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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 | #!/usr/bin/env python3
"""RESCORING v3 : ajoute (a) les monolingues lin comme rescoreurs (bons chemins),
(b) un TERME DE LONGUEUR — les scores etant des sommes de log-probs, ils penalisent
structurellement les hypotheses longues, ce qui peut expliquer une part de l'ecart a l'oracle.
score(h) = ac_cont(h) + lm_kenlm(h) + somme_i w_i*ac_i(h) + gamma*nb_mots(h)
"""
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"),
("lin_s4", "/root/models/lin_s4_best"),
("lin_r2", R + "/lin_r2_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):
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)
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: %.4f" % REF, flush=True)
cands, AC1, LM, 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]]
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))
NW.append(np.array([float(len(x.split())) for x in c]))
SC = {}
for tag, mdl in RESCORERS:
if not os.path.isdir(mdl):
print("%-8s ABSENT %s" % (tag, mdl), 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 scores OK (|V|=%d)" % (tag, len(t2.get_vocab())), flush=True)
def evaluate(W, gamma=0.0):
hyps = []
for i in range(len(cands)):
tot = AC1[i] + LM[i] + gamma * NW[i]
for t, w in W.items():
if w:
tot = tot + w * SC[t][i]
hyps.append(cc(cands[i][int(np.argmax(tot))], greedy[i]))
return comb(refs, hyps)[2]
print("\n--- (a) rescoreurs solo ---", flush=True)
for tag in SC:
bb = (9.0, 0.0)
for w in (0.3, 0.5, 1.0, 1.5, 2.5, 4.0):
m = evaluate({tag: w})
if m < bb[0]:
bb = (m, w)
print(" %-8s %.4f (w=%.1f) %+.4f" % (tag, bb[0], bb[1], bb[0] - REF), flush=True)
print("\n--- (b) terme de longueur seul (gamma), sans rescoreur ---", flush=True)
for gm in (-2.0, -1.0, 0.0, 1.0, 2.0, 4.0, 8.0):
print(" gamma=%+5.1f : %.4f (%+.4f)" % (gm, evaluate({}, gm), evaluate({}, gm) - REF), flush=True)
print("\n--- (c) cont2 + longueur ---", flush=True)
best = (9.0, None, None)
for w in (1.0, 1.5, 2.5):
for gm in (0.0, 1.0, 2.0, 4.0, 8.0):
m = evaluate({"cont2": w}, gm)
if m < best[0]:
best = (m, w, gm)
print(" cont2=%.1f gamma=%+5.1f : %.4f (%+.4f)" % (w, gm, m, m - REF), flush=True)
print("\nBEST_V3 %.4f cont2=%s gamma=%s (ref %.4f, gain %+.4f)"
% (best[0], best[1], best[2], REF, best[0] - REF), flush=True)
json.dump({"combine": best[0], "cont2": best[1], "gamma": best[2], "ref": REF},
open("/root/multi_v3.json", "w"))
print("V3_DONE", flush=True)
if __name__ == "__main__":
main()
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