mining: reject probe CER>0.5, drop unusable modulation gate

kaggle/voice_mining.py

@@ -153,6 +153,29 @@ def spectral_flatness(y: np.ndarray, sr: int) -> float:
     return float(np.median(np.exp(np.log(spec).mean(axis=1)) / spec.mean(axis=1)))
 
 
+def speech_modulation(y: np.ndarray, sr: int) -> float:
+    """Share of envelope energy in the 2-8 Hz syllabic band.
+
+    Speech has a syllabic rhythm; rustling, tapping and room tone do not. This
+    is the physical counterpart to the transcript: whisper-small happily
+    invents a caption over pure noise ("字幕by索兰娅"), so the transcript alone
+    cannot tell us whether anyone is talking.
+    """
+    hop = int(0.01 * sr)
+    if len(y) < hop * 50:
+        return 0.0
+    n = len(y) // hop
+    env = np.sqrt((y[: n * hop].reshape(n, hop) ** 2).mean(axis=1) + 1e-12)
+    env = env - env.mean()
+    spec = np.abs(np.fft.rfft(env))
+    freqs = np.fft.rfftfreq(len(env), 0.01)
+    total = spec.sum() + 1e-12
+    return float(spec[(freqs >= 2.0) & (freqs <= 8.0)].sum() / total)
+
+
+ASR_ARTIFACTS = ("字幕", "by索", "谢谢观看", "请订阅", "amara", "subtitles", "感谢观看")
+
+
 def repeated_run(text: str) -> float:
     """Share of the transcript taken by its longest repeated character run."""
     if not text:
@@ -213,13 +236,28 @@ def harvest(asr) -> list[dict]:
         if seg is None:
             continue
         flat = spectral_flatness(seg, sr)
+        mod = speech_modulation(seg, sr)
         st["flatness"] = flat
+        st["modulation"] = mod
         if st["snr_db"] < 12 or st["clip_ratio"] > 1e-3 or st["speech_ratio"] < 0.55 or flat > 0.35:
             continue
+        # Do not gate on `modulation`. Measured on clips a listener confirmed
+        # contain whisper speech versus clips that are pure ASMR noise, the two
+        # overlap almost exactly (0.239 vs 0.230), because whispering has weak
+        # syllabic rhythm. It is recorded for diagnostics only. The reliable
+        # discriminator is the round-trip probe CER: noise-only prompts score
+        # above 1.0, a usable whisper prompt scores 0.22.
 
         text = asr(seg, sr)
         if not (6 <= len(text) <= 70) or repeated_run(text) > 0.3 or not is_sfw(text):
             continue
+        if any(a in text for a in ASR_ARTIFACTS):
+            continue
+        # A hallucinated caption over noise tends to be far shorter or longer
+        # than anyone can actually say in six seconds.
+        rate = len(text) / (len(seg) / sr)
+        if not (2.5 <= rate <= 9.0):
+            continue
 
         idx = len(per_artist.get(creator, []))
         tag = hashlib.md5(creator.encode("utf-8")).hexdigest()[:4]
@@ -298,6 +336,10 @@ def probe(cands: list[dict], asr) -> list[dict]:
                 hfs.append(hf_ratio(arr, 44100))
             row = dict(c, cer=float(np.mean(ces)), cer_each=ces,
                        hf=float(np.mean(hfs)), wall=round(time.time() - t0, 1))
+            # A prompt whose round trip scores above 0.5 is not a voice, it is
+            # either a bad transcript or a clip with no speech in it. Both show
+            # up here as garbled output, and both are unusable downstream.
+            row["keep"] = bool(row["cer"] < 0.5)
             results.append(row)
             log(f"[{i+1}/{len(cands)}] {c['cid']} cer={row['cer']:.3f} hf={row['hf']:.4f} "
                 f"snr={c['snr_db']:.1f} {c['prompt_text'][:16]}")
@@ -313,7 +355,8 @@ def select(results: list[dict]) -> list[dict]:
     best: dict[str, dict] = {}
     for r in results:
         best.setdefault(r["artist"], r)
-    ranked = sorted(best.values(), key=lambda r: r["cer"])[:TOP_K]
+    ranked = [r for r in sorted(best.values(), key=lambda r: r["cer"])
+              if r.get("keep", True)][:TOP_K]
     (WORK / "best.json").write_text(json.dumps(ranked, ensure_ascii=False, indent=2))
     log("\n-- best per artist --")
     for r in ranked: