eval in two passes to fit a 15GB T4

kaggle/train_voices.py

@@ -357,8 +357,13 @@ def stage_eval() -> None:
 
     sents = json.loads((BUNDLE / "eval/sentences.json").read_text())["items"]
     want = EVAL_VOICES or [v["slot"] for v in voices()[:2]]
+    sel = [x for x in voices() if x["slot"] in want]
+    wav_dir = OUT / "eval_wavs"
+    wav_dir.mkdir(parents=True, exist_ok=True)
     rows = []
-    for v in [x for x in voices() if x["slot"] in want]:
+
+    # Pass 1: teacher only. Both DiTs resident does not fit on a 15 GB T4.
+    for v in sel:
         latent, spk = tts.prepare_prompt(str(BUNDLE / v["wav"]), use_spk_emb=True)
         latent = latent.to(device=device, dtype=next(mm.parameters()).dtype)
         spk = [x.to(device=device, dtype=next(mm.parameters()).dtype) for x in spk]
@@ -370,28 +375,53 @@ def stage_eval() -> None:
                                     prompt_text=v["prompt_text"], use_spk_emb=True,
                                     raw_text=True, max_decode_steps=max(32, min(400, int(len(text) * 3.5))))
                 tw = tw.cpu().numpy().reshape(-1)
+                p = wav_dir / f"{v['slot']}_{item['id']}_teacher.wav"
+                torch.save(torch.from_numpy(tw), p.with_suffix(".pt"))
+                hyp = asr(tw)
+                s = score_row({"text": text, "hyp_text": hyp}, table)
+                rows.append({"voice": v["slot"], "artist": v["artist"], "id": item["id"],
+                             "model": "teacher", "text": text, "cer": s["cer_norm"],
+                             "cer_head": s["cer_prefix"], "hyp": hyp})
+            except Exception:
+                log(f"teacher {v['slot']} {item['id']} failed\n{traceback.format_exc()}")
+            torch.cuda.empty_cache()
+        log(f"teacher {v['slot']} done ({len(rows)} rows)")
+
+    del teacher
+    torch.cuda.empty_cache()
+
+    # Pass 2: student only, reusing the teacher audio for frame count.
+    for v in sel:
+        latent, spk = tts.prepare_prompt(str(BUNDLE / v["wav"]), use_spk_emb=True)
+        latent = latent.to(device=device, dtype=next(mm.parameters()).dtype)
+        spk = [x.to(device=device, dtype=next(mm.parameters()).dtype) for x in spk]
+        for item in sents:
+            text = item["text"]
+            try:
+                tp = wav_dir / f"{v['slot']}_{item['id']}_teacher.pt"
+                tw = torch.load(tp, map_location="cpu").numpy()
                 frames = max(16, int(round(tw.size / 3528)))
                 torch.manual_seed(SEED)
                 sw = ar_generate(mm, student, STEPS, text, task_prompt, latent,
                                  v["prompt_text"], spk, frames)
-                for name, arr in (("teacher", tw), ("student", sw)):
-                    hyp = asr(arr)
-                    s = score_row({"text": text, "hyp_text": hyp}, table)
-                    rows.append({"voice": v["slot"], "artist": v["artist"], "id": item["id"],
-                                 "model": name, "text": text, "cer": s["cer_norm"],
-                                 "cer_head": s["cer_prefix"], "hyp": hyp})
+                hyp = asr(sw)
+                s = score_row({"text": text, "hyp_text": hyp}, table)
+                rows.append({"voice": v["slot"], "artist": v["artist"], "id": item["id"],
+                             "model": "student", "text": text, "cer": s["cer_norm"],
+                             "cer_head": s["cer_prefix"], "hyp": hyp})
             except Exception:
-                log(f"eval {v['slot']} {item['id']} failed\n{traceback.format_exc()}")
-        log(f"eval {v['slot']} done ({len(rows)} rows)")
+                log(f"student {v['slot']} {item['id']} failed\n{traceback.format_exc()}")
+            torch.cuda.empty_cache()
+        log(f"student {v['slot']} done ({len(rows)} rows)")
 
     (OUT / "eval_rows.json").write_text(json.dumps(rows, ensure_ascii=False, indent=1))
     for name in ("teacher", "student"):
-        sel = [r for r in rows if r["model"] == name]
-        if sel:
-            log(f"{name:<8} n={len(sel)} cer={np.mean([r['cer'] for r in sel]):.4f} "
-                f"head={np.mean([r['cer_head'] for r in sel]):.4f}")
-        for v in sorted({r["voice"] for r in sel}):
-            vs = [r["cer"] for r in sel if r["voice"] == v]
+        sel_rows = [r for r in rows if r["model"] == name]
+        if sel_rows:
+            log(f"{name:<8} n={len(sel_rows)} cer={np.mean([r['cer'] for r in sel_rows]):.4f} "
+                f"head={np.mean([r['cer_head'] for r in sel_rows]):.4f}")
+        for v in sorted({r["voice"] for r in sel_rows}):
+            vs = [r["cer"] for r in sel_rows if r["voice"] == v]
             log(f"   {v} n={len(vs)} cer={np.mean(vs):.4f}")