environ[k]='1' +os.environ['NUMBA_CACHE_DIR']=str(__import__('pathlib').Path(__file__).resolve().parent/'cache/numba') +import argparse,csv,hashlib,json,platform,sys,time +import importlib.metadata as md +from pathlib import Path +from collections import Counter +import numpy as np +import soundfile as sf +import librosa +import onnxruntime as ort +HERE=Path(__file__).resolve().parent +ROOT=HERE.parents[1] +OUT=HERE/'results' +LABEL_PATH=ROOT/'labels/EOT标注/工作数据/eot_labels.csv' +sys.path.insert(0,str(HERE/'vendor')) +from frontend import AudioFrontend +from infer import DEFAULT_FRONTEND_CONF,process_predictions,LABELS + +def sha(p): return hashlib.sha256(p.read_bytes()).hexdigest() +def prepare(user,sr,ms): + end=min(len(user),ms*sr//1000) + if end<=0: raise ValueError('No audio available') + prefix=user[:end].astype(np.float32)/32768. + if sr!=16000: prefix=librosa.resample(prefix,orig_sr=sr,target_sr=16000) + return np.ascontiguousarray(prefix[-128000:]) +class Model: + def __init__(self): + manifest=json.loads((HERE/'model-manifest.json').read_text()) + for name,digest in manifest['files'].items(): + if sha(HERE/'models'/name)!=digest: raise ValueError('Weight/CMVN checksum mismatch') + opts=ort.SessionOptions();opts.intra_op_num_threads=1;opts.inter_op_num_threads=1 + opts.graph_optimization_level=ort.GraphOptimizationLevel.ORT_ENABLE_ALL + self.session=ort.InferenceSession(str(HERE/'models/model_fp32.onnx'),sess_options=opts,providers=['CPUExecutionProvider']) + self.frontend=AudioFrontend(**{**DEFAULT_FRONTEND_CONF,'cmvn_file':str(HERE/'models/am.mvn')}) + assert LABELS==['complete','incomplete','invalid'] + def predict(self,audio): + t=time.perf_counter();feats,n=self.frontend.extract_features(audio) + inputs={'feats':np.asarray(feats,dtype=np.float32)[None,...],'feat_lengths':np.asarray([int(n)],dtype=np.int64)} + frontend_ms=(time.perf_counter()-t)*1000 + t=time.perf_counter();raw=self.session.run(None,inputs)[0];inference_ms=(time.perf_counter()-t)*1000 + probs,pred=process_predictions(raw) + if probs.shape!=(1,3) or not np.isfinite(probs).all() or not np.isclose(probs.sum(),1): raise ValueError('Invalid 3-class output') + return probs[0],int(pred[0]),frontend_ms,inference_ms + +def metrics(rows,rule): + c=Counter((int(r['can_reply_now']),int(r[rule])) for r in rows if r['can_reply_now'] in ('0','1')) + tn,fp,fn,tp=c[0,0],c[0,1],c[1,0],c[1,1];n=tn+fp+fn+tp + return dict(n=n,tn=tn,fp=fp,fn=fn,tp=tp,accuracy=(tn+tp)/n,false_reply_rate=fp/(tn+fp),false_hold_rate=fn/(tp+fn)) +def prov(): + return dict(machine=platform.platform(),hardware='Apple M4 Mac mini CPU / 16 GB',python=sys.version, + dependencies={p:md.version(p) for p in ['numpy','onnxruntime','librosa','kaldi-native-fbank','soundfile']}, + providers=['CPUExecutionProvider'],intra_op_threads=1,inter_op_threads=1, + model_manifest=json.loads((HERE/'model-manifest.json').read_text()),script_sha256=sha(Path(__file__)), + annotation_sha256=sha(LABEL_PATH),frontend_sha256=sha(HERE/'vendor/frontend/audio_frontend.py'), + official_infer_sha256=sha(HERE/'vendor/infer.py')) +def main(mode): + OUT.mkdir(exist_ok=True) + t=time.perf_counter();model=Model();load_ms=(time.perf_counter()-t)*1000 + rng=np.random.default_rng(42);test=rng.normal(0,.005,16000).astype(np.float32) + a=model.predict(test)[0];b=model.predict(test)[0];assert np.array_equal(a,b) + print('WITNESS CPU seed=42',a.tolist(),'repeat identical',flush=True) + if mode=='smoke': + print(json.dumps(dict(load_ms=load_ms,provenance=prov()),ensure_ascii=False));return + with LABEL_PATH.open(encoding='utf-8-sig',newline='') as f: labels=list(csv.DictReader(f)) + rows,calls=[],[] + 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='int16',always_2d=True);user=pcm[:,0];duration=round(len(user)*1000/sr,3) + exact={int(r['decision_ms']):r for r in labels if r['call_id']==call} + if any(t>duration for t in exact): raise ValueError('Label beyond recording') + times=sorted(set(range(200,int(duration)+1,200))|set(exact)|{int(duration)}) if mode=='all' else sorted(exact) + points=[];checkpoints=[] + for i,ms in enumerate(times): + t=time.perf_counter();audio=prepare(user,sr,ms);prep=(time.perf_counter()-t)*1000 + probs,pred,front,inf=model.predict(audio);points.append([ms,round(float(probs[0]),7)]) + if ms in exact: + assert np.array_equal(probs,model.predict(audio)[0]) + modified=user.copy();modified[ms*sr//1000:]=0 + assert np.array_equal(audio,prepare(modified,sr,ms)) + checkpoints.append([ms,round(float(probs[0]),7)]) + rows.append(dict(call_id=call,decision_ms=ms,can_reply_now=exact[ms]['can_reply_now'], + p_complete=float(probs[0]),p_incomplete=float(probs[1]),p_invalid=float(probs[2]), + native_class=LABELS[pred],native_can_reply=int(pred==0),prediction_05=int(probs[0]>=.5), + input_samples=len(audio),available_end_ms=(ms*sr//1000)*1000/sr, + preparation_ms=prep,frontend_ms=front,inference_ms=inf)) + if (i+1)%100==0: print(f'{call[:8]}: {i+1}/{len(times)} scores',flush=True) + calls.append(dict(call_id=call,audio_path=f'韩泰机器人抢话/音频/{call}.mp3',audio_sha256=sha(path), + duration_ms=duration,points=points,checkpoints=checkpoints)) + print(f'{call[:8]} DONE: {len(points)} scores, {len(checkpoints)} checkpoints; repeat/future-suffix checks passed',flush=True) + with (OUT/'predictions.csv').open('w',encoding='utf-8',newline='') as f: + w=csv.DictWriter(f,fieldnames=list(rows[0]));w.writeheader();w.writerows(rows) + method='TurnSense1.0 FP32, official code/frontend/CMVN and 3-class postprocessing. CPU one intra/inter-op thread. User channel 0 int16/32768, crop received prefix BEFORE librosa resample to 16kHz, last 8s, no future ASR/audio or silence/timeout gates. Score=P(complete), native argmax complete=>1 else0; separately report threshold0.5. Full-prefix resampling is offline causal-window evaluation, not an incremental frontend benchmark.' + summary=dict(model='TurnSense 1.0 FP32',native_argmax=metrics(rows,'native_can_reply'),threshold_05=metrics(rows,'prediction_05'), + class_counts=dict(Counter(r['native_class'] for r in rows)),load_ms=load_ms,method=method,provenance=prov(), + latency_ms={key:{name:float(np.percentile([r[key] for r in rows],q)) for name,q in [('p50',50),('p95',95)]} for key in ['preparation_ms','frontend_ms','inference_ms']}) + (OUT/'summary.json').write_text(json.dumps(summary,ensure_ascii=False,indent=2)+'\n') + if mode=='all': + output=dict(schema_version=1,model='turnsense',name='TurnSense 1.0',score='P(complete), 3-class output',sample_interval_ms=200, + method=method,provenance=prov(),calls=calls) + (OUT/'score-timeline.json').write_text(json.dumps(output,ensure_ascii=False,separators=(',',':'))+'\n') + print(json.dumps({k:summary[k] for k in ['native_argmax','threshold_05','class_counts']}),flush=True) +if __name__=='__main__': + p=argparse.ArgumentParser();p.add_argument('mode',choices=['smoke','evaluate','all']);main(p.parse_args().mode) diff --git a/experiments/turnsense-1.0/setup_assets.py b/experiments/turnsense-1.0/setup_assets.py new file mode 100644 index 0000000..b271bbd --- /dev/null +++ b/experiments/turnsense-1.0/setup_assets.py @@ -0,0 +1,28 @@ +"""Restore fixed official assets; no speech or labels are transmitted.""" +import hashlib,json,tarfile,urllib.request,io +from pathlib import Path +HERE=Path(__file__).resolve().parent +m=json.loads((HERE/'model-manifest.json').read_text()) + +def download(url): + with urllib.request.urlopen(url,timeout=120) as r: return r.read() + +def main(): + vendor=HERE/'vendor';vendor.mkdir(exist_ok=True) + revision=m['code_revision'];archive=download(f'https://codeload.github.com/{m["code_repo"]}/tar.gz/{revision}') + # Extract only the official inference code, frontend sources and license. + with tarfile.open(fileobj=io.BytesIO(archive),mode='r:gz') as tar: + for member in tar.getmembers(): + rel=Path(*Path(member.name).parts[1:]) + if not member.isfile() or rel.is_absolute() or '..' in rel.parts: continue + if str(rel) not in ('infer.py','LICENSE') and not (str(rel).startswith('frontend/') and rel.suffix=='.py'): continue + target=vendor/rel;target.parent.mkdir(parents=True,exist_ok=True);target.write_bytes(tar.extractfile(member).read()) + models=HERE/'models';models.mkdir(exist_ok=True) + for name,sha in m['files'].items(): + target=models/name + if target.exists() and hashlib.sha256(target.read_bytes()).hexdigest()==sha: continue + data=download(f'https://huggingface.co/{m["repo"]}/resolve/{m["revision"]}/TurnSense1.0/{name}') + if hashlib.sha256(data).hexdigest()!=sha: raise ValueError('Checksum mismatch') + target.write_bytes(data) + print('Official code and weight checksums restored') +if __name__=='__main__': main() diff --git 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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。 - 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。 - 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。 -- 三个模型名称旁各有独立显示开关，默认都打开。关闭后隐藏该模型的曲线、原标注点评分、参考线及分数读数；重新打开保留原阈值。全部关闭时显示提示，切换通话或收起再展开会保留当前显示选择。 +- “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： @@ -76,3 +76,11 @@ demo,5200,我明天下午,0,high,mid_turn_pause,A - `GET /api/audio?path=...`：仅提供 `data` 内的 MP3、WAV、M4A、OGG、FLAC 和 WebM 录音，支持 Range 请求和 HEAD，便于跳转播放。路径相对 `data` 目录，不能通过 `..` 或符号链接访问目录之外的文件。 录音 `call_id` 默认取文件名去除扩展名后的部分；同一通话有多个项目录音时，默认使用发现的第一条，可点击“更换录音”重新关联本地文件。 + +### LiveKit v1-mini + +新增蓝色 LiveKit v1-mini 曲线，默认显示、每 200 ms 更新，并包含 44 个判断点的精确分数。只接收判断前的用户声道，16 kHz 最近 1.2 秒音频。参考阈值默认采用锁定 SDK 的中文阈值 0.355，可切换 0.5 / 0.8。它通过 LiveKit Agents 本地推理入口运行，不加服务层 VAD 门槛、静音等待或超时；不将这些曲线称为完整在线策略表现。模型许可与复现说明见 [实验说明](../../experiments/livekit-v1-mini/README.md)。 + +### TurnSense 1.0 FP32 + +玫红色曲线为真实 ONNX 推理的 P(complete)，每 200 ms 及原判断点精确时刻更新。输入仅为判断前的用户左声道，重采样到 16 kHz 后取最近 8 秒；官方前端/CMVN不改动。支持独立开关和 0.5 / 0.8 参考线。原生输出为 complete/incomplete/invalid 三分类，完整三项概率保存在实验 predictions.csv，原生 argmax complete 映射为可接话；网页 0.5 参考线不是原生 argmax 的等价规则。复现和评估结果见 [TurnSense 实验说明](../../experiments/turnsense-1.0/README.md)。 diff --git a/labels/eot-studio/app/api/model-scores/route.ts b/labels/eot-studio/app/api/model-scores/route.ts index f3cfed2..47056f9 100644 --- a/labels/eot-studio/app/api/model-scores/route.ts +++ b/labels/eot-studio/app/api/model-scores/route.ts @@ -1,16 +1,14 @@ import { promises as fs } from "node:fs"; import { createHash } from "node:crypto"; import path from "node:path"; -import { parseScoreExport, type ModelId, type ScoreResponse } from "@/lib/model-scores"; +import { parseScoreExport, type ScoreResponse } from "@/lib/model-scores"; + +import { SCORE_MODELS, SCORE_MODEL_IDS } from "@/lib/score-models"; export const runtime = "nodejs"; export const dynamic = "force-dynamic"; const root = path.resolve(process.cwd(), "..", ".."); -const sources: { model: ModelId; directory: string; name: string }[] = [ - { model: "maai", directory: "maai-ch-kyoto", name: "MaAI" }, - { model: "smart_turn", directory: "smart-turn-v3.2", name: "Smart Turn" }, - { model: "silero_vad", directory: "silero-vad", name: "Silero VAD" }, -]; +const sources = SCORE_MODEL_IDS.map(model => ({ model, ...SCORE_MODELS[model] })); const json = (data: unknown, status = 200) => Response.json(data, { status, headers: { "Cache-Control": "no-store" } }); export async function GET(request: Request) { diff --git a/labels/eot-studio/components/model-score-track.tsx b/labels/eot-studio/components/model-score-track.tsx index a66989b..555f748 100644 --- a/labels/eot-studio/components/model-score-track.tsx +++ b/labels/eot-studio/components/model-score-track.tsx @@ -1,12 +1,14 @@ "use client"; -import { useEffect, useMemo, useState } from "react"; +import { useEffect, useMemo, useRef, useState, useId } from "react"; import { 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"; import styles from "./timeline.module.css"; const EMPTY: ScoreSeries[] = []; -const COLORS: Record = { maai: "#8866b2", smart_turn: "#278d95", silero_vad: "#c47b32" }; +const COLORS = Object.fromEntries(SCORE_MODEL_IDS.map(id => [id, SCORE_MODELS[id].color])) as Record; export const SCORE_HEIGHT = 172; export type ScoreThresholds = Record; export type ScoreVisibility = Record; @@ -44,33 +46,125 @@ export function useModelScores(callId: string, recordingPath: string | null, exp }; } -export function ScoreControls({ series, currentMs, thresholds, onThreshold, visibility, onVisibility }: { +export function useScorePreferences() { + const [preferences, setPreferences] = useState(() => readScorePreferences(null)); + const [ready, setReady] = useState(false); + useEffect(() => { + try { setPreferences(readScorePreferences(JSON.parse(localStorage.getItem("eot-score-preferences-v1") || "null"))); } catch { /* Defaults survive unavailable or invalid browser storage. */ } + setReady(true); + }, []); + useEffect(() => { + if (!ready) return; + try { localStorage.setItem("eot-score-preferences-v1", JSON.stringify({ version: 1, ...preferences })); } catch { /* Preferences are optional. */ } + }, [ready, preferences]); + return { ...preferences, + setVisibility: (visibility: ScoreVisibility) => setPreferences(previous => ({ ...previous, visibility })), + setThreshold: (id: ModelId, value: number) => setPreferences(previous => ({ ...previous, thresholds: { ...previous.thresholds, [id]: value } })), + }; +} + +export function ScoreControls({ series, currentMs, thresholds, onThreshold, visibility, onVisibility, onFocus, focusedModel, loading }: { series: ScoreSeries[]; currentMs: number; thresholds: ScoreThresholds; onThreshold: (model: ModelId, value: number) => void; - visibility: ScoreVisibility; onVisibility: (model: ModelId, visible: boolean) => void; + visibility: ScoreVisibility; onVisibility: (visibility: ScoreVisibility) => void; + onFocus: (model: ModelId | null) => void; focusedModel: ModelId | null; loading: boolean; }) { + const [panel, setPanel] = useState(null); + const [query, setQuery] = useState(""); + const [position, setPosition] = useState({ top: 0, left: 0 }); + const [previousSelection, setPreviousSelection] = useState(null); + const anchor = useRef(null); + const popup = useRef(null); + const search = useRef(null); + const panelId = useId(); + const available = new Set(series.map(row => row.model)); + const selected = series.filter(row => visibility[row.model]); + function close() { setPanel(null); anchor.current?.focus({ preventScroll: true }); } + function open(kind: ModelId | "choose", button: HTMLButtonElement) { + anchor.current = button; + if (panel === kind) { close(); return; } + setQuery(""); setPanel(kind); + } + useEffect(() => { + if (!panel) return; + const place = () => { + const r = anchor.current?.getBoundingClientRect(); if (!r) return; + const width = Math.min(360, window.innerWidth - 24); + const height = Math.min(popup.current?.getBoundingClientRect().height || 460, window.innerHeight - 24); + setPosition({ left: Math.max(12, Math.min(r.left, window.innerWidth - width - 12)), + top: Math.max(12, Math.min(r.bottom + 8, window.innerHeight - height - 12)) }); + }; + place(); + (panel === "choose" ? search.current : popup.current?.querySelector("button,select"))?.focus({ preventScroll: true }); + const outside = (event: PointerEvent) => { + if (!popup.current?.contains(event.target as Node) && !anchor.current?.contains(event.target as Node)) setPanel(null); + }; + const escape = (event: KeyboardEvent) => { + if (event.key === "Escape") { event.preventDefault(); event.stopPropagation(); close(); } + // Keep workbench hotkeys from changing annotations while browsing settings. + else if (popup.current?.contains(event.target as Node)) event.stopPropagation(); + }; + document.addEventListener("pointerdown", outside); document.addEventListener("keydown", escape, true); + window.addEventListener("resize", place); window.addEventListener("scroll", place, true); + return () => { document.removeEventListener("pointerdown", outside); document.removeEventListener("keydown", escape, true); + window.removeEventListener("resize", place); window.removeEventListener("scroll", place, true); }; + }, [panel]); + const manualSelection = (next: ScoreVisibility) => { setPreviousSelection(null); onVisibility(next); }; + const metadata = panel && panel !== "choose" ? SCORE_MODELS[panel] : null; return
- {series.map(row => { - const visible = visibility[row.model]; - const point = visible ? scoreAt(row.points, currentMs, row.sample_interval_ms * 2) : null; - return
- - {point ? point[1].toFixed(3) : "—"} -
; - })} + + + } +
, document.body)} ; } -export default function ModelScoreTrack({ series, width, durationMs, currentMs, thresholds, annotations, onSeek, onSelectAnnotation }: { +export default function ModelScoreTrack({ series, width, durationMs, currentMs, thresholds, annotations, onSeek, onSelectAnnotation, focusedModel }: { series: ScoreSeries[]; width: number; durationMs: number; currentMs: number; - thresholds: ScoreThresholds; annotations: TimelineAnnotation[]; + thresholds: ScoreThresholds; annotations: TimelineAnnotation[]; focusedModel: ModelId | null; onSeek: (ms: number) => void; onSelectAnnotation: (id: string, seekToPoint?: boolean) => void; }) { const [hoverMs, setHoverMs] = useState(null); @@ -86,7 +180,7 @@ 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))); @@ -94,10 +188,10 @@ export default function ModelScoreTrack({ series, width, durationMs, currentMs, {[0, .25, .5, .75, 1].map(p => )} {annotations.map(row => )} - {paths.map(row => + {paths.map(row => - + {row.checkpoints.map(([ms, score]) => span:last-child { color: #929b89; font-size: 10px; } .chevron, .chevronOpen { display: inline-block; width: 12px; font-size: 23px; line-height: 16px; text-align: center; } .chevronOpen { transform: rotate(90deg); } -.scoreControls { display: flex; flex-wrap: wrap; align-items: center; gap: 9px; } -.modelControl { display: flex; align-items: center; gap: 6px; padding: 5px 8px; border: 1px solid #e9e9e2; border-radius: 7px; background: #fafbf7; font-size: 10px; } -.modelControl strong { font-weight: 600; } -.modelControl output { width: 37px; font-family: monospace; font-variant-numeric: tabular-nums; text-align: center; } -.modelControl label { display: flex; align-items: center; gap: 4px; color: #88927e; padding-left: 4px; border-left: 1px solid #e2e5db; } -.modelControl select { padding: 1px 0; border: 0; background: transparent; font-size: 10px; color: #637258; cursor: pointer; } -.modelVisibility { display: inline-flex; align-items: center; gap: 6px; padding: 3px 0; border: 0; background: transparent; color: inherit; font: inherit; cursor: pointer; } -.modelSwitch { display: inline-block; position: relative; width: 26px; height: 15px; border-radius: 20px; background: currentColor; } -.modelSwitch::after { content: ""; position: absolute; top: 3px; left: 14px; width: 9px; height: 9px; border-radius: 50%; background: white; transition: left .15s ease; } -.modelVisibility[aria-checked="false"] { color: #92998c; } -.modelVisibility[aria-checked="false"] .modelSwitch { background: #c6cbbe; } -.modelVisibility[aria-checked="false"] .modelSwitch::after { left: 3px; } -.modelControl select:disabled { opacity: .45; cursor: default; } +.scoreControls { width: 100%; display: flex; flex-wrap: wrap; align-items: center; gap: 8px 14px; } .scoreDescription { padding: 0 22px 10px; font-size: 10px; line-height: 1.7; color: #88927f; } .scoreDescription button { border: 0; background: none; color: #536f40; cursor: pointer; text-decoration: underline; font-size: inherit; } .trackNames > .scoreAxis { padding: 12px 8px; flex-direction: column; justify-content: space-between; font-size: 10px; color: #929a87; border-top: 1px solid #e7e9e0; } @@ -150,10 +138,51 @@ .scoreInspection b { font-weight: 500; } .scoreEmpty { position: sticky; left: 0; display: grid; place-items: center; color: #929b88; font-size: 11px; background: #fdfefb; border-top: 1px solid #e7e9e0; } .scoreFootnote { margin: 0; padding: 9px 22px 0; color: #8c9583; font-size: 10px; line-height: 1.7; } -.scoreToggle:focus-visible, .modelVisibility:focus-visible, .modelControl select:focus-visible { outline: 2px solid #758962; outline-offset: 3px; border-radius: 3px; } +.scoreToggle:focus-visible, .modelPickerButton:focus-visible, .legendName:focus-visible { outline: 2px solid #758962; outline-offset: 3px; border-radius: 3px; } @media (max-width: 640px) { .scorePanelHeader { padding-inline: 12px; gap: 5px; } .scoreDescription, .scoreFootnote { padding-inline: 12px; } .scoreControls { gap: 5px; } - .modelControl { padding: 4px 5px; } } + +.modelPickerBar { display: flex; align-items: center; gap: 9px; } +.modelPickerButton { display: inline-flex; align-items: center; gap: 7px; padding: 6px 9px; border: 1px solid #dfe5d7; background: #f8faf3; border-radius: 6px; font: inherit; font-size: 11px; color: #526448; cursor: pointer; white-space: nowrap; } +.modelPickerButton > span:first-child { padding: 1px 5px; border-radius: 4px; background: #e9efdf; color: #69775f; font: 10px monospace; } +.restoreSelection { border: 0; background: none; color: #647d50; font: inherit; font-size: 10px; cursor: pointer; text-decoration: underline; text-underline-offset: 3px; } +.modelLegend { display: flex; align-items: center; flex-wrap: wrap; gap: 3px 10px; min-width: 0; } +.legendItem { transition: opacity .12s; } +.legendName { display: flex; align-items: center; gap: 5px; border: 0; border-radius: 5px; padding: 5px 2px; background: none; color: inherit; font-size: 10px; cursor: pointer; } +.legendName:hover, .legendName[aria-expanded="true"] { background: #f0f3e9; } +.legendName output { font: 10px monospace; font-variant-numeric: tabular-nums; min-width: 32px; } +.legendMore { color: #9aa18e; font-size: 15px; line-height: 10px; } +.modelColor { display: inline-block; width: 7px; height: 7px; flex-shrink: 0; background: currentColor; border-radius: 50%; } +.noModels { color: #929b88; font-size: 10px; } +.modelPopover { position: fixed; z-index: 100; width: min(360px, calc(100vw - 24px)); max-height: calc(100vh - 24px); overflow-y: auto; padding: 14px; border: 1px solid #dfe4d6; border-radius: 12px; background: #fffefb; box-shadow: 0 12px 40px #33402a26, 0 2px 8px #33402a0d; color: #46563e; font-family: inherit; font-size: 12px; } +.popoverHeading { display: flex; align-items: center; justify-content: space-between; gap: 12px; margin-bottom: 12px; } +.popoverHeading strong { font-size: 13px; font-weight: 600; } +.popoverHeading button { display: grid; place-items: center; width: 26px; height: 26px; border: 0; border-radius: 5px; background: #f4f6ee; font-size: 19px; color: #7c8872; cursor: pointer; } +.modelSearch { box-sizing: border-box; width: 100%; padding: 8px 10px; border: 1px solid #dfe5d7; border-radius: 7px; color: #526448; background: #fbfcf7; font: inherit; font-size: 12px; } +.modelSearch:focus-visible { outline: 2px solid #9aac87; outline-offset: 1px; } +.pickerActions { display: flex; align-items: center; gap: 9px; padding: 11px 0 7px; font-size: 10px; color: #8b9580; } +.pickerActions > span { margin-right: auto; } +.pickerActions button { padding: 2px 0; border: 0; background: none; color: #637e4d; cursor: pointer; font: inherit; } +.pickerActions button:disabled { opacity: .4; cursor: default; } +.modelOptions { max-height: min(360px, 50vh); overflow-y: auto; scrollbar-width: thin; } +.modelOptions fieldset { border: 0; margin: 6px 0 0; padding: 0; min-width: 0; } +.modelOptions legend { padding: 5px 0; color: #8c9781; font-size: 10px; } +.modelOption { display: flex; align-items: center; gap: 9px; padding: 8px 6px; border-radius: 6px; cursor: pointer; } +.modelOption:hover { background: #f4f7ed; } +.modelOption:has(input:disabled) { opacity: .55; cursor: default; } +.modelOption input { accent-color: #748c5f; margin: 0; width: 14px; height: 14px; } +.modelOption > span { display: flex; flex-direction: column; gap: 3px; min-width: 0; } +.modelOption strong { font-size: 11px; font-weight: 550; } +.modelOption small { font-size: 10px; color: #8c9781; } +.pickerHint { margin: 12px 0 0; border-top: 1px solid #ecede5; padding-top: 10px; color: #929b88; font-size: 10px; } +.noSearchResults { padding: 18px 0; text-align: center; color: #929b88; } +.modelExplanation { margin: 0 0 14px; line-height: 1.8; color: #839175; font-size: 11px; } +.thresholdSetting { display: flex; align-items: center; justify-content: space-between; gap: 12px; margin: 8px 0 14px; color: #66775a; font-size: 11px; } +.thresholdSetting select { border: 1px solid #dfe5d7; border-radius: 6px; padding: 6px 8px; background: #f9fbf4; color: #526448; font: inherit; } +.modelMenuAction { display: block; width: 100%; padding: 9px 6px; text-align: left; border: 0; border-top: 1px solid #eef0e6; color: #637a50; background: none; cursor: pointer; font: inherit; font-size: 11px; } +.modelMenuAction:hover { background: #f4f7ed; } +.modelPopover button:focus-visible, .modelPopover select:focus-visible { outline: 2px solid #91a67c; outline-offset: 2px; } +@media (prefers-reduced-motion: reduce) { .legendItem { transition: none; } } diff --git a/labels/eot-studio/components/timeline.tsx b/labels/eot-studio/components/timeline.tsx index eebb3ac..8d6c6b9 100644 --- a/labels/eot-studio/components/timeline.tsx +++ b/labels/eot-studio/components/timeline.tsx @@ -11,8 +11,9 @@ import { type PointerEvent, } from "react"; import styles from "./timeline.module.css"; +import type { ModelId } from "@/lib/model-scores"; import { resizeTranscriptRange, snapTimelineTime, type SnapPoint } from "@/lib/timeline-edit"; -import ModelScoreTrack, { ScoreControls, SCORE_HEIGHT, useModelScores, type ScoreThresholds, type ScoreVisibility } from "./model-score-track"; +import ModelScoreTrack, { ScoreControls, SCORE_HEIGHT, useModelScores, useScorePreferences } from "./model-score-track"; export interface TimelineTranscript { index: number; @@ -90,8 +91,8 @@ export default function Timeline({ const helpId = useId(); const scoreRegionId = useId(); const [scoresOpen, setScoresOpen] = useState(false); - const [thresholds, setThresholds] = useState({ maai: .5, smart_turn: .5, silero_vad: .5 }); - const [scoreVisibility, setScoreVisibility] = useState({ maai: true, smart_turn: true, silero_vad: true }); + const { thresholds, visibility: scoreVisibility, setVisibility, setThreshold } = useScorePreferences(); + const [focusedModel, setFocusedModel] = useState(null); const scores = useModelScores(callId, recordingPath, scoresOpen); const visibleScores = useMemo(() => scores.series.filter(row => scoreVisibility[row.model]), [scores.series, scoreVisibility]); const viewportRef = useRef(null); @@ -336,15 +337,15 @@ export default function Timeline({ - 模型分数对比{scoresOpen ? "收起" : "展开 VAP / Smart Turn / Silero VAD 对比"} + 模型分数对比{scoresOpen ? "收起" : "展开评分曲线"} {scoresOpen && setScoreVisibility(previous => ({ ...previous, [model]: visible }))} - thresholds={thresholds} onThreshold={(model, value) => setThresholds(previous => ({ ...previous, [model]: value }))} />} + visibility={scoreVisibility} onVisibility={setVisibility} + thresholds={thresholds} onThreshold={setThreshold} focusedModel={focusedModel} onFocus={setFocusedModel} loading={scores.status === "loading"} />}
{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 表示完成倾向；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：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(" ")}}
}
@@ -452,9 +453,9 @@ export default function Timeline({
{scoresOpen &&
- {visibleScores.length ? row.model === focusedModel) ? focusedModel : null} series={visibleScores} width={canvasWidth} durationMs={duration} currentMs={current} thresholds={thresholds} annotations={annotations} onSeek={onSeek} onSelectAnnotation={onSelectAnnotation} /> - :
{scores.status === "loading" ? "正在载入评分曲线…" : scores.series.length ? "已隐藏全部模型分数，请打开上方开关查看" : "当前录音暂无可显示的模型分数"}
} + :
{scores.status === "loading" ? "正在载入评分曲线…" : scores.series.length ? "已隐藏全部模型分数，请在「选择模型」中添加" : "当前录音暂无可显示的模型分数"}
}
} {activeSnap && - {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 更新。Silero 空心点取判断前最后完整帧；其他模型原标注点评分使用独立历史窗口，与连续曲线可能不同；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 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。

}
播放 {timeLabel(current, true)}
diff --git a/labels/eot-studio/lib/model-scores.ts b/labels/eot-studio/lib/model-scores.ts index 3225814..4a05897 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"; +export type ModelId = "maai" | "smart_turn" | "silero_vad" | "livekit_mini" | "turnsense"; export type ScorePoint = [timeMs: number, score: number]; export interface ScoreCall { diff --git a/labels/eot-studio/lib/score-models.ts b/labels/eot-studio/lib/score-models.ts new file mode 100644 index 0000000..727c44a --- /dev/null +++ b/labels/eot-studio/lib/score-models.ts @@ -0,0 +1,22 @@ +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。" }, + 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。" }, + turnsense: { name: "TurnSense 1.0", family: "话轮检测", color: "#b65378", directory: "turnsense-1.0", thresholds: [.5, .8], defaultThreshold: .5, defaultVisible: true, description: "FP32 · P(complete)；最近 8 秒用户音频，每 200 ms 更新。参考线不等同原生三分类 argmax。" }, +}; +export const SCORE_MODEL_IDS = Object.keys(SCORE_MODELS) as ModelId[]; +export interface ScorePreferences { visibility: Record; thresholds: Record } +export function readScorePreferences(value: unknown): ScorePreferences { + const saved = value && typeof value === "object" && "version" in value && value.version === 1 + ? value as { visibility?: Record; thresholds?: Record } : {}; + return { + visibility: Object.fromEntries(SCORE_MODEL_IDS.map(id => [id, typeof saved.visibility?.[id] === "boolean" ? saved.visibility[id] : SCORE_MODELS[id].defaultVisible])) as Record, + thresholds: Object.fromEntries(SCORE_MODEL_IDS.map(id => [id, SCORE_MODELS[id].thresholds.includes(saved.thresholds?.[id] as number) ? saved.thresholds![id] : SCORE_MODELS[id].defaultThreshold])) as Record, + }; +} diff --git a/labels/eot-studio/tests/model-scores.test.ts b/labels/eot-studio/tests/model-scores.test.ts index b25ca8c..af0dbeb 100644 --- a/labels/eot-studio/tests/model-scores.test.ts +++ b/labels/eot-studio/tests/model-scores.test.ts @@ -38,13 +38,15 @@ test("curve gaps are not connected and steps remain on the audio clock", () => { 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"], + ["maai", "maai-ch-kyoto"], ["smart_turn", "smart-turn-v3.2"], ["silero_vad", "silero-vad"], ["livekit_mini", "livekit-v1-mini"], ["turnsense", "turnsense-1.0"], ] 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 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) { assert.equal(data.calls.reduce((n, call) => n + call.checkpoints.length, 0), 44); for (const call of data.calls) { @@ -69,3 +71,25 @@ test("Silero checkpoints use only the last available frame, and speech is invert } } }); + + +test("LiveKit original checkpoints are real exact-time curve scores", async () => { + const data = parseScoreExport(JSON.parse(await readFile(new URL( + "../../../experiments/livekit-v1-mini/results/score-timeline.json", import.meta.url), "utf8")), "livekit_mini"); + assert.equal(data.sample_interval_ms, 200); + for (const call of data.calls) { + for (const [ms, score] of call.checkpoints) { + assert.deepEqual(call.points.find(point => point[0] === ms), [ms, score]); + } + } +}); + + +test("TurnSense curve checkpoints retain the complete probability at the exact decision", async () => { + const data = parseScoreExport(JSON.parse(await readFile(new URL( + "../../../experiments/turnsense-1.0/results/score-timeline.json", import.meta.url), "utf8")), "turnsense"); + assert.equal(data.sample_interval_ms, 200); + for (const call of data.calls) { + for (const point of call.checkpoints) assert.deepEqual(call.points.find(p => p[0] === point[0]), point); + } +}); diff --git a/labels/eot-studio/tests/score-preferences.test.ts b/labels/eot-studio/tests/score-preferences.test.ts new file mode 100644 index 0000000..7eac19a --- /dev/null +++ b/labels/eot-studio/tests/score-preferences.test.ts @@ -0,0 +1,28 @@ +import assert from "node:assert/strict"; +import test from "node:test"; +// @ts-expect-error Node's native TypeScript runner requires explicit extensions. +import { readScorePreferences } from "../lib/score-models.ts"; + +test("first visit starts with three comparison curves and each model's reference threshold", () => { + 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); +}); + +test("saved choices preserve clear-all and accept only supported reference thresholds", () => { + const saved = readScorePreferences({ version: 1, visibility: { maai: false, smart_turn: false, silero_vad: false, livekit_mini: false, turnsense: false }, + thresholds: { maai: .11, livekit_mini: .355, smart_turn: 99, turnsense: "0.8" } }); + assert.equal(Object.values(saved.visibility).some(Boolean), false); + assert.equal(saved.thresholds.maai, .11); + assert.equal(saved.thresholds.smart_turn, .5); + assert.equal(saved.thresholds.turnsense, .5); +}); + +test("partial and invalid preferences leave future models on their defaults", () => { + const saved = readScorePreferences({ version: 1, visibility: { maai: true, smart_turn: "false", unknown: true }, thresholds: { turnsense: .8 } }); + assert.equal(saved.visibility.smart_turn, true); + assert.equal(saved.visibility.turnsense, true); + assert.equal(saved.thresholds.turnsense, .8); + assert.equal("unknown" in saved.visibility, false); + assert.deepEqual(readScorePreferences({ version: 999, visibility: { maai: false } }), readScorePreferences(null)); +});