add granular logging + full stderr to diagnose preprocessing hang

app.py

@@ -343,7 +343,12 @@ try:
     torchaudio.load = _sf
 
     log("[Step 1/2] Preprocessing audio...")
+    log("  importing preprocess module...")
     from acestep.training_v2.preprocess import preprocess_audio_files
+    log("  import done, calling preprocess_audio_files...")
+    import resource
+    mem_before = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss // 1024
+    log(f"  RAM before preprocess: {{mem_before}} MB")
     result = preprocess_audio_files(
         audio_dir="{audio_dir}",
         output_dir="{output_dir}/preprocessed_tensors",
@@ -351,6 +356,8 @@ try:
         variant="turbo", max_duration=60.0,
         device="cpu", precision="float32",
     )
+    mem_after = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss // 1024
+    log(f"  RAM after preprocess: {{mem_after}} MB")
     processed = result.get("processed", 0)
     failed = result.get("failed", 0)
     log(f"  Preprocessed: {{processed}}/{{result.get('total',0)}} (failed: {{failed}})")
@@ -429,8 +436,8 @@ finally:
             parts.append(open(TRAIN_LOG).read())
         stderr_log = os.path.join(ADAPTER_DIR, "test-lora", "train_stderr.log")
         if os.path.exists(stderr_log) and os.path.getsize(stderr_log) > 0:
-            stderr = open(stderr_log).read()[-2000:]
-            parts.append(f"\n--- stderr (last 2000 chars) ---\n{stderr}")
+            stderr = open(stderr_log).read()[-8000:]
+            parts.append(f"\n--- stderr (last 8000 chars, {os.path.getsize(stderr_log)} bytes total) ---\n{stderr}")
         return "\n".join(parts) if parts else "No training log found."
 
     # -- Build UI --