File size: 6,727 Bytes
6eed659 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | #!/usr/bin/env python3
"""SHONA v2 — pousser le levier qui a payé (+0.001127 au LB).
Constat v1 : seul un SPÉCIALISTE shona marche comme rescoreur (sna_r2 −0.0029 ; cont2 −0.0002 ;
cont +0.0011 = nuit). Or 4 autres modèles shona n'ont JAMAIS été testés.
Oracle 10-best = 0.0949 (marge −0.0332) : la marge est dans la SÉLECTION.
Ici : (1) tous les rescoreurs shona en solo, (2) N-best élargi 25, (3) combinaison des 2 meilleurs
(avec garde-fou : on ne retient la combinaison que si elle bat nettement le meilleur solo,
sinon sur-apprentissage sur 433 clips — leçon §4c).
"""
import json, os, pickle
import jiwer, numpy as np, soundfile as sf, torch
from multiprocessing import Pool
from pyctcdecode import build_ctcdecoder
from transformers import AutoModelForCTC, AutoProcessor
M1 = "/root/models/sna_ps_best"
R = "/scratch/restore"
NBEST = int(os.environ.get("NBEST", "25"))
AUD = "/root/devhard_audio"
CANDS_R = [("sna_r2", R + "/sna_r2_best"), ("sna_r", R + "/sna_r_best"),
("sna_s1", R + "/sna_s1_best"), ("sna_s2", R + "/sna_s2_best"),
("sna_ws", R + "/sna_ws_best")]
def comb(refs, hyps):
pr = [(r, h) for r, h in zip(refs, hyps) if r.strip()]
a = [x for x, _ in pr]; b = [y for _, y in pr]
return 0.5 * jiwer.wer(a, b) + 0.5 * jiwer.cer(a, b)
def encode_for(tok, text):
v = tok.get_vocab()
delim = getattr(tok, "word_delimiter_token", "|")
s = text.replace(" ", delim)
keep = "".join(c for c in s if c in v)
if not keep:
keep = "".join(c for c in text.lower().replace(" ", delim) if c in v)
return [v[c] for c in keep if v[c] != tok.pad_token_id]
def ctc_score(logp, ids, blank):
T = logp.shape[0]
if not ids or len(ids) > T:
return -1e9
lp = torch.from_numpy(logp).unsqueeze(1)
return -float(torch.nn.functional.ctc_loss(
lp, torch.tensor(ids).unsqueeze(0), torch.tensor([T]), torch.tensor([len(ids)]),
blank=blank, reduction="sum", zero_infinity=True))
def compute_logits(model_dir, rows):
proc = AutoProcessor.from_pretrained(model_dir)
m = AutoModelForCTC.from_pretrained(model_dir, dtype=torch.float32).cuda().eval()
out = []
with torch.inference_mode():
for i in range(0, len(rows), 4):
b = rows[i:i + 4]
au = [sf.read(r["audio"], dtype="float32")[0] for r in b]
x = proc(au, sampling_rate=16000, return_tensors="pt", padding=True)
x = {k: v.cuda() for k, v in x.items()}
lg = m(**x).logits.log_softmax(-1).float().cpu().numpy()
for j in range(len(b)):
out.append(lg[j])
del m; torch.cuda.empty_cache()
return proc, out
def main():
rows = [json.loads(l) for l in open("/root/devhard/devhard_linsna.jsonl", encoding="utf-8")]
sub = [r for r in rows if r["lang"] == "sna"]
for r in sub:
r["audio"] = os.path.join(AUD, os.path.basename(r["audio"]))
sub = [r for r in sub if os.path.exists(r["audio"])]
refs = [r["text"] for r in sub]
print("devhard-sna %d clips | NBEST=%d" % (len(sub), NBEST), flush=True)
CACHE = "/scratch/lm/logits_sna.pkl"
L1 = pickle.load(open(CACHE, "rb")) if os.path.exists(CACHE) else compute_logits(M1, sub)[1]
tok = AutoProcessor.from_pretrained(M1).tokenizer
v = tok.get_vocab()
lab = [None] * len(v)
for t, i in v.items():
lab[i] = t
lab[tok.word_delimiter_token_id] = " "
lab[tok.unk_token_id] = "⁇"
lab[tok.pad_token_id] = ""
greedy = [" ".join(tok.decode(l.argmax(-1)).replace("|", " ").split()) for l in L1]
REF = comb(refs, greedy)
print("baseline greedy %.4f" % REF, flush=True)
dec = build_ctcdecoder(lab)
with Pool(8) as p:
allbeams = dec.decode_beams_batch(p, L1, beam_width=128)
with Pool(8) as p:
db = [" ".join(x.split()) for x in dec.decode_batch(p, L1, beam_width=128)]
cands, AC1, NW = [], [], []
for i, bs in enumerate(allbeams):
c = [" ".join(b[0].split()) for b in bs[:NBEST]]
a = [(b[3] if len(b) > 3 else 0.0) for b in bs[:NBEST]]
for extra in (db[i], greedy[i]):
if extra and extra not in c:
c.append(extra)
a.append(ctc_score(L1[i], encode_for(tok, extra), tok.pad_token_id))
cands.append(c); AC1.append(np.array(a))
NW.append(np.array([float(len(x.split())) for x in c]))
orc = [min(cands[i], key=lambda h: comb([refs[i]], [h]) if refs[i].strip() else 0)
for i in range(len(cands))]
print("ORACLE %d-best %.4f (marge %+.4f)" % (NBEST, comb(refs, orc), comb(refs, orc) - REF), flush=True)
SC = {}
for tag, mdl in CANDS_R:
if not os.path.isdir(mdl):
print("%-8s ABSENT" % tag, flush=True); continue
proc, LG = compute_logits(mdl, sub)
t2 = proc.tokenizer
SC[tag] = [np.array([ctc_score(LG[i], encode_for(t2, x), t2.pad_token_id)
for x in cands[i]]) for i in range(len(cands))]
print("%-8s OK" % tag, flush=True)
def ev(W):
hyps = []
for i in range(len(cands)):
tot = AC1[i].copy()
for t, w in W.items():
if w:
tot = tot + w * SC[t][i]
hyps.append(cands[i][int(np.argmax(tot))])
return comb(refs, hyps)
print("\n--- solo (ref %.4f) ---" % REF, flush=True)
solo = {}
for t in SC:
bb = (9.0, 0.0)
for w in (0.3, 0.5, 1.0, 1.5, 2.5, 4.0):
m = ev({t: w})
if m < bb[0]:
bb = (m, w)
solo[t] = bb
print(" %-8s %.4f (w=%.1f) %+.4f" % (t, bb[0], bb[1], bb[0] - REF), flush=True)
ranked = sorted(solo, key=lambda t: solo[t][0])
best_solo = solo[ranked[0]]
print("\n--- combinaison des 2 meilleurs (%s + %s) ---" % (ranked[0], ranked[1]), flush=True)
bc = (9.0, None, None)
for w1 in (0.5, 1.0, 1.5, 2.5):
for w2 in (0.0, 0.3, 0.5, 1.0, 1.5):
m = ev({ranked[0]: w1, ranked[1]: w2})
if m < bc[0]:
bc = (m, w1, w2)
print(" best %.4f (%s=%.1f %s=%.1f) %+.4f vs solo" % (bc[0], ranked[0], bc[1], ranked[1], bc[2], bc[0] - best_solo[0]), flush=True)
keep_combo = bc[0] < best_solo[0] - 0.0015 # garde-fou anti sur-apprentissage
print("\nRETENU : %s" % ("COMBINAISON" if keep_combo else "SOLO %s w=%.1f" % (ranked[0], best_solo[1])), flush=True)
json.dump({"ref": REF, "solo": {k: list(v) for k, v in solo.items()},
"combo": list(bc), "keep_combo": bool(keep_combo), "nbest": NBEST},
open("/root/sna_v2.json", "w"))
print("SNA_V2_DONE", flush=True)
if __name__ == "__main__":
main()
|