wangjia
25acf9db6e
横向对比云端 gummy 与本地开源模型(faster-whisper/SenseVoice/Paraformer)， 重点覆盖中英混说，产出准确率(CER/WER/MER)/速度(延迟/RTF)/资源(cpu/mem/模型大小) 对比报告。公共集(ASCEND/AISHELL/LibriSpeech)统一走 HF 适配器 + 自定义 JSONL manifest。 gummy 引擎对照 server/internal/asr/gummy.go 协议移植。 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
86 lines
2.6 KiB
Python
86 lines
2.6 KiB
Python
"""资源探针：本地引擎推理时采样峰值内存/CPU；模型大小读磁盘。
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注意：psutil 测的是整个 Python 进程 RSS（含解释器与已加载库），用于本地模型横向对比是
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合理代理量；报告会标注硬件环境，且只在本地引擎启用，云端 gummy 不测（填 N/A）。
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"""
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from __future__ import annotations
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import os
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import threading
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import time
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import psutil
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class ResourceProbe:
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"""上下文管理器：with 块内后台采样进程 RSS / CPU%。"""
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def __init__(self, interval: float = 0.05):
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self.interval = interval
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self._stop = threading.Event()
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self._thread: threading.Thread | None = None
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self._proc = psutil.Process(os.getpid())
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self.peak_rss = 0
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self._cpu: list[float] = []
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def __enter__(self) -> "ResourceProbe":
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self._proc.cpu_percent(None) # 预热：首次调用返回 0，丢弃
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self._thread = threading.Thread(target=self._run, daemon=True)
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self._thread.start()
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return self
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def _run(self) -> None:
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while not self._stop.is_set():
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try:
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self.peak_rss = max(self.peak_rss, self._proc.memory_info().rss)
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c = self._proc.cpu_percent(None)
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if c > 0:
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self._cpu.append(c)
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except Exception:
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pass
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time.sleep(self.interval)
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def __exit__(self, *_exc) -> None:
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self._stop.set()
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if self._thread:
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self._thread.join(timeout=1.0)
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@property
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def peak_rss_mb(self) -> float:
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return self.peak_rss / 1024 / 1024
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@property
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def avg_cpu(self) -> float:
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return sum(self._cpu) / len(self._cpu) if self._cpu else 0.0
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def dir_size_mb(path: str) -> float:
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"""目录下所有文件体积之和（MB）；路径不存在返回 0。"""
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total = 0
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if not path or not os.path.exists(path):
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return 0.0
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if os.path.isfile(path):
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return os.path.getsize(path) / 1024 / 1024
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for root, _dirs, files in os.walk(path):
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for f in files:
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try:
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total += os.path.getsize(os.path.join(root, f))
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except OSError:
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pass
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return total / 1024 / 1024
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def hardware_info() -> dict:
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"""报告里标注的硬件环境。"""
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import platform
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vm = psutil.virtual_memory()
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return {
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"platform": platform.platform(),
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"machine": platform.machine(),
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"cpu_count": psutil.cpu_count(logical=True),
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"cpu_count_physical": psutil.cpu_count(logical=False),
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"total_mem_gb": round(vm.total / 1024**3, 1),
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}
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