File size: 1,740 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 | import json, numpy as np, soundfile as sf, torch, jiwer
from transformers import Wav2Vec2BertForCTC, Wav2Vec2BertProcessor
SR=16000
proc=Wav2Vec2BertProcessor.from_pretrained("/root/models/joint_best")
model=Wav2Vec2BertForCTC.from_pretrained("/root/models/joint_best",torch_dtype=torch.bfloat16).cuda().eval()
mono={"lin":0.283,"lug":0.101,"sna":0.177}
@torch.inference_mode()
def transcribe(rows):
rows=sorted(rows,key=lambda r:-r["duration"]); out={}
b=[];bud=0;bs=[]
for r in rows:
if b and bud+r["duration"]>140: bs.append(b);b=[];bud=0
b.append(r);bud+=r["duration"]
if b:bs.append(b)
for bb in bs:
au=[sf.read(r["audio"],dtype="float32")[0] for r in bb]
f=proc.feature_extractor(au,sampling_rate=SR,return_tensors="pt",padding=True)
f={k:v.to("cuda",dtype=torch.bfloat16 if v.dtype==torch.float32 else v.dtype) for k,v in f.items()}
ids=model(**f).logits.float().argmax(-1).cpu().numpy()
for r,s in zip(bb,proc.tokenizer.batch_decode(ids)): out[r["id"]]=" ".join(s.replace("|"," ").split())
return out
print("=== JOINT par langue vs meilleur monolingue ===")
for lang in ("lin","lug","sna"):
rows=[json.loads(l) for l in open(f"/scratch/prep/manifests/waxal_{lang}_validation.jsonl") if json.loads(l)["text"].strip()]
for r in rows: r["text"]=" ".join(r["text"].replace("|"," ").split())
h=transcribe(rows)
refs=[r["text"] for r in rows]; hyps=[h.get(r["id"],"") for r in rows]
w,c=jiwer.wer(refs,hyps),jiwer.cer(refs,hyps); comb=0.5*w+0.5*c
verdict="JOINT GAGNE" if comb<mono[lang] else "mono gagne"
print(f" {lang}: joint {comb:.4f} vs mono {mono[lang]:.4f} -> {verdict} ({comb-mono[lang]:+.4f})")
print("EVALJOINT_DONE")
|