| """Microbenchmark: is this container CPU-throttled? Whisper tiny + piper RTFs |
| in isolation on the e2e flavor, plus piper's ORT session options.""" |
|
|
| import json |
| import os |
| import time |
| import urllib.request |
| from pathlib import Path |
|
|
| ROOT = Path("/tmp/dbgperf") |
| VOICE_DIR = ROOT / "voices" |
|
|
|
|
| def fetch(url, dest): |
| dest.parent.mkdir(parents=True, exist_ok=True) |
| with urllib.request.urlopen(url, timeout=120) as r: |
| dest.write_bytes(r.read()) |
|
|
|
|
| print("os.cpu_count():", os.cpu_count(), flush=True) |
| try: |
| print("cgroup cpu.max:", open("/sys/fs/cgroup/cpu.max").read().strip(), flush=True) |
| except OSError: |
| print("no cgroup v2 cpu.max", flush=True) |
|
|
| for name in ("en/en_US/amy/medium/en_US-amy-medium.onnx", |
| "en/en_US/amy/medium/en_US-amy-medium.onnx.json"): |
| fetch(f"https://huggingface.co/rhasspy/piper-voices/resolve/main/{name}", |
| VOICE_DIR / Path(name).name) |
|
|
| from piper import PiperVoice |
| import inspect |
|
|
| print("PiperVoice.load signature:", inspect.signature(PiperVoice.load), flush=True) |
| try: |
| src = inspect.getsource(PiperVoice.load) |
| print("--- PiperVoice.load source ---") |
| print(src[:2000], flush=True) |
| except Exception as e: |
| print("no source:", e, flush=True) |
|
|
| voice = PiperVoice.load(str(VOICE_DIR / "en_US-amy-medium.onnx")) |
| SENT = "Hi everyone, I'm Streamy, an AI assistant." |
| for run in range(3): |
| t0 = time.perf_counter() |
| chunks = list(voice.synthesize(SENT)) |
| n = sum(len(c.audio_int16_bytes) for c in chunks) |
| dt = time.perf_counter() - t0 |
| print(f"piper synth {run}: {dt*1000:.0f}ms for {n/2/22050:.2f}s audio " |
| f"(RTF {dt / (n/2/22050):.2f})", flush=True) |
|
|
| from piper.voice import AudioChunk |
| import numpy as np |
|
|
| pcm = np.concatenate([np.frombuffer(c.audio_int16_bytes, dtype=np.int16) |
| for c in chunks]).astype(np.float32) / 32768.0 |
| np.save("/tmp/dbg_perf_speech.npy", pcm.astype(np.float32)) |
|
|
| from faster_whisper import WhisperModel |
|
|
| for threads in (1, 2, 4, 8): |
| model = WhisperModel("tiny", device="cpu", compute_type="int8", cpu_threads=threads) |
| for rep in range(2): |
| t0 = time.perf_counter() |
| segs, _ = model.transcribe(pcm, language="en", beam_size=1, |
| condition_on_previous_text=False) |
| text = " ".join(s.text.strip() for s in segs).strip() |
| dt = time.perf_counter() - t0 |
| print(f"whisper tiny threads={threads} rep={rep}: {dt*1000:.0f}ms " |
| f"(RTF {dt / (len(pcm)/16000):.2f}) '{text[:40]}'", flush=True) |
|
|
| print("[dbg] done", flush=True) |
