Guía de programación de Cactus Whistle: ¿Un modelo de voz de 16,9 MB resiste frente a Whisper Base? Tasa de error de palabras, velocidad, transmisión, marcas de tiempo, palabras clave y llamadas de voz a herramientas en CPU

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In this tutorial, we use Whistle, Cactus Compute’s speech-to-text model, a 16.9 MB file that runs on a CPU, transcribes seven languages, and returns word timestamps and probabilities. The model card claims it beats Whisper base on LibriSpeech; we test it on seeded test-clean and test-other samples, comparing Whistle with Whisper tiny and base on identical clips, judged by paired speaker-cluster intervals with Holm correction. We find a lead over greedy Whisper base on test-other, no significant gap against beam search, and a speed advantage that narrows as clips grow. Beyond the headline, we probe a 30-second window where the pinned engine silently drops the middle of longer audio, plus streaming latency, timestamps, keyword biasing, other languages, noise, embeddings, and speech-to-tool calls. Our figures are one-run samples from an Apple M4, and published full-split numbers are quoted, not measured. Copy CodeCopiedUse a different Browserimport os import io import re import sys import json import math import time import random import hashlib import shutil import zipfile import platform import traceback import subprocess import collections import urllib.error import urllib.request RESULTS = {} VERDICTS = [] def banner(title): print("\n" + "=" * 78) print(title) print("=" * 78) def section(name): def wrap(fn): def run(*a, **kw): banner(name) t0 = time.time() try: out = fn(*a, **kw) RESULTS[name] = out if isinstance(out, str) else "ok" return out except Exception as e: RESULTS[name] = f"SKIPPED / FAILED -> {type(e).__name__}: {e}" print(f"\n[!] {name} did not complete: {type(e).__name__}: {e}") traceback.print_exc(limit=3) return None finally: print(f"\n[{time.time() - t0:.0f} s]") json.dump({"results": RESULTS, "verdicts": VERDICTS}