#!/usr/bin/env python3 """P0 基准 · STT 与 TTS 并发。 为什么必须测： 真实系统里助手说话时麦克风并没有关——要检测打断，STT 必须同时在跑。 这台机器只有 4 个核，且 bench_threads.py 已经证明 STT 超过 2 线程就会急剧退化。 那么"STT(2线程) + TTS(2线程)"极可能互相踩踏。 本脚本把两种负载跑在一起，和各自单独跑的结果对比，量化退化幅度。 只看单独指标会得出"两个都能实时"的错误结论。 """ import os import statistics import threading import time import wave import numpy as np import sherpa_onnx HERE = os.path.dirname(os.path.abspath(__file__)) STT = os.path.join(HERE, "models", "stt") WAVS = os.path.join(HERE, "wavs") SR = 16000 CHUNK = int(SR * 0.1) TTS_SENTENCES = [ "我查了一下，明天上午十点有个会议。", "地点在三号会议室，需要提前十分钟到。", "另外提醒你，后天是妈妈的生日。", "要不要我提前帮你订一束花？", "还有你上周说要买的那本书现在有货了。", "价格是四十九块八，要不要下单？", ] def read_wav(p): with wave.open(p, "rb") as w: return np.frombuffer(w.readframes(w.getnframes()), dtype=np.int16).astype(np.float32) / 32768.0 def build_stt(nt): return sherpa_onnx.OnlineRecognizer.from_transducer( tokens=os.path.join(STT, "tokens.txt"), encoder=os.path.join(STT, "encoder-epoch-99-avg-1.int8.onnx"), decoder=os.path.join(STT, "decoder-epoch-99-avg-1.int8.onnx"), joiner=os.path.join(STT, "joiner-epoch-99-avg-1.int8.onnx"), num_threads=nt, sample_rate=SR, feature_dim=80, decoding_method="greedy_search", enable_endpoint_detection=False) def build_tts(which, nt): if which == "matcha": d = os.path.join(HERE, "models", "matcha") matcha = sherpa_onnx.OfflineTtsMatchaModelConfig( acoustic_model=os.path.join(d, "model-steps-3.onnx"), vocoder=os.path.join(HERE, "models", "matcha_vocoder", "hifigan_v2.onnx"), lexicon=os.path.join(d, "lexicon.txt"), tokens=os.path.join(d, "tokens.txt"), dict_dir=os.path.join(d, "dict")) return sherpa_onnx.OfflineTts(sherpa_onnx.OfflineTtsConfig( model=sherpa_onnx.OfflineTtsModelConfig(matcha=matcha, num_threads=nt, provider="cpu"), rule_fsts=",".join(os.path.join(d, f) for f in ("date.fst", "number.fst", "phone.fst")), max_num_sentences=1)) if which == "kokoro_int8": d = os.path.join(HERE, "models", "kokoro_int8") kokoro = sherpa_onnx.OfflineTtsKokoroModelConfig( model=os.path.join(d, "model.int8.onnx"), voices=os.path.join(d, "voices.bin"), tokens=os.path.join(d, "tokens.txt"), data_dir=os.path.join(d, "espeak-ng-data"), dict_dir=os.path.join(d, "dict"), lexicon=",".join(os.path.join(d, f) for f in ("lexicon-us-en.txt", "lexicon-gb-en.txt", "lexicon-zh.txt"))) return sherpa_onnx.OfflineTts(sherpa_onnx.OfflineTtsConfig( model=sherpa_onnx.OfflineTtsModelConfig(kokoro=kokoro, num_threads=nt, provider="cpu"), max_num_sentences=1)) if which == "piper": d = os.path.join(HERE, "models", "tts_piper_xlow") vits = sherpa_onnx.OfflineTtsVitsModelConfig( model=os.path.join(d, "zh_CN-huayan-x_low.onnx"), lexicon=os.path.join(d, "lexicon.txt"), tokens=os.path.join(d, "tokens.txt")) return sherpa_onnx.OfflineTts(sherpa_onnx.OfflineTtsConfig( model=sherpa_onnx.OfflineTtsModelConfig(vits=vits, num_threads=nt, provider="cpu"), max_num_sentences=1)) d = os.path.join(HERE, "models", "tts") vits = sherpa_onnx.OfflineTtsVitsModelConfig( model=os.path.join(d, "model.onnx"), lexicon=os.path.join(d, "lexicon.txt"), tokens=os.path.join(d, "tokens.txt"), dict_dir=os.path.join(d, "dict")) return sherpa_onnx.OfflineTts(sherpa_onnx.OfflineTtsConfig( model=sherpa_onnx.OfflineTtsModelConfig(vits=vits, num_threads=nt, provider="cpu"), rule_fsts=",".join(os.path.join(d, f) for f in ("date.fst", "number.fst", "phone.fst", "new_heteronym.fst")), max_num_sentences=1)) def stt_pass(rec, samples): """喂一遍音频，返回 (rtf, 单块解码耗时列表)。""" s = rec.create_stream() durs = [] pos = 0 t_all = time.perf_counter() while pos < len(samples): block = samples[pos:pos + CHUNK] pos += CHUNK t0 = time.perf_counter() s.accept_waveform(SR, block) while rec.is_ready(s): rec.decode_stream(s) durs.append(time.perf_counter() - t0) total = time.perf_counter() - t_all return total / (len(samples) / SR), durs def tts_pass(tts, sents): """连续合成若干句，返回 (rtf, 每句耗时ms)。""" per = [] audio_s = 0.0 t_all = time.perf_counter() for s in sents: t0 = time.perf_counter() a = tts.generate(s, sid=0, speed=1.0) per.append((time.perf_counter() - t0) * 1000) audio_s += len(a.samples) / a.sample_rate total = time.perf_counter() - t_all return total / audio_s, per def main(): stt_nt = int(os.environ.get("STT_THREADS", "2")) tts_nt = int(os.environ.get("TTS_THREADS", "2")) tts_kind = os.environ.get("TTS_MODEL", "piper") wav = read_wav(os.path.join(WAVS, "stt_0.wav")) dur = len(wav) / SR print(f"音频 {dur:.2f}s STT线程={stt_nt} TTS={tts_kind}线程={tts_nt}\n") stt = build_stt(stt_nt) tts = build_tts(tts_kind, tts_nt) stt_pass(stt, wav[: SR * 2]) # 预热 tts.generate("预热", sid=0, speed=1.0) # 单独 solo_stt, _ = stt_pass(stt, wav) solo_tts, _ = tts_pass(tts, TTS_SENTENCES) print(f"单独: STT RTF={solo_stt:.2f} TTS RTF={solo_tts:.2f}") # 并发：TTS 在后台线程持续合成，主线程跑 STT stop = threading.Event() tts_stats = {"runs": 0, "rtf": [], "done": False} def tts_worker(): try: while not stop.is_set(): rtf, _ = tts_pass(tts, TTS_SENTENCES) tts_stats["rtf"].append(rtf) tts_stats["runs"] += 1 if tts_stats["runs"] >= 3: break finally: tts_stats["done"] = True th = threading.Thread(target=tts_worker) th.start() time.sleep(0.2) # 让 TTS 先跑起来，制造真实争抢 conc_rtfs, conc_durs = [], [] for _ in range(3): r, d = stt_pass(stt, wav) conc_rtfs.append(r) conc_durs.extend(d) stop.set() th.join() conc_tts = statistics.median(tts_stats["rtf"]) if tts_stats["rtf"] else float("nan") stt_med = statistics.median(conc_rtfs) ratios = sorted(x / (CHUNK / SR) for x in conc_durs) print(f"并发: STT RTF={stt_med:.2f} (×{stt_med/solo_stt:.2f}) " f"TTS RTF={conc_tts:.2f} (×{conc_tts/solo_tts:.2f})") print(f" 并发下单块解码 p50={statistics.median(ratios):.2f} " f"p95={ratios[int(len(ratios)*0.95)]:.2f} max={ratios[-1]:.2f}（>1 表示积压）") # 判定口径：看 RTF（聚合力，<1 表示跟得上）。单块 p95 只作观察项—— # 流式模型并非每块都解码（实测约每 3 块解一次），所以单块 >1 不等于积压。 # 留 0.7 的余量，因为还要给视觉检测等其它负载留 CPU。 if stt_med < 0.7 and conc_tts < 0.7: verdict = f"✅ 并发可实时（STT 余量 {1/stt_med:.1f}x，TTS 余量 {1/conc_tts:.1f}x）" elif stt_med < 1 and conc_tts < 1: verdict = "△ 并发勉强够用，视觉检测再加负载有风险" else: verdict = "⚠ 并发跟不上实时" print(f"\n判定: {verdict}") import json out = os.path.join(HERE, f"results_concurrent_{tts_kind}.json") with open(out, "w") as f: json.dump({"stt_threads": stt_nt, "tts_threads": tts_nt, "tts_model": tts_kind, "solo_stt_rtf": solo_stt, "solo_tts_rtf": solo_tts, "conc_stt_rtf": stt_med, "conc_tts_rtf": conc_tts, "stt_degrade": stt_med / solo_stt, "tts_degrade": conc_tts / solo_tts, "conc_chunk_p95": ratios[int(len(ratios) * 0.95)], "conc_chunk_max": ratios[-1]}, f, ensure_ascii=False, indent=2) print(f"结果已写入 {out}") if __name__ == "__main__": main()