| 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") |
|
|
