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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 | #!/usr/bin/env python3
"""RESCORING N-BEST SUR LE SHONA — jamais testé (le shona = la moitié du test).
Le KenLM DÉGRADE le shona (il déforme), mais le rescoring ne fait que CHOISIR parmi des
hypothèses déjà produites par sna_ps : mécanisme différent, non invalidé.
score(h) = ac_snaps(h) + somme_i w_i*ac_i(h) + gamma*nb_mots(h)
Gate = devhard-sna (433 clips), FIABLE pour le shona d'après §7 de l'AUTOPILOT.
Baseline à battre : sna_ps greedy = 0.1281 (combine).
"""
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" # modèle principal shona
NBEST = 10
R = "/scratch/restore"
RESCORERS = [
("cont2", R + "/joint_cont2_best"),
("cont", R + "/joint_cont_best"),
("sna_r2", R + "/sna_r2_best"),
]
AUD = "/root/devhard_audio"
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"] == "sna"]
for r in sub: # réécrire les chemins vers /root
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" % len(sub), flush=True)
# --- logits du modèle principal (cache) ---
CACHE = "/scratch/lm/logits_sna.pkl"
os.makedirs("/scratch/lm", exist_ok=True)
if os.path.exists(CACHE):
L1 = pickle.load(open(CACHE, "rb"))
else:
_, L1 = compute_logits(M1, sub)
pickle.dump(L1, open(CACHE, "wb"))
print("logits sna_ps OK", flush=True)
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 sna_ps : %.4f" % REF, flush=True)
# --- N-best par beam CTC PUR (aucun LM : il dégrade le shona) ---
dec = build_ctcdecoder(lab) # pas de kenlm_model_path
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)]
_, _, BEAM = comb(refs, db)
print("beam CTC pur (1-best) : %.4f (%+.4f vs greedy)" % (BEAM, BEAM - REF), flush=True)
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]): # injecter decode_batch ET le greedy
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]))
# --- ORACLE : plafond atteignable par simple sélection ---
orc = []
for i in range(len(cands)):
best = min(cands[i], key=lambda h: comb([refs[i]], [h])[2] if refs[i].strip() else 0)
orc.append(best)
_, _, ORACLE = comb(refs, orc)
print("ORACLE %d-best : %.4f (marge %+.4f)" % (NBEST, ORACLE, ORACLE - REF), flush=True)
# --- scores des rescoreurs ---
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] + gamma * NW[i]
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)[2]
print("\n--- (a) rescoreurs solo (ref greedy %.4f) ---" % REF, 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, 4.0):
m = evaluate({tag: w})
if m < bb[0]:
bb = (m, w)
solo[tag] = bb
print(" %-8s %.4f (w=%.1f) %+.4f" % (tag, bb[0], bb[1], bb[0] - REF), flush=True)
print("\n--- (b) terme de longueur seul ---", flush=True)
for gm in (-2.0, -1.0, 0.0, 1.0, 2.0, 4.0):
m = evaluate({}, gm)
print(" gamma=%+5.1f : %.4f (%+.4f)" % (gm, m, m - REF), flush=True)
print("\n--- (c) meilleur rescoreur + longueur ---", flush=True)
best = (9.0, None, None, None)
if solo:
btag = min(solo, key=lambda t: solo[t][0])
for w in (0.5, 1.0, 1.5, 2.5):
for gm in (0.0, 1.0, 2.0, 4.0):
m = evaluate({btag: w}, gm)
if m < best[0]:
best = (m, btag, w, gm)
print(" %s=%.1f gamma=%+5.1f : %.4f (%+.4f)" % (btag, w, gm, m, m - REF), flush=True)
print("\nBEST_SNA %.4f (%s w=%s gamma=%s) baseline %.4f gain %+.4f"
% (best[0], best[1], best[2], best[3], REF, best[0] - REF), flush=True)
json.dump({"best": best[0], "tag": best[1], "w": best[2], "gamma": best[3],
"ref": REF, "oracle": ORACLE},
open("/root/sna_rescore.json", "w"))
print("SNA_RESCORE_DONE", flush=True)
if __name__ == "__main__":
main()
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