v0.1.0

tests/debug_perf.py

@@ -0,0 +1,72 @@
+"""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  # noqa: E402
+import inspect  # noqa: E402
+
+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  # noqa: E402
+import numpy as np  # noqa: E402
+
+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  # noqa: E402
+
+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)