run4: unpack cached traj, seed teacher, 8000 steps

kaggle/train_voices.py

@@ -171,6 +171,13 @@ def stage_traj() -> None:
 def load_dataset():
     import torch
 
+    cache = BUNDLE / "traj.zip"
+    if not any(TRAJ.glob("*/*.pt")) and cache.exists():
+        import zipfile
+        log(f"unpacking {cache} ({cache.stat().st_size // 1024 // 1024} MB)")
+        with zipfile.ZipFile(cache) as z:
+            z.extractall(ROOT / "train")
+
     zs, hs, ns, ls = [], [], [], []
     for path in sorted(TRAJ.glob("*/*.pt")):
         row = torch.load(path, map_location="cpu", weights_only=False)
@@ -373,6 +380,10 @@ def stage_eval() -> None:
         for item in sents:
             text = item["text"]
             try:
+                # Seed the teacher too: unseeded teacher sampling moved the
+                # baseline CER by ~2 pp between runs, which is the same size as
+                # the effect we are trying to measure.
+                torch.manual_seed(SEED)
                 tw = tts.synthesize(task="TTS", text=text, language_id="Chinese",
                                     prompt_wav_path=str(BUNDLE / v["wav"]),
                                     prompt_text=v["prompt_text"], use_spk_emb=True,