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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 | #!/usr/bin/env python3
"""RESCORING MULTI-MODELES sur le lingala.
Point cle : pour du rescoring, un modele n'a PAS besoin du meme vocabulaire ni de la meme
frequence de trames que le decodeur — il doit seulement savoir evaluer log P(texte | ses logits).
On peut donc recruter des modeles ecartes comme decodeurs (MMS, monolingues) comme rescoreurs.
score(h) = ac_cont(h) + lm_kenlm(h) + somme_i w_i * ac_modele_i(h)
Recherche des poids par montee de coordonnees sur devhard-lin.
"""
import json
import os
import pickle
import jiwer
import numpy as np
import soundfile as sf
import torch
from multiprocessing import Pool
from pyctcdecode import build_ctcdecoder
from transformers import AutoModelForCTC, AutoProcessor
M1 = "/root/models/joint_cont_best"
ARPA = "/scratch/lm/lin_5g.arpa"
NBEST = 10
R = "/scratch/restore"
RESCORERS = [
("cont2", "/root/models/joint_cont2_best"),
("jbest", "/root/models/joint_best"),
("lin_s4", R + "/lin_s4_best"),
("mmsjoint", "/root/models/mmsjoint_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]
w = jiwer.wer(a, b)
c = jiwer.cer(a, b)
return w, c, 0.5 * w + 0.5 * c
def encode_for(tok, text):
"""Encode en restant dans le vocabulaire du modele : les caracteres absents sont retires.
Permet de recruter des modeles sans casse/ponctuation (ils jugent alors le contenu seul)."""
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:
low = text.lower().replace(" ", delim)
keep = "".join(c for c in low if c in v)
ids = [v[c] for c in keep]
return [i for i in ids if i != 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)
loss = 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)
return -float(loss)
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"] == "lin"]
refs = [r["text"] for r in sub]
L1 = pickle.load(open("/scratch/lm/logits_lin.pkl", "rb"))
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]
def cc(h, g):
return (g[:1] + h[1:]) if (h and g) else h
dec = build_ctcdecoder(lab, kenlm_model_path=ARPA, alpha=0.5, beta=0.5,
lm_score_boundary=False)
with Pool(8) as p:
allbeams = dec.decode_beams_batch(p, L1, beam_width=64)
with Pool(8) as p:
db = [" ".join(x.split()) for x in dec.decode_batch(p, L1, beam_width=64)]
_, _, REF = comb(refs, [cc(h, g) for h, g in zip(db, greedy)])
print("reference (meilleur 1-best connu): %.4f" % REF, flush=True)
# pool de candidats + scores du decodeur
cands, AC1, LM = [], [], []
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]]
l = [((b[4] - b[3]) if len(b) > 4 else 0.0) for b in bs[:NBEST]]
if db[i] not in c:
c.append(db[i])
a.append(ctc_score(L1[i], encode_for(tok, db[i]), tok.pad_token_id))
l.append(float(np.mean(l)) if l else 0.0)
cands.append(c)
AC1.append(np.array(a))
LM.append(np.array(l))
print("taille moyenne du pool: %.1f hypotheses" % np.mean([len(c) for c in cands]), flush=True)
# scores de chaque rescoreur
SC = {}
chars = set("".join(refs))
for tag, mdl in RESCORERS:
if not os.path.isdir(mdl):
print("%-9s ABSENT (%s)" % (tag, mdl), flush=True)
continue
try:
proc, LG = compute_logits(mdl, sub)
except Exception as e:
print("%-9s ERREUR %s" % (tag, str(e)[:60]), flush=True)
continue
t2 = proc.tokenizer
vv = set(t2.get_vocab())
miss = sorted(c for c in chars if c not in vv and c != " ")
s = []
for i, c in enumerate(cands):
s.append(np.array([ctc_score(LG[i], encode_for(t2, x), t2.pad_token_id) for x in c]))
SC[tag] = s
print("%-9s |V|=%d, caract. refs absents=%d -> scores calcules"
% (tag, len(vv), len(miss)), flush=True)
def evaluate(weights):
hyps = []
for i in range(len(cands)):
tot = AC1[i] + LM[i]
for tag, w in weights.items():
if w:
tot = tot + w * SC[tag][i]
hyps.append(cc(cands[i][int(np.argmax(tot))], greedy[i]))
return comb(refs, hyps)[2]
print("\n--- rescoreurs pris un par un ---", flush=True)
solo = {}
for tag in SC:
bb = (9.0, 0.0)
for w in (0.3, 0.5, 1.0, 1.5, 2.5):
m = evaluate({tag: w})
if m < bb[0]:
bb = (m, w)
solo[tag] = bb
print(" %-9s meilleur %.4f (w=%.1f) vs ref %.4f : %+.4f"
% (tag, bb[0], bb[1], REF, bb[0] - REF), flush=True)
print("\n--- montee de coordonnees (combinaison) ---", flush=True)
W = {t: 0.0 for t in SC}
order = sorted(solo, key=lambda t: solo[t][0])
for t in order:
W[t] = solo[t][1]
cur = evaluate(W)
print(" depart (chacun a son optimum solo): %.4f %s" % (cur, W), flush=True)
for it in range(3):
improved = False
for t in order:
base_w = W[t]
for w in (0.0, 0.3, 0.5, 1.0, 1.5, 2.5):
W[t] = w
m = evaluate(W)
if m < cur - 1e-6:
cur, base_w, improved = m, w, True
W[t] = base_w
print(" passe %d: %.4f %s" % (it + 1, cur, {k: v for k, v in W.items() if v}), flush=True)
if not improved:
break
print("\nBEST_MULTI %.4f poids=%s (ref %.4f, gain %+.4f)"
% (cur, {k: v for k, v in W.items() if v}, REF, cur - REF), flush=True)
json.dump({"combine": cur, "weights": W, "ref": REF, "solo": {k: list(v) for k, v in solo.items()}},
open("/root/multi_rescore.json", "w"))
print("MULTI_DONE", flush=True)
if __name__ == "__main__":
main()
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