| |
| """Replacement for vpgen.procedural_caption(). |
| |
| WHY A REPLACEMENT AND NOT A PATCH |
| --------------------------------- |
| The VoiceNet classification heads return, as `label`, the multi-sentence TRAINING ANCHOR for the |
| predicted level, e.g. AGEV bucket 4 is: |
| |
| "The acoustic signature reflects a fully developed, mature adult, characterized by a highly |
| settled, grounded, and authoritative resonant weight. The vocal folds exhibit ..." |
| |
| vpgen's `lab()` does `" ".join(t.split(".")[0].split(",")[0].strip().split()[:9])` and pastes the |
| result into a noun slot, producing the known-broken output |
| "A The acoustic signature reflects a fully developed, The audio delivers a standard masculine..." |
| Truncating prose can never yield an adjective, so there is no patch to the slicing that fixes it. |
| The anchors are not tags and must not be used as tags. |
| |
| WHAT THIS DOES INSTEAD |
| ---------------------- |
| An ordinal tag table keyed by (dimension, bucket, n_levels). Every VoiceNet head is an ordinal |
| classifier with a known level count (57 dims: 54 x 7 levels, BKGN 5, EXPL 3), so bucket index + |
| level count is exactly the information a short tag needs. Curated wording for the dimensions that |
| actually carry a caption; a generic ordinal ("markedly high Roughness") for the rest, built from |
| the head's own `name` field so nothing is invented. Style dimensions (S_*) are reported only when |
| their regression score clears a threshold, and emotions come from Empathic Insight, not VoiceNet. |
| """ |
| import json, os, re |
|
|
| |
| L7 = { |
| "AGEV": ["infant","child","adolescent","young adult","adult","middle-aged","elderly"], |
| |
| |
| |
| |
| |
| |
| "GEND": ["strongly feminine","feminine","somewhat feminine","androgynous", |
| "somewhat masculine","masculine","strongly masculine"], |
| "AROU": ["lethargic","very low-energy","subdued","normally alert","energised","highly aroused","frantic"], |
| "VALN": ["deeply negative","negative","mildly negative","neutral","mildly positive","positive","elated"], |
| "TEMP": ["very slow","slow","measured","normal-paced","brisk","fast","very fast"], |
| "WARM": ["cold","cool","slightly cool","neutral-toned","slightly warm","warm","very warm"], |
| "BRGT": ["very dark","dark","slightly dark","neutral-bright","slightly bright","bright","very bright"], |
| "TENS": ["fully relaxed","relaxed","slightly relaxed","neutral tension","slightly tense","tense","very tense"], |
| "VOLT": ["completely steady","steady","fairly steady","moderately variable","variable","volatile","highly volatile"], |
| "ROUG": ["very smooth","smooth","fairly smooth","slightly rough","rough","very rough","gravelly"], |
| "RCQL": ["very poor recording","poor recording","below-average recording","average recording", |
| "good recording","very good recording","studio-grade recording"], |
| "CLRT": ["very slurred","slurred","somewhat unclear","average clarity","clear","very clear","crisply articulate"], |
| "DFLU": ["no disfluency","almost no disfluency","little disfluency","some disfluency", |
| "frequent disfluency","heavy disfluency","severely disfluent"], |
| "RESP": ["no audible breath","minimal breath","light breath","normal breath", |
| "audible breath","heavy breath","breathless"], |
| "RANG": ["monotone pitch","narrow pitch range","fairly narrow pitch","moderate pitch range", |
| "wide pitch range","very wide pitch range","extreme pitch range"], |
| "FULL": ["very thin","thin","slightly thin","balanced body","full","very full","booming"], |
| "VULN": ["guarded","fairly guarded","slightly guarded","neutral openness", |
| "slightly vulnerable","vulnerable","very vulnerable"], |
| "STNC": ["very submissive","submissive","slightly submissive","neutral stance", |
| "slightly dominant","dominant","very dominant"], |
| "ESTH": ["very unpleasant","unpleasant","slightly unpleasant","neutral","pleasant","very pleasant","beautiful"], |
| } |
| |
| |
| |
| |
| L5 = {"BKGN": ["very noisy background","noisy background","some background noise", |
| "quiet background","no background noise"]} |
| L3 = {"EXPL": ["clean content","mildly explicit content","explicit content"]} |
| GENERIC7 = ["minimal","very low","low","moderate","elevated","high","extreme"] |
| STYLE_NAME = {"S_ASMR":"ASMR","S_AUTH":"authoritative","S_CART":"cartoonish","S_CASU":"casual", |
| "S_CONV":"conversational","S_DRAM":"dramatic","S_FORM":"formal","S_MONO":"monologue", |
| "S_NARR":"narration","S_NEWS":"newsreading","S_PLAY":"playful","S_RANT":"ranting", |
| "S_STRY":"storytelling","S_TECH":"didactic","S_WHIS":"whispered"} |
|
|
| def tag(code, d): |
| """(dim code, its score dict) -> short human tag, or '' if the dimension has nothing to say.""" |
| if not d: return "" |
| b = d.get("bucket") |
| if b is None: return "" |
| b = int(b) |
| if code in L3: return L3[code][min(b, 2)] |
| if code in L5: return L5[code][min(b, 4)] |
| if code in L7: |
| t = L7[code] |
| return t[min(b, len(t)-1)] |
| |
| name = (d.get("name") or code).replace(" Style", "") |
| return f"{GENERIC7[min(b,6)]} {name.lower()}" |
|
|
| def procedural_caption(f, text, dur, lang, *, style_thr=3.0, emo_thr=1.0, max_text=400, |
| emo_rel=0.45, bursts=None): |
| """f: one FastScorer.score_batch() dict. Returns (general, script). |
| |
| PRODUCTION CHANGES over the survey version (see WORKLOG "caption"): |
| * emotions are gated relatively, not by a fixed 0.30 on an Empathic-Insight 0-7 scale -- |
| 0.30 admitted essentially every emotion, which is why the survey's sample captions all |
| read "concentration, emotional numbness, contemplation" regardless of the clip. |
| * a stance/affect clause (VALN, STNC, VULN, RESP) and an explicit-content flag (EXPL) are |
| emitted; those five heads were computed and then thrown away. |
| * language is dropped when unknown instead of being rendered as "XX". |
| * vocal bursts, when they cannot be placed inline, are named here (generic-GENERAL policy). |
| """ |
| vn = f.get("voicenet", {}) or {} |
| bits = f.get("_caption_bits", {}) or {} |
| g = lambda c: tag(c, vn.get(c)) |
|
|
| who = " ".join(x for x in (g("AGEV"), g("GEND")) if x) or "unspecified" |
| art = "An" if who[:1].lower() in "aeiou" else "A" |
| clauses = [f"{art} {who} voice"] |
|
|
| delivery = [x for x in (g("AROU"), g("TEMP"), g("TENS"), g("VOLT")) if x] |
| if delivery: clauses.append("delivery is " + ", ".join(delivery)) |
| timbre = [x for x in (g("WARM"), g("BRGT"), g("ROUG"), g("FULL")) if x] |
| if timbre: clauses.append("timbre is " + ", ".join(timbre)) |
| speech = [x for x in (g("CLRT"), g("DFLU"), g("RANG"), g("RESP")) if x] |
| if speech: clauses.append(", ".join(speech)) |
| stance = [x for x in (g("VALN"), g("STNC"), g("VULN")) if x] |
| if stance: clauses.append("affect is " + ", ".join(stance)) |
|
|
| scored = (bits.get("top_emotions_scored") or []) |
| if scored: |
| hi = max(v for _, v in scored) |
| thr = max(emo_thr, emo_rel * hi) |
| emo = [e.replace("_", " ").replace("/", " or ").lower() for e, v in scored[:3] if v >= thr] |
| if emo: clauses.append("reads as " + ", ".join(emo)) |
|
|
| st = [STYLE_NAME.get(c, c) for c, r, _ in (bits.get("styles_scored") or []) if r >= style_thr] |
| if st: clauses.append("style: " + ", ".join(st[:2])) |
|
|
| rec = [x for x in (g("RCQL"), g("BKGN")) if x] |
| if rec: clauses.append(", ".join(rec)) |
| ex = vn.get("EXPL") or {} |
| if int(ex.get("bucket", 0) or 0) > 0: clauses.append(tag("EXPL", ex)) |
|
|
| if bursts: |
| clauses.append("contains vocal bursts: " + ", ".join(sorted({b["label"] for b in bursts}))) |
| clauses.append(f"genuineness {f.get('genuineness_0_6', 0):.1f}/6") |
| clauses.append(f"vocal-burst blend {f.get('blend_0_10', 0):.1f}/10") |
| tail = f"{dur:.1f}s" |
| if lang and str(lang).lower() not in ("xx", "none", ""): tail += f", {str(lang).upper()}" |
| clauses.append(tail) |
| general = "; ".join(clauses) + "." |
| script = (text or "").strip()[:max_text] |
| return general, script |
|
|
| def render(general, script): |
| return f"GENERAL: {general}\nSCRIPT:\n{script}" if script else f"GENERAL: {general}" |
|
|
