RadarAI· MarkTechPost（RSS）·· 3 小时前AI 评分58
Whistle 语音识别实测：16.9 MB CPU 模型与 Whisper base 的 WER、速度、流式与工具调用对比
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MarkTechPost 在 Apple M4 的 CPU 上实测 Cactus Compute 的 16.9 MB 语音转写模型 Whistle，与 Whisper tiny、base 在 LibriSpeech 同一批音频上对比。
正文 · AI 翻译
MarkTechPost· Sana Hassan·2026-10-22 12:17· 57 分钟前AI 评分65
Cactus Whistle 编码指南：16.9 MB 的语音模型能否与 Whisper Base 抗衡？在 CPU 上的词错误率、速度、流式处理、时间戳、关键词与语音转工具调用
AI 导读
这篇教程在 Apple M4 的 CPU 上实测 Cactus Compute 的 16.9 MB 语音转写模型 Whistle，用配对说话人聚类 bootstrap 加 Holm 校正与 Whisper tiny 和 base 对比。
正文 · 原文
在本教程中，我们使用 Whistle，这是 Cactus Compute 的语音转文本模型，一个 16.9 MB 的文件，可在 CPU 上运行，可转写七种语言，并返回词级时间戳和概率。模型卡声称它在 LibriSpeech 上优于 Whisper base；我们在带种子的 test-clean 和 test-other 样本上测试它，在相同音频片段上比较 Whistle 与 Whisper tiny 和 base，采用配对说话人聚类区间加 Holm 校正来评判。我们发现它在 test-other 上领先贪心解码的 Whisper base，与束搜索相比没有显著差距，并且速度优势随片段变长而缩小。除主要结论外，我们还探查了一个 30 秒窗口，其中固定版本的引擎会静默丢弃较长音频的中间部分，此外还考察了流式延迟、时间戳、关键词偏置、其他语言、噪声、嵌入以及语音转工具调用。我们的数据是来自 Apple M4 的单次运行样本，已发表的完整划分数字为引用而非实测。
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import 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}, open("results.json", "w"), indent=1)
return run
return wrap
banner("1. Install Whistle, pin its weights and engine by Hub revision, transcribe one clip")
subprocess.run([sys.executable, "-m", "pip", "install", "-q", "cactus-needle==3.2.0", "openai-whisper==20250625",
"jiwer==4.0.0", "soundfile==0.14.0", "soxr==1.1.0"], check=True)
os.environ["NEEDLE_TELEMETRY"] = "0" # the package sends anonymous usage counts unless told not to
os.environ["HF_HUB_DISABLE_PROGRESS_BARS"] = "1"
import numpy as np
import soundfile as sf
import soxr
from huggingface_hub import hf_hub_download
# cactus-needle fetches three files on first use, from Hugging Face repos whose files are replaced in place: the 16.9 MB
# whistle.cact weights, the ~1 MB engine library (version 3.3.0 in this package; the needle3 repo received new binaries
# on 2026-10-10) and the 35 MB needle3.cact used in step 12. Pinning the Hub revision of each, and printing the hashes,
# is what makes this run reproducible. The engine version is read from the package, not typed in.
WHISTLE_REV = "ca5287601bef25af26dcf2e1b2bdc0843a7c19e5" # Cactus-Compute/whistle, 2026-10-10
NEEDLE3_REV = "93e88ede867e4311c39151285e74df513e4b835b" # Cactus-Compute/needle3 (engine + Needle 3 weights), 2026-10-10
from needle.agent import fetch as needle_fetch
TAG = needle_fetch._platform_tag()
ENGINE_VERSION = needle_fetch.engine_version(3)
weights = hf_hub_download("Cactus-Compute/whistle", "whistle.cact", revision=WHISTLE_REV)
wheel = hf_hub_download("Cactus-Compute/needle3", needle_fetch.engine_wheel(ENGINE_VERSION, TAG, 3), revision=NEEDLE3_REV)
with zipfile.ZipFile(wheel) as z:
member = next(n for n in z.namelist() if n.startswith("needle/libneedle"))
os.makedirs("engine", exist_ok=True)
engine = os.path.abspath(os.path.join("engine", os.path.basename(member)))
open(engine, "wb").write(z.read(member))
needle3_weights = hf_hub_download("Cactus-Compute/needle3", "needle3.cact", revision=NEEDLE3_REV)
os.makedirs(needle_fetch.cache_dir(3), exist_ok=True) # Needle(tools=...) reads its weights from this cache path
shutil.copyfile(needle3_weights, os.path.join(needle_fetch.cache_dir(3), needle_fetch.base_weights(3)))
os.environ["NEEDLE_WHISTLE_WEIGHTS"] = weights # both variables are honoured by the package
os.environ["NEEDLE3_LIB_PATH"] = engine
import needle
import importlib.metadata as md
sha = lambda p: hashlib.sha256(open(p, "rb").read()).hexdigest()[:12]
CPU = platform.processor() or platform.machine()
if sys.platform == "linux" and os.path.exists("/proc/cpuinfo"):
CPU = next((l.split(":", 1)[1].strip() for l in open("/proc/cpuinfo") if l.startswith("model name")), CPU)
elif sys.platform == "darwin":
CPU = subprocess.run(["sysctl", "-n", "machdep.cpu.brand_string"], capture_output=True, text=True).stdout.strip() or CPU
print(f"cactus-needle {needle.__version__} (engine {ENGINE_VERSION}, wheel tag {TAG}) python {platform.python_version()} "
f"soundfile {md.version('soundfile')} soxr {md.version('soxr')} numpy {np.__version__}")
print(f"hardware: {CPU}, {os.cpu_count()} cpus visible")
print(f"whistle.cact {os.path.getsize(weights)/1e6:.1f} MB sha256 {sha(weights)} @ {WHISTLE_REV[:12]}")
print(f"engine {os.path.getsize(engine)/1e6:.1f} MB sha256 {sha(engine)} @ {NEEDLE3_REV[:12]}")
print(f"needle3.cact {os.path.getsize(needle3_weights)/1e6:.1f} MB sha256 {sha(needle3_weights)} @ {NEEDLE3_REV[:12]}")
SR = 16000
W = needle.Whistle() # one model per process, not thread-safe; every later step reuses this object
def fetch(url, tries=7):
"""GET with retries. The datasets-server rate-limits anonymous callers (HTTP 429): honour Retry-After when it is
sent, otherwise back off 5, 10, 20, 40 ... seconds, and never give up on the first refusal."""
for k in range(tries):
try:
with urllib.request.urlopen(url, timeout=60) as r:
return r.read()
except urllib.error.HTTPError as e:
if k == tries - 1:
raise
wait = int(e.headers.get("Retry-After", 0) or 0) if e.code == 429 else 0
time.sleep(max(wait, 5 * 2 ** k) if e.code in (429, 500, 502, 503, 504) else 2)
except Exception:
if k == tries - 1:
raise
time.sleep(3 * (k + 1))
def page(dataset, config, split, offset, length=100):
"""One page of a Hub dataset through the datasets-server rows API: metadata plus signed audio URLs."""
url = (f"https://datasets-server.huggingface.co/rows?dataset={dataset}&config={config}"
f"&split={split}&offset={offset}&length={length}")
time.sleep(0.25) # stay under the anonymous rate limit when paging a whole split
return json.loads(fetch(url))
def load_audio(row):
"""Download one row's clip; return 16 kHz mono float32 samples and the sha256 of the original file."""
data = fetch(row["audio"][0]["src"])
audio, sr = sf.read(io.BytesIO(data), dtype="float32")
if audio.ndim > 1:
audio = audio.mean(axis=1)
if sr != SR:
audio = soxr.resample(audio, sr, SR, quality="HQ")
return audio, hashlib.sha256(data).hexdigest()[:12]
first = page("openslr/librispeech_asr", "clean", "test", 0, 1)["rows"][0]["row"]
clip, _ = load_audio(first)
t0 = time.perf_counter()
out = W.transcribe(clip, word_timestamps=True)
wall = (time.perf_counter() - t0) * 1000
print(f"\nclip {first['id']} {len(clip)/SR:.1f} s reference: {first['text']}")
print(f"-> {out['text']!r}")
print(f" language {out['language']} engine time to first token {out['ttft_ms']:.1f} ms decode {out['decode_tps']:.0f} tok/s"
f" wall {wall:.0f} ms")
print(" first words:", [(w["word"], w["start"], w["end"], w["probability"]) for w in out["words"][:3]])
RESULTS["1. Install Whistle, pin its weights and engine by Hub revision, transcribe one clip"] = \
f"{os.path.getsize(weights)/1e6:.1f} MB weights, {len(clip)/SR:.1f} s clip in {wall:.0f} ms"
我们安装 cactus-needle 3.2.0 并固定其依赖版本，同时关闭其匿名遥测。该包从 Hugging Face 仓库下载 Whistle 的权重、其引擎以及 Needle 3 的权重，这些仓库的文件会被原地替换，因此我们将两个仓库都固定到特定修订版本并打印 SHA-256 哈希。page() 辅助函数通过带限流退避的方式读取 datasets-server 的行 API，load_audio() 将片段重采样到 16 kHz。第一个 test-clean 片段是 3.5 秒的语音，转写耗时 33 毫秒实际时间，每个词都带有其开始、结束和概率。
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import jiwer
import whisper
import torch
from whisper.normalizers import EnglishTextNormalizer, BasicTextNormalizer
NORM_EN = EnglishTextNormalizer() # the normalisers Whistle's own benchmarks use; applied to both sides, always
NORM_BASIC = BasicTextNormalizer()
SEED = 2026
N_WHISTLE = 300 # clips per LibriSpeech split that Whistle transcribes (it is cheap)
N_PAIRED = {"other": 100, "clean": 60} # the first N of them also go through Whisper (it is not); test-other is primary
MAX_CLIP_S = 30.0 # Whistle's single-pass window; longer clips are declared out of scope here
WHISPER_SECONDS = collections.Counter()
def all_rows(dataset, config, split):
out, offset = [], 0
while True:
rows = page(dataset, config, split, offset)["rows"]
out += [r["row"] for r in rows]
offset += 100
if len(rows) < 100:
return out
def dataset_sha(dataset):
return json.loads(fetch(f"https://huggingface.co/api/datasets/{dataset}"))["sha"][:12]
def wer_counts(ref, hyp):
"""Errors (S+D+I) and reference words for one normalised pair; an empty hypothesis is all deletions."""
n = len(ref.split())
if not hyp.strip():
return n, n
o = jiwer.process_words(ref, hyp)
return o.substitutions + o.deletions + o.insertions, n
def sdi(ref, hyp):
if not hyp.strip():
return 0, len(ref.split()), 0
o = jiwer.process_words(ref, hyp)
return o.substitutions, o.deletions, o.insertions
def corpus_wer(errs, words):
return 100.0 * sum(errs) / max(sum(words), 1)
def _by_cluster(clusters, *arrays):
tot = collections.defaultdict(lambda: [0.0] * len(arrays))
for i, c in enumerate(clusters):
for j, a in enumerate(arrays):
tot[c][j] += a[i]
return tot
def wer_ci(clusters, errs, words, B=2000, seed=0):
"""95% percentile interval of a corpus WER, resampling clusters (speakers) with replacement."""
tot = _by_cluster(clusters, errs, words)
keys, rng = sorted(tot), random.Random(seed)
boots = []
for _ in range(B):
d = rng.choices(keys, k=len(keys))
boots.append(100 * sum(tot[c][0] for c in d) / max(sum(tot[c][1] for c in d), 1))
return np.percentile(boots, [2.5, 97.5])
def paired_contrast(clusters, errs_a, words, errs_b, label_a, label_b, B=2000, seed=0):
"""WER(a) - WER(b) with a paired speaker-cluster bootstrap: the same resampled speakers for both systems."""
tot = _by_cluster(clusters, errs_a, errs_b, words)
keys, rng = sorted(tot), random.Random(seed)
boots = []
for _ in range(B):
d = rng.choices(keys, k=len(keys))
w = max(sum(tot[c][2] for c in d), 1)
boots.append(100 * (sum(tot[c][0] for c in d) - sum(tot[c][1] for c in d)) / w)
boots = np.array(boots)
lo, hi = np.percentile(boots, [2.5, 97.5])
p = 2 * min((boots <= 0).mean(), (boots >= 0).mean())
return {"a": label_a, "b": label_b, "diff": corpus_wer(errs_a, words) - corpus_wer(errs_b, words),
"lo": lo, "hi": hi, "p": max(p, 1 / B), "clusters": len(keys)}
def holm(ps, alpha=0.05):
order, keep = np.argsort(ps), [False] * len(ps)
for rank, i in enumerate(order):
if ps[i] <= alpha / (len(ps) - rank):
keep[i] = True
else:
break
return keep
def verdict(c, significant):
"""Verdict rule: diff = WER(other system) - WER(Whistle). CI entirely > 0 and Holm-significant -> Whistle ahead;
entirely < 0 and significant -> Whistle behind; otherwise no significant difference at this sample size."""
if c["lo"] > 0 and significant:
return "Whistle ahead"
if c["hi"] < 0 and significant:
return "Whistle behind"
return "no significant difference at this n"
def fmt_contrast(c):
return f"{c['diff']:+.2f} pts (95% CI {c['lo']:+.2f}..{c['hi']:+.2f}, {c['clusters']} speakers)"
@section("2. Fixed, seeded samples of LibriSpeech test-clean and test-other, and the rules for the verdicts")
def sample_english():
global EN
print("Rules for the verdicts below, written into this notebook before its final run (exploratory probes on the")
print("same splits came first; the prose says which):")
print(f" * simple random sample of {N_WHISTLE} rows per split (seed {SEED}); the first {N_PAIRED['other']} (test-other) and "
f"{N_PAIRED['clean']} (test-clean) are the paired Whisper subsets")
print(f" * clips longer than {MAX_CLIP_S:.0f} s are excluded and counted (Whistle's single-pass window), as are empty references")
print(" * the same normaliser on both sides of each comparison (Whisper's EnglishTextNormalizer for LibriSpeech, its")
print(" BasicTextNormalizer for MLS); corpus WER = summed errors / summed reference words")
print(" * interval: 95% speaker-cluster paired bootstrap, 2,000 draws, the same resampled speakers for every system")
print(" * verdict per contrast: unadjusted CI of WER(other) - WER(Whistle) entirely > 0 and Holm-adjusted p < 0.05 ->")
print(" Whistle ahead; entirely < 0 and significant -> behind; otherwise no significant difference at this n")
print(" family A (published claims): test-other and test-clean vs base greedy and vs base beam 5, MLS pooled vs base greedy;")
print(" family B: the keyword contrasts. Holm at alpha = 0.05 within each family. Whisper tiny rows are descriptive.\n")
global MANIFEST
EN, manifest = {}, {"dataset_sha": dataset_sha("openslr/librispeech_asr"), "seed": SEED, "splits": {}}
MANIFEST = manifest
for split in ("other", "clean"):
t0 = time.time()
rows = all_rows("openslr/librispeech_asr", split, "test")
idx = random.Random(SEED).sample(range(len(rows)), N_WHISTLE)
kept, too_long, empty = [], 0, 0
for i in idx:
r = rows[i]
audio, h = load_audio(r)
ref = NORM_EN(r["text"])
if len(audio) / SR > MAX_CLIP_S:
too_long += 1
continue
if not ref:
empty += 1
continue
kept.append({"id": r["id"], "spk": str(r["speaker_id"]), "text": r["text"], "ref": ref,
"audio": audio, "dur": len(audio) / SR, "sha": h})
EN[split] = kept
manifest["splits"][split] = {"rows": len(rows), "sampled": N_WHISTLE, "too_long": too_long, "empty_ref": empty,
"ids": [c["id"] for c in kept], "sha": [c["sha"] for c in kept]}
durs = [c["dur"] for c in kept]
pk = kept[:N_PAIRED[split]]
print(f"test-{split}: {len(rows)} rows -> {len(kept)} clips kept ({too_long} over {MAX_CLIP_S:.0f} s, {empty} empty), "
f"{len(set(c['spk'] for c in kept))} speakers, {sum(durs)/60:.1f} min, clip {np.mean(durs):.1f} s mean / {max(durs):.1f} s max"
f" [{time.time()-t0:.0f} s]")
print(f" paired subset: {len(pk)} clips, {len(set(c['spk'] for c in pk))} speakers, {sum(c['dur'] for c in pk):.0f} s of audio, "
f"{sum(len(c['ref'].split()) for c in pk)} reference words")
json.dump(manifest, open("manifest.json", "w"))
print(f"\nmanifest.json: LibriSpeech revision {manifest['dataset_sha']}, clip ids and file hashes (the MLS and MInDS-14 clips are added to it in")
print("steps 9 and 12), so a rerun can prove it used the same audio")
return f"{len(EN['other'])} + {len(EN['clean'])} clips, manifest written"
sample_english()
如果判定规则是在最终数字出来之后才设定的，那么结论就没什么意义，因此我们在最终运行之前就把规则写进了 notebook，此前进行了一些探索性探查，触及了部分相同片段。我们为每个划分抽取 300 行的带种子样本，并丢弃长于 Whistle 单次处理窗口的片段，每个划分各留下 299 个片段。配对子集，即前 100 个 test-other 和 60 个 test-clean 片段，经过 Whisper 处理，说话人较少使得其区间只是近似。只有当差异的 95% 配对说话人聚类 bootstrap 区间不包含零，且其 Holm 校正后的 p 值低于 0.05 时，才计为有效差异。
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WHISPER = {}
def whisper_model(size):
if size not in WHISPER:
WHISPER[size] = whisper.load_model(size, device="cpu")
return WHISPER[size]
def run_whisper(size, audio, language="en", **kw):
"""openai-whisper with its defaults (greedy with temperature fallback) unless kw says otherwise. Seeded per call,
because the fallback samples. Returns text, whether any segment needed the fallback, and the wall time."""
torch.manual_seed(0)
WHISPER_SECONDS[size] += len(audio) / SR
t0 = time.perf_counter()
r = whisper_model(size).transcribe(audio, language=language, fp16=False, **kw)
fell_back = any(s.get("temperature", 0) > 0 for s in r["segments"])
return r["text"], fell_back, time.perf_counter() - t0
SYSTEMS = {"other": [("whisper tiny, greedy", "tiny", {}), ("whisper base, greedy", "base", {}), ("whisper base, beam 5", "base", {"beam_size": 5})],
"clean": [("whisper tiny, greedy", "tiny", {}), ("whisper base, greedy", "base", {}), ("whisper base, beam 5", "base", {"beam_size": 5})]}
FAMILY_A = {"test-clean: Whistle vs whisper base, greedy", "test-clean: Whistle vs whisper base, beam 5",
"test-other: Whistle vs whisper base, greedy", "test-other: Whistle vs whisper base, beam 5",
"MLS pooled: Whistle vs whisper base, greedy"}
PUBLISHED = {"other": "Whistle 10.49 / Whisper base 11.0 (paper table 9, beam) / 12.4 (table 8, greedy)",
"clean": "Whistle 4.31 / Whisper base 4.9 (paper table 9, beam) / 5.0 (table 8, greedy)"}
CONTRASTS = []
@section("3. Word error rate against Whisper tiny and base, paired by clip, with a verdict per contrast")
def english_wer():
global HYP
HYP = {}
for split in ("other", "clean"):
clips = EN[split]
t0 = time.perf_counter()
hw = [W.transcribe(c["audio"], language="en")["text"] for c in clips]
tw = time.perf_counter() - t0
HYP[(split, "Whistle")] = hw
e_w, n_w = zip(*[wer_counts(c["ref"], NORM_EN(h)) for c, h in zip(clips, hw)])
lo, hi = wer_ci([c["spk"] for c in clips], e_w, n_w)
print(f"test-{split}: Whistle on all {len(clips)} clips: WER {corpus_wer(e_w, n_w):.2f}% (95% speaker-cluster CI {lo:.2f}..{hi:.2f}) "
f"in {tw:.1f} s ({1000*tw/len(clips):.0f} ms per clip)")
print(f" published full-split figures, different sample: {PUBLISHED[split]}")
pk = clips[:N_PAIRED[split]]
spk = [c["spk"] for c in pk]
e_wp, n_p = list(e_w[:len(pk)]), list(n_w[:len(pk)])
s = np.sum([sdi(c["ref"], NORM_EN(h)) for c, h in zip(pk, hw[:len(pk)])], axis=0)
print(f" paired subset, {len(pk)} clips / {len(set(spk))} speakers / {sum(n_p)} words:")
print(f" {'Whistle, 5-beam':22s} {corpus_wer(e_wp, n_p):6.2f}% S/D/I {s[0]:3d}/{s[1]:3d}/{s[2]:3d}")
for label, size, kw in SYSTEMS[split]:
res = [run_whisper(size, c["audio"], **kw) for c in pk]
hyps = [r[0] for r in res]
HYP[(split, label)] = hyps
e_o = [wer_counts(c["ref"], NORM_EN(h))[0] for c, h in zip(pk, hyps)]
s = np.sum([sdi(c["ref"], NORM_EN(h)) for c, h in zip(pk, hyps)], axis=0)
con = paired_contrast(spk, e_o, n_p, e_wp, label, "Whistle")
con["name"] = f"test-{split}: Whistle vs {label}"
CONTRASTS.append(con)
better, worse = sum(a < b for a, b in zip(e_wp, e_o)), sum(a > b for a, b in zip(e_wp, e_o))
print(f" {label:22s} {corpus_wer(e_o, n_p):6.2f}% S/D/I {s[0]:3d}/{s[1]:3d}/{s[2]:3d} fallback on {sum(r[1] for r in res):2d} clips "
f"{np.median([r[2] for r in res])*1000:5.0f} ms/clip | {fmt_contrast(con)} clips Whistle better/worse {better}/{worse}")
fam = [c for c in CONTRASTS if c["name"] in FAMILY_A]
print(f"\nContrasts so far (family A: {len(fam)} of 5; the Holm correction and the verdicts come once the MLS contrast is in):")
for c in CONTRASTS:
tag = "family A" if c["name"] in FAMILY_A else "descriptive"
print(f" {c['name']:48s} {fmt_contrast(c)} p~{c['p']:.3f} [{tag}]")
print("\n Whistle decodes with a 5-beam length-normalised search. openai-whisper's default is greedy with temperature")
print(" fallback; the Whisper paper's LibriSpeech numbers come from beam search, so the beam-5 rows are the like-for-like ones.")
print(" Every gap here is a system gap (model plus decoder), not a model-only one.")
return " | ".join(f"test-{s}: Whistle {corpus_wer(*zip(*[wer_counts(c['ref'], NORM_EN(h)) for c, h in zip(EN[s][:N_PAIRED[s]], HYP[(s, 'Whistle')][:N_PAIRED[s]])])):.2f}% "
f"vs base greedy {corpus_wer(*zip(*[wer_counts(c['ref'], NORM_EN(h)) for c, h in zip(EN[s][:N_PAIRED[s]], HYP[(s, 'whisper base, greedy')])])):.2f}%" for s in ("other", "clean"))
english_wer()
Whisper 的默认解码器是带温度回退的贪心解码。同时，论文中的 LibriSpeech 数字来自束搜索，而 Whistle 使用五束搜索解码，因此每次比较都必须指明其基线。使用同一归一化器评分时，Whistle 在 test-other 样本上的 WER 为 10.47%，接近已发表的完整划分 10.49，后者我们引用而非实测。在配对子集上它得分为 10.27%，而贪心 Whisper base 为 12.46%，束搜索为 10.83%。对贪心解码的 2.19 个百分点领先通过了 Holm 校正，但与束搜索的差距未通过，因此基线胜出。
def audio_seconds(clips, seconds):
"""Exactly `seconds` of speech: clips concatenated in sample order, then trimmed."""
out, total = [], 0
for c in clips:
out.append(c["audio"])
total += c["dur"]
if total >= seconds:
break
return np.concatenate(out)[:int(seconds * SR)]
PROBE_SCRIPT = r"""
import sys, json, time, resource, numpy as np
kind, paths = sys.argv[1], sys.argv[2:]
scale = 1 << 20 if sys.platform == "darwin" else 1 << 10 # ru_maxrss is bytes on macOS, kilobytes on Linux
rss = lambda: resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / scale
out = {"kind": kind}
if kind.startswith("whistle"):
import needle
out["after_import"] = rss()
W = needle.Whistle()
run = lambda a: W.transcribe(a, language="en")
else:
import whisper, torch
out["after_import"] = rss()
size, kw = kind.split("-")[1], ({"beam_size": 5} if kind.endswith("beam5") else {})
m = whisper.load_model(size, device="cpu")
def run(a):
torch.manual_seed(0)
return m.transcribe(a, language="en", fp16=False, **kw)
clips = {p: np.load(p) for p in paths}
run(clips[paths[0]]) # warm-up
reps = 5 if kind.startswith("whistle") else 3
for p, a in clips.items():
ts = []
for _ in range(reps):
t0 = time.perf_counter(); r = run(a); ts.append(time.perf_counter() - t0)
out[p] = {"wall_ms": 1000 * float(np.median(ts)), "spread_ms": 1000 * float(np.ptp(ts))}
if kind.startswith("whistle"):
out[p].update(ttft_ms=r["ttft_ms"], tps=r["decode_tps"])
out["peak"] = rss()
print(json.dumps(out))
"""
@section("4. Speed and footprint: identical arrays, one fresh process per engine, total wall time and peak memory")
def speed():
pool = EN["other"]
print(f"hardware: {CPU}, {os.cpu_count()} cpus visible. Each engine runs in its own fresh process so torch's threads cannot slow")
print("Whistle down (or the reverse); Whistle's engine picks its own thread count. Primary measure: total wall time on the same")
print("float32 array, median of 5 (Whistle) or 3 (Whisper) after a warm-up. ttft_ms is the engine's own clock, for reference.\n")
for secs in (5, 10, 30):
np.save(f"clip{secs}.npy", audio_seconds(pool, secs))
open("probe.py", "w").write(PROBE_SCRIPT)
res = {}
for kind in ("whistle", "whisper-tiny", "whisper-base", "whisper-base-beam5"):
out = subprocess.run([sys.executable, "probe.py", kind, "clip5.npy", "clip10.npy", "clip30.npy"], capture_output=True, text=True, env=os.environ)
if out.returncode != 0:
raise RuntimeError(f"{kind} probe failed: {out.stderr[-300:]}")
res[kind] = json.loads(out.stdout.strip().splitlines()[-1])
print(f"{'clip':>6s} | {'Whistle wall':>12s} {'spread':>7s} {'ttft':>6s} {'tok/s':>6s} | {'tiny greedy':>11s} | {'base greedy':>11s} | {'base beam 5':>11s}")
for secs in (5, 10, 30):
p = f"clip{secs}.npy"
w = res["whistle"][p]
print(f"{secs:5d} s | {w['wall_ms']:9.0f} ms {w['spread_ms']:5.0f} ms {w['ttft_ms']:6.1f} {w['tps']:6.0f} | "
f"{res['whisper-tiny'][p]['wall_ms']:8.0f} ms | {res['whisper-base'][p]['wall_ms']:8.0f} ms | {res['whisper-base-beam5'][p]['wall_ms']:8.0f} ms")
w5, w10, w30 = (res["whistle"][p]["wall_ms"] for p in ("clip5.npy", "clip10.npy", "clip30.npy"))
b5, b10, b30 = (res["whisper-base"][p]["wall_ms"] for p in ("clip5.npy", "clip10.npy", "clip30.npy"))
bb5, bb10, bb30 = (res["whisper-base-beam5"][p]["wall_ms"] for p in ("clip5.npy", "clip10.npy", "clip30.npy"))
print(f"\nreal-time factor on the 10 s clip: Whistle {w10/10000:.4f}, base greedy {b10/10000:.3f}, base beam 5 {bb10/10000:.3f}")
print(f"base greedy / Whistle wall time: {b5/w5:.1f}x at 5 s, {b10/w10:.1f}x at 10 s, {b30/w30:.1f}x at 30 s; beam 5 / Whistle: "
f"{bb5/w5:.1f}x, {bb10/w10:.1f}x, {bb30/w30:.1f}x")
print(f"from 5 to 10 s the wall time grows {w10/w5:.2f}x for Whistle, {b10/b5:.2f}x for base greedy, {bb10/bb5:.2f}x for base beam 5;")
print(f"from 10 to 30 s: {w30/w10:.2f}x, {b30/b10:.2f}x, {bb30/bb10:.2f}x. Whisper's encoder always sees a padded 30 s window; its decoder")
print("still grows with the words it emits, so the padding fixes only part of its cost. The encoder is not timed separately here.")
print("\npeak RSS of each process (model loaded, three clips transcribed) and the RSS right after its imports:")
for kind in ("whistle", "whisper-tiny", "whisper-base"):
r = res[kind]
print(f" {kind:13s} peak {r['peak']:6.0f} MB after imports {r['after_import']:4.0f} MB -> {r['peak']-r['after_import']:4.0f} MB for the model and the run")
sizes = {"whistle.cact + engine": (os.path.getsize(weights) + os.path.getsize(engine)) / 1e6}
for size in ("tiny", "base"):
p = os.path.expanduser(f"~/.cache/whisper/{size}.pt")
sizes[f"whisper {size}.pt"] = os.path.getsize(p) / 1e6 if os.path.exists(p) else float("nan")
print("files on disk: " + ", ".join(f"{k} {v:.1f} MB" for k, v in sizes.items()) + " (file sizes, not deployed footprint)")
off = subprocess.run([sys.executable, "-c", "import needle, numpy as np; W = needle.Whistle(); print(W.transcribe(np.zeros(16000, np.float32))['text'] == '')"],
capture_output=True, text=True, env={**os.environ, "HF_HUB_OFFLINE": "1"})
print(f"fresh process with HF_HUB_OFFLINE=1 and the pinned paths: {'loads and runs' if 'True' in off.stdout else 'FAILED: ' + off.stderr[-200:]}")
return f"10 s clip: Whistle {w10:.0f} ms vs base greedy {b10:.0f} ms ({b10/w10:.1f}x, {b5/w5:.1f}x at 5 s, {b30/w30:.1f}x at 30 s); peak RSS {res['whistle']['peak']:.0f} vs {res['whisper-base']['peak']:.0f} MB"
speed()
当各引擎共享同一进程时，速度对比会失真，因为 torch 线程可能拖慢其中一个。因此每个引擎都在全新进程中运行，使用完全相同的 float32 数组，在预热后取中位数计时。随着音频片段变长，差距逐渐缩小：5 秒时 Whistle 的耗时是 Whisper 的 7.7 倍，30 秒时降至 2.8 倍，部分原因是 Whisper 使用固定的填充窗口。峰值内存方面，Whistle 为 124 MB，Whisper base 为 823 MB。Whistle 在两个数据划分上的单片段耗时相差近三倍，而片段长度无法解释这一差异，因此这些比值只能视为近似值。
def quiet_windows(a, limit=30.0, search=5.0, hop=0.2):
"""Cut before `limit` seconds at the quietest `hop`-second patch inside the last `search` seconds of each window."""
out, i, n, s, h = [], 0, int(limit * SR), int(search * SR), int(hop * SR)
while len(a) - i > n:
seg = a[i + n - s:i + n]
energy = [np.mean(seg[j:j + h] ** 2) for j in range(0, len(seg) - h, h // 4)]
cut = i + n - s + int(np.argmin(energy)) * (h // 4) + h // 2
out.append(a[i:cut])
i = cut
out.append(a[i:])
return out
def speaker_sessions(min_s=40, max_s=100, k=8):
"""Same-speaker concatenations (0.4 s gaps) from the sampled clips of both splits, longest speakers first."""
by = collections.defaultdict(list)
for split in ("other", "clean"):
for c in EN[split]:
by[c["spk"]].append(c)
out, gap = [], np.zeros(int(0.4 * SR), np.float32)
for spk, cs in sorted(by.items(), key=lambda kv: -sum(c["dur"] for c in kv[1])):
parts, used, total = [], [], 0
for c in cs:
if total + c["dur"] + 0.4 > max_s:
break
parts += [c["audio"], gap]
used.append(c)
total += c["dur"] + 0.4
if total >= min_s:
out.append({"spk": spk, "clips": used, "audio": np.concatenate(parts), "ref": " ".join(c["ref"] for c in used), "dur": total})
if len(out) == k:
break
return out
OTHER_ENGINE_SCRIPT = r"""
import sys, os, numpy as np
os.environ["NEEDLE3_LIB_PATH"] = sys.argv[1]
import needle
W = needle.Whistle()
a = np.load(sys.argv[2])
for secs in (30.0, 30.01, 45.0):
try:
r = W.transcribe(a[:int(secs * 16000)], language="en", word_timestamps=True)
print(f" {secs:6.2f} s -> {len(r['words'])} words, last word ends at {r['words'][-1]['end']:.2f} s")
except RuntimeError as e:
print(f" {secs:6.2f} s -> RuntimeError: {e}")
"""
@section("5. The 30-second window: what transcribe() does with longer audio, and two ways around it")
def long_audio():
global SESSIONS
SESSIONS = speaker_sessions()
s = SESSIONS[0]
gap = np.zeros(int(0.4 * SR), np.float32)
print(f"speaker {s['spk']}: {len(s['clips'])} utterances joined with 0.4 s gaps, {s['dur']:.0f} s, {len(s['ref'].split())} reference words")
print("the docs say transcribe() takes 'audio of any length, one pass up to 30 seconds and windowed beyond it'.")
print(f"the pinned engine {ENGINE_VERSION}, on whole utterances cut from the session so the reference is exact:\n")
print(f"{'audio':>7s} | {'ref words':>9s} {'hyp words':>9s} {'WER':>7s} | gaps longer than 3 s in the word timeline")
for target in (20, 29, 35, 45, 60, 90):
parts, refs, total = [], [], 0
for c in s["clips"]:
if total + c["dur"] + 0.4 > target:
break
parts += [c["audio"], gap]
refs.append(c["ref"])
total += c["dur"] + 0.4
if total < min(target, s["dur"]) - 8:
continue
a, ref = np.concatenate(parts), " ".join(refs)
r = W.transcribe(a, language="en", word_timestamps=True)
hyp = NORM_EN(r["text"])
gaps = [f"{w1['end']:.0f}-{w2['start']:.0f} s" for w1, w2 in zip(r["words"], r["words"][1:]) if w2["start"] - w1["end"] > 3]
print(f"{total:5.0f} s | {len(ref.split()):9d} {len(hyp.split()):9d} {100*jiwer.wer(ref, hyp or '<empty>'):6.1f}% | {', '.join(gaps) or 'none'}")
full = W.transcribe(s["audio"], language="en", word_timestamps=True)
hyp = NORM_EN(full["text"])
print(f"\nthe whole {s['dur']:.0f} s in one call: {len(hyp.split())} of {len(s['ref'].split())} words, WER {100*jiwer.wer(s['ref'], hyp or '<empty>'):.1f}%, "
f"nothing raised, engine ttft {full['ttft_ms']:.0f} ms")
# the same repo revision also holds a 3.2.0 engine wheel; a child process shows what that build does with the same audio
other_wheel = hf_hub_download("Cactus-Compute/needle3", needle_fetch.engine_wheel("3.2.0", TAG, 3), revision=NEEDLE3_REV)
with zipfile.ZipFile(other_wheel) as z:
member = next(n for n in z.namelist() if n.startswith("needle/libneedle"))
other = os.path.abspath(os.path.join("engine", "v3.2.0-" + os.path.basename(member)))
open(other, "wb").write(z.read(member))
np.save("session0.npy", s["audio"])
open("other_engine.py", "w").write(OTHER_ENGINE_SCRIPT)
out = subprocess.run([sys.executable, "other_engine.py", other, "session0.npy"], capture_output=True, text=True, env=os.environ)
print(f"\nthe 3.2.0 engine wheel from the same repo revision (sha256 {sha(other)}), in a child process, on the same audio:")
print(out.stdout.rstrip() or out.stderr[-300:])
print(" two builds, two behaviours: one windows and loses the middle, the other refuses. Either way the split is yours to make.\n")
print("workarounds on the whole clip, scored against the exact reference (one clip; the joins are 0.4 s of silence):")
results = {}
for name, wins in (("fixed 30 s windows", [s["audio"][i:i + 30 * SR] for i in range(0, len(s["audio"]), 30 * SR)]),
("fixed 25 s windows", [s["audio"][i:i + 25 * SR] for i in range(0, len(s["audio"]), 25 * SR)]),
("quiet-point cuts <= 30 s", quiet_windows(s["audio"], 30)),
("quiet-point cuts <= 20 s", quiet_windows(s["audio"], 20))):
t0 = time.perf_counter()
hyp = NORM_EN(" ".join(W.transcribe(w, language="en")["text"] for w in wins))
results[name] = 100 * jiwer.wer(s["ref"], hyp or "<empty>")
print(f" {name:26s} {len(wins):2d} calls WER {results[name]:5.2f}% {1000*(time.perf_counter()-t0):4.0f} ms "
f"windows of {', '.join(f'{len(w)/SR:.1f}' for w in wins)} s")
t0 = time.perf_counter()
steps = list(W.stream(iter([s["audio"][i:i + SR] for i in range(0, len(s["audio"]), SR)]), language="en"))
hyp = NORM_EN(" ".join(x["text"] for x in steps if x["text"]))
results["stream(), 1 s chunks"] = 100 * jiwer.wer(s["ref"], hyp or "<empty>")
print(f" {'stream(), 1 s chunks':26s} {len(steps):2d} passes WER {results['stream(), 1 s chunks']:5.2f}% {1000*(time.perf_counter()-t0):4.0f} ms")
best = min(results, key=results.get)
return (f"one call on {s['dur']:.0f} s keeps {len(NORM_EN(full['text']).split())}/{len(s['ref'].split())} words; {best}: {results[best]:.2f}% WER "
f"(fixed 30 s windows {results['fixed 30 s windows']:.2f}%)")
long_audio()
将同一说话人的话语拼接起来，可得到一个带有精确参考文本的长片段，而 README 承诺支持超出 30 秒限制的窗口化处理。较短的截取几乎能逐词返回，但固定为 3.3.0 的引擎会静默丢弃完整片段中间的部分，返回的单词不足一半且不报错。3.2.0 引擎则对此类音频抛出 RuntimeError 拒绝处理，因此两个构建版本行为不一致，如何切分需由我们决定。在静音点切分的 WER 为 3.75%，而固定窗口为 6.74%，不过仅凭一个片段无法定论。
@section("6. Streaming: cost per pass, how long a word waits to be committed, and what chunk size changes")
def streaming():
durs = [s["dur"] for s in SESSIONS]
print(f"{len(SESSIONS)} same-speaker sessions of {', '.join(f'{x:.0f}' for x in durs)} s, "
f"{sum(len(s['ref'].split()) for s in SESSIONS)} reference words; the joins between utterances are artificial")
off = {c["id"]: h for split in ("other", "clean") for c, h in zip(EN[split], HYP[(split, "Whistle")])}
off_err = [sum(wer_counts(c["ref"], NORM_EN(off[c["id"]]))[0] for c in s["clips"]) for s in SESSIONS]
words = [len(s["ref"].split()) for s in SESSIONS]
print(f"the same utterances one at a time through transcribe() (the step 3 outputs): WER {100*sum(off_err)/sum(words):.2f}%\n")
print(f"{'chunk':>6s} {'sessions':>8s} | {'WER':>6s} | {'pass ms mean / max':>18s} | {'passes > 1 s':>12s} | {'commit latency median / p90':>27s} | {'words only in tail':>18s} | {'pass vs time r':>14s}")
summary, stream_err = {}, None
for chunk_s, sessions in ((1.0, SESSIONS), (0.5, SESSIONS[:2]), (1.0, SESSIONS[:2]), (2.0, SESSIONS[:2])):
n = int(chunk_s * SR)
errs, tail, allw = [], 0, 0
pass_ms, lat, rs = [], [], []
for s in sessions:
steps = list(W.stream(iter([s["audio"][i:i + n] for i in range(0, len(s["audio"]), n)]), language="en"))
errs.append(wer_counts(s["ref"], NORM_EN(" ".join(x["text"] for x in steps if x["text"])))[0])
body = steps[:-1]
pass_ms += [x["pass_ms"] for x in body]
lat += [x["received"] - wd["end"] for x in steps for wd in x["words"]]
tail += len(steps[-1]["words"])
allw += sum(len(x["words"]) for x in steps)
if len(body) > 2:
rs.append(np.corrcoef([x["received"] for x in body], [x["pass_ms"] for x in body])[0, 1])
w = words[:len(sessions)]
summary[(chunk_s, len(sessions))] = (100 * sum(errs) / sum(w), np.median(lat), np.mean(pass_ms))
if chunk_s == 1.0 and len(sessions) == len(SESSIONS):
stream_err = errs
print(f"{chunk_s:5.1f} s {len(sessions):8d} | {100*sum(errs)/sum(w):5.2f}% | {np.mean(pass_ms):9.0f} / {max(pass_ms):4.0f} | {sum(p > 1000 for p in pass_ms):12d} | "
f"{np.median(lat):12.2f} / {np.percentile(lat, 90):4.2f} s | {100*tail/allw:17.1f}% | {np.mean(rs):+.2f}")
c = paired_contrast(list(range(len(SESSIONS))), stream_err, words, off_err, "stream, 1 s chunks", "one utterance at a time")
print(f"\nstream (1 s chunks, 8 sessions) minus utterance-by-utterance: {c['diff']:+.2f} pts (95% session-cluster CI {c['lo']:+.2f}..{c['hi']:+.2f}, "
f"{c['clusters']} sessions) -> {'no significant difference' if c['lo'] <= 0 <= c['hi'] else 'a real gap'} at this n")
print(" pass_ms is the engine time per chunk; below the chunk length means real time. Commit latency is the stream time")
print(" at which a word was committed minus the word's end: each chunk re-transcribes the recent audio and commits what two")
print(" passes agree on, so the wait scales with the chunk. r is the correlation of pass time with stream time (buffer growth).")
print(" The three chunk sizes are compared on the same two sessions; the eight-session row is the headline for 1 s chunks.")
a8, a1, a05, a2 = summary[(1.0, len(SESSIONS))], summary[(1.0, 2)], summary[(0.5, 2)], summary[(2.0, 2)]
return (f"1 s chunks on 8 sessions: WER {a8[0]:.2f}% vs {100*sum(off_err)/sum(words):.2f}% utterance by utterance, median commit {a8[1]:.2f} s; "
f"on 2 sessions the median commit is {a05[1]:.2f} / {a1[1]:.2f} / {a2[1]:.2f} s for 0.5 / 1 / 2 s chunks")
streaming()
通过 stream() 进行流式处理会重新转写最近的音频，当连续两次处理结果一致时才确认一个词，因此等待时间随分块大小增加。将话语拼接而成的八个同说话人会话，以一秒分块流式处理，WER 为 10.05%；而将同样的话语逐条转写时 WER 为 10.41%，差异不显著。一个词在其结束后中位 1.68 秒被确认；在两个会话上，中位数从半秒分块的 0.78 秒升至两秒分块的 3.44 秒。这些拼接是人工构造的，因此测试的是机制而非真实对话。
def auroc(scores, labels):
"""Probability that a random positive scores above a random negative (Mann-Whitney)."""
pos = [s for s, l in zip(scores, labels) if l]
neg = [s for s, l in zip(scores, labels) if not l]
if not pos or not neg:
return float("nan")
order = sorted((s, i) for i, s in enumerate(scores))
ranks, i = {}, 0
while i < len(order):
j = i
while j + 1 < len(order) and order[j + 1][0] == order[i][0]:
j += 1
for k in range(i, j + 1):
ranks[order[k][1]] = (i + j) / 2 + 1
i = j + 1
pos_idx = [i for i, l in enumerate(labels) if l]
return (sum(ranks[i] for i in pos_idx) - len(pos) * (len(pos) + 1) / 2) / (len(pos) * len(neg))
@section("7. Word timestamps and word probabilities: consistency, a known shift, agreement with Whisper, and what p < 0.7 means")
def timestamps():
import difflib
pk = EN["other"][:40]
key = lambda w: re.sub(r"[^a-z']", "", w.lower())
diffs, same, mono, shifts = [], 0, 0, []
probs, correct, durs = [], [], []
replay_hits = replay_n = 0
for i, c in enumerate(pk):
ws = W.transcribe(c["audio"], language="en", word_timestamps=True)["words"]
mono += all(a["start"] <= a["end"] <= b["start"] + 1e-6 for a, b in zip(ws, ws[1:]))
if i < 20: # a known shift: 2.000 s of zeros in front should move every word by 2.000 s
ws2 = W.transcribe(np.concatenate([np.zeros(2 * SR, np.float32), c["audio"]]), language="en", word_timestamps=True)["words"]
if ws2 and ws:
shifts.append(ws2[0]["start"] - ws[0]["start"])
if i < 20 and len(ws) >= 6: # cut the audio where word k ends and transcribe only the cut
k = len(ws) // 2
got = NORM_EN(W.transcribe(c["audio"][:int(ws[k]["end"] * SR)], language="en")["text"]).split()
exp = NORM_EN(" ".join(w["word"] for w in ws[:k + 1])).split()
replay_n += 1
replay_hits += got == exp
torch.manual_seed(0)
WHISPER_SECONDS["base"] += c["dur"]
ww = [w for s in whisper_model("base").transcribe(c["audio"], language="en", fp16=False, word_timestamps=True)["segments"] for w in s["words"]]
if [key(w["word"]) for w in ws] == [key(w["word"]) for w in ww]:
same += 1
diffs += [x["start"] - y["start"] for x, y in zip(ws, ww)] + [x["end"] - y["end"] for x, y in zip(ws, ww)]
H, tp, td = [], [], []
for w in ws:
for t in NORM_EN(w["word"]).split(): # "hmm" normalises to nothing, "didn't" to two tokens, as in the reference
H.append(t)
tp.append(w["probability"])
td.append(w["end"] - w["start"])
ok = [False] * len(H)
for tag, i1, i2, j1, j2 in difflib.SequenceMatcher(a=c["ref"].split(), b=H).get_opcodes():
if tag == "equal":
for j in range(j1, j2):
ok[j] = True
probs += tp
correct += ok
durs += td
diffs, shifts = np.abs(diffs), np.array(shifts)
print(f"{len(pk)} test-other clips: word times monotonic on {mono}/{len(pk)}")
print(f"2.000 s of silence prepended on 20 clips: first word moved by {np.median(shifts):.3f} s (median), within 50 ms of 2.000 on "
f"{100*np.mean(np.abs(shifts-2) <= 0.05):.0f}% of clips")
print(f"cut-and-replay: cutting the audio at a word's 'end' and transcribing the cut reproduced the exact word prefix on {replay_hits}/{replay_n} clips")
print(f"agreement with Whisper base's DTW word times, on the {same}/{len(pk)} clips where both emit the same words ({len(diffs)//2} words; a selected, easier subset):")
print(f" |boundary difference| median {np.median(diffs):.3f} s, within 100 ms {100*np.mean(diffs <= 0.1):.0f}%, within 200 ms "
f"{100*np.mean(diffs <= 0.2):.0f}% (agreement between two systems, not accuracy: there is no gold alignment)")
probs, correct, durs = np.array(probs), np.array(correct), np.array(durs)
print(f"\nword probability over {len(probs)} normalised tokens from {len(pk)} clips: median {np.median(probs):.3f}, 10th percentile {np.percentile(probs, 10):.3f}")
for lo, hi in ((0, 0.5), (0.5, 0.7), (0.7, 0.9), (0.9, 1.01)):
m = (probs >= lo) & (probs < hi)
if m.sum():
print(f" p in [{lo:.1f}, {min(hi, 1):.1f}): {m.sum():4d} words, {100*(~correct[m]).mean():5.1f}% wrong")
print(f" AUROC for 'this word is wrong': 1 - probability {auroc(-probs, ~correct):.3f}; word duration as a baseline (shorter = riskier) {auroc(-durs, ~correct):.3f}")
low, high = probs < 0.7, probs >= 0.7
return (f"shift {np.median(shifts):.2f} s; within 200 ms of Whisper on {100*np.mean(diffs <= 0.2):.0f}% of {len(diffs)//2} matched words; "
f"p<0.7 words wrong {100*(~correct[low]).mean():.0f}% vs {100*(~correct[high]).mean():.1f}%")
timestamps()
在没有黄金对齐的情况下，我们依据时间戳必须满足的条件来检验它们。在我们检查的每个片段上，词时间都是单调的；在二十个片段前加上两秒静音后，第一个词的时间中位偏移为 2.000 秒。在某个词的结束处切分应能复现其前缀，但这一条件在二十个片段中仅五分之一成立；这检验的是稳定性，而非结束时间的准确性。概率是更强的信号：评分低于 0.5 的词有 69.2% 是错的，而评分在 0.9 及以上的词错误率为 2.5%；对于错误词，AUROC 为 0.916，而时长仅为 0.509。
@section("8. Keyword biasing: an oracle ceiling, a matched wrong-keyword control and a distractor list")
def keywords():
import difflib
clips = EN["other"]
base = HYP[("other", "Whistle")]
freq = collections.Counter(w for c in clips for w in c["ref"].split())
def error_words(ref, hyp):
out = []
for tag, i1, i2, j1, j2 in difflib.SequenceMatcher(a=ref.split(), b=NORM_EN(hyp).split()).get_opcodes():
if tag in ("replace", "delete"):
out += ref.split()[i1:i2]
return sorted(set(out))
oracle = [error_words(c["ref"], h) for c, h in zip(clips, base)]
idx = [i for i, k in enumerate(oracle) if k]
print(f"{len(idx)}/{len(clips)} test-other clips have at least one Whistle error ({sum(len(oracle[i]) for i in idx)} distinct wrong words)")
print(" oracle keywords = the reference words Whistle got wrong on that clip (a CEILING: it reads the answer)")
print(" matched wrong = the next error clip's oracle list, minus every word the target clip actually says")
print(" distractor (8) = the 8 most frequent reference words of the whole sample that the target clip does not say")
print(" distractor (2) = the same list cut to 2 words, so its size matches the oracle and matched arms\n")
def matched_wrong(i):
donor = oracle[idx[(idx.index(i) + 1) % len(idx)]]
return [w for w in donor if w not in clips[i]["ref"].split()] or None
arms = {}
distractor = lambda i, k: [w for w, _ in freq.most_common(60) if w not in clips[i]["ref"].split()][:k]
for name, keylist in (("no keywords", lambda i: None), ("oracle (ceiling)", lambda i: oracle[i]), ("matched wrong", matched_wrong),
("distractor (8)", lambda i: distractor(i, 8)), ("distractor (2)", lambda i: distractor(i, 2))):
errs, words, ins, recovered, total_kw, bad_kw, bad_kw_base, n_kw = [], [], 0, 0, 0, 0, 0, 0
for i in idx:
c = clips[i]
kws = keylist(i)
n_kw += len(kws or [])
h = base[i] if name == "no keywords" else W.transcribe(c["audio"], language="en", keywords=kws)["text"]
s, d, ins_i = sdi(c["ref"], NORM_EN(h))
errs.append(s + d + ins_i)
words.append(len(c["ref"].split()))
ins += ins_i
still_wrong = set(error_words(c["ref"], h))
recovered += sum(w not in still_wrong for w in oracle[i])
total_kw += len(oracle[i])
if kws and name != "oracle (ceiling)":
bad_kw += sum(w in NORM_EN(h).split() for w in kws)
bad_kw_base += sum(w in NORM_EN(base[i]).split() for w in kws)
arms[name] = (errs, words)
extra = f" wrong keywords in the output {bad_kw:3d} (already {bad_kw_base} without biasing)" if name not in ("no keywords", "oracle (ceiling)") else ""
print(f" {name:18s} {n_kw/len(idx):4.1f} keywords/clip WER {corpus_wer(errs, words):6.2f}% insertions {ins:3d} oracle words now right {recovered:3d}/{total_kw}{extra}")
spk = [clips[i]["spk"] for i in idx]
w = arms["no keywords"][1]
fam = []
for a, b in (("no keywords", "oracle (ceiling)"), ("matched wrong", "no keywords"), ("distractor (8)", "no keywords"),
("distractor (2)", "no keywords"), ("matched wrong", "oracle (ceiling)")):
c = paired_contrast(spk, arms[a][0], w, arms[b][0], a, b)
fam.append(c)
sig = holm([c["p"] for c in fam])
print(f"\n family B, {len(fam)} contrasts, Holm at 0.05:")
for c, s in zip(fam, sig):
print(f" WER({c['a']}) - WER({c['b']}) = {fmt_contrast(c)} p~{c['p']:.3f} {'significant' if s else 'not significant'}")
print(" the decisive contrast is matched wrong minus oracle: how much of the gain needs the right words, not just any words")
return (f"oracle ceiling {corpus_wer(*arms['oracle (ceiling)']):.2f}% vs {corpus_wer(*arms['no keywords']):.2f}% baseline; "
f"wrong keywords {corpus_wer(*arms['matched wrong']):.2f}%")
keywords()
关键词偏置需要对照，因为一个能读到答案的 oracle 看起来就像胜利。该 oracle 列出 Whistle 转错的词；匹配对照组则使用另一片段的错误词，并剔除本片段中出现的词。在 185 个含错误的片段上，oracle 将 WER 从 14.74% 降至 7.62%，而匹配的错误词仅使其变为 14.82%，变化不显著。加入片段中不存在的八个高频词会使 WER 上升 2.05 个百分点。与 oracle 相比，匹配的错误词差 7.20 个百分点，经 Holm 校正后显著，因此正确的词确实重要。
MLS = {"de": "german", "fr": "french", "es": "spanish", "it": "italian", "nl": "dutch", "pl": "polish"}
WHISTLE_LANGS = ("en", "de", "fr", "es", "it", "nl", "pl")
N_PER_LANG = 12
PUBLISHED_MLS_BASE = {"nl": 28.4, "fr": 26.6, "de": 17.7, "it": 31.1, "pl": 22.8, "es": 12.8} # Whisper paper, table 10 (full splits)
def clopper_pearson_lower(k, n, alpha=0.05):
"""Exact one-sided 95% lower bound for a binomial proportion, by bisection on the binomial tail."""
lo, hi = 0.0, 1.0
for _ in range(60):
p = (lo + hi) / 2
tail = sum(math.comb(n, j) * p ** j * (1 - p) ** (n - j) for j in range(k, n + 1))
lo, hi = (p, hi) if tail < alpha else (lo, p)
return lo
@section("9. Six more languages on Multilingual LibriSpeech, pooled, with a symmetric language-ID test")
def multilingual():
rng = random.Random(SEED)
print(f"sampling rule, fixed in advance: {N_PER_LANG} clips per language from 3 random pages of 100 rows of each test split (seed {SEED});")
print("MLS pages are speaker-sorted, so the pages are the first stage of the sample. Normaliser: Whisper's BasicTextNormalizer on both sides.\n")
pooled = {"Whistle (forced)": [], "Whistle (detected)": [], "whisper base, greedy": []}
words, clusters, lid_w, lid_b, rows_out = [], [], 0, 0, []
wb = whisper_model("base")
for code, cfg in MLS.items():
total = page("facebook/multilingual_librispeech", cfg, "test", 0, 1)["num_rows_total"]
offsets = rng.sample(range(0, total, 100), 3)
rows = [r["row"] for off in offsets for r in page("facebook/multilingual_librispeech", cfg, "test", off)["rows"]]
sample = rng.sample(rows, N_PER_LANG)
e, n_words, spk = collections.defaultdict(list), [], []
for r in sample:
audio, h = load_audio(r)
MANIFEST.setdefault("mls", {}).setdefault(code, []).append([r["id"], h])
ref = NORM_BASIC(r["transcript"])
forced = W.transcribe(audio, language=code)["text"]
det = W.transcribe(audio)
lid_w += det["language"] == code
hb, _, _ = run_whisper("base", audio, language=code)
mel = whisper.log_mel_spectrogram(whisper.pad_or_trim(audio), n_mels=wb.dims.n_mels)
_, probs = wb.detect_language(mel)
lid_b += max(WHISTLE_LANGS, key=lambda l: probs.get(l, 0)) == code
for name, h in (("Whistle (forced)", forced), ("Whistle (detected)", det["text"]), ("whisper base, greedy", hb)):
e[name].append(wer_counts(ref, NORM_BASIC(h))[0])
n_words.append(len(ref.split()))
spk.append(f"{code}-{r['speaker_id']}")
for name in pooled:
pooled[name] += e[name]
words += n_words
clusters += spk
rows_out.append((code, len(sample), len(set(spk)), sum(n_words), corpus_wer(e["Whistle (forced)"], n_words), corpus_wer(e["whisper base, greedy"], n_words)))
print(f"{'lang':>4s} {'clips':>5s} {'spk':>3s} {'words':>5s} | {'Whistle':>8s} {'base':>8s} | Whisper base published on the full split (paper table 10)")
for code, n, s, nw, ww, wbase in rows_out:
print(f"{code:>4s} {n:5d} {s:3d} {nw:5d} | {ww:7.1f}% {wbase:7.1f}% | {PUBLISHED_MLS_BASE[code]}")
print(" per-language rows are descriptive: 12 clips from a handful of speakers cannot rank the systems within a language\n")
c = paired_contrast(clusters, pooled["whisper base, greedy"], words, pooled["Whistle (forced)"], "whisper base, greedy", "Whistle")
c["name"] = "MLS pooled: Whistle vs whisper base, greedy"
CONTRASTS.append(c)
print(f"pooled over {len(words)} clips / {c['clusters']} speakers / {sum(words)} words: Whistle (forced) {corpus_wer(pooled['Whistle (forced)'], words):.2f}%, "
f"whisper base greedy {corpus_wer(pooled['whisper base, greedy'], words):.2f}%; {fmt_contrast(c)} [family A]")
print(f"Whistle with the language detected instead of forced: {corpus_wer(pooled['Whistle (detected)'], words):.2f}%")
n = len(words)
print(f"language identification over the same seven labels: Whistle {lid_w}/{n} (one-sided 95% lower bound {100*clopper_pearson_lower(lid_w, n):.0f}%, "
f"treating clips as independent), whisper base {lid_b}/{n} (lower bound {100*clopper_pearson_lower(lid_b, n):.0f}%)")
json.dump(MANIFEST, open("manifest.json", "w"))
fam = [x for x in CONTRASTS if x["name"] in FAMILY_A]
sig = dict(zip([x["name"] for x in fam], holm([x["p"] for x in fam])))
print(f"\nfamily A complete ({len(fam)} contrasts), Holm at 0.05 (the printed CIs are unadjusted; the verdict uses the adjusted p):")
for x in fam:
v = verdict(x, sig[x["name"]])
VERDICTS.append((x["name"], fmt_contrast(x), v))
print(f" {x['name']:48s} {fmt_contrast(x)} p~{x['p']:.3f} -> {v}")
return f"pooled WER Whistle {corpus_wer(pooled['Whistle (forced)'], words):.1f}% vs base {corpus_wer(pooled['whisper base, greedy'], words):.1f}%; language ID {lid_w}/{n} vs {lid_b}/{n}"
multilingual()
强制使用真实语言会掩盖检测失败，因此每个系统也必须检测语言。我们从三个预先固定的随机页面中为每种语言抽取 12 个片段，并用 BasicTextNormalizer 对双方评分。我们不固定 MLS 修订版本，因此改为对每个片段做哈希。在来自 22 位说话人的 72 个片段上汇总，强制 Whistle 得分为 26.40%，而贪心 Whisper base 为 24.06%，这一差距有利于 Whisper，但不显著。两个系统在全部 72 个片段上都检测到了语言。按语言分列的行仅作描述之用。
@section("10. Non-speech, quiet speech and white noise at three SNRs, Whistle against Whisper base")
def noise():
rng = np.random.default_rng(SEED)
quiet = EN["other"][0]["audio"] * (10 ** (-30 / 20) / np.sqrt(np.mean(EN["other"][0]["audio"] ** 2)))
probes = {"5 s digital silence": np.zeros(5 * SR, np.float32),
"white noise, -40 dBFS": (rng.standard_normal(5 * SR) * 0.01).astype(np.float32),
"white noise, -20 dBFS": (rng.standard_normal(5 * SR) * 0.1).astype(np.float32),
"440 Hz tone, -20 dBFS": (0.1 * np.sqrt(2) * np.sin(2 * np.pi * 440 * np.arange(5 * SR) / SR)).astype(np.float32),
"speech scaled to -30 dBFS": quiet.astype(np.float32)}
print(f"{'input':>26s} | Whistle text / language | Whisper base text")
for name, x in probes.items():
w = W.transcribe(x, language="en")
b = run_whisper("base", x)[0].strip()
print(f"{name:>26s} | {w['text']!r} {w['language']!r} | {b!r}")
print(f" reference for the quiet clip: {EN['other'][0]['ref']}")
print(" an empty Whisper output is its no-speech gate, not evidence of quality; an empty Whistle output on real speech would be a miss\n")
clips = EN["other"][:20]
spk = [c["spk"] for c in clips]
words = [len(c["ref"].split()) for c in clips]
print(f"white noise on {len(clips)} test-other clips, {sum(words)} words, {len(set(spk))} speakers (directional at this n):")
print(f"{'condition':>14s} | {'Whistle':>8s} {'base greedy':>11s} | WER(base) - WER(Whistle), 95% speaker-cluster CI")
out = []
for snr in (None, 20, 10, 5):
xs = []
for c in clips:
if snr is None:
xs.append(c["audio"])
else:
n = rng.standard_normal(len(c["audio"])).astype(np.float32)
n *= np.sqrt((c["audio"] ** 2).mean() / ((n ** 2).mean() * 10 ** (snr / 10)))
y = c["audio"] + n
xs.append((y * min(1, 0.99 / np.abs(y).max())).astype(np.float32))
e_w = [wer_counts(c["ref"], NORM_EN(W.transcribe(x, language="en")["text"]))[0] for c, x in zip(clips, xs)]
e_b = [wer_counts(c["ref"], NORM_EN(run_whisper("base", x)[0]))[0] for c, x in zip(clips, xs)]
c = paired_contrast(spk, e_b, words, e_w, "base", "Whistle")
label = "clean" if snr is None else f"{snr} dB SNR"
out.append((label, corpus_wer(e_w, words), corpus_wer(e_b, words)))
print(f"{label:>14s} | {corpus_wer(e_w, words):7.1f}% {corpus_wer(e_b, words):10.1f}% | {c['diff']:+6.1f} ({c['lo']:+.1f}..{c['hi']:+.1f})")
print("\n Whistle's front end keeps roughly 250-3500 Hz while Whisper's covers 0-8 kHz, so the two systems do not see the same")
print(" noise; this compares them as shipped, and at n = 20 none of the differences is distinguishable from zero.")
return "; ".join(f"{l}: {w:.0f}% vs {b:.0f}%" for l, w, b in out[1:])
noise()
数字静音、两个噪声水平和纯音都给出空文本。对 Whisper 而言，这是无语音门控，而非质量证据；而 Whistle 在语音上输出为空则会是漏检。一个 -30 dBFS 的真实句子由 Whistle 逐词返回，尽管 Whisper base 把“traced”写成“crazed”。在我们运行的最严苛 SNR 下，白噪声使 20 个 test-other 片段上的 WER 对 Whistle 从 12.4% 升至 37.1%，对 Whisper base 从 15.2% 升至 39.9%，且没有差距可与零区分。
@section("11. Speech embeddings: the measured frame rate, and finding a spoken word inside another clip")
def embeddings():
frames = {}
for secs in (1, 10, 30):
v = np.asarray(W.embed(audio_seconds(EN["other"], secs)), np.float32)
frames[secs] = len(v) // 256
print(f"{secs:2d} s of audio -> {len(v):7d} floats = {len(v)//256:4d} frames x 256 ({len(v)/256/secs:.1f} frames/s, {1000*secs/(len(v)/256):.1f} ms per frame)")
fps = frames[10] / 10
try:
W.embed(np.zeros(31 * SR, np.float32))
except RuntimeError as e:
print(f"31 s -> RuntimeError: {e}")
print("\nspoken-term search: embed clip A whole, keep the frames under one word's own timestamps as the query (the encoder is")
print("non-causal, so those frames carry context), slide the query over clip B's frames with a cosine score. Pool: the first 120")
print("test-other clips; words of 5+ letters and 0.25 s+, first two occurrences in different clips; a hit is a peak within 0.3 s.")
clips = [c for c in EN["other"][:120]]
key = lambda w: re.sub(r"[^a-z']", "", w.lower())
occurrences = collections.defaultdict(list)
for i, c in enumerate(clips):
for w in W.transcribe(c["audio"], language="en", word_timestamps=True)["words"]:
if len(key(w["word"])) >= 5 and w["end"] - w["start"] >= 0.25:
occurrences[key(w["word"])].append((i, w["start"], w["end"]))
pairs = [(k, o[0], o[1]) for k, o in occurrences.items() if len(o) >= 2 and o[0][0] != o[1][0]]
cache = {}
def frames_of(i):
if i not in cache:
v = np.asarray(W.embed(clips[i]["audio"]), np.float32).reshape(-1, 256)
cache[i] = v / (np.linalg.norm(v, axis=1, keepdims=True) + 1e-8)
return cache[i]
hits = n = 0
chance, tol = [], 0.3
for k, (ia, sa, ea), (ib, sb, eb) in pairs[:60]:
q = frames_of(ia)[int(sa * fps):max(int(sa * fps) + 2, int(ea * fps))]
T = frames_of(ib)
if len(T) <= len(q):
continue
scores = np.array([(q * T[j:j + len(q)]).sum(1).mean() for j in range(len(T) - len(q) + 1)])
peak = (scores.argmax() + len(q) / 2) / fps
hit = sb - tol <= peak <= eb + tol
hits += hit
n += 1
chance.append((eb - sb + 2 * tol) / clips[ib]["dur"])
if n <= 3:
print(f" {k!r:14s} query {ea-sa:.2f} s from clip {ia:3d} -> peak at {peak:5.2f} s in clip {ib:3d}; the word is spoken at {sb:.2f}-{eb:.2f} s: {'hit' if hit else 'miss'}")
print(f" {hits}/{n} peaks inside the target word (+-{tol} s); a random position would land there about {100*np.mean(chance):.0f}% of the time")
print(" the encoder output is a content representation at frame level; it is not a speaker or sentence embedding")
return f"{fps:.1f} frames/s x 256 floats; spoken-term search {hits}/{n} hits (chance ~{100*np.mean(chance):.0f}%)"
embeddings()
测量帧率是因为搜索将帧映射到秒。一个十秒片段每秒产生 25 帧，每帧是一个 256 浮点向量，超过嵌入限制的片段会引发 RuntimeError。我们整体嵌入一个片段，因为其帧能看到两侧，并通过余弦得分将一个词的帧滑过另一个片段。在 34 对含五个或更多字母的词对中，有 32 对的峰值落在该词 0.3 秒以内，而随机概率约为 17%。输出是帧级内容，而非句子嵌入。
INTENTS = {"abroad": "Using the card or account while travelling or abroad",
"address": "Change, update or confirm the postal address on the account",
"app_error": "The banking app or website shows an error or does not work",
"atm_limit": "The daily ATM cash withdrawal limit",
"balance": "Check the current account balance",
"business_loan": "Apply for or ask about a business loan",
"card_issues": "A card is lost, stolen, blocked, declined or not working",
"cash_deposit": "Deposit or transfer cash into the account",
"direct_debit": "Set up, change or cancel a direct debit or standing order",
"freeze": "Freeze, block or unfreeze the account or card",
"high_value_payment": "Make or authorize a large or high-value payment or transfer",
"joint_account": "Open or ask about a joint account with another person",
"latest_transactions": "See the latest or recent transactions and payments",
"pay_bill": "Pay a bill or make a payment to a company"}
N_MINDS = 200
@section("12. From speech to a tool call: Whistle transcripts into Needle 3 on MInDS-14 banking requests")
def speech_to_tools():
tools = [{"name": n, "description": d, "parameters": {"type": "object", "properties": {}}} for n, d in INTENTS.items()]
print(f"14 tools, one per MInDS-14 intent, descriptions fixed before the first call (sha256 {hashlib.sha256(json.dumps(tools).encode()).hexdigest()[:12]})")
first = page("PolyAI/minds14", "en-US", "train", 0, 1)
names = next(f for f in first["features"] if f["name"] == "intent_class")["type"]["names"]
assert names == list(INTENTS), names
rows = all_rows("PolyAI/minds14", "en-US", "train")
sample = random.Random(SEED).sample(rows, N_MINDS)
clips, too_long = [], 0
for r in sample:
audio, h = load_audio(r) # 8 kHz telephone wav, resampled to 16 kHz by load_audio
MANIFEST.setdefault("minds14", []).append([r["path"], h])
if len(audio) / SR > MAX_CLIP_S: # the same pre-declared filter as step 2: the engine refuses longer clips
too_long += 1
continue
clips.append({"audio": audio, "ref": r["transcription"], "intent": names[r["intent_class"]], "dur": len(audio) / SR})
t0 = time.perf_counter()
hyps = [W.transcribe(c["audio"], language="en")["text"] for c in clips]
tw = time.perf_counter() - t0
e, n = zip(*[wer_counts(NORM_EN(c["ref"]), NORM_EN(h)) for c, h in zip(clips, hyps)])
json.dump(MANIFEST, open("manifest.json", "w"))
print(f"{len(clips)} of the {N_MINDS} sampled clips ({len(rows)} rows in the split; {too_long} dropped for exceeding {MAX_CLIP_S:.0f} s; "
f"{sum(c['dur'] for c in clips)/60:.1f} min of 8 kHz telephone speech): Whistle WER {corpus_wer(e, n):.1f}% "
f"against the casual MInDS-14 transcriptions, {1000*tw/len(clips):.0f} ms per clip")
for c, h in list(zip(clips, hyps))[:2]:
print(f" [{c['intent']}] ref: {c['ref'][:72]}\n {' ' * (len(c['intent']) + 2)} hyp: {h[:72]}")
t0 = time.perf_counter()
agent = needle.Needle(tools=tools, auto_date=False) # no date fact in the prompt, so the result does not depend on the clock
print(f"\nNeedle 3 loaded in {time.perf_counter()-t0:.1f} s into the same engine; Whistle stays loaded beside it")
def route(texts):
out = []
for t in texts:
r = agent.complete(t)
agent.reset()
calls = [c["name"] for c in r.get("function_calls", [])]
out.append((calls[0] if calls else None, r.get("confidence") or 0.0, r.get("peak_ram_mb")))
return out
truth = [c["intent"] for c in clips]
res = {}
for label, texts in (("reference transcripts", [c["ref"] for c in clips]), ("Whistle transcripts", hyps)):
t0 = time.perf_counter()
res[label] = route(texts)
right = np.array([p == y for (p, _, _), y in zip(res[label], truth)])
conf = np.array([c for _, c, _ in res[label]])
print(f" routing from {label:21s}: {100*right.mean():.1f}% correct, {sum(p is None for p, _, _ in res[label])} refusals, "
f"{1000*(time.perf_counter()-t0)/len(texts):.0f} ms/call, confidence when right {conf[right].mean():.2f} vs wrong {conf[~right].mean():.2f}")
a = [p == y for (p, _, _), y in zip(res["reference transcripts"], truth)]
b = [p == y for (p, _, _), y in zip(res["Whistle transcripts"], truth)]
only_ref, only_wh = sum(x and not y for x, y in zip(a, b)), sum(y and not x for x, y in zip(a, b))
m = only_ref + only_wh
p_mc = min(1.0, 2 * sum(math.comb(m, j) for j in range(min(only_ref, only_wh) + 1)) / 2 ** m) if m else 1.0
print(f" discordant clips: right only from the reference text {only_ref}, right only from Whistle's text {only_wh} (exact McNemar p = {p_mc:.2f})")
conf = collections.Counter((y, p) for (p, _, _), y in zip(res["Whistle transcripts"], truth) if p != y)
print(" most common confusions (truth -> call): " + ", ".join(f"{y} -> {p} x{k}" for (y, p), k in conf.most_common(3)))
off = route([NORM_EN(h) for h in HYP[("other", "Whistle")][:40]])
print(f" 40 LibriSpeech sentences (off-topic): {sum(p is None for p, _, _ in off)} refused, {sum(p is not None for p, _, _ in off)} given a banking tool call")
print(f" peak_ram_mb reported by Needle: {res['Whistle transcripts'][-1][2]} MB (this whole process, Whisper models included). MInDS-14 is public,")
print(" so training-set overlap cannot be ruled out for either model, and the tool descriptions are ours: the number moves with them.")
return f"routing {100*np.mean(b):.0f}% right from Whistle's text vs {100*np.mean(a):.0f}% from the reference text; WER {corpus_wer(e, n):.0f}%"
speech_to_tools()
Whistle 转写采样的 MInDS-14 银行请求，相对于随意参考转录的 WER 为 31.9%。Needle 3 将每个转录在十四个工具之间路由，每个工具对应一个银行意图，其描述由我们撰写。返回的函数调用成为路由，无调用的回复计为拒绝。从 Whistle 的文本路由正确率为 50.3%，从参考文本为 51.8%，差距不显著，因此在这些描述下，限制在于路由而非转写。在离题的 LibriSpeech 句子中，有 17 个被拒绝，其余获得银行工具调用。
banner("SUMMARY")
for name, res in RESULTS.items():
print(f" {name:<104s} {res}")
print("\nVerdicts on the published-claim contrasts (family A, 95% speaker-cluster paired bootstrap, unadjusted CIs, Holm over 5):")
for claim, measured, v in VERDICTS:
print(f" {claim:<48s} {measured:<58s} {v}")
print(f"\nWhisper audio transcribed in this run: " + ", ".join(f"{k} {v/60:.1f} min" for k, v in WHISPER_SECONDS.items())
+ f"; hardware {CPU}")
print("""
What to carry over
- Pin the Hub revisions of whistle.cact, the engine wheel and needle3.cact and print their hashes; both repos replace files in
place, and the engine version the package wants is in needle.agent.fetch.ENGINE_VERSIONS, not in the package version.
- Score with the same normalizer on both sides of each comparison, sample with a fixed seed, cluster the bootstrap by speaker.
- Say which Whisper you compared against: greedy (the package default) and beam search (the paper's numbers) differ.
- Keep a single transcribe() call under 30 s; cut longer audio yourself (quiet points worked best on this clip) or use stream().
- Word probability is a usable error signal, keyword biasing has a ceiling, embeddings are content frames.
Where to go next
- pip install "cactus-needle[mic,compare]" then needle whistle compare clip.wav runs one clip through Whistle, Whisper and Moonshine.
- needle download <platform> fetches the engine for macOS, Linux, Android, iOS, RISC-V, MIPS, the browser or WASI; the native
runner takes --model needle3.cact --model whistle.cact --tools tools.json --audio clip.wav for audio-to-tool-calls in one call.
- The weights also ship as safetensors, and fine-tuning for new languages is being discussed in the repository's issues.
- Docs: cactuscompute.com/whistle Code: github.com/cactus-compute/needle Weights: huggingface.co/Cactus-Compute/whistle
""")
摘要打印每个部分的一行结果，然后是五个已发表声明的判定，其中只有 test-other 对贪心 Whisper base 有利于 Whistle。它还报告 Whisper 转写的音频，tiny 为 18.4 分钟，base 为 68.7 分钟，且全部 13 个单元运行无故障。需要沿用的习惯是固定 Hub 修订版本、固定种子、用同一个归一化器对双方评分，并指明 Whisper 解码器。
总之，在 Apple M4 CPU 上，Whistle 在 test-other 样本上得分为 10.47%，接近已发表的完整划分 10.49（引用值，非实测值）。在配对子集上，它比贪心 Whisper base 高出 2.19 个百分点，而束搜索、test-clean 和合并 MLS 均未显示出显著差距。在十秒音频片段上，它的运行速度比贪心 base 快 5.7 倍。以一秒为块进行流式处理时，单词在其结束后中位数 1.68 秒被确认，且无显著损失。30 秒限制最为关键：固定引擎会静默丢弃较长音频的中间部分，因此需拆分或流式处理。这些均为单次运行样本，任何单个片段或小规模集合都无法定论。
查看完整代码。 所有功劳归功于本项目的研究者。另外，欢迎在 Twitter 上关注我们，别忘了加入我们的 150k+ ML SubReddit 并订阅我们的新闻通讯。等等！你在 Telegram 上吗？现在你也可以在 Telegram 上加入我们了。
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文章 Cactus Whistle 编码指南：一个 16.9 MB 的语音模型能否与 Whisper Base 抗衡？CPU 上的词错误率、速度、流式处理、时间戳、关键词及语音到工具调用 首次出现在 MarkTechPost。
来源：MarkTechPost · marktechpost.com
来源：RadarAI · aihot.news